Connectivity Patterns Research Articles

Convergent functional effects of antidepressants in major depressive disorder: a neuroimaging meta-analysis.

Neuroimaging studies have provided valuable insights into the macroscale impacts of antidepressants on brain functions in patients with major depressive disorder. However, the findings of individual studies are inconsistent. Here, we aimed to provide a quantitative synthesis of the literature to identify convergence of the reported findings at both regional and network levels and to examine their associations with neurotransmitter systems. Through a comprehensive search in PubMed and Scopus databases, we reviewed 5258 abstracts and identified 36 eligible functional neuroimaging studies on antidepressant effects in major depressive disorder. Activation likelihood estimation was used to investigate regional convergence of the reported foci of antidepressant effects, followed by functional decoding and connectivity mapping of the convergent clusters. Additionally, utilizing group-averaged data from the Human Connectome Project, we assessed convergent resting-state functional connectivity patterns of the reported foci. Next, we compared the convergent circuit with the circuits targeted by transcranial magnetic stimulation therapy. Last, we studied the association of regional and network-level convergence maps with selected neurotransmitter receptors/transporters maps. No regional convergence was found across foci of treatment-associated alterations in functional imaging. Subgroup analysis in the Treated > Untreated contrast revealed a convergent cluster in the left dorsolateral prefrontal cortex, which was associated with working memory and attention behavioral domains. Moreover, we found network-level convergence of the treatment-associated alterations in a circuit more prominent in the frontoparietal areas. This circuit was co-aligned with circuits targeted by "anti-subgenual" and "Beam F3"transcranial magnetic stimulation therapy. We observed no significant correlations between our meta-analytic findings with the maps of neurotransmitter receptors/transporters. Our findings highlight the importance of the frontoparietal network and the left dorsolateral prefrontal cortex in the therapeutic effects of antidepressants, which may relate to their role in improving executive functions and emotional processing.

Quantifying metadata relevance to network block structure using description length

Network analysis is often enriched by including an examination of node metadata. In the context of understanding the mesoscale of networks it is often assumed that node groups based on metadata and node groups based on connectivity patterns are intrinsically linked. This assumption is increasingly being challenged, whereby metadata might be entirely unrelated to structure or, similarly, multiple sets of metadata might be relevant to the structure of a network in different ways. We propose the metablox tool to quantify the relationship between a network’s node metadata and its mesoscale structure, measuring the strength of the relationship and the type of structural arrangement exhibited by the metadata. We show on a number of synthetic and empirical networks that our tool distinguishes relevant metadata and allows for this in a comparative setting, demonstrating that it can be used as part of systematic meta analyses for the comparison of networks from different domains.

SpinalTRAQ: A novel volumetric cervical spinal cord atlas identifies the corticospinal tract synaptic projectome in healthy and post-stroke mice.

Descending corticospinal tract (CST) connections to the neurons of the cervical spinal cord are vital for performance of forelimb-specific fine motor skills. In rodents, CST axons are almost entirely crossed at the level of the medullary decussation. While specific contralateral axon projections have been well-characterized using anatomic and molecular approaches, the field currently lacks a cohesive imaging modality allowing rapid quantitative assessment of the entire, bilateral cervical cord projectome at the level of individual laminae and cervical levels. This is potentially important as the CST is known to undergo marked structural remodeling in development, injury, and disease. We developed SpinalTRAQ (Spinal cord Tomographic Registration and Automated Quantification), a novel volumetric cervical spinal cord atlas and machine learning-driven microscopy acquisition and analysis pipeline that uses serial two-photon tomography- images to generate unbiased, region-specific quantification of the fluorescent pixels of anterograde AAV-labeled CST pre-synaptic terminals. In adult mice, the CST synaptic projectome densely innervates the contralateral hemicord, particularly in laminae 5 and 7, with sparse, monosynaptic input to motoneurons in lamina 9. Motor pools supplying axial musculature in the upper cervical cord are bilaterally innervated. The remainder of the ipsilateral cord has sparse labeling in a distinct distribution compared to the contralateral side. Following a focal stroke of the motor cortex, there is a complete loss of descending corticospinal axons from the injured side. Consistent with prior reports of axon collateralization, the CST spinal projectome increases at four weeks post-stroke and continues to elevate by six weeks post stroke. At six weeks post-stroke, we observed striking synapse formation in the denervated hemicord from the uninjured CST in a homotopic distribution. Additionally, CST synaptic reinnervation increases in the denervated lamina 9 in nearly all motoneuron pools, exhibiting novel patterns of connectivity. Detailed level- and lamina-specific quantification of the bilateral cervical spinal cord synaptic projectome reveals previously undescribed patterns of CST connectivity in health and injury-related plasticity.

Nucleus reuniens: Modulating emotional overgeneralization in peri-adolescents with anxiety.

