Microstructural and metabolic changes in the longitudinal progression of white matter hyperintensities

Cerebral small vessel disease increases risk for epilepsy: a Mendelian randomization study

BACKGROUND

Despite previous research suggesting a potential association between cerebral small vessel disease (CSVD) and epilepsy, the precise causality and directionality between cerebral small vessel disease (CSVD) and epilepsy remain incompletely understood. We aimed to investigate the causal link between CSVD and epilepsy.

METHOD

A bidirectional two-sample Mendelian randomization (MR) analysis was performed to evaluate the causal relationship between CSVD and epilepsy. The analysis included five dimensions of CSVD, namely small vessel ischemic stroke (SVS), intracerebral hemorrhage (ICH), white matter damage (including white matter hyperintensity [WMH], fractional anisotropy, and mean diffusivity), lacunar stroke, and cerebral microbleeds. We also incorporated epilepsy encompassing both focal epilepsy and generalized epilepsy. Inverse variance weighted (IVW) was used as the primary estimate while other four MR techniques were used to validate the results. Pleiotropic effects were controlled by adjusting vascular risk factors through multivariable MR.

RESULT

The study found a significant association between SVS (odds ratio [OR] 1.117, PFDR = 0.022), fractional anisotropy (OR 0.961, PFDR = 0.005), mean diffusivity (OR 1.036, PFDR = 0.004), and lacunar stroke (OR 1.127, PFDR = 0.007) with an increased risk of epilepsy. The aforementioned correlations primarily occurred in focal epilepsy rather than generalized epilepsy on subgroup analysis and retained their significance in the multivariable MR analysis.

CONCLUSION

Our study demonstrated that genetic susceptibility to CSVD independently elevates the risk of epilepsy, especially focal epilepsy. Diffusion tensor imaging may help screen patients at high risk for epilepsy in CSVD. Improved management of CSVD may be a significant approach in reducing the overall prevalence of epilepsy.

Magnetic Resonance Imaging Tissue Signatures Associated With White Matter Changes Due to Sporadic Cerebral Small Vessel Disease Indicate That White Matter Hyperintensities Can Regress

BACKGROUND

White matter hyperintensities (WMHs) might regress and progress contemporaneously, but we know little about underlying mechanisms. We examined WMH change and underlying quantitative magnetic resonance imaging tissue measures over 1 year in patients with minor ischemic stroke with sporadic cerebral small vessel disease.

METHODS AND RESULTS

We defined areas of stable normal-appearing white matter, stable WMHs, progressing and regressing WMHs based on baseline and 1-year brain magnetic resonance imaging. In these areas we assessed tissue characteristics with quantitative T1, fractional anisotropy (FA), mean diffusivity (MD), and neurite orientation dispersion and density imaging (baseline only). We compared tissue signatures cross-sectionally between areas, and longitudinally within each area. WMH change masks were available for N=197. Participants' mean age was 65.61 years (SD, 11.10), 59% had a lacunar infarct, and 68% were men. FA and MD were available for N=195, quantitative T1 for N=182, and neurite orientation dispersion and density imaging for N=174. Cross-sectionally, all 4 tissue classes differed for FA, MD, T1, and Neurite Density Index. Longitudinally, in regressing WMHs, FA increased with little change in MD and T1 (difference estimate, 0.011 [95% CI, 0.006-0.017]; -0.002 [95% CI, -0.008 to 0.003] and -0.003 [95% CI, -0.009 to 0.004]); in progressing and stable WMHs, FA decreased (-0.022 [95% CI, -0.027 to -0.017] and -0.009 [95% CI, -0.011 to -0.006]), whereas MD and T1 increased (progressing WMHs, 0.057 [95% CI, 0.050-0.063], 0.058 [95% CI, 0.050 -0.066]; stable WMHs, 0.054 [95% CI, 0.045-0.063], 0.049 [95% CI, 0.039-0.058]); and in stable normal-appearing white matter, MD increased (0.004 [95% CI, 0.003-0.005]), whereas FA and T1 slightly decreased and increased (-0.002 [95% CI, -0.004 to -0.000] and 0.005 [95% CI, 0.001-0.009]).

CONCLUSIONS

Quantitative magnetic resonance imaging shows that WMHs that regress have less abnormal microstructure at baseline than stable WMHs and follow trajectories indicating tissue improvement compared with stable and progressing WMHs.

