Connectivity changes in two-channel prefrontal ERP associated with early cognitive decline in the elderly population: beta band responses to the auditory oddball stimuli.

Background: This study utilized recent advancements in electroencephalography (EEG) technology that enable the measurement of prefrontal event-related potentials (ERPs) to facilitate the early detection of mild cognitive impairment (MCI). We investigated two-channel prefrontal ERP signals obtained from a large cohort of elderly participants and compare among cognitively normal (CN), subjective cognitive decline (SCD), amnestic MCI (aMCI), and nonamnestic MCI (naMCI) groups. Methods: Signal processing and ERP component analyses, specifically adapted for two-channel prefrontal ERP signals evoked by the auditory oddball task, were performed on a total of 1,754 elderly participants. Connectivity analyses were conducted to assess brain synchronization, especially in the beta band involving the phase locking value (PLV) and coherence (COH). Time-frequency, time-trial, grand average, and further statistical analyses of the standard and target epochs were also conducted to explore differences among the cognition groups. Results: The MCI group's response to target stimuli was characterized by greater response time variability (p < 0.001) and greater variability in the P300 latency (p < 0.05), leading to less consistent responses than those of the healthy control (HC) group (CN+SCD subgroups). In the connectivity analyses of PLV and COH waveforms, significant differences were observed, indicating a loss of synchronization in the beta band in response to standard stimuli in the MCI group. In addition, the absence of event-related desynchronization (ERD) indicated that information processing related to readiness and task performance in the beta band was not efficient in the MCI group. Furthermore, the observed decline in the P200 amplitude as the standard trials progressed suggests the impaired attention and inhibitory processes in the MCI group compared to the HC group. The aMCI subgroup showed high variability in COH values, while the naMCI subgroup showed impairments in their overall behavioral performance. Conclusion: These findings highlight the variability and connectivity measures can be used as markers of early cognitive decline; such measures can be assessed with simple and fast two-channel prefrontal ERP signals evoked by both standard and target stimuli. Our study provides deeper insight of cognitive impairment and the potential use of the prefrontal ERP connectivity measures to assess early cognitive decline.

Econeurobiology and brain development in children: key factors affecting development, behavioral outcomes, and school interventions.

The Econeurobiology of the brain describes the environment in which an individual's brain develops. This paper explores the complex neural mechanisms that support and evaluate enrichment at various stages of development, providing an overview of how they contribute to plasticity and enhancement of both achievement and health. It explores the deep benefits of enrichment and contrasts them with the negative effects of trauma and stress on brain development. In addition, the paper strongly emphasizes the integration of Gardner's intelligence types into the school curriculum environment. It emphasizes the importance of linking various intelligence traits to educational strategies to ensure a holistic approach to cognitive development. In the field of Econeurobiology, this work explains the central role of the environment in shaping the development of the brain. It examines brain connections and plasticity and reveals the impact of certain environmental factors on brain development in early and mid-childhood. In particular, the six key factors highlighted are an environment of support, nutrition, physical activity, music, sleep, and cognitive strategies, highlighting their potential to improve cognitive abilities, memory, learning, self-regulation, and social and emotional development. This paper also investigates the social determinants of health and education in the context of Econeurobiology. It emphasizes the transformative power of education in society, especially in vulnerable communities facing global challenges in accessing quality education.

Salience network segregation mediates the effect of tau pathology on mild behavioral impairment.

INTRODUCTION: A recently developed mild behavioral impairment (MBI) diagnostic framework standardizes the early characterization of neuropsychiatric symptoms in older adults. However, the joint contributions of Alzheimer's disease (AD) pathology and brain function to MBI remain unclear. METHODS: We test a novel model assessing direct relationships between AD biomarker status and MBI symptoms, as well as mediated effects through segregation of the salience and default-mode networks, using data from 128 participants with diagnosis of amnestic mild cognitive impairment or mild dementia-AD type. RESULTS: We identified a mediated effect of tau positivity on MBI through functional segregation of the salience network from the other high-level, association networks. There were no direct effects of AD biomarkers status on MBI. DISCUSSION: Our findings suggest that tau pathology contributes to MBI primarily by disrupting salience network function and emphasize the role of the salience network in mediating relationships between neuropathological changes and behavioral manifestations. HIGHLIGHTS: Network segregation mediates Alzheimer's disease (AD) pathology impact on mild behavioral impairment (MBI). The salience network is pivotal in linking tau pathology and MBI. This study used path analysis with AD biomarkers and network integrity. The study evaluated the roles of salience, default mode, and frontoparietal networks. This is the first study to integrate MBI with AD biomarkers and network functionality.

