Dysfunctional Architecture Underlies White Matter Hyperintensities with and without Cognitive Impairment

The characteristic patterns of individual brain susceptibility networks underlie Alzheimer's disease and white matter hyperintensity-related cognitive impairment

Excessive iron accumulation in the brain cortex increases the risk of cognitive deterioration. However, interregional relationships (defined as susceptibility connectivity) of local brain iron have not been explored, which could provide new insights into the underlying mechanisms of cognitive decline. Seventy-six healthy controls (HC), 58 participants with mild cognitive impairment due to probable Alzheimer's disease (MCI-AD) and 66 participants with white matter hyperintensity (WMH) were included. We proposed a novel approach to construct a brain susceptibility network by using Kullback‒Leibler divergence similarity estimation from quantitative susceptibility mapping and further evaluated its topological organization. Moreover, sparse logistic regression (SLR) was applied to classify MCI-AD from HC and WMH with normal cognition (WMH-NC) from WMH with MCI (WMH-MCI).The altered susceptibility connectivity in the MCI-AD patients indicated that relatively more connectivity was involved in the default mode network (DMN)-related and visual network (VN)-related connectivity, while more altered DMN-related and subcortical network (SN)-related connectivity was found in the WMH-MCI patients. For the HC vs. MCI-AD classification, the features selected by the SLR were primarily distributed throughout the DMN-related and VN-related connectivity (accuracy = 76.12%). For the WMH-NC vs. WMH-MCI classification, the features with high appearance frequency were involved in SN-related and DMN-related connectivity (accuracy = 84.85%). The shared and specific patterns of the susceptibility network identified in both MCI-AD and WMH-MCI may provide a potential diagnostic biomarker for cognitive impairment, which could enhance the understanding of the relationships between brain iron burden and cognitive decline from a network perspective.

Effect of Menopause Status on Brain Perfusion Hemodynamics

BACKGROUND

The menopause transition is associated with an increasing risk of cerebrovascular disorders. However, the direct effect of menopause status on brain perfusion hemodynamics remains unclear. This study aimed to explore the influence of menopause status on cerebral blood flow (CBF) using arterial spin labeling magnetic resonance imaging.

METHODS

In this cross-sectional study, 185 subjects underwent arterial spin labeling magnetic resonance imaging at a hospital in China between September 2020 and December 2022, including 38 premenopausal women (mean age, 47.74±2.02 years), 42 perimenopausal women (mean age, 50.62±3.15 years), 42 postmenopausal women (mean age, 54.02±4.09 years), and 63 men (mean age, 52.70±4.33 years) of a similar age range. Mean CBF values in the whole brain, gray matter, white matter, cortical gray matter, subcortical gray matter, juxtacortical white matter, deep white matter, and periventricular white matter were extracted. ANCOVA was used to compare mean CBF among the 4 groups, controlling for confounding factors. Student t test was applied to compare mean CBF between the 3 female groups and age-matched males, respectively. Multivariable regression analysis was used to analysis the effect of age, sex, and menopause status on the CBF of the whole brain, gray matter, white matter, and subregions.

RESULTS

Perimenopausal and postmenopausal women showed a higher proportion of white matter hyperintensities compared with the other 2 groups (P<0.001). Premenopausal women exhibited higher CBF in the whole brain, gray matter, white matter, and subregions, compared with perimenopausal, postmenopausal women and men (P≤0.001). Multivariable regression analysis demonstrated significant effect of age and insignificant effect of sex on CBF for all participants. In addition, menopause status and the interaction between age and menopause status on CBF of whole brain, gray matter, white matter, and the subregions were observed in female participants, except for the deep and periventricular white matter regions, with premenopausal women exhibited a slight increase in CBF with age, while perimenopausal and postmenopausal women exhibited declines in CBF with age.

CONCLUSIONS

The current findings suggest that alterations of brain perfusion hemodynamics begin during the perimenopause period, which may be due to the increased burden of white matter hyperintensities.

