White matter hyperintensity burden and collateral circulation in acute ischemic stroke with large artery occlusion.

OBJECTIVE: This study aimed to investigate the association between white matter hyperintensity (WMH) burden and pial collaterals in acute strokes caused by intracranial large artery occlusion treated with mechanical thrombectomy in the anterior circulation, focusing on stroke subtypes. METHODS: Consecutive patients undergoing mechanical thrombectomy between December 2019 and June 2022 were retrospectively screened. The Fazekas scale assessed WMH burden. Pial collaterals were categorized as either poor (0-2) or good (3-4) based on the Higashida score. A multivariable analysis was used to determine the relationship between WMH burden and pial collaterals. Subgroup analyses delved into associations stratified by stroke subtypes, namely cardioembolism (CE), tandem lesions (TLs), and intracranial atherosclerosis (ICAS). RESULTS: Of the 573 patients included, 274 (47.8%) demonstrated poor pial collaterals. Multivariable regression indicated a strong association between extensive WMH burden (Fazekas score of 3-6) and poor collaterals [adjusted OR 3.04, 95% CI 1.70-5.46, P < 0.001]. Additional independent predictors of poor collaterals encompassed ICAS-related occlusion (aOR 0.26, 95% CI 0.09-0.76, P = 0.014), female sex (aOR 0.63, 95% CI 0.41-0.96, P = 0.031), and baseline Alberta Stroke Program Early Computed Tomography scores (aOR 0.80, 95% CI 0.74-0.88, P < 0.001). Notably, an interaction between extensive WMH burden and stroke subtypes was observed in predicting poor collaterals (P = 0.001), being pronounced for CE (adjusted OR 2.30, 95% CI 1.21-4.37) and TLs (adjusted OR 5.09, 95% CI 2.32-11.16), but was absent in ICAS (adjusted OR 1.24, 95% CI 0.65-2.36). CONCLUSIONS: Among patients treated with mechanical thrombectomy for anterior circulation large artery occlusion, extensive WMH burden correlates with poor pial collaterals in embolic occlusion cases (CE and TLs), but not in ICAS-related occlusion.

Young versus older subject diffusion magnetic resonance imaging data for virtual white matter lesion tractography.

White matter hyperintensity (WMH) lesions on T2 fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) and changes in adjacent normal-appearing white matter can disrupt computerized tract reconstruction and result in inaccurate measures of structural brain connectivity. The virtual lesion approach provides an alternative strategy for estimating structural connectivity changes due to WMH. To assess the impact of using young versus older subject diffusion MRI data for virtual lesion tractography, we leveraged recently available diffusion MRI data from the Human Connectome Project (HCP) Lifespan database. Neuroimaging data from 50 healthy young (39.2 ± 1.6 years) and 46 healthy older (74.2 ± 2.5 years) subjects were obtained from the publicly available HCP-Aging database. Three WMH masks with low, moderate, and high lesion burdens were extracted from the WMH lesion frequency map of locally acquired FLAIR MRI data. Deterministic tractography was conducted to extract streamlines in 21 WM bundles with and without the WMH masks as regions of avoidance in both young and older cohorts. For intact tractography without virtual lesion masks, 7 out of 21 WM pathways showed a significantly lower number of streamlines in older subjects compared to young subjects. A decrease in streamline count with higher native lesion burden was found in corpus callosum, corticostriatal tract, and fornix pathways. Comparable percentages of affected streamlines were obtained in young and older groups with virtual lesion tractography using the three WMH lesion masks of increasing severity. We conclude that using normative diffusion MRI data from young subjects for virtual lesion tractography of WMH is, in most cases, preferable to using age-matched normative data.

Effect of leukoaraiosis on collateral circulation in acute ischemic stroke treated with endovascular therapy: a meta-analysis.