EEG biomarkers in Alzheimer's and prodromal Alzheimer's: a comprehensive analysis of spectral and connectivity features.

BACKGROUND: Biomarkers of Alzheimer's disease (AD) and mild cognitive impairment (MCI, or prodromal AD) are highly significant for early diagnosis, clinical trials and treatment outcome evaluations. Electroencephalography (EEG), being noninvasive and easily accessible, has recently been the center of focus. However, a comprehensive understanding of EEG in dementia is still needed. A primary objective of this study is to investigate which of the many EEG characteristics could effectively differentiate between individuals with AD or prodromal AD and healthy individuals. METHODS: We collected resting state EEG data from individuals with AD, prodromal AD, and normal cognition. Two distinct preprocessing pipelines were employed to study the reliability of the extracted measures across different datasets. We extracted 41 different EEG features. We have also developed a stand-alone software application package, Feature Analyzer, as a comprehensive toolbox for EEG analysis. This tool allows users to extract 41 EEG features spanning various domains, including complexity measures, wavelet features, spectral power ratios, and entropy measures. We performed statistical tests to investigate the differences in AD or prodromal AD from age-matched cognitively normal individuals based on the extracted EEG features, power spectral density (PSD), and EEG functional connectivity. RESULTS: Spectral power ratio measures such as theta/alpha and theta/beta power ratios showed significant differences between cognitively normal and AD individuals. Theta power was higher in AD, suggesting a slowing of oscillations in AD; however, the functional connectivity of the theta band was decreased in AD individuals. In contrast, we observed increased gamma/alpha power ratio, gamma power, and gamma functional connectivity in prodromal AD. Entropy and complexity measures after correcting for multiple electrode comparisons did not show differences in AD or prodromal AD groups. We thus catalogued AD and prodromal AD-specific EEG features. CONCLUSIONS: Our findings reveal that the changes in power and connectivity in certain frequency bands of EEG differ in prodromal AD and AD. The spectral power, power ratios, and the functional connectivity of theta and gamma could be biomarkers for diagnosis of AD and prodromal AD, measure the treatment outcome, and possibly a target for brain stimulation.

Assessing neurocognitive maturation in early adolescence based on baby and adult functional brain landscapes.

Adolescence is a period of growth in cognitive performance and functioning. Recently, data-driven measures of brain-age gap, which can index cognitive decline in older populations, have been utilized in adolescent data with mixed findings. Instead of using a data-driven approach, here we assess the maturation status of the brain functional landscape in early adolescence by directly comparing an individual's resting-state functional connectivity (rsFC) to the canonical early-life and adulthood communities. Specifically, we hypothesized that the degree to which a youth's connectome is better captured by adult networks compared to infant/toddler networks is predictive of their cognitive development. To test this hypothesis across individuals and longitudinally, we utilized the Adolescent Brain Cognitive Development (ABCD) Study at baseline (9-10 years; n = 6,489) and 2-year-follow-up (Y2: 11-12 years; n = 5,089). Adjusted for demographic factors, our anchored rsFC score (AFC) was associated with better task performance both across and within participants. AFC was related to age and aging across youth, and change in AFC statistically mediated the age-related change in task performance. In conclusion, we showed that a model-fitting-free index of the brain at rest that is anchored to both adult and baby connectivity landscapes predicts cognitive performance and development in youth.

Improved ADHD Diagnosis Using EEG Connectivity and Deep Learning through Combining Pearson Correlation Coefficient and Phase-Locking Value.

