کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6028975 1580922 2013 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Advancing understanding of affect labeling with dynamic causal modeling
ترجمه فارسی عنوان
درک پیشرفت در مورد برچسب گذاری تاثیر با مدل سازی علیت پویا
کلمات کلیدی
تاثیر برچسب زدن، مقررات احساسات اتفاقی، ارتباط موثر مدل سازی علت دینامیکی،
موضوعات مرتبط
علوم زیستی و بیوفناوری علم عصب شناسی علوم اعصاب شناختی
چکیده انگلیسی
Mechanistic understandings of forms of incidental emotion regulation have implications for basic and translational research in the affective sciences. In this study we applied Dynamic Causal Modeling (DCM) for fMRI to a common paradigm of labeling facial affect to elucidate prefrontal to subcortical influences. Four brain regions were used to model affect labeling, including right ventrolateral prefrontal cortex (vlPFC), amygdala and Broca's area. 64 models were compared, for each of 45 healthy subjects. Family level inference split the model space to a likely driving input and Bayesian Model Selection within the winning family of 32 models revealed a strong pattern of endogenous network connectivity. Modulatory effects of labeling were most prominently observed following Bayesian Model Averaging, with the dampening influence on amygdala originating from Broca's area but much more strongly from right vlPFC. These results solidify and extend previous correlation and regression-based estimations of negative corticolimbic coupling.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: NeuroImage - Volume 82, 15 November 2013, Pages 481-488
نویسندگان
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