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13 - Concepts and Principles of Clinical Functional Magnetic Resonance Imaging

from Part III - Experimental and Biological Approaches

Published online by Cambridge University Press:  23 March 2020

Aidan G. C. Wright
Affiliation:
University of Pittsburgh
Michael N. Hallquist
Affiliation:
Pennsylvania State University
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Summary

Fueled by rapid methodological and analytic advances, functional magnetic resonance imaging (fMRI) has become the dominant method to characterize the relationship between brain function, the environment, and symptoms of psychiatric illness. The widespread adoption of this in vivo imaging approach has allowed for the study of brain systems that underlie symptom expression, treatment response, and risk for illness onset. Yet a host of approaches exist for the collection and analysis of fMRI data, and researchers often struggle to select appropriate study designs and analytic methods. Here we take a critical look at how recent advances in fMRI methods can inform our understanding of brain functions in mental illness. The benefits and limitations of different experimental approaches, from task-evoked fMRI to resting-state designs are described, and how these data provide complementary perspectives on the neurobiological basis of psychiatric illness. Established and cutting-edge analytic techniques for fMRI data are covered. Finally, some of the constraints and limitations on the interpretation of fMRI analyses are reviewed, highlighting common pitfalls to avoid, including issues pertaining to assumptions of mechanistic specificity, causality, diagnostic and symptom specificity, as well as controversial inferential strategies utilized by much of the field.

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Publisher: Cambridge University Press
Print publication year: 2020

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References

Amaro, E. Jr., & Barker, G. J. (2006). Study Design in fMRI: Basic Principles. Brain and Cognition, 60(3), 220232.CrossRefGoogle ScholarPubMed
Anderson, A. W., Heptulla, R. A., Driesen, N., Flanagan, D., Goldberg, P. A., Jones, T. W., … Gore, J. C. (2006). Effects of Hypoglycemia on Human Brain Activation Measured with fMRI. Magnetic Resonance Imaging, 24, 693697.Google Scholar
Anderson, K. M., Krienen, F. M., Choi, E. Y., Reinen, J. M., Yeo, B. T. T., & Holmes, A. J. (2018). Gene Expression Links Functional Networks across Cortex and Striatum. Nature Communications, 9, 1428.Google Scholar
Baker, J. T., Holmes, A. J., Masters, G. A., Yeo, B. T. T., Krienen, F. M., Buckner, R. L., & Öngür, D. (2014). Disruption of Cortical Association Networks in Schizophrenia and Psychotic Bipolar Disorder. JAMA Psychiatry, 71(2), 109118.CrossRefGoogle ScholarPubMed
Barch, D. M., Burgess, G. C., Harms, M. P., Petersen, S. E., Schlaggar, B. L., Corbetta, M., … Van Essen, D. C. (2013). Function in the Human Connectome: Task-fMRI and Individual Differences in Behavior. NeuroImage, 80, 169189.Google Scholar
Biswal, B. B., Mennes, M., Zuo, X.-N., Gohel, S., Kelly, C., Smith, S. M., … Milham, M. P. (2010). Toward Discovery Science of Human Brain Function. Proceedings of the National Academy of Sciences, 107(10), 47344739.CrossRefGoogle ScholarPubMed
Biswal, B. B., Yetkin, F. Z., Haughton, V. M., & Hyde, J. S. (1995). Functional Connectivity in the Motor Cortex of Resting Human Brain Using Echo-Planar MRI. Magnetic Resonance in Medicine, 34(4), 537541.CrossRefGoogle ScholarPubMed
Braver, T. S., Reynolds, J. R., & Donaldson, D. I. (2003). Neural Mechanisms of Transient and Sustained Cognitive Control during Task Switching. Neuron, 39(4), 713726.Google Scholar
