Previous studies have shown that hippocampal subfields may be differentially affected by Alzheimer’s disease (AD). This study used an automated analysis technique and two large cohorts to (1) investigate patterns of subfield volume loss in mild cognitive impairment (MCI) and AD, (2) determine the pattern of subfield volume loss due to age, gender, education, APOE ε4 genotype, and neuropsychological test scores, (3) compare combined subfield volumes to hippocampal volume alone at discriminating between AD and healthy controls (HC), and predicting future MCI conversion to AD at 12 months. 1,069 subjects were selected from the AddNeuroMed and Alzheimer’s disease neuroimaging initiative (ADNI) cohorts. Freesurfer was used for automated segmentation of the hippocampus and hippocampal subfields. Orthogonal partial least squares to latent structures (OPLS) was used to train models on AD and HC subjects using one cohort for training and the other for testing and the combined cohort was used to predict MCI conversion. MANCOVA and linear regression analyses showed multiple subfield volumes including Cornu Ammonis 1 (CA1), subiculum and presubiculum were atrophied in AD and MCI and were related to age, gender, education, APOE ε4 genotype, and neuropsychological test scores. For classifying AD from HC, combined subfield volumes achieved comparable classification accuracy (81.7 %) to total hippocampal (80.7 %), subiculum (81.2 %) and presubiculum (80.6 %) volume. For predicting MCI conversion to AD combined subfield volumes and presubiculum volume were more accurate (81.1 %) than total hippocampal volume. (76.7 %).

The analysis of structural and functional neuroimaging data using graph theory has increasingly become a popular approach for visualising and understanding anatomical and functional relationships between different cerebral areas. In this work we applied a network-based approach for brain PET studies using population-based covariance matrices, with the aim to explore topological tracer kinetic differences in cross-sectional investigations. Simulations, test-retest studies and applications to cross-sectional datasets from three different tracers ([ 18 F]FDG, [ 18 F]FDOPA and [ 11 C]SB217045) and more than 400 PET scans were investigated to assess the applicability of the methodology in healthy controls and patients. A validation of statistics, including the assessment of false positive differences in parametric versus permutation testing, was also performed. Results showed good reproducibility and general applicability of the method within the range of experimental settings typical of PET neuroimaging studies, with permutation being the method of choice for the statistical analysis. The use of graph theory for the quantification of [ 18 F]FDG brain PET covariance, including the definition of an entropy metric, proved to be particularly relevant for Alzheimer’s disease, showing an association with the progression of the pathology. This study shows that covariance statistics can be applied to PET neuroimaging data to investigate the topological characteristics of the tracer kinetics and its related targets, although sensitivity to experimental variables, group inhomogeneities and image resolution need to be considered when the method is applied to cross-sectional studies.

Background and Purpose— We examined if ischemic stroke is associated with white matter degeneration predominantly confined to the ipsi-lesional tracts or with widespread bilateral axonal loss independent of lesion laterality. Methods— We applied a novel fixel-based analysis, sensitive to fiber tract–specific differences within a voxel, to assess axonal loss in stroke (N=104, 32 women) compared to control participants (N=40, 15 women) across the whole brain. We studied microstructural differences in fiber density and macrostructural (morphological) changes in fiber cross-section. Results— In participants with stroke, we observed significantly lower fiber density and cross-section in areas adjacent, or connected, to the lesions (eg, ipsi-lesional corticospinal tract). In addition, the changes extended beyond directly connected tracts, independent of the lesion laterality (eg, corpus callosum, bilateral inferior fronto-occipital fasciculus, right superior longitudinal fasciculus). Conclusions— We conclude that ischemic stroke is associated with extensive neurodegeneration that significantly affects white matter integrity across the whole brain. These findings expand our understanding of the mechanisms of brain volume loss and delayed cognitive decline in stroke.