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In cerebral small vessel disease (cSVD), whole brain MRI markers of cSVD-related brain injury explain limited variance to support individualized prediction. Here, we investigate whether considering abnormalities in brain tracts by integrating multimodal metrics from diffusion MRI (dMRI) and structural MRI (sMRI), can better capture cognitive performance in cSVD patients than established approaches based on whole brain markers. We selected 102 patients (73.7 ± 10.2 years old, 59 males) with MRI-visible SVD lesions and both sMRI and dMRI. Conventional linear models using demographics and established whole brain markers were used as benchmark of predicting individual cognitive scores. Multi-modal metrics of 73 major brain tracts were derived from dMRI and sMRI, and used together with established markers as input of a feed-forward artificial neural network (ANN) to predict individual cognitive scores. A feature selection strategy was implemented to reduce the risk of overfitting. Prediction was performed with leave-one-out cross-validation and evaluated with the R2 of the correlation between measured and predicted cognitive scores. Linear models predicted memory and processing speed with R2 = 0.26 and R2 = 0.38, respectively. With ANN, feature selection resulted in 13 tract-specific metrics and 5 whole brain markers for predicting processing speed, and 28 tract-specific metrics and 4 whole brain markers for predicting memory. Leave-one-out ANN prediction with the selected features achieved R2 = 0.49 and R2 = 0.40 for processing speed and memory, respectively. Our results show proof-of-concept that combining tract-specific multimodal MRI metrics can improve the prediction of cognitive performance in cSVD by leveraging tract-specific multi-modal metrics.

ObjectiveTo determine the occurrence of acute cerebral microinfarcts (ACMIs) in memory clinic patients and relate their presence to vascular risk and cognitive profile, CSF and neuroimaging markers, and clinical outcome.MethodsThe TRACE-VCI study is a memory clinic cohort of patients with vascular brain injury on MRI (i.e., possible vascular cognitive impairment [VCI]). We included 783 patients (mean age 67.6 ± 8.5, 46% female) with available 3T diffusion-weighted imaging (DWI). ACMIs were defined as supratentorial DWI hyperintensities ResultsA total of 23 ACMIs were found in 16 of the 783 patients (2.0%). Patients with ACMIs did not differ in vascular risk or cognitive profile, but were more often diagnosed with vascular dementia (odds ratio [OR] 5.1; 95% confidence interval [CI] 1.4–18.9, p = 0.014). ACMI presence was associated with lower levels of β-amyloid (p < 0.004) and with vascular imaging markers (lacunar infarcts: OR 3.5, CI 1.3–9.6, p = 0.015; nonlacunar infarcts: OR 4.1, CI 1.4–12.5, p = 0.012; severe white matter hyperintensities: OR 4.8, CI 1.7–13.8, p = 0.004; microbleeds: OR 18.9, CI 2.5–144.0, p = 0.0001). After a median follow-up of 2.1 years, the risk of poor clinical outcome (composite of marked cognitive decline, major vascular event, death, and institutionalization) was increased among patients with ACMIs (hazard ratio 3.0; 1.4–6.0, p = 0.005).ConclusionIn patients with possible VCI, ACMI presence was associated with a high burden of cerebrovascular disease of both small and large vessel etiology and poor clinical outcome. ACMIs may thus be a novel marker of active vascular brain injury in these patients.

White matter hyperintensities (WMHs) are a common manifestation of cerebral small vessel disease. WMHs are also frequently observed in patients with familial and sporadic Alzheimer's disease, often with a particular posterior predominance. Whether amyloid and tau pathologies are linked to WMH occurrence is still debated. We examined whether cerebral amyloid and tau burden, reflected in cerebrospinal fluid amyloid-beta 1-42 (A beta-42) and phosphorylated tau (p-tau), are related to WMH location in a cohort of 517 memory clinic patients. Two lesion mapping techniques were performed: voxel-based analyses and region of interest-based linear regression. Voxelwise associations were found between lower (A beta-42) and parieto-occipital periventricular WMHs. Regression analyses demonstrated that lower A beta-42 correlated with larger WMH volumes in the splenium of the corpus callosum and posterior thalamic radiation, also after controlling for markers of vascular disease. P-tau was not consistently related to WMH occurrence. Our findings indicate that cerebral amyloid burden is associated with WMHs located in specific posterior white matter regions, possibly reflecting region-specific effects of amyloid pathology on the white matter. (C) 2019 The Authors. Published by Elsevier Inc.