White matter hyperintensity volume modifies the association between CSF vascular inflammatory biomarkers and regional FDG-PET along the Alzheimer's disease continuum

In older adults with abnormal levels of Alzheimer's disease neuropathology, lower cerebrospinal fluid (CSF) vascular endothelial growth factor (VEGF) levels are associated with lower [¹⁸F]-fluorodeoxyglucose positron emission tomography (FDG-PET) signal, but whether this association is (1) specific to VEGF or broadly driven by vascular inflammation, or (2) modified by vascular risk (e.g., white matter hyperintensities [WMHs]) remains unknown. To address this and build upon our past work, we evaluated whether 5 CSF vascular inflammation biomarkers (vascular cell adhesion molecule 1, VEGF, C-reactive protein, fibrinogen, and von Willebrand factor)-previously associated with CSF amyloid levels-were related to FDG-PET signal and whether WMH volume modified these associations in 158 Alzheimer's Disease Neuroimaging Initiative participants (55-90 years old, 39 cognitively normal, 80 mild cognitive impairment, 39 Alzheimer's disease). We defined regions both by cortical boundary and by the 3 major vascular territories: anterior, middle, and posterior cerebral arteries. We found that WMH volume had interactive effects with CSF biomarkers (VEGF and C-reactive protein) on FDG-PET throughout the cortex in both vascular territories and conventionally defined regions of interest.

Characterizing the penumbras of white matter hyperintensities in patients with cerebral small vessel disease

PURPOSE

The white matter hyperintensity penumbra (WMH-P) is the subtly changed normal-appearing white matter (NAWM) that surrounds white matter hyperintensities (WMHs). The goal of this study was to define WMH-P in cerebral small vessel disease (CSVD) by arterial spin labeling (ASL) and diffusion tensor imaging (DTI)/diffusion kurtosis imaging (DKI).

MATERIALS AND METHODS

We prospectively analyzed 42 patients with CSVD. To determine the range of cerebral blood flow (CBF) and DTI/DKI penumbras around white matter hyperintensities, we generated NAWM layer masks from periventricular WMHs (PVWMHs) and deep WMHs (DWMHs). Mean values of CBF, fractional anisotropy, mean diffusivity, axial diffusivity, radial diffusivity, mean kurtosis, axial kurtosis, and radial kurtosis within the WMHs and their corresponding NAWM layer masks were analyzed. Paired sample t tests were used for analysis, and differences were considered statistically significant if the associated p value was ≤ 0.05.

RESULTS

For DWMHs, the CBF penumbras were 13 mm, and the DTI/DKI penumbras were 8 mm. For PVWMHs, the CBF penumbras were 14 mm, and the DTI/DKI penumbras were 14 mm.

CONCLUSIONS

Our findings revealed that DTI/DKI and ASL can show structural and blood flow changes in brain tissue surrounding WMHs. In DWMHs, the blood flow penumbra was larger than the structural penumbra, while in PVWMHs, the blood flow penumbra was almost the same as the structural penumbra.

White matter hyperintensities and post-stroke depression: A systematic review and meta-analysis

INTRODUCTION

Post-stroke depression (PSD) is the most common emotional problem following a stroke. White matter hyperintensities (WMHs) are often reported in patients with a stroke, and are often divided into deep WMHs (DWMHs) and periventricular WMHs (PVWMHs). The relationship between WMHs and PSD remains controversial. This review aims to resolve this controversy.

METHODS

A systematic search of electronic databases was conducted for studies. We extracted the relevant data and evaluated the study quality by using the Newcastle-Ottawa Scale. We pooled odds ratios (OR) for the same type of WMHs that were present in the relevant PSD period.

RESULTS

15 studies (n = 4133 patients) met our inclusion criteria. In the acute phase, WMHs, DWMHs, severe WMHs, and severe DWMHs were not significant risk factors for incident depression, but PVWMHs (pooled OR, 1.21; 95 % CI, 1.01-1.44) and severe PVWMHs (pooled OR, 1.72; 95 % CI, 1.12-2.65) had a significant association with PSD. In the subacute phase, DWMHs, DWMHs, and severe WMHs were not significantly associated with PSD, but PVWMHs (pooled OR, 2.44; 95 % CI, 1.25-4.76) showed a significant association with PSD. In the chronic phase, severe PVWMHs had no significant association with PSD, while WMHs (pooled OR, 1.063; 95 % CI, 1.03-1.09), DWMHs (pooled OR, 1.40; 95 % CI, 1.11-1.76), PVWMHs (pooled OR, 1.28; 95 % CI, 1.11-1.48), and severe DWMHs (pooled OR, 1.52; 95 % CI, 1.12-2.05) showed a significant association with PSD.

