کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
8687127 1580840 2018 39 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Predicting functional neuroanatomical maps from fusing brain networks with genetic information
ترجمه فارسی عنوان
پیش بینی نقشه های عملکردی نوروآنتیومیک از شبکه های مغز با استفاده از اطلاعات ژنتیکی
کلمات کلیدی
نقشه های نورولوژیک، رفتار - اخلاق، اتصال عصب شناسی عملکردی، تجزیه و تحلیل محاسباتی،
موضوعات مرتبط
علوم زیستی و بیوفناوری علم عصب شناسی علوم اعصاب شناختی
چکیده انگلیسی
Functional neuroanatomical maps provide a mesoscale reference framework for studies from molecular to systems neuroscience and psychiatry. The underlying structure-function relationships are typically derived from functional manipulations or imaging approaches. Although highly informative, these are experimentally costly. The increasing amount of publicly available brain and genetic data offers a rich source that could be mined to address this problem computationally. Here, we developed an algorithm that fuses gene expression and connectivity data with functional genetic meta data and exploits cumulative effects to derive neuroanatomical maps related to multi-genic functions. We validated the approach by using public available mouse and human data. The generated neuroanatomical maps recapture known functional anatomical annotations from literature and functional MRI data. When applied to multi-genic meta data from mouse quantitative trait loci (QTL) studies and human neuropsychiatric databases, this method predicted known functional maps underlying behavioral or psychiatric traits. Taken together, genetically weighted connectivity analysis (GWCA) allows for high throughput functional exploration of brain anatomy in silico. It maps functional genetic associations onto brain circuitry for refining functional neuroanatomy, or identifying trait-associated brain circuitry, from genetic data.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: NeuroImage - Volume 170, 15 April 2018, Pages 113-120
نویسندگان
, , , , , ,