@article{Abdulrahman2016,
    title={Effect of trial-to-trial variability on optimal event-related fMRI design: Implications for Beta-series correlation and multi-voxel pattern analysis},
    author={Abdulrahman, Hunar and Henson, Richard N},
    journal={NeuroImage},
    volume={125},
    pages={756--766},
    year={2016},
    publisher={Elsevier}
}

@misc{Abraham2014,
    TITLE = {{Region segmentation for sparse decompositions: better brain parcellations from rest fMRI}},
    AUTHOR = {Abraham, Alexandre and Dohmatob, Elvis and Thirion, Bertrand and Samaras, Dimitris and Varoquaux, Gael},
    URL = {https://inria.hal.science/hal-01093944},
    HOWPUBLISHED = {{Sparsity Techniques in Medical Imaging}},
    PAGES = {8},
    YEAR = {2014},
    MONTH = Sep,
    KEYWORDS = {resting state fMRI ; region extraction ; brain networks ; clustering},
    PDF = {https://inria.hal.science/hal-01093944/file/paper16.pdf},
    HAL_ID = {hal-01093944},
    HAL_VERSION = {v1},
}

@inproceedings{AdDabbagh2006,
    author={Baldassarre, Luca and Mourao-Miranda, Janaina and Pontil, Massimiliano},
    booktitle={Proceedings of the 12th Annual Meeting of the Human Brain Mapping Organization},
    title={The CIVET Image-Processing Environment: A Fully Automated Comprehensive Pipeline for Anatomical Neuroimaging Research.},
    year={2006},
    pdf = {https://www.bic.mni.mcgill.ca/users/yaddab/Yasser-HBM2006-Poster.pdf},
}

@article{Allen2011,
	AUTHOR={Allen, Elena and Erhardt, Erik and Damaraju, Eswar and Gruner, William and Segall, Judith and Silva, Rogers and Havlicek, Martin and Rachakonda, Srinivas and Fries, Jill and Kalyanam, Ravi and Michael, Andrew and Caprihan, Arvind and Turner, Jessica and Eichele, Tom and Adelsheim, Steven and Bryan, Angela and Bustillo, Juan and Clark, Vincent and Feldstein Ewing, Sarah and Filbey, Francesca and Ford, Corey and Hutchison, Kent and Jung, Rex and Kiehl, Kent and Kodituwakku, Piyadasa and Komesu, Yuko and Mayer, Andrew and Pearlson, Godfrey and Phillips, John and Sadek, Joseph and Stevens, Michael and Teuscher, Ursina and Thoma, Robert and Calhoun, Vince},
	TITLE={A Baseline for the Multivariate Comparison of Resting-State Networks},
	JOURNAL={Frontiers in Systems Neuroscience},
	VOLUME={5},
	PAGES={2},
	YEAR={2011},
	DOI={10.3389/fnsys.2011.00002},
	ISSN={1662-5137},
	ABSTRACT={As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease.}
}

@article{Allen2012,
	AUTHOR={Allen, Elena A and Erhardt, Erik B and Calhoun, Vince D},
	title={Data Visualization in the Neurosciences: overcoming the Curse of Dimensionality.},
	journal={Neuron},
	year={2011},
	doi={10.1016/j.neuron.2012.05.001 },
}

@article{Amunts2020,
	title = {Julich-{Brain}: {A} {3D} probabilistic atlas of the human brain’s cytoarchitecture},
	volume = {369},
	issn = {0036-8075, 1095-9203},
	shorttitle = {Julich-{Brain}},
	doi = {10.1126/science.abb4588},
	language = {en},
	number = {6506},
	urldate = {2024-02-01},
	journal = {Science},
	author = {Amunts, Katrin and Mohlberg, Hartmut and Bludau, Sebastian and Zilles, Karl},
	month = aug,
	year = {2020},
	pages = {988--992},
}

@article{Anderson2001,
    author = {Anderson, Marti J. and Robinson, John},
    title = {Permutation Tests for Linear Models},
    journal = {Australian \& New Zealand Journal of Statistics},
    volume = {43},
    number = {1},
    pages = {75-88},
    keywords = {asymptotics, partial correlations, power, resampling.},
    doi = {10.1111/1467-842X.00156},
    abstract = {Several approximate permutation tests have been proposed for tests of partial regression coefficients in a linear model based on sample partial correlations. This paper begins with an explanation and notation for an exact test. It then compares the distributions of the test statistics under the various permutation methods proposed, and shows that the partial correlations under permutation are asymptotically jointly normal with means 0 and variances 1. The method of Freedman \& Lane (1983) is found to have asymptotic correlation 1 with the exact test, and the other methods are found to have smaller correlations with this test. Under local alternatives the critical values of all the approximate permutation tests converge to the same constant, so they all have the same asymptotic power. Simulations demonstrate these theoretical results.},
    year = {2001}
}

@inproceedings{Baldassarre2012,
    author={Baldassarre, Luca and Mourao-Miranda, Janaina and Pontil, Massimiliano},
    booktitle={2012 Second International Workshop on Pattern Recognition in NeuroImaging},
    title={Structured Sparsity Models for Brain Decoding from fMRI Data},
    year={2012},
    volume={},
    number={},
    pages={5-8},
    doi={10.1109/PRNI.2012.31},
    pdf = {http://www0.cs.ucl.ac.uk/staff/M.Pontil/reading/neurosparse_prni.pdf},
}

@article{Behzadi2007,
	title = {A component based noise correction method (CompCor) for BOLD and perfusion based fMRI},
	journal = {NeuroImage},
	volume = {37},
	number = {1},
	pages = {90-101},
	year = {2007},
	issn = {1053-8119},
	doi = {10.1016/j.neuroimage.2007.04.042},
	author = {Yashar Behzadi and Khaled Restom and Joy Liau and Thomas T. Liu},
	abstract = {A component based method (CompCor) for the reduction of noise in both blood oxygenation level-dependent (BOLD) and perfusion-based functional magnetic resonance imaging (fMRI) data is presented. In the proposed method, significant principal components are derived from noise regions-of-interest (ROI) in which the time series data are unlikely to be modulated by neural activity. These components are then included as nuisance parameters within general linear models for BOLD and perfusion-based fMRI time series data. Two approaches for the determination of the noise ROI are considered. The first method uses high-resolution anatomical data to define a region of interest composed primarily of white matter and cerebrospinal fluid, while the second method defines a region based upon the temporal standard deviation of the time series data. With the application of CompCor, the temporal standard deviation of resting-state perfusion and BOLD data in gray matter regions was significantly reduced as compared to either no correction or the application of a previously described retrospective image based correction scheme (RETROICOR). For both functional perfusion and BOLD data, the application of CompCor significantly increased the number of activated voxels as compared to no correction. In addition, for functional BOLD data, there were significantly more activated voxels detected with CompCor as compared to RETROICOR. In comparison to RETROICOR, CompCor has the advantage of not requiring external monitoring of physiological fluctuations.}
}

@inproceedings{Bellec2013,
	author={Bellec, Pierre},
	booktitle={2013 International Workshop on Pattern Recognition in Neuroimaging},
	title={Mining the Hierarchy of Resting-State Brain Networks: Selection of Representative Clusters in a Multiscale Structure},
	year={2013},
	volume={},
	number={},
	pages={54-57},
	abstract={The hierarchical organization of brain networks can be captured by clustering time series using multiple numbers of clusters, or scales, in resting-state functional magnetic resonance imaging. However, the systematic examination of all scales is a tedious task. Here, I propose a method to select a limited number of scales that are representative of the full hierarchy. A bootstrap analysis is first performed to estimate stability matrices, which quantify the reliability of the clustering for every pair of brain regions, over a grid of possible scales. A subset of scales is then selected to approximate linearly all stability matrices with a specified level of accuracy. On real data, the method was found to select a relatively small (~7) number of scales to explain 95% of the energy of 73 scales ranging from 2 to 1100 clusters. The number of selected scales was very consistent across 43 subjects, and the actual scales also showed some good level of agreement. This approach thus provides a principled approach to mine hierarchical brain networks, in the form of a few scales amenable to detailed examination.},
	keywords={},
	doi={10.1109/PRNI.2013.23},
	ISSN={},
	month={06},
}

@article{Bellec2010,
	title = {Multi-level bootstrap analysis of stable clusters in resting-state fMRI},
	journal = {NeuroImage},
	volume = {51},
	number = {3},
	pages = {1126-1139},
	year = {2010},
	issn = {1053-8119},
	doi = {10.1016/j.neuroimage.2010.02.082},
	author = {Pierre Bellec and Pedro Rosa-Neto and Oliver C. Lyttelton and Habib Benali and Alan C. Evans},
	keywords = {Bootstrap, Clustering, Functional MRI, Hierarchical clustering, -Means, Multi-level analysis, Resting-state networks, Stability analysis},
	abstract = {A variety of methods have been developed to identify brain networks with spontaneous, coherent activity in resting-state functional magnetic resonance imaging (fMRI). We propose here a generic statistical framework to quantify the stability of such resting-state networks (RSNs), which was implemented with k-means clustering. The core of the method consists in bootstrapping the available datasets to replicate the clustering process a large number of times and quantify the stable features across all replications. This bootstrap analysis of stable clusters (BASC) has several benefits: (1) it can be implemented in a multi-level fashion to investigate stable RSNs at the level of individual subjects and at the level of a group; (2) it provides a principled measure of RSN stability; and (3) the maximization of the stability measure can be used as a natural criterion to select the number of RSNs. A simulation study validated the good performance of the multi-level BASC on purely synthetic data. Stable networks were also derived from a real resting-state study for 43 subjects. At the group level, seven RSNs were identified which exhibited a good agreement with the previous findings from the literature. The comparison between the individual and group-level stability maps demonstrated the capacity of BASC to establish successful correspondences between these two levels of analysis and at the same time retain some interesting subject-specific characteristics, e.g. the specific involvement of subcortical regions in the visual and fronto-parietal networks for some subjects.}
}

@article{Bowring2019,
    title={Exploring the impact of analysis software on task fMRI results},
    author={Bowring, Alexander and Maumet, Camille and Nichols, Thomas E},
    journal={Human brain mapping},
    volume={40},
    number={11},
    pages={3362--3384},
    year={2019},
    publisher={Wiley Online Library},
}

@article{Chen2022,
	title = {Sources of information waste in neuroimaging: mishandling structures, thinking dichotomously, and over-reducing data.},
	journal = {Aperture Neuro},
	year = {2022},
	doi = {10.52294/ApertureNeuro.2022.2.ZRJI8542},
	author = {Chen, G. and Taylor, P. A. and Stoddard, J. and Cox, R. W. and Bandettini, P. A. and Pessoa, L.},
}

@article{Chen2011,
	title = {Cortical surface-based searchlight decoding},
	journal = {NeuroImage},
	volume = {56},
	number = {2},
	pages = {582-592},
	year = {2011},
	note = {Multivariate Decoding and Brain Reading},
	issn = {1053-8119},
	doi = {10.1016/j.neuroimage.2010.07.035},
	author = {Yi Chen and Praneeth Namburi and Lloyd T. Elliott and Jakob Heinzle and Chun Siong Soon and Michael W.L. Chee and John-Dylan Haynes},
	abstract = {Local voxel patterns of fMRI signals contain specific information about cognitive processes ranging from basic sensory processing to high level decision making. These patterns can be detected using multivariate pattern classification, and localization of these patterns can be achieved with searchlight methods in which the information content of spherical sub-volumes of the fMRI signal is assessed. The only assumption made by this approach is that the patterns are spatially local. We present a cortical surface-based searchlight approach to pattern localization. Voxels are grouped according to distance along the cortical surface—the intrinsic metric of cortical anatomy—rather than Euclidean distance as in volumetric searchlights. Using a paradigm in which the category of visually presented objects is decoded, we compare the surface-based method to a standard volumetric searchlight technique. Group analyses of accuracy maps produced by both methods show similar distributions of informative regions. The surface-based method achieves a finer spatial specificity with comparable peak values of significance, while the volumetric method appears to be more sensitive to small informative regions and might also capture information not located directly within the gray matter. Furthermore, our findings show that a surface centered in the middle of the gray matter contains more information than to the white–gray boundary or the pial surface.}
}

@article{Ciric2017,
	title = {Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity},
	volume = {154},
	issn = {10959572},
	doi = {10.1016/j.neuroimage.2017.03.020},
	abstract = {Since initial reports regarding the impact of motion artifact on measures of functional connectivity, there has been a proliferation of participant-level confound regression methods to limit its impact. However, many of the most commonly used techniques have not been systematically evaluated using a broad range of outcome measures. Here, we provide a systematic evaluation of 14 participant-level confound regression methods in 393 youths. Specifically, we compare methods according to four benchmarks, including the residual relationship between motion and connectivity, distance-dependent effects of motion on connectivity, network identifiability, and additional degrees of freedom lost in confound regression. Our results delineate two clear trade-offs among methods. First, methods that include global signal regression minimize the relationship between connectivity and motion, but result in distance-dependent artifact. In contrast, censoring methods mitigate both motion artifact and distance-dependence, but use additional degrees of freedom. Importantly, less effective de-noising methods are also unable to identify modular network structure in the connectome. Taken together, these results emphasize the heterogeneous efficacy of existing methods, and suggest that different confound regression strategies may be appropriate in the context of specific scientific goals.},
	pages = {174--187},
	number = {1},
	journal = {NeuroImage},
	author = {Ciric, Rastko and Wolf, Daniel H. and Power, Jonathan D. and Roalf, David R. and Baum, Graham L. and Ruparel, Kosha and Shinohara, Russell T. and Elliott, Mark A. and Eickhoff, Simon B. and Davatzikos, Christos and Gur, Ruben C. and Gur, Raquel E. and Bassett, Danielle S. and Satterthwaite, Theodore D.},
	year = {2017},
	pmid = {28302591},
	keywords = {{fMRI}, Functional connectivity, \#nosource, Artifact, Confound, Motion, Noise},
}

@article{Cisler2014,
    title={A comparison of statistical methods for detecting context-modulated functional connectivity in fMRI},
    author={Cisler, Josh M and Bush, Keith and Steele, J Scott},
    journal={Neuroimage},
    volume={84},
    pages={1042--1052},
    year={2014},
    publisher={Elsevier}
}

@article {Clarke2014,
	author = {Clarke, Alex and Tyler, Lorraine K.},
	title = {Object-Specific Semantic Coding in Human Perirhinal Cortex},
	volume = {34},
	number = {14},
	pages = {4766--4775},
	year = {2014},
	doi = {10.1523/JNEUROSCI.2828-13.2014},
	publisher = {Society for Neuroscience},
	abstract = {Category-specificity has been demonstrated in the human posterior ventral temporal cortex for a variety of object categories. Although object representations within the ventral visual pathway must be sufficiently rich and complex to support the recognition of individual objects, little is known about how specific objects are represented. Here, we used representational similarity analysis to determine what different kinds of object information are reflected in fMRI activation patterns and uncover the relationship between categorical and object-specific semantic representations. Our results show a gradient of informational specificity along the ventral stream from representations of image-based visual properties in early visual cortex, to categorical representations in the posterior ventral stream. A key finding showed that object-specific semantic information is uniquely represented in the perirhinal cortex, which was also increasingly engaged for objects that are more semantically confusable. These findings suggest a key role for the perirhinal cortex in representing and processing object-specific semantic information that is more critical for highly confusable objects. Our findings extend current distributed models by showing coarse dissociations between objects in posterior ventral cortex, and fine-grained distinctions between objects supported by the anterior medial temporal lobes, including the perirhinal cortex, which serve to integrate complex object information.},
	issn = {0270-6474},
	pdf = {https://www.jneurosci.org/content/34/14/4766.full.pdf},
	journal = {Journal of Neuroscience}
}

@article{Collins1997,
  title={Animal: validation and applications of nonlinear registration-based segmentation},
  author={Collins, D. Louis and Evans, Alan C.},
  journal={International journal of pattern recognition and artificial intelligence},
  volume={11},
  number={08},
  pages={1271--1294},
  year={1997},
  publisher={World Scientific}
}