Free Water MR Imaging of White Matter Microstructural Changes is a Sensitive Marker of Amyloid Positivity in Alzheimer's Disease

BACKGROUND

Extracellular free water (FW) resulting from white matter degeneration limits the sensitivity of diffusion tensor imaging (DTI) in predicting Alzheimer's disease (AD).

PURPOSE

To evaluate the sensitivity of FW-DTI in detecting white matter microstructural changes in AD. To validate the effectiveness of FW-DTI indices to predict amyloid-beta (Aβ) positivity in mild cognitive impairment (MCI) subtypes.

STUDY TYPE

Retrospective.

POPULATION

Thirty-eight Aβ-negative cognitively healthy (CH) controls (68.74 ± 8.28 years old, 55% female), 15 Aβ-negative MCI patients (MCI-n) (68.87 ± 8.83 years old, 60% female), 29 Aβ-positive MCI patients (MCI-p) (73.03 ± 7.05 years old, 52% female), and 29 Aβ-positive AD patients (72.93 ± 9.11 years old, 55% female).

FIELD STRENGTH/SEQUENCE

3.0T; DTI, T1 -weighted, T2 -weighted, T2 star-weighted angiography, and Aβ PET (18 F-florbetaben or 11 C-PIB).

ASSESSMENT

FW-corrected and standard diffusion indices were analyzed using trace-based spatial statistics. Area under the curve (AUC) in distinguishing MCI subtypes were compared using support vector machine (SVM).

STATISTICAL TESTS

Chi-squared test, one-way analysis of covariance, general linear regression analyses, nonparametric permutation tests, partial Pearson's correlation, receiver operating characteristic curve analysis, and linear SVM. A P value <0.05 was considered statistically significant.

RESULTS

Compared with CH/MCI-n/MCI-p, AD showed significant change in tissue compartment indices of FW-DTI. No difference was found in the FW index among pair-wise group comparisons (the minimum FWE-corrected P = 0.114). There was a significant association between FW-DTI indices and memory and visuospatial function. The SVM classifier with tissue radial diffusivity as an input feature had the best classification performance of MCI subtypes (AUC = 0.91), and the classifying accuracy of FW-DTI was all over 89.89%.

DATA CONCLUSION

FW-DTI indices prove to be potential biomarkers of AD. The classification of MCI subtypes based on SVM and FW-DTI indices has good accuracy and could help early diagnosis.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Altered static and dynamic functional network connectivity related to cognitive decline in individuals with white matter hyperintensities

White matter hyperintensities (WMH) of assumed vascular origin are common in elderly individuals and are closely associated with cognitive decline. However, the underlying neural mechanisms of WMH-related cognitive impairment remain unclear. After strict screening, 59 healthy controls (HC, n = 59), 51 patients with WMH and normal cognition (WMH-NC, n = 51) and 68 patients with WMH and mild cognitive impairment (WMH-MCI, n = 68) were included in the final analyses. All individuals underwent multimodal magnetic resonance imaging (MRI) and cognitive evaluations. We investigated the neural mechanism underlying WMH-related cognitive impairment based on static and dynamic functional network connectivity (sFNC and dFNC) approaches. Finally, the support vector machine (SVM) method was performed to identify WMH-MCI individuals. The sFNC analysis indicated that functional connectivity within the visual network (VN) could mediate the impairment of information processing speed related to WMH (indirect effect: 0.24; 95% CI: 0.03, 0.88 and indirect effect: 0.05; 95% CI: 0.001, 0.14). WMH may regulate the dFNC between the higher-order cognitive network and other networks and enhance the dynamic variability between the left frontoparietal network (lFPN) and the VN to compensate for the decline in high-level cognitive functions. The SVM model achieved good prediction ability for WMH-MCI patients based on the above characteristic connectivity patterns. Our findings shed light on the dynamic regulation of brain network resources to maintain cognitive processing in individuals with WMH. Crucially, dynamic reorganization of brain networks could be regarded as a potential neuroimaging biomarker for identifying WMH-related cognitive impairment.