BACKGROUND AND OBJECTIVE: The recruitment of collateral circulation correlates with a balance of the microvasculature. Uncertainty remains to be made about the association of leukoaraiosis with leptomeningeal collaterals. To explore the effect of leukoaraiosis on leptomeningeal collaterals in patients treated with endovascular therapy. METHODS: Observational studies exploring the correlation between leukoaraiosis and leptomeningeal collaterals in large vessel occlusion treated with endovascular therapy were searched from PubMed, EMBASE, and Cochrane Libraries databases. Two independent reviewers retrieved eligible literature, extracted purpose-related data, and utilized the Newcastle-Ottawa Scale to evaluate the risk of bias. A Mantel-Haenszel method was used to calculate the odds ratio (OR). Meta-regression and subgroup analyses were conducted to clarify heterogeneity. RESULTS: Data from 10 studies with 1606 patients were extracted for pooled analysis. Compared to non-severe leukoaraiosis, patients with severe leukoaraiosis showed significant relevance to poor leptomeningeal collaterals (OR, 2.13; 95% confidence interval [1.27-3.57]; P = 0.004). Meta-regression indicated that sample size (coefficient = -0.007299, P = 0.035) and the number of female patients (coefficient = -0.0174709, P = 0.020) were sources of heterogeneity. Furthermore, all of the countries (USA versus France versus China, Q = 3.67, P = 0.159), various assessment scales of leukoaraiosis (the Fazekas scale versus Non-Fazekas scales, Q = 0.77, P = 0.379), and different imaging methods of leukoaraiosis (computed tomography versus magnetic resonance imaging, Q = 2.12, P = 0.146) and leptomeningeal collaterals (computed tomography angiography versus digital subtraction angiography, Q = 1.21, P = 0.271) showed no contribution to the effect size. CONCLUSION: Severe leukoaraiosis is associated with poor leptomeningeal collaterals in patients treated with endovascular therapy. Further studies may focus on whether the finding applies to different stroke subtypes.

Pre-existing brain damage and association between severity and prior cognitive impairment in ischemic stroke patients.

BACKGROUND: We evaluated whether pre-existing brain damage may explain greater severity in cognitively-impaired patients with ischemic stroke (IS). METHODS: IS patients were retrieved from the population-based registry of Dijon, France. Pre-existing damage (leukoaraiosis, old vascular brain lesions, cortical and central brain atrophy) was assessed on initial CT-scan. Association between prestroke cognitive status defined as no impairment, mild cognitive impairment (MCI), or dementia, and clinical severity at IS onset assessed with the NIHSS score was evaluated using ordinal regression analysis. Mediation analysis was performed to assess pre-existing brain lesions as mediators of the relationship between cognitive status and severity. RESULTS: Among the 916 included patients (mean age 76.8 ± 15.0 years, 54.3% women), those with pre-existing MCI (n = 115, median NIHSS [IQR]: 6 [2-15]) or dementia (n = 147, median NIHSS: 6 [3-15]) had a greater severity than patients without (n = 654, median NIHSS: 3 [1-9]) in univariate analysis (OR=1.69; 95% CI: 1.18-2.42, p = 0.004, and OR=2.06; 95% CI: 1.49-2.84, p < 0.001, respectively). Old cortical lesion (OR=1.53, p = 0.002), central atrophy (OR=1.41, p = 0.005), cortical atrophy (OR=1.90, p < 0.001) and moderate (OR=1.41, p = 0.005) or severe (OR=1.84, p = 0.002) leukoaraiosis were also associated with greater severity. After adjustments, pre-existing MCI (OR=1.52; 95% CI: 1.03-2.26, p = 0.037) or dementia (OR=1.94; 95% CI: 1.32-2.86, p = 0.001) remained associated with higher severity at IS onset, independently of confounding factors including imaging variables. Association between cognitive impairment and severity was not mediated by pre-existing visible brain damages. CONCLUSION: Impaired brain ischemic tolerance in IS patients with prior cognitive impairment could involve other mechanisms than pre-existing visible brain damage.

White matter hyperintensities segmentation using an ensemble of neural networks.