@article{Collins1998,
  author={Collins, D.L. and Zijdenbos, A.P. and Kollokian, V. and Sled, J.G. and Kabani, N.J. and Holmes, C.J. and Evans, A.C.},
  journal={IEEE Transactions on Medical Imaging},
  title={Design and construction of a realistic digital brain phantom},
  year={1998},
  volume={17},
  number={3},
  pages={463-468},
  keywords={Imaging phantoms;Brain modeling;Algorithm design and analysis;Image analysis;Testing;Biomedical image processing;Medical simulation;In vivo;Computational modeling;Solid modeling},
  doi={10.1109/42.712135}
}

@inproceedings{Collins1999,
	author="Collins, D. Louis
	and Zijdenbos, Alex P.
	and Baar{\'e}, Wim F. C.
	and Evans, Alan C.",
	editor="Kuba, Attila
	and {\v{S}}{\'a}amal, Martin
	and Todd-Pokropek, Andrew",
	title="ANIMAL+INSECT: Improved Cortical Structure Segmentation",
	booktitle="Information Processing in Medical Imaging",
	year="1999",
	publisher="Springer Berlin Heidelberg",
	address="Berlin, Heidelberg",
	pages="210--223",
	abstract="An algorithm for improved automatic segmentation of gross anatomical structures of the human brain is presented that merges the output of a tissue classification process with gross anatomical region masks, automatically defined by non-linear registration of a given data set with a probabilistic anatomical atlas. Experiments with 20 real MRI volumes demonstrate that the method is reliable, robust and accurate. Manually and automatically defined labels of specific gyri of the frontal lobe are similar, with a Kappa index of 0.657.",
	isbn="978-3-540-48714-2"
}

@article{Craddock2012,
	author = {Craddock, R. Cameron and James, G.Andrew and Holtzheimer III, Paul E. and Hu, Xiaoping P. and Mayberg, Helen S.},
	title = {A whole brain fMRI atlas generated via spatially constrained spectral clustering},
	journal = {Human Brain Mapping},
	volume = {33},
	number = {8},
	pages = {1914-1928},
	keywords = {resting state, functional connectivity, regions of interest, clustering, atlas},
	doi = {10.1002/hbm.21333},
	abstract = {Abstract Connectivity analyses and computational modeling of human brain function from fMRI data frequently require the specification of regions of interests (ROIs). Several analyses have relied on atlases derived from anatomical or cyto-architectonic boundaries to specify these ROIs, yet the suitability of atlases for resting state functional connectivity (FC) studies has yet to be established. This article introduces a data-driven method for generating an ROI atlas by parcellating whole brain resting-state fMRI data into spatially coherent regions of homogeneous FC. Several clustering statistics are used to compare methodological trade-offs as well as determine an adequate number of clusters. Additionally, we evaluate the suitability of the parcellation atlas against four ROI atlases (Talairach and Tournoux, Harvard-Oxford, Eickoff-Zilles, and Automatic Anatomical Labeling) and a random parcellation approach. The evaluated anatomical atlases exhibit poor ROI homogeneity and do not accurately reproduce FC patterns present at the voxel scale. In general, the proposed functional and random parcellations perform equivalently for most of the metrics evaluated. ROI size and hence the number of ROIs in a parcellation had the greatest impact on their suitability for FC analysis. With 200 or fewer ROIs, the resulting parcellations consist of ROIs with anatomic homology, and thus offer increased interpretability. Parcellation results containing higher numbers of ROIs (600 or 1,000) most accurately represent FC patterns present at the voxel scale and are preferable when interpretability can be sacrificed for accuracy. The resulting atlases and clustering software have been made publicly available at: https://www.nitrc.org/projects/cluster\_roi/. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc},
	year = {2012}
}

@article{Dadi2019,
	title = {Benchmarking functional connectome-based predictive models for resting-state fMRI},
	journal = {NeuroImage},
	volume = {192},
	pages = {115-134},
	year = {2019},
	issn = {1053-8119},
	doi = {10.1016/j.neuroimage.2019.02.062},
	author = {Kamalaker Dadi and Mehdi Rahim and Alexandre Abraham and Darya Chyzhyk and Michael Milham and Bertrand Thirion and Gaël Varoquaux},
	keywords = {Resting-state fMRI, Functional connectomes, Predictive modeling, Classification, Population study},
	abstract = {Functional connectomes reveal biomarkers of individual psychological or clinical traits. However, there is great variability in the analytic pipelines typically used to derive them from rest-fMRI cohorts. Here, we consider a specific type of studies, using predictive models on the edge weights of functional connectomes, for which we highlight the best modeling choices. We systematically study the prediction performances of models in 6 different cohorts and a total of 2000 individuals, encompassing neuro-degenerative (Alzheimer's, Post-traumatic stress disorder), neuro-psychiatric (Schizophrenia, Autism), drug impact (Cannabis use) clinical settings and psychological trait (fluid intelligence). The typical prediction procedure from rest-fMRI consists of three main steps: defining brain regions, representing the interactions, and supervised learning. For each step we benchmark typical choices: 8 different ways of defining regions –either pre-defined or generated from the rest-fMRI data– 3 measures to build functional connectomes from the extracted time-series, and 10 classification models to compare functional interactions across subjects. Our benchmarks summarize more than 240 different pipelines and outline modeling choices that show consistent prediction performances in spite of variations in the populations and sites. We find that regions defined from functional data work best; that it is beneficial to capture between-region interactions with tangent-based parametrization of covariances, a midway between correlations and partial correlation; and that simple linear predictors such as a logistic regression give the best predictions. Our work is a step forward to establishing reproducible imaging-based biomarkers for clinical settings.}
}

@article{Dadi2020,
	title = {Fine-grain atlases of functional modes for fMRI analysis},
	journal = {NeuroImage},
	volume = {221},
	pages = {117126},
	year = {2020},
	issn = {1053-8119},
	doi = {10.1016/j.neuroimage.2020.117126},
	url = {https://inria.hal.science/hal-02904869},
	author = {Kamalaker Dadi and Gaël Varoquaux and Antonia Machlouzarides-Shalit and Krzysztof J. Gorgolewski and Demian Wassermann and Bertrand Thirion and Arthur Mensch},
	keywords = {Brain imaging atlases, Functional networks, Functional parcellations, Multi-resolution},
	abstract = {Population imaging markedly increased the size of functional-imaging datasets, shedding new light on the neural basis of inter-individual differences. Analyzing these large data entails new scalability challenges, computational and statistical. For this reason, brain images are typically summarized in a few signals, for instance reducing voxel-level measures with brain atlases or functional modes. A good choice of the corresponding brain networks is important, as most data analyses start from these reduced signals. We contribute finely-resolved atlases of functional modes, comprising from 64 to 1024 networks. These dictionaries of functional modes (DiFuMo) are trained on millions of fMRI functional brain volumes of total size 2.4 ​TB, spanned over 27 studies and many research groups. We demonstrate the benefits of extracting reduced signals on our fine-grain atlases for many classic functional data analysis pipelines: stimuli decoding from 12,334 brain responses, standard GLM analysis of fMRI across sessions and individuals, extraction of resting-state functional-connectomes biomarkers for 2500 individuals, data compression and meta-analysis over more than 15,000 statistical maps. In each of these analysis scenarii, we compare the performance of our functional atlases with that of other popular references, and to a simple voxel-level analysis. Results highlight the importance of using high-dimensional “soft” functional atlases, to represent and analyze brain activity while capturing its functional gradients. Analyses on high-dimensional modes achieve similar statistical performance as at the voxel level, but with much reduced computational cost and higher interpretability. In addition to making them available, we provide meaningful names for these modes, based on their anatomical location. It will facilitate reporting of results.}
}

@article{Dale1999,
	title = {Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction},
	journal = {NeuroImage},
	volume = {9},
	number = {2},
	pages = {179-194},
	year = {1999},
	issn = {1053-8119},
	doi = {10.1006/nimg.1998.0395},
	author = {Anders M. Dale and Bruce Fischl and Martin I. Sereno},
	keywords = {segmentation, cortical surface reconstruction.},
	abstract = {Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the cortical surface. In order to study such cortical properties in humans, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Here we describe a set of automated procedures for obtaining accurate reconstructions of the cortical surface, which have been applied to data from more than 100 subjects, requiring little or no manual intervention. Automated routines for unfolding and flattening the cortical surface are described in a companion paper. These procedures allow for the routine use of cortical surface-based analysis and visualization methods in functional brain imaging.}
}

@book{Davidson2004,
    address = {New York, NY [u.a.]},
    author = {Davidson, {Russell} and MacKinnon, {James G.}},
    isbn = {978-0-19-512372-2},
    keywords = {Econometrics Methoden_und_Techniken_der_Betriebswirtschaft Methoden_und_Techniken_der_Volkswirtschaft Schätzfunktion Ökonometrie},
    publisher = {Oxford Univ. Press},
    title = {Econometric theory and methods},
    year = 2004,
    pages = {662}
}

@article{Dehaene2006,
	title={Functional segregation of cortical language areas by sentence repetition},
	author={Dehaene-Lambertz, Ghislaine and Dehaene, Stanislas and Anton, Jean-Luc and Campagne, Aurelie and Ciuciu, Philippe and Dehaene, Guillaume P and Denghien, Isabelle and Jobert, Antoinette and LeBihan, Denis and Sigman, Mariano and others},
	journal={Human brain mapping},
	volume={27},
	number={5},
	pages={360--371},
	year={2006},
	publisher={Wiley Online Library},
	url={https://pmc.ncbi.nlm.nih.gov/articles/PMC6871319/}
}

@article{Desikan2006,
  title={An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest},
  author={Desikan, Rahul S and S{\'e}gonne, Florent and Fischl, Bruce and Quinn, Brian T and Dickerson, Bradford C and Blacker, Deborah and Buckner, Randy L and Dale, Anders M and Maguire, R Paul and Hyman, Bradley T and others},
  journal={Neuroimage},
  volume={31},
  number={3},
  pages={968--980},
  year={2006},
  publisher={Elsevier}
}

@article{Destrieux2010,
	title = {Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature},
	journal = {NeuroImage},
	volume = {53},
	number = {1},
	pages = {1-15},
	year = {2010},
	issn = {1053-8119},
	doi = {10.1016/j.neuroimage.2010.06.010},
	author = {Christophe Destrieux and Bruce Fischl and Anders Dale and Eric Halgren},
	keywords = {Anatomy, Atlas, Brain, Cerebral cortex, MRI},
	abstract = {Precise localization of sulco-gyral structures of the human cerebral cortex is important for the interpretation of morpho-functional data, but requires anatomical expertise and is time consuming because of the brain's geometric complexity. Software developed to automatically identify sulco-gyral structures has improved substantially as a result of techniques providing topologically correct reconstructions permitting inflated views of the human brain. Here we describe a complete parcellation of the cortical surface using standard internationally accepted nomenclature and criteria. This parcellation is available in the FreeSurfer package. First, a computer-assisted hand parcellation classified each vertex as sulcal or gyral, and these were then subparcellated into 74 labels per hemisphere. Twelve datasets were used to develop rules and algorithms (reported here) that produced labels consistent with anatomical rules as well as automated computational parcellation. The final parcellation was used to build an atlas for automatically labeling the whole cerebral cortex. This atlas was used to label an additional 12 datasets, which were found to have good concordance with manual labels. This paper presents a precisely defined method for automatically labeling the cortical surface in standard terminology.}
}

@article{Destrieux2009,
	author = {Destrieux, C and Fischl, B and Dale, AM and Halgren, E},
	doi = {10.1016/S1053-8119(09)71561-7},
	journal = {NeuroImage},
	language = {English},
	number = {Supplement 1},
	pages = {S151},
	title = {A sulcal depth-based anatomical parcellation of the cerebral cortex.},
	volume = {47},
	year = {2009}
}

@inproceedings{Dohmatob2015,
    TITLE = {{Speeding-up model-selection in GraphNet via early-stopping and univariate feature-screening}},
    AUTHOR = {Dohmatob, Elvis and Eickenberg, Michael and Thirion, Bertrand and Varoquaux, Ga{\"e}l},
    URL = {https://inria.hal.science/hal-01147731},
    BOOKTITLE = {{PRNI}},
    ADDRESS = {Stanford, United States},
    YEAR = {2015},
    MONTH = Jun,
    KEYWORDS = {univariate feature-screening ; model-selection ; cross-validation ; MRI ; supervised learning ; pattern-recognition ; sparsity ; GraphNet ; S-Lasso ; TV-L1 ; spatial priors},
    PDF = {https://inria.hal.science/hal-01147731/file/paper.pdf},
    HAL_ID = {hal-01147731},
    HAL_VERSION = {v1},
}

@inproceedings{Dohmatob2014,
    TITLE = {{Benchmarking solvers for TV-l1 least-squares and logistic regression in brain imaging}},
    AUTHOR = {Dohmatob, Elvis and Gramfort, Alexandre and Thirion, Bertrand and Varoquaux, Ga{\"e}l},
    URL = {https://inria.hal.science/hal-00991743},
    BOOKTITLE = {{PRNI 2014 - 4th International Workshop on Pattern Recognition in NeuroImaging}},
    ADDRESS = {T{\"u}bingen, Germany},
    PUBLISHER = {{IEEE}},
    YEAR = {2014},
    MONTH = Jun,
    KEYWORDS = {sparse models ; fMRI ; non-smooth convex optimization ; regression ; classification ; Total Variation},
    PDF = {https://inria.hal.science/hal-00991743/file/PRNI2014_TVl1.pdf},
    HAL_ID = {hal-00991743},
    HAL_VERSION = {v1},
}

@article {Dosenbach2010,
	author = {Dosenbach, Nico U. F. and Nardos, Binyam and Cohen, Alexander L. and Fair, Damien A. and Power, Jonathan D. and Church, Jessica A. and Nelson, Steven M. and Wig, Gagan S. and Vogel, Alecia C. and Lessov-Schlaggar, Christina N. and Barnes, Kelly Anne and Dubis, Joseph W. and Feczko, Eric and Coalson, Rebecca S. and Pruett, John R. and Barch, Deanna M. and Petersen, Steven E. and Schlaggar, Bradley L.},
	title = {Prediction of Individual Brain Maturity Using fMRI},
	volume = {329},
	number = {5997},
	pages = {1358--1361},
	year = {2010},
	doi = {10.1126/science.1194144},
	publisher = {American Association for the Advancement of Science},
	abstract = {Group functional connectivity magnetic resonance imaging (fcMRI) studies have documented reliable changes in human functional brain maturity over development. Here we show that support vector machine-based multivariate pattern analysis extracts sufficient information from fcMRI data to make accurate predictions about individuals{\textquoteright} brain maturity across development. The use of only 5 minutes of resting-state fcMRI data from 238 scans of typically developing volunteers (ages 7 to 30 years) allowed prediction of individual brain maturity as a functional connectivity maturation index. The resultant functional maturation curve accounted for 55\% of the sample variance and followed a nonlinear asymptotic growth curve shape. The greatest relative contribution to predicting individual brain maturity was made by the weakening of short-range functional connections between the adult brain{\textquoteright}s major functional networks.},
	issn = {0036-8075},
	journal = {Science}
}

@article{Duchi2012,
	title = {Projected subgradient methods for learning sparse gaussians},
	author = {Duchi, John and Gould, Stephen and Koller, Daphne},
	year = {2012},
	month = {06},
	abstract = {Gaussian Markov random fields (GMRFs) are useful in a broad range of applications. In this paper we tackle the problem of learning a sparse GMRF in a high-dimensional space. Our approach uses the l1-norm as a regularization on the inverse covariance matrix. We utilize a novel projected gradient method, which is faster than previous methods in practice and equal to the best performing of these in asymptotic complexity. We also extend the l1-regularized objective to the problem of sparsifying entire blocks within the inverse covariance matrix. Our methods generalize fairly easily to this case, while other methods do not. We demonstrate that our extensions give better generalization performance on two real domains--biological network analysis and a 2D-shape modeling image task.},
	eprint = {1206.3249},
	journal={arXiv},
}

