Effects of White Matter Hyperintensities on Brain Connectivity and Hippocampal Volume in Healthy Subjects According to Their Localization

Impact of white matter hyperintensity volumes estimated by automated methods using deep learning on stroke outcomes in small vessel occlusion stroke

Introduction

Although white matter hyperintensity (WMH) shares similar vascular risk and pathology with small vessel occlusion (SVO) stroke, there were few studies to evaluate the impact of the burden of WMH volume on early and delayed stroke outcomes in SVO stroke.

Materials and methods

Using a multicenter registry database, we enrolled SVO stroke patients between August 2013 and November 2022. The WMH volume was estimated by automated methods using deep learning (VUNO Med-DeepBrain, Seoul, South Korea), which was a commercially available segmentation model. After propensity score matching (PSM), we evaluated the impact of WMH volume on early neurological deterioration (END) and poor functional outcomes at 3-month modified Ranking Scale (mRS), defined as mRS score >2 at 3 months, after an SVO stroke.

Results

Among 1,718 SVO stroke cases, the prevalence of subjects with severe WMH (Fazekas score ≥ 3) was 68.9%. After PSM, END and poor functional outcomes at 3-month mRS (mRS > 2) were higher in the severe WMH group (END: 6.9 vs. 13.5%, p < 0.001; 3-month mRS > 2: 11.4 vs. 24.7%, p < 0.001). The logistic regression analysis using the PSM cohort showed that total WMH volume increased the risk of END [odd ratio [OR], 95% confidence interval [CI]; 1.01, 1.00-1.02, p = 0.048] and 3-month mRS > 2 (OR, 95% CI; 1.02, 1.01-1.03, p < 0.001). Deep WMH was associated with both END and 3-month mRS > 2, but periventricular WMH was associated with 3-month mRS > 2 only.

Conclusion

This study used automated methods using a deep learning segmentation model to assess the impact of WMH burden on outcomes in SVO stroke. Our findings emphasize the significance of WMH burden in SVO stroke prognosis, encouraging tailored interventions for better patient care.

Impact of corpus callosum integrity on functional interhemispheric connectivity and cognition in healthy subjects

To examine the corpus callosum's (CC) integrity in terms of fractional anisotropy (FA) and how it affects resting-state hemispheric connectivity (rs-IHC) and cognitive function in healthy individuals. Sixty-eight healthy individuals were recruited for the study. The global FA (gFA) and FA values of each CC tract (forceps minor, body, tapetum, and forceps major) were evaluated using diffusion-weighted imaging (DWI) sequences. The homotopic functional connectivity technique was used to quantify the effects of FA in the CC tracts on bilateral functional connectivity, including the confounding effect of gFA. Brain regions with higher or lower rs-IHC were identified using the threshold-free cluster enhancement family-wise error-corrected p-value of 0.05. The null hypothesis was rejected if the p-value was ≤ 0.05 for the nonparametric partial correlation technique. Several clusters of increased rs-IHC were identified in relation to the FA of individual CC tracts, each with a unique topographic distribution and extension. Only forceps minor FA values correlated with cognitive scores. The integrity of CC influences rs-IHC differently in healthy subjects. Specifically, forceps minor anisotropy impacts rs-IHC and cognition more than other CC tracts do.

Effect of pre-stroke antiplatelet use on stroke outcomes in acute small vessel occlusion stroke with moderate to severe white matter burden

BACKGROUND

Cerebral small vessel disease is characterized by white matter hyperintensities (WMH) and acute small vessel occlusion (SVO) stroke. We investigated the effect of prior antiplatelet use (APU) on stroke outcome in 1151 patients with acute SVO stroke patients and moderate to severe WMH.

METHODS

Using a multicenter database, this retrospective study used quantitative WMH volume measurements and propensity score matching (PSM) for comparisons between patients with prior APU and without APU. Primary outcomes were stroke progression and poor functional outcome (modified Rankin Scale>2) at 3 months. Logistic regression analyses assessed associations between prior APU, WMH burden, and stroke outcomes.

