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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\u2010weighted brain MRI dataset of 90 monocentric FASD patients (53 FAS, 37 NS\u2010FASD) and 95 typically developing controls (ages 6\u201320), using volBrain\u2010vol2Brain as reference, and both Freesurfer\u2010SAMSEG and FSL\u2010FIRST 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<jats:sub>i<\/jats:sub>) and TBV (V<jats:sub>i<\/jats:sub>\u2009=\u2009<jats:italic>b<\/jats:italic>\u2009\u00d7\u2009TBV<jats:sup>a<\/jats:sup>) and evaluated the effect of FAS on scaling. We then estimated the volumetric deviation from typical scaling (<jats:sub>v<\/jats:sub>DTS) 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 <jats:sub>v<\/jats:sub>DTS or total brain deviation from typical volume, by adding the five nuclear <jats:sub>v<\/jats:sub>DTS, both in terms of performance and generalizability to NS\u2010FASD. Scaling was significantly different between the FAS and control groups for all deep grey matter nuclei (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05). We confirmed the undersizing of total deep grey matter in FAS (<jats:sub>v<\/jats:sub>DTS\u2009=\u2009\u22126%) and identified a pattern of volumetric undersizing, most pronounced in the caudate (\u221213%) and globus pallidus (\u221211%), less so in the thalamus (\u22124%) and putamen (\u22122%) and sparing the accumbens (0%). These findings were consistent across segmentation tools, despite variations in magnitude. The pattern\u2010based classifier was more efficient than the one based on total deep grey matter alone (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001) and identified 32.4% of the NS\u2010FASD as having a FAS\u2010like deep grey matter phenotype, compared to 18.9% with the classifier based on total deep grey matter alone (<jats:italic>p<\/jats:italic>\u2009=\u20090.113). Added to a classifier based on TBV only, the pattern improved the performance (<jats:italic>p<\/jats:italic>\u2009=\u20090.033) of the model and increased identification of NS\u2010FASD with a FAS\u2010like neuroanatomical phenotype from 37.8% to 62.2% (<jats:italic>p<\/jats:italic>\u2009=\u20090.002). 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\u2010FASD by means of MRI\u2010based diagnostic classifiers.<\/jats:p>","DOI":"10.1002\/hbm.70233","type":"journal-article","created":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T12:35:49Z","timestamp":1747658149000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Pattern of Deep Grey Matter Undersizing Boosts <scp>MRI<\/scp>\u2010Based Diagnostic Classifiers in Fetal Alcohol Spectrum Disorders"],"prefix":"10.1002","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4071-2008","authenticated-orcid":false,"given":"Eliot","family":"Kerdreux","sequence":"first","affiliation":[{"name":"Universit\u00e9 Paris\u2010Saclay, CEA, Joliot Institute, NeuroSpin, UNIACT  Gif\u2010sur\u2010Yvette 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