Abstract
Machine learning is becoming an increasingly popular approach for investigating spatially distributed and subtle neuroanatomical alterations in brain‐based disorders. However, some machine learning models have been criticized for requiring a large number of cases in each experimental group, and for resembling a “black box” that provides little or no insight into the nature of the data. In this article, we propose an alternative conceptual and practical approach for investigating brain‐based disorders which aim to overcome these limitations. We used an artificial neural network known as “deep autoencoder” to create a normative model using structural magnetic resonance imaging data from 1,113 healthy people. We then used this model to estimate total and regional neuroanatomical deviation in individual patients with schizophrenia and autism spectrum disorder using two independent data sets (n = 263). We report that the model was able to generate different values of total neuroanatomical deviation for each disease under investigation relative to their control group (p < .005). Furthermore, the model revealed distinct patterns of neuroanatomical deviations for the two diseases, consistent with the existing neuroimaging literature. We conclude that the deep autoencoder provides a flexible and promising framework for assessing total and regional neuroanatomical deviations in neuropsychiatric populations.
Highlights
Structural magnetic resonance imaging enables the in vivo investigation of the morphological features of the human brain
The data used in this study were obtained from three public data sets: Human Connectome Project (HCP) data set, Northwestern University Schizophrenia Data and Software Tool (NUSDAST) data set, and Autism Brain Imaging Data Exchange (ABIDE) data set
We reported the median performance of the Support Vector Machines (SVM) and its confidence intervals (CI)
Summary
Structural magnetic resonance imaging (sMRI) enables the in vivo investigation of the morphological features of the human brain. This can be challenging, for example when comparing specific clinical sub-groups who are difficult to recruit in large numbers (e.g., patients with schizophrenia who did and did not respond to a specific treatment) Besides this limitation, some machine learning algorithms (e.g., deep neural networks) have been criticized for resembling a “black box” due to the difficulty of interpreting their inner workings. The resulting model learns to encode the healthy patterns from the input data and from the encoded representation, tries to reconstruct the input data as close as possible to the original After training this model, we used it to encode and reconstruct the data from two public data sets with psychiatry patients. We hypothesized that (a) the autoencoder would generate different deviation metrics in patients and controls, with higher mean deviation metrics in the former relative to the latter, and that (b) the autoencoder would reveal different patterns of neuroanatomical deviations for SCZ and ASD, consistent with the existing neuroimaging literature on these disorders
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