In Alzheimer’s disease, tau pathology spreads hierarchically from the inferior temporal lobe throughout the cortex, ensuing cognitive decline and dementia. Similarly, circumscribed patterns of pathological tau have been observed in normal ageing and small vessel disease, suggesting a spatially ordered distribution of tau pathology across normal ageing and different diseases. In vitro findings suggest that pathological tau may spread ‘prion-like’ across neuronal connections in an activity-dependent manner. Supporting this notion, functional brain networks show a spatial correspondence to tau deposition patterns. However, it remains unclear whether higher network-connectivity facilitates tau propagation. To address this, we included 55 normal aged elderly (i.e. cognitively normal, amyloid-negative), 50 Alzheimer’s disease patients (i.e. amyloid-positive) covering the preclinical to dementia spectrum, as well as 36 patients with pure (i.e. amyloid-negative) vascular cognitive impairment due to small vessel disease. All subjects were assessed with AV1451 tau-PET and resting-state functional MRI. Within each group, we computed atlas-based resting-state functional MRI functional connectivity across 400 regions of interest covering the entire neocortex. Using the same atlas, we also assessed within each group the covariance of tau-PET levels among the 400 regions of interest. We found that higher resting-state functional MRI assessed functional connectivity between any given region of interest pair was associated with higher covariance in tau-PET binding in corresponding regions of interest. This result was consistently found in normal ageing, Alzheimer’s disease and vascular cognitive impairment. In particular, inferior temporal tau-hotspots, as defined by highest tau-PET uptake, showed high predictive value of tau-PET levels in functionally closely connected regions of interest. These associations between functional connectivity and tau-PET uptake were detected regardless of presence of dementia symptoms (mild cognitive impairment or dementia), amyloid deposition (as assessed by amyloid-PET) or small vessel disease. Our findings suggest that higher functional connectivity between brain regions is associated with shared tau-levels, supporting the view of prion-like tau spreading facilitated by neural activity.

Diffusion alterations and clinical status in SVD are largely determined by extracellular fluid increase rather than alterations of white matter fiber organization.

ObjectiveTo test the hypothesis that multi-shell diffusion models improve the characterization of microstructural alterations in cerebral small vessel disease (SVD), we assessed associations with processing speed performance, longitudinal change and reproducibility of diffusion metrics.MethodsWe included 50 sporadic and 59 genetically defined SVD patients (CADASIL) with cognitive testing and standardized 3T MRI, including multi-shell diffusion imaging. We applied the simple diffusion tensor imaging (DTI) model and 2 advanced models: diffusion kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI). Linear regression and multivariable random forest regression (including conventional SVD markers) were used to determine associations between diffusion metrics and processing speed performance. The detection of short-term disease progression was assessed by linear mixed models in 49 sporadic SVD patients with longitudinal high-frequency imaging (in total 459 MRIs). Inter-site reproducibility was determined in 10 CADASIL patients scanned back-to-back on 2 different 3T MRI scanners.ResultsMetrics from DKI showed the strongest associations with processing speed performance (R2 up to 21%) and the largest added benefit on top of conventional SVD imaging markers in sporadic SVD and CADASIL patients with lower SVD burden. Several metrics from DTI and DKI performed similarly in detecting disease progression. Reproducibility was excellent (intraclass correlation coefficient >0.93) for DTI and DKI metrics. NODDI metrics were less reproducible.ConclusionMulti-shell diffusion imaging and DKI improve the detection and characterization of cognitively relevant microstructural white matter alterations in SVD. Excellent reproducibility of diffusion metrics endorses their use as SVD markers in research and clinical care. Our publicly available inter-site dataset facilitates future studies.Classification of evidenceThis study provides Class I evidence that in patients with SVD, diffusion MRI metrics are associated with processing speed performance.

* Data used in preparation of this article were obtained from the Dominantly Inherited Alzheimer Network (DIAN) database, the DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE) database, and the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within DIAN, DELCODE, and ADNI contributed to the design and implementation of the respective studies and/or provided data but did not participate in analysis or writing of this report. A complete listing of the DIAN consortium, the DELCODE study group, and ADNI investigators can be found in the Supplement (DIAN and DELCODE) and at http://adni.loni.usc.edu/wp-content/uploads/ how_to_apply/ADNI_Acknowledgement_List. pdf (ADNI). Sofia Finsterwalder and Naomi Vlegels both contributed equally to the study.

While structural network analysis consolidated the hypothesis of cerebral small vessel disease (SVD) being a disconnection syndrome, little is known about functional changes on the level of brain networks. In patients with genetically defined SVD (CADASIL, n = 41) and sporadic SVD (n = 46), we independently tested the hypothesis that functional networks change with SVD burden and mediate the effect of disease burden on cognitive performance, in particular slowing of processing speed. We further determined test–retest reliability of functional network measures in sporadic SVD patients participating in a high‐frequency (monthly) serial imaging study (RUN DMC—InTENse, median: 8 MRIs per participant). Functional networks for the whole brain and major subsystems (i.e., default mode network, DMN; fronto‐parietal task control network, FPCN; visual network, VN; hand somatosensory‐motor network, HSMN) were constructed based on resting‐state multi‐band functional MRI. In CADASIL, global efficiency (a graph metric capturing network integration) of the DMN was lower in patients with high disease burden (standardized beta = −.44; p [corrected] = .035) and mediated the negative effect of disease burden on processing speed (indirect path: std. beta = −.20, p = .047; direct path: std. beta = −.19, p = .25; total effect: std. beta = −.39, p = .02). The corresponding analyses in sporadic SVD showed no effect. Intraclass correlations in the high‐frequency serial MRI dataset of the sporadic SVD patients revealed poor test–retest reliability and analysis of individual variability suggested an influence of age, but not disease burden, on global efficiency. In conclusion, our results suggest that changes in functional connectivity networks mediate the effect of SVD‐related brain damage on cognitive deficits. However, limited reliability of functional network measures, possibly due to age‐related comorbidities, impedes the analysis in elderly SVD patients.