Pattern of Deep Grey Matter Undersizing Boosts MRI-Based Diagnostic Classifiers in Fetal Alcohol Spectrum Disorders

Abstract

In fetal alcohol spectrum disorders (FASD), brain growth deficiency is a hallmark of subjects with both fetal alcohol syndrome (FAS) and nonsyndromic FASD (NS-FASD, that is, those without specific diagnostic features). Although previous studies have suggested that the deep grey matter is heterogeneously affected at the group level, it has not yet been established within proper scaling modeling, nor has it been given a place in the FASD diagnostic criteria where neuroanatomical features still contribute almost nothing to diagnostic specificity. We segmented a 1.5T T1-weighted brain MRI dataset of 90 monocentric FASD patients (53 FAS, 37 NS-FASD) and 95 typically developing controls (ages 6-20), using volBrain-vol2Brain as reference, and both Freesurfer-SAMSEG and FSL-FIRST to estimate result robustness. The segmentation resulted in seven anatomical volumes: total brain (TBV), total deep grey matter, caudate, putamen, globus pallidus, thalamus, and accumbens. After adjusting for confounds, we fitted the scaling relationship between deep grey matter nuclei volumes (V_i) and TBV (V_i = b × TBV^a) and evaluated the effect of FAS on scaling. We then estimated the volumetric deviation from typical scaling (_vDTS) for each deep grey nucleus volume in the FAS sample. Finally, we tested the improvement of FAS versus control classifiers based on total deep grey matter _vDTS or total brain deviation from typical volume, by adding the five nuclear _vDTS, both in terms of performance and generalizability to NS-FASD. Scaling was significantly different between the FAS and control groups for all deep grey matter nuclei (p < 0.05). We confirmed the undersizing of total deep grey matter in FAS (_vDTS = -6%) and identified a pattern of volumetric undersizing, most pronounced in the caudate (-13%) and globus pallidus (-11%), less so in the thalamus (-4%) and putamen (-2%) and sparing the accumbens (0%). These findings were consistent across segmentation tools, despite variations in magnitude. The pattern-based classifier was more efficient than the one based on total deep grey matter alone (p < 0.001) and identified 32.4% of the NS-FASD as having a FAS-like deep grey matter phenotype, compared to 18.9% with the classifier based on total deep grey matter alone (p = 0.113). Added to a classifier based on TBV only, the pattern improved the performance (p = 0.033) of the model and increased identification of NS-FASD with a FAS-like neuroanatomical phenotype from 37.8% to 62.2% (p = 0.002). This study details the volumetric undersizing of deep grey matter in a large series of FASD patients. It reveals a differential pattern of vulnerability to prenatal alcohol exposure partially convergent across automatic segmentation tools. It also strongly suggests that this pattern of volumetric undersizing in the deep grey matter may contribute to a neuroanatomical signature of FAS that is usable to improve the probabilistic diagnosis of NS-FASD by means of MRI-based diagnostic classifiers.

Keywords: deep grey matter; diagnostic classifier; fetal alcohol spectrum disorders; magnetic resonance imaging; neuroimaging biomarkers; normative analysis; supervised learning.