Multimodal tract-based MRI metrics outperform whole brain markers in determining cognitive impact of small vessel disease-related brain injury

Preoperative brain volume loss is associated with postoperative delirium in advanced heart failure patients supported by left ventricular assist device

Delirium is a common neurological complication in patients with advanced heart failure (ADHF) following left ventricular assist device (LVAD) implantation, significantly impacting recovery. This study aimed to analyze non-contrast computed tomography (CT) scans of the brain in ADHF patients undergoing LVAD implantation to determine the association between pre-existing brain atrophy and postoperative delirium. A study involving 166 ADHF patients was conducted from March 2020 to July 2023. Non-contrast CT scans were analyzed using advanced quantitative neuroimaging techniques before implantation. The primary marker assessed was the lateral ventricle fraction (LVF), with secondary markers including cortical gray matter fraction (cGMF), white matter fraction (WMF), basal ganglia fraction (BGF), and thalamus fraction (TLF). A total of 56 patients (33%) experienced postoperative delirium within two weeks of implantation. Patients with delirium were older and exhibited greater brain atrophy, indicated by higher LVF and lower cGMF, WMF, BGF, and TLF values. The occurrence of delirium was strongly associated with age, and ventricular enlargement, primarily in the lateral ventricles. LVF effectively predicted delirium development, regardless of age. Preoperative brain volumetric analysis, particularly of the lateral ventricles, may be crucial in identifying patients at risk for postoperative delirium, enhancing postoperative management, and improving outcomes for LVAD recipients.

Enhancing cognitive performance prediction by white matter hyperintensity connectivity assessment

White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating brain health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. Lesion network mapping (LNM) enables us to infer if brain networks are connected to lesions and could be a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed LNM to test the following hypotheses: (i) LNM-informed markers surpass WMH volumes in predicting cognitive performance; and (ii) WMH contributing to cognitive impairment map to specific brain networks. We analysed cross-sectional data of 3485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in four cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity to 480 atlas-based grey and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. We compared the capacity of total and regional WMH volumes and LNM scores in predicting cognitive function using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention/executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater connectivity to WMH, in grey and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance. Measures of WMH-related brain network connectivity significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network integrity, particularly in attention-related brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders.

Cognitive impairment and amygdala subregion volumes in elderly with cerebral small vessel disease: A large prospective cohort study

Although the amygdala is associated with cognitive impairment resulting from cerebral small vessel disease, the relationship between alterations in amygdala structure and cerebral small vessel disease (CSVD) remains controversial. Given that the amygdala comprises several subregions, detecting subtle regional changes through total amygdala volume measurement is challenging. This study aimed to identify the patterns of amygdala subregion atrophy in cerebral small vessel disease patients and their relationship with cognitive impairment. A total of 114 participants diagnosed with cerebral small vessel disease and 129 healthy participants, aged 40 to 70, underwent 3 T magnetic resonance imaging scans. The amygdala subregions were automatically segmented using FreeSurfer. In the Propensity Score Matching (PSM)-matched cohort, Lasso regression was employed to identify subregions associated with cerebral small vessel disease, and restricted cubic splines (RCS) were used to explore their nonlinear relationship with cognitive abilities. Subsequently, multivariate linear regression models were used to investigate the impact of amygdala subregion volumes on various cognitive abilities. Compared to healthy controls (HC), the volume of the left cortical nucleus was significantly reduced in cerebral small vessel disease patients. The volume of the left cortical nucleus was significantly negatively correlated with cerebral small vessel disease progression, and atrophy in this region was also identified as an independent risk factor for decreased cognitive control and processing ability. Our findings suggest that patients with cerebral small vessel disease exhibit atrophy in specific amygdala subregions compared to healthy controls, which correlates with poorer cognitive control and processing abilities. These insights may advance our understanding of the pathogenesis of cerebral small vessel disease.

Enhancing Cognitive Performance Prediction through White Matter Hyperintensity Connectivity Assessment: A Multicenter Lesion Network Mapping Analysis of 3,485 Memory Clinic Patients

Introduction

White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating cognitive health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. We propose that lesion network mapping (LNM), enables to infer if brain networks are connected to lesions, and could be a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed this approach to test the following hypotheses: (1) LNM-informed markers surpass WMH volumes in predicting cognitive performance, and (2) WMH contributing to cognitive impairment map to specific brain networks.

Methods & results

We analyzed cross-sectional data of 3,485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in 4 cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity across 480 atlas-based gray and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. The capacity of total and regional WMH volumes and LNM scores in predicting cognitive function was compared using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention and executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater disruptive effects of WMH on regional connectivity, in gray and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance.

Conclusion

Measures of WMH-related brain network connectivity significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network effects, particularly in attentionrelated brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders.