Multi-modal MRI classifiers identify excessive alcohol consumption and treatment effects in the brain.

Robust neuroimaging markers of neuropsychiatric disorders have proven difficult to obtain. In alcohol use disorders, profound brain structural deficits can be found in severe alcoholic patients, but the heterogeneity of unimodal MRI measurements has so far precluded the identification of selective biomarkers, especially for early diagnosis. In the present work we used a combination of multiple MRI modalities to provide comprehensive and insightful descriptions of brain tissue microstructure. We performed a longitudinal experiment using Marchigian-Sardinian (msP) rats, an established model of chronic excessive alcohol consumption, and acquired multi-modal images before and after 1 month of alcohol consumption (6.8 ± 1.4 g/kg/day, mean ± SD), as well as after 1 week of abstinence with or without concomitant treatment with the antirelapse opioid antagonist naltrexone (2.5 mg/kg/day). We found remarkable sensitivity and selectivity to accurately classify brains affected by alcohol even after the relative short exposure period. One month drinking was enough to imprint a highly specific signature of alcohol consumption. Brain alterations were regionally specific and affected both gray and white matter and persisted into the early abstinence state without any detectable recovery. Interestingly, naltrexone treatment during early abstinence resulted in subtle brain changes that could be distinguished from non-treated abstinent brains, suggesting the existence of an intermediate state associated with brain recovery from alcohol exposure induced by medication. The presented framework is a promising tool for the development of biomarkers for clinical diagnosis of alcohol use disorders, with capacity to further inform about its progression and response to treatment.

Brain size regulations by cbp haploinsufficiency evaluated by in-vivo MRI based volumetry.

The Rubinstein-Taybi Syndrome (RSTS) is a congenital disease that affects brain development causing severe cognitive deficits. In most cases the disease is associated with dominant mutations in the gene encoding the CREB binding protein (CBP). In this work, we present the first quantitative analysis of brain abnormalities in a mouse model of RSTS using magnetic resonance imaging (MRI) and two novel self-developed automated algorithms for image volumetric analysis. Our results quantitatively confirm key syndromic features observed in RSTS patients, such as reductions in brain size (-16.31%, p < 0.05), white matter volume (-16.00%, p < 0.05), and corpus callosum (-12.40%, p < 0.05). Furthermore, they provide new insight into the developmental origin of the disease. By comparing brain tissues in a region by region basis between cbp(+/-) and cbp(+/+) littermates, we found that cbp haploinsufficiency is specifically associated with significant reductions in prosencephalic tissue, such us in the olfactory bulb and neocortex, whereas regions evolved from the embryonic rhombencephalon were spared. Despite the large volume reductions, the proportion between gray-, white-matter and cerebrospinal fluid were conserved, suggesting a role of CBP in brain size regulation. The commonalities with holoprosencephaly and arhinencephaly conditions suggest the inclusion of RSTS in the family of neuronal migration disorders.

Unsupervised segmentation of brain regions with similar microstructural properties: application to alcoholism.

In this work, a novel brain MRI segmentation approach evaluates microstructural differences between groups. Going further from the traditional segmentation of brain tissues (white matter -WM-, gray matter -GM- and cerebrospinal fluid -CSF- or a mixture of them), a new way to classify brain areas is proposed using their microstructural MR properties. Eight rats were studied using the proposed methodology identifying regions which present microstructural differences as a consequence on one month of hard alcohol consumption. Differences in relaxation times of the tissues have been found in different brain regions (p<0.05). Furthermore, these changes allowed the automatic classification of the animals based on their drinking history (hit rate of 93.75 % of the cases).