Machine-learning classification of 22q11.2 deletion syndrome: A diffusion tensor imaging study

Abstract

Chromosome 22q11.2 deletion syndrome (22q11.2DS) is a genetic neurodevelopmental syndrome that has been studied intensively in order to understand relationships between the genetic microdeletion, brain development, cognitive function, and the emergence of psychiatric symptoms. White matter microstructural abnormalities identified using diffusion tensor imaging methods have been reported to affect a variety of neuroanatomical tracts in 22q11.2DS. In the present study, we sought to combine two discovery-based approaches: (1) white matter query language was used to parcellate the brain's white matter into tracts connecting pairs of 34, bilateral cortical regions and (2) the diffusion imaging characteristics of the resulting tracts were analyzed using a machine-learning method called support vector machine in order to optimize the selection of a set of imaging features that maximally discriminated 22q11.2DS and comparison subjects. With this unique approach, we both confirmed previously-recognized 22q11.2DS-related abnormalities in the inferior longitudinal fasciculus (ILF), and identified, for the first time, 22q11.2DS-related anomalies in the middle longitudinal fascicle and the extreme capsule, which may have been overlooked in previous, hypothesis-guided studies. We further observed that, in participants with 22q11.2DS, ILF metrics were significantly associated with positive prodromal symptoms of psychosis.

Individualized Diagnostic and Prognostic Models for Patients With Psychosis Risk Syndromes: A Meta-analytic View on the State of the Art

“…A total of 19 ML studies (73%) employed a support vector machine algorithm (10,30,(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52)(53), while the rest used Gaussian process (11) or convex hull classification (54), randomized trees (55), greedy algorithm (20), random forest (5), or LASSO regression (56,57). All ML models were computed with CV, whereas studies using Cox regression applied bootstrapping (28,(58)(59)(60)(61)(62), reported apparent results (i.e., the model is tested in the same sample from which it was derived) (63-68), or lacked a validation procedure.…”

“…Diagnostic models included the use of functional (37,39,43,47,48) and structural (46,50,70) magnetic resonance imaging (MRI) and diffusion tensor imaging (49), and behavioral models were based on neurocognitive functions (42,43).…”

Altered white matter microstructure in 22q11.2 deletion syndrome: a multisite diffusion tensor imaging study

“…WM differences associated with psychosis are of interest in 22q11DS. Psychotic symptoms in 22q11DS have been associated with higher FA and lower WM diffusivities, but not always in the same regions across studies [22,25,30,31,33,34]. In addition, there is variability in deletion breakpoints; 85-90% of individuals with the deletion have a~3 Mb (A-D) deletion, containing 46 protein-coding genes, whereas~10% of cases have a nested 1.5 Mb (A-B) deletion [1].…”

Improving the classification of neuropsychiatric conditions using gene ontology terms as features

“…Such tests would prove especially valuable to the field of neuropsychiatry, where diagnostic criteria do not have a strong molecular foundation, and where early diagnosis can improve patient outcomes (as previously demonstrated for children with ASD [Elder, Kreider, Brasher, & Ansell, ]). Machine learning techniques like artificial neural nets and SVM have grown in popularity (Jensen & Bateman, ), and have been used successfully for classifying neuropsychiatric conditions based on neuro‐imaging (Tylee, Hess, et al, ) and gene expression (Tylee et al, ).…”