Alcohol drinking and brain morphometry in apparently healthy community-dwelling Japanese men.

The clinical implications of alcohol consumption have been extensively examined; however, its effects on brain structures in apparently healthy community-dwellers remain unclear. Therefore, we investigated the relationship between alcohol consumption and brain gray matter volume (GMV) in community-dwelling Japanese men using voxel-based morphometry (VBM). We recruited cognitively intact Japanese men, aged 40-79 years, from a population-based cohort in Shiga, Japan. Brain magnetic resonance imaging was performed, on average, 2 years after demographic and medical information was obtained in 2010-2014. A multivariable linear regression analysis of 639 men was conducted to elucidate the relationship between the amount of alcohol consumed and GMV. VBM statistics were analyzed by threshold-free cluster enhancement with a family-wise error rate of <0.05. The results obtained demonstrated that the amount of alcohol consumed was associated with lower GMV. The VBM analysis showed lower GMV within the parahippocampal, entorhinal, cingulate, insular, temporal, and frontal cortices and cerebellum in very heavy drinkers (≥42 ethanol g/day) than in non-drinkers. Furthermore, alcohol consumption was associated with a higher white matter lesion volume. These results suggest subclinical structural changes similar to alcohol-related neurological diseases.

Machine Learning for Diagnosis of AD and Prediction of MCI Progression From Brain MRI Using Brain Anatomical Analysis Using Diffeomorphic Deformation.

Background: With the growing momentum for the adoption of machine learning (ML) in medical field, it is likely that reliance on ML for imaging will become routine over the next few years. We have developed a software named BAAD, which uses ML algorithms for the diagnosis of Alzheimer's disease (AD) and prediction of mild cognitive impairment (MCI) progression. Methods: We constructed an algorithm by combining a support vector machine (SVM) to classify and a voxel-based morphometry (VBM) to reduce concerned variables. We grouped progressive MCI and AD as an AD spectrum and trained SVM according to this classification. We randomly selected half from the total 1,314 subjects of AD neuroimaging Initiative (ADNI) from North America for SVM training, and the remaining half were used for validation to fine-tune the model hyperparameters. We created two types of SVMs, one based solely on the brain structure (SVMst), and the other based on both the brain structure and Mini-Mental State Examination score (SVMcog). We compared the model performance with two expert neuroradiologists, and further evaluated it in test datasets involving 519, 592, 69, and 128 subjects from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Aging (AIBL), Japanese ADNI, the Minimal Interval Resonance Imaging in AD (MIDIAD) and the Open Access Series of Imaging Studies (OASIS), respectively. Results: BAAD's SVMs outperformed radiologists for AD diagnosis in a structural magnetic resonance imaging review. The accuracy of the two radiologists was 57.5 and 70.0%, respectively, whereas, that of the SVMst was 90.5%. The diagnostic accuracy of the SVMst and SVMcog in the test datasets ranged from 88.0 to 97.1% and 92.5 to 100%, respectively. The prediction accuracy for MCI progression was 83.0% in SVMst and 85.0% in SVMcog. In the AD spectrum classified by SVMst, 87.1% of the subjects were Aß positive according to an AV-45 positron emission tomography. Similarly, among MCI patients classified for the AD spectrum, 89.5% of the subjects progressed to AD. Conclusion: Our ML has shown high performance in AD diagnosis and prediction of MCI progression. It outperformed expert radiologists, and is expected to provide support in clinical practice.