@article{Etzel2013,
    title = {Searchlight analysis: Promise, pitfalls, and potential},
    journal = {NeuroImage},
    volume = {78},
    pages = {261-269},
    year = {2013},
    issn = {1053-8119},
    doi = {10.1016/j.neuroimage.2013.03.041},
    author = {Joset A. Etzel and Jeffrey M. Zacks and Todd S. Braver},
    keywords = {Functional magnetic resonance imaging (fMRI), Multivariate pattern analysis (MVPA), Searchlight analysis, Support vector machines (SVM), Information mapping, Classification},
    abstract = {Multivariate pattern analysis (MVPA) is an increasingly popular approach for characterizing the information present in neural activity as measured by fMRI. For neuroimaging researchers, the searchlight technique serves as the most intuitively appealing means of implementing MVPA with fMRI data. However, searchlight approaches carry with them a number of special concerns and limitations that can lead to serious interpretation errors in practice, such as misidentifying a cluster as informative, or failing to detect truly informative voxels. Here we describe how such distorted results can occur, using both schematic illustrations and examples from actual fMRI datasets. We recommend that confirmatory and sensitivity tests, such as the ones prescribed here, should be considered a necessary stage of searchlight analysis interpretation, and that their adoption will allow the full potential of searchlight analysis to be realized.}
}

@article {Filippini2009,
	author = {Filippini, Nicola and MacIntosh, Bradley J. and Hough, Morgan G. and Goodwin, Guy M. and Frisoni, Giovanni B. and Smith, Stephen M. and Matthews, Paul M. and Beckmann, Christian F. and Mackay, Clare E.},
	title = {Distinct patterns of brain activity in young carriers of the APOE-ε4 allele},
	volume = {106},
	number = {17},
	pages = {7209--7214},
	year = {2009},
	doi = {10.1073/pnas.0811879106},
	publisher = {National Academy of Sciences},
	abstract = {The APOE ε4 allele is a risk factor for late-life pathological changes that is also associated with anatomical and functional brain changes in middle-aged and elderly healthy subjects. We investigated structural and functional effects of the APOE polymorphism in 18 young healthy APOE ε4-carriers and 18 matched noncarriers (age range: 20{\textendash}35 years). Brain activity was studied both at rest and during an encoding memory paradigm using blood oxygen level-dependent fMRI. Resting fMRI revealed increased {\textquotedblleft}default mode network{\textquotedblright} (involving retrosplenial, medial temporal, and medial-prefrontal cortical areas) coactivation in ε4-carriers relative to noncarriers. The encoding task produced greater hippocampal activation in ε4-carriers relative to noncarriers. Neither result could be explained by differences in memory performance, brain morphology, or resting cerebral blood flow. The APOE ε4 allele modulates brain function decades before any clinical or neurophysiological expression of neurodegenerative processes.},
	issn = {0027-8424},
	pdf = {https://www.pnas.org/content/106/17/7209.full.pdf},
	journal = {Proceedings of the National Academy of Sciences}
}

@article{Fischl2004,
	author = {Fischl, Bruce and van der Kouwe, André and Destrieux, Christophe and Halgren, Eric and Ségonne, Florent and Salat, David H. and Busa, Evelina and Seidman, Larry J. and Goldstein, Jill and Kennedy, David and Caviness, Verne and Makris, Nikos and Rosen, Bruce and Dale, Anders M.},
	title = "{Automatically Parcellating the Human Cerebral Cortex}",
	journal = {Cerebral Cortex},
	volume = {14},
	number = {1},
	pages = {11-22},
	year = {2004},
	month = {01},
	abstract = "{We present a technique for automatically assigning a neuroanatomical label to each location on a cortical surface model based on probabilistic information estimated from a manually labeled training set. This procedure incorporates both geometric information derived from the cortical model, and neuroanatomical convention, as found in the training set. The result is a complete labeling of cortical sulci and gyri. Examples are given from two different training sets generated using different neuroanatomical conventions, illustrating the flexibility of the algorithm. The technique is shown to be comparable in accuracy to manual labeling.}",
	issn = {1047-3211},
	doi = {10.1093/cercor/bhg087},
}

@article{Fischl1999,
	author = {Fischl, Bruce and Sereno, Martin I. and Tootell, Roger B.H. and Dale, Anders M.},
	title = {High-resolution intersubject averaging and a coordinate system for the cortical surface},
	journal = {Human Brain Mapping},
	volume = {8},
	number = {4},
	pages = {272-284},
	keywords = {intersubject averaging, coordinate systems, atlas},
	doi = {10.1002/(SICI)1097-0193(1999)8:4<272::AID-HBM10>3.0.CO;2-4},
	abstract = {Abstract The neurons of the human cerebral cortex are arranged in a highly folded sheet, with the majority of the cortical surface area buried in folds. Cortical maps are typically arranged with a topography oriented parallel to the cortical surface. Despite this unambiguous sheetlike geometry, the most commonly used coordinate systems for localizing cortical features are based on 3-D stereotaxic coordinates rather than on position relative to the 2-D cortical sheet. In order to address the need for a more natural surface-based coordinate system for the cortex, we have developed a means for generating an average folding pattern across a large number of individual subjects as a function on the unit sphere and of nonrigidly aligning each individual with the average. This establishes a spherical surface-based coordinate system that is adapted to the folding pattern of each individual subject, allowing for much higher localization accuracy of structural and functional features of the human brain. Hum. Brain Mapping 8:272–284, 1999. © 1999 Wiley-Liss, Inc.},
	year = {1999}
}

@article{Fischl1999b,
	title = {Cortical Surface-Based Analysis: II: Inflation, Flattening, and a Surface-Based Coordinate System},
	journal = {NeuroImage},
	volume = {9},
	number = {2},
	pages = {195-207},
	year = {1999},
	issn = {1053-8119},
	doi = {10.1006/nimg.1998.0396},
	author = {Bruce Fischl and Martin I. Sereno and Anders M. Dale},
	keywords = {Cortical surface reconstruction, flattening, coordinate systems, atlas.},
	abstract = {The surface of the human cerebral cortex is a highly folded sheet with the majority of its surface area buried within folds. As such, it is a difficult domain for computational as well as visualization purposes. We have therefore designed a set of procedures for modifying the representation of the cortical surface to (i) inflate it so that activity buried inside sulci may be visualized, (ii) cut and flatten an entire hemisphere, and (iii) transform a hemisphere into a simple parameterizable surface such as a sphere for the purpose of establishing a surface-based coordinate system.}
}

@book{Fisher1935,
    added-at = {2009-10-28T04:42:52.000+0100},
    address = {Edinburgh},
    author = {Fisher, R.A.},
    biburl = {https://www.bibsonomy.org/bibtex/2848c0d82200028f9225bc3a62b773289/jwbowers},
    date-added = {2007-09-03 22:45:16 -0500},
    date-modified = {2007-09-03 22:45:16 -0500},
    interhash = {9342665c9b607c072447f16c0b9127df},
    intrahash = {848c0d82200028f9225bc3a62b773289},
    journal = {Oliver \& Boyd Edinburgh, Scotland},
    keywords = {imported},
    publisher = {Oliver and Boyd},
    timestamp = {2009-10-28T04:43:07.000+0100},
    title = {{The design of experiments. 1935}},
    year = 1935
}

@article{Fletcher2007,
	title = {Riemannian geometry for the statistical analysis of diffusion tensor data},
	journal = {Signal Processing},
	volume = {87},
	number = {2},
	pages = {250-262},
	year = {2007},
	note = {Tensor Signal Processing},
	issn = {0165-1684},
	doi = {10.1016/j.sigpro.2005.12.018},
	author = {P. Thomas Fletcher and Sarang Joshi},
	keywords = {Diffusion tensor MRI, Statistics, Riemannian manifolds},
	abstract = {The tensors produced by diffusion tensor magnetic resonance imaging (DT-MRI) represent the covariance in a Brownian motion model of water diffusion. Under this physical interpretation, diffusion tensors are required to be symmetric, positive-definite. However, current approaches to statistical analysis of diffusion tensor data, which treat the tensors as linear entities, do not take this positive-definite constraint into account. This difficulty is due to the fact that the space of diffusion tensors does not form a vector space. In this paper we show that the space of diffusion tensors is a type of curved manifold known as a Riemannian symmetric space. We then develop methods for producing statistics, namely averages and modes of variation, in this space. We show that these statistics preserve natural geometric properties of the tensors, including the constraint that their eigenvalues be positive. The symmetric space formulation also leads to a natural definition for interpolation of diffusion tensors and a new measure of anisotropy. We expect that these methods will be useful in the registration of diffusion tensor images, the production of statistical atlases from diffusion tensor data, and the quantification of the anatomical variability caused by disease. The framework presented in this paper should also be useful in other applications where symmetric, positive-definite tensors arise, such as mechanics and computer vision.}
}

@article{Fonov2011,
	title = {Unbiased average age-appropriate atlases for pediatric studies},
	journal = {NeuroImage},
	volume = {54},
	number = {1},
	pages = {313-327},
	year = {2011},
	issn = {1053-8119},
	doi = {10.1016/j.neuroimage.2010.07.033},
	author = {Vladimir Fonov and Alan C. Evans and Kelly Botteron and C. Robert Almli and Robert C. McKinstry and D. Louis Collins},
	keywords = {Atlas template, Registration, Pediatric image analysis},
	abstract = {Spatial normalization, registration, and segmentation techniques for Magnetic Resonance Imaging (MRI) often use a target or template volume to facilitate processing, take advantage of prior information, and define a common coordinate system for analysis. In the neuroimaging literature, the MNI305 Talairach-like coordinate system is often used as a standard template. However, when studying pediatric populations, variation from the adult brain makes the MNI305 suboptimal for processing brain images of children. Morphological changes occurring during development render the use of age-appropriate templates desirable to reduce potential errors and minimize bias during processing of pediatric data. This paper presents the methods used to create unbiased, age-appropriate MRI atlas templates for pediatric studies that represent the average anatomy for the age range of 4.5–18.5years, while maintaining a high level of anatomical detail and contrast. The creation of anatomical T1-weighted, T2-weighted, and proton density-weighted templates for specific developmentally important age-ranges, used data derived from the largest epidemiological, representative (healthy and normal) sample of the U.S. population, where each subject was carefully screened for medical and psychiatric factors and characterized using established neuropsychological and behavioral assessments. Use of these age-specific templates was evaluated by computing average tissue maps for gray matter, white matter, and cerebrospinal fluid for each specific age range, and by conducting an exemplar voxel-wise deformation-based morphometry study using 66 young (4.5–6.9years) participants to demonstrate the benefits of using the age-appropriate templates. The public availability of these atlases/templates will facilitate analysis of pediatric MRI data and enable comparison of results between studies in a common standardized space specific to pediatric research.}
}

@article{Fonov2009,
	author = {Fonov, VS and Evans, AC and McKinstry, RC and Almli, CR and Collins, DL},
	doi = {10.1016/S1053-8119(09)70884-5},
	journal = {NeuroImage},
	language = {English},
	number = {Supplement 1},
	pages = {S102},
	title = {Unbiased nonlinear average age-appropriate brain templates from birth to adulthood},
	volume = {47},
	year = {2009},
}

@article{Fox2005,
	title = {The human brain is intrinsically organized into dynamic, anticorrelated functional networks},
	volume = {102},
	issn = {0027-8424},
	doi = {10.1073/pnas.0504136102},
	number = {27},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Fox, Michael D. and Snyder, Abraham Z. and Vincent, Justin L and Corbetta, Maurizio and Van Essen, David C. and Raichle, Marcus E.},
	month = jul,
	year = {2005},
	pmid = {15976020},
	pages = {9673--9678},
}

@article{Frazier2005,
	title={Structural brain magnetic resonance imaging of limbic and thalamic volumes in pediatric bipolar disorder},
	author={Frazier, Jean A and Chiu, Sufen and Breeze, Janis L and Makris, Nikos and Lange, Nicholas and Kennedy, David N and Herbert, Martha R and Bent, Eileen K and Koneru, Vamsi K and Dieterich, Megan E and others},
	journal={American Journal of Psychiatry},
	volume={162},
	number={7},
	pages={1256--1265},
	year={2005},
	publisher={Am Psychiatric Assoc}
}

@article{Freedman1983,
    author = {David Freedman and David Lane},
    title = {A Nonstochastic Interpretation of Reported Significance Levels},
    journal = {Journal of Business \& Economic Statistics},
    volume = {1},
    number = {4},
    pages = {292-298},
    year  = {1983},
    publisher = {Taylor & Francis},
    doi = {10.1080/07350015.1983.10509354},
    abstract = {Tests of significance are often made in situations where the standard assumptions underlying the probability calculations do not hold. As a result, the reported significance levels become difficult to interpret. This article sketches an alternative interpretation of a reported significance level, valid in considerable generality. This level locates the given data set within the spectrum of other data sets derived from the given one by an appropriate class of transformations. If the null hypothesis being tested holds, the derived data sets should be equivalent to the original one. Thus, a small reported significance level indicates an unusual data set. This development parallels that of randomization tests, but there is a crucial technical difference: our approach involves permuting observed residuals; the classical randomization approach involves permuting unobservable, or perhaps nonexistent, stochastic disturbance terms.}
}

@article{Friedman2008,
	title = {Sparse inverse covariance estimation with the graphical lasso},
	volume = {9},
	issn = {1465-4644, 1468-4357},
	doi = {10.1093/biostatistics/kxm045},
	language = {en},
	number = {3},
	urldate = {2024-02-02},
	journal = {Biostatistics},
	author = {Friedman, J. and Hastie, T. and Tibshirani, R.},
	month = jul,
	year = {2008},
	pages = {432--441},
	file = {Full Text:/home/remi/Zotero/storage/7CMGBPTH/Friedman et al. - 2008 - Sparse inverse covariance estimation with the grap.pdf:application/pdf},
}

@article{Friston1994,
	author = {Friston, K. J. and Holmes, A. P. and Worsley, K. J. and Poline, J.-P. and Frith, C. D. and Frackowiak, R. S. J.},
	title = {Statistical parametric maps in functional imaging: A general linear approach},
	journal = {Human Brain Mapping},
	volume = {2},
	number = {4},
	pages = {189-210},
	keywords = {statistical parametric maps, analysis of variance, general linear model, statistics, functional imaging, Gaussian fields, functional anatomy},
	doi = {10.1002/hbm.460020402},
	abstract = {Abstract Statistical parametric maps are spatially extended statistical processes that are used to test hypotheses about regionally specific effects in neuroimaging data. The most established sorts of statistical parametric maps (e.g., Friston et al. [1991]: J Cereb Blood Flow Metab 11:690–699; Worsley et al. [1992]: J Cereb Blood Flow Metab 12:900–918) are based on linear models, for example ANCOVA, correlation coefficients and t tests. In the sense that these examples are all special cases of the general linear model it should be possible to implement them (and many others) within a unified framework. We present here a general approach that accommodates most forms of experimental layout and ensuing analysis (designed experiments with fixed effects for factors, covariates and interaction of factors). This approach brings together two well established bodies of theory (the general linear model and the theory of Gaussian fields) to provide a complete and simple framework for the analysis of imaging data. The importance of this framework is twofold: (i) Conceptual and mathematical simplicity, in that the same small number of operational equations is used irrespective of the complexity of the experiment or nature of the statistical model and (ii) the generality of the framework provides for great latitude in experimental design and analysis. © 1995 Wiley-Liss, Inc.},
	year = {1994}
}

@article{Giove2009,
	title = {Images-based suppression of unwanted global signals in resting-state functional connectivity studies},
	volume = {27},
	issn = {0730725X},
	doi = {10.1016/j.mri.2009.06.004},
	language = {en},
	number = {8},
	urldate = {2024-02-01},
	journal = {Magnetic Resonance Imaging},
	author = {Giove, Federico and Gili, Tommaso and Iacovella, Vittorio and Macaluso, Emiliano and Maraviglia, Bruno},
	month = oct,
	year = {2009},
	pages = {1058--1064},
}

@article{Goldstein2007,
	title={Hypothalamic abnormalities in schizophrenia: sex effects and genetic vulnerability},
	author={Goldstein, Jill M and Seidman, Larry J and Makris, Nikos and Ahern, Todd and O’Brien, Liam M and Caviness Jr, Verne S and Kennedy, David N and Faraone, Stephen V and Tsuang, Ming T},
	journal={Biological psychiatry},
	volume={61},
	number={8},
	pages={935--945},
	year={2007},
	publisher={Elsevier}
}


