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ObjectivesTo investigate spatial heterogeneity of white matter lesions or hyperintensities (WMH).MethodsMRI scans of 1,836 participants (median age 52.2 ± 13.16 years) encompassing a wide age range (22–84 years) from the cross-sectional Study of Health in Pomerania (Germany) were included as discovery set identifying spatially distinct components of WMH using a structural covariance approach. Scans of 307 participants (median age 73.8 ± 10.2 years, with 747 observations) from the Baltimore Longitudinal Study of Aging (United States) were included to examine differences in longitudinal progression of these components. The associations of these components with vascular risk factors, cortical atrophy, Alzheimer disease (AD) genetics, and cognition were then investigated using linear regression.ResultsWMH were found to occur nonuniformly, with higher frequency within spatially heterogeneous patterns encoded by 4 components, which were consistent with common categorizations of deep and periventricular WMH, while further dividing the latter into posterior, frontal, and dorsal components. Temporal trends of the components differed both cross-sectionally and longitudinally. Frontal periventricular WMH were most distinctive as they appeared in the fifth decade of life, whereas the other components appeared later in life during the sixth decade. Furthermore, frontal WMH were associated with systolic blood pressure and with pronounced atrophy including AD-related regions. AD polygenic risk score was associated with the dorsal periventricular component in the elderly. Cognitive decline was associated with the dorsal component.ConclusionsThese results support the hypothesis that the appearance of WMH follows age and disease-dependent regional distribution patterns, potentially influenced by differential underlying pathophysiologic mechanisms, and possibly with a differential link to vascular and neurodegenerative changes.

ObjectiveTo investigate the association of enlarged perivascular spaces (ePVS) with cognition in elderly without dementia.MethodsWe included 5 studies from the Uniform Neuro-Imaging of Virchow-Robin Space Enlargement (UNIVRSE) consortium, namely the Austrian Stroke Prevention Family Study, Study of Health in Pomerania, Rotterdam Study, Epidemiology of Dementia in Singapore study, and Risk Index for Subclinical Brain Lesions in Hong Kong study. ePVS were counted in 4 regions (mesencephalon, hippocampus, basal ganglia, and centrum semiovale) with harmonized rating across studies. Mini-Mental State Examination (MMSE) and general fluid cognitive ability factor (G-factor) were used to assess cognitive function. For each study, a linear regression model was performed to estimate the effect of ePVS on MMSE and G-factor. Estimates were pooled across studies with the use of inverse variance meta-analysis with fixed- or random-effect models when appropriate.ResultsThe final sample size consisted of 3,575 persons (age range 63.4–73.2 years, 50.6% women). Total ePVS counts were not significantly associated with MMSE score (mean difference per ePVS score increase 0.001, 95% confidence interval [CI] −0.007 to 0.008, p = 0.885) or G-factor (mean difference per ePVS score increase 0.002, 95% CI −0.001 to 0.006, p = 0.148) in age-, sex-, and education-adjusted models. Adjustments for cardiovascular risk factors and MRI markers did not change the results. Repeating the analyses with region-specific ePVS rendered similar results.ConclusionsIn this study, we found that ePVS counts were not associated with cognitive dysfunction in the general population. Future studies with longitudinal designs are warranted to examine whether ePVS contribute to cognitive decline.