Attention Deficit Hyperactivity Disorder (ADHD) is a widespread neurobehavioral disorder affecting children and adolescents, requiring early detection for effective treatment. EEG connectivity measures can reveal the interdependencies between EEG recordings, highlighting brain network patterns and functional behavior that improve diagnostic accuracy. This study introduces a novel ADHD diagnostic method by combining linear and nonlinear brain connectivity maps with an attention-based convolutional neural network (Att-CNN). Pearson Correlation Coefficient (PCC) and Phase-Locking Value (PLV) are used to create fused connectivity maps (FCMs) from various EEG frequency subbands, which are then inputted into the Att-CNN. The attention module is strategically placed after the latest convolutional layer in the CNN. The performance of different optimizers (Adam and SGD) and learning rates are assessed. The suggested model obtained 98.88%, 98.41%, 98.19%, and 98.30% for accuracy, precision, recall, and F1 Score, respectively, using the SGD optimizer in the FCM of the theta band with a learning rate of 1e-1. With the use of FCM, Att-CNN, and advanced optimizers, the proposed technique has the potential to produce trustworthy instruments for the early diagnosis of ADHD, greatly enhancing both patient outcomes and diagnostic accuracy.

Testing Directed Acyclic Graph via Structural, Supervised and Generative Adversarial Learning.

In this article, we propose a new hypothesis testing method for directed acyclic graph (DAG). While there is a rich class of DAG estimation methods, there is a relative paucity of DAG inference solutions. Moreover, the existing methods often impose some specific model structures such as linear models or additive models, and assume independent data observations. Our proposed test instead allows the associations among the random variables to be nonlinear and the data to be time-dependent. We build the test based on some highly flexible neural networks learners. We establish the asymptotic guarantees of the test, while allowing either the number of subjects or the number of time points for each subject to diverge to infinity. We demonstrate the efficacy of the test through simulations and a brain connectivity network analysis.

Investigating the effects of cortico-cortical paired associative stimulation in the human brain: A systematic review and meta-analysis.

Recent decades have witnessed a rapid development of novel neuromodulation techniques that allow direct manipulation of cortical pathways in the human brain. These techniques, known as cortico-cortical paired stimulation (ccPAS), apply magnetic stimulation over two cortical regions altering interregional connectivity. This review evaluates ccPAS's effectiveness to induce plastic changes in cortical pathways in the healthy brain. A systematic database search identified 41 studies investigating the effect of ccPAS on neurophysiological or behavioural measures, and a subsequent multilevel meta-analysis focused on the standardized mean differences to assess ccPAS's efficacy. Most studies report significant neurophysiological and behavioural changes from ccPAS interventions across several brain networks, consistently showing medium effect sizes. Moderator analyses revealed limited influence of experimental manipulations on effect sizes. The multivariate approach and lack of small-study bias suggest reliable effect estimates. ccPAS is a promising tool to manipulate neuroplasticity in cortical pathways, showing reliable effects on brain cortical networks. Important areas for further research on the influence of experimental procedures and the potential of ccPAS for clinical interventions are highlighted.

Metabolic Brain PET Connectivity.

This review examines the role of metabolic connectivity based on fluorodeoxyglucose-PET in understanding brain network organization across neurologic disorders, with a focus on neurodegenerative diseases. The article explores key methodologies for metabolic connectivity study and highlights altered connectivity patterns in Alzheimer's, Parkinson's, frontotemporal dementia, and other conditions. It also discusses emerging applications, including single-subject analyses and brain-organ interactions.

Autism Spectrum Disorder and Atypical Brain Connectivity: Novel Insights from Brain Connectivity-Associated Genes by Combining Random Forest and Support Vector Machine Algorithm.