Breiter, H. C., Etcoff, N. L., Whalen, P. J., Kennedy, W. A., Rauch, S. L., Buckner, R. L., … Rosen, B. R. (1996). Response and Habituation of the Human Amygdala during Visual Processing of Facial Expression. Neuron, 17(5), 875887.Google Scholar
Buckholtz, J. W., & Meyer-Lindenberg, A. (2012). Psychopathology and the Human Connectome: Toward a Transdiagnostic Model of Risk for Mental Illness. Neuron, 74(6), 9901004.CrossRefGoogle Scholar
Calhoun, V. D., Adali, T., Pearlson, G. D., & Pekar, J. J. (2001). A Method for Making Group Inferences from Functional MRI Data Using Independent Component Analysis. Human Brain Mapping, 14, 140151.Google Scholar
Chekroud, A. M., Ward, E. J., Rosenberg, M. D., & Holmes, A. J. (2016). Patterns in the Human Brain Mosaic Discriminate Males from Females. Proceedings of the National Academy of Sciences, 113(14), E1968.CrossRefGoogle ScholarPubMed
Ciric, R., Wolf, D. H., Power, J. D., Roalf, D. R., Baum, G. L., Ruparel, K., … Satterthwaite, T. D. (2018). Benchmarking of Participant-Level Confound Regression Strategies for the Control of Motion Artifact in Studies of Functional Connectivity. NeuroImage, 154, 174187.Google Scholar
Cole, M. W., Repov, G., & Anticevic, A. (2014). The Frontoparietal Control System: A Central Role in Mental Health. The Neuroscientist, 20(6), 652664.CrossRefGoogle ScholarPubMed
Crossley, N. A., Mechelli, A., Vértes, P. E., Winton-Brown, T. T., Patel, A. X., Ginestet, C. E., … Bullmore, E. T. (2013). Cognitive Relevance of the Community Structure of the Human Brain Functional Coactivation Network. Proceedings of the National Academy of Sciences of the United States of America, 110(28), 1158311588.Google Scholar
Dale, A. M., & Buckner, R. L. (1997). Selective Averaging of Rapidly Presented Individual Trials Using fMRI. Human Brain Mapping, 5(5), 329340.Google Scholar
Deco, G., Jirsa, V. K., & McIntosh, A. R. (2011). Emerging Concepts for the Dynamical Organization of Resting-State Activity in the Brain. Nature Reviews Neuroscience, 12(1), 4356.CrossRefGoogle ScholarPubMed
Deng, C., Yuan, H., & Dai, J. (2018). Behavioral Manipulation by Optogenetics in the Nonhuman Primate. The Neuroscientist; 24(5), 526539.Google Scholar
Farkas, T., Wolf, A. P., Jaeger, J., Brodie, J. D., Christman, D. R., & Fowler, J. S. (1984). Regional Brain Glucose Metabolism in Chronic Schizophrenia: A Positron Emission Transaxial Tomographic Study. Archives of General Psychiatry, 41(3), 293300.CrossRefGoogle ScholarPubMed
Fox, P. T., Mintun, M. A., Reiman, E. M., & Raichle, M. E. (1988). Enhanced Detection of Focal Brain Responses Using Intersubject Averaging and Change-Distribution Analysis of Subtracted PET Images. Journal of Cerebral Blood Flow and Metabolism, 8(5), 642653.Google Scholar
Fox, P. T., Parsons, L. M., & Lancaster, J. L. (1998). Beyond the Single Study: Function/Location Metanalysis in Cognitive Neuroimaging. Current Opinion in Neurobiology, 8(2), 178187.Google Scholar
Friston, K.J., Frith, C. D., Liddle, P. F., & Frackowiak, R. S. J. (1993). Functional Connectivity: The Principal-Component Analysis of Large (PET) Data Sets. Journal of Cerebral Blood Flow and Metabolism, 13(1), 514.Google Scholar
Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J.-P., Frith, C. D., & Frackowiak, R. S. J. (1994). Statistical Parametric Maps in Functional Imaging: A General Linear Approach. Human Brain Mapping, 2(4), 189210.Google Scholar
Friston, K. J., Buechel, C., Fink, G. R., Morris, J., Rolls, E., & Dolan, R. J. (1997). Psychophysiological and Modulatory Interactions in Neuroimaging. Neuroimage, 6(3), 218229.CrossRefGoogle ScholarPubMed
Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic Causal Modelling. Neuroimage, 19(4), 12731302.Google Scholar