Converging evidence from structural, metabolic and functional connectivity MRI suggests that neurodegenerative diseases, such as Alzheimer's disease, target specific neural networks. However, age-related network changes commonly co-occur with neuropathological cascades, limiting efforts to disentangle disease-specific alterations in network function from those associated with normal ageing. Here we elucidate the differential effects of ageing and Alzheimer's disease pathology through simultaneous analyses of two functional connectivity MRI datasets: (i) young participants harbouring highly-penetrant mutations leading to autosomal-dominant Alzheimer's disease from the Dominantly Inherited Alzheimer's Network (DIAN), an Alzheimer's disease cohort in which age-related comorbidities are minimal and likelihood of progression along an Alzheimer's disease trajectory is extremely high; and (ii) young and elderly participants from the Harvard Aging Brain Study, a cohort in which imaging biomarkers of amyloid burden and neurodegeneration can be used to disambiguate ageing alone from preclinical Alzheimer's disease. Consonant with prior reports, we observed the preferential degradation of cognitive (especially the default and dorsal attention networks) over motor and sensory networks in early autosomal-dominant Alzheimer's disease, and found that this distinctive degradation pattern was magnified in more advanced stages of disease. Importantly, a nascent form of the pattern observed across the autosomal-dominant Alzheimer's disease spectrum was also detectable in clinically normal elderly with clear biomarker evidence of Alzheimer's disease pathology (preclinical Alzheimer's disease). At the more granular level of individual connections between node pairs, we observed that connections within cognitive networks were preferentially targeted in Alzheimer's disease (with between network connections relatively spared), and that connections between positively coupled nodes (correlations) were preferentially degraded as compared to connections between negatively coupled nodes (anti-correlations). In contrast, ageing in the absence of Alzheimer's disease biomarkers was characterized by a far less network-specific degradation across cognitive and sensory networks, of between- and within-network connections, and of connections between positively and negatively coupled nodes. We go on to demonstrate that formalizing the differential patterns of network degradation in ageing and Alzheimer's disease may have the practical benefit of yielding connectivity measurements that highlight early Alzheimer's disease-related connectivity changes over those due to age-related processes. Together, the contrasting patterns of connectivity in Alzheimer's disease and ageing add to prior work arguing against Alzheimer's disease as a form of accelerated ageing, and suggest multi-network composite functional connectivity MRI metrics may be useful in the detection of early Alzheimer's disease-specific alterations co-occurring with age-related connectivity changes. More broadly, our findings are consistent with a specific pattern of network degradation associated with the spreading of Alzheimer's disease pathology within targeted neural networks.

Background Little is known of possible associations between Alzheimer's disease (AD) biomarkers and instrumental activities of daily living (IADL) change over time. Objective The present study seeks to identify relationships between baseline imaging and fluid biomarker profiles, and decline in IADL utilizing data collated from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Methods Generalized estimating equations analysis, adjusted for cognitive deterioration, was applied to a cohort of 509 individuals from all stages of ADNI, including 156 healthy controls, 189 early mild cognitive impairment (MCI) patients and 164 MCI patients. Results A significant correlation was found between baseline biomarkers, specifically CSF Aβ and FDG PET, and IADL change over a 3-year period in individuals with MCI. Importantly, comparable correlations between presence of pathological biomarker levels and temporal decline in both functional and cognitive performance were also noted. Discussion We show that distinct baseline biomarkers may predict latent changes in IADL. Our results necessitate a revision of the commonly held view upholding cognitive changes as the predominant endpoint measure associated with presence of abnormal baseline biomarkers.

Patients with Alzheimer's disease (AD) show heterogeneity in profile of cognitive impairment. We aimed to identify cognitive subtypes in four large AD cohorts using a data-driven clustering approach.We included probable AD dementia patients from the Amsterdam Dementia Cohort (n = 496), Alzheimer's Disease Neuroimaging Initiative (n = 376), German Dementia Competence Network (n = 521), and University of California, San Francisco (n = 589). Neuropsychological data were clustered using nonnegative matrix factorization. We explored clinical and neurobiological characteristics of identified clusters.In each cohort, a two-clusters solution best fitted the data (cophenetic correlation0.9): one cluster was memory-impaired and the other relatively memory spared. Pooled analyses showed that the memory-spared clusters (29%-52% of patients) were younger, more often apolipoprotein E (APOE) ɛ4 negative, and had more severe posterior atrophy compared with the memory-impaired clusters (all P .05).We could identify two robust cognitive clusters in four independent large cohorts with distinct clinical characteristics.Alzheimer's disease (AD) is a heterogeneous disorder. We identified two cognitive AD subtypes in four cohorts with a data-driven approach. Nonamnestic AD is associated with distinct neurobiological characteristics.

Risk of Alzheimer’s disease (AD) can be predicted by volumetric analyses of MRI data in the medial temporal lobe. The present study compared a volumetric measurement of the hippocampus to a novel measure of hippocampal integrity derived from the ratio of parenchyma volume over total volume. Participants were cognitively intact and aged 60 or older at baseline, and were tested twice, roughly three years apart. Participants had been recruited for a study on late-life major 34 depression (LLMD) and were evenly split between depressed and controls. Linear regression models were applied to the data with a cognitive composite score as outcome, and hippocampal integrity (HI) and volume (HV), together or separately, as predictors. Subsequent cognitive performance was predicted well by models that include an interaction between HI and LLMD-status, such that lower HI scores predicted more cognitive decline in depressed subjects. More research is needed, but tentative results from this study appear to suggest that the newly introduced measure HI is an effective tool for the purpose of predicting future changes in general cognitive ability, and especially so in individuals with LLMD.

