Brain Network Connectivity Research Articles

Depression in dementia with Lewy bodies: a critical update.

Depression with an estimated prevalence of 35% is a frequent manifestation of dementia with Lewy bodies (DLB), having negative effects on cognitive performance and life expectancy, yet the underlying neurobiology is poorly understood and most likely heterogeneous. Depressive symptoms in DLB can occur during the clinical course and, together with apathy, is a common prodromal neuropsychiatric symptom of this neurocognitive disorder in the group of Lewy body synucleinopathies. There are no essential differences in the frequency of depression in DLB and Parkinson disease-dementia (PDD), while its severity is up to twice as high as in Alzheimer disease (AD). Depression in DLB that is frequently underdiagnosed and undertreated, has been related to a variety of pathogenic mechanisms associated with the basic neurodegenerative process, in particular dysfunctions of neurotransmitter systems (decreased monoaminergic/serotonergic, noradrenergic and dopaminergic metabolism), α-synuclein pathology, synaptic zinc dysregulation, proteasome inhibition, gray matter volume loss in prefrontal and temporal areas as well as dysfunction of neuronal circuits with decreased functional connectivity of specific brain networks. Pharmacotherapy should avoid tricyclic antidepressants (anticholinergic adverse effects), second-generation antidepressants being a better choice, while modified electroconvulsive therapy, transcranial magnetic stimulation therapy and deep brain stimulation may be effective for pharmacotherapy-resistant cases. Since compared to depression in other dementias like Alzheimer disease and other parkinsonian syndromes, our knowledge of its molecular basis is limited, and further studies to elucidate the heterogeneous pathogenesis of depression in DLB are warranted.

Precision functional MRI mapping reveals distinct connectivity patterns for depression associated with traumatic brain injury.

Depression associated with traumatic brain injury (TBI) is believed to be clinically distinct from primary major depressive disorder (MDD) and may be less responsive to conventional treatments. Brain connectivity differences between the dorsal attention network (DAN), default mode network (DMN), and subgenual cingulate have been implicated in TBI and MDD. To characterize these distinctions, we applied precision functional mapping of brain network connectivity to resting-state functional magnetic resonance imaging data from five published patient cohorts, four discovery cohorts (n = 93), and one replication cohort (n = 180). We identified a distinct brain connectivity profile in TBI-associated depression that was independent of TBI, MDD, posttraumatic stress disorder (PTSD), depression severity, and cohort. TBI-associated depression was independently associated with decreased DAN-subgenual cingulate connectivity, increased DAN-DMN connectivity, and the combined effect of both. This effect was stronger when using precision functional mapping relative to group-level network maps. Our results support the possibility of a physiologically distinct "TBI affective syndrome," which may benefit from individualized neuromodulation approaches to target its distinct neural circuitry.

Estimation of Discriminative Multimodal Brain Network Connectivity Using Message-Passing-Based Nonlinear Network Fusion.

Effective estimation of brain network connectivity enables better unraveling of the extraordinary complexity interactions of brain regions and helps in auxiliary diagnosis of psychiatric disorders. Considering different modalities can provide comprehensive characterizations of brain connectivity, we propose the message-passing-based nonlinear network fusion (MP-NNF) algorithm to estimate multimodal brain network connectivity. In the proposed method, the initial functional and structural networks were computed from fMRI and DTI separately. Then, we update every unimodal network iteratively, making it more similar to the others in every iteration, and finally converge to one unified network. The estimated brain connectivities integrate complementary information from multiple modalities while preserving their original structure, by adding the strong connectivities present in unimodal brain networks and eliminating the weak connectivities. The effectiveness of the method was evaluated by applying the learned brain connectivity for the classification of major depressive disorder (MDD). Specifically, 82.18% classification accuracy was achieved even with the simple feature selection and classification pipeline, which significantly outperforms the competing methods. Exploration of brain connectivity contributed to MDD identification suggests that the proposed method not only improves the classification performance but also was sensitive to critical disease-related neuroimaging biomarkers.

Elevated corticosterone after fear learning impairs remote auditory memory retrieval and alters brain network connectivity.