Funahashi, S., Bruce, C. J., & Goldman-Rakic, P. S. (1989). Mnemonic Coding of Visual Space in the Monkey’s Dorsolateral Prefrontal Cortex. Journal of Neurophysiology, 61(2), 331349.Google Scholar
Ge, T., Holmes, A. J., Buckner, R. L., Smoller, J. W., & Sabuncu, M. R. (2018). Heritability Analysis with Repeat Measurements and Its Application to Resting-State Functional Connectivity. Proceedings of the National Academy of Sciences, 114(21), 55215526.Google Scholar
Gee, D. G., Gabard-Durnam, L. J., Flannery, J., Goff, B., Humphreys, K. L., Telzer, E. H., … Tottenham, N. (2013). Early Developmental Emergence of Human Amygdala-Prefrontal Connectivity after Maternal Deprivation. Proceedings of the National Academy of Sciences of the United States of America, 110(39), 1563815643.Google Scholar
Glasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., … Van Essen, D. C. (2016). A Multi-Modal Parcellation of Human Cerebral Cortex. Nature, 536(7615), 171178.Google Scholar
Goldstein, R. Z., Leskovjan, A. C., Hoff, A. L., Hitzemann, R., Bashan, F., Khalsa, S. S., … Volkow, N. D. (2004). Severity of Neuropsychological Impairment in Cocaine and Alcohol Addiction: Association with Metabolism in the Prefrontal Cortex. Neuropsychologia, 42(11), 14471458.Google Scholar
Goodwin, G. M. (1997). Neuropsychological and Neuroimaging Evidence for the Involvement of the Frontal Lobes in Depression. Journal of Psychopharmacology, 11(2), 115122.Google Scholar
Gur, R. C., & Gur, R. E. (1995). Hypofrontality in Schizophrenia: RIP. Lancet, 345(8962), 13381340.CrossRefGoogle ScholarPubMed
Haut, K. M., Lim, K. O., & MacDonald, A. (2010). Prefrontal Cortical Changes following Cognitive Training in Patients with Chronic Schizophrenia: Effects of Practice, Generalization, and Specificity. Neuropsychopharmacology, 35(9), 18501859.Google Scholar
Henson, R. (2006). Forward Inference Using Functional Neuroimaging: Dissociations versus Associations. Trends in Cognitive Sciences, 10(2), 6469.CrossRefGoogle ScholarPubMed
Holmes, A. J., & Patrick, L. M. (2018). The Myth of Optimality in Clinical Neuroscience. Trends in Cognitive Sciences, 22(3), 241257.Google Scholar
Holmes, A. J., & Yeo, B. T. T. (2015). From Phenotypic Chaos to Neurobiological Order. Nature Neuroscience, 18(11), 15321534.Google Scholar
Holmes, A. J., Hollinshead, M. O., O’Keefe, T. M., Petrov, V. I., Fariello, G. R., Wald, L. L., … Buckner, R. L. (2015). Brain Genomics Superstruct Project Initial Data Release with Structural, Functional, and Behavioral Measures. Scientific Data, 2, 150031.Google Scholar
Huettel, S. A. (2012). Event-Related fMRI in Cognition. Neuroimage, 62(2), 11521156.CrossRefGoogle ScholarPubMed
Huettel, S. A., Song, A. W., & McCarthy, G. (2004). Functional Magnetic Resonance Imaging (Vol. 1). Sunderland, MA: Sinauer Associates Sunderland.Google ScholarPubMed
Hutchison, R. M., Womelsdorf, T., Allen, E. A., Bandettini, P. A., Calhoun, V. D., Corbetta, M., … Chang, C. (2013). Dynamic Functional Connectivity: Promise, Issues, and Interpretations. NeuroImage, 80, 360378.Google Scholar
Ingvar, D. H., & Franzen, G. (1974). Distribution of Cerebral Activity in Chronic Schizophrenia. Lancet, 304(7895), 14841486.Google Scholar
Kamiński, M., Ding, M., Truccolo, W. A., & Bressler, S. L. (2001). Evaluating Causal Relations in Neural Systems: Granger Causality, Directed Transfer Function and Statistical Assessment of Significance. Biological Cybernetics, 85(2), 145157.Google ScholarPubMed
Koch, M. A., Norris, D. G., & Hund-Georgiadis, M. (2002). An Investigation of Functional and Anatomical Connectivity Using Magnetic Resonance Imaging. Neuroimage, 16(1), 241250.Google Scholar
