Barkhof, Frederik

AbstractBackgroundGrey matter covariance networks become disrupted early in Alzheimer’s disease (AD), when amyloid starts aggregating, and before the onset of irreversible atrophy. However, the precise relationship of grey matter network breakdown and the AD pathophysiological process remains unclear. Here, we studied to what extent shared familial (genes and family environment) or environmental factors unique to a twin contribute to these relationships in a sample of older monozygotic twins with intact cognition.MethodFrom the EMIF‐AD PreclinAD study we included monozygotic twins who had at least an MRI scan (n=197) and CSF available (n=124). We assessed the upper limit of genetic contribution to grey matter network measures (size, degree, connectivity density, clustering, path length, small‐world measures) with within‐twin pair correlations. Associations between CSF AD markers (Aβ42/40, t‐tau, p‐tau) and other markers related to AD pathophysiology (neurogranin, Aβ38, Aβ40, BACE1) with grey matter network disruptions were assessed with linear mixed models. Twin analyses (cross‐twin cross‐trait, twin difference analyses) were used to assess the contribution of shared familial and/or unique environmental factors to these associations.ResultTwenty individuals (16%) had abnormal CSF Aβ42/40 levels. Grey matter network measures were highly correlated within twin pairs (r=0.54‐0.97; all p<0.001, Fig.1), suggesting a moderate to strong genetic background. Across the whole sample, more abnormal Aβ42/40 ratios were moderately related to lower connectivity density (β±SE = 0.176±0.09, p=0.055, Fig.2). The strongest associations with grey matter network measures were observed for t‐tau and p‐tau levels (range β=‐0.344‐0.182), followed by Aβ production markers (Aβ38, Aβ40, BACE1) (range β=‐0.202‐0.165). We observed no relationships between neurogranin and grey matter network measures. Cross‐twin cross‐trait analyses showed that levels for t‐tau, p‐tau and Aβ38 in one twin were associated with grey matter network measures in their co‐twin (r=‐0.27 to ‐0.21; p range=0.02 to 0.07, Fig.3). Within‐pair differences in BACE1 were related to within‐pair differences in normalized path length values (r=0.35; p=0.01; Fig.4).ConclusionOur results suggest a shared familial background for the relationship of grey matter network disruptions and AD‐related processes as measured in CSF. In addition, unique environmental factors influencing BACE1 may also affect grey matter network disruptions.

AbstractBackgroundBrain amyloid‐β (Aβ) is the pathological hallmark of Alzheimer's disease (AD). In logistic disease models, Aβ accumulation is a sigmoid function of time‐since‐disease‐onset (TSDO) (figure 1). Previous positron emission tomography (PET)‐based models vary accumulation onset(t50) and duration(r) globally; capacity(K) and baseline(NS) regionally (Whittington2018). We confirm existing approaches and propose a more powerful ICA‐based approach to quantify disease severity and estimate TSDO.MethodWe used 1071 18F‐florbetapir standard uptake value ratio (SUVR) images from the ADNI‐2 study (adni.loni.usc.edu/data‐samples/data‐types/pet). Images were mapped into MNI space. Averages were extracted using the Harvard‐Oxford brain‐atlas. Whole‐brain tracer‐specific sigmoid parameters (Jack2013) obtained from the literature were used to estimate TSDO.Of 16 models of regional Aβ accumulation (each of the 4 regional sigmoid parameters varied either regionally or globally), the optimal Bayesian information criterion was found with global t50 and r, and regional NS and K (figure 1) with global values r=6.16y and t50=4.10y.Linearised maps of NS and K were obtained by regressing the SUVR maps onto the global sigmoid. We also estimated these maps as independent components, using a 2‐component ICA on the SUVR maps. Both outcomes were used to quantify Aβ accumulation from SUVR images as weighting factors of the accumulation map.We compared the weights from the logistic model and the ICA model in ADNI, using effect size measured with Hedges' g between cognitively normal (CN), subjective memory complaints (SMC), mild cognitive impairment (EMCI/MCI/LMCI) and AD groups. We compared 3 longitudinal visits (N=112) in the OASIS‐3 study (see www.oasis‐brains.org) with both methods, global SUVR and Centiloid (Klunk2015) using 11C‐PiB PET SUVR images.ResultMaps of accumulation capacity from both models had spatial correlation of 0.86 (figure 2); baseline maps had spatial correlation of 0.95. Hedges' g between ADNI groups was 2.25 for K, and 2.42 for ICA (1.46 for global SUVR). In OASIS‐3, Hedges' g between visits was 1.24 for K, 1.46 for ICA (global SUVR 0.15, Centiloid 0.4).ConclusionWe demonstrate that linear accumulation models can be used to quantify brain Aβ with PET; maps obtained by ICA yield larger effect sizes than the logistic method for differentiating groups and measuring changes between visits.

