Journal article
Autoconnectivity: A new perspective on human brain function
Journal of neuroscience methods, Vol.323, pp.68-76
07/15/2019
DOI: 10.1016/j.jneumeth.2019.03.015
PMID: 31005575
Abstract
Autocorrelation (AC) in fMRI time-series is a well-known phenomenon, typically attributed to colored noise and therefore removed from the data. We hypothesize that AC reflects systematic and meaningful signal fluctuations that may be tied to neural activity and provide evidence to support this hypothesis.
Each fMRI time-series is modeled as an autoregressive process from which the autocorrelation is quantified. Then, autocorrelation during resting-state fMRI and auditory oddball (AOD) task in schizophrenia and healthy volunteers is examined.
During resting-state, AC was higher in the visual cortex while during AOD task, frontal part of the brain exhibited higher AC in both groups. AC values were significantly lower in specific brain regions in schizophrenia patients (such as thalamus during resting-state) compared to healthy controls in two independent datasets. Moreover, AC values had significant negative correlation with patients' symptoms. AC differences discriminated patients from healthy controls with high accuracy (resting-state).
Contrary to most prior works, the results suggest AC shows meaningful patterns that are discriminative between patients and controls. Our results are in line with recent works attributing autocorrelation to feedback loop of brain's regulatory circuit.
Autoconnectivity is cognitive state dependent (resting-state vs. task) and mental state dependent (healthy vs. schizophrenia). The concept of autoconnectivity resembles a recurrent neural network and provides a new perspective of functional integration in the brain. These findings may have important implications for understanding of brain function in health and disease as well as for analysis of fMRI time-series.
Details
- Title: Subtitle
- Autoconnectivity: A new perspective on human brain function
- Creators
- Mohammad R Arbabshirani - The Mind Research Network, Albuquerque, NM, USA. Electronic address: marbabshirani@mrn.orgAdrian Preda - Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USAJatin G Vaidya - Department of Psychiatry, University of Iowa, IA, USASteven G Potkin - Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USAGodfrey Pearlson - Department of Psychiatry, Yale University School of Medicine, CT, USAJames Voyvodic - Brain Imaging and Analysis Center, Duke University, Durham, NC, USADaniel Mathalon - Department of Psychiatry, University of California, San Francisco, CA, USA; San Francisco VA Medical Center, San Francisco, CA, USATheo van Erp - Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USAAndrew Michael - Duke Institute for Brain Sciences, Duke University, Durham, NC, USAKent A Kiehl - The Mind Research Network, Albuquerque, NM, USAJessica A Turner - Department of Psychology, Georgia State University, Atlanta, GA, USAVince D Calhoun - The Mind Research Network, Albuquerque, NM, USA; Department of ECE, University of New Mexico, Albuquerque, NM, USA
- Resource Type
- Journal article
- Publication Details
- Journal of neuroscience methods, Vol.323, pp.68-76
- Publisher
- Netherlands
- DOI
- 10.1016/j.jneumeth.2019.03.015
- PMID
- 31005575
- ISSN
- 0165-0270
- eISSN
- 1872-678X
- Grant note
- name: NIH, award: U24RR021992, 2R01EB000840, P20GM103472, R01REB020407; name: NSF, award: 1539067
- Language
- English
- Date published
- 07/15/2019
- Academic Unit
- Psychiatry; Iowa Neuroscience Institute; University College Courses
- Record Identifier
- 9984003966002771
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