Discovering the gene-brain-behavior link in autism via generative machine learning

Abstract

Autism is traditionally diagnosed behaviorally but has a strong genetic basis. A genetics-first approach could transform understanding and treatment of autism. However, isolating the gene-brain-behavior relationship from confounding sources of variability is a challenge. We demonstrate a novel technique, 3D transport-based morphometry (TBM), to extract the structural brain changes linked to genetic copy number variation (CNV) at the 16p11.2 region. We identified two distinct endophenotypes. In data from the Simons Variation in Individuals Project, detection of these endophenotypes enabled 89 to 95% test accuracy in predicting 16p11.2 CNV from brain images alone. Then, TBM enabled direct visualization of the endophenotypes driving accurate prediction, revealing dose-dependent brain changes among deletion and duplication carriers. These endophenotypes are sensitive to articulation disorders and explain a portion of the intelligence quotient variability. Genetic stratification combined with TBM could reveal new brain endophenotypes in many neurodevelopmental disorders, accelerating precision medicine, and understanding of human neurodiversity.

Fig. 1.. 3D TBM system diagram.

Images that are not easily separable in the image domain are transformed to the transport domain, where they are represented as points on a high-dimensional Riemannian manifold. Supervised learning is performed in the transport space. The classifier decision boundaries are inverted to visualize the discriminant patterns causally driving classification as computer-generated images in the image domain. 3D TBM is performed on volumetric images, although a single white matter axial slice is shown here for illustration purposes.

Fig. 2.. Principal components.

The (A) white matter and (B) gray matter structure are better captured using fewer components in the transport domain than the image domain.

Fig. 3.. 3D TBM discriminant subspace.

Each subject in the study is represented by a point on the scatterplot. Subject data is projected onto the most discriminant subspace computed by pLDA for (A) white matter (WM) and (B) gray matter (GM). Boundaries between the classes computed based on nearest centroid classification for (C) white matter and (D) gray matter.

Fig. 4.. TBM generated images showing spatially diffuse changes associated with 16p11.2 CNV.

The resulting 3D TBM-generated images, obtained by sampling the discriminant subspace in Fig. 2 along discriminant direction 1, depict the physical changes in white (WM) and gray matter (GM) tissue density. Red indicates relative increase in tissue density, while blue represents relative decrease. Our findings reveal diffuse tissue overgrowth in deletion carriers and tissue undergrowth in duplication carriers compared to controls, as highlighted by the black arrows in selected regions.

Fig. 5.. Reciprocal changes along direction 1.

Z-score maps demonstrating reciprocal changes among deletion and duplication carriers. These were derived from the determinant of the Jacobian of the transport maps.

Fig. 6.. Changes along direction 2.

Z-score maps demonstrating the changes among controls and deletion/duplication carriers. These were derived from the determinant of the Jacobian of the transport maps.

Fig. 7.. Articulation disorder.

The presence of articulation disorder among deletion and duplication carriers is highly associated with projection scores along discriminant direction 1 for both (A) gray matter (GM) (P < 0.0001) and (B) white matter (WM) (P = 0.0002). Among duplication/deletion carriers, having a negative TBM score for either gray matter or white matter was 96% sensitive and 62.9% specific for having an articulation disorder using logistic regression.

Fig. 8.. Sample images.

The brains of 15 different subjects are shown (five control, five duplication, and five deletion carriers). Parenchymal tissue has been segmented, and the same axial slice is shown for each individual. Visual inspection does not reveal a pattern that differentiates the three cohorts. Furthermore, histograms of the brain tissue volumes across all three genetic cohorts demonstrate that although there is a statistically significant difference among the groups (P < 0.001), volume alone is insufficient to differentiate CNV.