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      A prefrontal–habenular circuitry regulates social fear behaviour

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          Abstract

          The medial prefrontal cortex (mPFC) has been implicated in the pathophysiology of social impairments, including social fear. However, the precise subcortical partners that mediate mPFC dysfunction on social fear behaviour have not been identified.

          Using a social fear conditioning paradigm, we induced robust social fear in mice and found that the lateral habenula (LHb) neurons and LHb-projecting mPFC neurons are activated synchronously during social fear expression. Moreover, optogenetic inhibition of the mPFC–LHb projection significantly reduced social fear responses. Importantly, consistent with animal studies, we observed an elevated prefrontal–habenular functional connectivity in subclinical individuals with higher social anxiety characterized by heightened social fear.

          These results unravel a crucial role of the prefrontal–habenular circuitry in social fear regulation and suggest that this pathway could serve as a potential target for the treatment of social fear symptoms often observed in many psychiatric disorders.

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          Most cited references83

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            AFNI: software for analysis and visualization of functional magnetic resonance neuroimages.

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            A package of computer programs for analysis and visualization of three-dimensional human brain functional magnetic resonance imaging (FMRI) results is described. The software can color overlay neural activation maps onto higher resolution anatomical scans. Slices in each cardinal plane can be viewed simultaneously. Manual placement of markers on anatomical landmarks allows transformation of anatomical and functional scans into stereotaxic (Talairach-Tournoux) coordinates. The techniques for automatically generating transformed functional data sets from manually labeled anatomical data sets are described. Facilities are provided for several types of statistical analyses of multiple 3D functional data sets. The programs are written in ANSI C and Motif 1.2 to run on Unix workstations.
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              Large-scale automated synthesis of human functional neuroimaging data

              The explosive growth of the human neuroimaging literature has led to major advances in understanding of human brain function, but has also made aggregation and synthesis of neuroimaging findings increasingly difficult. Here we describe and validate an automated brain mapping framework that uses text mining, meta-analysis and machine learning techniques to generate a large database of mappings between neural and cognitive states. We demonstrate the capacity of our approach to automatically conduct large-scale, high-quality neuroimaging meta-analyses, address long-standing inferential problems in the neuroimaging literature, and support accurate ‘decoding’ of broad cognitive states from brain activity in both entire studies and individual human subjects. Collectively, our results validate a powerful and generative framework for synthesizing human neuroimaging data on an unprecedented scale.
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                Author and article information

                Journal
                Brain
                Oxford University Press (OUP)
                0006-8950
                1460-2156
                July 04 2024
                July 04 2024
                Article
                10.1093/brain/awae209
                2bb94c42-6db0-4640-b934-9006bc422724
                © 2024

                https://academic.oup.com/pages/standard-publication-reuse-rights

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