Progressively Disrupted Brain Functional Connectivity Network in Subcortical Ischemic Vascular Cognitive Impairment Patients

Aberrant baseline brain activity and disrupted functional connectivity in patients with vascular cognitive impairment due to cerebral small vessel disease

Objective

This study aims to examine the alterations in aberrant brain activity and network connectivity between individuals with mild and major vascular cognitive impairment (VCI).

Materials and methods

A total of 114 patients with cerebral small vessel disease (CSVD) were included in this study, comprising 61 individuals with mild VCI (mean age, 55.7 ± 6.9 years; male, 42.6%) and 53 cases with major VCI (mean age, 57.6 ± 5.5 years; male, 58.5%). Additionally, 53 age-, gender-, and education-matched healthy subjects were recruited as normal controls (NC) (mean age, 54.9 ± 7.9 years; male, 52.9%). All participants underwent neuropsychological assessments and magnetic resonance imaging scans. One-way analysis of variance was used to compare fractional amplitude of low-frequency fluctuation (fALFF) values among the three groups. Two-sample t-tests were conducted to assess functional connectivity matrices between different groups for each connection. Moreover, mediation analyses were performed to explore the mediating effect of aberrant brain activity on the relationship between cognitive impairment and CSVD total burden.

Results

VCI patients exhibited aberrant brain activity in regions such as the right thalamus (THA_R), right cuneus (CUN_R), left postcentral gyrus (PoCG_L), right postcentral gyrus (PoCG_R), right median cingulate, paracingulate gyri (PCG_R), and left precuneus (PCUN_L). Reduced positive functional connectivity was predominantly observed among nodes including PCUN_L, CUN_R, PoCG_L, PoCG_R, right posterior cingulate (PCG_R), and left occipital gyrus (IOG_L) in VCI patients. The aberrant baseline brain activity and disrupted brain network were more pronounced with worsening cognitive function. Increased fALFF values in THA_R, CUN_R, and PoCG_L mediated cognitive impairment in CSVD patients.

Conclusion

Abnormal brain activities in THA_R, CUN_R, and PoCG_L, along with their associated abnormal functional connections, play a significant role in VCI. The study revealed a progressive increase in aberrant brain activity and network connectivity with advancing stages of VCI.

Language Impairment in Vascular Dementia: A Clinical Review

Vascular cognitive impairment (VCI) encompasses a wide range of conditions, including cognitive impairment associated with stroke or vascular brain injury, mild vascular cognitive impairment, and vascular dementia (VD). Knowledge of language impairment associated with VD is far less extensive than that of Alzheimer's disease. Although not prevalent in VD, impairment in language skills has been reported. A better understanding of the neurolinguistic features associated with the different presentations of VD could facilitate medical diagnosis. In this article, we report data on language impairment in VD, with particular attention to their primary or secondary functional origin. To better appreciate this functional origin, we also outline the main characteristics of impairment in other cognitive functions. Key elements that should be considered in the speech-language assessment of individuals with possible or proven VD are also highlighted.

Study of gray matter atrophy pattern with subcortical ischemic vascular disease-vascular cognitive impairment no dementia based on structural magnetic resonance imaging

Objective

This study explored the structural imaging changes in patients with subcortical ischemic vascular disease (SIVD)-vascular cognitive impairment no dementia (VCIND) and the correlation between the changes in gray matter volume and the field of cognitive impairment to provide new targets for early diagnosis and treatment.

Methods

Our study included 15 patients with SIVD-normal cognitive impairment (SIVD-NCI), 63 with SIVD-VCIND, 26 with SIVD-vascular dementia (SIVD-VD), and 14 normal controls (NC). T1-weighted images of all participants were collected, and DPABI and SPM12 software were used to process the gray matter of the four groups based on voxels. Fisher's exact test, one-way ANOVA and Kruskal-Wallis H test were used to evaluate all clinical and demographic data and compare the characteristics of diencephalic gray matter atrophy in each group. Finally, the region of interest (ROI) of the SIVD-VCIND was extracted, and Pearson correlation analysis was performed between the ROI and the results of the neuropsychological scale.

