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Background and purpose: Cerebral amyloid angiopathy (CAA) is characterized by β‐amyloid deposition in cortical and leptomeningeal arterioles, which might result from glymphatic dysfunction. The aim was to explore glymphatic function in CAA using the non‐invasive diffusion tensor image analysis along the perivascular space method. Methods: Sixty‐three patients with CAA were prospectively recruited together with seventy age‐ and sex‐matched normal controls. The Mini‐Mental State Examination and Montreal Cognitive Assessment were applied to screen global cognitive status. Magnetic resonance imaging scans were conducted to calculate the index for diffusivity along the perivascular space (ALPS index), and linear regression models were used to assess its relationships with cerebral small vessel disease (CSVD) markers, cognitive status and blood biomarkers. Cox proportional hazard models were applied to explore the role of the baseline ALPS index in disease recurrence. Results: Patients with CAA exhibited a lower ALPS index than controls globally (p < 0.001). In addition, a lower ALPS index was related to more enlarged perivascular space in basal ganglia (p = 0.026), more lacunes (p < 0.001), higher white matter hyperintensity Fazekas score (p = 0.049), elevated total magnetic resonance imaging burden of CSVD (p = 0.034) and lower Mini‐Mental State Examination (p = 0.001) as well as Montreal Cognitive Assessment (p < 0.001) in CAA. During a median follow‐up of 4.1 years, a higher ALPS index was associated with lower disease recurrence (p = 0.022). The ALPS index was also negatively correlated with serum soluble intercellular adhesion molecule‐1, neurofilament light and chitinase‐3‐like protein 1 in CAA. Conclusions: Patients with CAA showed impaired glymphatic function. The ALPS index was significantly related to CSVD severity, cognitive impairment and disease recurrence in CAA. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The aim of this study was to automatically detect focal cortical dysplasia (FCD) lesions in patients with extratemporal lobe epilepsy by relying on diffusion tensor imaging (DTI) and T2-weighted magnetic resonance imaging (MRI) data. We implemented an automated classifier using voxel-based multimodal features to identify gray and white matter abnormalities of FCD in patient cohorts. In addition to the commonly used T2-weighted image intensity feature, DTI-based features were also utilized. A Gaussian processes for machine learning (GPML) classifier was tested on 12 patients with FCD (8 with histologically confirmed FCD) scanned at 1.5 T and cross-validated using a leave-one-out strategy. Moreover, we compared the multimodal GPML paradigm's performance with that of single modal GPML and classical support vector machine (SVM). Our results demonstrated that the GPML performance on DTI-based features (mean AUC = 0.63) matches with the GPML performance on T2-weighted image intensity feature (mean AUC = 0.64). More promisingly, GPML yielded significantly improved performance (mean AUC = 0.76) when applying DTI-based features to multimodal paradigm. Based on the results, it can also be clearly stated that the proposed GPML strategy performed better and is robust to unbalanced dataset contrary to SVM that performed poorly (AUC = 0.69). Therefore, the GPML paradigm using multimodal MRI data containing DTI modality has promising result towards detection of the FCD lesions and provides an effective direction for future researches. [ABSTRACT FROM AUTHOR]