Kong, R., Li, J., Sun, N., Sabuncu, M. R., Schaefer, A., Scholz, M., … Yeo, B. T. T. (2019). Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality and Emotion. Cerebral Cortex, 29(6), 25332551.Google Scholar
Krienen, F. M., Yeo, B. T. T., & Buckner, R. L. (2014). Reconfigurable Task-Dependent Functional Coupling Modes Cluster around a Core Functional Architecture. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 369(1653), 20130526.CrossRefGoogle ScholarPubMed
Lahey, B. B., Applegate, B., Hakes, J. K., Zald, D. H., Hariri, A. R., & Rathouz, P. J. (2012). Is There a General Factor of Prevalent Psychopathology during Adulthood? Journal of Abnormal Psychology, 121(4), 971977.Google Scholar
Laird, A. R., Mickle Fox, P., Price, C. J., Glahn, D. C., Uecker, A. M., Lancaster, J. L., … Fox, P. T. (2005). ALE Meta-Analysis: Controlling the False Discovery Rate and Performing Statistical Contrasts. Human Brain Mapping, 25(1), 155164.Google Scholar
Laurienti, P. J., Field, A. S., Burdette, J. H., Maldjian, J. A., Yen, Y.-F., & Moody, D. M. (2002). Dietary Caffeine Consumption Modulates fMRI Measures. Neuroimage, 17, 751757.Google Scholar
Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., & Oeltermann, A. (2001). Neurophysiological Investigation of the Basis of the fMRI Signal. Nature, 412, 150157.CrossRefGoogle ScholarPubMed
MacDonald, A. W., Becker, T. M., & Carter, C. S. (2006). Functional Magnetic Resonance Imaging Study of Cognitive Control in the Healthy Relatives of Schizophrenia Patients. Biological Psychiatry, 60(11), 12411249.Google Scholar
MacDonald, A. W. (2015). Differential Deficit. In Cautin, R. & Lilienfeld, S. (Eds.), The Encyclopedia of Clinical Psychology (1st edn.). Hoboken, NJ: John Wiley.Google Scholar
Mayberg, H. S. (1997). Limbic-Cortical Dysregulation: A Proposed Model of Depression. Journal of Neuropsychiatry and Clinical Neurosciences, 9(3), 471481.Google Scholar
McIntosh, A. R., & Gonzalez-Lima, F. (1991). Structural Modeling of Functional Neural Pathways Mapped with 2-Deoxyglucose: Effects of Acoustic Startle Habituation on the Auditory System. Brain Research, 547(2), 295302.Google Scholar
McKeown, M. J., & Sejnowski, T. J. (1998). Independent Component Analysis of fMRI Data: Examining the Assumptions. Human Brain Mapping, 6, 368372.Google Scholar
Monti, M. M. (2011). Statistical Analysis of fMRI Time-Series: A Critical Review of the GLM Approach. Frontiers in Human Neuroscience, 5(28), 113.Google Scholar
Ollier, W., Sprosen, T., & Peakman, T. (2005). UK Biobank: From Concept to Reality. Pharmacogenomics, 6(6), 639646.Google Scholar
Ollinger, J. M., Shulman, G. L., & Corbetta, M. (2001). Separating Processes Within a Trial in Event-Related Functional MRI I: The Method. NeuroImage, 13(1), 210217.Google Scholar
Park, S., Holzman, P. S., & Goldman-Rakic, P. S. (1995). Spatial Working Memory Deficits in the Relatives of Schizophrenic Patients. Archives of General Psychiatry, 52(10), 821828.Google Scholar
Penny, W. D., Stephan, K. E., Mechelli, A., & Friston, K. J. (2004). Modelling Functional Integration: A Comparison of Structural Equation and Dynamic Causal Models. Neuroimage, 23(Suppl. 1), S264–274.Google Scholar
Poldrack, R. A. (2006). Can Cognitive Processes Be Inferred from Neuroimaging Data? Trends in Cognitive Sciences, 10(2), 5963.Google Scholar
Poldrack, R. A. (2010). Mapping Mental Function to Brain Structure: How Can Cognitive Neuroimaging Succeed? Perspectives on Psychological Science, 5, 753761.Google Scholar
Poppe, A. B., Wisner, K., Atluri, G., Lim, K. O., Kumar, V., & MacDonald, A. W. (2013). Toward a Neurometric Foundation for Probabilistic Independent Component Analysis of fMRI Data. Cognitive, Affective, & Behavioral Neuroscience, 13(3), 641659.Google Scholar