Results

Compared to the NC, changes in gray matter atrophy were observed in the bilateral orbitofrontal gyrus, right middle temporal gyrus, superior temporal gyrus, and precuneus in the SIVD-VCIND. Gray matter atrophy was observed in the left cerebellar region 6, cerebellar crural region 1, bilateral thalamus, right precuneus, and calcarine in the SIVD-VD. Compared with the SIVD-VCIND, gray matter atrophy changes were observed in the bilateral thalamus in the SIVD-VD (p < 0.05, family-wise error corrected). In the SIVD-VCIND, the total gray matter volume, bilateral medial orbital superior frontal gyrus, right superior temporal gyrus, middle temporal gyrus, and precuneus were positively correlated with Boston Naming Test score, whereas the total gray matter volume, right superior temporal gyrus, and middle temporal gyrus were positively correlated with overall cognition.

Conclusion

Structural magnetic resonance imaging can detect extensive and subtle structural changes in the gray matter of patients with SIVD-VCIND and SIVD-VD, providing valuable evidences to explain the pathogenesis of subcortical vascular cognitive impairment and contributing to the early diagnosis of SIVD-VCIND and early warning of SIVD-VD.

The older adult brain is less modular, more integrated, and less efficient at rest: A systematic review of large-scale resting-state functional brain networks in aging

The literature on large-scale resting-state functional brain networks across the adult lifespan was systematically reviewed. Studies published between 1986 and July 2021 were retrieved from PubMed. After reviewing 2938 records, 144 studies were included. Results on 11 network measures were summarized and assessed for certainty of the evidence using a modified GRADE method. The evidence provides high certainty that older adults display reduced within-network and increased between-network functional connectivity. Older adults also show lower segregation, modularity, efficiency and hub function, and decreased lateralization and a posterior to anterior shift at rest. Higher-order functional networks reliably showed age differences, whereas primary sensory and motor networks showed more variable results. The inflection point for network changes is often the third or fourth decade of life. Age effects were found with moderate certainty for within- and between-network altered patterns and speed of dynamic connectivity. Research on within-subject bold variability and connectivity using glucose uptake provides low certainty of age differences but warrants further study. Taken together, these age-related changes may contribute to the cognitive decline often seen in older adults.

Associations of white matter hyperintensities with networks of gray matter blood flow and volume in midlife adults: A coronary artery risk development in young adults magnetic resonance imaging substudy

White matter hyperintensities (WMHs) are emblematic of cerebral small vessel disease, yet effects on the brain have not been well characterized at midlife. Here, we investigated whether WMH volume is associated with brain network alterations in midlife adults. Two hundred and fifty‐four participants from the Coronary Artery Risk Development in Young Adults study were selected and stratified by WMH burden into Lo‐WMH (mean age = 50 ± 3.5 years) and Hi‐WMH (mean age = 51 ± 3.7 years) groups of equal size. We constructed group‐level covariance networks based on cerebral blood flow (CBF) and gray matter volume (GMV) maps across 74 gray matter regions. Through consensus clustering, we found that both CBF and GMV covariance networks partitioned into modules that were largely consistent between groups. Next, CBF and GMV covariance network topologies were compared between Lo‐ and Hi‐WMH groups at global (clustering coefficient, characteristic path length, global efficiency) and regional (degree, betweenness centrality, local efficiency) levels. At the global level, there were no between‐group differences in either CBF or GMV covariance networks. In contrast, we found between‐group differences in the regional degree, betweenness centrality, and local efficiency of several brain regions in both CBF and GMV covariance networks. Overall, CBF and GMV covariance analyses provide evidence that WMH‐related network alterations are present at midlife.

Diagnosis of Subcortical Ischemic Vascular Cognitive Impairment With No Dementia Using Radiomics of Cerebral Cortex and Subcortical Nuclei in High-Resolution T1-Weighted MR Imaging

Purpose

To investigate whether the combination of radiomics derived from brain high-resolution T1-weighted imaging and automatic machine learning could diagnose subcortical ischemic vascular cognitive impairment with no dementia (SIVCIND) accurately.

Methods

A total of 116 right-handed participants involving 40 SIVCIND patients and 76 gender-, age-, and educational experience-matched normal controls (NM) were recruited. A total of 7,106 quantitative features from the bilateral thalamus, hippocampus, globus pallidus, amygdala, nucleus accumbens, putamen, caudate nucleus, and 148 areas of the cerebral cortex were automatically calculated from each subject. Six methods including least absolute shrinkage and selection operator (LASSO) were utilized to lessen the redundancy of features. Three supervised machine learning approaches of logistic regression (LR), random forest (RF), and support vector machine (SVM) employing 5-fold cross-validation were used to train and establish diagnosis models, and 10 times 10-fold cross-validation was used to evaluate the generalization performance of each model. Correlation analysis was performed between the optimal features and the neuropsychological scores of the SIVCIND patients.