CONCLUSION

We found a significant association between WMHs/DWMHs/PVWMHs and PSD in the chronic post-stroke phase. PVWMHs had a stronger correlation with PSD in each period after stroke than WMHs and DWMHs. High-quality prospective studies are still needed to fully resolve this relationship.

Associations between cerebral blood flow and progression of white matter hyperintensity in community-dwelling adults: a longitudinal cohort study

Background

White matter hyperintensity (WMH) is prevalent in elderly populations. Ischemia is characterized by a decline in cerebral blood flow (CBF) and may play a key role in the pathogenesis of WMH. However, the association between CBF reduction and WMH progression remains controversial. This study aimed to investigate the association between CBF and the progression of WMH at a 2-year follow-up of community-based, asymptomatic adults in a longitudinal cohort study across the lifespan.

Methods

Asymptomatic adults who participated in a community-based study were recruited and underwent brain structural and perfusion magnetic resonance imaging (MRI) at baseline and at a 2-year follow-up visit. The CBF was measured on pseudo-continuous arterial spin-labeling (pCASL) MRI. The WMH was evaluated on T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) images. Tissue segmentation was conducted on T1-weighted (T1W) images to derive binary masks of gray matter and normal-appearing white matter. Linear mixed effect models were conducted to analyze the cross-sectional and longitudinal associations between CBF and WMH.

Results

A total of 229 adults (mean age 57.3±12.6 years; 94 males) were enrolled at baseline, among whom 84 participants (mean age 54.1±11.9 years; 41 males) completed a follow-up visit with a mean time interval of 2.77±0.44 years. At baseline, there was a decreasing trend in gray matter (GM) CBF with an increase of WMH burden (P=0.063), but this association was attenuated after adjusting for age (P=0.362). In the longitudinal analysis, baseline WMH volume was significantly associated with the reduction of perfusion in GM [coefficient =−1.96, 95% confidence interval (CI): −3.25 to −0.67; P=0.004] and normal appearing white matter (coefficient =−0.99, 95% CI: −1.66 to −0.31; P=0.005) during follow-up. On the contrary, neither baseline CBF in GM (P=0.888) nor normal appearing white matter (P=0.850) was associated with WMH progression. In addition, CBF changes within WMH were significantly associated with both baseline (coefficient =−0.014, 95% CI: −0.025 to −0.003; P=0.017) and progression (coefficient =−1.01, 95% CI: −1.81 to −0.20; P=0.015) of WMH volume.

Conclusions

A WMH burden was not found to be directly associated with cortex perfusion at baseline due to the effects of age on both CBF and WMH. However, baseline WMH volume could predict the reduction of perfusion.

Five years of exercise intervention at different intensities and development of white matter hyperintensities in community dwelling older adults, a Generation 100 sub-study

We investigated if a five-year supervised exercise intervention with moderate-intensity continuous training (MICT) or high-intensity interval training (HIIT) versus control; physical activity according to national guidelines, attenuated the growth of white matter hyperintensities (WMH). We hypothesized that supervised exercise, in particular HIIT, reduced WMH growth. Older adults from the general population participating in the RCT Generation 100 Study were scanned at 3T MRI at baseline (age 70–77), and after 1-, 3- and 5-years. At each follow-up, cardiorespiratory fitness was measured with ergospirometry, and physical activity plus clinical data collected. Manually delineated total WMH, periventricular (PWMH), deep (DWMH), and automated total white matter hypointensity volumes were obtained. No group by time interactions were present in linear mixed model analyses with the different WMH measurements as outcomes. In the combined exercise (MICT&HIIT) group, a significant group by time interaction was uncovered for PWMH volume, with a larger increase in the MICT&HIIT group. Cardiorespiratory fitness at the follow-ups or change in cardiorespiratory fitness over time were not associated with any WMH measure. Contrary to our hypothesis, taking part in MICT or HIIT over a five-year period did not attenuate WMH growth compared to being in a control group following national physical activity guidelines.

Amide Proton Transfer MRI Could Be Used to Evaluate the Pathophysiological Status of White Matter Hyperintensities

BACKGROUND

The pathophysiology of white matter hyperintensities (WMH) remains unclear, investigations of amide proton transfer (APT) signals in WMH disease may provide relevant pathophysiological information.

PURPOSE

To evaluate the APT signals differences and heterogeneity of WMH and adjacent normal-appearing white matter (NAWM) at different Fazekas grades and different locations.

STUDY TYPE

Prospective.

POPULATION

In all, 180 WMH patients (age, 40-76; male/female, 77/103) and 59 healthy controls (age, 42-70; male/female, 23/36).

FIELD STRENGTH/SEQUENCE

A 3 T; 3D fluid-attenuated inversion recovery (FLAIR), 3D APT-weighted (APTw).

ASSESSMENT

The mean APTw values (APTw_mean ) and the APTw signals heterogeneity (APTw_max-min ) among different grades WMH and NAWM and the APTw_mean of the same grade deep WMH (DWMH) and paraventricular WMH (PWMH) were calculated and compared. Regions of interests were delineated on WMH lesions, NAWM and healthy white matter.

STATISTICAL TESTS

One-way analysis of variance (ANOVA); independent sample t test; Chi-square test. Significance level: P < 0.05.

RESULTS

APTw_mean among different grade WMH (from grade 0 to 3, 0.58 ± 0.14% vs. 0.29 ± 0.23% vs. 0.37 ± 0.24% vs. 0.61 ± 0.22%, respectively) were significantly different except between grade 1 and 2 (P = 0.27) and between grade 0 and 3 (P = 0.97). The differences in APTw_mean between WMH and NAWM were significant (WMH vs. NAWM from grade 1 to 3, 0.29% ± 0.23% vs. 0.55% ± 0.27%; 0.37% ± 0.24% vs. 0.59% ± 0.22%; 0.61% ± 0.22% vs. 0.42% ± 0.24%, respectively). Lower APTw_mean values were found only in grade 3 NAWM than other grades NAWM and controls. The APTw_max-min values of grade 1-3 WMH (0.38% ± 0.27% vs. 0.51% ± 0.31% vs. 0.67% ± 0.34%, respectively) were significantly different. Higher APT_mean values were found only in grade 2 PWMH (0.47% ± 0.22% vs. 0.32% ± 0.24%).

DATA CONCLUSION

Significant differences of APT signals were found in WMH of different Fazekas grades and different locations.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 3.

Dilated perivascular space is related to reduced free-water in surrounding white matter among healthy adults and elderlies but not in patients with severe cerebral small vessel disease

Perivascular space facilitates cerebral interstitial water clearance. However, it is unclear how dilated perivascular space (dPVS) affects the interstitial water of surrounding white matter. We aimed to determine the presence and extent of changes in normal-appearing white matter water components around dPVS in different populations. Twenty healthy elderly subjects and 15 elderly subjects with severe cerebral small vessel disease (CSVD, with lacunar infarction 6 months before the scan) were included in our study. And other 28 healthy adult subjects were enrolled under a different scanning parameter to see if the results are comparable. The normal-appearing white matter around dPVS was categorized into 10 layers (1 mm thickness each) based on their distance to dPVS. We evaluated the mean isotropic-diffusing water volume fraction in each layer. We discovered a significantly reduced free-water content in the layers closely adjacent to the dPVS in the healthy elderlies. however, this reduction around dPVS was weaker in the CSVD subjects. We also discovered an elevated free-water content within dPVS. DPVS played different roles in healthy subjects or CSVD subjects. The reduced water content around dPVS in healthy subjects suggests these MR-visible PVSs are not always related to the stagnation of fluid.

