title: Coupled Intrinsic Connectivity Distribution analysis: a method for exploratory connectivity analysis of paired FMRI data.
abstract: We present a novel voxel-based connectivity approach for paired functional magnetic resonance imaging (fMRI) data collected under two different conditions labeled the Coupled Intrinsic Connectivity Distribution (coupled-ICD). Our proposed method jointly models both conditions to incorporate additional paired information into the connectivity metric. Voxel-based connectivity holds promise as a clinical tool to characterize a wide range of neurological and psychiatric diseases, and monitor their treatment. As such, examining paired connectivity data such as scans acquired pre- and post-intervention is an important application for connectivity methodologically. When presented with data from paired conditions, conventional voxel-based methods analyze each condition separately. However, summarizing each connection separately can misrepresent patterns of changes in connectivity. We show that commonly used methods can underestimate functional changes and subsequently introduce and evaluate our solution to this problem, the coupled-ICD metric, using two studies: 1) healthy controls scanned awake and under anesthesia, and 2) cocaine-dependent subjects and healthy controls scanned while being presented with relaxing or drug-related imagery cues. The coupled-ICD approach detected differences between paired conditions in similar brain regions as the conventional approaches while also revealing additional changes in regions not identified using conventional voxel-based connectivity analyses. Follow-up seed-based analyses on data independent from the voxel-based results also showed connectivity differences between conditions in regions detected by coupled-ICD. This approach of jointly analyzing paired resting-state scans provides a new and important tool with many applications for clinical and basic neuroscience research.
authors.affiliation: Department of Biomedical Engineering, Yale University, New Haven, Connecticut, United States of America.
authors.forename: Dustin
authors.initials: D
authors.lastname: Scheinost
authors.affiliation: Department of Diagnostic Radiology, Yale University, New Haven, Connecticut, United States of America.
authors.forename: Xilin
authors.initials: X
authors.lastname: Shen
authors.affiliation: Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut, United States of America.
authors.forename: Emily
authors.initials: E
authors.lastname: Finn
authors.affiliation: Department of Psychiatry, Yale University, New Haven, Connecticut, United States of America.
authors.forename: Rajita
authors.initials: R
authors.lastname: Sinha
authors.affiliation: Department of Biomedical Engineering, Yale University, New Haven, Connecticut, United States of America; Department of Diagnostic Radiology, Yale University, New Haven, Connecticut, United States of America; Department of Neurosurgery, Yale University, New Haven, Connecticut, United States of America.
authors.forename: R Todd
authors.initials: RT
authors.lastname: Constable
authors.affiliation: Department of Biomedical Engineering, Yale University, New Haven, Connecticut, United States of America; Department of Diagnostic Radiology, Yale University, New Haven, Connecticut, United States of America.
authors.forename: Xenophon
authors.initials: X
authors.lastname: Papademetris
mesh_terms: D000465:Algorithms; D000758:Anesthesia; D001921:Brain / Q000473:pathology / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods / Q000706:statistics & numerical data*; D016022:Case-Control Studies; D019970:Cocaine-Related Disorders / Q000473:pathology / Q000503:physiopathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D009434:Neural Pathways / Q000502:physiology* / Q000503:physiopathology; D012146:Rest / Q000523:psychology; D014851:Wakefulness / Q000502:physiology