Subsystem mechanisms of default mode network underlying white matter hyperintensity-related cognitive impairment

Functional changes of default mode network (DMN) have been proven to be closely associated with white matter hyperintensity (WMH) related cognitive impairment (CI). However, subsystem mechanisms of DMN underlying WMH-related CI remain unclear. The present study recruited WMH patients (n = 206) with mild CI and normal cognition, as well as healthy controls (HC, n = 102). Static/dynamic functional connectivity (FC) of the DMN's three subsystems were calculated using resting-state functional MRI. K-means clustering analyses were performed to extract distinct dynamic connectivity states. Compared with the WMH-NC group, the WMH-MCI group displayed lower static FC within medial temporal lobe (MTL) and core subsystem, between core-MTL subsystem, as well as between core and dorsal medial prefrontal cortex subsystem. All these static alterations were positively associated with information processing speed (IPS). Regarding dynamic FC, the WMH-MCI group exhibited higher dynamic FC within MTL subsystem than the HC and WMH-NC groups. Altered dynamic FC within MTL subsystem mediated the relationship between WMH and memory span (indirect effect: -0.2251, 95% confidence interval [-0.6295, -0.0267]). Additionally, dynamic FCs of DMN subsystems could be clustered into two recurring states. For dynamic FCs within MTL subsystem, WMH-MCI subjects exhibited longer mean dwell time (MDT) and higher reoccurrence fraction (RF) in a sparsely connected state (State 2). Altered MDT and RF in State 2 were negatively associated with IPS. Taken together, these findings indicated static/dynamic FC of DMN subsystems can provide relevant information on cognitive decline from different aspects, which provides a comprehensive view of subsystem mechanisms of DMN underlying WMH-related CI.

Automatic segmentation of white matter hyperintensities in routine clinical brain MRI by 2D VB-Net: A large-scale study

White matter hyperintensities (WMH) are imaging manifestations frequently observed in various neurological disorders, yet the clinical application of WMH quantification is limited. In this study, we designed a series of dedicated WMH labeling protocols and proposed a convolutional neural network named 2D VB-Net for the segmentation of WMH and other coexisting intracranial lesions based on a large dataset of 1,045 subjects across various demographics and multiple scanners using 2D thick-slice protocols that are more commonly applied in clinical practice. Using our labeling pipeline, the Dice consistency of the WMH regions manually depicted by two observers was 0.878, which formed a solid basis for the development and evaluation of the automatic segmentation system. The proposed algorithm outperformed other state-of-the-art methods (uResNet, 3D V-Net and Visual Geometry Group network) in the segmentation of WMH and other coexisting intracranial lesions and was well validated on datasets with thick-slice magnetic resonance (MR) images and the 2017 medical image computing and computer assisted intervention WMH Segmentation Challenge dataset (with thin-slice MR images), all showing excellent effectiveness. Furthermore, our method can subclassify WMH to display the WMH distributions and is very lightweight. Additionally, in terms of correlation to visual rating scores, our algorithm showed excellent consistency with the manual delineations and was overall better than those from other competing methods. In conclusion, we developed an automatic WMH quantification framework for multiple application scenarios, exhibiting a promising future in clinical practice.

Research Progress on MRI for White Matter Hyperintensity of Presumed Vascular Origin and Cognitive Impairment

White matter hyperintensity of presumed vascular origin (WMH) is a common medical imaging manifestation in the brains of middle-aged and elderly individuals. WMH can lead to cognitive decline and an increased risk of cognitive impairment and dementia. However, the pathogenesis of cognitive impairment in patients with WMH remains unclear. WMH increases the risk of cognitive impairment, the nature and severity of which depend on lesion volume and location and the patient's cognitive reserve. Abnormal changes in microstructure, cerebral blood flow, metabolites, and resting brain function are observed in patients with WMH with cognitive impairment. Magnetic resonance imaging (MRI) is an indispensable tool for detecting WMH, and novel MRI techniques have emerged as the key approaches for exploring WMH and cognitive impairment. This article provides an overview of the association between WMH and cognitive impairment and the application of dynamic contrast-enhanced MRI, structural MRI, diffusion tensor imaging, 3D-arterial spin labeling, intravoxel incoherent motion, magnetic resonance spectroscopy, and resting-state functional MRI for examining WMH and cognitive impairment.