RESULTS

Stroke progression was lower in the prior APU group in both the total cohort (14.8% vs. 6.9%, p < 0.001) and the PSM cohort (16.3% vs. 6.9%, p < 0.001). The proportion of poor functional outcomes at 3 months was not significantly different in the total cohort, but the PSM cohort showed a lower proportion in the prior APU group (30.8% vs. 20.2%, p = 0.002). Logistic regression analysis confirmed that prior APU was associated with a reduced risk of stroke progression (OR, 0.39; 95% CI, 0.22-0.70; p = 0.001) and poor functional outcome at 3 months (OR, 0.37; 95% CI, 0.23-0.59; p < 0.001).

CONCLUSION

Prior APU is associated with reduced stroke progression and improved functional outcome at 3 months in acute SVO stroke patients with moderate to severe WMH. Early treatment of WMH and acute SVO stroke may have potential benefits in improving stroke outcomes.

Effect of anti-P ribosomal and anti-NR2 antibodies on depression and cognitive processes in SLE: an integrated clinical and functional MRI study

OBJECTIVES

To explore the effects of anti-ribosomal P protein (anti-P) and anti-N-methyl-D-aspartic acid receptor subunit 2 (anti-NR2) autoantibodies on depression and cognitive dysfunction and their relationships with functional brain connectivity in SLE.

METHODS

This cross-sectional study included adult patients who fulfilled the American College of Rheumatology/European Alliance of Associations for Rheumatology 2019 SLE criteria. Anti-P and anti-NR2 were quantified using ELISA. A 1-hour battery of neuropsychological testing interpreted by a neuropsychologist explored depressive symptoms (Center for Epidemiologic Studies Depression Scale, CES-D), cognitive domains and quality of life (SF-12). Resting-state functional connectivity (rs-fc) MRI analysis was performed within 1 month, and region-of-interest to region-of-interest (ROI-to-ROI) analyses with the graph theory were performed.

RESULTS

Thirty-three patients with SLE (9% male) were enrolled, mean age (SD) of 43.5 (14) years and median disease duration of 10.4 years (2.9-25.4). Anti-P was positive in 6 (18.2%) and anti-NR2 in 14 (42.4%) patients. Depressive symptoms were found in 14 (42.4%) patients using the CES-D (range 0-51). After correction for age, disease duration, disease activity and white matter lesion load, the CES-D score was independently associated with anti-P serum level (β=0.32; p=0.049) and prednisone daily dose (β=0.38; p=0.023). Nineteen patients (57.6%) showed at least a cognitive test alteration, but no significant association with autoantibodies was found. The rs-fc MRI analysis revealed an independent association between the anti-P serum levels and many altered brain ROI properties but no anti-NR2 and prednisone effects on the cerebral network.

CONCLUSIONS

Anti-P was associated with brain network perturbation, which may be responsible for depressive symptoms in patients with SLE.

Discrete Ricci curvatures capture age-related changes in human brain functional connectivity networks

Introduction

Geometry-inspired notions of discrete Ricci curvature have been successfully used as markers of disrupted brain connectivity in neuropsychiatric disorders, but their ability to characterize age-related changes in functional connectivity is unexplored.

Methods

We apply Forman-Ricci curvature and Ollivier-Ricci curvature to compare functional connectivity networks of healthy young and older subjects from the Max Planck Institute Leipzig Study for Mind-Body-Emotion Interactions (MPI-LEMON) dataset (N = 225).

Results

We found that both Forman-Ricci curvature and Ollivier-Ricci curvature can capture whole-brain and region-level age-related differences in functional connectivity. Meta-analysis decoding demonstrated that those brain regions with age-related curvature differences were associated with cognitive domains known to manifest age-related changes-movement, affective processing, and somatosensory processing. Moreover, the curvature values of some brain regions showing age-related differences exhibited correlations with behavioral scores of affective processing. Finally, we found an overlap between brain regions showing age-related curvature differences and those brain regions whose non-invasive stimulation resulted in improved movement performance in older adults.