Results

Thirteen features from the right amygdala, right hippocampus, left caudate nucleus, left putamen, left thalamus, and bilateral nucleus accumbens were included in the optimal subset. Among all the three models, the RF produced the highest diagnostic performance with an area under the receiver operator characteristic curve (AUC) of 0.990 and an accuracy of 0.948. According to the correlation analysis, the radiomics features of the right amygdala, left caudate nucleus, left putamen, and left thalamus were found to be significantly correlated with the neuropsychological scores of the SIVCIND patients.

Conclusions

The combination of radiomics derived from brain high-resolution T1-weighted imaging and machine learning could diagnose SIVCIND accurately and automatically. The optimal radiomics features are mostly located in the right amygdala, left caudate nucleus, left putamen, and left thalamus, which might be new biomarkers of SIVCIND.

Impaired functional network properties contribute to white matter hyperintensity related cognitive decline in patients with cerebral small vessel disease

Background

White matter hyperintensity (WMH) is one of the typical neuroimaging manifestations of cerebral small vessel disease (CSVD), and the WMH correlates closely to cognitive impairment (CI). CSVD patients with WMH own altered topological properties of brain functional network, which is a possible mechanism that leads to CI. This study aims to identify differences in the characteristics of some brain functional network among patients with different grades of WMH and estimates the correlations between these different brain functional network characteristics and cognitive assessment scores.

Methods

110 CSVD patients underwent 3.0 T Magnetic resonance imaging scans and neuropsychological cognitive assessments. WMH of each participant was graded on the basis of Fazekas grade scale and was divided into two groups: (A) WMH score of 1–2 points (n = 64), (B) WMH score of 3–6 points (n = 46). Topological indexes of brain functional network were analyzed using graph-theoretical method. T-test and Mann–Whitney U test was used to compare the differences in topological properties of brain functional network between groups. Partial correlation analysis was applied to explore the relationship between different topological properties of brain functional networks and overall cognitive function.

Results

Patients with high WMH scores exhibited decreased clustering coefficient values, global and local network efficiency along with increased shortest path length on whole brain level as well as decreased nodal efficiency in some brain regions on nodal level (p < 0.05). Nodal efficiency in the left lingual gyrus was significantly positively correlated with patients' total Montreal Cognitive Assessment (MoCA) scores (p < 0.05). No significant difference was found between two groups on the aspect of total MoCA and Mini-mental State Examination (MMSE) scores (p > 0.05).

Conclusion

Therefore, we come to conclusions that patients with high WMH scores showed less optimized small-world networks compared to patients with low WMH scores. Global and local network efficiency on the whole-brain level, as well as nodal efficiency in certain brain regions on the nodal level, can be viewed as markers to reflect the course of WMH.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-022-00769-7.

Dissociable Functional Brain Networks Associated With Apathy in Subcortical Ischemic Vascular Disease and Alzheimer’s Disease

Few studies have investigated differences in functional connectivity (FC) between patients with subcortical ischemic vascular disease (SIVD) and Alzheimer’s disease (AD), especially in relation to apathy. Therefore, the aim of this study was to compare apathy-related FC changes among patients with SIVD, AD, and cognitively normal subjects. The SIVD group had the highest level of apathy as measured using the Apathy Evaluation Scale-clinician version (AES). Dementia staging, volume of white matter hyperintensities (WMH), and the Beck Depression Inventory were the most significant clinical predictors for apathy. Group-wise comparisons revealed that the SIVD patients had the worst level of “Initiation” by factor analysis of the AES. FCs from four resting state networks (RSNs) were compared, and the connectograms at the level of intra- and inter-RSNs revealed dissociable FC changes, shared FC in the dorsal attention network, and distinct FC in the salient network across SIVD and AD. Neuronal correlates for “Initiation” deficits that underlie apathy were explored through a regional-specific approach, which showed that the right inferior frontal gyrus, left middle frontal gyrus, and left anterior insula were the critical hubs. These findings broaden the disconnection theory by considering the effect of FC interactions across multiple RSNs on apathy formation.

