@article{oai:kanazawa-u.repo.nii.ac.jp:00047555,
 author = {坪本, 真 and 川端, 梨加 and 三邉, 義雄 and 橋本, 隆紀 and Tsubomoto, Makoto and Kawabata, Rika and Zhu, Xiaonan and Minabe, Yoshio and Chen, Kehui and Lewis, David A. and Hashimoto, Takanori},
 issue = {8},
 journal = {Cerebral Cortex},
 month = {Aug},
 note = {PMID: 30247542, Visuospatial working memory (WM), which is impaired in schizophrenia, depends on a distributed network including visual, posterior parietal, and dorsolateral prefrontal cortical regions. Within each region, information processing is differentially regulated by subsets of γ-aminobutyric acid (GABA) neurons that express parvalbumin (PV), somatostatin (SST), or vasoactive intestinal peptide (VIP). In schizophrenia, WM impairments have been associated with alterations of PV and SST neurons in the dorsolateral prefrontal cortex. Here, we quantified transcripts selectively expressed in GABA neuron subsets across four cortical regions in the WM network from comparison and schizophrenia subjects. In comparison subjects, PV mRNA levels declined and SST mRNA levels increased from posterior to anterior regions, whereas VIP mRNA levels were comparable across regions except for the primary visual cortex (V1). In schizophrenia subjects, each transcript in PV and SST neurons exhibited similar alterations across all regions, whereas transcripts in VIP neurons were unaltered in any region except for V1. These findings suggest that the contribution of each GABA neuron subset to inhibitory regulation of local circuitry normally differs across cortical regions of the visuospatial WM network and that in schizophrenia alterations of PV and SST neurons are a shared feature across these regions, whereas VIP neurons are affected only in V1., Embargo Period 12 months, 金沢大学医薬保健総合研究域医学系 / Graduate School of Medical Sciences},
 pages = {3540--3550},
 title = {Expression of Transcripts Selective for GABA Neuron Subpopulations across the Cortical Visuospatial Working Memory Network in the Healthy State and Schizophrenia},
 volume = {29},
 year = {2019}
}