A research paper co-authored by Dr Suhail Ahmad, Post-Doctoral Fellow, Psychology, SIAS has recently been published in Current Alzheimer Research (Bentham Sciences), with an impact factor of 1.9 and major indexing in Scopus, SCIE, and PubMed. The paper is titled Early Diagnosis of Alzheimer’s: Machine Learning Analysis Leveraging Structural MRI.
Brief: This longitudinal study investigated whether structural MRI–based surface-based morphometry (SBM) of subcortical brain regions can support early detection of Alzheimer’s disease. Using ADNI data, morphological changes (cortical thickness, sulcal depth, and gyrification index) were tracked over 6 months to 3 years in individuals with mild cognitive impairment (MCI) who later progressed to Alzheimer’s, compared with healthy controls. Significant progressive atrophy—especially in cortical thickness—was identified. Machine learning models trained on these features showed improving performance over time, achieving the highest accuracy near the point of diagnosis. The findings suggest that SBM-derived subcortical atrophy patterns, particularly cortical thickness, serve as sensitive biomarkers and, when combined with machine learning, provide a scalable framework for early Alzheimer’s prediction.