A Mutual Multi-Scale Triplet Graph Convolutional Network for Classification of Brain Disorders Using Functional or Structural Connectivity

Brain connectivity alterations associated with mental disorders have been widely reported in both functional MRI (fMRI) and diffusion MRI (dMRI). However, extracting useful information from the vast amount of information afforded by brain networks remains a great challenge. Capturing network topology, graph convolutional networks (GCNs) have demonstrated to be superior in learning network representations tailored for identifying specific brain disorders. Existing graph construction techniques generally rely on a specific brain parcellation to define regions-of-interest (ROIs) to construct networks, often limiting the analysis into a single spatial scale. In addition, most methods focus on the pairwise relationships between the ROIs and ignore high-order associations between subjects. In this letter, we propose a mutual multi-scale triplet graph convolutional network (MMTGCN) to analyze functional and structural connectivity for brain disorder diagnosis. We first employ several templates with different scales of ROI parcellation to construct coarse-to-fine brain connectivity networks for each subject. Then, a triplet GCN (TGCN) module is developed to learn functional/structural representations of brain connectivity networks at each scale, with the triplet relationship among subjects explicitly incorporated into the learning process. Finally, we propose a template mutual learning strategy to train different scale TGCNs collaboratively for disease classification. Experimental results on 1,160 subjects from three datasets with fMRI or dMRI data demonstrate that our MMTGCN outperforms several state-of-the-art methods in identifying three types of brain disorders.

Disentangling the pathologies linking white matter hyperintensity and geriatric depressive symptoms in subjects with different degrees of vascular impairment

BACKGROUND

White matter hyperintensity (WMH) is closely associated with geriatric depressive symptoms, but its underlying neural mechanism is unclear. We aim to disentangle the contribution of vascular degeneration and fiber disruption to depressive symptoms in elderly subjects at different clinical status.

METHODS

One hundred and thirty-three normal elderly subjects, as well as 43 patients with cerebral small vessel disease (CSVD) were included. The Hamilton Depression Rating Scale (HAMD) was used to measure depressive symptoms. Based on the diffusion tensor imaging data, a free water elimination analytical model was adopted to reflect fiber tract disruption (measure: tissue fractional anisotropy, tFA) and increased white matter water content (measure: free water fraction, FW).

RESULTS

We found that WMH severity was significantly correlated with decreased tFA and increased FW in all subjects. In normal elderly subjects, the HAMD score was correlated with mean tFA, but not FW. Compared to the traditional fractional anisotropy measure, tFA showed stronger correlation with clinical symptoms. In CSVD subjects, the correlation was only significant for FW, and marginally significant for tFA.

LIMITATIONS

Most subjects had only mild to moderate depressive symptoms. Further validation in patients with major depressive disorder is needed to confirm these findings.

CONCLUSIONS

The neural mechanisms of depressive symptoms may be different in elderly people with or without severe vascular damage. The free water elimination model may disentangle the effects of fiber disruption and increased free water, providing sensitive imaging markers that could potentially be used on monitoring disease treatment.

Loss of Integrity of Corpus Callosum White Matter Hyperintensity Penumbra Predicts Cognitive Decline in Patients With Subcortical Vascular Mild Cognitive Impairment

Loss of white matter (WM) integrity contributes to subcortical vascular mild cognitive impairment (svMCI). Diffusion tensor imaging (DTI) has revealed damage beyond the area of WM hyperintensity (WMH) including in normal-appearing WM (NAWM); however, the functional significance of this observation is unclear. To answer this question, in this study we investigated the relationship between microstructural changes in the WMH penumbra (WMH-P) and cognitive function in patients with svMCI by regional tract-based analysis. A total of 111 patients with svMCI and 72 patients with subcortical ischemic vascular disease (SIVD) without cognitive impairment (controls) underwent DTI and neuropsychological assessment. WMH burden was determined before computing mean values of fractional anisotropy (FA) and mean diffusivity (MD) within WMHs and WMH-Ps. Pearson’s partial correlations were used to assess the relationship between measurements showing significant intergroup differences and composite Z-scores representing global cognitive function. Multiple linear regression analysis was carried out to determine the best model for predicting composite Z-scores. We found that WMH burden in the genu, body, and splenium of the corpus callosum (GCC, BCC, and SCC respectively); bilateral anterior, superior, and posterior corona radiata; left sagittal stratum was significantly higher in the svMCI group than in the control group (p < 0.05). The WMH burden of the GCC, BCC, SCC, and bilateral anterior corona radiata was negatively correlated with composite Z-scores. Among diffusion parameters showing significant differences across the 10 WM regions, mean FA values of WMH and WMH-P of the BCC were correlated with composite Z-scores in svMCI patients. The results of the multiple linear regression analysis showed that the FA of WMH-P of the BCC and WMH burden of the SCC and GCC were independent predictors of composite Z-score, with the FA of WMH-P of the BCC making the largest contribution. These findings indicate that disruption of the CC microstructure—especially the WMH-P of the BCC—may contribute to the cognitive deficits associated with SIVD.

