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      Spatio‐temporal dynamics of resting‐state brain networks improve single‐subject prediction of schizophrenia diagnosis

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          Abstract

          Correlation in functional MRI activity between spatially separated brain regions can fluctuate dynamically when an individual is at rest. These dynamics are typically characterized temporally by measuring fluctuations in functional connectivity between brain regions that remain fixed in space over time. Here, dynamics in functional connectivity were characterized in both time and space . Temporal dynamics were mapped with sliding‐window correlation, while spatial dynamics were characterized by enabling network regions to vary in size (shrink/grow) over time according to the functional connectivity profile of their constituent voxels. These temporal and spatial dynamics were evaluated as biomarkers to distinguish schizophrenia patients from controls, and compared to current biomarkers based on static measures of resting‐state functional connectivity. Support vector machine classifiers were trained using: (a) static, (b) dynamic in time, (c) dynamic in space, and (d) dynamic in time and space characterizations of functional connectivity within canonical resting‐state brain networks. Classifiers trained on functional connectivity dynamics mapped over both space and time predicted diagnostic status with accuracy exceeding 91%, whereas utilizing only spatial or temporal dynamics alone yielded lower classification accuracies. Static measures of functional connectivity yielded the lowest accuracy (79.5%). Compared to healthy comparison individuals, schizophrenia patients generally exhibited functional connectivity that was reduced in strength and more variable. Robustness was established with replication in an independent dataset. The utility of biomarkers based on temporal and spatial functional connectivity dynamics suggests that resting‐state dynamics are not trivially attributable to sampling variability and head motion.

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          Most cited references46

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          Competition between functional brain networks mediates behavioral variability.

          Increased intraindividual variability (IIV) is a hallmark of disorders of attention. Recent work has linked these disorders to abnormalities in a "default mode" network, comprising brain regions routinely deactivated during goal-directed cognitive tasks. Findings from a study of the neural basis of attentional lapses suggest that a competitive relationship between the "task-negative" default mode network and regions of a "task-positive" attentional network is a potential locus of dysfunction in individuals with increased IIV. Resting state studies have shown that this competitive relationship is intrinsically represented in the brain, in the form of a negative correlation or antiphase relationship between spontaneous activity occurring in the two networks. We quantified the negative correlation between these two networks in 26 subjects, during active (Eriksen flanker task) and resting state scans. We hypothesized that the strength of the negative correlation is an index of the degree of regulation of activity in the default mode and task-positive networks and would be positively related to consistent behavioral performance. We found that the strength of the correlation between the two networks varies across individuals. These individual differences appear to be behaviorally relevant, as interindividual variation in the strength of the correlation was significantly related to individual differences in response time variability: the stronger the negative correlation (i.e., the closer to 180 degrees antiphase), the less variable the behavioral performance. This relationship was moderately consistent across resting and task conditions, suggesting that the measure indexes moderately stable individual differences in the integrity of functional brain networks. We discuss the implications of these findings for our understanding of the behavioral significance of spontaneous brain activity, in both healthy and clinical populations.
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            MINIMUM REDUNDANCY FEATURE SELECTION FROM MICROARRAY GENE EXPRESSION DATA

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              Resting-state functional connectivity in neuropsychiatric disorders.

              This review considers recent advances in the application of resting-state functional magnetic resonance imaging to the study of neuropsychiatric disorders. Resting-state functional magnetic resonance imaging is a relatively novel technique that has several potential advantages over task-activation functional magnetic resonance imaging in terms of its clinical applicability. A number of research groups have begun to investigate the use of resting-state functional magnetic resonance imaging in a variety of neuropsychiatric disorders including Alzheimer's disease, depression, and schizophrenia. Although preliminary results have been fairly consistent in some disorders (for example, Alzheimer's disease) they have been less reproducible in others (schizophrenia). Resting-state connectivity has been shown to correlate with behavioral performance and emotional measures. It's potential as a biomarker of disease and an early objective marker of treatment response is genuine but still to be realized. Resting-state functional magnetic resonance imaging has made some strides in the clinical realm but significant advances are required before it can be used in a meaningful way at the single-patient level.
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                Author and article information

                Journal
                Human Brain Mapping
                Hum. Brain Mapp.
                Wiley
                1065-9471
                1097-0193
                August 11 2018
                September 2018
                May 10 2018
                September 2018
                : 39
                : 9
                : 3663-3681
                Affiliations
                [1 ]Department of Biomedical EngineeringThe University of Melbourne Victoria3010 Australia
                [2 ]Department of Electrical and Electronic EngineeringThe University of Melbourne Victoria3010 Australia
                [3 ]Florey Institute for Neurosciences and Mental healthParkville Victoria3052 Australia
                [4 ]Melbourne Neuropsychiatry CentreThe University of Melbourne Victoria3010 Australia
                [5 ]Cooperative Research Centre for Mental HealthCarlton Victoria3053 Australia
                [6 ]Department of PsychiatryThe University of Melbourne Victoria3010 Australia
                [7 ]North Western Mental Health, Melbourne HealthParkville Victoria Australia
                [8 ]Centre for Neural Engineering, Department of Electrical and Electronic EngineeringThe University of Melbourne Victoria3053 Australia
                [9 ]Department of Computing and Information SystemsThe University of MelbourneVictoria3010 Australia
                Article
                10.1002/hbm.24202
                6866493
                29749660
                c2e72fa4-f5a9-4b09-868b-056d7382469d
                © 2018

                http://onlinelibrary.wiley.com/termsAndConditions#vor

                http://doi.wiley.com/10.1002/tdm_license_1.1

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