Long-Term Connectome Analysis Reveals Reshaping of Visual, Spatial Networks in a Model With Vascular Dementia Features

Connectome analysis of neuroimaging data is a rapidly expanding field that offers the potential to diagnose, characterize, and predict neurological disease. Animal models provide insight into biological mechanisms that underpin disease, but connectivity approaches are currently lagging in the rodent.

Altered Dynamic Functional Connectivity in Subcortical Ischemic Vascular Disease With Cognitive Impairment

Subcortical ischemic vascular disease (SIVD) can cause cognitive impairment and affect the static functional connectivity of resting functional magnetic resonance imaging (fMRI). Numerous previous studies have demonstrated that functional connectivities (FCs) fluctuate dynamically over time. However, little is known about the impact of cognitive impairment on brain dynamic functional connectivity (DFC) in SIVD patients with MCI. In the present study, the DFC analysis method was applied to the resting functional magnetic resonance imaging (fMRI) data of 37 SIVD controls (SIVD-Control) without cognitive impairment, 34 SIVD patients with amnestic MCI (SIVD-aMCI) and 30 SIVD patients with nonamnestic MCI (SIVD-naMCI). The results indicated that the cognitive impairment of SIVD mainly reduced the mean dwell time of State 3 with overall strong positive connections. The reduction degree of SIVD-aMCI was larger than that of SIVD-naMCI. The memory/execution function impairment of SIVD also changed the relationship between the mean dwell time of State 3 and the behavioral performance of the memory/execution task from significant to non-significant correlation. Moreover, SIVD-aMCI showed significantly lower system segregation of FC states than SIVD-Control and SIVD-naMCI. The system segregation of State 5 with overall weak connections was significantly positive correlated with the memory performance. The results may suggest that the mean dwell time of State 3 and the system segregation of State 5 may be used as important neural measures of cognitive impairments of SIVD.

Alterations in Resting-State Functional MRI Connectivity Related to Cognitive Changes in Intracranial Dural Arteriovenous Fistulas Before and After Embolization Treatment

BACKGROUND

Cognitive decline is a non-hemorrhagic, major complication of intracranial dural arteriovenous fistula (DAVF), thought to be primarily related to venous hypertension. However, imaging features to predict cognitive decline are scanty in the literature.

PURPOSE

To evaluate functional connectivity (FC) changes of resting-state networks (RSNs) in DAVF before and after treatment and its relation to cognitive impairment.

STUDY TYPE

Prospective.

SUBJECTS

DAVF subjects were screened for inclusion. Pre-embolization (N = 33, mean age 45.9 years, 29 males), 1 month post-embolization (N = 20, mean age 42.7 years, 19 males), and healthy controls (HC, N = 33, mean age 45.09 years, 27 males).

FIELD STRENGTH/SEQUENCE

3.0 T, resting-state functional magnetic resonance imaging (MRI), three-dimensional (3D) T1, T₂ fast spin echo (FSE), diffusion weighted imaging (DWI), susceptibility weighted imaging (SWI), fluid-attenuated inversion recovery, and time of flight.

ASSESSMENT

Data quality assessment was performed. FC analysis was done using group independent component analysis (ICA) and seed to voxel analysis. Neuropsychology (NP) scores of patients were compared with HC and correlated with FC changes.

STATISTICAL TESTS

Voxel-wise parametric T-statistics for F-test was executed in FC analysis (p-FDR corrected <0.05). NP scores between DAVF group and HC group were compared using one-way analysis of variance with post hoc Bonferroni correction (P < 0.05).

RESULTS

Both RSNs analysis methods showed reduced FC at the precuneus-posterior cingulate cortex (PC-PCC) of default mode network (DMN), anterior cingulate cortex (ACC) of the salience network (SN), and possible compensatory increased connectivity at the frontoparietal (FPN) and dorsal attention (DAN) networks. DAVF with low NP scores showed reduced FC at DMN and SN and minimal to absent connectivity at FPN and DAN. At post-embolization 1-month follow-up, improvement in FC at PC-PCC of DMN and ACC of SN were noted.

