Clinical applications of the functional connectome
Central to the development of clinical applications of functional connectomics for neurology and psychiatry is the discovery and validation of biomarkers. Resting state fMRI (R-fMRI) is emerging as a mainstream approach for imaging-based biomarker identification, detecting variations in the function...
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Veröffentlicht in: | NeuroImage (Orlando, Fla.) Fla.), 2013-10, Vol.80, p.527-540 |
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creator | Castellanos, F. Xavier Di Martino, Adriana Craddock, R. Cameron Mehta, Ashesh D. Milham, Michael P. |
description | Central to the development of clinical applications of functional connectomics for neurology and psychiatry is the discovery and validation of biomarkers. Resting state fMRI (R-fMRI) is emerging as a mainstream approach for imaging-based biomarker identification, detecting variations in the functional connectome that can be attributed to clinical variables (e.g., diagnostic status). Despite growing enthusiasm, many challenges remain. Here, we assess evidence of the readiness of R-fMRI based functional connectomics to lead to clinically meaningful biomarker identification through the lens of the criteria used to evaluate clinical tests (i.e., validity, reliability, sensitivity, specificity, and applicability). We focus on current R-fMRI-based prediction efforts, and survey R-fMRI used for neurosurgical planning. We identify gaps and needs for R-fMRI-based biomarker identification, highlighting the potential of emerging conceptual, analytical and cultural innovations (e.g., the Research Domain Criteria Project (RDoC), open science initiatives, and Big Data) to address them. Additionally, we note the need to expand future efforts beyond identification of biomarkers for disease status alone to include clinical variables related to risk, expected treatment response and prognosis.
•Resting-state fMRI methods can lead to biomarker identification for brain disorders.•Prospective biomarkers must be assessed with criteria for clinical tests.•Predictive modeling approaches are providing proof-of-concept of diagnostic utility.•The convergence of dimensional approaches, data sharing & Big Data is propitious. |
doi_str_mv | 10.1016/j.neuroimage.2013.04.083 |
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Xavier</au><au>Di Martino, Adriana</au><au>Craddock, R. Cameron</au><au>Mehta, Ashesh D.</au><au>Milham, Michael P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Clinical applications of the functional connectome</atitle><jtitle>NeuroImage (Orlando, Fla.)</jtitle><addtitle>Neuroimage</addtitle><date>2013-10-15</date><risdate>2013</risdate><volume>80</volume><spage>527</spage><epage>540</epage><pages>527-540</pages><issn>1053-8119</issn><eissn>1095-9572</eissn><abstract>Central to the development of clinical applications of functional connectomics for neurology and psychiatry is the discovery and validation of biomarkers. Resting state fMRI (R-fMRI) is emerging as a mainstream approach for imaging-based biomarker identification, detecting variations in the functional connectome that can be attributed to clinical variables (e.g., diagnostic status). Despite growing enthusiasm, many challenges remain. Here, we assess evidence of the readiness of R-fMRI based functional connectomics to lead to clinically meaningful biomarker identification through the lens of the criteria used to evaluate clinical tests (i.e., validity, reliability, sensitivity, specificity, and applicability). We focus on current R-fMRI-based prediction efforts, and survey R-fMRI used for neurosurgical planning. We identify gaps and needs for R-fMRI-based biomarker identification, highlighting the potential of emerging conceptual, analytical and cultural innovations (e.g., the Research Domain Criteria Project (RDoC), open science initiatives, and Big Data) to address them. Additionally, we note the need to expand future efforts beyond identification of biomarkers for disease status alone to include clinical variables related to risk, expected treatment response and prognosis.
•Resting-state fMRI methods can lead to biomarker identification for brain disorders.•Prospective biomarkers must be assessed with criteria for clinical tests.•Predictive modeling approaches are providing proof-of-concept of diagnostic utility.•The convergence of dimensional approaches, data sharing & Big Data is propitious.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>23631991</pmid><doi>10.1016/j.neuroimage.2013.04.083</doi><tpages>14</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Anatomy & physiology Animals Biomarkers Brain - physiopathology Brain Diseases - diagnosis Brain Diseases - physiopathology Brain research Connectome - methods Data processing Evidence-Based Medicine Fourier transforms Functional connectome Humans Illnesses Magnetic Resonance Imaging - methods Models, Neurological Mortality Nerve Net - physiopathology Neurosciences Predictive modeling Principal components analysis Reliability Sensitivity Specificity Validity |
title | Clinical applications of the functional connectome |
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