Small vessel disease more than Alzheimer's disease determines diffusion MRI alterations in memory clinic patients

INTRODUCTION

Microstructural alterations as assessed by diffusion tensor imaging (DTI) are key findings in both Alzheimer's disease (AD) and small vessel disease (SVD). We determined the contribution of each of these conditions to diffusion alterations.

METHODS

We studied six samples (N = 365 participants) covering the spectrum of AD and SVD, including genetically defined samples. We calculated diffusion measures from DTI and free water imaging. Simple linear, multivariable random forest, and voxel-based regressions were used to evaluate associations between AD biomarkers (amyloid beta, tau), SVD imaging markers, and diffusion measures.

RESULTS

SVD markers were strongly associated with diffusion measures and showed a higher contribution than AD biomarkers in multivariable analysis across all memory clinic samples. Voxel-wise analyses between tau and diffusion measures were not significant.

DISCUSSION

In memory clinic patients, the effect of SVD on diffusion alterations largely exceeds the effect of AD, supporting the value of diffusion measures as markers of SVD.

Automatic spatial estimation of white matter hyperintensities evolution in brain MRI using disease evolution predictor deep neural networks

Previous studies have indicated that white matter hyperintensities (WMH), the main radiological feature of small vessel disease, may evolve (i.e., shrink, grow) or stay stable over a period of time. Predicting these changes are challenging because it involves some unknown clinical risk factors that leads to a non-deterministic prediction task. In this study, we propose a deep learning model to predict the evolution of WMH from baseline to follow-up (i.e., 1-year later), namely "Disease Evolution Predictor" (DEP) model, which can be adjusted to become a non-deterministic model. The DEP model receives a baseline image as input and produces a map called "Disease Evolution Map" (DEM), which represents the evolution of WMH from baseline to follow-up. Two DEP models are proposed, namely DEP-UResNet and DEP-GAN, which are representatives of the supervised (i.e., need expert-generated manual labels to generate the output) and unsupervised (i.e., do not require manual labels produced by experts) deep learning algorithms respectively. To simulate the non-deterministic and unknown parameters involved in WMH evolution, we modulate a Gaussian noise array to the DEP model as auxiliary input. This forces the DEP model to imitate a wider spectrum of alternatives in the prediction results. The alternatives of using other types of auxiliary input instead, such as baseline WMH and stroke lesion loads are also proposed and tested. Based on our experiments, the fully supervised machine learning scheme DEP-UResNet regularly performed better than the DEP-GAN which works in principle without using any expert-generated label (i.e., unsupervised). However, a semi-supervised DEP-GAN model, which uses probability maps produced by a supervised segmentation method in the learning process, yielded similar performances to the DEP-UResNet and performed best in the clinical evaluation. Furthermore, an ablation study showed that an auxiliary input, especially the Gaussian noise, improved the performance of DEP models compared to DEP models that lacked the auxiliary input regardless of the model's architecture. To the best of our knowledge, this is the first extensive study on modelling WMH evolution using deep learning algorithms, which deals with the non-deterministic nature of WMH evolution.

A Novel Joint Brain Network Analysis Using Longitudinal Alzheimer's Disease Data

There is well-documented evidence of brain network differences between individuals with Alzheimer's disease (AD) and healthy controls (HC). To date, imaging studies investigating brain networks in these populations have typically been cross-sectional, and the reproducibility of such findings is somewhat unclear. In a novel study, we use the longitudinal ADNI data on the whole brain to jointly compute the brain network at baseline and one-year using a state of the art approach that pools information across both time points to yield distinct visit-specific networks for the AD and HC cohorts, resulting in more accurate inferences. We perform a multiscale comparison of the AD and HC networks in terms of global network metrics as well as at the more granular level of resting state networks defined under a whole brain parcellation. Our analysis illustrates a decrease in small-worldedness in the AD group at both the time points and also identifies more local network features and hub nodes that are disrupted due to the progression of AD. We also obtain high reproducibility of the HC network across visits. On the other hand, a separate estimation of the networks at each visit using standard graphical approaches reveals fewer meaningful differences and lower reproducibility.

