Assessing the Predictive Validity of Simple Dementia Risk Models in Harmonized Stroke Cohorts.

BACKGROUND AND PURPOSE: Stroke is associated with an increased risk of dementia. To assist in the early identification of individuals at high risk of future dementia, numerous prediction models have been developed for use in the general population. However, it is not known whether such models also provide accurate predictions among stroke patients. Therefore, the aim of this study was to determine whether existing dementia risk prediction models that were developed for use in the general population can also be applied to individuals with a history of stroke to predict poststroke dementia with equivalent predictive validity. METHODS: Data were harmonized from 4 stroke studies (follow-up range, ≈12-18 months poststroke) from Hong Kong, the United States, the Netherlands, and France. Regression analysis was used to test 3 risk prediction models: the Cardiovascular Risk Factors, Aging and Dementia score, the Australian National University Alzheimer Disease Risk Index, and the Brief Dementia Screening Indicator. Model performance or discrimination accuracy was assessed using the C statistic or area under the curve. Calibration was tested using the Grønnesby and Borgan and the goodness-of-fit tests. RESULTS: The predictive accuracy of the models varied but was generally low compared with the original development cohorts, with the Australian National University Alzheimer Disease Risk Index (C-statistic, 0.66) and the Brief Dementia Screening Indicator (C-statistic, 0.61) both performing better than the Cardiovascular Risk Factors, Aging and Dementia score (area under the curve, 0.53). CONCLUSIONS: Dementia risk prediction models developed for the general population do not perform well in individuals with stroke. Their poor performance could have been due to the need for additional or different predictors related to stroke and vascular risk factors or methodological differences across studies (eg, length of follow-up, age distribution). Future work is needed to develop simple and cost-effective risk prediction models specific to poststroke dementia.

Eating and hypothalamus changes in behavioral-variant frontotemporal dementia.

OBJECTIVE: Behavioral-variant frontotemporal dementia (bvFTD) is a progressive neurodegenerative brain disorder, clinically characterized by changes in cognition, personality, and behavior. Marked disturbances in eating behavior, such as overeating and preference for sweet foods, are also commonly reported. The hypothalamus plays a critical role in feeding regulation, yet the relation between pathology in this region and eating behavior in FTD is unknown. This study aimed to address this issue using 2 complementary approaches. METHODS: First, 18 early stage bvFTD patients and 16 healthy controls underwent high-resolution structural magnetic resonance imaging and assessment of eating behavior. Hypothalamic volumes were traced manually on coronal images. Second, postmortem analyses of 12 bvFTD cases and 6 matched controls were performed. Fixed hypothalamic tissue sections were stained for a cell marker and for peptides regulating feeding behaviors using immunohistochemistry. Stereological estimates of the hypothalamic volume and the number of neurons and glia were performed. RESULTS: Significant atrophy of the hypothalamus in bvFTD was present in both analyses. Patients with high feeding disturbance exhibited significant atrophy of the posterior hypothalamus. Neuronal loss, which was observed only in bvFTD cases with Tar DNA protein-43 deposition, was also predominant posteriorly. In contrast, orexin (hypocretin), neuropeptide Y, cocaine- and amphetamine-regulating transcript, and vasopressin-containing neurons that regulate appetite were spared in posterior nuclei known to participate in feeding regulation. INTERPRETATION: Degeneration and consequent dysregulation within the hypothalamus relates to significant feeding disturbance in bvFTD. These findings provide a basis for the development of therapeutic models.

Diffusion MRI harmonization enables joint-analysis of multicentre data of patients with cerebral small vessel disease.

OBJECTIVES: Acquisition-related differences in diffusion magnetic resonance imaging (dMRI) hamper pooling of multicentre data to achieve large sample sizes. A promising solution is to harmonize the raw diffusion signal using rotation invariant spherical harmonic (RISH) features, but this has not been tested in elderly subjects. Here we aimed to establish if RISH harmonization effectively removes acquisition-related differences in multicentre dMRI of elderly subjects with cerebral small vessel disease (SVD), while preserving sensitivity to disease effects. METHODS: Five cohorts of patients with SVD (N = 397) and elderly controls (N = 175) with 3 Tesla MRI on different systems were included. First, to establish effectiveness of harmonization, the RISH method was trained with data of 13 to 15 age and sex-matched controls from each site. Fractional anisotropy (FA) and mean diffusivity (MD) were compared in matched controls between sites using tract-based spatial statistics (TBSS) and voxel-wise analysis, before and after harmonization. Second, to assess sensitivity to disease effects, we examined whether the contrast (effect sizes of FA, MD and peak width of skeletonized MD - PSMD) between patients and controls within each site remained unaffected by harmonization. Finally, we evaluated the association between white matter hyperintensity (WMH) burden, FA, MD and PSMD using linear regression analyses both within individual cohorts as well as with pooled scans from multiple sites, before and after harmonization. RESULTS: Before harmonization, significant differences in FA and MD were observed between matched controls of different sites (p < 0.05). After harmonization these site-differences were removed. Within each site, RISH harmonization did not alter the effect sizes of FA, MD and PSMD between patients and controls (relative change in Cohen's d = 4 %) nor the strength of association with WMH volume (relative change in R2 = 2.8 %). After harmonization, patient data of all sites could be aggregated in a single analysis to infer the association between WMH volume and FA (R2 = 0.62), MD (R2 = 0.64), and PSMD (R2 = 0.60). CONCLUSIONS: We showed that RISH harmonization effectively removes acquisition-related differences in dMRI of elderly subjects while preserving sensitivity to SVD-related effects. This study provides proof of concept for future multicentre SVD studies with pooled datasets.