Discussion

Our results suggest that both Forman-Ricci curvature and Ollivier-Ricci curvature correctly identify brain regions that are known to be functionally or clinically relevant. Our results add to a growing body of evidence demonstrating the sensitivity of discrete Ricci curvature measures to changes in the organization of functional connectivity networks, both in health and disease.

Abnormal dynamic functional connectivity of hippocampal subregions associated with working memory impairment in melancholic depression

BACKGROUND

Previous studies have demonstrated structural and functional changes of the hippocampus in patients with major depressive disorder (MDD). However, no studies have analyzed the dynamic functional connectivity (dFC) of hippocampal subregions in melancholic MDD. We aimed to reveal the patterns for dFC variability in hippocampus subregions - including the bilateral rostral and caudal areas and its associations with cognitive impairment in melancholic MDD.

METHODS

Forty-two treatment-naive MDD patients with melancholic features and 55 demographically matched healthy controls were included. The sliding-window analysis was used to evaluate whole-brain dFC for each hippocampal subregions seed. We assessed between-group differences in the dFC variability values of each hippocampal subregion in the whole brain and cognitive performance on the MATRICS Consensus Cognitive Battery (MCCB). Finally, association analysis was conducted to investigate their relationships.

RESULTS

Patients with melancholic MDD showed decreased dFC variability between the left rostral hippocampus and left anterior lobe of cerebellum compared with healthy controls (voxel p < 0.005, cluster p < 0.0125, GRF corrected), and poorer cognitive scores in working memory, verbal learning, visual learning, and social cognition (all p < 0.05). Association analysis showed that working memory was positively correlated with the dFC variability values of the left rostral hippocampus-left anterior lobe of the cerebellum (r = 0.338, p = 0.029) in melancholic MDD.

CONCLUSIONS

These findings confirmed the distinct dynamic functional pathway of hippocampal subregions in patients with melancholic MDD, and suggested that the dysfunction of hippocampus-cerebellum connectivity may be underlying the neural substrate of working memory impairment in melancholic MDD.

Quantitative morphometric changes in vascular mild cognitive impairment patients: early diagnosis of dementia

Vascular mild cognitive impairment (VMCI) is an early and reversible stage of dementia. Volume differences in regional gray matter may reveal the development and prognosis of VMCI. This study selected 2 of the most common types of VMCI, namely, periventricular white matter hyperintensities (PWMH, n = 14) and strategic single infarctions (SSI, n = 10), and used the voxel-based morphometry method to quantify their morphological characteristics. Meanwhile, age- and sex-matched healthy volunteers were included (n = 16). All the participants were neuropsychologically tested to characterize their cognitive function and underwent whole-brain magnetic resonance imaging scanning. Our results showed that the volumes of the bilateral temporal lobes and bilateral frontal gray matter were obviously diminished in the PWMH group. The atrophy volume difference was 4,086 voxels in the left temporal lobe, 4,154 voxels in the right temporal lobe, 1,718 voxels in the left frontal lobe, and 1,141 voxels in the right frontal lobe (P ≤ 0.001). Moreover, the characteristics of the gray matter atrophy associated with the PWMH were more similar to those associated with Alzheimer's disease than SSI, which further revealed the susceptibility for escalation from PWMH to dementia. In conclusion, PWMH patients and SSI patients have different morphological characteristics, which explain the different prognoses of VMCI.

Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework

Brain tumor characterization (BTC) is the process of knowing the underlying cause of brain tumors and their characteristics through various approaches such as tumor segmentation, classification, detection, and risk analysis. The substantial brain tumor characterization includes the identification of the molecular signature of various useful genomes whose alteration causes the brain tumor. The radiomics approach uses the radiological image for disease characterization by extracting quantitative radiomics features in the artificial intelligence (AI) environment. However, when considering a higher level of disease characteristics such as genetic information and mutation status, the combined study of "radiomics and genomics" has been considered under the umbrella of "radiogenomics". Furthermore, AI in a radiogenomics' environment offers benefits/advantages such as the finalized outcome of personalized treatment and individualized medicine. The proposed study summarizes the brain tumor's characterization in the prospect of an emerging field of research, i.e., radiomics and radiogenomics in an AI environment, with the help of statistical observation and risk-of-bias (RoB) analysis. The PRISMA search approach was used to find 121 relevant studies for the proposed review using IEEE, Google Scholar, PubMed, MDPI, and Scopus. Our findings indicate that both radiomics and radiogenomics have been successfully applied aggressively to several oncology applications with numerous advantages. Furthermore, under the AI paradigm, both the conventional and deep radiomics features have made an impact on the favorable outcomes of the radiogenomics approach of BTC. Furthermore, risk-of-bias (RoB) analysis offers a better understanding of the architectures with stronger benefits of AI by providing the bias involved in them.

Global Fractional Anisotropy: Effect on Resting-state Neural Activity and Brain Networking in Healthy Participants

The global fractional anisotropy (gFA) is a structural marker of white matter myelination and integrity. Previous studies already evidenced that aging-related reduced integrity of specific white matter tracts is associated with decreased functional connectivity in several hubs. However, the correlations between gFA and functional brain connectivity remain unknown. In this cross-sectional study, we analyzed structural and functional MR datasets of 79 healthy participants from the Leipzig Study for Mind-Body-Emotion Interactions. DTI model-based method was used to quantify gFA values. We tested associations between gFA, age, and gender. The fractional amplitude of low-frequency fluctuations (fALFF) and ROI-to-ROI connectivity were analyzed in a regression model for evaluating the effects of gFA on brain activity and networking, respectively. A negative correlation was found between gFA and age (ρ = -0.343; p = 0.002). No statistically significant correlation as found between gFA and gender (p = 0.229). Higher values of gFA were associated with increased brain regional activity, including areas of the default mode network. There was a higher degree of correlation between some regions, particularly those that conform to the limbic system. Our study demonstrates that gFA influences regional neural activity and brain networking on resting, particularly the limbic system.

The association between white matter hyperintensities, cognition and regional neural activity in healthy subjects

White matter hyperintensities (WMH) are common findings that can be found in physiological ageing. Several studies suggest that the disruption of white matter tracts included in WMH could induce abnormal functioning of the respective linked cortical structures, with consequent repercussion on the cerebral functions, included the cognitive sphere. In this cross-sectional research, we analysed the effects of the total WMH burden (tWMHb) on resting-state functional magnetic resonance imaging (rs-fMRI) and cognition. Functional and structural MR data, as well as the scores of the trail making test subtests A (TMT-A) and B (TMT-B) of 75 healthy patients, were extracted from the public available Leipzig Study for Mind-Body-Emotion Interactions dataset. tWMHb was extracted from structural data. Spearman's correlation analyses were made for investigating correlations between WMHb and the scores of the cognitive tests. The fractional amplitude of low-frequency fluctuations (fALFF) method was applied for analysing the rs-fMRI data, adopting a multiple regression model for studying the effects of tWMHb on brain activity. Three different subanalyses were conducted using different statistical methods. We observed statistically significant correlations between WMHb and the scores of the cognitive tests. The fALFF analysis revealed that tWMHb is associated with the reduction of regional neural activity of several brain areas (in particular the prefrontal cortex, precuneus and cerebellar crus I/II). We conclude that our findings clarify better the relationships between WMH and cognitive impairment, evidencing that tWMHb is associated with impairments of the neurocognitive function in healthy subjects by inducing a diffuse reduction of the neural activity.