It is estimated that approximately one in every 100 children is diagnosed with autism spectrum disorder (ASD) around the globe. Currently, there are no curative pharmacological treatments for ASD. Discoveries on key molecular mechanisms of ASD are essential for precision medicine strategies. Considering that atypical brain connectivity patterns have been observed in individuals with ASD, this study examined the brain connectivity-associated genes and their putatively distinct expression patterns in brain samples from individuals diagnosed with ASD and using an iterative strategy based on random forest and support vector machine algorithms. We discovered a potential gene signature capable of differentiating ASD from control samples with a 92% accuracy. This gene signature comprised 14 brain connectivity-associated genes exhibiting enrichment in synapse-related terms. Of these genes, 11 were previously associated with ASD in varying degrees of evidence. Notably, NFKBIA, WNT10B, and IFT22 genes were identified as ASD-related for the first time in this study. Subsequent clustering analysis revealed the existence of two distinct ASD subtypes based on our gene signature. The expression levels of signature genes have the potential to influence brain connectivity patterns, potentially contributing to the manifestation of ASD. Further studies on the omics of ASD are called for so as to elucidate the molecular basis of ASD and for diagnostic and therapeutic innovations. Finally, we underscore that advances in ASD research can benefit from integrative bioinformatics and data science approaches.

Assessment of hyperacute cerebral ischemia using laser speckle contrast imaging.

Accurate diagnosis of cerebral ischemia severity is crucial for clinical decision-making. Laser speckle contrast imaging based cerebral blood flow imaging can help assess the severity of cerebral ischemia by monitoring changes in blood flow. In this study, we simulated hyperacute ischemia in rats, isolating arterial and venous flow-related signals from cortical vasculature. Pearson correlation was used to examine the correlation between damaged vessels. Granger causality analysis was utilized to investigate causality correlation in ischemic vessels. Resting state analysis revealed a negative Pearson correlation between regional arteries and veins. Following cerebral ischemia induction, a positive artery-vein correlation emerged, which vanished after blood flow reperfusion. Granger causality analysis demonstrating enhanced causality coefficients for middle artery-vein pairs during occlusion, with a stronger left-right arterial effect than that of right-left, which persisted after reperfusion. These processing approaches amplify the understanding of cerebral ischemic images, promising potential future diagnostic advancements.

Enhanced Cognition and Modulation of Brain Connectivity in Mild Neurocognitive Disorder: The Promise of Transcranial Pulse Stimulation.

Existing pharmacological treatments for mild neurocognitive disorder (NCD) offer limited effectiveness and adverse side effects. Transcranial pulse stimulation (TPS) utilizing ultrashort ultrasound pulses reaches deep brain regions and may circumvent conductivity issues associated with brain stimulation. This study addresses the gap in TPS research for mild NCD during a critical intervention period before irreversible cognitive degradation. Our objective was to explore the effectiveness and tolerability of TPS in older adults with mild NCD. In an open-label study, 17 older adults (including 10 females and 7 males) with mild NCD underwent TPS for two weeks with three sessions per week. Cognitive evaluations and fMRI scans were conducted pre- and post-intervention. The results indicated changes in functional connectivity in key brain regions, correlating with cognitive improvement at B = 0.087 (CI, 0.007-0.167; p = 0.038). However, cortical thickness measurements showed no significant differences. Here we show that TPS can enhance cognitive function within mild NCD. This proof-of-concept study suggests that TPS has potential as a non-invasive therapy used to attenuate cognitive decline, encouraging further investigation in larger randomized trials. The findings could influence clinical practice by introducing TPS as an adjunctive treatment option and potentially impact policy by promoting its inclusion in new treatment strategies for mild NCD.

Harmonization of Structural Brain Connectivity through Distribution Matching.

The increasing prevalence of multi-site diffusion-weighted magnetic resonance imaging (dMRI) studies potentially offers enhanced statistical power for investigating brain structure. However, these studies face challenges due to variations in scanner hardware and acquisition protocols. While several methods exist for dMRI data harmonization, few specifically address structural brain connectivity. We introduce a new distribution-matching approach to harmonizing structural brain connectivity across different sites and scanners. We evaluate our method using structural brain connectivity data from two distinct datasets of OASIS-3 and ADNI-2, comparing its performance to the widely used ComBat method. Our approach is meant to align the statistical properties of connectivity data from these two datasets. We examine the impact of harmonization on the correlation of brain connectivity with the Mini-Mental State Examination score and age. Our results demonstrate that our distribution-matching technique more effectively harmonizes structural brain connectivity, often producing stronger and more significant correlations compared to ComBat. Qualitative assessments illustrate the desired distributional alignment of ADNI-2 with OASIS-3, while quantitative evaluations confirm robust performance. This work contributes to the growing field of dMRI harmonization, potentially improving the reliability and comparability of structural connectivity studies that combine data from different sources in neuroscientific and clinical research.