White matter hyperintensities (WMHs) are a common manifestation of cerebral small vessel disease, that is increasingly studied with large, pooled multicenter datasets. This data pooling increases statistical power, but poses challenges for automated WMH segmentation. Although there is extensive literature on the evaluation of automated WMH segmentation methods, such evaluations in a multicenter setting are lacking. We performed WMH segmentations in sixty patients scanned on six different magnetic resonance imaging (MRI) scanners (10 patients per scanner) using five freely available and fully-automated WMH segmentation methods (Cascade, kNN-TTP, Lesion-TOADS, LST-LGA and LST-LPA). Different MRI scanner vendors and field strengths were included. We compared these automated WMH segmentations with manual WMH segmentations as a reference. Performance of each method both within and across scanners was assessed using spatial and volumetric correspondence with the reference segmentations by Dice’s similarity coefficient (DSC) and intra-class correlation coefficient (ICC) respectively. We found the best performance, both within and across scanners, for kNN-TTP, followed by LST-LPA and LST-LGA, with worse performance for Lesion-TOADS and Cascade. Our findings can serve as a guide for choosing a method and also highlight the importance to further improve and evaluate consistency of methods in a multicenter setting.

Fragestellung: Bary- und Polytraumata gelten als eine der häufigsten Todesursachen und Verursacher bleibender Behinderungen weltweit bei Personen unter 40 Jahren und nehmen in ihrer Fallzahl, insbesondere in Entwicklungsländern, zu. Ein drohendes letales Multiorganversagen stellt auch nach[zum vollständigen Text gelangen Sie über die oben angegebene URL], Deutscher Kongress für Orthopädie und Unfallchirurgie (DKOU 2019)

Background and purpose: Cerebral microinfarcts (CMIs) are associated with cognitive impairment and dementia. CMIs might affect cognitive performance through disruption of cerebral networks. We investigated in memory clinic patients whether cortical CMIs are clustered in specific brain regions and if presence of cortical CMIs is associated with reduced white matter (WM) connectivity in tracts projecting to these regions.Methods: 164 memory clinic patients with vascular brain injury with a mean age of 72 +/- 11 years (54% male) were included. All underwent 3 tesla MRI, including a diffusion MRI and cognitive testing. Cortical CMIs were rated according to established criteria and their spatial location was marked. Diffusion imaging-based tractography was used to reconstruct WM connections and voxel based analysis (VBA) to assess integrity of WM directly below the cortex. WM connectivity and integrity were compared between patients with and without cortical CMIs for the whole brain and regions with a high CMI burden.Results: 30 patients (18%) had at least 1 cortical CMI [range 1-46]. More than 70% of the cortical CMIs were located in the superior frontal, middle frontal, and pre- and postcentral brain regions (covering 16% of the cortical surface). In these high CMI burden regions, presence of cortical CMIs was not associated with WM connectivity after correction for conventional neuroimaging markers of vascular injury. WM connectivity in the whole brain and WM voxels directly underneath the cortical surface did not differ between patients with and without cortical CMIs.Conclusion: Cortical CMIs displayed a strong local clustering in highly interconnected frontal, pre- and postcentral brain regions. Nevertheless, WM connections projecting to these regions were not disproportionally impaired in patients with compared to patients without cortical CMIs. Alternative mechanisms, such as focal disturbances in cortical structure and functioning, may better explain CMI associated cognitive impairment.

Item does not contain fulltext BACKGROUND: In patients with Alzheimer's disease (AD), atrophy of the entorhinal cortex (ERC) and hippocampal formation may induce degeneration of connecting white matter tracts. OBJECTIVE: We examined the association of hippocampal subfield and ERC atrophy at 7 tesla MRI with fornix and parahippocampal cingulum (PHC) microstructure in patients with early AD. METHODS: Twenty-five patients with amnestic mild cognitive impairment (aMCI) (n = 15) or early AD (n = 10) and 17 controls underwent 3 tesla diffusion MRI to obtain fractional anisotropy (FA) of the fornix and PHC and 7 tesla MRI to obtain ERC and hippocampal subfield volumes. Linear regression analyses were performed, adjusted for age, gender, and intracranial volume. RESULTS: Fornix FA was significantly lower and subiculum, cornu ammonis (CA) 1, and dentate gyrus &CA4 volume were significantly smaller in patients with MCI or AD as compared to controls. In patients with MCI or AD, fornix FA was positively associated with subiculum volume (beta = 0.53, 95% CI 0.10; 0.96), but not with ERC/other subfield volumes. PHC FA was not associated with ERC/subfield volumes. CONCLUSION: These findings indicate that in early AD subiculum atrophy is associated with lower FA of the fornix, which primarily consists of axons originating in the subiculum. This suggests that degeneration of subicular cell bodies and their axons are related processes in early AD.