DATA CONCLUSION

RS-fMRI in DAVF displayed FC changes that may be related to cognitive decline and its subsequent reversibility after treatment. FC changes at DMN, SN, FPN, and DAN were linked to cognitive decline and the corresponding NP scores.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

Selective Aberrant Functional-Structural Coupling of Multiscale Brain Networks in Subcortical Vascular Mild Cognitive Impairment

Subcortical vascular mild cognitive impairment (svMCI) is a common prodromal stage of vascular dementia. Although mounting evidence has suggested abnormalities in several single brain network metrics, few studies have explored the consistency between functional and structural connectivity networks in svMCI. Here, we constructed such networks using resting-state fMRI for functional connectivity and diffusion tensor imaging for structural connectivity in 30 patients with svMCI and 30 normal controls. The functional networks were then parcellated into topological modules, corresponding to several well-defined functional domains. The coupling between the functional and structural networks was finally estimated and compared at the multiscale network level (whole brain and modular level). We found no significant intergroup differences in the functional-structural coupling within the whole brain; however, there was significantly increased functional-structural coupling within the dorsal attention module and decreased functional-structural coupling within the ventral attention module in the svMCI group. In addition, the svMCI patients demonstrated decreased intramodular connectivity strength in the visual, somatomotor, and dorsal attention modules as well as decreased intermodular connectivity strength between several modules in the functional network, mainly linking the visual, somatomotor, dorsal attention, ventral attention, and frontoparietal control modules. There was no significant correlation between the altered module-level functional-structural coupling and cognitive performance in patients with svMCI. These findings demonstrate for the first time that svMCI is reflected in a selective aberrant topological organization in multiscale brain networks and may improve our understanding of the pathophysiological mechanisms underlying svMCI.

Conditional entropy approach to analyze cognitive dynamics in autism spectrum disorder

OBJECTIVE

Preliminary evidence has documented functional connectivity during the cognitive task in Autism Spectrum Disorder (ASD). However, evidence of effective neural connectivity with respect to information flow between different brain regions during complex tasks is missing. The present paper aims to provide insights into the cognition-based neural dynamics reflecting information exchange in brain network under cognitive load in ASD.

METHODS

Twenty-two individuals with ASD (8-18 years) and 18 Typically Developing (TD; 6-17 years) individuals participated in the cognitive task of differentiating risky from neutral stimuli. The Conditional Entropy (CE) technique is applied upon task-activated Electroencephalogram (EEG) to measure the causal influence of the activity of brain's one Region of interest (ROI) over another.

RESULTS

A higher CE in frontal ROI and left hemisphere reflected atypical brain complexity in ASD. The absence of causal effect, poor Coupling Strength (CS; measured using CE) and hemisphere lateralization is responsible for lower cognition in ASD. However, the persistent information exchange during the task reflects the existence of certain alternative paths when other direct paths remained disconnected due to cognitive impairment. The Support Vector Machine (SVM) classifier showed that CE can identify the atypical information exchange with an accuracy of 96.89% and area under curve = 0.987.

DISCUSSION

The statistical results reflect a significant change in the information flow between different ROIs in ASD. A correlation of CS and behavioral domain suggests that the cognitive decline could be predicted from the connectivity patterns. Thus, CS could be a potential biomarker to identify cognitive status at a higher discrimination rate in ASD.

Network topology and machine learning analyses reveal microstructural white matter changes underlying Chinese medicine Dengzhan Shengmai treatment on patients with vascular cognitive impairment

With the increasing incidence of cerebrovascular diseases and dementia, considerable efforts have been made to develop effective treatments on vascular cognitive impairment (VCI), among which accumulating practice-based evidence has shown great potential of the traditional Chinese medicine (TCM). Current randomized double-blind controlled trial has been designed to evaluate the 6-month treatment effects of Dengzhan Shengmai (DZSM) capsules, one TCM herbal preparations on VCI, and to explore the underlying neural mechanisms with graph theory-based analysis and machine learning method based on diffusion tensor imaging (DTI) data. A total of 82 VCI patients were recruited and randomly assigned to drug (45 with DZSM) and placebo (37 with placebo) groups, and neuropsychological and neuroimaging data were acquired at baseline and after 6-month treatment. After treatment, compared to the placebo group, the drug groups showed significantly improved performance in Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-cog) score (p < 0.001) and the other cognitive domains. And with the reconstruction of white matter structural network, there were more streamlines connecting the left thalamus and right hippocampus in the drug groups (p < 0.001 uncorrected), with decreasing nodal efficiency of the right olfactory associated with slower decline in the general cognition (r = -0.364, p = 0.048). Moreover, support vector machine classification analyses revealed significant white matter network alterations after treatment in the drug groups (accuracy of baseline vs. 6-month later, 68.18 %). Taking together, the present study showed significant efficacy of DZSM treatment on VCI, which might result from white matter microstructure alterations and the topological changes in brain structural network.