Poppe, A. B., Barch, D. M., Carter, C. S., Gold, J. M., Ragland, J. D., Silverstein, S. M., & MacDonald, A. W. (2016). Reduced Frontoparietal Activity in Schizophrenia Is Linked to a Specific Deficit in Goal Maintenance: A Multisite Functional Imaging Study. Schizophrenia Bulletin, 42(5), 11491157.Google Scholar
Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., … Petersen, S E. (2011). Functional Network Organization of the Human Brain. Neuron, 72(4), 665678.Google Scholar
Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014). Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI. NeuroImage, 84, 320341.Google Scholar
Price, J. L., & Drevets, W. C. (2012). Neural Circuits Underlying the Pathophysiology of Mood Disorders. Trends in Cognitive Sciences, 16(1), 6171.Google Scholar
Raichle, M. E. (2009). A Brief History of Human Brain Mapping. Trends in Neurosciences, 32(2), 118126.CrossRefGoogle ScholarPubMed
Reinen, J. M., Chen, O. Y., Hutchison, R. M., Yeo, B. T. T., Anderson, K. M., Sabuncu, M. R., … Holmes, A. J. (2018). The Human Cortex Possesses a Reconfigurable Dynamic Network Architecture That Is Disrupted in Psychosis. Nature Communications, 9, 1157.Google Scholar
Richiardi, J., Altmann, A., Milazzo, A.-C., Chang, C., Chakravarty, M. M., Banaschewski, T., … Greicius, M. D. (2015). Correlated Gene Expression Supports Synchronous Activity in Brain Networks. Science, 348(6240), 12411244.Google Scholar
Rosenberg, M. D., Finn, E. S., Scheinost, D., Papademetris, X., Shen, X., Constable, R. T., & Chun, M. M. (2016). A Neuromarker of Sustained Attention from Whole-Brain Functional Connectivity. Nature Neuroscience, 19(1), 165171.Google Scholar
Rosenberg, M. D., Casey, B. J., & Holmes, A. J. (2018). Prediction Complements Explanation in Understanding the Developing Brain. Nature Communications, 9, 589.Google Scholar
Salem, J. E., Kring, A. M., & Kerr, S. L. (1996). More Evidence for Generalized Poor Performance in Facial Emotion Expression in Schizophrenia. Journal of Abnormal Psychology, 105(3), 480483.Google Scholar
Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X.-N., Holmes, A., … Yeo, B. T. (2018). Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cerebral Cortex, 28, 30953114.Google Scholar
Seifritz, E., Bilecen, D., Hänggi, D., Haselhorst, R., Radü, E. W., Wetzel, S., … Scheffler, K. (2000). Effect of Ethanol on BOLD Response to Acoustic Stimulation: Implications for Neuropharmacological fMRI. Psychiatry Research Neuroimaging, 99(1), 113.Google Scholar
Sepulcre, J., Liu, H., Talukdar, T., Martincorena, I., Yeo, B. T. T., & Buckner, R. L. (2010). The Organization of Local and Distant Functional Connectivity in the Human Brain. PLoS Computational Biology, 6(6), e1000808.Google Scholar
Shen, X., Tokoglu, F., Papademetris, X., & Constable, R. T. (2013). Groupwise Whole-Brain Parcellation from Resting-State fMRI Data for Network Node Identification. Neuroimage, 82, 403415.Google Scholar
Shmueli, K., van Gelderen, P., de Zwart, J. A., Horovitz, S. G., Fukunaga, M., Jansma, J. M., & Duyn, J. H. (2007). Low-Frequency Fluctuations in the Cardiac Rate as a Source of Variance in the Resting-State fMRI BOLD Signal. Neuroimage, 38(2), 306320.Google Scholar
Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Mickle Fox, P., Mackay, C. E., … Beckmann, C. F. (2009). Correspondence of the Brain’s Functional Architecture during Activation and Rest. Proceedings of the National Academy of Sciences, 106(31), 1304013045.Google Scholar
Smith, S. M., Vidaurre, D., Beckmann, C. F., Glasser, M. F., Jenkinson, M., Miller, K. L., … Van Essen, D. C. (2013). Functional Connectomics from Resting-State fMRI. Trends in Cognitive Sciences, 17(12), 666682.Google Scholar