Glucocorticoids are potent memory modulators that can modify behavior in an adaptive or maladaptive manner. Elevated glucocorticoid levels after learning promote memory consolidation at recent time points, but their effects on remote time points are not well established. Here we set out to assess whether corticosterone (CORT) given after learning modifies remote fear memory. To that end, mice were exposed to a mild auditory fear conditioning paradigm followed by a single 2 mg/kg CORT injection, and after 28 d, auditory memory was assessed. Neuronal activation was investigated using immunohistochemistry for the immediate early gene c-Fos, and coactivation of brain regions was determined using a correlation matrix analysis. CORT-treated mice displayed significantly less remote auditory memory retrieval. While the net activity of studied brain regions was similar compared with the control condition, CORT-induced remote memory impairment was associated with altered correlated activity between brain regions. Specifically, connectivity of the lateral amygdala with the basal amygdala and the dorsal dentate gyrus was significantly reduced in CORT-treated mice, suggesting disrupted network connectivity that may underlie diminished remote memory retrieval. Elucidating the pathways underlying these effects could help provide mechanistic insight into the effects of stress on memory and possibly provide therapeutic targets for psychopathology.

Similar connectivity of gut microbiota and brain activity networks is mediated by animal protein and lipid intake in children from a Mexican indigenous population.

The gut microbiota-brain axis is a complex communication network essential for host health. Any long-term disruption can affect higher cognitive functions, or it may even result in several chronic neurological diseases. The type and diversity of nutrients an individual consumes are essential for developing the gut microbiota (GM) and the brain. Hence, dietary patterns might influence networks communication of this axis, especially at the age that both systems go through maturation processes. By implementing Mutual Information and Minimum Spanning Tree (MST); we proposed a novel combination of Machine Learning and Network Theory techniques to study the effect of animal protein and lipid intake on the connectivity of GM and brain cortex activity (BCA) networks in children from 5-to 10 years old from an indigenous community in the southwest of México. Socio-ecological conditions in this nonwestern lifestyle community are very homogeneous among its inhabitants but it shows high individual heterogeneity in the consumption of animal products. Results suggest that MST, the critical backbone of information flow, diminishes under low protein and lipid intake. So, under these nonwestern regimens, deficient animal protein and lipid consumption diets may significantly affect the GM-BCA connectivity in crucial development stages. Finally, MST offers us a metric that unifies biological systems of different nature to evaluate the change in their complexity in the face of environmental pressures or disturbances. Effect of Diet on gut microbiota and brain networks connectivity.

Frequency-dependent connectivity in large-scale resting-state brain networks during sleep.

Functional connectivity (FC) during sleep has been shown to break down as non-rapid eye movement (NREM) sleep deepens before returning to a state closer to wakefulness during rapid eye movement (REM) sleep. However, the specific spatial and temporal signatures of these fluctuations in connectivity patterns remain poorly understood. This study aimed to investigate how frequency-dependent network-level FC fluctuates during nocturnal sleep in healthy young adults using high-density electroencephalography (hdEEG). Specifically, we examined source-localized FC in resting-state networks during NREM2, NREM3 and REM sleep (sleep stages scored using a semi-automatic procedure) in the first three sleep cycles of 29 participants. Our results showed that FC within and between all resting-state networks decreased from NREM2 to NREM3 sleep in multiple frequency bands and all sleep cycles. The data also highlighted a complex modulation of connectivity patterns during the transition to REM sleep whereby delta and sigma bands hosted a persistence of the connectivity breakdown in all networks. In contrast, a reconnection occurred in the default mode and the attentional networks in frequency bands characterizing their organization during wake (i.e., alpha and beta bands, respectively). Finally, all network pairs (except the visual network) showed higher gamma-band FC during REM sleep in cycle three compared to earlier sleep cycles. Altogether, our results unravel the spatial and temporal characteristics of the well-known breakdown in connectivity observed as NREM sleep deepens. They also illustrate a complex pattern of connectivity during REM sleep that is consistent with network- and frequency-specific breakdown and reconnection processes.

Abnormal dynamic functional network connectivity in patients with early-onset bipolar disorder.

