Convolutional Neural Network with an Elementwise Filter to Classify Dynamic Functional Connectivity The dynamic nature of the brain functional connectivity (FC) is well accepted in recent years. However, most of the current FC classification methods are based on the static estimation of FC. In this paper, we propose a novel convolutional neural network with an elementwise filter for classifying dynamic functional connectivity (DFCCNN). First, a DFC matrix is estimated to quantify the DFC. Then, taking the DFC matrix as input, the DFCCNN model employs onedimensional convolutional kernels to extract the highlevel features of DFC. Moreover, an elementwise filter is specially designed for the DFC matrix, which further improves the classification performance. The experimental results on the autism brain imaging data exchange I (ABIDE I) indicate that the proposed model can distinguish subject groups more accurately, and also can be used to identify the abnormal brain regions.

A Hybrid 3DCNN and 3DCLSTM based model for 4D Spatiotemporal fMRI data: An ABIDE Autism Classification study. Functional Magnetic Resonance Imaging (fMRI) captures the temporal dynamics of neural activity as a function of spatial location in the brain. Thus, fMRI scans are represented as 4Dimensional (3space  1time) tensors. And it is widely believed that the spatiotemporal patterns in fMRI manifests as behaviour and clinical symptoms. Because of the high dimensionality (sim 1 Million) of fMRI, and the added constraints of limited cardinality of data sets, extracting such patterns are challenging. A standard approach to overcome these hurdles is to reduce the dimensionality of the data by either summarizing activation over time or space at the expense of possible loss of useful information. Here, we introduce an endtoend algorithm capable of extracting spatiotemporal features from the full 4D data using 3D CNNs and 3D Convolutional LSTMs. We evaluate our proposed model on the publicly available ABIDE dataset to demonstrate the capability of our model to classify Autism Spectrum Disorder (ASD) from restingstate fMRI data. Our results show that the proposed model achieves state of the art results on single sites with F1scores of 0.78 and 0.7 on NYU and UM sites, respectively.