White matter hyperintensities (WMHs) represent the most common neuroimaging marker of cerebral small vessel disease (CSVD). The volume and location of WMHs are important clinical measures. We present a pipeline using deep fully convolutional network and ensemble models, combining U-Net, SE-Net, and multi-scale features, to automatically segment WMHs and estimate their volumes and locations. We evaluated our method in two datasets: a clinical routine dataset comprising 60 patients (selected from Chinese National Stroke Registry, CNSR) and a research dataset composed of 60 patients (selected from MICCAI WMH Challenge, MWC). The performance of our pipeline was compared with four freely available methods: LGA, LPA, UBO detector, and U-Net, in terms of a variety of metrics. Additionally, to access the model generalization ability, another research dataset comprising 40 patients (from Older Australian Twins Study and Sydney Memory and Aging Study, OSM), was selected and tested. The pipeline achieved the best performance in both research dataset and the clinical routine dataset with DSC being significantly higher than other methods (p < .001), reaching .833 and .783, respectively. The results of model generalization ability showed that the model trained on the research dataset (DSC = 0.736) performed higher than that trained on the clinical dataset (DSC = 0.622). Our method outperformed widely used pipelines in WMHs segmentation. This system could generate both image and text outputs for whole brain, lobar and anatomical automatic labeling WMHs. Additionally, software and models of our method are made publicly available at https://www.nitrc.org/projects/what_v1.

Deep Bayesian networks for uncertainty estimation and adversarial resistance of white matter hyperintensity segmentation.

White matter hyperintensities (WMHs) are frequently observed on structural neuroimaging of elderly populations and are associated with cognitive decline and increased risk of dementia. Many existing WMH segmentation algorithms produce suboptimal results in populations with vascular lesions or brain atrophy, or require parameter tuning and are computationally expensive. Additionally, most algorithms do not generate a confidence estimate of segmentation quality, limiting their interpretation. MRI-based segmentation methods are often sensitive to acquisition protocols, scanners, noise-level, and image contrast, failing to generalize to other populations and out-of-distribution datasets. Given these concerns, we propose a novel Bayesian 3D convolutional neural network with a U-Net architecture that automatically segments WMH, provides uncertainty estimates of the segmentation output for quality control, and is robust to changes in acquisition protocols. We also provide a second model to differentiate deep and periventricular WMH. Four hundred thirty-two subjects were recruited to train the CNNs from four multisite imaging studies. A separate test set of 158 subjects was used for evaluation, including an unseen multisite study. We compared our model to two established state-of-the-art techniques (BIANCA and DeepMedic), highlighting its accuracy and efficiency. Our Bayesian 3D U-Net achieved the highest Dice similarity coefficient of 0.89 ± 0.08 and the lowest modified Hausdorff distance of 2.98 ± 4.40 mm. We further validated our models highlighting their robustness on "clinical adversarial cases" simulating data with low signal-to-noise ratio, low resolution, and different contrast (stemming from MRI sequences with different parameters). Our pipeline and models are available at: https://hypermapp3r.readthedocs.io.

Associations of white matter hyperintensities with networks of gray matter blood flow and volume in midlife adults: A coronary artery risk development in young adults magnetic resonance imaging substudy.

White matter hyperintensities (WMHs) are emblematic of cerebral small vessel disease, yet effects on the brain have not been well characterized at midlife. Here, we investigated whether WMH volume is associated with brain network alterations in midlife adults. Two hundred and fifty-four participants from the Coronary Artery Risk Development in Young Adults study were selected and stratified by WMH burden into Lo-WMH (mean age = 50 ± 3.5 years) and Hi-WMH (mean age = 51 ± 3.7 years) groups of equal size. We constructed group-level covariance networks based on cerebral blood flow (CBF) and gray matter volume (GMV) maps across 74 gray matter regions. Through consensus clustering, we found that both CBF and GMV covariance networks partitioned into modules that were largely consistent between groups. Next, CBF and GMV covariance network topologies were compared between Lo- and Hi-WMH groups at global (clustering coefficient, characteristic path length, global efficiency) and regional (degree, betweenness centrality, local efficiency) levels. At the global level, there were no between-group differences in either CBF or GMV covariance networks. In contrast, we found between-group differences in the regional degree, betweenness centrality, and local efficiency of several brain regions in both CBF and GMV covariance networks. Overall, CBF and GMV covariance analyses provide evidence that WMH-related network alterations are present at midlife.

Validation of an automatic tool for the rapid measurement of brain atrophy and white matter hyperintensity: QyScore®.