To explore the changes in dynamic functional brain network connectivity (dFNC) in patients with early-onset bipolar disorder (BD). Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected from 39 patients with early-onset BD and 22 healthy controls (HCs). Four repeated and stable dFNC states were characterised by independent component analysis (ICA), sliding time windows and k-means clustering, and three dFNC temporal metrics (fraction of time, mean dwell time and number of transitions) were obtained. The dFNC temporal metrics and the differences in dFNC between the two groups in different states were evaluated, and the correlations between the differential dFNC metrics and neuropsychological scores were analysed. The dFNC analysis showed four connected patterns in all subjects. Compared with the HCs, the dFNC patterns of early-onset BD were significantly altered in all four states, mainly involving impaired cognitive and perceptual networks. In addition, early-onset BD patients had a decreased fraction of time and mean dwell time in state 2 and an increased mean dwell time in state 3 (p < 0.05). The mean dwell time in state 3 of BD showed a positive correlation trend with the HAMA score (r = 0.4049, p = 0.0237 × 3 > 0.05 after Bonferroni correction). Patients with early-onset BD had abnormal dynamic properties of brain functional network connectivity, suggesting that their dFNC was unstable, mainly manifesting as impaired coordination between cognitive and perceptual networks. This study provided a new imaging basis for the neuropathological study of emotional and cognitive deficits in early-onset BD.

Functional Brain Network Connectivity Changes in Children and Adolescents with Attention- Deficit Hyperactivity Disorder: A Resting-State Brain Functional Magnetic Resonance Imaging Study

Background: ADHD is one of the most common psychiatric disorders in children and adolescents. Altered functional connectivity has been associated with ADHD symptoms. This study aimed to investigate abnormal changes in the functional connectivity of resting-state brain networks (RSNs) among children and adolescents with different subtypes of ADHD.&#x0D; Methods: This study was performed on 50 participants Group A: included 30 children and adolescents with ADHD Group B: included 20 normal typically developing controls. All participants aged between 7 and 16 years. All study patients. All participants underwent a neuropsychological assessment by applying the DSM-5 criteria to them. All study children and adolescents will undergo a R-fMRI examination, including Blood oxygenation level dependent BOLD-MRI and pulsed Arterial spin labeling MRI (PASL-MRI).&#x0D; Results: According to Conner’s Rating scale, there was a significant difference between ADHD subtypes and severity. Children with combined subtype were more likely to have severe degree in both teacher and parent editions of Conner’s Rating Scale. On the other hand, children with inattentive subtype had less severe degrees by both teacher and parent edition. There were a statistically significance difference between both genders as regard ADHD severity according to Conner’s Rating scale (Teacher and parent editions) (P value = 0.049, 0.028 respectively). Males were significantly affected by moderate and severe type compared to females whose affection were mostly mild and moderate. According to CBCL, most of ADHD children had low competence parameters. There was a statistically significant difference (P value &lt; 0.001) on the type of ADHD and social competence in the studied children, where the lowest parameters were found in children with hyperactive type.&#x0D; Conclusion: There was a statistically significant difference on the type of ADHD and social competence, where the lowest parameters were found in children with hyperactive type.

Simple topological task-based functional connectivity features predict longitudinal behavioral change of fluid reasoning in the RANN cohort

Recent attention has been given to topological data analysis (TDA), and more specifically persistent homology (PH), to identify the underlying shape of brain network connectivity beyond simple edge pairings by computing connective components across different connectivity thresholds (see Sizemore et al., 2019). In the present study, we applied PH to task-based functional connectivity, computing 0-dimension Betti (B0) curves and calculating the area under these curves (AUC); AUC indicates how quickly a single connected component is formed across correlation filtration thresholds, with lower values interpreted as potentially analogous to lower whole-brain system segregation (e.g., Gracia-Tabuenca et al., 2020). One hundred sixty-three participants from the Reference Ability Neural Network (RANN) longitudinal lifespan cohort (age 20–80 years) were tested in-scanner at baseline and five-year follow-up on a battery of tests comprising four domains of cognition (i.e., Stern et al., 2014). We tested for 1.) age-related change in the AUC of the B0 curve over time, 2.) the predictive utility of AUC in accounting for longitudinal change in behavioral performance and 3.) compared system segregation to the PH approach. Results demonstrated longitudinal age-related decreases in AUC for Fluid Reasoning, with these decreases predicting longitudinal declines in cognition, even after controlling for demographic and brain integrity factors; moreover, change in AUC partially mediated the effect of age on change in cognitive performance. System segregation also significantly decreased with age in three of the four cognitive domains but did not predict change in cognition. These results argue for greater application of TDA to the study of aging.