Anxiety affects 4.4-million children in the USA with an onset between childhood and adolescence, a period marked by neural changes that impact emotions and memory. Negative overgeneralization - or responding similarly to innocuous events that share features with past aversive experiences - is common in anxiety but remains mechanistically underspecified. The nucleus reuniens (RE) has been considered a crucial candidate in the modulation of memory specificity. Our study investigated its activation and functional connectivity with the medial prefrontal cortex (mPFC) and hippocampus (HPC) as neurobiological mechanisms of negative overgeneralization in anxious youth. As part of a secondary data analysis, we examined data from 34 participants between 9 and 14years of age (mean age ± SD, 11.4 ± 2.0years; 16 females) with varying degrees of anxiety severity. During the Study session participants rated images as negative, neutral, and positive. After 12h, participants returned for a Test session, where they performed a memory recognition test with repeated (targets) and similar (lures) images. Labeling negative relative to neutral lures as "old" (false alarms) was our operational definition of negative overgeneralization. Negative relative to neutral false alarmed stimuli displayed elevated RE activation (at Study and Test) and increased functional connectivity with the Cornu Ammonis (CA) 1 (at Test). Elevated anxiety severity was associated with reductions in the RE-mPFC functional coupling for neutral relative to negative stimuli. Exploratory analyses revealed similar patterns in activation and functional connectivity with positive stimuli. Our findings demonstrate the importance of the RE in negative overgeneralization and anxiety.

Distinct functional connectivity phenotypes in preadolescent children with binge eating disorder by BMI status.

The neurobiological mechanisms underpinning binge eating disorder (BED) in children remain largely unclear, as the alterations that have been identified to date may be attributable to BED, obesity, or compound effects. This study aimed to delineate functional connectivity (FC) patterns in inhibitory control and reward networks in preadolescent children with and without BED from the Adolescent Brain Cognitive Development (ABCD) Study according to BMI. Resting-state FC was examined in the inhibitory control network by using seeds in the dorsolateral prefrontal cortex, the anterior cingulate cortex, and the posterior cingulate cortex, whereas the reward network included seeds in the orbitofrontal cortex, nucleus accumbens, and amygdala. Seed-to-voxel analyses characterized FC differences between preadolescent children with BED with a high BMI and those with BED with a low BMI. We identified that BED was characterized by reduced connectivity between the reward network and regions in the default mode network, irrespective of weight status. Participants with BED also presented with hypoconnectivity in fronto-amygdalar circuits, which has been consistently associated with impaired emotion regulation capacity. Our findings support that FC alterations between the reward network and the default mode network may be specifically impacted by the presence of BED as opposed to weight status.

EEG synchronization patterns during a Go/No-Go task in individuals with aphasia in subacute and chronic phases of stroke

Stroke and subsequent neuroregenerative processes cause changes in neural organization of attentional functions. In this study, we attempted to identify differences in neural synchronization patterns during a visual Go/No-Go task in people with post-stroke aphasia in both subacute and chronic stroke phases. To identify neuronal underpinnings of the behavioral differences we investigated pairwise connectivity patterns using corrected imaginary phase locking value and graph-theoretic measures (efficiency, modularity and clustering coefficient) at global and local level in subacute (n = 13) and chronic stroke phases (n = 14) during a Go/No-Go task. We observed significantly lower phase synchronization in the Subacute Group in the alpha band in the connections spanning frontal and central areas of both hemispheres alongside lower local efficiency and clustering coefficient in the left frontal region. Additionally, we observed higher modularity in the beta band in the unaffected right parietal region in the Subacute Group which may denote inhibition of motor and attention functions. Those mechanisms could serve to align cognitive abilities between the damaged and healthy hemispheres, harmonizing the activity of the neuronal networks of both hemispheres disrupted by the effects of the stroke. Our findings have potential implications for rehabilitation therapies, which should take into account the pattern of connectivity changes during different phases of reovery.

Connector hubs in semantic network contribute to creative thinking.

Semantic memory offers a rich repository of raw materials (e.g., various concepts and connections between concepts) for creative thinking, represented as a semantic network. Similar to other networks, the semantic network exhibits a modular structure characterized by modules with dense internal connections and sparse connections between them. This organizational principle facilitates the routine storage and retrieval of information but may impede creativity. The present study investigated the effect of hub concepts with varying connection patterns on creative thinking from the perspective of a modular structured semantic network. By analyzing a large-scale semantic network, connector hubs (C-hubs) and provincial hubs (P-hubs) were identified based on their intra- and intermodule connections. These hubs were used as cue words in the alternative uses task, a widely used measure of creative thinking. Across four experiments, behavioral and neural evidence indicated that C-hubs facilitate the generation of more novel and remote ideas compared to P-hubs. However, this effect is predominantly observed in the early stage of the creative thinking process, involving changes in brain activation and functional connectivity in core regions of the default mode network and the frontoparietal network, including the dorsolateral prefrontal cortex, angular gyrus, and precuneus. Neural findings suggest that the superior performance of C-hubs relies on stronger interactions between automatic spreading activation, controlled semantic retrieval, and attentional regulation of salient information. These results provide insight into how concepts with varying semantic connection patterns facilitate and constrain different stages of the creative thinking process through the modular structure of semantic network. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The neural plasticity and efficacy of acupuncture for post-stroke dysphagia: protocol for a randomized controlled trial with fMRI and DTI

