Accumulating evidence implicates small vessel cerebrovascular disease, visualized as white matter hyperintensities (WMH) on T2-weighted MRI, in the pathogenesis and diagnosis of Alzheimer's disease (AD). Cross-sectional volumetric measures of WMH, particularly in the parietal lobes, are associated with increased risk of AD. In the current study, we sought to determine whether the longitudinal regional progression of WMH predicts incident AD above-and-beyond traditional radiological markers of neurodegeneration (i.e., hippocampal atrophy, cortical thickness). Three hundred three non-demented older adults (mean age = 79.24±5.29) received high-resolution MRI at baseline and then again 4.6 years (SD=1.01) later. Over the follow-up interval 26 participants progressed to AD. Using structural equation modeling (SEM), we calculated latent difference scores of parietal/non-parietal WMH, hippocampus volumes, and cortical thickness values in AD-related regions. Within the SEM framework, we determined whether baseline or change scores or both predicted AD conversion, while controlling for several time-invariant relevant variables. Smaller baseline hippocampus volume, change in hippocampus volume (i.e., atrophy), higher baseline parietal lobe WMH, and increasing parietal lobe WMH volume but not WMH in other regions or measures of cortical thickness, independently predicted progression to AD. The findings provide strong evidence that regionally accumulating WMH, in addition to degenerative changes in the medial temporal lobe, predict AD onset in addition to hallmark neurodegenerative changes typically associated with AD.

Background The retrogenesis hypothesis postulates that late-myelinated white matter fibers are most vulnerable to age- and disease-related degeneration, which in turn mediate cognitive decline. While recent evidence supports this hypothesis in the context of Alzheimer’s disease, it has not been tested systematically in normal cognitive aging. Methods In the current study, we examined the retrogenesis hypothesis in a group (n=282) of cognitively normal individuals ranging in age from 7 to 87 years from the Brain Resource International Database. Participants were evaluated with a comprehensive neuropsychological battery and were imaged with diffusion tensor imaging. Fractional anisotropy (FA), radial diffusivity (RD), and axial diffusivity (DA), measures of white matter coherence, were computed in two prototypical early-myelinated fiber tracts (posterior limb of the internal capsule, cerebral peduncles) and two prototypical late-myelinated fiber tracts (superior longitudinal fasciculus, inferior longitudinal fasciculus) chosen to parallel previous studies; mean summary values were also computed for other early- and late-myelinated fiber tracts. We examined age-associated differences in FA, RD, and DA in the developmental trajectory (ages 7 to 30 years) and degenerative trajectory (ages 31 to 87 years), and tested whether the measures of white matter coherence mediated age-related cognitive decline in the older group. Results FA and DA values were greater for early-myelinated fibers than for late-myelinated fibers, and RD values were lower for early-myelinated than late-myelinated fibers. There were age-associated differences in FA, RD, and DA across early- and late-myelinated fiber tracts in the younger group, but the magnitude of differences did not vary as a function of early or late myelinating status. FA and RD in most fiber tracts showed reliable age-associated differences in the older age group, but the magnitudes were greatest for the late-myelinated tract summary measure, inferior longitudinal fasciculus (late fiber tract), and cerebral peduncles (early fiber tract). Finally, FA in the inferior longitudinal fasciculus and cerebral peduncles and RD in the cerebral peduncles mediated age-associated differences in an executive functioning factor. Discussion Taken together, the findings highlight the importance of white matter coherence in cognitive aging and provide some, but not complete, support for the white matter retrogenesis hypothesis in normal cognitive aging.

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Background Microbleeds, small perivascular collections of hemosiderin manifested radiologically as hypointensities on gradient-echo magnetic resonance imaging (MRI), are important markers of small vessel pathology. Despite their clinical relevance, little is known about their prevalence and demographic correlates, particularly among ethnically diverse older adults. We examined demographic and clinical correlates of regional microbleeds in a multi-ethnic cohort and examined categorization schemes of microbleed distribution and severity. Methods Between 2005 and 2007, 769 individuals participated in a MRI study as part of the Washington Heights/Inwood Columbia Aging Project. Approximately four years later, 243 out of 339 participants (mean age=84.50) who returned for a repeat MRI had gradient-echo scans for microbleed assessment and comprised the sample. We examined the association of deep and lobar microbleeds with age, sex, education, vascular factors, cognitive status and markers of small vessel disease. Results Sixty-seven of the 243(27%) participants had at least one microbleed. Individuals with microbleeds were more likely to have a history of stroke than individuals without. When categorized as having either no microbleeds, microbleeds in deep regions only, in lobar regions only, and both deep and lobar microbleeds, hypertension, proportion of strokes, and white matter hyperintensity volume (WMH) increased monotonically across the four groups. Number of lobar microbleeds correlated with WMH volume and diastolic blood pressure. Conclusions Microbleeds in deep and lobar locations are associated with worse outcomes than microbleeds in either location alone, although presence of lobar microbleeds appears to be more clinically relevant.

Background Research investigating treatments and interventions for cognitive decline fail due to difficulties in accurately recognizing behavioral signatures in the presymptomatic stages of the disease. For this validation study, we took our previously constructed digital biomarker‐based prognostic models and focused on generalizability and robustness of the models. Method We validated prognostic models characterizing subjects using digital biomarkers in a longitudinal, multi‐site, 40‐month prospective study collecting data in memory clinics, general practitioner offices, and home environments. Results Our models were able to accurately discriminate between healthy subjects and individuals at risk to progress to dementia within 3 years. The model was also able to differentiate between people with or without amyloid neuropathology and classify fast and slow cognitive decliners with a very good diagnostic performance. Conclusion Digital biomarker prognostic models can be a useful tool to assist large‐scale population screening for the early detection of cognitive impairment and patient monitoring over time.