Altered functional connectivity between cortical networks associated with inhibitory control in trauma-exposed females

Post-traumatic stress disorder (PTSD) is associated with impaired inhibitory control and alterations in large-scale brain network connectivity. However, few studies to date have examined the construct of inhibitory control as it relates to resting-state functional connectivity (rsFC) in a population with PTSD or trauma-exposure. The present study investigated the relationship between impaired inhibitory control and rsFC within the default mode network (DMN), central executive network (CEN), and salience network (SN) in a sample of females exposed to interpersonal trauma with and without PTSD (n = 67). Participants completed a classic Color-Word Stroop task as a measure of inhibitory control and two resting-state fMRI scans. We conducted voxelwise rsFC analyses with seed regions in the DMN, CEN, and SN and voxelwise linear regression analyses to examine the relationship between inhibitory control and rsFC of these networks across the sample. Better Stroop performance was negatively associated with total self-reported PTSD symptoms. An analysis of PTSD symptom clusters indicated that better Stroop performance was also associated with re-experiencing and hyperarousal symptoms, but not avoidance PTSD symptoms. Decreased coupling between the CEN and the DMN was associated with better inhibitory control in this sample of trauma-exposed females. These findings lend support to the hypothesis that efficient switching between these networks may contribute to better performance on cognitive and attentional tasks in trauma-exposed individuals.

Generative AI for brain image computing and brain network computing: a review.

Recent years have witnessed a significant advancement in brain imaging techniques that offer a non-invasive approach to mapping the structure and function of the brain. Concurrently, generative artificial intelligence (AI) has experienced substantial growth, involving using existing data to create new content with a similar underlying pattern to real-world data. The integration of these two domains, generative AI in neuroimaging, presents a promising avenue for exploring various fields of brain imaging and brain network computing, particularly in the areas of extracting spatiotemporal brain features and reconstructing the topological connectivity of brain networks. Therefore, this study reviewed the advanced models, tasks, challenges, and prospects of brain imaging and brain network computing techniques and intends to provide a comprehensive picture of current generative AI techniques in brain imaging. This review is focused on novel methodological approaches and applications of related new methods. It discussed fundamental theories and algorithms of four classic generative models and provided a systematic survey and categorization of tasks, including co-registration, super-resolution, enhancement, classification, segmentation, cross-modality, brain network analysis, and brain decoding. This paper also highlighted the challenges and future directions of the latest work with the expectation that future research can be beneficial.

820-nm Transcranial near-infrared stimulation on the left DLPFC relieved anxiety: A randomized, double-blind, sham-controlled study

ObjectiveGeneralized anxiety disorder (GAD) is a chronic mood disease associated with abnormal brain network connections, including decreased activity in the left dorsolateral prefrontal cortex (DLPFC). Cortical excitability can be increased with 820-nm transcranial near-infrared stimulation (tNIRS), while transcranial magnetic stimulation with electroencephalography (TMS-EEG) can help evaluate time-varying brain network connectivity. A randomized, double-blind, sham-controlled trial was conducted to assess the efficacy of tNIRS on the left DLPFC and the impact on time-varying brain network connections in GAD patients. MethodsA total of 36 GAD patients were randomized to receive active or sham tNIRS for 2 weeks. Clinical psychological scales were assessed before, after, and at the 2-, 4-, and 8-week follow-ups. TMS-EEG was performed for 20 min before and immediately after tNIRS treatment. The healthy controls did not receive tNIRS and only had TMS-EEG data collected once in the resting state. ResultsThe Hamilton Anxiety Scale (HAMA) scores of the active stimulation group decreased post-treatment compared with the sham group (P = 0.021). The HAMA scores of the active stimulation group at the 2-, 4-, and 8-week follow-up assessments were lower than those before treatment (P < 0.05). The time-varying EEG network pattern showed an information outflow from the left DLPFC and the left posterior temporal region after active treatment. ConclusionHerein, 820-nm tNIRS targeting the left DLPFC had significant positive effects on therapy for GAD that lasted at least 2 months. tNIRS may reverse the abnormality of time-varying brain network connections in GAD.