Enhanced network synchronization connectivity following transcranial direct current stimulation (tDCS) in bipolar depression: Effects on EEG oscillations and deep learning-based predictors of clinical remission.

AIM: To investigate oscillatory networks in bipolar depression, effects of a home-based tDCS treatment protocol, and potential predictors of clinical response. METHODS: 20 participants (14 women) with bipolar disorder, mean age 50.75 ± 10.46 years, in a depressive episode of severe severity (mean Montgomery-Åsberg Rating Scale (MADRS) score 24.60 ± 2.87) received home-based transcranial direct current stimulation (tDCS) treatment for 6 weeks. Clinical remission defined as MADRS score < 10. Resting-state EEG data were acquired at baseline, prior to the start of treatment, and at the end of treatment, using a portable 4-channel EEG device (electrode positions: AF7, AF8, TP9, TP10). EEG band power was extracted for each electrode and phase locking value (PLV) was computed as a functional connectivity measure of phase synchronization. Deep learning was applied to pre-treatment PLV features to examine potential predictors of clinical remission. RESULTS: Following treatment, 11 participants (9 women) attained clinical remission. A significant positive correlation was observed with improvements in depressive symptoms and delta band PLV in frontal and temporoparietal regional channel pairs. An interaction effect in network synchronization was observed in beta band PLV in temporoparietal regions, in which participants who attained clinical remission showed increased synchronization following tDCS treatment, which was decreased in participants who did not achieve clinical remission. Main effects of clinical remission status were observed in several PLV bands: clinical remission following tDCS treatment was associated with increased PLV in frontal and temporal regions and in several frequency bands, including delta, theta, alpha and beta, as compared to participants who did not achieve clinical remission. The highest deep learning prediction accuracy 69.45 % (sensitivity 71.68 %, specificity 66.72 %) was obtained from PLV features combined from theta, beta, and gamma bands. CONCLUSIONS: tDCS treatment enhances network synchronization, potentially increasing inhibitory control, which underscores improvement in depressive symptoms. Baseline EEG-based measures might aid predicting clinical response.

Repetitive Transcranial Magnetic Stimulation on individualized spots based on task fMRI improves swallowing function in post-stroke dysphagia.

BACKGROUND: Functional magnetic resonance imaging (fMRI) has not previously been used to localize the swallowing functional area in repetitive transcranial magnetic stimulation (rTMS) treatment for post-stroke dysphagia; Traditionally, the target area for rTMS is the hotspot, which is defined as the specific region of the brain identified as the optimal location for transcranial magnetic stimulation (TMS). This study aims to compare the network differences between the TMS hotspot and the saliva swallowing fMRI activation to determine the better rTMS treatment site and investigate changes in functional connectivity related to post-stroke dysphagia using resting-state fMRI. METHODS: Using an information-based approach, we conducted a single case study to explore neural functional connectivity in a patient with post-stroke dysphagia before, immediately after rTMS, and four weeks after rTMS intervention. 20 healthy participants underwent fMRI and TMS hotspot localization as a control group. Neural network alterations were assessed , and functional connections related to post-stroke dysphagia were examined using resting-state fMRI. RESULTS: Compared to the TMS-induced hotspots, the fMRI activation peaks were located significantly more posteriorly and exhibited stronger functional connectivity with bilateral postcentral gyri. Following rTMS treatment, this patient developed functional connection between the brainstem and the bilateral insula, caudate, anterior cingulate cortex, and cerebellum. CONCLUSION: The saliva swallowing fMRI activation peaks show more intense functional connectivity with bilateral postcentral gyri compared to the TMS hotspots. Activation peak-guided rTMS treatment improves swallowing function in post-stroke dysphagia. This study proposes a novel and potentially more efficacious therapeutic target for rTMS, expanding its therapeutic options for treating post-stroke dysphagia.

Connectivity Changes Following Episodic Future Thinking in Alcohol Use Disorder.