@article{Gorgolewski2015,
	AUTHOR={Gorgolewski, Krzysztof J. and Varoquaux, Gael and Rivera, Gabriel and Schwarz, Yannick and Ghosh, Satrajit S. and Maumet, Camille and Sochat, Vanessa V. and Nichols, Thomas E. and Poldrack, Russell A. and Poline, Jean-Baptiste and Yarkoni, Tal and Margulies, Daniel S.},
	TITLE={NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain},
	JOURNAL={Frontiers in Neuroinformatics},
	VOLUME={9},
	PAGES={8},
	YEAR={2015},
	DOI={10.3389/fninf.2015.00008},
	ISSN={1662-5196},
	ABSTRACT={Here we present NeuroVault—a web based repository that allows researchers to store, share, visualize, and decode statistical maps of the human brain. NeuroVault is easy to use and employs modern web technologies to provide informative visualization of data without the need to install additional software. In addition, it leverages the power of the Neurosynth database to provide cognitive decoding of deposited maps. The data are exposed through a public REST API enabling other services and tools to take advantage of it. NeuroVault is a new resource for researchers interested in conducting meta- and coactivation analyses.}
}

@inproceedings{Gramfort2013,
    title = {{Identifying predictive regions from fMRI with TV-L1 prior}},
    author = {Gramfort, Alexandre and Thirion, Bertrand and Varoquaux, Ga{\"e}l},
    url = {https://inria.hal.science/hal-00839984},
    booktitle = {{Pattern Recognition in Neuroimaging (PRNI)}},
    address = {Philadelphia, United States},
    publisher = {{IEEE}},
    year = {2013},
    month = Jun,
    keywords = {fMRI ; supervised learning ; total-variation ; sparse ; decoding ; primal-dual optimization ; support recovery},
    pdf = {https://inria.hal.science/hal-00839984/file/paper.pdf},
    HAL_ID = {hal-00839984},
    HAL_VERSION = {v1},
}

@book{Greene2003,
    author = {Greene, William H.},
    edition = {Fifth},
    isbn = {0-13-066189-9},
    keywords = {textbook statistics economics},
    publisher = {Pearson Education},
    title = {Econometric Analysis},
    url = {https://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm},
    year = 2003
}

@article{Grosenick2013,
    title = {Interpretable whole-brain prediction analysis with GraphNet},
    journal = {NeuroImage},
    volume = {72},
    pages = {304-321},
    year = {2013},
    issn = {1053-8119},
    doi = {10.1016/j.neuroimage.2012.12.062},
    author = {Logan Grosenick and Brad Klingenberg and Kiefer Katovich and Brian Knutson and Jonathan E. Taylor},
    abstract = {Multivariate machine learning methods are increasingly used to analyze neuroimaging data, often replacing more traditional “mass univariate” techniques that fit data one voxel at a time. In the functional magnetic resonance imaging (fMRI) literature, this has led to broad application of “off-the-shelf” classification and regression methods. These generic approaches allow investigators to use ready-made algorithms to accurately decode perceptual, cognitive, or behavioral states from distributed patterns of neural activity. However, when applied to correlated whole-brain fMRI data these methods suffer from coefficient instability, are sensitive to outliers, and yield dense solutions that are hard to interpret without arbitrary thresholding. Here, we develop variants of the Graph-constrained Elastic-Net (GraphNet), a fast, whole-brain regression and classification method developed for spatially and temporally correlated data that automatically yields interpretable coefficient maps (Grosenick et al., 2009b). GraphNet methods yield sparse but structured solutions by combining structured graph constraints (based on knowledge about coefficient smoothness or connectivity) with a global sparsity-inducing prior that automatically selects important variables. Because GraphNet methods can efficiently fit regression or classification models to whole-brain, multiple time-point data sets and enhance classification accuracy relative to volume-of-interest (VOI) approaches, they eliminate the need for inherently biased VOI analyses and allow whole-brain fitting without the multiple comparison problems that plague mass univariate and roaming VOI (“searchlight”) methods. As fMRI data are unlikely to be normally distributed, we (1) extend GraphNet to include robust loss functions that confer insensitivity to outliers, (2) equip them with “adaptive” penalties that asymptotically guarantee correct variable selection, and (3) develop a novel sparse structured Support Vector GraphNet classifier (SVGN). When applied to previously published data (Knutson et al., 2007), these efficient whole-brain methods significantly improved classification accuracy over previously reported VOI-based analyses on the same data (Grosenick et al., 2008, Knutson et al., 2007) while discovering task-related regions not documented in the original VOI approach. Critically, GraphNet estimates fit to the Knutson et al. (2007) data generalize well to out-of-sample data collected more than three years later on the same task but with different subjects and stimuli (Karmarkar et al., submitted for publication). By enabling robust and efficient selection of important voxels from whole-brain data taken over multiple time points (>100,000 “features”), these methods enable data-driven selection of brain areas that accurately predict single-trial behavior within and across individuals.}
}

@article {Haxby2001,
	author = {Haxby, James V. and Gobbini, M. Ida and Furey, Maura L. and Ishai, Alumit and Schouten, Jennifer L. and Pietrini, Pietro},
	title = {Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex},
	volume = {293},
	number = {5539},
	pages = {2425--2430},
	year = {2001},
	doi = {10.1126/science.1063736},
	publisher = {American Association for the Advancement of Science},
	abstract = {The functional architecture of the object vision pathway in the human brain was investigated using functional magnetic resonance imaging to measure patterns of response in ventral temporal cortex while subjects viewed faces, cats, five categories of man-made objects, and nonsense pictures. A distinct pattern of response was found for each stimulus category. The distinctiveness of the response to a given category was not due simply to the regions that responded maximally to that category, because the category being viewed also could be identified on the basis of the pattern of response when those regions were excluded from the analysis. Patterns of response that discriminated among all categories were found even within cortical regions that responded maximally to only one category. These results indicate that the representations of faces and objects in ventral temporal cortex are widely distributed and overlapping.},
	issn = {0036-8075},
	journal = {Science}
}

@article{Henson2003,
    author = {Henson, R.N. and Goshen-Gottstein, Y. and Ganel, T. and Otten, L.J. and Quayle, A. and Rugg, M.D.},
    title = "{Electrophysiological and Haemodynamic Correlates of Face Perception, Recognition and Priming}",
    journal = {Cerebral Cortex},
    volume = {13},
    number = {7},
    pages = {793-805},
    year = {2003},
    month = {07},
    abstract = "{Face perception, recognition and priming were examined with event-related functional magnetic resonance imaging (fMRI) and scalp event-related potentials (ERPs). Face perception was associated with haemodynamic increases in regions including bilateral fusiform and right superior temporal cortices, and a right posterior negativity (N170), most likely generated in the superior temporal region. Face recognition was associated with haemodynamic increases in fusiform, medial frontal and orbitofrontal cortices, and with a frontocentral positivity from 550 ms poststimulus. Face repetition was associated with a positivity from 400 to 600 ms and behavioural priming. Repetition of familiar faces was also associated with earlier onset of the ERP familiarity effect, and haemodynamic decreases in fusiform cortex. These data support a multi-component model of face-processing, with priming arising from more than one stage.}",
    issn = {1047-3211},
    doi = {10.1093/cercor/13.7.793},
}

@article{Honorio2012,
	title = {On the statistical efficiency of l1,p multi-task learning of Gaussian graphical models},
	author = {Honorio, Jean and Jaakkola, Tommi and Samaras, Dimitris},
	year = {2012},
	month = {10},
	abstract = {In this paper, we present l1,p multi-task structure learning for Gaussian graphical models. We analyze the sufficient number of samples for the correct recovery of the support union and edge signs. We also analyze the necessary number of samples for any conceivable method by providing information-theoretic lower bounds. We compare the statistical efficiency of multi-task learning versus that of single-task learning. For experiments, we use a block coordinate descent method that is provably convergent and generates a sequence of positive definite solutions. We provide experimental validation on synthetic data as well as on two publicly available real-world data sets, including functional magnetic resonance imaging and gene expression data.},
	eprint = {1207.4255},
	journal = {arXiv},
}


@article{Hoyos2019,
	author = {Hoyos-Idrobo, Andres and Varoquaux, Gael and Kahn, Jonas and Thirion, Bertrand},
	title = {Recursive Nearest Agglomeration (ReNA): Fast Clustering for Approximation of Structured Signals},
	year = {2019},
	publisher = {IEEE Computer Society},
	address = {USA},
	volume = {41},
	number = {3},
	issn = {0162-8828},
	doi = {10.1109/TPAMI.2018.2815524},
	abstract = {In this work, we revisit fast dimension reduction approaches, as with random projections and random sampling. Our goal is to summarize the data to decrease computational costs and memory footprint of subsequent analysis. Such dimension reduction can be very efficient when the signals of interest have a strong structure, such as with images. We focus on this setting and investigate feature clustering schemes for data reductions that capture this structure. An impediment to fast dimension reduction is then that good clustering comes with large algorithmic costs. We address it by contributing a linear-time agglomerative clustering scheme, Recursive Nearest Agglomeration (ReNA). Unlike existing fast agglomerative schemes, it avoids the creation of giant clusters. We empirically validate that it approximates the data as well as traditional variance-minimizing clustering schemes that have a quadratic complexity. In addition, we analyze signal approximation with feature clustering and show that it can remove noise, improving subsequent analysis steps. As a consequence, data reduction by clustering features with ReNA yields very fast and accurate models, enabling to process large datasets on budget. Our theoretical analysis is backed by extensive experiments on publicly-available data that illustrate the computation efficiency and the denoising properties of the resulting dimension reduction scheme.},
	journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
	month = {3},
	pages = {669–681},
	numpages = {13}
}

@article{Hoyos-Idrobo2018,
	title = {FReM – Scalable and stable decoding with fast regularized ensemble of models},
	journal = {NeuroImage},
	volume = {180},
	pages = {160-172},
	year = {2018},
	note = {New advances in encoding and decoding of brain signals},
	issn = {1053-8119},
	doi = {10.1016/j.neuroimage.2017.10.005},
	author = {Andrés Hoyos-Idrobo and Gaël Varoquaux and Yannick Schwartz and Bertrand Thirion},
	keywords = {fMRI, Supervised learning, Decoding, Bagging, MVPA},
	abstract = {Brain decoding relates behavior to brain activity through predictive models. These are also used to identify brain regions involved in the cognitive operations related to the observed behavior. Training such multivariate models is a high-dimensional statistical problem that calls for suitable priors. State of the art priors –eg small total-variation– enforce spatial structure on the maps to stabilize them and improve prediction. However, they come with a hefty computational cost. We build upon very fast dimension reduction with spatial structure and model ensembling to achieve decoders that are fast on large datasets and increase the stability of the predictions and the maps. Our approach, fast regularized ensemble of models (FReM), includes an implicit spatial regularization by using a voxel grouping with a fast clustering algorithm. In addition, it aggregates different estimators obtained across splits of a cross-validation loop, each time keeping the best possible model. Experiments on a large number of brain imaging datasets show that our combination of voxel clustering and model ensembling improves decoding maps stability and reduces the variance of prediction accuracy. Importantly, our method requires less samples than state-of-the-art methods to achieve a given level of prediction accuracy. Finally, FreM is much faster than other spatially-regularized methods and, in addition, it can better exploit parallel computing resources.}
}

@article{Jimura2012,
	title = {Analyses of regional-average activation and multivoxel pattern information tell complementary stories},
	journal = {Neuropsychologia},
	volume = {50},
	number = {4},
	pages = {544-552},
	year = {2012},
	note = {Multivoxel pattern analysis and cognitive theories},
	issn = {0028-3932},
	doi = {10.1016/j.neuropsychologia.2011.11.007},
	author = {Koji Jimura and Russell A. Poldrack},
	keywords = {fMRI, Decision-making, Support vector regression, MVPA, Univariate analysis},
	abstract = {Multivariate pattern analysis (MVPA) has recently received increasing attention in functional neuroimaging due to its ability to decode mental states from fMRI signals. However, questions remain regarding both the empirical and conceptual relationships between results from MVPA and standard univariate analyses. In the current study, whole-brain univariate and searchlight MVPAs of parametric manipulations of monetary gain and loss in a decision making task (Tom et al., 2007) were compared to identify the differences in the results across these methods and the implications for understanding the underlying mental processes. The MVPA and univariate results did identify some overlapping regions in whole brain analyses. However, an analysis of consistency revealed that in many regions the effect size estimates obtained from MVPA and univariate analysis were uncorrelated. Moreover, comparison of sensitivity showed a general trend towards greater sensitivity to task manipulations by MVPA compared to univariate analysis. These results demonstrate that MVPA methods may provide a different view of the functional organization of mental processing compared to univariate analysis, wherein MVPA is more sensitive to distributed coding of information whereas univariate analysis is more sensitive to global engagement in ongoing tasks. The results also highlight the need for better ways to integrate these methods.}
}

@article{Kahnt2011,
    title = {Perceptual Learning and Decision-Making in Human Medial Frontal Cortex},
    journal = {Neuron},
    volume = {70},
    number = {3},
    pages = {549-559},
    year = {2011},
    issn = {0896-6273},
    doi = {10.1016/j.neuron.2011.02.054},
    author = {Thorsten Kahnt and Marcus Grueschow and Oliver Speck and John-Dylan Haynes},
    abstract = {The dominant view that perceptual learning is accompanied by changes in early sensory representations has recently been challenged. Here we tested the idea that perceptual learning can be accounted for by reinforcement learning involving changes in higher decision-making areas. We trained subjects on an orientation discrimination task involving feedback over 4 days, acquiring fMRI data on the first and last day. Behavioral improvements were well explained by a reinforcement learning model in which learning leads to enhanced readout of sensory information, thereby establishing noise-robust representations of decision variables. We find stimulus orientation encoded in early visual and higher cortical regions such as lateral parietal cortex and anterior cingulate cortex (ACC). However, only activity patterns in the ACC tracked changes in decision variables during learning. These results provide strong evidence for perceptual learning-related changes in higher order areas and suggest that perceptual and reward learning are based on a common neurobiological mechanism.}
}

@article{Knops2009,
	title = {Recruitment of an {Area} {Involved} in {Eye} {Movements} {During} {Mental} {Arithmetic}},
	volume = {324},
	issn = {0036-8075, 1095-9203},
	doi = {10.1126/science.1171599},
	language = {en},
	number = {5934},
	urldate = {2024-02-02},
	journal = {Science},
	author = {Knops, André and Thirion, Bertrand and Hubbard, Edward M. and Michel, Vincent and Dehaene, Stanislas},
	month = jun,
	year = {2009},
	pages = {1583--1585},
	file = {Full Text:/home/remi/Zotero/storage/C5EYUKDA/Knops et al. - 2009 - Recruitment of an Area Involved in Eye Movements D.pdf:application/pdf},
}

@article{Kriegeskorte2006,
	author = {Kriegeskorte, Nikolaus and Goebel, Rainer and Bandettini, Peter},
	title = {Information-based functional brain mapping},
	volume = {103},
	number = {10},
	pages = {3863--3868},
	year = {2006},
	doi = {10.1073/pnas.0600244103},
	publisher = {National Academy of Sciences},
	abstract = {The development of high-resolution neuroimaging and multielectrode electrophysiological recording provides neuroscientists with huge amounts of multivariate data. The complexity of the data creates a need for statistical summary, but the local averaging standardly applied to this end may obscure the effects of greatest neuroscientific interest. In neuroimaging, for example, brain mapping analysis has focused on the discovery of activation, i.e., of extended brain regions whose average activity changes across experimental conditions. Here we propose to ask a more general question of the data: Where in the brain does the activity pattern contain information about the experimental condition? To address this question, we propose scanning the imaged volume with a {\textquotedblleft}searchlight,{\textquotedblright} whose contents are analyzed multivariately at each location in the brain.},
	issn = {0027-8424},
	journal = {Proceedings of the National Academy of Sciences}
}