Altered regional brain activity and functional connectivity in resting-state brain networks associated with psychological erectile dysfunction.

Erectile dysfunction (ED), especially psychological ED (pED), is usually accompanied with psychological factors, which are related to abnormal activity in brain regions involved in sexual behavior. However, the mechanisms underlying functional changes in the brain of pED are still unclear. The present study aimed to explore the abnormalities of brain function, as well as their relationships with sexual behavior and emotion in pED patients. Resting state functional magnetic resonance imaging (rs-fMRI) data were collected from 31 pED patients to 31 healthy controls (HCs). The values of amplitude of fractional amplitude of low-frequency fluctuation (fALFF) and functional connectivity (FC) were calculated and compared between groups. In addition, the associations between abnormal brain regions and clinical features were evaluated by Pearson correlation analyses. Compared to HCs, pED patients demonstrated decreased fALFF values in the left medial superior frontal gyrus (had decreased FC values with the left dorsolateral superior frontal gyrus), the left lingual gyrus (had decreased FC values with the left parahippocamal gyrus and insula), the left putamen (had decreased FC values with the right caudate) and the right putamen (had decreased FC values with the left putamen and the right caudate). The fALFF values of the left medial superior frontal gyrus were negatively correlated with the fifth item scores of International Index of Erectile Function (IIEF-5). Negative relationships were found between fALFF values of the left putamen and the second item scores of Arizona Sexual Scale (ASEX). FC values between the right putamen and caudate were negatively related to the state scores of State-Trait Anxiety Inventory (STAI-S). Altered brain function were found in the medial superior frontal gyrus and caudate-putamen of pED patients, which were associated with sexual function and psychological condition. These findings provided new insights into the central pathological mechanisms of pED.

EEG-based brain connectivity analysis in autism spectrum disorder: Unraveling the effects of bumetanide treatment

Emerging evidence suggests that cognitive impairment associated with brain network disorders in people with autism could be improved with medications such as bumetanide. However, the extent to which bumetanide is effective in improving brain function in these individuals has not been adequately studied. The main purpose of this study is to investigate the nonlinear brain connectivity and topological changes in brain networks of people with autism spectrum disorders (ASD) after a three-month course of bumetanide treatment. We used electroencephalography (EEG) data of nine participants recorded during the face emotion recognition activity in two stages before and after bumetanide treatment. Brain connectivity matrix was calculated using a neural network-based estimator. Graph criteria and statistical tests have been used to determine the effects of bumetanide treatment on children and adolescents with autism. Bumetanide treatment significantly alters the brain connectivity networks based on stimuli type. Differences in brain connectivity related to the sad stimuli are more significant. The most of the significant changes of the strength graph metric was in the occipital electrodes and electrodes related to the right hemisphere. These results suggest that bumetanide may affect effective connectivity and be used a promising treatment for improving social interactions in patients with autism. It also suggests that brain connectivity patterns can be considered as a neural marker to be used in the development of new therapies.

Evidence of neuroplasticity with brain-computer interface in a randomized trial for post-stroke rehabilitation: a graph-theoretic study of subnetwork analysis.

Brain-computer interface (BCI) has been widely used for functional recovery after stroke. Understanding the brain mechanisms following BCI intervention to optimize BCI strategies is crucial for the benefit of stroke patients. Forty-six patients with upper limb motor dysfunction after stroke were recruited and randomly divided into the control group or the BCI group. The primary outcome was measured by the assessment of Fugl-Meyer Assessment of Upper Extremity (FMA-UE). Meanwhile, we performed resting-state functional magnetic resonance imaging (rs-fMRI) in all patients, followed by independent component analysis (ICA) to identify functionally connected brain networks. Finally, we assessed the topological efficiency of both groups using graph-theoretic analysis in these brain subnetworks. The FMA-UE score of the BCI group was significantly higher than that of the control group after treatment (p = 0.035). From the network topology analysis, we first identified seven subnetworks from the rs-fMRI data. In the following analysis of subnetwork properties, small-world properties including γ (p = 0.035) and σ (p = 0.031) within the visual network (VN) decreased in the BCI group. For the analysis of the dorsal attention network (DAN), significant differences were found in assortativity (p = 0.045) between the groups. Additionally, the improvement in FMA-UE was positively correlated with the assortativity of the dorsal attention network (R = 0.498, p = 0.011). Brain-computer interface can promote the recovery of upper limbs after stroke by regulating VN and DAN. The correlation trend of weak intensity proves that functional recovery in stroke patients is likely to be related to the brain's visuospatial processing ability, which can be used to optimize BCI strategies. The trial is registered in the Chinese Clinical Trial Registry, number ChiCTR2000034848. Registered 21 July 2020.

A multi-head residual connection GCN for EEG emotion recognition

Electroencephalography (EEG) emotion recognition is a crucial aspect of human-computer interaction. However, conventional neural networks have limitations in extracting profound EEG emotional features. This paper introduces a novel multi-head residual graph convolutional neural network (MRGCN) model that incorporates complex brain networks and graph convolution networks. The decomposition of multi-band differential entropy (DE) features exposes the temporal intricacy of emotion-linked brain activity, and the combination of short and long-distance brain networks can explore complex topological characteristics. Moreover, the residual-based architecture not only enhances performance but also augments classification stability across subjects. The visualization of brain network connectivity offers a practical technique for investigating emotional regulation mechanisms. The MRGCN model exhibits average classification accuracies of 95.8% and 98.9% for the DEAP and SEED datasets, respectively, highlighting its excellent performance and robustness.

Genetic association of apolipoprotein E genotype with EEG alpha rhythm and resting‐state functional MRI connectivity in non‐demented adults during aging

AbstractBackgroundThe ε4 allele of the apolipoprotein E (APOE) genotype is a major genetic risk factor for Alzheimer’s disease (AD), but the mechanisms underlying its influence are not completely understood. The study was aimed at determining the possible effect of APOE genotype on spontaneous EEG alpha rhythm characteristics and on the resting‐state functional MRI (fMRI) connectivity (rsFC) in large brain networks in non‐demented adults during aging.MethodWe examined individual alpha peak frequency (IAPF), the EEG alpha sub‐bands power and the fMRI rsFC in 137 non‐demented volunteers, age range 26‐79 years, stratified by APOE genotype. APOE ε3/ε3 subgroup (APOE4‐) included 91 subjects, APOE ε4/ε3+ (APOE4+) subgroup – 46 subjects. The individuals underwent resting‐state fMRI of the brain, the CONN Matlab/SPM‐based toolbox was used to study connectivity in brain networks (Whitfield‐Gabrieli and Nieto‐Castanon, 2012). Informed written consent was obtained from all participants. All subjects underwent a neurological examination and cognitive screening.ResultThe presence of the APOE4+ genotype was associated with lower IAPF and lower relative power of the 11‐13Hz alpha sub‐bands. The age‐related alpha rhythm slowing was more pronounced in the APOE4+ than in the APOE4+ non‐carriers. The negative rsFC between the left hippocampus and the right posterior parietal cortex was reduced for the APOE4+ carriers compared to the APOE4‐ carriers. EEG IAFT showed a significant inverse correlation between rsFC of the left hippocampus and the right posterior parietal cortex.ConclusionAPOE4+ genotype is associated with marked age‐related alpha rhythm slowing and the dysfunction of the inhibitory large‐scale hippocampal networks. Inhibitory deficit of these networks function, interconnected with alpha rhythm slowing, may be related to learning and memory impairments and contribute to the subsequent AD development.This study was supported by Russian Science Foundation (Project No. 19‐75‐30039, genotyping)