Individual prediction of hemispheric similarity of functional connectivity during normal aging

In the aging process of normal people, the functional activity pattern of brain is in constant change, and the change of brain runs through the whole life cycle, which plays a crucial role in the track of individual development. In recent years, some studies had been carried out on the brain functional activity pattern during individual aging process from different perspectives, which provided an opportunity for the problem we want to study. In this study, we used the resting-state functional magnetic resonance imaging (rs-fMRI) data from Cambridge Center for Aging and Neuroscience (Cam-CAN) database with large sample and long lifespan, and computed the functional connectivity (FC) values for each individual. Based on these values, the hemispheric similarity of functional connectivity (HSFC) obtained by Pearson correlation was used as the starting point of this study. We evaluated the ability of individual recognition of HSFC in the process of aging, as well as the variation trend with aging process. The results showed that HSFC could be used to identify individuals effectively, and it could reflect the change rule in the process of aging. In addition, we observed a series of results at the sub-module level and find that the recognition rate in the sub-module was different from each other, as well as the trend with age. Finally, as a validation, we repeated the main results by human brainnetome atlas (BNA) template and without global signal regression, found that had a good robustness. This also provides a new clue to hemispherical change patterns during normal aging.

Abnormal Cerebral Blood Flow and Functional Connectivity Strength in Subjects With White Matter Hyperintensities

White matter hyperintensities (WMHs) are common neuroimaging findings in the aging population and are associated with various clinical symptoms, especially cognitive impairment. Abnormal global cerebral blood flow (CBF) and specific functional connections have been reported in subjects with higher WMH loads. Nevertheless, the comprehensive functional mechanisms underlying WMH are yet to be established. In this study, by combining resting-state functional magnetic resonance imaging and arterial spin labeling, we investigated the neurovascular dysfunction in subjects with WMH in CBF, functional connectivity strength (FCS), and CBF–FCS coupling. The whole-brain alterations of all these measures were explored among non-dementia subjects with different WMH loads using a fine-grained Human Brainnetome Atlas. In addition, exploratory mediation analyses were conducted to further determine the relationships between these neuroimaging indicators, WMH load, and cognition. The results showed that subjects with higher WMH loads displayed decreased CBF and FCS mainly in regions involving the cognitive- and emotional-related brain networks, including the default mode network, salience network, and central executive network. Notably, subjects with higher WMH loads also showed an abnormal regional CBF–FCS coupling in several regions of the thalamus, posterior cingulate cortex, and parahippocampal gyrus involving the default mode network. Furthermore, regional CBF in the right inferior temporal gyrus and right dorsal caudate may mediate the relationship between WMH load and cognition in WMH subjects. These findings indicated characteristic changes in cerebral blood supply, brain activity, and neurovascular coupling in regions involving specific brain networks with the development of WMH, providing further information on pathophysiology underpinnings of the WMH and related cognitive impairment.

Cortical and Subcortical Grey Matter Abnormalities in White Matter Hyperintensities and Subsequent Cognitive Impairment

Grey matter (GM) alterations may contribute to cognitive decline in individuals with white matter hyperintensities (WMH) but no consensus has yet emerged. Here, we investigated cortical thickness and grey matter volume in 23 WMH patients with mild cognitive impairment (WMH-MCI), 43 WMH patients without cognitive impairment, and 55 healthy controls. Both WMH groups showed GM atrophy in the bilateral thalamus, fronto-insular cortices, and several parietal-temporal regions, and the WMH-MCI group showed more extensive and severe GM atrophy. The GM atrophy in the thalamus and fronto-insular cortices was associated with cognitive decline in the WMH-MCI patients and may mediate the relationship between WMH and cognition in WMH patients. Furthermore, the main results were well replicated in an independent dataset from the Alzheimer's Disease Neuroimaging Initiative database and in other control analyses. These comprehensive results provide robust evidence of specific GM alterations underlying WMH and subsequent cognitive impairment.

