  These days, computational diagnosis strategies of neuropsychiatric disorders
are gaining attention day by day. It's critical to determine the brain's
functional connectivity based on Functional-Magnetic-Resonance-Imaging(fMRI) to
diagnose the disorder. It's known as a chronic disease, and millions of
children amass the symptoms of this disease, so there is much vacuum for the
researcher to formulate a model to improve the accuracy to diagnose ADHD
accurately. In this paper, we consider the functional connectivity of a brain
extracted using various time templates/Atlases. Local-Binary Encoding-Method
(LBEM) algorithm is utilized for feature extraction, while Hierarchical-
Extreme-Learning-Machine (HELM) is used to classify the extracted features. To
validate our approach, fMRI data of 143 normal and 100 ADHD affected children
is used for experimental purpose. Our experimental results are based on
comparing various Atlases given as CC400, CC200, and AAL. Our model achieves
high performance with CC400 as compared to other Atlases