@article{Laird2011,
	author = {Laird, Angela R. and Fox, P. Mickle and Eickhoff, Simon B. and Turner, Jessica A. and Ray, Kimberly L. and McKay, D. Reese and Glahn, David C. and Beckmann, Christian F. and Smith, Stephen M. and Fox, Peter T.},
	title = "{Behavioral Interpretations of Intrinsic Connectivity Networks}",
	journal = {Journal of Cognitive Neuroscience},
	volume = {23},
	number = {12},
	pages = {4022-4037},
	year = {2011},
	month = {12},
	abstract = "{An increasingly large number of neuroimaging studies have investigated functionally connected networks during rest, providing insight into human brain architecture. Assessment of the functional qualities of resting state networks has been limited by the task-independent state, which results in an inability to relate these networks to specific mental functions. However, it was recently demonstrated that similar brain networks can be extracted from resting state data and data extracted from thousands of task-based neuroimaging experiments archived in the BrainMap database. Here, we present a full functional explication of these intrinsic connectivity networks at a standard low order decomposition using a neuroinformatics approach based on the BrainMap behavioral taxonomy as well as a stratified, data-driven ordering of cognitive processes. Our results serve as a resource for functional interpretations of brain networks in resting state studies and future investigations into mental operations and the tasks that drive them.}",
	issn = {0898-929X},
	doi = {10.1162/jocn_a_00077},
}

@article{Lancaster2000,
	author = {Lancaster, Jack L. and Woldorff, Marty G. and Parsons, Lawrence M. and Liotti, Mario and Freitas, Catarina S. and Rainey, Lacy and Kochunov, Peter V. and Nickerson, Dan and Mikiten, Shawn A. and Fox, Peter T.},
	title = {Automated Talairach Atlas labels for functional brain mapping},
	journal = {Human Brain Mapping},
	volume = {10},
	number = {3},
	pages = {120-131},
	keywords = {Talairach Daemon, volume occupancy, Talairach Labels, brain labels},
	doi = {10.1002/1097-0193(200007)10:3<120::AID-HBM30>3.0.CO;2-8},
	abstract = {Abstract An automated coordinate-based system to retrieve brain labels from the 1988 Talairach Atlas, called the Talairach Daemon (TD), was previously introduced [Lancaster et al., 1997]. In the present study, the TD system and its 3-D database of labels for the 1988 Talairach atlas were tested for labeling of functional activation foci. TD system labels were compared with author-designated labels of activation coordinates from over 250 published functional brain-mapping studies and with manual atlas-derived labels from an expert group using a subset of these activation coordinates. Automated labeling by the TD system compared well with authors' labels, with a 70\% or greater label match averaged over all locations. Author-label matching improved to greater than 90\% within a search range of ±5 mm for most sites. An adaptive grey matter (GM) range-search utility was evaluated using individual activations from the M1 mouth region (30 subjects, 52 sites). It provided an 87\% label match to Brodmann area labels (BA 4 \& BA 6) within a search range of ±5 mm. Using the adaptive GM range search, the TD system's overall match with authors' labels (90\%) was better than that of the expert group (80\%). When used in concert with authors' deeper knowledge of an experiment, the TD system provides consistent and comprehensive labels for brain activation foci. Additional suggested applications of the TD system include interactive labeling, anatomical grouping of activation foci, lesion-deficit analysis, and neuroanatomy education. Hum. Brain Mapping 10:120–131, 2000. © 2000 Wiley-Liss, Inc.},
	year = {2000}
}

@article{Lancaster1997,
	author = {Lancaster, J.L. and Rainey, L.H. and Summerlin, J.L. and Freitas, C.S. and Fox, P.T. and Evans, A.C. and Toga, A.W. and Mazziotta, J.C.},
	title = {Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method},
	journal = {Human Brain Mapping},
	volume = {5},
	number = {4},
	pages = {238-242},
	keywords = {spatial normalization, segmentation, Talairach Atlas, Talairach Daemon, naming hierarchy, brain labeling},
	doi = {10.1002/(SICI)1097-0193(1997)5:4<238::AID-HBM6>3.0.CO;2-4},
	abstract = {Abstract A forward-transform method for retrieving brain labels from the 1988 Talairach Atlas using x-y-z coordinates is presented. A hierarchical volume-occupancy labeling scheme was created to simplify the organization of atlas labels using volume and subvolumetric components. Segmentation rules were developed to define boundaries that were not given explicitly in the atlas. The labeling scheme and segmentation rules guided the segmentation and labeling of 160 contiguous regions within the atlas. A unique three-dimensional (3-D) database label server called the Talairach Daemon (https://rii.uthscsa.edu/projects) was developed for serving labels keyed to the Talairach coordinate system. Given an x-y-z Talairach coordinate, a corresponding hierarchical listing of labels is returned by the server. The accuracy and precision of the forward-transform labeling method is now under evaluation. Hum. Brain Mapping 5:238–242, 1997. © 1997 Wiley-Liss, Inc.},
	year = {1997}
}

@article {Lindquist2018,
	author = {Lindquist, Martin A. and Geuter, Stephan and Wager, Tor D. and Caffo, Brian S.},
	title = {Modular preprocessing pipelines can reintroduce artifacts into fMRI data},
	year = {2018},
	doi = {10.1101/407676},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {The preprocessing pipelines typically used in both task and restingstate fMRI (rs-fMRI) analysis are modular in nature: They are composed of a number of separate filtering/regression steps, including removal of head motion covariates and band-pass filtering, performed sequentially and in a flexible order. In this paper we illustrate the shortcomings of this approach, as we show how later preprocessing steps can reintroduce artifacts previously removed from the data in prior preprocessing steps. We show that each regression step is a geometric projection of data onto a subspace, and that performing a sequence of projections can move the data into subspaces no longer orthogonal to those previously removed, reintroducing signal related to nuisance covariates. Thus, linear filtering operations are not commutative, and the order in which the preprocessing steps are performed is critical. These issues can arise in practice when any combination of standard preprocessing steps{\textemdash}including motion regression, scrubbing, component-based correction, global signal regression, and temporal filtering{\textemdash}are performed sequentially. In this work we focus primarily on rs-fMRI. We illustrate the problem both theoretically and empirically through application to a test-retest rs-fMRI data set, and suggest remedies. These include (a) combining all steps into a single linear filter, or (b) sequential orthogonalization of covariates/linear filters performed in series.},
	pdf = {https://www.biorxiv.org/content/early/2018/09/04/407676.full.pdf},
	journal = {bioRxiv}
}

@article{Liu2009,
	title = {Evidence from intrinsic activity that asymmetry of the human brain is controlled by multiple factors},
	volume = {106},
	issn = {0027-8424, 1091-6490},
	doi = {10.1073/pnas.0908073106},
	abstract = {Cerebral lateralization is a fundamental property of the human brain and a marker of successful development. Here we provide evidence that multiple mechanisms control asymmetry for distinct brain systems. Using intrinsic activity to measure asymmetry in 300 adults, we mapped the most strongly lateralized brain regions. Both men and women showed strong asymmetries with a significant, but small, group difference. Factor analysis on the asymmetric regions revealed 4 separate factors that each accounted for significant variation across subjects. The factors were associated with brain systems involved in vision, internal thought (the default network), attention, and language. An independent sample of right- and left-handed individuals showed that hand dominance affects brain asymmetry but differentially across the 4 factors supporting their independence. These findings show the feasibility of measuring brain asymmetry using intrinsic activity fluctuations and suggest that multiple genetic or environmental mechanisms control cerebral lateralization.},
	language = {en},
	number = {48},
	urldate = {2024-02-01},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Liu, Hesheng and Stufflebeam, Steven M. and Sepulcre, Jorge and Hedden, Trey and Buckner, Randy L.},
	month = dec,
	year = {2009},
	pages = {20499--20503},
}

@article{Makris2006,
	title = {Decreased volume of left and total anterior insular lobule in schizophrenia},
	volume = {83},
	issn = {09209964},
	doi = {10.1016/j.schres.2005.11.020},
	language = {en},
	number = {2-3},
	urldate = {2024-02-01},
	journal = {Schizophrenia Research},
	author = {Makris, Nikos and Goldstein, Jill M. and Kennedy, David and Hodge, Steven M. and Caviness, Verne S. and Faraone, Stephen V. and Tsuang, Ming T. and Seidman, Larry J.},
	month = apr,
	year = {2006},
	pages = {155--171},
}

@article{Marcus2007,
    author = {Marcus, Daniel S. and Wang, Tracy H. and Parker, Jamie and Csernansky, John G. and Morris, John C. and Buckner, Randy L.},
    title = "{Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults}",
    journal = {Journal of Cognitive Neuroscience},
    volume = {19},
    number = {9},
    pages = {1498-1507},
    year = {2007},
    month = {09},
    abstract = "{The Open Access Series of Imaging Studies is a series of magnetic resonance imaging data sets that is publicly available for study and analysis. The initial data set consists of a cross-sectional collection of 416 subjects aged 18 to 96 years. One hundred of the included subjects older than 60 years have been clinically diagnosed with very mild to moderate Alzheimer's disease. The subjects are all right-handed and include both men and women. For each subject, three or four individual T1-weighted magnetic resonance imaging scans obtained in single imaging sessions are included. Multiple within-session acquisitions provide extremely high contrast-to-noise ratio, making the data amenable to a wide range of analytic approaches including automated computational analysis. Additionally, a reliability data set is included containing 20 subjects without dementia imaged on a subsequent visit within 90 days of their initial session. Automated calculation of whole-brain volume and estimated total intracranial volume are presented to demonstrate use of the data for measuring differences associated with normal aging and Alzheimer's disease.}",
    issn = {0898-929X},
    doi = {10.1162/jocn.2007.19.9.1498},
    pdf = {https://direct.mit.edu/jocn/article-pdf/19/9/1498/1756878/jocn.2007.19.9.1498.pdf},
}

@INPROCEEDINGS{Mensch2016,
  author={Mensch, Arthur and Varoquaux, Gael and Thirion, Bertrand},
  booktitle={2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)},
  title={Compressed online dictionary learning for fast resting-state fMRI decomposition},
  year={2016},
  volume={},
  number={},
  pages={1282-1285},
  doi={10.1109/ISBI.2016.7493501}}

@article{Michel2011,
    TITLE = {{Total variation regularization for fMRI-based prediction of behaviour.}},
    AUTHOR = {Michel, Vincent and Gramfort, Alexandre and Varoquaux, Ga{\"e}l and Eger, Evelyn and Thirion, Bertrand},
    URL = {https://inria.hal.science/inria-00563468},
    JOURNAL = {{IEEE Transactions on Medical Imaging}},
    PUBLISHER = {{Institute of Electrical and Electronics Engineers}},
    VOLUME = {30},
    NUMBER = {7},
    PAGES = {1328 - 1340},
    YEAR = {2011},
    MONTH = Feb,
    DOI = {10.1109/TMI.2011.2113378},
    PDF = {https://inria.hal.science/inria-00563468/file/arxiv_version.pdf},
    HAL_ID = {inria-00563468},
    HAL_VERSION = {v1},
}

@article{Michel2011b,
	title = {A supervised clustering approach for fMRI-based inference of brain states},
	journal = {Pattern Recognition},
	volume = {45},
	number = {6},
	pages = {2041-2049},
	year = {2012},
	note = {Brain Decoding},
	issn = {0031-3203},
	doi = {10.1016/j.patcog.2011.04.006},
	author = {Vincent Michel and Alexandre Gramfort and Gaël Varoquaux and Evelyn Eger and Christine Keribin and Bertrand Thirion},
	keywords = {, Brain reading, Prediction, Hierarchical clustering, Dimension reduction, Multi-scale analysis, Feature agglomeration},
	abstract = {We propose a method that combines signals from many brain regions observed in functional Magnetic Resonance Imaging (fMRI) to predict the subject's behavior during a scanning session. Such predictions suffer from the huge number of brain regions sampled on the voxel grid of standard fMRI data sets: the curse of dimensionality. Dimensionality reduction is thus needed, but it is often performed using a univariate feature selection procedure, that handles neither the spatial structure of the images, nor the multivariate nature of the signal. By introducing a hierarchical clustering of the brain volume that incorporates connectivity constraints, we reduce the span of the possible spatial configurations to a single tree of nested regions tailored to the signal. We then prune the tree in a supervised setting, hence the name supervised clustering, in order to extract a parcellation (division of the volume) such that parcel-based signal averages best predict the target information. Dimensionality reduction is thus achieved by feature agglomeration, and the constructed features now provide a multi-scale representation of the signal. Comparisons with reference methods on both simulated and real data show that our approach yields higher prediction accuracy than standard voxel-based approaches. Moreover, the method infers an explicit weighting of the regions involved in the regression or classification task.}
}

@article{Miyawaki2008,
	title = {Visual Image Reconstruction from Human Brain Activity using a Combination of Multiscale Local Image Decoders},
	journal = {Neuron},
	volume = {60},
	number = {5},
	pages = {915-929},
	year = {2008},
	issn = {0896-6273},
	doi = {10.1016/j.neuron.2008.11.004},
	author = {Yoichi Miyawaki and Hajime Uchida and Okito Yamashita and Masa-aki Sato and Yusuke Morito and Hiroki C. Tanabe and Norihiro Sadato and Yukiyasu Kamitani},
	keywords = {SYSNEURO},
	abstract = {Perceptual experience consists of an enormous number of possible states. Previous fMRI studies have predicted a perceptual state by classifying brain activity into prespecified categories. Constraint-free visual image reconstruction is more challenging, as it is impractical to specify brain activity for all possible images. In this study, we reconstructed visual images by combining local image bases of multiple scales, whose contrasts were independently decoded from fMRI activity by automatically selecting relevant voxels and exploiting their correlated patterns. Binary-contrast, 10 × 10-patch images (2100 possible states) were accurately reconstructed without any image prior on a single trial or volume basis by measuring brain activity only for several hundred random images. Reconstruction was also used to identify the presented image among millions of candidates. The results suggest that our approach provides an effective means to read out complex perceptual states from brain activity while discovering information representation in multivoxel patterns.}
}

@book{Montgomery2006,
    author = {Montgomery, Douglas C. and Peck, Elizabeth A. and Vining, Geoffrey G.},
    isbn = {0471754951},
    keywords = {montgomery},
    publisher = {Wiley \& Sons},
    title = {Introduction to Linear Regression Analysis (4th ed.)},
    year = 2006
}


@article{Mourao-miranda2012,
	title = {Pattern {Recognition} and {Functional} {Neuroimaging} {Help} to {Discriminate} {Healthy} {Adolescents} at {Risk} for {Mood} {Disorders} from {Low} {Risk} {Adolescents}},
	volume = {7},
	issn = {1932-6203},
	doi = {10.1371/journal.pone.0029482},
	language = {en},
	number = {2},
	urldate = {2024-02-02},
	journal = {PLoS ONE},
	author = {Mourão-Miranda, Janaina and Oliveira, Leticia and Ladouceur, Cecile D. and Marquand, Andre and Brammer, Michael and Birmaher, Boris and Axelson, David and Phillips, Mary L.},
	editor = {Op De Beeck, Hans P.},
	month = feb,
	year = {2012},
	pages = {e29482},
	file = {Full Text:/home/remi/Zotero/storage/LZ95ZG8B/Mourão-Miranda et al. - 2012 - Pattern Recognition and Functional Neuroimaging He.pdf:application/pdf},
}

@article{Mumford2012,
    title={Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses},
    author={Mumford, Jeanette A and Turner, Benjamin O and Ashby, F Gregory and Poldrack, Russell A},
    journal={Neuroimage},
    volume={59},
    number={3},
    pages={2636--2643},
    year={2012},
    publisher={Elsevier}
}

@article{Naselaris2011,
    author={Naselaris, Thomas and Kay, Kendrick N. and Nishimoto, Shinji and Gallant, Jack L.},
    title={Encoding and decoding in fMRI},
    journal={NeuroImage},
    year={2011},
    month={May},
    day={15},
    edition={2010/08/04},
    volume={56},
    number={2},
    pages={400-410},
    keywords={Brain/*physiology; Brain Mapping/*methods; Humans; Image Processing, Computer-Assisted/*methods; *Magnetic Resonance Imaging; *Models, Neurological},
    abstract={Over the past decade fMRI researchers have developed increasingly sensitive techniques for analyzing the information represented in BOLD activity. The most popular of these techniques is linear classification, a simple technique for decoding information about experimental stimuli or tasks from patterns of activity across an array of voxels. A more recent development is the voxel-based encoding model, which describes the information about the stimulus or task that is represented in the activity of single voxels. Encoding and decoding are complementary operations: encoding uses stimuli to predict activity while decoding uses activity to predict information about the stimuli. However, in practice these two operations are often confused, and their respective strengths and weaknesses have not been made clear. Here we use the concept of a linearizing feature space to clarify the relationship between encoding and decoding. We show that encoding and decoding operations can both be used to investigate some of the most common questions about how information is represented in the brain. However, focusing on encoding models offers two important advantages over decoding. First, an encoding model can in principle provide a complete functional description of a region of interest, while a decoding model can provide only a partial description. Second, while it is straightforward to derive an optimal decoding model from an encoding model it is much more difficult to derive an encoding model from a decoding model. We propose a systematic modeling approach that begins by estimating an encoding model for every voxel in a scan and ends by using the estimated encoding models to perform decoding.},
    note={20691790[pmid]},
    issn={1095-9572},
    doi={10.1016/j.neuroimage.2010.07.073},
    url={https://pubmed.ncbi.nlm.nih.gov/20691790},
    language={eng}
}