OBJECTIVES: QyScore® is an imaging analysis tool certified in Europe (CE marked) and the US (FDA cleared) for the automatic volumetry of grey and white matter (GM and WM respectively), hippocampus (HP), amygdala (AM), and white matter hyperintensity (WMH). Here we compare QyScore® performances with the consensus of expert neuroradiologists. METHODS: Dice similarity coefficient (DSC) and the relative volume difference (RVD) for GM, WM volumes were calculated on 50 3DT1 images. DSC and the F1 metrics were calculated for WMH on 130 3DT1 and FLAIR images. For each index, we identified thresholds of reliability based on current literature review results. We hypothesized that DSC/F1 scores obtained using QyScore® markers would be higher than the threshold. In contrast, RVD scores would be lower. Regression analysis and Bland-Altman plots were obtained to evaluate QyScore® performance in comparison to the consensus of three expert neuroradiologists. RESULTS: The lower bound of the DSC/F1 confidence intervals was higher than the threshold for the GM, WM, HP, AM, and WMH, and the higher bounds of the RVD confidence interval were below the threshold for the WM, GM, HP, and AM. QyScore®, compared with the consensus of three expert neuroradiologists, provides reliable performance for the automatic segmentation of the GM and WM volumes, and HP and AM volumes, as well as WMH volumes. CONCLUSIONS: QyScore® represents a reliable medical device in comparison with the consensus of expert neuroradiologists. Therefore, QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases. KEY POINTS: • QyScore® provides reliable automatic segmentation of brain structures in comparison with the consensus of three expert neuroradiologists. • QyScore® automatic segmentation could be performed on MRI images using different vendors and protocols of acquisition. In addition, the fast segmentation process saves time over manual and semi-automatic methods. • QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases.

A deep learning algorithm for white matter hyperintensity lesion detection and segmentation.

PURPOSE: White matter hyperintensity (WMHI) lesions on MR images are an important indication of various types of brain diseases that involve inflammation and blood vessel abnormalities. Automated quantification of the WMHI can be valuable for the clinical management of patients, but existing automated software is often developed for a single type of disease and may not be applicable for clinical scans with thick slices and different scanning protocols. The purpose of the study is to develop and validate an algorithm for automatic quantification of white matter hyperintensity suitable for heterogeneous MRI data with different disease types. METHODS: We developed and evaluated "DeepWML", a deep learning method for fully automated white matter lesion (WML) segmentation of multicentre FLAIR images. We used MRI from 507 patients, including three distinct white matter diseases, obtained in 9 centres, with a wide range of scanners and acquisition protocols. The automated delineation tool was evaluated through quantitative parameters of Dice similarity, sensitivity and precision compared to manual delineation (gold standard). RESULTS: The overall median Dice similarity coefficient was 0.78 (range 0.64 ~ 0.86) across the three disease types and multiple centres. The median sensitivity and precision were 0.84 (range 0.67 ~ 0.94) and 0.81 (range 0.64 ~ 0.92), respectively. The tool's performance increased with larger lesion volumes. CONCLUSION: DeepWML was successfully applied to a wide spectrum of MRI data in the three white matter disease types, which has the potential to improve the practical workflow of white matter lesion delineation.

A pilot study of the association between leukoaraiosis and cerebral atherosclerosis using synthetic magnetic resonance imaging.

BACKGROUND: Leukoaraiosis is a type of lesion characterized by tissue rarefaction or myelin pallor resulting from axons loss and gliosis. Synthetic magnetic resonance imaging (MRI) could yield quantitative T1, T2, proton density (PD) values of leukoaraiosis in addition to information on the volume of the lesion. PURPOSE: To investigate the feasibility of quantifying leukoaraiosis using synthetic MRI and to explore the association between leukoaraiosis and cerebral small vascular diseases and cerebral atherosclerosis. MATERIAL AND METHODS: Patients with acute ischemic stroke were enrolled in this study. All participants underwent a conventional T2-weighted image, brain volume, CUBE fluid attenuated inversion recovery, and synthetic MRI acquisition using a 3.0-T MR system. A time-of-flight magnetic resonance angiography was also obtained. We evaluated the T1, T2, PD values and leukoaraiosis volume. RESULTS: Analysis of the leukoaraiosis volume ratios demonstrated a positive association with T2 values, a negative association with T1 values, and no association with PD values. Leukoaraiosis volume ratios were independently correlated with age (P < 0.001), lacunes (P = 0.022), and cerebral microbleeds (P = 0.010). A statistical association was found between both age (P < 0.001) and lacunes (P = 0.047) and leukoaraiosis T2 values. CONCLUSION: Synthetic MRI may enhance the evaluation of leukoaraiosis, in addition to providing information on its volume. Leukoaraiosis may represent a type of cerebral small vascular disease rather than cerebral atherosclerosis and may share the same pathological mechanism as lacunes and cerebral microbleeds.