[Please note that in order to respond to reviewers request we had exceed the 300 word limit. The following is NOT revised from the first submission, please see the actual revised manuscript file for the reviewer-driven changes]. INTRODUCTION: Recent addiction and obesity-related research suggest that episodic future thinking (EFT) can serve as a promising intervention to promote healthy decision making. This study investigated the neural effects of EFT in alcohol use disorder (AUD). METHODS: Participants received either a brief EFT or control intervention to examine differences in resting-state connectivity. We then used these findings to characterize psychophysiological interaction (PPI) differences during a delay discounting (DD) fMRI task. In addition, we used a second control group of AUD participants without any intervention to reproduce and aid in interpreting our key findings. RESULTS: EFT participants, but not controls, showed statistically improved discounting rates - a behavioral marker for addiction. Resting state analyses of the left hippocampus revealed connectivity differences in the frontal poles. The directionality of this difference suggested that EFT reduced a hypoconnectivity relationship between these regions in AUD. We also found resting state connectivity differences between the salience network and the right dorsolateral prefrontal cortex (R DLPFC), which then led us to discover R-to-L DLPFC PPI differences during DD. Moreover, the resting state salience-to-DLPFC functional connectivity showed an inverse relationship to discounting rate while hyperconnectivity between left and right DLPFC reflected slower reaction times during difficult DD trials. DISCUSSION: These findings suggest that EFT produces beneficial changes in neural connectivity patterns in AUD. The alterations in connectivity highlight potential mechanisms underlying the effectiveness of EFT in improving decision-making in AUD. Understanding these neural effects may contribute to the further development of targeted interventions for AUD and related disorders.

Rapid reconfiguration of cortical networks after repeated exposure to visual-vestibular conflicts.

Visual-vestibular conflicts can induce motion sickness and further postural instability. Visual-vestibular habituation is recommended to reduce the symptoms of motion sickness and improve postural stability with an altered multisensory reweighting progress. However, it is unclear how the human brain reweights multisensory information after repeated exposure to visual-vestibular conflicts. Therefore, we synchronized a rotating platform and a virtual scene to present visual-vestibular congruent (natural visual stimulation) and incongruent (conflicted visual stimulation) conditions and collected EEG and center of pressure (COP) data. We constructed the effective brain connectivity of region of interest (ROI) derived from source-space EEG in theta-band activity, and quantified the postural stability and the inflow and outflow of each ROI. We found repeated exposure to congruent and incongruent conditions both decreased COP path length and increased COP complexity. Besides, we found that repeated exposure to the incongruent environment decreased the inflow into visual cortex, suggesting the brain down-weighted the less reliable visual information for postural stability. In contrast, repeated exposure to the congruent environment increased the inflow into posterior parietal cortex and the outflow from visual cortex and S1, suggesting an increase in efficiency of multisensory integration. We concluded that repeated exposure to congruent and incongruent conditions both improved postural stability with different multisensory reweighting patterns as revealed by different dynamic changes of brain networks.

Differences in Topography of Individual Amyloid Brain Networks by Amyloid PET Images in Healthy Control, Mild Cognitive Impairment, and Alzheimer's Disease.

Amyloid plaques, implicated in Alzheimer's disease, exhibit a spatial propagation pattern through interconnected brain regions, suggesting network-driven dissemination. This study utilizes PET imaging to investigate these brain connections and introduces an innovative method for analyzing the amyloid network. A modified version of a previously established method is applied to explore distinctive patterns of connectivity alterations across cognitive performance domains. PET images illustrate differences in amyloid accumulation, complemented by quantitative network indices. The normal control group shows minimal amyloid accumulation and preserved network connectivity. The MCI group displays intermediate amyloid deposits and partial similarity to normal controls and AD patients, reflecting the evolving nature of cognitive decline. Alzheimer's disease patients exhibit high amyloid levels and pronounced disruptions in network connectivity, which are reflected in low levels of global efficiency (Eg) and local efficiency (Eloc). It is mostly in the temporal lobe where connectivity alterations are found, particularly in regions related to memory and cognition. Network connectivity alterations, combined with amyloid PET imaging, show potential as discriminative markers for different cognitive states. Dataset-specific variations must be considered when interpreting connectivity patterns. The variability in MCI and AD overlap emphasizes the heterogeneity in cognitive decline progression, suggesting personalized approaches for neurodegenerative disorders. This study contributes to understanding the evolving network characteristics associated with normal cognition, MCI, and AD, offering valuable insights for developing diagnostic and prognostic markers.