@article{Nielsen2013,
	AUTHOR={Nielsen, Jared and Zielinski, Brandon and Fletcher, P and Alexander, Andrew and Lange, Nicholas and Bigler, Erin and Lainhart, Janet and Anderson, Jeffrey},
	TITLE={Multisite functional connectivity MRI classification of autism: ABIDE results},
	JOURNAL={Frontiers in Human Neuroscience},
	VOLUME={7},
	PAGES={599},
	YEAR={2013},
	DOI={10.3389/fnhum.2013.00599},
	ISSN={1662-5161},
	ABSTRACT={Background: Systematic differences in functional connectivity MRI metrics have been consistently observed in autism, with predominantly decreased cortico-cortical connectivity. Previous attempts at single subject classification in high-functioning autism using whole brain point-to-point functional connectivity have yielded about 80% accurate classification of autism vs. control subjects across a wide age range. We attempted to replicate the method and results using the Autism Brain Imaging Data Exchange (ABIDE) including resting state fMRI data obtained from 964 subjects and 16 separate international sites.Methods: For each of 964 subjects, we obtained pairwise functional connectivity measurements from a lattice of 7266 regions of interest covering the gray matter (26.4 million “connections”) after preprocessing that included motion and slice timing correction, coregistration to an anatomic image, normalization to standard space, and voxelwise removal by regression of motion parameters, soft tissue, CSF, and white matter signals. Connections were grouped into multiple bins, and a leave-one-out classifier was evaluated on connections comprising each set of bins. Age, age-squared, gender, handedness, and site were included as covariates for the classifier.Results: Classification accuracy significantly outperformed chance but was much lower for multisite prediction than for previous single site results. As high as 60% accuracy was obtained for whole brain classification, with the best accuracy from connections involving regions of the default mode network, parahippocampaland fusiform gyri, insula, Wernicke Area, and intraparietal sulcus. The classifier score was related to symptom severity, social function, daily living skills, and verbal IQ. Classification accuracy was significantly higher for sites with longer BOLD imaging times.Conclusions: Multisite functional connectivity classification of autism outperformed chance using a simple leave-one-out classifier, but exhibited poorer accuracy than for single site results. Attempts to use multisite classifiers will likely require improved classification algorithms, longer BOLD imaging times, and standardized acquisition parameters for possible future clinical utility.}
}

@article{Nooner2012,
	AUTHOR={Nooner, Kate and Colcombe, Stanley and Tobe, Russell and Mennes, Maarten and Benedict, Melissa and Moreno, Alexis and Panek, Laura and Brown, Shaquanna and Zavitz, Stephen and Li, Qingyang and Sikka, Sharad and Gutman, David and Bangaru, Saroja and Schlachter, Rochelle Tziona and Kamiel, Stephanie and Anwar, Ayesha and Hinz, Caitlin and Kaplan, Michelle and Rachlin, Anna and Adelsberg, Samantha and Cheung, Brian and Khanuja, Ranjit and Yan, Chaogan and Craddock, Cameron and Calhoun, Vincent and Courtney, William and King, Margaret and Wood, Dylan and Cox, Christine and Kelly, Clare and DiMartino, Adriana and Petkova, Eva and Reiss, Philip and Duan, Nancy and Thompsen, Dawn and Biswal, Bharat and Coffey, Barbara and Hoptman, Matthew and Javitt, Daniel and Pomara, Nunzio and Sidtis, John and Koplewicz, Harold and Castellanos, Francisco and Leventhal, Bennett and Milham, Michael},
	TITLE={The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry},
	JOURNAL={Frontiers in Neuroscience},
	VOLUME={6},
	PAGES={152},
	YEAR={2012},
	DOI={10.3389/fnins.2012.00152},
	ISSN={1662-453X},
	ABSTRACT={The National Institute of Mental Health strategic plan for advancing psychiatric neuroscience calls for an acceleration of discovery and the delineation of developmental trajectories for risk and resilience across the lifespan. To attain these objectives, sufficiently powered datasets with broad and deep phenotypic characterization, state-of-the-art neuroimaging, and genetic samples must be generated and made openly available to the scientific community. The enhanced Nathan Kline Institute-Rockland Sample (NKI-RS) is a response to this need. NKI-RS is an ongoing, institutionally centered endeavor aimed at creating a large-scale (N > 1000), deeply phenotyped, community-ascertained, lifespan sample (ages 6–85 years old) with advanced neuroimaging and genetics. These data will be publicly shared, openly, and prospectively (i.e., on a weekly basis). Herein, we describe the conceptual basis of the NKI-RS, including study design, sampling considerations, and steps to synchronize phenotypic and neuroimaging assessment. Additionally, we describe our process for sharing the data with the scientific community while protecting participant confidentiality, maintaining an adequate database, and certifying data integrity. The pilot phase of the NKI-RS, including challenges in recruiting, characterizing, imaging, and sharing data, is discussed while also explaining how this experience informed the final design of the enhanced NKI-RS. It is our hope that familiarity with the conceptual underpinnings of the enhanced NKI-RS will facilitate harmonization with future data collection efforts aimed at advancing psychiatric neuroscience and nosology.}
}

@article{Papadopoulos-Orfanos2017,
	title = {The Brainomics/Localizer database},
	journal = {NeuroImage},
	volume = {144},
	pages = {309-314},
	year = {2017},
	note = {Data Sharing Part II},
	issn = {1053-8119},
	doi = {10.1016/j.neuroimage.2015.09.052},
	author = {Dimitri {Papadopoulos Orfanos} and Vincent Michel and Yannick Schwartz and Philippe Pinel and Antonio Moreno and Denis {Le Bihan} and Vincent Frouin},
	keywords = {Imaging genetics, Database, Semantic web},
	abstract = {The Brainomics/Localizer database exposes part of the data collected by the in-house Localizer project, which planned to acquire four types of data from volunteer research subjects: anatomical MRI scans, functional MRI data, behavioral and demographic data, and DNA sampling. Over the years, this local project has been collecting such data from hundreds of subjects. We had selected 94 of these subjects for their complete datasets, including all four types of data, as the basis for a prior publication; the Brainomics/Localizer database publishes the data associated with these 94 subjects. Since regulatory rules prevent us from making genetic data available for download, the database serves only anatomical MRI scans, functional MRI data, behavioral and demographic data. To publish this set of heterogeneous data, we use dedicated software based on the open-source CubicWeb semantic web framework. Through genericity in the data model and flexibility in the display of data (web pages, CSV, JSON, XML), CubicWeb helps us expose these complex datasets in original and efficient ways.}
}

@article{Parker2018,
	title = {An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional {MRI}},
	volume = {171},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2017.12.073},
	journal = {NeuroImage},
	author = {Parkes, Linden and Fulcher, Ben and Yücel, Murat and Fornito, Alex},
	month = may,
	year = {2018},
	pmid = {29278773},
	pages = {415--436},
}

@article{Pauli2018,
	author={Pauli, Wolfgang M.
	and Nili, Amanda N.
	and Tyszka, J. Michael},
	title={A high-resolution probabilistic in vivo atlas of human subcortical brain nuclei},
	journal={Scientific Data},
	year={2018},
	month={Apr},
	day={17},
	volume={5},
	number={1},
	pages={180063},
	abstract={Recent advances in magnetic resonance imaging methods, including data acquisition, pre-processing and analysis, have benefited research on the contributions of subcortical brain nuclei to human cognition and behavior. At the same time, these developments have led to an increasing need for a high-resolution probabilistic in vivo anatomical atlas of subcortical nuclei. In order to address this need, we constructed high spatial resolution, three-dimensional templates, using high-accuracy diffeomorphic registration of T1- and T2- weighted structural images from 168 typical adults between 22 and 35 years old. In these templates, many tissue boundaries are clearly visible, which would otherwise be impossible to delineate in data from individual studies. The resulting delineations of subcortical nuclei complement current histology-based atlases. We further created a companion library of software tools for atlas development, to offer an open and evolving resource for the creation of a crowd-sourced in vivo probabilistic anatomical atlas of the human brain.},
	issn={2052-4463},
	doi={10.1038/sdata.2018.63},
}

@article{Pinel2007,
	title={Fast reproducible identification and large-scale databasing of individual functional cognitive networks},
	journal={BMC Neuroscience},
	volume={8},
	year={2007},
	author={Pinel, Philippe
	and Thirion, Bertrand
	and Meriaux, S{\'e}bastien
	and Jobert, Antoinette
	and Serres, Julien
	and Le Bihan, Denis
	and Poline, Jean-Baptiste
	and Dehaene, Stanislas},
	abstract = {Although cognitive processes such as reading and calculation are associated with reproducible cerebral networks, inter-individual variability is considerable. Understanding the origins of this variability will require the elaboration of large multimodal databases compiling behavioral, anatomical, genetic and functional neuroimaging data over hundreds of subjects. With this goal in mind, we designed a simple and fast acquisition procedure based on a 5-minute functional magnetic resonance imaging (fMRI) sequence that can be run as easily and as systematically as an anatomical scan, and is therefore used in every subject undergoing fMRI in our laboratory. This protocol captures the cerebral bases of auditory and visual perception, motor actions, reading, language comprehension and mental calculation at an individual level.}
}

@article{Poldrack2016,
	title = {A phenome-wide examination of neural and cognitive function},
	volume = {3},
	issn = {2052-4463},
	doi = {10.1038/sdata.2016.110},
	abstract = {This data descriptor outlines a shared neuroimaging dataset from the UCLA Consortium for Neuropsychiatric Phenomics, which focused on understanding the dimensional structure of memory and cognitive control (response inhibition) functions in both healthy individuals (130 subjects) and individuals with neuropsychiatric disorders including schizophrenia (50 subjects), bipolar disorder (49 subjects), and attention deficit/hyperactivity disorder (43 subjects). The dataset includes an extensive set of task-based fMRI assessments, resting fMRI, structural MRI, and high angular resolution diffusion MRI. The dataset is shared through the OpenfMRI project, and is formatted according to the Brain Imaging Data Structure (BIDS) standard.},
	number = {1},
	journal = {Scientific Data},
	author = {Poldrack, R.A. and Congdon, E. and Triplett, W. and Gorgolewski, K.J. and Karlsgodt, K.H. and Mumford, J.A. and Sabb, F.W. and Freimer, N.B. and London, E.D. and Cannon, T.D. and Bilder, R.M.},
	month = dec,
	year = {2016},
	pages = {160110},
}

@article{Power2017,
	title = {A simple but useful way to assess fMRI scan qualities},
	journal = {NeuroImage},
	volume = {154},
	pages = {150-158},
	year = {2017},
	note = {Cleaning up the fMRI time series: Mitigating noise with advanced acquisition and correction strategies},
	issn = {1053-8119},
	doi = {10.1016/j.neuroimage.2016.08.009},
	author = {Jonathan D. Power},
	abstract = {This short “how to” article describes a plot I find useful for assessing fMRI data quality. I discuss the reasoning behind the plot and how it is constructed. I create the plot in scans from several publicly available datasets to illustrate different kinds of fMRI signal variance, ranging from thermal noise to motion artifacts to respiratory-related signals. I also show how the plot can be used to understand the variance removed during denoising. Code to make the plot is provided with the article, and supplemental movies show plots for hundreds of additional subjects.}
}

@article{Power2014,
	title = {Methods to detect, characterize, and remove motion artifact in resting state {fMRI}},
	volume = {84},
	issn = {10538119},
	doi = {10.1016/j.neuroimage.2013.08.048},
	abstract = {Head motion systematically alters correlations in resting state functional connectivity {fMRI} ({RSFC}). In this report we examine impact of motion on signal intensity and {RSFC} correlations. We find that motion-induced signal changes (1) are often complex and variable waveforms, (2) are often shared across nearly all brain voxels, and (3) often persist more than 10. s after motion ceases. These signal changes, both during and after motion, increase observed {RSFC} correlations in a distance-dependent manner. Motion-related signal changes are not removed by a variety of motion-based regressors, but are effectively reduced by global signal regression. We link several measures of data quality to motion, changes in signal intensity, and changes in {RSFC} correlations. We demonstrate that improvements in data quality measures during processing may represent cosmetic improvements rather than true correction of the data. We demonstrate a within-subject, censoring-based artifact removal strategy based on volume censoring that reduces group differences due to motion to chance levels. We note conditions under which group-level regressions do and do not correct motion-related effects. © 2013 Elsevier Inc.},
	pages = {320--341},
	journal = {NeuroImage},
	author = {Power, Jonathan D. and Mitra, Anish and Laumann, Timothy O. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
	year = {2014},
	pmid = {23994314},
	keywords = {Functional connectivity, \#nosource, Resting state, Artifact, Motion, Movement, {MRI}},
}

@article{Power2012,
	title = {Spurious but systematic correlations in functional connectivity {MRI} networks arise from subject motion},
	volume = {59},
	issn = {10538119},
	doi = {10.1016/j.neuroimage.2011.10.018},
	abstract = {Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity {MRI} (rs-{fcMRI}) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-{fcMRI} correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-{fcMRI} analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-{fcMRI} work that may not have adequately controlled for effects of transient subject movements. © 2011 Elsevier Inc.},
	pages = {2142--2154},
	number = {3},
	journal = {NeuroImage},
	author = {Power, Jonathan D. and Barnes, Kelly A. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
	year = {2012},
	pmid = {22019881},
	keywords = {{FMRI}, Resting state, Artifact, Motion, Noise, Movement, {FcMRI}, Network},
}

@article{Power2011,
	author={Power, Jonathan D.
	and Cohen, Alexander L.
	and Nelson, Steven M.
	and Wig, Gagan S.
	and Barnes, Kelly Anne
	and Church, Jessica A.
	and Vogel, Alecia C.
	and Laumann, Timothy O.
	and Miezin, Fran M.
	and Schlaggar, Bradley L.
	and Petersen, Steven E.},
	title={Functional Network Organization of the Human Brain},
	journal={Neuron},
	year={2011},
	month={Nov},
	day={17},
	publisher={Elsevier},
	volume={72},
	number={4},
	pages={665-678},
	abstract={Real-world complex systems may be mathematically modeled as graphs, revealing properties of the system. Here we study graphs of functional brain organization in healthy adults using resting state functional connectivity MRI. We propose two novel brain-wide graphs, one of 264 putative functional areas, the other a modification of voxelwise networks that eliminates potentially artificial short-distance relationships. These graphs contain many subgraphs in good agreement with known functional brain systems. Other subgraphs lack established functional identities; we suggest possible functional characteristics for these subgraphs. Further, graph measures of the areal network indicate that the default mode subgraph shares network properties with sensory and motor subgraphs: it is internally integrated but isolated from other subgraphs, much like a ?processing? system. The modified voxelwise graph also reveals spatial motifs in the patterning of systems across the cortex.},
	issn={0896-6273},
	doi={10.1016/j.neuron.2011.09.006},
}