Quantitative Analysis of White Matter Hyperintensity: Comparison of Magnetic Resonance Imaging Image Analysis Software.

OBJECTIVE: White matter hyperintensity (WMH), defined as abnormal signals on magnetic resonance imaging (MRI), is an important clinical indicator of aging and dementia. Although MRI image analysis software can automatically detect WMH, the quantitative accuracy of periventricular hyperintensity (PVH) and deep white matter hyperintensity (DWMH) is unknown. MATERIALS AND METHODS: This study was a sub-analysis of MRI data from an ongoing hospital-based prospective cohort study (the Gimlet study). Between March 2016 and March 2017, we enrolled patients who visited our memory clinic and agreed to undergo medical assessments of cognitive function and fecal examination to study the gut microbiome. Participants with a history of stroke were excluded. WMH was independently quantitatively analyzed using two MRI imaging analysis software modalities: SNIPER and FUSION. Intraclass correlation coefficients and the mean difference in volume were calculated and compared between modalities. RESULTS: The data of 87 patients (49 women, mean age 74.8 ± 7.9 years) were analyzed. Both total WMH and DWMH volumes obtained using FUSION were greater (p < 0.001), and PVH volume was smaller (p < 0.001) than those obtained using SNIPER. Intraclass correlation coefficients for the lesion measurements of WMH, PVH, and DWMH between the different software were 0.726 (p < 0.001), 0.673 (p < 0.001), and 0.048 (p = 0.231), respectively. CONCLUSIONS: There were significant differences in the quantitative data of WMH between the two MRI imaging analysis software modalities. Thus, care should be taken for quantitative assessments of WMH.

Central vein sign: comparison of multiple sclerosis and leukoaraiosis.

BACKGROUND: Leukoaraiosis produces white matter lesions (WML) similar to multiple sclerosis (MS) on brain magnetic resonance imaging (MRI), and the distinction between these two conditions is difficult radiologically. This study aimed to investigate the role of the central vein sign (CVS) in susceptibility-weighted imaging (SWI) sequence in distinguishing MS lesions from leukoaraiosis lesions in Turkish population. METHODS: In this prospective study, axial SWI and sagittal three-dimensional fluid-attenuated inversion recovery (3DFLAIR) were obtained in 374 consecutive patients. The study consisted of 169 (89 MS patients, 80 patients with leukoaraiosis) patients according to the inclusion and exclusion criteria. Two observers evaluated MR images by consensus, and observers were unaware of the patient's clinical findings. Locations (periventricular, juxtacortical, and deep white matter) and the presence of CVS were investigated for each of the lesions. Differences between patients in the leukoaraiosis and MS groups were investigated using the Mann-Whitney U test or chi-square analysis. In addition, receiver operating characteristic (ROC) analysis was used to analyze the diagnostic performance of CVS. RESULTS: A total of 1908 WMLs (1265 MS lesions, 643 leukoaraiosis) were detected in 169 patients. The CVS was significantly higher in the MS lesions (p < 0.001). The CVS positivity rate in periventricular WMLs was higher than in juxtacortical WMLs or deep WMLs, both for all patients and for patients with MS (p < 0.001). The area under the curve (AUC) of the ROC analysis was 0.88 (95% confidence interval 0.83-0.93) for CVS in the distinction of MS lesions and leukoaraiosis. DISCUSSION: The presence of CVS in the SWI sequence can be used as an auxiliary finding for the diagnosis of MS in the differentiation of MS and leukoaraiosis lesions.

White matter hyperintensities segmentation using the ensemble U-Net with multi-scale highlighting foregrounds.

