# Simple 1-D Convolutional Networks for Resting-State fMRI Based Classification in Autism

## TL;DR

This paper introduces a simple 1-D convolutional neural network that efficiently classifies autism using resting-state fMRI data by transforming high-dimensional signals into a manageable form, achieving competitive accuracy with minimal preprocessing.

## Contribution

The study presents a novel, straightforward transformation of rsfMRI data enabling effective 1-D CNN classification of autism with minimal preprocessing.

## Key findings

- Achieves comparable accuracy to state-of-the-art methods
- Requires minimal preprocessing and fast training
- Effectively captures temporal dynamics of rsfMRI signals

## Abstract

Deep learning methods are increasingly being used with neuroimaging data like structural and function magnetic resonance imaging (MRI) to predict the diagnosis of neuropsychiatric and neurological disorders. For psychiatric disorders in particular, it is believed that one of the most promising modality is the resting-state functional MRI (rsfMRI), which captures the intrinsic connectivity between regions in the brain. Because rsfMRI data points are inherently high-dimensional (~1M), it is impossible to process the entire input in its raw form. In this paper, we propose a very simple transformation of the rsfMRI images that captures all of the temporal dynamics of the signal but sub-samples its spatial extent. As a result, we use a very simple 1-D convolutional network which is fast to train, requires minimal preprocessing and performs at par with the state-of-the-art on the classification of Autism spectrum disorders.