@article{Pruim2015,
	title = {{ICA}-{AROMA}: A robust {ICA}-based strategy for removing motion artifacts from {fMRI} data},
	volume = {112},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2015.02.064},
	shorttitle = {{ICA}-{AROMA}},
	abstract = {Head motion during functional {MRI} ({fMRI}) scanning can induce spurious findings and/or harm detection of true effects. Solutions have been proposed, including deleting ('scrubbing') or regressing out ('spike regression') motion volumes from {fMRI} time-series. These strategies remove motion-induced signal variations at the cost of destroying the autocorrelation structure of the {fMRI} time-series and reducing temporal degrees of freedom. {ICA}-based {fMRI} denoising strategies overcome these drawbacks but typically require re-training of a classifier, needing manual labeling of derived components (e.g. {ICA}-{FIX}; Salimi-Khorshidi et al. (2014)). Here, we propose an {ICA}-based strategy for Automatic Removal of Motion Artifacts ({ICA}-{AROMA}) that uses a small (n=4), but robust set of theoretically motivated temporal and spatial features. Our strategy does not require classifier re-training, retains the data's autocorrelation structure and largely preserves temporal degrees of freedom. We describe {ICA}-{AROMA}, its implementation, and initial validation. {ICA}-{AROMA} identified motion components with high accuracy and robustness as illustrated by leave-N-out cross-validation. We additionally validated {ICA}-{AROMA} in resting-state (100 participants) and task-based {fMRI} data (118 participants). Our approach removed (motion-related) spurious noise from both {rfMRI} and task-based {fMRI} data to larger extent than regression using 24 motion parameters or spike regression. Furthermore, {ICA}-{AROMA} increased sensitivity to group-level activation. Our results show that {ICA}-{AROMA} effectively reduces motion-induced signal variations in {fMRI} data, is applicable across datasets without requiring classifier re-training, and preserves the temporal characteristics of the {fMRI} data.},
	pages = {267--277},
	journal = {Neuroimage},
	author = {Pruim, Raimon H. R. and Mennes, Maarten and van Rooij, Daan and Llera, Alberto and Buitelaar, Jan K. and Beckmann, Christian F.},
	year = {2015},
	pmid = {25770991},
	keywords = {Algorithms, Artifact, Artifacts, Artificial Intelligence, Cerebrospinal Fluid, Connectivity, Functional {MRI}, Humans, Image Processing, Computer-Assisted, Independent component analysis, Magnetic Resonance Imaging, Motion, Principal Component Analysis, Reproducibility of Results, Rest, Resting state},
}

@article{Reilly2009,
    author = {O'Reilly, Jill X. and Beckmann, Christian F. and Tomassini, Valentina and Ramnani, Narender and Johansen-Berg, Heidi},
    title = "{Distinct and Overlapping Functional Zones in the Cerebellum Defined by Resting State Functional Connectivity}",
    journal = {Cerebral Cortex},
    volume = {20},
    number = {4},
    pages = {953-965},
    year = {2009},
    month = {08},
    abstract = "{The cerebellum processes information from functionally diverse regions of the cerebral cortex. Cerebellar input and output nuclei have connections with prefrontal, parietal, and sensory cortex as well as motor and premotor cortex. However, the topography of the connections between the cerebellar and cerebral cortices remains largely unmapped, as it is relatively unamenable to anatomical methods. We used resting-state functional magnetic resonance imaging to define subregions within the cerebellar cortex based on their functional connectivity with the cerebral cortex. We mapped resting-state functional connectivity voxel-wise across the cerebellar cortex, for cerebral–cortical masks covering prefrontal, motor, somatosensory, posterior parietal, visual, and auditory cortices. We found that the cerebellum can be divided into at least 2 zones: 1) a primary sensorimotor zone (Lobules V, VI, and VIII), which contains overlapping functional connectivity maps for domain-specific motor, somatosensory, visual, and auditory cortices; and 2) a supramodal zone (Lobules VIIa, Crus I, and II), which contains overlapping functional connectivity maps for prefrontal and posterior-parietal cortex. The cortical connectivity of the supramodal zone was driven by regions of frontal and parietal cortex which are not directly involved in sensory or motor processing, including dorsolateral prefrontal cortex and the frontal pole, and the inferior parietal lobule.}",
    issn = {1047-3211},
    doi = {10.1093/cercor/bhp157},
}

@article{Richardson2018,
	title={Development of the social brain from age three to twelve years},
	author={Richardson, Hilary and Lisandrelli, Grace and Riobueno-Naylor, Alexa and Saxe, Rebecca},
	journal={Nature communications},
	volume={9},
	number={1},
	pages={1--12},
	year={2018},
	publisher={Nature Publishing Group}
}

@article{Rissman2004,
    title={Measuring functional connectivity during distinct stages of a cognitive task},
    author={Rissman, Jesse and Gazzaley, Adam and D'Esposito, Mark},
    journal={Neuroimage},
    volume={23},
    number={2},
    pages={752--763},
    year={2004},
    publisher={Elsevier}
}

@article{Rosenblatt2018,
    title = "All-Resolutions Inference for brain imaging",
    author = "Rosenblatt, {Jonathan D.} and Livio Finos and Weeda, {Wouter D.} and Aldo Solari and Goeman, {Jelle J.}",
    year = "2018",
    month = nov,
    doi = "10.1016/j.neuroimage.2018.07.060",
    language = "English",
    volume = "181",
    pages = "786--796",
    journal = "NeuroImage",
    issn = "1053-8119",
    publisher = "Academic Press Inc.",
}

@article{Schaefer2017,
	author = {Schaefer, Alexander and Kong, Ru and Gordon, Evan M and Laumann, Timothy O and Zuo, Xi-Nian and Holmes, Avram J and Eickhoff, Simon B and Yeo, B T Thomas},
	title = "{Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI}",
	journal = {Cerebral Cortex},
	volume = {28},
	number = {9},
	pages = {3095-3114},
	year = {2017},
	month = {07},
	abstract = "{A central goal in systems neuroscience is the parcellation of the cerebral cortex into discrete neurobiological “atoms”. Resting-state functional magnetic resonance imaging (rs-fMRI) offers the possibility of in vivo human cortical parcellation. Almost all previous parcellations relied on 1 of 2 approaches. The local gradient approach detects abrupt transitions in functional connectivity patterns. These transitions potentially reflect cortical areal boundaries defined by histology or visuotopic fMRI. By contrast, the global similarity approach clusters similar functional connectivity patterns regardless of spatial proximity, resulting in parcels with homogeneous (similar) rs-fMRI signals. Here, we propose a gradient-weighted Markov Random Field (gwMRF) model integrating local gradient and global similarity approaches. Using task-fMRI and rs-fMRI across diverse acquisition protocols, we found gwMRF parcellations to be more homogeneous than 4 previously published parcellations. Furthermore, gwMRF parcellations agreed with the boundaries of certain cortical areas defined using histology and visuotopic fMRI. Some parcels captured subareal (somatotopic and visuotopic) features that likely reflect distinct computational units within known cortical areas. These results suggest that gwMRF parcellations reveal neurobiologically meaningful features of brain organization and are potentially useful for future applications requiring dimensionality reduction of voxel-wise fMRI data. Multiresolution parcellations generated from 1489 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable\_projects/brain\_parcellation/Schaefer2018\_LocalGlobal).}",
	issn = {1047-3211},
	doi = {10.1093/cercor/bhx179},
}

@article{Seitzman2020,
	title = {A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum},
	journal = {NeuroImage},
	volume = {206},
	pages = {116290},
	year = {2020},
	issn = {1053-8119},
	doi = {10.1016/j.neuroimage.2019.116290},
	author = {Benjamin A. Seitzman and Caterina Gratton and Scott Marek and Ryan V. Raut and Nico U.F. Dosenbach and Bradley L. Schlaggar and Steven E. Petersen and Deanna J. Greene},
	keywords = {Subcortical, Cerebellum, Networks, Resting-state, Functional connectivity},
	abstract = {An important aspect of network-based analysis is robust node definition. This issue is critical for functional brain network analyses, as poor node choice can lead to spurious findings and misleading inferences about functional brain organization. Two sets of functional brain nodes from our group are well represented in the literature: (1) 264 volumetric regions of interest (ROIs) reported in Power et al., 2011, and (2) 333 cortical surface parcels reported in Gordon et al., 2016. However, subcortical and cerebellar structures are either incompletely captured or missing from these ROI sets. Therefore, properties of functional network organization involving the subcortex and cerebellum may be underappreciated thus far. Here, we apply a winner-take-all partitioning method to resting-state fMRI data to generate novel functionally-constrained ROIs in the thalamus, basal ganglia, amygdala, hippocampus, and cerebellum. We validate these ROIs in three datasets using several criteria, including agreement with existing literature and anatomical atlases. Further, we demonstrate that combining these ROIs with established cortical ROIs recapitulates and extends previously described functional network organization. This new set of ROIs is made publicly available for general use, including a full list of MNI coordinates and functional network labels.}
}

@article{Shehzad2009,
    author = {Shehzad, Zarrar and Kelly, A. M. Clare and Reiss, Philip T. and Gee, Dylan G. and Gotimer, Kristin and Uddin, Lucina Q. and Lee, Sang Han and Margulies, Daniel S. and Roy, Amy Krain and Biswal, Bharat B. and Petkova, Eva and Castellanos, F. Xavier and Milham, Michael P.},
    title = "{The Resting Brain: Unconstrained yet Reliable}",
    journal = {Cerebral Cortex},
    volume = {19},
    number = {10},
    pages = {2209-2229},
    year = {2009},
    month = {02},
    abstract = "{Recent years have witnessed an upsurge in the usage of resting-state functional magnetic resonance imaging (fMRI) to examine functional connectivity (fcMRI), both in normal and pathological populations. Despite this increasing popularity, concerns about the psychologically unconstrained nature of the “resting-state” remain. Across studies, the patterns of functional connectivity detected are remarkably consistent. However, the test–retest reliability for measures of resting state fcMRI measures has not been determined. Here, we quantify the test–retest reliability, using resting scans from 26 participants at 3 different time points. Specifically, we assessed intersession (\\&gt;5 months apart), intrasession (\\&lt;1 h apart), and multiscan (across all 3 scans) reliability and consistency for both region-of-interest and voxel-wise analyses. For both approaches, we observed modest to high reliability across connections, dependent upon 3 predictive factors: 1) correlation significance (significantly nonzero \\&gt; nonsignificant), 2) correlation valence (positive \\&gt; negative), and 3) network membership (default mode \\&gt; task positive network). Short- and long-term measures of the consistency of global connectivity patterns were highly robust. Finally, hierarchical clustering solutions were highly reproducible, both across participants and sessions. Our findings provide a solid foundation for continued examination of resting state fcMRI in typical and atypical populations.}",
    issn = {1047-3211},
    doi = {10.1093/cercor/bhn256},
}


@article{Smith2011,
	title = {Network modelling methods for {FMRI}},
	volume = {54},
	issn = {10538119},
	doi = {10.1016/j.neuroimage.2010.08.063},
	language = {en},
	number = {2},
	urldate = {2024-02-02},
	journal = {NeuroImage},
	author = {Smith, Stephen M. and Miller, Karla L. and Salimi-Khorshidi, Gholamreza and Webster, Matthew and Beckmann, Christian F. and Nichols, Thomas E. and Ramsey, Joseph D. and Woolrich, Mark W.},
	month = jan,
	year = {2011},
	pages = {875--891},
}

@article{Smith2015a,
	title={A positive-negative mode of population covariation links brain connectivity, demographics and behavior},
	author={Smith, Stephen M and Nichols, Thomas E and Vidaurre, Diego and Winkler, Anderson M and Behrens, Timothy EJ and Glasser, Matthew F and Ugurbil, Kamil and Barch, Deanna M and Van Essen, David C and Miller, Karla L},
	journal={Nature neuroscience},
	volume={18},
	number={11},
	pages={1565--1567},
	year={2015},
	publisher={Nature Publishing Group}
}

@inproceedings{Smith2015b,
	title={HCP beta-release of the Functional Connectivity MegaTrawl},
	author={Stephen M. Smith and Diego Vidaurre and Matthew F. Glasser and Anderson M. Winkler and Paul McCarthy and Emma Claire Robinson and Xu Chen and William Horton and Mark Jenkinson and Eugene P. Duff and Christian F. Beckmann and Mark W. Woolrich and Daniel S. Marcus and Deanna M. Barch and K{\^a}mil Uğurbil and Thomas E. Nichols and David C. Van Essen},
	pdf = {https://www.humanconnectome.org/storage/app/media/documentation/s500/HCP500_MegaTrawl_April2015.pdf},
	booktitle={humanconnectome},
	year={2015}
}

@inproceedings{Smith2014,
	title={Methods for network modelling from high quality rfMRI data},
	author={Smith, SM and Glasser, MF and Robinson, E and Salimi-Khorshidi, G and Duff, E and Van Essen, DC and Woolrich, MW and Jenkinson, M and Beckmann, CF},
	booktitle={OHBM 2014 Annual Meeting},
	year={2014}
}

@article{Smith2009a,
	title={Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference},
	author={Smith, Stephen M and Nichols, Thomas E},
	journal={Neuroimage},
	volume={44},
	number={1},
	pages={83--98},
	year={2009},
	publisher={Elsevier},
	doi={10.1016/j.neuroimage.2008.03.061}
}

@article {Smith2009b,
	author = {Smith, Stephen M. and Fox, Peter T. and Miller, Karla L. and Glahn, David C. and Fox, P. Mickle and Mackay, Clare E. and Filippini, Nicola and Watkins, Kate E. and Toro, Roberto and Laird, Angela R. and Beckmann, Christian F.},
	title = {Correspondence of the brain{\textquoteright}s functional architecture during activation and rest},
	volume = {106},
	number = {31},
	pages = {13040--13045},
	year = {2009},
	doi = {10.1073/pnas.0905267106},
	publisher = {National Academy of Sciences},
	abstract = {Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are continuously interacting when the brain is {\textquotedblleft}at rest.{\textquotedblright} In this work, we identify the major explicit activation networks by carrying out an image-based activation network analysis of thousands of separate activation maps derived from the BrainMap database of functional imaging studies, involving nearly 30,000 human subjects. Independently, we extract the major covarying networks in the resting brain, as imaged with functional magnetic resonance imaging in 36 subjects at rest. The sets of major brain networks, and their decompositions into subnetworks, show close correspondence between the independent analyses of resting and activation brain dynamics. We conclude that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically {\textquotedblleft}active{\textquotedblright} even when at {\textquotedblleft}rest.{\textquotedblright}},
	issn = {0027-8424},
	journal = {Proceedings of the National Academy of Sciences}
}

@article {Sundermann2024,
	author = {Sundermann, B. and Pfleiderer, B. and McLeod, A. and Mathys, C.},
	title = {Seeing more than the Tip of the Iceberg: Approaches to Subthreshold Effects in Functional Magnetic Resonance Imaging of the Brain},
	year = {2023},
	doi = {10.1007/s00062-024-01422-2},
	journal = {Clinical Neuroradiology}
}

@article {Taylor2023,
	author = {Taylor, P. A. and Reynolds, R. C. and Calhoun, V. and Gonzalez-Castillo, J. and Handwerker, D. A. and Bandettini, P. A. and Mejia, A. F. and Chen, G},
	title = {Highlight Results, Don't Hide Them: Enhance interpretation, reduce biases and improve reproducibility},
	year = {2023},
	doi = {10.1016/j.neuroimage.2023.120138},
	journal = {Neuroimage}
}

@article{Thirion2014,
	author={Thirion, Bertrand and Varoquaux, Gaël and Dohmatob, Elvis and Poline, Jean-Baptiste},
	title={Which fMRI clustering gives good brain parcellations?},
	journal={Frontiers in Neuroscience},
	volume={8},
	year={2014},
	doi={10.3389/fnins.2014.00167},
	ISSN={1662-453X},
	abstract={Analysis and interpretation of neuroimaging data often require one to divide the brain into a number of regions, or parcels, with homogeneous characteristics, be these regions defined in the brain volume or on the cortical surface. While predefined brain atlases do not adapt to the signal in the individual subject images, parcellation approaches use brain activity (e.g., found in some functional contrasts of interest) and clustering techniques to define regions with some degree of signal homogeneity. In this work, we address the question of which clustering technique is appropriate and how to optimize the corresponding model. We use two principled criteria: goodness of fit (accuracy), and reproducibility of the parcellation across bootstrap samples. We study these criteria on both simulated and two task-based functional Magnetic Resonance Imaging datasets for the Ward, spectral and k-means clustering algorithms. We show that in general Ward’s clustering performs better than alternative methods with regard to reproducibility and accuracy and that the two criteria diverge regarding the preferred models (reproducibility leading to more conservative solutions), thus deferring the practical decision to a higher level alternative, namely the choice of a trade-off between accuracy and stability.}
}

