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Delirium is associated with long-term cognitive dysfunction and with increased brain atrophy. However, it is unclear whether these problems result from or predisposes to delirium. We aimed to investigate preoperative to postoperative brain changes, as well as the role of delirium in these changes over time. We investigated the effects of surgery and postoperative delirium with brain MRIs made before and 3 months after major elective surgery in 299 elderly patients, and an MRI with a 3 months follow-up MRI in 48 non-surgical control participants. To study the effects of surgery and delirium, we compared brain volumes, white matter hyperintensities and brain infarcts between baseline and follow-up MRIs, using multiple regression analyses adjusting for possible confounders. Within the patients group, 37 persons (12%) developed postoperative delirium. Surgical patients showed a greater decrease in grey matter volume than non-surgical control participants [linear regression: B (95% confidence interval) = -0.65% of intracranial volume (-1.01 to -0.29, P < 0.005)]. Within the surgery group, delirium was associated with a greater decrease in grey matter volume [B (95% confidence interval): -0.44% of intracranial volume (-0.82 to -0.06, P = 0.02)]. Furthermore, within the patients, delirium was associated with a non-significantly increased risk of a new postoperative brain infarct [logistic regression: odds ratio (95% confidence interval): 2.8 (0.7-11.1), P = 0.14]. Our study was the first to investigate the association between delirium and preoperative to postoperative brain volume changes, suggesting that delirium is associated with increased progression of grey matter volume loss.

BACKGROUND AND PURPOSE: The clinical relevance of cortical microinfarcts has recently been established; however, studies on microinfarcts in the deep gray matter are lacking. We examined the risk factors and MR imaging correlates of microinfarcts in the deep gray matter on 7T MR imaging and their relation to cognitive functioning.\MATERIALS AND METHODS: Within the Second Manifestations of ARTerial disease?Magnetic Resonance (SMART-MR) study, 213 patients (mean age, 68 [SD, 8]?years) had a risk-factor assessment, 7T and 1.5T brain MR imaging, and a cognitive examination. Microinfarcts on 7T MR imaging were defined as lesions of < 5 mm. Regression models were used to examine the age-adjusted associations among risk factors, MR imaging markers, and microinfarcts. Cognitive function was summarized as composite and domain-specific z scores. RESULTS: A total of 47 microinfarcts were found in 28 patients (13%), most commonly in the thalamus. Older age, history of stroke, hypertension, and intima-media thickness were associated with microinfarcts. On 1.5T MR imaging, cerebellar infarcts (relative risk = 2.75; 95% CI, 1.4?5.33) and lacunes in the white (relative risk = 3.28; 95% CI, 3.28?6.04) and deep gray matter (relative risk?= 3.06; 95% CI, 1.75?5.35) were associated with microinfarcts, and on 7T MR imaging cortical microinfarcts (relative risk = 2.33; 95% CI, 1.32?4.13). Microinfarcts were also associated with poorer global cognitive functioning (mean difference in the global z score between patients with multiple microinfarcts versus none = ?0.97; 95% CI, ?1.66 to ?0.28, P = .006) and across all cognitive domains. CONCLUSIONS: Microinfarcts in the deep gray matter on 7T MR imaging were associated with worse cognitive functioning and risk factors and MR imaging markers of small-vessel and large-vessel disease. Our findings suggest that microinfarcts in the deep gray matter may represent a novel imaging marker of vascular brain injury.

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.