White matter hyperintensities (WMHs) are abnormal signals within the white matter region on the human brain MRI and have been associated with aging processes, cognitive decline, and dementia. In the current study, we proposed a U-Net with multi-scale highlighting foregrounds (HF) for WMHs segmentation. Our method, U-Net with HF, is designed to improve the detection of the WMH voxels with partial volume effects. We evaluated the segmentation performance of the proposed approach using the Challenge training dataset. Then we assessed the clinical utility of the WMH volumes that were automatically computed using our method and the Alzheimer's Disease Neuroimaging Initiative database. We demonstrated that the U-Net with HF significantly improved the detection of the WMH voxels at the boundary of the WMHs or in small WMH clusters quantitatively and qualitatively. Up to date, the proposed method has achieved the best overall evaluation scores, the highest dice similarity index, and the best F1-score among 39 methods submitted on the WMH Segmentation Challenge that was initially hosted by MICCAI 2017 and is continuously accepting new challengers. The evaluation of the clinical utility showed that the WMH volume that was automatically computed using U-Net with HF was significantly associated with cognitive performance and improves the classification between cognitive normal and Alzheimer's disease subjects and between patients with mild cognitive impairment and those with Alzheimer's disease. The implementation of our proposed method is publicly available using Dockerhub (https://hub.docker.com/r/wmhchallenge/pgs).

Painting by lesions: White matter hyperintensities disrupt functional networks and global cognition.

White matter hyperintensities (WMH) are a prominent feature of cerebral small vessel disease and are associated with cognitive impairment. These deficits in cognition may be caused by the disruption of large-scale functional networks due to the presence of WMHs. However, knowledge regarding the relevance of these lesions on functional networks remains inconclusive. These inconsistencies may derive from issues with interpreting functional imaging data from clinical populations. Lesion network mapping is a technique that allows the overlaying of lesions from a patient population to the functional connectivity of a human connectome derived from healthy adults. This allows researchers to identify functional networks that would be disrupted in a healthy population should the WMHs seen in cerebral small vessel disease be present. We hypothesized that the extent to which these functional networks are disrupted by WMHs is associated with cognitive performance in older adults with cerebral small vessel disease. This cross-sectional study combined baseline data from four studies to create a total sample of 164 older adults (aged ≥55) from metropolitan Vancouver with cerebral small vessel disease. Using lesion network mapping, we assessed the percentage overlap between voxels functionally connected with both the WMHs (lesion network) and five common functional networks: (1) visual; (2) dorsal attention; (3) ventral attention; (4) sensorimotor; and (5) frontoparietal. Cognition was assessed using: (1) Montreal Cognitive Assessment (MoCA); (2) Stroop Colour Word Test (3-2); (3) Trail Making Tests (Part B-A); and (4) Digit Symbol Substitution Test. A One-Way ANOVA and Tukey post-hoc tests were performed to identify the functional networks with greatest percentage overlap with the lesion network. Partial correlations controlling for age were used to analyse whether the extent of the overlap between the lesion and functional networks was associated with poorer cognition. The visual, ventral attention, and frontoparietal networks had significantly greater overlap with the lesion network. After controlling for multiple comparisons, level of lesion network overlap with both the sensorimotor network (p<.001) and ventral attention network (p <. 001) was significantly correlated with MoCA score. Thus, the greater the disruption to the sensorimotor and ventral attention networks, the poorer the global cognition. Our results reveal that the visual, ventral attention, and frontoparietal networks are most vulnerable to disruptions stemming from WMHs. Additionally, we identified that disruption to the sensorimotor and ventral attention networks, as a result of WMHs, may underlie deficits in global cognition in older adults with cerebral small vessel disease.

White matter hyperintensities induce distal deficits in the connected fibers.

White matter hyperintensities (WMH) are common in elderly individuals and cause brain network deficits. However, it is still unclear how the global brain network is affected by the focal WMH. We aimed to investigate the diffusion of WMH-related deficits along the connecting white matters (WM). Brain magnetic resonance imaging data and neuropsychological evaluations of 174 participants (aged 74 ± 5 years) were collected and analyzed. For each participant, WMH lesions were segmented using a deep learning method, and 18 major WM tracts were reconstructed using automated quantitative tractography. The diffusion characteristics of distal WM tracts (with the WMH penumbra excluded) were calculated. Multivariable linear regression analysis was performed. We found that a high burden of tract-specific WMH was related to worse diffusion characteristics of distal WM tracts in a wide range of WM tracts, including the forceps major (FMA), forceps minor (FMI), anterior thalamic radiation (ATR), cingulum cingulate gyrus (CCG), corticospinal tract (CST), inferior longitudinal fasciculus (ILF), superior longitudinal fasciculus-parietal (SLFP), superior longitudinal fasciculus-temporal (SLFT), and uncinate fasciculus (UNC). Furthermore, a higher mean diffusivity (MD) of distal tracts was linked to worse attention and executive function in the FMI, right CCG, left ILF, SLFP, SLFT, and UNC. The effect of WMH on the microstructural integrity of WM tracts may propagate along tracts to distal regions beyond the penumbra and might eventually affect attention and executive function.