@article{Turner2012,
    title={Spatiotemporal activity estimation for multivoxel pattern analysis with rapid event-related designs},
    author={Turner, Benjamin O and Mumford, Jeanette A and Poldrack, Russell A and Ashby, F Gregory},
    journal={NeuroImage},
    volume={62},
    number={3},
    pages={1429--1438},
    year={2012},
    publisher={Elsevier}
}

@article{Tzourio-Mazoyer2002,
	title = {Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain},
	journal = {NeuroImage},
	volume = {15},
	number = {1},
	pages = {273-289},
	year = {2002},
	issn = {1053-8119},
	doi = {10.1006/nimg.2001.0978},
	author = {N. Tzourio-Mazoyer and B. Landeau and D. Papathanassiou and F. Crivello and O. Etard and N. Delcroix and B. Mazoyer and M. Joliot},
	abstract = {An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute (MNI) (D. L. Collins et al., 1998, Trans. Med. Imag. 17, 463–468) was performed. The MNI single-subject main sulci were first delineated and further used as landmarks for the 3D definition of 45 anatomical volumes of interest (AVOI) in each hemisphere. This procedure was performed using a dedicated software which allowed a 3D following of the sulci course on the edited brain. Regions of interest were then drawn manually with the same software every 2 mm on the axial slices of the high-resolution MNI single subject. The 90 AVOI were reconstructed and assigned a label. Using this parcellation method, three procedures to perform the automated anatomical labeling of functional studies are proposed: (1) labeling of an extremum defined by a set of coordinates, (2) percentage of voxels belonging to each of the AVOI intersected by a sphere centered by a set of coordinates, and (3) percentage of voxels belonging to each of the AVOI intersected by an activated cluster. An interface with the Statistical Parametric Mapping package (SPM, J. Ashburner and K. J. Friston, 1999, Hum. Brain Mapp. 7, 254–266) is provided as a freeware to researchers of the neuroimaging community. We believe that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain in which deformations are well known. However, this tool does not alleviate the need for more sophisticated labeling strategies based on anatomical or cytoarchitectonic probabilistic maps.}
}

@article{Varoquaux2013,
	title = {Learning and comparing functional connectomes across subjects},
	journal = {NeuroImage},
	volume = {80},
	pages = {405-415},
	year = {2013},
	note = {Mapping the Connectome},
	issn = {1053-8119},
	doi = {10.1016/j.neuroimage.2013.04.007},
	author = {Gaël Varoquaux and R. Cameron Craddock},
	keywords = {Functional connectivity, Connectome, Group study, Effective connectivity, fMRI, Resting-state},
	abstract = {Functional connectomes capture brain interactions via synchronized fluctuations in the functional magnetic resonance imaging signal. If measured during rest, they map the intrinsic functional architecture of the brain. With task-driven experiments they represent integration mechanisms between specialized brain areas. Analyzing their variability across subjects and conditions can reveal markers of brain pathologies and mechanisms underlying cognition. Methods of estimating functional connectomes from the imaging signal have undergone rapid developments and the literature is full of diverse strategies for comparing them. This review aims to clarify links across functional-connectivity methods as well as to expose different steps to perform a group study of functional connectomes.}
}

@inproceedings{Varoquaux2011,
	author="Varoquaux, Gael
	and Gramfort, Alexandre
	and Pedregosa, Fabian
	and Michel, Vincent
	and Thirion, Bertrand",
	title="Multi-subject Dictionary Learning to Segment an Atlas of Brain Spontaneous Activity",
	booktitle="Information Processing in Medical Imaging",
	year="2011",
	publisher="Springer Berlin Heidelberg",
	address="Berlin, Heidelberg",
	pages="562--573",
	abstract="Fluctuations in brain on-going activity can be used to reveal its intrinsic functional organization. To mine this information, we give a new hierarchical probabilistic model for brain activity patterns that does not require an experimental design to be specified. We estimate this model in the dictionary learning framework, learning simultaneously latent spatial maps and the corresponding brain activity time-series. Unlike previous dictionary learning frameworks, we introduce an explicit difference between subject-level spatial maps and their corresponding population-level maps, forming an atlas. We give a novel algorithm using convex optimization techniques to solve efficiently this problem with non-smooth penalties well-suited to image denoising. We show on simulated data that it can recover population-level maps as well as subject specificities. On resting-state fMRI data, we extract the first atlas of spontaneous brain activity and show how it defines a subject-specific functional parcellation of the brain in localized regions.",
	isbn="978-3-642-22092-0"
}

@article{Varoquaux2010a,
	title = {Brain covariance selection: Better individual functional connectivity models using population prior},
	author = {Varoquaux, Gael and Gramfort, Alexandre and Poline, Jean Baptiste and Thirion, Bertrand},
	abstract = {Spontaneous brain activity, as observed in functional neuroimaging, has been shown to display reproducible structure that expresses brain architecture and carries markers of brain pathologies. An important view of modern neuroscience is that such large-scale structure of coherent activity reflects modularity properties of brain connectivity graphs. However, to date, there has been no demonstration that the limited and noisy data available in spontaneous activity observations could be used to learn full-brain probabilistic models that generalize to new data. Learning such models entails two main challenges: i) modeling full brain connectivity is a difficult estimation problem that faces the curse of dimensionality and ii) variability between subjects, coupled with the variability of functional signals between experimental runs, makes the use of multiple datasets challenging. We describe subject-level brain functional connectivity structure as a multivariate Gaussian process and introduce a new strategy to estimate it from group data, by imposing a common structure on the graphical model in the population. We show that individual models learned from functional Magnetic Resonance Imaging (fMRI) data using this population prior generalize better to unseen data than models based on alternative regularization schemes. To our knowledge, this is the first report of a cross-validated model of spontaneous brain activity. Finally, we use the estimated graphical model to explore the large-scale characteristics of functional architecture and show for the first time that known cognitive networks appear as the integrated communities of functional connectivity graph.},
	eprint = {1008.5071},
	journal={Advances in neural information processing systems},
	volume={23},
	year={2010},
	url={https://inria.hal.science/inria-00512451}
}

@article{varoquaux2010brain,
  title={Brain covariance selection: better individual functional connectivity models using population prior},
  author={Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Poline, Jean-Baptiste and Thirion, Bertrand},
  journal={Advances in neural information processing systems},
  volume={23},
  year={2010}
}


@inproceedings{Varoquaux2010b,
	title = {Detection of brain functional-connectivity difference in post-stroke patients using group-level covariance modeling},
	booktitle = {Medical image computing and computer-assisted intervention - {{MICCAI}} 2010},
	author = {Varoquaux, Gael and Baronnet, Flore and Kleinschmidt, Andreas and Fillard, Pierre and Thirion, Bertrand},
	editor = {Jiang, Tianzi and Navab, Nassir and Pluim, Josien P. W. and Viergever, Max A.},
	year = {2010},
	pages = {200--208},
	publisher = {{Springer}},
	address = {{Berlin, Heidelberg}},
	doi = {10/cn2h9c},
	abstract = {Functional brain connectivity, as revealed through distant correlations in the signals measured by functional Magnetic Resonance Imaging (fMRI), is a promising source of biomarkers of brain pathologies. However, establishing and using diagnostic markers requires probabilistic inter-subject comparisons. Principled comparison of functional-connectivity structures is still a challenging issue. We give a new matrix-variate probabilistic model suitable for inter-subject comparison of functional connectivity matrices on the manifold of Symmetric Positive Definite (SPD) matrices. We show that this model leads to a new algorithm for principled comparison of connectivity coefficients between pairs of regions. We apply this model to comparing separately post-stroke patients to a group of healthy controls. We find neurologically-relevant connection differences and show that our model is more sensitive that the standard procedure. To the best of our knowledge, these results are the first report of functional connectivity differences between a single-patient and a group and thus establish an important step toward using functional connectivity as a diagnostic tool.},
	isbn = {978-3-642-15705-9},
	series = {Lecture notes in computer science}
}

@article{Varoquaux2010c,
	title = {A group model for stable multi-subject ICA on fMRI datasets},
	author = {Varoquaux, Gael
	and Sadaghiani, Sepideh
	and Pinel, Philippe
	and Kleinschmidt, Andreas
	and Poline, Jean-Baptiste
	and Thirion, Bertrand},
	year = {2010},
	journal = {NeuroImage},
	volume = {51},
	number = {1},
	pages = {288--299},
	issn = {1095-9572},
    doi={10.1016/j.neuroimage.2010.02.010},
    url={https://pubmed.ncbi.nlm.nih.gov/20153834}
}

@INPROCEEDINGS{Varoquaux2010d,
  author={Varoquaux, Gaël
  and Keller, Merlin
  and Poline, Jean-Baptiste
  and Ciuciu, Philippe
  and Thirion, Bertrand},
  booktitle={2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro},
  title={ICA-based sparse features recovery from fMRI datasets},
  year={2010},
  volume={},
  number={},
  pages={1177-1180},
  doi={10.1109/ISBI.2010.5490204}}

@article{Varoquaux2012,
  title={Small-sample brain mapping: sparse recovery on spatially correlated designs with randomization and clustering},
  author={Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Thirion, Bertrand},
  journal={arXiv},
  eprint={1206.6447},
  year={2012},
  pdf={https://icml.cc/2012/papers/688.pdf},
}

@article{Varoquaux2017,
	title={Assessing and tuning brain decoders: cross-validation, caveats, and guidelines},
	author={Varoquaux, Ga{\"e}l and Raamana, Pradeep Reddy and Engemann, Denis A and Hoyos-Idrobo, Andr{\'e}s and Schwartz, Yannick and Thirion, Bertrand},
	journal={NeuroImage},
	volume={145},
	pages={166--179},
	year={2017},
	publisher={Elsevier},
	eprint={1606.05201}
}

@article{Winkler2014,
    title = {Permutation inference for the general linear model},
    journal = {NeuroImage},
    volume = {92},
    pages = {381-397},
    year = {2014},
    issn = {1053-8119},
    doi = {10.1016/j.neuroimage.2014.01.060},

    author = {Anderson M. Winkler and Gerard R. Ridgway and Matthew A. Webster and Stephen M. Smith and Thomas E. Nichols},
    keywords = {Permutation inference, Multiple regression, General linear model, Randomise},
    abstract = {Permutation methods can provide exact control of false positives and allow the use of non-standard statistics, making only weak assumptions about the data. With the availability of fast and inexpensive computing, their main limitation would be some lack of flexibility to work with arbitrary experimental designs. In this paper we report on results on approximate permutation methods that are more flexible with respect to the experimental design and nuisance variables, and conduct detailed simulations to identify the best method for settings that are typical for imaging research scenarios. We present a generic framework for permutation inference for complex general linear models (glms) when the errors are exchangeable and/or have a symmetric distribution, and show that, even in the presence of nuisance effects, these permutation inferences are powerful while providing excellent control of false positives in a wide range of common and relevant imaging research scenarios. We also demonstrate how the inference on glm parameters, originally intended for independent data, can be used in certain special but useful cases in which independence is violated. Detailed examples of common neuroimaging applications are provided, as well as a complete algorithm – the “randomise” algorithm – for permutation inference with the glm.}
}

@article{Yarkoni2011,
	title={Large-scale automated synthesis of human functional neuroimaging data},
	author={Yarkoni, Tal and Poldrack, Russell A and Nichols, Thomas E and Van Essen, David C and Wager, Tor D},
	journal={Nature methods},
	volume={8},
	number={8},
	pages={665--670},
	year={2011},
	publisher={Nature Publishing Group}
}

@article{Yeo2011,
	author = {Thomas Yeo, B. T. and Krienen, Fenna M. and Sepulcre, Jorge and Sabuncu, Mert R. and Lashkari, Danial and Hollinshead, Marisa and Roffman, Joshua L. and Smoller, Jordan W. and Zöllei, Lilla and Polimeni, Jonathan R. and Fischl, Bruce and Liu, Hesheng and Buckner, Randy L.},
	title = {The organization of the human cerebral cortex estimated by intrinsic functional connectivity},
	journal = {Journal of Neurophysiology},
	volume = {106},
	number = {3},
	pages = {1125-1165},
	year = {2011},
	doi = {10.1152/jn.00338.2011},
	note = {PMID: 21653723},
	abstract = { Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition. }
}

@article{zalesky2012use,
  title={On the use of correlation as a measure of network connectivity},
  author={Zalesky, Andrew and Fornito, Alex and Bullmore, Ed},
  journal={Neuroimage},
  volume={60},
  number={4},
  pages={2096--2106},
  year={2012},
  publisher={Elsevier}
}

@article{Zuo2010a,
	title = {The oscillating brain: Complex and reliable},
	journal = {NeuroImage},
	volume = {49},
	number = {2},
	pages = {1432-1445},
	year = {2010},
	issn = {1053-8119},
	doi = {10.1016/j.neuroimage.2009.09.037},
	author = {Xi-Nian Zuo and Adriana {Di Martino} and Clare Kelly and Zarrar E. Shehzad and Dylan G. Gee and Donald F. Klein and F. Xavier Castellanos and Bharat B. Biswal and Michael P. Milham},
	abstract = {The human brain is a complex dynamic system capable of generating a multitude of oscillatory waves in support of brain function. Using fMRI, we examined the amplitude of spontaneous low-frequency oscillations (LFO) observed in the human resting brain and the test–retest reliability of relevant amplitude measures. We confirmed prior reports that gray matter exhibits higher LFO amplitude than white matter. Within gray matter, the largest amplitudes appeared along mid-brain structures associated with the “default-mode” network. Additionally, we found that high-amplitude LFO activity in specific brain regions was reliable across time. Furthermore, parcellation-based results revealed significant and highly reliable ranking orders of LFO amplitudes among anatomical parcellation units. Detailed examination of individual low frequency bands showed distinct spatial profiles. Intriguingly, LFO amplitudes in the slow-4 (0.027–0.073 Hz) band, as defined by Buzsáki et al., were most robust in the basal ganglia, as has been found in spontaneous electrophysiological recordings in the awake rat. These results suggest that amplitude measures of LFO can contribute to further between-group characterization of existing and future “resting-state” fMRI datasets.}
}

@article{Zuo2010b,
	title = {Reliable intrinsic connectivity networks: Test–retest evaluation using ICA and dual regression approach},
	journal = {NeuroImage},
	volume = {49},
	number = {3},
	pages = {2163-2177},
	year = {2010},
	issn = {1053-8119},
	doi = {10.1016/j.neuroimage.2009.10.080},
	author = {Xi-Nian Zuo and Clare Kelly and Jonathan S. Adelstein and Donald F. Klein and F. Xavier Castellanos and Michael P. Milham},
	keywords = {Test–retest reliability, Intrinsic connectivity network, ICA, Dual regression, Resting state},
	abstract = {Functional connectivity analyses of resting-state fMRI data are rapidly emerging as highly efficient and powerful tools for in vivo mapping of functional networks in the brain, referred to as intrinsic connectivity networks (ICNs). Despite a burgeoning literature, researchers continue to struggle with the challenge of defining computationally efficient and reliable approaches for identifying and characterizing ICNs. Independent component analysis (ICA) has emerged as a powerful tool for exploring ICNs in both healthy and clinical populations. In particular, temporal concatenation group ICA (TC-GICA) coupled with a back-reconstruction step produces participant-level resting state functional connectivity maps for each group-level component. The present work systematically evaluated the test–retest reliability of TC-GICA derived RSFC measures over the short-term (<45 min) and long-term (5–16 months). Additionally, to investigate the degree to which the components revealed by TC-GICA are detectable via single-session ICA, we investigated the reproducibility of TC-GICA findings. First, we found moderate-to-high short- and long-term test–retest reliability for ICNs derived by combining TC-GICA and dual regression. Exceptions to this finding were limited to physiological- and imaging-related artifacts. Second, our reproducibility analyses revealed notable limitations for template matching procedures to accurately detect TC-GICA based components at the individual scan level. Third, we found that TC-GICA component's reliability and reproducibility ranks are highly consistent. In summary, TC-GICA combined with dual regression is an effective and reliable approach to exploratory analyses of resting state fMRI data.}
}
