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      Assessing Variations in Areal Organization for the Intrinsic Brain: From Fingerprints to Reliability

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          Abstract

          Resting state fMRI (R-fMRI) is a powerful in-vivo tool for examining the functional architecture of the human brain. Recent studies have demonstrated the ability to characterize transitions between functionally distinct cortical areas through the mapping of gradients in intrinsic functional connectivity (iFC) profiles. To date, this novel approach has primarily been applied to iFC profiles averaged across groups of individuals, or in one case, a single individual scanned multiple times. Here, we used a publically available R-fMRI dataset, in which 30 healthy participants were scanned 10 times (10 min per session), to investigate differences in full-brain transition profiles (i.e., gradient maps, edge maps) across individuals, and their reliability. 10-min R-fMRI scans were sufficient to achieve high accuracies in efforts to “fingerprint” individuals based upon full-brain transition profiles. Regarding test–retest reliability, the image-wise intraclass correlation coefficient (ICC) was moderate, and vertex-level ICC varied depending on region; larger durations of data yielded higher reliability scores universally. Initial application of gradient-based methodologies to a recently published dataset obtained from twins suggested inter-individual variation in areal profiles might have genetic and familial origins. Overall, these results illustrate the utility of gradient-based iFC approaches for studying inter-individual variation in brain function.

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          Individual variability in functional connectivity architecture of the human brain.

          The fact that people think or behave differently from one another is rooted in individual differences in brain anatomy and connectivity. Here, we used repeated-measurement resting-state functional MRI to explore intersubject variability in connectivity. Individual differences in functional connectivity were heterogeneous across the cortex, with significantly higher variability in heteromodal association cortex and lower variability in unimodal cortices. Intersubject variability in connectivity was significantly correlated with the degree of evolutionary cortical expansion, suggesting a potential evolutionary root of functional variability. The connectivity variability was also related to variability in sulcal depth but not cortical thickness, positively correlated with the degree of long-range connectivity but negatively correlated with local connectivity. A meta-analysis further revealed that regions predicting individual differences in cognitive domains are predominantly located in regions of high connectivity variability. Our findings have potential implications for understanding brain evolution and development, guiding intervention, and interpreting statistical maps in neuroimaging. Copyright © 2013 Elsevier Inc. All rights reserved.
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            Frequencies contributing to functional connectivity in the cerebral cortex in "resting-state" data.

            In subjects performing no specific cognitive task ("resting state"), time courses of voxels within functionally connected regions of the brain have high cross-correlation coefficients ("functional connectivity"). The purpose of this study was to measure the contributions of low frequencies and physiological noise to cross-correlation maps. In four healthy volunteers, task-activation functional MR imaging and resting-state data were acquired. We obtained four contiguous slice locations in the "resting state" with a high sampling rate. Regions of interest consisting of four contiguous voxels were selected. The correlation coefficient for the averaged time course and every other voxel in the four slices was calculated and separated into its component frequency contributions. We calculated the relative amounts of the spectrum that were in the low-frequency (0 to 0.1 Hz), the respiratory-frequency (0.1 to 0.5 Hz), and cardiac-frequency range (0.6 to 1.2 Hz). For each volunteer, resting-state maps that resembled task-activation maps were obtained. For the auditory and visual cortices, the correlation coefficient depended almost exclusively on low frequencies (<0.1 Hz). For all cortical regions studied, low-frequency fluctuations contributed more than 90% of the correlation coefficient. Physiological (respiratory and cardiac) noise sources contributed less than 10% to any functional connectivity MR imaging map. In blood vessels and cerebrospinal fluid, physiological noise contributed more to the correlation coefficient. Functional connectivity in the auditory, visual, and sensorimotor cortices is characterized predominantly by frequencies slower than those in the cardiac and respiratory cycles. In functionally connected regions, these low frequencies are characterized by a high degree of temporal coherence.
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              Decreased segregation of brain systems across the healthy adult lifespan.

              Healthy aging has been associated with decreased specialization in brain function. This characterization has focused largely on describing age-accompanied differences in specialization at the level of neurons and brain areas. We expand this work to describe systems-level differences in specialization in a healthy adult lifespan sample (n = 210; 20-89 y). A graph-theoretic framework is used to guide analysis of functional MRI resting-state data and describe systems-level differences in connectivity of individual brain networks. Young adults' brain systems exhibit a balance of within- and between-system correlations that is characteristic of segregated and specialized organization. Increasing age is accompanied by decreasing segregation of brain systems. Compared with systems involved in the processing of sensory input and motor output, systems mediating "associative" operations exhibit a distinct pattern of reductions in segregation across the adult lifespan. Of particular importance, the magnitude of association system segregation is predictive of long-term memory function, independent of an individual's age.
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                Author and article information

                Journal
                Cereb Cortex
                Cereb. Cortex
                cercor
                cercor
                Cerebral Cortex (New York, NY)
                Oxford University Press
                1047-3211
                1460-2199
                October 2016
                17 October 2016
                17 October 2016
                : 26
                : 11
                : 4192-4211
                Affiliations
                [1 ]Key Laboratory of Behavioral Sciences and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences , Beijing100101, China
                [2 ]Center for the Developing Brain, Child Mind Institute , New York, NY10022, USA
                [3 ]Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research , Orangeburg, NY10962, USA
                [4 ]Queensland Brain Institute and Centre for Advanced Imaging , University of Queensland, St Lucia, QLD 4072, Australia
                Author notes
                Address correspondence to Michael P. Milham, MD, PhD, Center for the Developing Brain, Child Mind Institute, 445 Park Avenue, New York, NY 10022, USA. Email: Michael.Milham@ 123456childmind.org
                Article
                bhw241
                10.1093/cercor/bhw241
                5066830
                27600846
                662b0129-c04e-45df-b886-f4ceafb7a779
                © The Author 2016. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 16 July 2016
                : 15 July 2016
                : 15 July 2016
                Page count
                Pages: 20
                Funding
                Funded by: International Exchange for Post-doctoral Scientists of China (2013);
                Funded by: NIH;
                Award ID: NIMH U01MH099059
                Funded by: NIMH BRAINS;
                Award ID: R01-MH101555
                Funded by: Natural Science Foundation of China;
                Award ID: 81270023, 81278412, 81171409, 81000583, 81471740, 81220108014
                Funded by: National Science Foundation for Post-doctoral Scientists of China;
                Award ID: 2013M530073
                Funded by: Key Research Program and the Hundred Talents Program of the Chinese Academy of Sciences;
                Award ID: KSZD-EW-TZ-002
                Categories
                Original Articles

                Neurology
                areal organization,individual differences,reliability,resting-state fmri,transition zones

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