Smoller, J. W., Gallagher, P. J., Duncan, L. E., McGrath, L. M., Haddad, S. A., Holmes, A. J., … Cohen, B. M. (2014). The Human Ortholog of Acid-Sensing Ion Channel Gene ASIC1a Is Associated with Panic Disorder and Amygdala Structure and Function. Biological Psychiatry, 76(11), 902910.Google Scholar
Sporns, O. (2014). Contributions and Challenges for Network Models in Cognitive Neuroscience. Nature Neuroscience, 17(5), 652660.Google Scholar
Sprooten, E., Rasgon, A., Goodman, M., Carlin, A., Leibu, E., Lee, W. H., & Frangou, S. (2018). Addressing Reverse Inference in Psychiatric Neuroimaging: Meta-Analyses of Task-Related Brain Activation in Common Mental Disorders. Human Brain Mapping, 38(4), 18461864.CrossRefGoogle Scholar
Tavor, I., Parker Jones, O., Mars, R. B., & Smith, S. M. (2016). Task-Free MRI Predicts Individual Differences in Brain Activity during Task Performance. Science, 352(6282), 216220.Google Scholar
Thiel, C. M., & Fink, G. R. (2007). Visual and Auditory Alertness: Modality-Specific and Supramodal Neural Mechanisms and Their Modulation by Nicotine. Journal of Neurophysiology, 97(4), 27582768.Google Scholar
Van Dijk, K. R. A., Hedden, T., Venkataraman, A., Evans, K. C., Lazar, S. W., & Buckner, R. L. (2010). Intrinsic Functional Connectivity as a Tool for Human Connectomics: Theory, Properties, and Optimization. Journal of Neurophysiology, 103(1), 297321.Google Scholar
Van Dijk, K. R. A., Sabuncu, M. R., & Buckner, R. L. (2012). The Influence of Head Motion on Intrinsic Functional Connectivity MRI. Neuroimage, 59(1), 431438.Google Scholar
Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., & Ugurbil, K. (2013). The WU-Minn Human Connectome Project: An Overview. Neuroimage, 80, 6279.Google Scholar
Vincent, J. L., Snyder, A. Z., Fox, M. D., Shannon, B. J., Andrews, J. R., Raichle, M. E., & Buckner, R. L. (2006). Coherent Spontaneous Activity Identifies a Hippocampal-Parietal Memory Network. Journal of Neurophysiology, 96(6), 35173531.Google Scholar
Viviani, R., Grön, G., & Spitzer, M., (2005). Functional Principal Component Analysis of fMRI Data. Human Brain Mapping, 24, 109129.Google Scholar
Wang, D., Buckner, R. L., Fox, M. D., Holt, D. J., Holmes, A. J., Stoecklein, S., … Liu, H. (2015). Parcellating Cortical Functional Networks in Individuals. Nature Neuroscience, 18(12), 18531860.Google Scholar
Wisner, K. M., Atluri, G., Lim, K. O., & MacDonald, A. W. (2013). Neurometrics of Intrinsic Connectivity Networks at Rest Using fMRI: Retest Reliability and Cross-Validation Using a Meta-Level Method. Neuroimage, 76, 236251.Google Scholar
Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C., & Wager, T. D. (2011). Large-Scale Automated Synthesis of Human Functional Neuroimaging Data. Nature Methods, 8(8), 665670.Google Scholar
Yeo, B. T. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., … Buckner, R. L. (2011). The Organization of the Human Cerebral Cortex Estimated by Intrinsic Functional Connectivity. Journal of Neurophysiology, 106(3), 11251165.Google ScholarPubMed
Zang, Y., Jiang, T., Lu, Y., He, T., & Tian, L. (2004). Regional Homogeneity Approach to fMRI Data Analysis. NeuroImage, 22, 394400.Google Scholar
Zhang, X., Mormino, E. C., Sun, N., Sperling, R. A., Sabuncu, M. R., & Yeo, B. T. T. (2016). Bayesian Model Reveals Latent Atrophy Factors with Dissociable Cognitive Trajectories in Alzheimer’s Disease. Proceedings of the National Academy of Sciences of the United States of America, 113(42), E6535E6544.Google Scholar
Zuo, X.-N., Anderson, J. S., Bellec, P., Birn, R. M., Biswal, B. B., Blautzik, J., … Milham, M. P. (2014). An Open Science Resource for Establishing Reliability and Reproducibility in Functional Connectomics. Scientific Data, 1, 140049.Google Scholar

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