Beware of white matter hyperintensities causing systematic errors in FreeSurfer gray matter segmentations!

Volumetric estimates of subcortical and cortical structures, extracted from T1-weighted MRIs, are widely used in many clinical and research applications. Here, we investigate the impact of the presence of white matter hyperintensities (WMHs) on FreeSurfer gray matter (GM) structure volumes and its possible bias on functional relationships. T1-weighted images from 1,077 participants (4,321 timepoints) from the Alzheimer's Disease Neuroimaging Initiative were processed with FreeSurfer version 6.0.0. WMHs were segmented using a previously validated algorithm on either T2-weighted or Fluid-attenuated inversion recovery images. Mixed-effects models were used to assess the relationships between overlapping WMHs and GM structure volumes and overall WMH burden, as well as to investigate whether such overlaps impact associations with age, diagnosis, and cognitive performance. Participants with higher WMH volumes had higher overlaps with GM volumes of bilateral caudate, cerebral cortex, putamen, thalamus, pallidum, and accumbens areas (p < .0001). When not corrected for WMHs, caudate volumes increased with age (p < .0001) and were not different between cognitively healthy individuals and age-matched probable Alzheimer's disease patients. After correcting for WMHs, caudate volumes decreased with age (p < .0001), and Alzheimer's disease patients had lower caudate volumes than cognitively healthy individuals (p < .01). Uncorrected caudate volume was not associated with ADAS13 scores, whereas corrected lower caudate volumes were significantly associated with poorer cognitive performance (p < .0001). Presence of WMHs leads to systematic inaccuracies in GM segmentations, particularly for the caudate, which can also change clinical associations. While specifically measured for the Freesurfer toolkit, this problem likely affects other algorithms.

Systemic oxidative stress and cognitive function in Parkinson's disease with different PWMH or DWMH lesions.

BACKGROUND: Parkinson's disease (PD), frequently accompanied by cognitive impairments, is associated with systemic oxidative stress and abnormal structural changes on brain images. We aimed to identify the correlation between systemic oxidative stress and cognitive function in PD patients with different periventricular white matter hyperintensities (PWMH) and deep white matter hyperintensities (DWMH). METHODS: A total of 146 participants with idiopathic PD underwent brain MRI, which revealed PWMH and DWMH. The number of lesions were evaluated using the Fazekas criteria. Systemic oxidative stress was determined as early or late phase changes in leukocyte apoptosis and its subsets by flow cytometry. Cognitive functions, including attention, executive function, memory, language, and visual space, were assessed. RESULTS: For different DWMH, the leukocyte apoptosis and its subsets were significantly different.. However, there were no significant differences in oxidative stress biomarkers in PD patients with different PWMH. Attention and memory were significantly decreased in patients with more advanced DWMH injuries. Attention, memory, and language were significantly impaired in patients with worse PWMH lesions. CONCLUSION: Significant oxidative stress biomarker alternations in PD patients with DWMH, but not PWMH, might be associated with white matter injury. Systemic inflammatory responses may contribute to deep white matter damage in PD. Further, more cognitive deficits were seen in PD patients with worse deep white matter lesions, especially in moderate to severe periventricular white matter injury. TRIAL REGISTRATION: Retrospective study.

Cerebral white matter diffusion properties and free-water with obstructive sleep apnea severity in older adults.