Abstract Background. Dementia with Lewy bodies (DLB) is a neurodegenerative disorder with a wide heterogeneity of symptoms, which suggests the existence of different subtypes. Objectives. We used data-driven analysis of MRI data to investigate DLB subtypes. Methods. We included 165 probable DLB from the Mayo Clinic and 3 centers from the European DLB consortium and performed a hierarchical cluster analysis to identify subtypes based on gray matter (GM) volumes. To characterize the subtypes, we used demographic and clinical data, as well as β-amyloid and cerebrovascular biomarkers at baseline, and cognitive decline over three years. Results. We identified 3 subtypes: an older subtype with reduced cortical GM volumes, worse cognition, and faster cognitive decline (n=49, 30%); a subtype with low GM volumes in fronto-occipital regions (n=76, 46%); and a subtype of younger patients with the highest cortical GM volumes, proportionally lower GM volumes in basal ganglia and the highest frequency of cognitive fluctuations (n=40, 24%). Conclusions. This study shows the existence of MRI subtypes in DLB, which may have implications for clinical workout, research, and therapeutic decisions.

AbstractIndividuals with prodromal Alzheimer’s disease show considerable variability in rates of cognitive decline, which hampers the ability to detect potential treatment effects in clinical trials. Prognostic markers to select those individuals who will decline rapidly within a trial time frame are needed. Brain network measures based on grey matter covariance patterns have been associated with future cognitive decline in Alzheimer’s disease. In this longitudinal cohort study, we investigated whether cut-offs for grey matter networks could be derived to detect fast disease progression at an individual level. We further tested whether detection was improved by adding other biomarkers known to be associated with future cognitive decline [i.e. CSF tau phosphorylated at threonine 181 (p-tau181) levels and hippocampal volume]. We selected individuals with mild cognitive impairment and abnormal CSF amyloid β1–42 levels from the Amsterdam Dementia Cohort and the Alzheimer’s Disease Neuroimaging Initiative, when they had available baseline structural MRI and clinical follow-up. The outcome was progression to dementia within 2 years. We determined prognostic cut-offs for grey matter network properties (gamma, lambda and small-world coefficient) using time-dependent receiver operating characteristic analysis in the Amsterdam Dementia Cohort. We tested the generalization of cut-offs in the Alzheimer’s Disease Neuroimaging Initiative, using logistic regression analysis and classification statistics. We further tested whether combining these with CSF p-tau181 and hippocampal volume improved the detection of fast decliners. We observed that within 2 years, 24.6% (Amsterdam Dementia Cohort, n = 244) and 34.0% (Alzheimer’s Disease Neuroimaging Initiative, n = 247) of prodromal Alzheimer’s disease patients progressed to dementia. Using the grey matter network cut-offs for progression, we could detect fast progressors with 65% accuracy in the Alzheimer’s Disease Neuroimaging Initiative. Combining grey matter network measures with CSF p-tau and hippocampal volume resulted in the best model fit for classification of rapid decliners, increasing detecting accuracy to 72%. These data suggest that single-subject grey matter connectivity networks indicative of a more random network organization can contribute to identifying prodromal Alzheimer’s disease individuals who will show rapid disease progression. Moreover, we found that combined with p-tau and hippocampal volume this resulted in the highest accuracy. This could facilitate clinical trials by increasing chances to detect effects on clinical outcome measures.

