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Neuroimage Clin. 2017 Jun 17;15:761-768. doi: 10.1016/j.nicl.2017.06.023. eCollection 2017.

A joint time-frequency analysis of resting-state functional connectivity reveals novel patterns of connectivity shared between or unique to schizophrenia patients and healthy controls.

Author information

1
The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106, USA.
2
Dept. of ECE, University of New Mexico, Albuquerque, NM 87131, USA.
3
Dept. of Psychiatry and Behavioral Science, University of New Mexico, Albuquerque, NM 87131, USA.
4
Dept. of Psychiatry, University of Minnesota, Minneapolis, MN 55414, USA.
5
Dept. of Psychiatry, University of Iowa, Iowa City, IA 52242, USA.

Abstract

Functional connectivity of the resting-state (RS) brain is a vehicle to study brain dysconnectivity aspects of diseases such as schizophrenia and bipolar. Methods that are developed to measure functional connectivity are based on the underlying hypotheses regarding the actual nature of RS-connectivity including evidence of temporally dynamic versus static RS-connectivity and evidence of frequency-specific versus hemodynamically-driven connectivity over a wide frequency range. This study is derived by these observations of variation of RS-connectivity in temporal and frequency domains and evaluates such characteristics of RS-connectivity in clinical population and jointly in temporal and frequency domains (the spectro-temporal domain). We base this study on the hypothesis that by studying functional connectivity of schizophrenia patients and comparing it to the one of healthy controls in the spectro-temporal domain we might be able to make new observations regarding the differences and similarities between diseased and healthy brain connectivity and such observations could be obscured by studies which investigate such characteristics separately. Interestingly, our results include, but are not limited to, a spectrally localized (mostly mid-range frequencies) modular dynamic connectivity pattern in which sensory motor networks are anti-correlated with visual, auditory and sub-cortical networks in schizophrenia, as well as evidence of lagged dependence between default-mode and sensory networks in schizophrenia. These observations are unique to the proposed augmented domain of connectivity analysis. We conclude this study by arguing not only resting-state connectivity has structured spectro-temporal variability, but also that studying properties of connectivity in this joint domain reveals distinctive group-based differences and similarities between clinical and healthy populations.

KEYWORDS:

Dynamic and frequency-specific connectivity; Resting-state functional connectivity; Time-frequency analysis; Wavelet transform coherence

PMID:
28706851
PMCID:
PMC5496209
DOI:
10.1016/j.nicl.2017.06.023
[Indexed for MEDLINE]
Free PMC Article

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