The effects of network-targeted tDCS on the connectivity of core brain networks

The effects of network-targeted tDCS on the connectivity of core brain networks

Functional hyperconnectivity is associated with higher levels of sPDGFRβ, a marker of blood brain barrier breakdown, in older adults

AbstractBackgroundApolipoprotein E4 (APOE4) has toxic effects on the cerebrovascular system, neuronal function, and functional connectivity of brain networks (e.g., default‐mode, frontal‐attention, and salience networks). The thalamus, a vital structure that acts as a relay station between many networks, has become of interest in AD research with studies showing atrophy among those with early AD and diminished cortical‐thalamic connections among those with vascular impairment. Additionally, CSF soluble platelet‐derived growth factor receptor‐β (sPDGFRβ), a marker of blood‐brain barrier breakdown, has been associated with AD risk, especially in APOE4 carriers. The present study investigated the relationship between CSF sPDGFRβ and functional connectivity in APOE4 carriers and non‐carriers.MethodOne hundred sixty‐six older adults from the Los Angeles area were selected from a larger cohort (96 females, mean age±SD = 67.4±10.7, 68 APOE4 carriers) based on availability of rsfMRI data. Image data was processed using CONN‐toolbox v19c. Grey matter was parcellated using Harvard/Oxford brain atlas, excluding cerebellum. ROI‐to‐ROI analyses were conducted selecting the thalamus as seed region averaging mean connectivity between the left and right structure of other parcellated brain regions. Linear regression models evaluated significant associations between blood oxygen level‐dependent (BOLD) and network connectivity among seed‐target regions and CSF sPDGFRβvalues, while covarying for age and sex. Comparison of averaged functional connectivity values between seed and all target regions among APOE4 carriers and non‐carriers was also examined.ResultHigher functional connectivity between the thalamus and resting state network hub regions were associated with higher CSF sPDGFRβ (Fig 1A, 1C). Cortical regions within the networks included posterior cingulate gyrus, anterior cingulate gyrus, and frontal eye field. Averaged functional connectivity between these areas was significantly higher in APOE4 carriers compared to non‐carriers (Fig 1B).ConclusionThe thalamus is a key subcortical structure for relaying signals across the brain for memory, attention and sleep/wakefulness cycles, and more recently with relevance to AD. These findings suggest a link between early BBB breakdown and elevated functional connectivity in brain regions known to have high synaptic activity and early pathology among non‐demented older adults at risk for AD.

The Effect of Multiple Factors on Working Memory Capacities: Aging, Task Difficulty, and Training.

Working memory (WM) is a memory system with a limited capacity that can process and store information temporarily in the performing of cognitive tasks. Despite WM is known to be influenced by age, the difficulty of tasks and trained or not from behavior studies, little is known about their relationships from the aspect of the brain functional network. Our goal was to explore the factor of aging-related changes of WM with brain functional networks. In this study, 25 healthy elderly and 23 healthy young volunteers were recruited for electroencephalogram (EEG) recording during the visual WM task with four difficulty levels (1-4 backs). In each back, we repeat the experiment with four sessions, and we add training sections between session one and session two as well as between session two and session three. However, we remove any training section between session three and session four in order to evaluate the impact of forgetting on WM in different age groups. After the experiment, we utilized graph theoretical analysis to characterize the brain functional network in three frequency bands (alpha, beta, and theta). From the well-designed experiment, we found that physiological aging influences brain network connectivity and makes the functional brain network less differentiated. Moreover, there is an inverse relationship between alpha activity and WM load for the elderly group, which is absent in the young group. At the same time, theta band activity will be correlated with behavioral performance for the elderly group with WM training between sessions, which is also absent in the young group. To further study the influence of difficulty of tasks and training on the WM, we distinguish the tasks with quantified topological characteristics, and the classification results manifest that the training is more effective for the young group. Finally, through the establishment of a brain map before and after training, we find that the right parietal lobe plays an important role in the training of WM for the elderly group whereas the beta band plays an important role in WM for both the elderly group and the young group. Taken together, our findings clarify the underlying mechanism of WM under different frequency bands in terms of physiological aging, the influence of training, and task difficulty. the working memory capacities can be uncovered in terms of the combination of three-way ANOVA and EEG-based graph theoretical analysis.