Summary: Researchers advanced brain imaging and machine learning to uncover altered functional brain connectivity in individuals with Autism Spectrum Disorder (ASD), acknowledging the diversity within the disorder.
The research distinguishes between shared and individual-specific connectivity patterns in ASD, revealing both common and unique brain alterations. This approach marks a significant shift from group-based analysis to a more personalized understanding of ASD.
The findings open pathways for tailored treatments, addressing the unique needs of individuals with ASD.
Personalized Brain Imaging Insights: The study differentiates between shared and individual-specific altered brain connectivity in ASD, highlighting both group-level differences and individual variations.
Machine Learning Application: Advanced machine learning techniques are employed to dissect the complex neuroimaging data, enabling this nuanced analysis.
Implications for Tailored Treatments: The findings suggest that personalized treatment strategies, targeting specific neural characteristics of individuals with ASD, could be more effective.
What happens in the brain to cause many neurodevelopmental disorders, including autism spectrum disorder (ASD), remains a mystery. A major limitation for researchers is the lack of biomarkers, or objective biological outputs, for these disorders, and in the case of ASD, for specific subtypes of disease.
Now, a new study uses brain imaging and machine learning to identify altered functional brain connectivity (FC) in people with ASD – importantly, taking into consideration differences between individuals.
The study appears in Biological Psychiatry, published by Elsevier.
John Krystal, MD, Editor of Biological Psychiatry, said of the work, “ASD has long been known to be a highly heterogeneous condition. While genetic studies have provided some clues to different causes of the disorder in different groups of ASD patients, it has been challenging to separate subtypes of ASD using other types of biomarkers, such as brain imaging.”
Brain imaging scans are also extremely heterogenous, varying greatly from one individual to another, making such data difficult to use as a biomarker. Previous studies have identified both increased and decreased FC in people with ASD compared to healthy controls, but because those studies focused on groups of participants, they failed to appreciate heterogeneous autism-related atypical FC.
In the new study, the researchers showed that although heterogenous brain imaging subtypes could be distinguished among participants with ASD.
Xujun Duan, PhD, senior author of the work at the University of Electronic Science and Technology of China, explained, “In this study, we used a technique to project altered FC of autism onto two subspaces: an individual-shared subspace, which represents altered connectivity pattern shared across autism, and an individual-specific subspace, which represents the remaining individual characteristics after eliminating the individual-shared altered connectivity patterns.”
The investigators found that the individual-shared subspace altered FC of autism reflects differences at the group level, while individual-specific subspace altered FC represents individual variation in autistic traits. These findings suggest a requirement to move beyond group effects and to capture and capitalize on the individual-specific brain features for dissecting clinical heterogeneity.
Dr. Krystal added, “Part of the challenge to finding subtypes of ASD has been the enormous complexity of neuroimaging data. This study uses a sophisticated computational approach to identify aspects of brain circuit alterations that are common to ASD and others that are associated with particular ASD traits.
“This type of strategy may help to more effectively guide the development of personalized treatments for ASD, i.e., treatments that meet the specific needs of particular patients.”
About this autism and neuroimaging research news
Author: Eileen Leahy Source: Elsevier Contact: Eileen Leahy – Elsevier Image: The image is credited to Neuroscience News
Disentangling the Individual-Shared and Individual-Specific Subspace of Altered Brain Functional Connectivity in Autism Spectrum Disorder
Despite considerable effort toward understanding the neural basis of autism spectrum disorder (ASD) using case-control analyses of resting-state functional magnetic resonance imaging data, findings are often not reproducible, largely due to biological and clinical heterogeneity among individuals with ASD. Thus, exploring the individual-shared and individual-specific altered functional connectivity (AFC) in ASD is important to understand this complex, heterogeneous disorder.
We considered 254 individuals with ASD and 295 typically developing individuals from the Autism Brain Imaging Data Exchange to explore the individual-shared and individual-specific subspaces of AFC. First, we computed AFC matrices of individuals with ASD compared with typically developing individuals. Then, common orthogonal basis extraction was used to project AFC of ASD onto 2 subspaces: an individual-shared subspace, which represents altered connectivity patterns shared across ASD, and an individual-specific subspace, which represents the remaining individual characteristics after eliminating the individual-shared altered connectivity patterns.
Analysis yielded 3 common components spanning the individual-shared subspace. Common components were associated with differences of functional connectivity at the group level. AFC in the individual-specific subspace improved the prediction of clinical symptoms. The default mode network–related and cingulo-opercular network–related magnitudes of AFC in the individual-specific subspace were significantly correlated with symptom severity in social communication deficits and restricted, repetitive behaviors in ASD.
Our study decomposed AFC of ASD into individual-shared and individual-specific subspaces, highlighting the importance of capturing and capitalizing on individual-specific brain connectivity features for dissecting heterogeneity. Our analysis framework provides a blueprint for parsing heterogeneity in other prevalent neurodevelopmental conditions.