Resting state connectivity within the basal ganglia and gait speed in older adults with cerebral small vessel disease and locomotor risk factors

BACKGROUND AND AIM

The basal ganglia are critical for planned locomotion, but their role in age-related gait slowing is not well known. Spontaneous regional co-activation of brain activity at rest, known as resting state connectivity, is emerging as a biomarker of functional neural specialization of varying human processes, including gait. We hypothesized that greater connectivity amongst regions of the basal ganglia would be associated with faster gait speed in the elderly. We further investigated whether this association was similar in strength to that of other risk factors for gait slowing, specifically white matter hyperintensities (WMH).

METHODS

A cohort of 269 adults (79-90 years, 146 females, 164 White) were assessed for gait speed (m/sec) via stopwatch; brain activation during resting state functional magnetic resonance imaging, WMH, and gray matter volume (GMV) normalized by intracranial volume via 3T neuroimaging; and risk factors of poorer locomotion via clinical exams (body mass index (BMI), muscle strength, vision, musculoskeletal pain, cardiometabolic conditions, depressive symptoms, and cognitive function). To understand whether basal ganglia connectivity shows distinct clusters of connectivity, we conducted a k-means clustering analysis of regional co-activation among the substantia nigra, nucleus accumbens, subthalamic nucleus, putamen, pallidum, and caudate. We conducted two multivariable linear regression models: (1) with gait speed as the dependent variable and connectivity, demographics, WMH, GMV, and locomotor risk factors as independent variables and (2) with basal ganglia connectivity as the dependent variable and demographics, WMH, GMV, and locomotor risk factors as independent variables.

RESULTS

We identified two clusters of basal ganglia connectivity: high and low without a distinct spatial distribution allowing us to compute an average connectivity index of the entire basal ganglia regional connectivity (representing a continuous measure). Lower connectivity was associated with slower gait, independent of other locomotor risk factors, including WMH; the coefficient of this association was similar to those of other locomotor risk factors. Lower connectivity was significantly associated with lower BMI and greater WMH.

CONCLUSIONS

Lower resting state basal ganglia connectivity is associated with slower gait speed. Its contribution appears comparable to WMH and other locomotor risk factors. Future studies should assess whether promoting higher basal ganglia connectivity in older adults may reduce age-related gait slowing.

Nodal Global Efficiency in Front-Parietal Lobe Mediated Periventricular White Matter Hyperintensity (PWMH)-Related Cognitive Impairment

White matter hyperintensity (WMH) is widely observed in the elderly population and serves as a key indicator of cognitive impairment (CI). However, the underlying mechanism remains to be elucidated. Herein, we investigated the topological patterns of resting state functional networks in WMH subjects and the relationship between the topological measures and CI. A graph theory-based analysis was employed in the resting-state functional magnetic resonance scans of 112 subjects (38 WMH subjects with cognitive impairment without dementia (CIND), 36 WMH subjects with normal cognition and 38 healthy controls (HCs), and we found that WMH-CIND subjects displayed decreased global efficiency at the levels of the whole brain, specific subnetworks [fronto-parietal network (FPN) and cingulo-opercular network (CON)] and certain nodes located in the FPN and CON, as well as decreased local efficiency in subnetworks. Our results demonstrated that nodal global efficiency in frontal and parietal regions mediated the impairment of information processing speed related to periventricular WMH (PWMH). Additionally, we performed support vector machine (SVM) analysis and found that altered functional efficiency can identify WMH-CIND subjects with high accuracy, sensitivity and specificity. These findings suggest impaired functional networks in WMH-CIND individuals and that decreased functional efficiency may be a feature of CI in WMH subjects.