Interactions between sleep disturbances and Alzheimer's disease on brain function: a preliminary study combining the static and dynamic functional MRI

Though sleep disturbance constitutes the risk factor for Alzheimer's disease (AD), the underlying mechanism is still unclear. This study aims to explore the interaction between sleep disturbances and AD on brain function. We included 192 normal controls, 111 mild cognitive impairment (MCI), and 30 AD patients, with either poor or normal sleep (PS, NS, respectively). To explore the strength and stability of brain activity, we used static amplitude of low-frequency fluctuation (sALFF) and dynamic ALFF (dALFF) variance. Further, we examined white matter hyperintensities (WMH) and amyloid PET deposition, representing the vascular risk factor and AD-related hallmark, respectively. We observed that sleep disturbance significantly interacted with disease severity, exposing distinct effects on sALFF and dALFF variance. Interestingly, PS groups showed the dALFF variance trajectory of initially increased, then decreased and finally increased along the AD spectrum, while showing the opposite trajectory of sALFF. Further correlation analysis showed that the WMH burden correlates with dALFF variance in PS groups. Conclusively, our study suggested that sleep disturbance interacts with AD severity, expressing as effects of compensatory in MCI and de-compensatory in AD, respectively. Further, vascular impairment might act as important pathogenesis underlying the interaction effect between sleep and AD.

Oxygenation differs among white matter hyperintensities, intersected fiber tracts and unaffected white matter

White matter hyperintensities of presumed vascular origin are frequently observed on magnetic resonance imaging in normal aging. They are typically found in cerebral small vessel disease and suspected culprits in the etiology of complex age- and small vessel disease-related conditions, such as late-onset depression. White matter hyperintensities may interfere with surrounding white matter metabolic demands by disrupting fiber tract integrity. Meanwhile, risk factors for small vessel disease are thought to reduce tissue oxygenation, not only by reducing regional blood supply, but also by impairing capillary function. To address white matter oxygen supply–demand balance, we estimated voxel-wise capillary density as an index of resting white matter metabolism, and combined estimates of blood supply and capillary function to calculate white matter oxygen availability. We conducted a cross-sectional study with structural, perfusion- and diffusion-weighted magnetic resonance imaging in 21 patients with late-onset depression and 21 controls. We outlined white matter hyperintensities and used tractography to identify the tracts they intersect. Perfusion data comprised cerebral blood flow, blood volume, mean transit time and relative transit time heterogeneity—the latter a marker of capillary dysfunction. Based on these, white matter oxygenation was calculated as the steady state cerebral metabolic rate of oxygen under the assumption of normal tissue oxygen tension and vice versa. The number, volume and perfusion characteristics of white matter hyperintensities did not differ significantly between groups. Hemodynamic data showed white matter hyperintensities to have lower blood flow and blood volume, but higher relative transit time heterogeneity, than normal-appearing white matter, resulting in either reduced capillary metabolic rate of oxygen or oxygen tension. Intersected tracts showed significantly lower blood flow, blood volume and capillary metabolic rate of oxygen than normal-appearing white matter. Across groups, lower lesion oxygen tension was associated with higher lesion number and volume. Compared with normal-appearing white matter, tissue oxygenation is significantly reduced in white matter hyperintensities as well as the fiber tracts they intersect, independent of parallel late-onset depression. In white matter hyperintensities, reduced microvascular blood volume and concomitant capillary dysfunction indicate a severe oxygen supply–demand imbalance with hypoxic tissue injury. In intersected fiber tracts, parallel reductions in oxygenation and microvascular blood volume are consistent with adaptations to reduced metabolic demands. We speculate, that aging and vascular risk factors impair white matter hyperintensity perfusion and capillary function to create hypoxic tissue injury, which in turn affect the function and metabolic demands of the white matter tracts they disrupt.

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.

Application of T1-/T2-Weighted Ratio Mapping to Elucidate Intracortical Demyelination Process in the Alzheimer's Disease Continuum

Background The biological diagnosis criteria of the Alzheimer’s disease (AD) suggests that previous work may misclassify the cognitive impairment caused by other factors into AD. Consequently, re-assessing the imaging profile of AD continuum is needed. Considering the high vulnerability of cortical association fibers, we aimed to elucidate the cortical demyelination process in the AD continuum biologically defined.