Disrupted Brain Structural Connectivity Network in Subcortical Ischemic Vascular Cognitive Impairment With No Dementia

The alteration of the functional topological organization in subcortical ischemic vascular cognitive impairment with no dementia (SIVCIND) patients has been illuminated by previous neuroimaging studies. However, in regard to the changes in the structural connectivity of brain networks, little has been reported. In this study, a total of 27 subjects, consisting of 13 SIVCIND patients, and 14 normal controls, were recruited. Each of the structural connectivity networks was constructed by diffusion tensor tractography. Subsequently, graph theory, and network-based statistics (NBS) were employed to analyze the whole-brain mean factional anisotropy matrix. After removing the factor of age, gender, and duration of formal education, the clustering coefficients (C_p) and global efficiency (E_glob) were significantly decreased and the mean path length (L_p) was significantly increased in SIVCIND patients compared with normal controls. Using the combination of four network topological parameters as the classification feature, a classification accuracy of 78% was obtained by leave-one-out cross-validation for all subjects with a sensitivity of 69% and a specificity of 86%. Moreover, we also found decreased structural connections in the SIVCIND patients, which mainly concerned fronto-occipital, fronto-subcortical, and tempo-occipital connections (NBS corrected, p < 0.01). Additionally, significantly altered nodal centralities were found in several brain regions of the SIVCIND patients, mainly located in the prefrontal, subcortical, and temporal cortices. These results suggest that cognitive impairment in SIVCIND patients is associated with disrupted topological organization and provide structural evidence for developing reliable biomarkers related to cognitive decline in SIVCIND.

Accelerated Aging of Functional Brain Networks Supporting Cognitive Function in Psychotic Disorders

BACKGROUND

Across networks, connectivity within the frontoparietal network (FPN) and cingulo-opercular network (CON) exhibits reductions earliest during healthy aging, contributing to cognitive impairment. Individuals with psychotic disorders demonstrate evidence of accelerated aging across multiple biological systems. By leveraging a large sample of patients with psychosis from early to chronic illness stages, this study sought to determine whether the CON and FPN exhibit evidence of accelerated aging in psychotic disorders, confirm associations between network efficiency and cognition, and determine whether reduced network efficiency is observed in early-stage psychosis.

METHODS

Resting-state functional magnetic resonance imaging and cognitive data were obtained on 240 patients with psychotic disorder and 178 healthy control participants (HCs). Global efficiency, a measure of functional integration, was calculated for the CON, FPN, subcortical network, and visual network. Associations with age and cognition were assessed and compared between groups.

RESULTS

Consistent with accelerated aging, significant group by age interactions reflected significantly stronger relationships between efficiency and age in patients with psychosis than in HCs for both the CON (psychosis: r = -.37; HC: r = -.16) and FPN (psychosis: r = -.31; HC: r = -.05). Accelerated aging was not observed in either the subcortical or visual network, suggesting specificity for cognitive networks that decline earliest in healthy aging. Replicating prior findings, efficiency of both the CON and FPN correlated with cognitive function across all participants (rs > .11, ps < .031). Furthermore, patients with chronic psychosis (p = .004), but not patients with early psychosis (p = .553), exhibited significantly lower FPN efficiency compared with HCs.

CONCLUSIONS

Functional integration of higher-order cognitive networks is intact in early psychosis but exhibits evidence of accelerated aging, suggesting the potential for intervention targeting cognition within the early psychosis period.

Altered static and dynamic functional network connectivity in Alzheimer's disease and subcortical ischemic vascular disease: shared and specific brain connectivity abnormalities

Subcortical ischemic vascular disease (SIVD) is a major subtype of vascular dementia with features that overlap clinically with Alzheimer's disease (AD), confounding diagnosis. Neuroimaging is a more specific and biologically based approach for detecting brain changes and thus may help to distinguish these diseases. There is still a lack of knowledge regarding the shared and specific functional brain abnormalities, especially functional connectivity changes in relation to AD and SIVD. In this study, we investigated both static functional network connectivity (sFNC) and dynamic FNC (dFNC) between 54 intrinsic connectivity networks in 19 AD patients, 19 SIVD patients, and 38 age-matched healthy controls. The results show that both patient groups have increased sFNC between the visual and cerebellar (CB) domains but decreased sFNC between the cognitive-control and CB domains. SIVD has specifically decreased sFNC within the sensorimotor domain while AD has specifically altered sFNC between the default-mode and CB domains. In addition, SIVD has more occurrences and a longer dwell time in the weakly connected dFNC states, but with fewer occurrences and a shorter dwell time in the strongly connected dFNC states. AD has both similar and opposite changes in certain dynamic features. More importantly, the dynamic features are found to be associated with cognitive performance. Our findings highlight similar and distinct functional connectivity alterations in AD and SIVD from both static and dynamic perspectives and indicate dFNC to be a more important biomarker for dementia since its progressively altered patterns can better track cognitive impairment in AD and SIVD.