AbstractBackgroundDementia with Lewy bodies (DLB) is a neurodegenerative disorder that presents with a variety of clinical symptoms. Part of the clinical heterogeneity within DLB has been related to the atrophy/sparing of the hippocampus. We extended that previous research by investigating whether different atrophy patterns exist in DLB, beyond and above the atrophy/sparing of the hippocampus, and whether those patterns contribute to the clinical heterogeneity in DLB. We aimed to identify DLB atrophy subtypes using a hypothesis‐free data‐driven clustering approach based on regional gray matter (GM) volumes and characterize the resulting subtypes across key measures.MethodWe included data from three centers of the European DLB consortium. A high‐resolution 3D T1‐weigthed magnetization prepared rapid gradient echo (MPRAGE) sequence was acquired for 97 DLB patients. Eighty‐four cortical regions and 12 subcortical regions from Mayo Clinic Adult Lifespan Template (https://www.nitrc.org/projects/mcalt/) atlases were obtained. Clusters of DLB patients were then determined using the random forest method. Between‐group differences in key demographic, clinical, MRI volumes and cerebrospinal fluid biomarkers (CSF) (amyloid‐beta, phosphorylated‐tau) were performed for clusters characterization.ResultWe identified three subtypes of DLB patients, with distinct patterns of GM volumes and differences in several clinical and demographic measures. Cluster 1 (C1) included 32 DLB patients (33%), cluster 2 (C2) included 29 DLB patients (30%), and cluster 3 (C3) included 36 DLB patients (37%). The clusters differed in age, with C1 being significantly older (72±8.6) than C3 (66±9.5) (F=3.93, p=0.02). C1 was characterized by lower volumes in fronto‐temporal regions and had a higher frequency of visual hallucinations (68%, vs. C2 35%). C2 showed lower volumes in occipital regions. C3 had higher GM volumes overall compared with the other two clusters and presented with a higher frequency of cognitive fluctuations (91%, vs. C1 67%). There were no differences between groups in CSF biomarkers. These results were independent of cluster differences in age and global GM volume.ConclusionThere are distinct patterns of brain atrophy in patients with DLB. These patterns seem to differentially contribute to the frequencies of visual hallucinations and cognitive fluctuations, independently of differences in age and global GM volume across subtypes.

AbstractIntroductionHarboring two copies of the apolipoprotein E (APOE) ε2 allele strongly protects against Alzheimer's disease (AD). However, the effect of this genotype on gray matter (GM) volume in cognitively unimpaired individuals has not yet been described.MethodsMulticenter brain magnetic resonance images (MRIs) from cognitively unimpaired ε2 homozygotes were matched (1:1) against all other APOE genotypes for relevant confounders (n = 223). GM volumes of ε2 genotypic groups were compared to each other and to the reference group (APOE ε3/ε3).ResultsCarrying at least one ε2 allele was associated with larger GM volumes in brain areas typically affected by AD and also in areas associated with cognitive resilience. APOE ε2 homozygotes, but not APOE ε2 heterozygotes, showed larger GM volumes in areas related to successful aging.DiscussionIn addition to the known resistance against amyloid‐β deposition, the larger GM volumes in key brain regions may confer APOE ε2 homozygotes additional protection against AD‐related cognitive decline.

