Multivariate Pattern Classification of Facial Expressions Based on Large-Scale Functional Connectivity
نویسندگان
چکیده
منابع مشابه
Multivariate Pattern Classification of Facial Expressions Based on Large-Scale Functional Connectivity
It is an important question how human beings achieve efficient recognition of others' facial expressions in cognitive neuroscience, and it has been identified that specific cortical regions show preferential activation to facial expressions in previous studies. However, the potential contributions of the connectivity patterns in the processing of facial expressions remained unclear. The present...
متن کاملMultivariate Pattern Connectivity
Whenever we engage in a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural mechanisms that make behavior possible. The majority of the investigations of interactions between brain regions have focused on the overall univariate responses in the regions. However, in the context of ‘static’ analyses, drastic adva...
متن کاملMultivariate pattern classification of pediatric Tourette syndrome using functional connectivity MRI
Tourette syndrome (TS) is a developmental neuropsychiatric disorder characterized by motor and vocal tics. Individuals with TS would benefit greatly from advances in prediction of symptom timecourse and treatment effectiveness. As a first step, we applied a multivariate method - support vector machine (SVM) classification - to test whether patterns in brain network activity, measured with resti...
متن کاملA large-scale analysis of sex differences in facial expressions
There exists a stereotype that women are more expressive than men; however, research has almost exclusively focused on a single facial behavior, smiling. A large-scale study examines whether women are consistently more expressive than men or whether the effects are dependent on the emotion expressed. Studies of gender differences in expressivity have been somewhat restricted to data collected i...
متن کاملLarge Scale Functional Connectivity for Brain Decoding
Functional Magnetic Resonance Imaging (fMRI) data consists of time series for each voxel recorded during a cognitive task. In order to extract useful information from this noisy and redundant data, techniques are proposed to select the voxels that are relevant to the underlying cognitive task. We propose a simple and efficient algorithm for decoding the brain states by modelling the correlation...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Frontiers in Human Neuroscience
سال: 2018
ISSN: 1662-5161
DOI: 10.3389/fnhum.2018.00094