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Front Hum Neurosci. 2014 Nov 7;8:897. doi: 10.3389/fnhum.2014.00897. eCollection 2014.

Dynamic connectivity states estimated from resting fMRI Identify differences among Schizophrenia, bipolar disorder, and healthy control subjects.

Author information

  • 1The Mind Research Network, Albuquerque NM, USA ; Department of Electrical and Computer Engineering, University of New Mexico Albuquerque, NM, USA.
  • 2Olin Neuropsychiatry Research Center - Institute of Living, Hartford CT, USA ; Departments of Psychiatry, Yale University School of Medicine New Haven, CT, USA ; Departments of Neurobiology, Yale University School of Medicine New Haven, CT, USA.
  • 3The Mind Research Network, Albuquerque NM, USA ; Department of Electrical and Computer Engineering, University of New Mexico Albuquerque, NM, USA ; Olin Neuropsychiatry Research Center - Institute of Living, Hartford CT, USA ; Departments of Psychiatry, Yale University School of Medicine New Haven, CT, USA.

Abstract

Schizophrenia (SZ) and bipolar disorder (BP) share significant overlap in clinical symptoms, brain characteristics, and risk genes, and both are associated with dysconnectivity among large-scale brain networks. Resting state functional magnetic resonance imaging (rsfMRI) data facilitates studying macroscopic connectivity among distant brain regions. Standard approaches to identifying such connectivity include seed-based correlation and data-driven clustering methods such as independent component analysis (ICA) but typically focus on average connectivity. In this study, we utilize ICA on rsfMRI data to obtain intrinsic connectivity networks (ICNs) in cohorts of healthy controls (HCs) and age matched SZ and BP patients. Subsequently, we investigated difference in functional network connectivity, defined as pairwise correlations among the timecourses of ICNs, between HCs and patients. We quantified differences in both static (average) and dynamic (windowed) connectivity during the entire scan duration. Disease-specific differences were identified in connectivity within different dynamic states. Notably, results suggest that patients make fewer transitions to some states (states 1, 2, and 4) compared to HCs, with most such differences confined to a single state. SZ patients showed more differences from healthy subjects than did bipolars, including both hyper and hypo connectivity in one common connectivity state (dynamic state 3). Also group differences between SZ and bipolar patients were identified in patterns (states) of connectivity involving the frontal (dynamic state 1) and frontal-parietal regions (dynamic state 3). Our results provide new information about these illnesses and strongly suggest that state-based analyses are critical to avoid averaging together important factors that can help distinguish these clinical groups.

KEYWORDS:

bipolar disorder; dynamic functional connectivity; independent component analysis; intrinsic connectivity networks; schizophrenia

PMID:
25426048
PMCID:
PMC4224100
DOI:
10.3389/fnhum.2014.00897
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