Abstract MRI has improved the diagnostic work-up of multiple sclerosis, but inappropriate image interpretation and application of MRI diagnostic criteria contribute to misdiagnosis. Some diseases, now recognized as conditions distinct from multiple sclerosis, may satisfy the MRI criteria for multiple sclerosis (e.g. neuromyelitis optica spectrum disorders, Susac syndrome), thus making the diagnosis of multiple sclerosis more challenging, especially if biomarker testing (such as serum anti-AQP4 antibodies) is not informative. Improvements in MRI technology contribute and promise to better define the typical features of multiple sclerosis lesions (e.g. juxtacortical and periventricular location, cortical involvement). Greater understanding of some key aspects of multiple sclerosis pathobiology has allowed the identification of characteristics more specific to multiple sclerosis (e.g. central vein sign, subpial demyelination and lesional rims), which are not included in the current multiple sclerosis diagnostic criteria. In this review, we provide the clinicians and researchers with a practical guide to enhance the proper recognition of multiple sclerosis lesions, including a thorough definition and illustration of typical MRI features, as well as a discussion of red flags suggestive of alternative diagnoses. We also discuss the possible place of emerging qualitative features of lesions which may become important in the near future.

Objectives: The objectives of this paper are to compare in a multicenter setting patterns of regional cortical thickness in patients with relapsing–remitting multiple sclerosis (RRMS) and cognitive impairment (CI) and those cognitively preserved (CP), and explore the relationship between cortical thinning and cognitive performance. Methods: T1-weighted isotropic brain scans were collected at 3T from seven European centers in 60 RRMS patients and 65 healthy controls (HCs). Patients underwent clinical and neuropsychological examinations. Cortical thickness (CTh) measures were calculated using FreeSurfer (failing in four) and both lobar and vertex-based general linear model (GLM) analyses were compared between study groups. Results: Twenty (36%) MS patients were classified as CI. Mean global CTh was smaller in RRMS patients compared to HCs (left 2.43 vs. 2.53 mm, right 2.44 vs. 2.54 mm, p < 0.001). Multivariate GLM regional analysis showed significantly more temporal thinning in CI compared to CP patients. Verbal memory scores correlated to regional cortical thinning in the insula whereas visual memory scores correlated to parietal thinning. Conclusions: This multicenter study showed mild global cortical thinning in RRMS. The extent of thinning is less pronounced than previously reported. Only subtle regional differences between CI and CP patients were observed, some of which related to specific cognitive domains.

AbstractObjectiveMetachromatic leukodystrophy (MLD) is an inherited lysosomal disorder due to a deficiency in arylsulfatase A with progressive demyelination and neurological decline. This retrospective MRI study investigated the extent of cortical involvement at time of diagnosis, and clinical correlates to both conventional and regional volumetric measures of brain involvement.Methods3D‐T1‐weighted MRI scans were used to determine cortical thickness and surface‐based cerebral cortical gray matter (GM) and cerebral white matter (WM) volume (GMV and WMV), WM lesions, thalamus, and cerebellum. MRI‐MLD severity scores were obtained from FLAIR images. Associations between clinical and imaging data were examined using correlation coefficients.ResultsTwenty patients with MLD (mean age 13.7 years, range 2–35) and 20 controls (mean age 13.9 years, range 2–40) were included. Compared with control subjects, late‐infantile, and juvenile patients (n = 14) had significantly diminished cerebral cortical GMV and thalamus volume (P < 0.05), but did not differ in WMV and cortical thickness. Adult patients (n = 6) showed significantly reduced GMV, WMV and cortical thickness (all P < 0.05). Regional analysis showed statistically significant cortical thinning in the cingulate gyrus and most pronounced thinning with age in the frontal lobe of MLD patients. Intelligence quotient (IQ) correlated with MRI‐MLD scores (r = −0.87, P < 0.001).InterpretationSignificant cerebral cortical GMV loss is already present in early stages of MLD. IQ correlates with WM severity scores and lesion volume, but not with volumetric measures. In adult presentations, there is more pronounced global atrophy with GMV and WMV loss and accelerated cortical thinning, most prominently in the cingulate gyrus and frontal lobes.