Identifying Autism Spectrum Disorder With Multi-Site fMRI via Low-Rank Domain Adaptation

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is characterized by a wide range of symptoms. Identifying biomarkers for accurate diagnosis is crucial for early intervention of ASD. While multi-site data increase sample size and statistical power, they suffer from inter-site heterogeneity. To address this issue, we propose a multi-site adaption framework via low-rank representation decomposition (maLRR) for ASD identification based on functional MRI (fMRI). The main idea is to determine a common low-rank representation for data from the multiple sites, aiming to reduce differences in data distributions. Treating one site as a target domain and the remaining sites as source domains, data from these domains are transformed (i.e., adapted) to a common space using low-rank representation. To reduce data heterogeneity between the target and source domains, data from the source domains are linearly represented in the common space by those from the target domain. We evaluated the proposed method on both synthetic and real multi-site fMRI data for ASD identification. The results suggest that our method yields superior performance over several state-of-the-art domain adaptation methods.

Fig. 1.

Illustration of the proposed method with K source domains and a target domain. Each source domain X_Si and the target domain X_T include samples from two categories (marked as triangles and circles). The first strategy of the proposed method is to transform each source domain and target domain into a latent representation domain via the specific projection P_i and the common projection P (with P_i = P + E_Pi, E_Pi is the sparse error term), respectively. Based on the transformed target domain data PX_T, the second strategy is to linearly represent each sample from each source domain using all samples from the target domain in the latent space (e.g., P_iX_Si = PX_TZ_Si + E_Si). Dotted arrows represent the first strategy, while the solid arrow represents the second strategy.

Fig. 2.

Results on synthetic data achieved by our method, with the same color denoting data points from the same domain. (a) denotes the data distributions before transformation, and (b) denotes the data distributions after transformation (based on our proposed common domain transformation strategy). Here, samples represented by blue are treated as target domain, while samples represented by green and red are treated as source domains.

Fig. 3.

Performance of seven different methods in ASD classification using the KNN classifier on the multi-site ABIDE database, where (a)-(e) denote the classification results using target domains.

Fig. 4.

Classification accuracies with respect to different parameter values of β and k in the proposed maLRR model (with α = 0.1), where (a)-(e) denote results generated by maLRR using different target domains.

Fig. 5.

Classification accuracies with respect to different parameter values of α and k in the proposed maLRR model (with β = 1), where (a)-(e) denotes different selection of target domain.

Fig. 6.

Classification accuracies with respect to different parameter values α and β in the proposed maLRR model (with k = 5), where (a)-(e) denotes different selection of target domain.