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Studying Brain Circuit Function with Dynamic Causal Modeling for Optogenetic fMRI.
Bernal-Casas, David; Lee, Hyun Joo; Weitz, Andrew J; Lee, Jin Hyung.
Affiliation
  • Bernal-Casas D; Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA.
  • Lee HJ; Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA.
  • Weitz AJ; Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
  • Lee JH; Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA; Department of Electrical Engineering, Stanford Univer
Neuron ; 93(3): 522-532.e5, 2017 Feb 08.
Article in En | MEDLINE | ID: mdl-28132829
ABSTRACT
Defining the large-scale behavior of brain circuits with cell type specificity is a major goal of neuroscience. However, neuronal circuit diagrams typically draw upon anatomical and electrophysiological measurements acquired in isolation. Consequently, a dynamic and cell-type-specific connectivity map has never been constructed from simultaneous measurements across the brain. Here, we introduce dynamic causal modeling (DCM) for optogenetic fMRI experiments-which uniquely allow cell-type-specific, brain-wide functional measurements-to parameterize the causal relationships among regions of a distributed brain network with cell type specificity. Strikingly, when applied to the brain-wide basal ganglia-thalamocortical network, DCM accurately reproduced the empirically observed time series, and the strongest connections were key connections of optogenetically stimulated pathways. We predict that quantitative and cell-type-specific descriptions of dynamic connectivity, as illustrated here, will empower novel systems-level understanding of neuronal circuit dynamics and facilitate the design of more effective neuromodulation therapies.
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Full text: 1 Database: MEDLINE Main subject: Brain / Models, Neurological / Nerve Net / Neurons Type of study: Prognostic_studies Language: En Journal: Neuron Year: 2017 Type: Article Affiliation country: United States

Full text: 1 Database: MEDLINE Main subject: Brain / Models, Neurological / Nerve Net / Neurons Type of study: Prognostic_studies Language: En Journal: Neuron Year: 2017 Type: Article Affiliation country: United States