Characterizing the effects of obstructive sleep apnea (OSA) on the aging brain could be key in our understanding of neurodegeneration in this population. Our objective was to assess white matter properties in newly diagnosed and untreated adults with mild to severe OSA. Sixty-five adults aged 55 to 85 were recruited and divided into three groups: control (apnea-hypopnea index ≤5/hr; n = 18; 65.2 ± 7.2 years old), mild (>5 to ≤15 hr; n = 27; 64.2 ± 5.3 years old) and moderate to severe OSA (>15/hr; n = 20; 65.2 ± 5.5 years old). Diffusion tensor imaging metrics (fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity, and mean diffusivity) were compared between groups with Tract-Based Spatial Statistics within the white matter skeleton created by the technique. Groups were also compared for white matter hyperintensities volume and the free-water (FW) fraction. Compared with controls, mild OSA participants showed widespread areas of lower diffusivity (p < .05 corrected) and lower FW fraction (p < .05). Participants with moderate to severe OSA showed lower AD in the corpus callosum compared with controls (p < .05 corrected). No between-group differences were observed for FA or white matter hyperintensities. Lower white matter diffusivity metrics is especially marked in mild OSA, suggesting that even the milder form may lead to detrimental outcomes. In moderate to severe OSA, competing pathological responses might have led to partial normalization of diffusion metrics.

Understanding the association between psychomotor processing speed and white matter hyperintensity: A comprehensive multi-modality MR imaging study.

Cognitive processing speed is crucial for human cognition and declines with aging. White matter hyperintensity (WMH), a common sign of WM vascular damage in the elderly, is closely related to slower psychomotor processing speed. In this study, we investigated the association between WMH and psychomotor speed changes through a comprehensive assessment of brain structural and functional features. Multi-modal MRIs were acquired from 60 elderly adults. Psychomotor processing speeds were assessed using the Trail Making Test Part A (TMT-A). Linear regression analyses were performed to assess the associations between TMT-A and brain features, including WMH volumes in five cerebral regions, diffusivity parameters in the major WM tracts, regional gray matter volume, and brain activities across the whole brain. Hierarchical regression analysis was used to demonstrate the contribution of each index to slower psychomotor processing speed. Linear regression analysis demonstrated that WMH volume in the occipital lobe and fractional anisotropy of the forceps major, an occipital association tract, were associated with TMT-A. Besides, resting-state brain activities in the visual cortex connected to the forceps major were associated with TMT-A. Hierarchical regression showed fractional anisotropy of the forceps major and regional brain activities were significant predictors of TMT-A. The occurrence of WMH, combined with the disruption of passing-through fiber integrity and altered functional activities in areas connected by this fiber, are associated with a decline of psychomotor processing speed. While the causal relationship of this WMH-Tract-Function-Behavior link requires further investigation, this study enhances our understanding of these complex mechanisms.

Clinical and Diffusion Tensor Imaging to Evaluate Falls, Balance and Gait Dysfunction in Leukoaraiosis: an Observational, Prospective Cohort Study.

OBJECTIVE: To assess the correlation between leukoaraiosis (LA) and falls, to determine the risk factors for falls in patients with LA, and to detect specific white matter tracts are associated with the falls by using the diffusion tensor magnetic resonance imaging (DTI) screen. METHODS: For the elderly patients with LA, we collected demographic information and scores for the Tinetti Balance and Gait Scale, Berg Balance Scale, Timed up-and-go test, and Cognitive, Emotional, Sleep-related Scale. All the patients underwent DTI scanning and were followed up for 1 year. RESULTS: Ninety-four individuals were prospectively enrolled. After multivariable analyses, age, history of falls in the past year, antidepressants usage, and LA-Fazekas grade were reported to be risk factors for falls. In patients with severe LA, the fall incidence was higher than in those with mild LA. Tract-Based Spatial Statistics showed that fractional anisotropy values of the corpus callosum, cingulate gyrus, anterior limb of internal capsule, cerebral peduncle, anterior corona, and fronto-occipital fasciculus were significantly reduced in the patients who fell. The body of the corpus callosum and anterior corona radiate were significantly related to balance and gait function. CONCLUSIONS: Our findings indicated that age, history of falls in the past year, antidepressants usage, and LA-Fazekas grade were risk factors for falls in elderly patients with LA. Leukoaraiosis was relevant for falls, but LA severity had a threshold effect with falls. The loss of integrity of some white matter tracts might influence balance and gait function. The DTI had preeminent clinical application prospects for identifying fall risk in patients with LA.