Introduction/PurposeSystemic lupus erythematosus (SLE) is a chronic auto-immune disease with a broad spectrum of clinical presentations, including heterogeneous and uncommon neuropsychiatric (NP) syndromes. Accurate diagnosis of neuropsychiatric SLE (NPSLE) is challenging due to lack of clinically useful biomarkers. Despite structural brain abnormalities on MRI in NPSLE being a common finding, a robust link between structural abnormalities and NPSLE has not been established, thus their contribution to the distinction between NPSLE patients and patients in which the NP symptoms are not primarily attributed to SLE is limited. Self-supervised contrastive learning algorithms do not require labels, and have been shown to be useful in classification tasks in rare diseases with limited number of datasets. The aim of our study was to apply self-supervised contrastive learning on T1-weighted images acquired from a well-defined cohort of SLE patients to distinguish between SLE patients with NP symptoms due to the disease (NPSLE) or and SLE patients with similar symptoms due to other causes (non-NPSLE).Subjects and Methods163 patients were included. We used 3T MRI T1-weighted images registered to the MNI152 template. The training set comprised 68 non-NPSLE and 34 NPSLE patients. During the training procedure, we applied random geometric transformations (cropping, left-right flipping and rotations) between iterations to enrich our data sets. Our ML pipeline consisted of convolutional base encoder and linear projector. To test the classification task, the projector was removed and one linear layer was measured. We trained the encoder and projector with the Normalized Temperature-scaled Cross Entropy Loss (NT-xent) loss function. We performed a Monte Carlo validation that consisted of 6 repeated random sub-samplings each using a random selection of a small group of samples from each group.ResultsIn the 6 trials described above, between 79% and 83% of the patients were correctly classified as NPSLE or non-NPSLE. For a qualitative evaluation of spatial distribution of the common features found in the NPSLE population, Gradient-weighted Class Activation Maps (Grad-CAM) were examined voxel-wise. Thresholded Grad-CAM maps show areas of common features identified for the NPSLE cohort, with no such communality found for the non-NPSLE group.Discussion/conclusionThe self-supervised contrastive learning model was effective in capturing diagnostic brain MRI features from a limited but well-defined cohort of SLE patients with NP symptoms. The interpretation of the Grad-CAM results is not straightforward, but points to involvement of the lateral and third ventricles, periventricular white matter and basal cisterns. We believe that the common features found in the NPSLE population in this study indicate a combination of tissue loss, local atrophy and to some extent that of periventricular white matter lesions, which are commonly found in NPSLE patients and appear hypointense on T1-weighted images.

Background: We investigated the association between white matter hyperintensity location and depressive symptoms in a memoryclinic population using lesion–symptom mapping.Methods: We included 680 patients with vascular brain injury from the TRACE-VCI cohort (mean age ± standard deviation: 67 ± 8 years; 52% female): 168 patients with subjective cognitive decline, 164 with mild cognitive impairment and 348 with dementia. We assessed depressive symptoms using the Geriatric Depression Scale. We applied assumptionfree voxel-based lesion–symptom mapping, adjusted for age, sex, total white matter hyperintensity volume and multiple testing. Next, we applied exploratory region-of-interest linear regression analyses of major white matter tracts, with additional adjustment for diagnosis.Results: Voxel-based lesion–symptom mapping identified voxel clusters related to the Geriatric Depression Scale in the left corticospinal tract. Region-of-interest analyses showed no relation between white matter hyperintensity volume and the Geriatric Depression Scale, but revealed an interaction with diagnosis in the forceps minor, where larger regional white matter hyperintensity volume was associated with more depressive symptoms in subjective cognitive decline (β = 0.26, p < 0.05), but not in mild cognitive impairment or dementia.Limitations: We observed a lack of convergence of findings between voxel-based lesion–symptom mapping and region-of-interest analyses, which may have been due to small effect sizes and limited lesion coverage despite the large sample size. This warrants replication of our findings and further investigation in other cohorts.Conclusion: This lesion–symptom mapping study in depressive symptoms indicates the corticospinal tract and forceps minor as strategic tracts in which white matter hyperintensity is associated with depressive symptoms in memory-clinic patients with vascular brain injury. The impact of white matter hyperintensity on depressive symptoms is modest, but it appears to depend on the location of white matter hyperintensity and disease severity.

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.

Frailty is a common syndrome in older individuals that is associated with poor cognitive outcome. The underlying brain correlates of frailty are unclear. The aim of this study was to investigate the association between frailty and MRI features of cerebral small vessel disease in a group of non-demented older individuals. We included 170 participants who were classified as frail (n = 30), pre-frail (n = 85) or non-frail (n = 55). The association of frailty and white matter hyperintensity volume and shape features, lacunar infarcts and cerebral perfusion was investigated by regression analyses adjusted for age and sex. Frail and pre-frail participants were older, more often female and showed higher white matter hyperintensity volume (0.69 [95%-CI 0.08 to 1.31], p = 0.03 respectively 0.43 [95%-CI: 0.04 to 0.82], p = 0.03) compared to non-frail participants. Frail participants showed a non-significant trend, and pre-frail participants showed a more complex shape of white matter hyperintensities (concavity index: 0.04 [95%-CI: 0.03 to 0.08], p = 0.03; fractal dimensions: 0.07 [95%-CI: 0.00 to 0.15], p = 0.05) compared to non-frail participants. No between group differences were found in gray matter perfusion or in the presence of lacunar infarcts. In conclusion, increased white matter hyperintensity volume and a more complex white matter hyperintensity shape may be structural brain correlates of the frailty phenotype.

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.