Methods According to the biological diagnosis criteria, we determined the positive amyloid status (A+) as cerebrospinal fluid (CSF) amyloid_1–42 < 192 pg/ml, Florbetapir Positron emission tomography (PET) composite standardized uptake value ratio (SUVR) >1.11. Also, the positive Tau status (T+) was determined as p-Tau₁₈₁ > 23 pg/ml. Based on the cognitive characterization, we further categorized 252 subjects into 27 cognitively unimpaired with normal AD biomarkers (A−T−, controls), 49 preclinical AD (A+T+), 113 AD with mild cognitive impairment (MCI) (A+T+), and 63 AD dementia (A+T+). We estimated the intracortical myelin content used the T1- and T2-weighted (T1W/T2W) ratio mapping. To investigate the sensitivity of the ratio mapping, we also utilized well-validated AD imaging biomarkers as the reference, including gray matter volume and Fludeoxyglucose PET (FDG-PET). Based on the general linear model, we conducted the voxel-wise two-sample T-tests between the controls and each group in the AD continuum.

Results Compared to the controls, the results showed that the preclinical AD patients exhibited decreased T1W/T2W ratio value in the right inferior parietal lobule (IPL); as the disease progresses, the prodromal AD patients demonstrated lower ratio value in bilateral IPL, with hippocampus (HP) atrophy. Lastly, the AD dementia patients exhibited decreased ratio value in bilateral IPL and hippocampus; also, we observed the bilateral temporal cortices atrophy and widespread decreased metabolism in the AD dementia patients. After corrected with gray volume, the results remained mostly unchanged.

Conclusion Our study implied the decreased right IPL T1W/T2W ratio might represent early AD-related demyelination in disease continuum. Additionally, we demonstrated that the T1W/T2W ratio mapping is an easy-to-implement and sensitive metric to assess the intracortical myelin content in AD, particularly in the early stage.

Predicting the Development of Normal-Appearing White Matter With Radiomics in the Aging Brain: A Longitudinal Clinical Study

Background: Normal-appearing white matter (NAWM) refers to the normal, yet diseased tissue around the white matter hyperintensities (WMH) on conventional MR images. Radiomics is an emerging quantitative imaging technique that provides more details than a traditional visual analysis. This study aims to explore whether WMH could be predicted during the early stages of NAWM, using a textural analysis in the general elderly population.

Methods: Imaging data were obtained from PACS between 2012 and 2017. The subjects (≥60 years) received two or more MRI exams on the same scanner with time intervals of more than 1 year. By comparing the baseline and follow-up images, patients with noted progression of WMH were included as the case group (n = 51), while age-matched subjects without WMH were included as the control group (n = 51). Segmentations of the regions of interest (ROIs) were done with the ITK software. Two ROIs of developing NAWM (dNAWM) and non-developing NAWM (non-dNAWM) were drawn separately on the FLAIR images of each patient. dNAWM appeared normal on the baseline images, yet evolved into WMH on the follow-up images. Non-dNAWM appeared normal on both the baseline and follow-up images. A third ROI of normal white matter (NWM) was extracted from the control group, which was normal on both baseline and follow-up images. Textural features were dimensionally reduced with ANOVA+MW, correlation analysis, and LASSO. Three models were built based on the optimal parameters of dimensional reduction, including Model 1 (NWM vs. dNAWM), Model 2 (non-dNAWM vs. dNAWM), and Model 3 (NWM vs. non-dNAWM). The ROC curve was adopted to evaluate the classification validity of these models.

Results: Basic characteristics of the patients and controls showed no significant differences. The AUC of Model 1 in training and test groups were 0.967 (95% CI: 0.831–0.999) and 0.954 (95% CI: 0.876–0.989), respectively. The AUC of Model 2 were 0.939 (95% CI: 0.856–0.982) and 0.846 (95% CI: 0.671–0.950). The AUC of Model 3 were 0.713 (95% CI: 0.593–0.814) and 0.667 (95% CI: 0.475–0.825).

Conclusion: Radiomics textural analysis can distinguish dNAWM from non-dNAWM on FLAIR images, which could be used for the early detection of NAWM lesions before they develop into visible WHM.