The effect of cognitive intervention on brain metabolism in Alzheimer's disease (AD) is largely unexplored. Therefore, we aimed to investigate clinical cognitive parameters and 18FDG PET to test for effects of a cognitive intervention in patients with amnestic mild cognitive impairment (aMCI) or mild AD. Patients with aMCI (n = 24) or mild AD (n = 15) were randomly assigned either to cognitive intervention groups (IGs), receiving weekly sessions of group-based multicomponent cognitive intervention, or active control groups (CGs), receiving pencil-paper exercises for self-study. We obtained resting-state FDG-PET scans and neuropsychological testing at baseline and after six-months. Normalized FDG-PET images were analyzed using voxel-based SPM5 approaches to determine longitudinal changes, group-by-time interactions, and correlations with neuropsychological outcome parameters. Primary global cognitive outcome was determined by analyses of covariance with MMSE and ADAS-cog scores as dependent measures. Both, aMCI and AD subgroups of CGs showed widespread bilateral cortical declines in FDG uptake, while the AD subgroup of IGs showed discrete decline or rather no decline in case of the aMCI subgroup. Group by time analyses revealed strongest attenuation of metabolic decline in the aMCI subgroup of the IGs, involving left superior temporal- and anterior cingulate gyrus. However, correlation analyses revealed only weak non-significant associations between increased FDG uptake and improvement in primary or secondary outcome parameters. Concurrently, there was significant improvement in global cognitive status in the aMCI subgroup of the IGs. A six-month cognitive intervention imparted cognitive benefits in patients with aMCI, which were concurrent with an attenuated decline of glucose metabolism in cortical regions affected by neurodegenerative AD.

Objective: To investigate the associations of cerebral amyloidosis with concurrent cognitive performance and with longitudinal cognitive decline in asymptomatic and symptomatic stages of autosomal dominant Alzheimer disease (ADAD). Methods: Two hundred sixty-three participants enrolled in the Dominantly Inherited Alzheimer Network observational study underwent neuropsychological evaluation as well as PET scans with Pittsburgh compound B. One hundred twenty-one participants completed at least 1 follow-up neuropsychological evaluation. Four composite cognitive measures representing global cognition, episodic memory, language, and working memory were generated using z scores from a battery of 13 standard neuropsychological tests. General linear mixed-effects models were used to investigate the relationship between baseline cerebral amyloidosis and baseline cognitive performance and whether baseline cerebral amyloidosis predicts cognitive change over time (mean follow-up 2.32 years ± 0.92, range 0.89–4.19) after controlling for estimated years from expected symptom onset, APOE e4 allelic status, and education. Results: In asymptomatic mutation carriers, amyloid burden was not associated with baseline cognitive functioning but was significantly predictive of longitudinal decline in episodic memory. In symptomatic mutation carriers, cerebral amyloidosis was correlated with worse baseline performance in multiple cognitive composites and predicted greater decline over time in global cognition, working memory, and Mini-Mental State Examination. Conclusions: Cerebral amyloidosis predicts longitudinal episodic memory decline in presymptomatic ADAD and multidomain cognitive decline in symptomatic ADAD. These findings imply that amyloidosis in the brain is an indicator of early cognitive decline and provides a useful outcome measure for early assessment and prevention treatment trials.

Rapid developments in medical neuroimaging have made it possible to reconstruct the trajectory of Alzheimer's disease (AD) as it spreads through the living brain. The current review focuses on the progressive signature of brain changes throughout the different stages of AD. We integrate recent findings on changes in cortical gray matter volume, white matter fiber tracts, neuropathological alterations, and brain metabolism assessed with molecular positron emission tomography (PET). Neurofibrillary tangles accumulate first in transentorhinal and cholinergic brain areas, and 4-D maps of cortical volume changes show early progressive temporo-parietal cortical thinning. Findings from diffusion tensor imaging (DTI) for assessment fiber tract integrity show cortical disconnection in corresponding brain networks. Importantly, the developmental trajectory of brain changes is not uniform and may be modulated by several factors such as onset of disease mechanisms, risk-associated and protective genes, converging comorbidity, and individual brain reserve. There is a general agreement between in vivo brain maps of cortical atrophy and amyloid pathology assessed through PET, reminiscent of post mortem histopathology studies that paved the way in the staging of AD. The association between in vivo and post mortem findings will clarify the temporal dynamics of pathophysiological alterations in the development of preclinical AD. This will be important in designing effective treatments that target specific underlying disease AD mechanisms.