Abnormal amygdala functional connectivity and deep learning classification in multifrequency bands in autism spectrum disorder: A multisite functional magnetic resonance imaging study

Abstract

Previous studies have explored resting‐state functional connectivity (rs‐FC) of the amygdala in patients with autism spectrum disorder (ASD). However, it remains unclear whether there are frequency‐specific FC alterations of the amygdala in ASD and whether FC in specific frequency bands can be used to distinguish patients with ASD from typical controls (TCs). Data from 306 patients with ASD and 314 age‐matched and sex‐matched TCs were collected from 28 sites in the Autism Brain Imaging Data Exchange database. The bilateral amygdala, defined as the seed regions, was used to perform seed‐based FC analyses in the conventional, slow‐5, and slow‐4 frequency bands at each site. Image‐based meta‐analyses were used to obtain consistent brain regions across 28 sites in the three frequency bands. By combining generative adversarial networks and deep neural networks, a deep learning approach was applied to distinguish patients with ASD from TCs. The meta‐analysis results showed frequency band specificity of FC in ASD, which was reflected in the slow‐5 frequency band instead of the conventional and slow‐4 frequency bands. The deep learning results showed that, compared with the conventional and slow‐4 frequency bands, the slow‐5 frequency band exhibited a higher accuracy of 74.73%, precision of 74.58%, recall of 75.05%, and area under the curve of 0.811 to distinguish patients with ASD from TCs. These findings may help us to understand the pathological mechanisms of ASD and provide preliminary guidance for the clinical diagnosis of ASD.

Measuring functional connectivity in frequency‐domain helps to better characterize brain function

“…In contrast, this finer parcellation was not achieved in earlier research or in the current correlation‐based framework, which might result from the flatness of correlation‐based FC features within the ventral precuneus (Glasser et al, 2016 ; Luo et al, 2019 ). With the introduction of coherence‐based FC, functional interaction patterns under multiple frequency bands could be integrated (Davoudi et al, 2021 ; Ma et al, 2023 ), which helped distinguish the potential functional boundaries and generate the finest functional parcellation of the human precuneus to date.…”

Multi-View and Multimodal Graph Convolutional Neural Network for Autism Spectrum Disorder Diagnosis

“…The model, trained with hinge loss, achieved higher accuracy than prior methods, highlighting the value of multi-atlas data fusion for improving ASD detection. Ma et al [11] utilized deep learning with GANs on ABIDE fMRI data to explore amygdala FC in ASD. They identified the slow-5 frequency band as the most accurate for classifying ASD, revealing frequencyspecific neural markers and advancing our understanding of ASD's pathology.…”

“…where T k (•) is a Chebyshev polynomial of order k, β k is the corresponding coefficient (iteratively updated during training), and Λ = 2Λ λ max − I N is the rescaled eigenvalue diagonal matrix. We substitute (11) into (10) and substitute the matrix operation into the Chebyshev polynomial to obtain the convolution operation for a GCN layer.…”

An Umbrella Review of the Fusion of fMRI and AI in Autism

“…Articles such as [82,84,86,108,110] emphasized the significance of studying functional connectivity and resting state fMRI data in the context of autism. These articles investigate how patterns of brain activity at rest can reveal insights into ASD.…”