Effect of Dialysis on Structural Brain Connectivity in Patients with End-Stage Renal Disease.

INTRODUCTION: Patients with end-stage renal disease (ESRD) are known to have reduced structural and functional brain connectivity in the brain regions associated with cognitive function. However, the effect of dialysis on brain connectivity remains unclear. This study aimed to evaluate the effects of dialysis on structural brain connectivity in patients with ESRD. METHODS: This prospective study included 20 patients with ESRD in the pre-dialysis stage and 35 healthy controls. The patients underwent T2-weighted and three-dimensional T1-weighted magnetic resonance imaging before and 3 months after dialysis initiation. Moreover, the cortical thickness was calculated. We applied graph theoretical analysis to calculate the structural covariance network based on cortical thickness. We compared the cortical thickness and structural covariance network of patients with ESRD in the pre-dialysis stage with those of healthy controls and with those of patients with ESRD in the post-dialysis stage. RESULTS: The mean cortical thickness in both hemispheres was lower in patients with ESRD in the pre-dialysis stage than in healthy controls (2.296 vs. 2.354, p = 0.030; 2.282 vs. 2.362, p = 0.004, respectively) and was higher in patients with ESRD in the post-dialysis stage than in those in the pre-dialysis stage (2.333 vs. 2.296, p = 0.001; 2.322 vs. 2.282, p = 0.002, respectively). Analysis of the structural covariance network revealed that the assortative coefficient was lower in patients with ESRD in the pre-dialysis stage than in healthy controls (-0.062 vs. -0.031, p = 0.029) and was higher in patients with ESRD in the post-dialysis stage than in those in the pre-dialysis stage (-0.002 vs. -0.062, p = 0.042). CONCLUSION: We observed differences in the cortical thickness and structural covariance networks before and after dialysis in patients with ESRD. This indicates that dialysis affects structural brain connectivity, contributing to the understanding of the pathophysiological mechanism of cognitive function alterations resulting from dialysis in patients with ESRD.

Regional desynchronization of microglial activity is associated with cognitive decline in Alzheimer's disease.

BACKGROUND: Microglial activation is one hallmark of Alzheimer disease (AD) neuropathology but the impact of the regional interplay of microglia cells in the brain is poorly understood. We hypothesized that microglial activation is regionally synchronized in the healthy brain but experiences regional desynchronization with ongoing neurodegenerative disease. We addressed the existence of a microglia connectome and investigated microglial desynchronization as an AD biomarker. METHODS: To validate the concept, we performed microglia depletion in mice to test whether interregional correlation coefficients (ICCs) of 18 kDa translocator protein (TSPO)-PET change when microglia are cleared. Next, we evaluated the influence of dysfunctional microglia and AD pathophysiology on TSPO-PET ICCs in the mouse brain, followed by translation to a human AD-continuum dataset. We correlated a personalized microglia desynchronization index with cognitive performance. Finally, we performed single-cell radiotracing (scRadiotracing) in mice to ensure the microglial source of the measured desynchronization. RESULTS: Microglia-depleted mice showed a strong ICC reduction in all brain compartments, indicating microglia-specific desynchronization. AD mouse models demonstrated significant reductions of microglial synchronicity, associated with increasing variability of cellular radiotracer uptake in pathologically altered brain regions. Humans within the AD-continuum indicated a stage-depended reduction of microglia synchronicity associated with cognitive decline. scRadiotracing in mice showed that the increased TSPO signal was attributed to microglia. CONCLUSION: Using TSPO-PET imaging of mice with depleted microglia and scRadiotracing in an amyloid model, we provide first evidence that a microglia connectome can be assessed in the mouse brain. Microglia synchronicity is closely associated with cognitive decline in AD and could serve as an independent personalized biomarker for disease progression.