Brain network disintegration as a final common pathway for delirium: a systematic review and qualitative meta-analysis

Delirium is an acute neuropsychiatric syndrome characterized by altered levels of attention and awareness with cognitive deficits. It is most prevalent in elderly hospitalized patients and related to poor outcomes. Predisposing risk factors, such as older age, determine the baseline vulnerability for delirium, while precipitating factors, such as use of sedatives, trigger the syndrome. Risk factors are heterogeneous and the underlying biological mechanisms leading to vulnerability for delirium are poorly understood. We tested the hypothesis that delirium and its risk factors are associated with consistent brain network changes. We performed a systematic review and qualitative meta-analysis and included 126 brain network publications on delirium and its risk factors. Findings were evaluated after an assessment of methodological quality, providing N=99 studies of good or excellent quality on predisposing risk factors, N=10 on precipitation risk factors and N=7 on delirium. Delirium was consistently associated with functional network disruptions, including lower EEG connectivity strength and decreased fMRI network integration. Risk factors for delirium were associated with lower structural connectivity strength and less efficient structural network organization. Decreased connectivity strength and efficiency appear to characterize structural brain networks of patients at risk for delirium, possibly impairing the functional network, while functional network disintegration seems to be a final common pathway for the syndrome.

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Delirium is consistently associated with functional network impairments.

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Risk factors are associated with lower structural connectivity strength.

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Risk factors are associated with a less efficient structural network organization.

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Structural impairments make the functional network more vulnerable to deterioration.

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Functional network disintegration seems to be a final common pathway for delirium.

Structural brain network measures are superior to vascular burden scores in predicting early cognitive impairment in post stroke patients with small vessel disease

Objectives

In this cross-sectional study, we aimed to explore the mechanisms of early cognitive impairment in a post stroke non-dementia cerebral small vessel disease (SVD) cohort by comparing the SVD score with the structural brain network measures.

Method

127 SVD patients were recruited consecutively from a stroke clinic, comprising 76 individuals with mild cognitive impairment (MCI) and 51 with no cognitive impairment (NCI). Detailed neuropsychological assessments and multimodal MRI were performed. SVD scores were calculated on a standard scale, and structural brain network measures were analyzed by diffusion tensor imaging (DTI). Between-group differences were analyzed, and logistic regression was applied to determine the predictive value of SVD and network measures for cognitive status. Mediation analysis with structural equation modeling (SEM) was used to better understand the interactions of SVD burden, brain networks and cognitive deficits.

Results

Group difference was found on all global brain network measures. After adjustment for age, gender, education and depression, significant correlations were found between global brain network measures and diverse neuropsychological tests, including TMT-B (r = −0.209, p < .05), DSST (r = 0.206, p < .05), AVLT short term free recall (r = 0.233, p < .05), AVLT long term free recall (r = 0.264, p < .05) and Rey-O copy (r = 0.272, p < .05). SVD score showed no group difference and was not correlated with cognition tests. Network global efficiency (E_Global) was significantly related to cognitive state (p < .01) but not the SVD score. Mediation analysis showed that the standardized total effect (p = .013) and the standardized indirect effect (p = .016) of SVD score on cognition was significant, but the direct effect was not.

Conclusions

Brain network measures, but not the SVD score, are significantly correlated with cognition in post-stroke SVD patients. Mediation analysis showed that the cerebral vascular lesions produce cognitive dysfunction by interfering with the structural brain network in SVD patients. The brain network measures may be regarded as direct and independent surrogate markers of cognitive impairment in SVD.

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Network measures instead of SVD score are significantly correlated with cognition.

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Network measures are superior to brain lesions in predicting cognitive impairment.

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Brain networks mediated the relationship between brain lesions and cognition.

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Brain network measures were direct and independent surrogate markers of SVD.