Anticorrelated resting-state functional connectivity in awake rat brain

UMMS Affiliation

Department of Psychiatry

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Document Type



Algorithms; Animals; *Artifacts; Brain; Image Enhancement; Image Interpretation, Computer-Assisted; Magnetic Resonance Imaging; Male; Nerve Net; Neural Pathways; Rats; Rats, Long-Evans; Reproducibility of Results; Rest; Sensitivity and Specificity; Statistics as Topic; Wakefulness


Neuroscience and Neurobiology | Psychiatry | Psychiatry and Psychology


Resting-state functional connectivity (RSFC) measured by functional magnetic resonance imaging has played an essential role in understanding neural circuitry and brain diseases. The vast majority of RSFC studies have been focused on positive RSFC, whereas our understanding about its conceptual counterpart - negative RSFC (i.e. anticorrelation) - remains elusive. To date, anticorrelated RSFC has yet been observed without the commonly used preprocessing step of global signal correction. However, this step can induce artifactual anticorrelation (Murphy et al., 2009), making it difficult to determine whether the observed anticorrelation in humans is a processing artifact (Fox et al., 2005). In this report we demonstrated robust anticorrelated RSFC in a well characterized frontolimbic circuit between the infralimbic cortex (IL) and amygdala in the awake rat. This anticorrelation was anatomically specific, highly reproducible and independent of preprocessing methods. Interestingly, this anticorrelated relationship was absent in anesthetized rats even with global signal correction, further supporting its functional significance. Establishing negative RSFC independent of data preprocessing methods will significantly enhance the applicability of RSFC in better understanding neural circuitries and brain networks. In addition, combining the neurobiological data of the IL-amygdala circuit in rodents, the finding of the present study will enable further investigation of the neurobiological basis underlying anticorrelation.

DOI of Published Version



Neuroimage. 2012 Jan 16;59(2):1190-9. Epub 2011 Aug 12. Link to article on publisher's site

Journal/Book/Conference Title



Co-author Zhifeng Liang is a student in the Program in Neuroscience in the Graduate School of Biomedical Sciences (GSBS) at UMass Medical School.

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