BackgroundDysphagia, a common complication of acute stroke, is associated with increased mortality and morbidity. Acupuncture, a widely used swallowing therapy in China, has been suggested as an effective therapy for treating Post-Stroke Dysphagia (PSD) by recent meta-analyses and guidelines. The use of resting-state functional Magnetic Resonance Imaging (rs-fMRI) and Diffusion Tensor Imaging (DTI) could explore the change of regional spontaneous neural activity, functional relationships between brain regions, and white matter connectivity patterns after acupuncture intervention for PSD. This trial aims to evaluate the efficacy of acupuncture treatment for PSD and explore its central mechanism by neuroimaging.Methods/designThis randomized controlled trial will recruit 40 PSD patients. All patients will be randomized to either the Real Acupuncture (RA) or Sham Acupuncture (SA) group by a ratio of 1:1. All patients will receive immediate acupuncture treatment in the MRI scanning room, followed by four weeks of long-term acupuncture treatment. The primary outcomes are the rs-fMRI and DTI indicators, which will be evaluated after the immediate and long-term acupuncture treatment. The secondary outcomes are the scales that assess the efficacy, including the Functional Oral Intake Scale (FOIS), Water Swallowing Test (WST), Swallowing Quality Of Life Questionnaire (SWAL-QOL), and National Institute of Health Stroke Scale (NIHSS). The modified version of the Massachusetts General Hospital Acupuncture Sensation Scale (M-MASS) and fMRI sensation record table will also be evaluated.DiscussionThis protocol presents the design of a randomized, single-blind trial that will evaluate the efficacy and explore the neural plasticity of acupuncture treatment for PSD. This trial will deepen our insight into the clinical value of acupuncture for PSD and initially probe into the time-dosage-effect mechanism of acupuncture.Trial registration numbersChinese Clinical Trial Registry (www.chictr.org.cn) ChiCTR2300067480. This study was registered on 9th January 2023.

Explaining slow seizure propagation with white matter tractography.

Epileptic seizures recorded with stereo-EEG can take a fraction of a second or several seconds to propagate from one region to another. What explains such propagation patterns? We combine tractography and stereo-EEG to determine the relationship between seizure propagation and the white matter architecture and to describe seizure propagation mechanisms. Patient-specific spatiotemporal seizure propagation maps were combined with tractography from diffusion imaging of matched subjects from the Human Connectome Project. The onset of seizure activity was marked on a channel-by-channel basis by two board-certified neurologists for all channels involved in the seizure. We measured the tract connectivity (number of tracts) between regions-of-interest pairs among the seizure onset zone, regions of seizure spread and non-involved regions. We also investigated how tract-connected the seizure onset zone is to regions of early seizure spread compared with regions of late spread. Comparisons were made after correcting for differences in distance. Sixty-nine seizures were marked across 26 patients with drug-resistant epilepsy; 11 were seizure free after surgery (Engel IA) and 15 were not (Engel IB-Engel IV). The seizure onset zone was more tract-connected to regions of seizure spread than to non-involved regions (P < 0.0001); however, regions of seizure spread were not differentially tract-connected to other regions of seizure spread compared with non-involved regions. In seizure-free patients only, regions of seizure spread were more tract-connected to the seizure onset zone than to other regions of spread (P < 0.0001). Over the temporal evolution of a seizure, the seizure onset zone was significantly more tract-connected to regions of early spread compared with regions of late spread in seizure-free patients only (P < 0.0001). By integrating information on structure, we demonstrate that seizure propagation is likely to be mediated by white matter tracts. The pattern of connectivity between seizure onset zone, regions of spread and non-involved regions demonstrates that the onset zone might be largely responsible for seizures propagating throughout the brain, rather than seizures propagating to intermediate points, from which further propagation takes place. Our findings also suggest that seizure propagation over seconds might be the result of a continuous bombardment of action potentials from the seizure onset zone to regions of spread. In non-seizure-free patients, the paucity of tracts from the presumed seizure onset zone to regions of spread suggests that the onset zone was missed. Fully understanding the structure-propagation relationship might eventually provide insight into selecting the correct targets for epilepsy surgery.

Functional connectivity across the lifespan: a cross-sectional analysis of changes.

In the era of functional brain networks, our understanding of how they evolve across life in a healthy population remains limited. Here, we investigate functional connectivity across the human lifespan using magnetoencephalography in a cohort of 792 healthy individuals, categorized into young (13 to 30 yr), middle (31 to 54 yr), and late adulthood (55 to 80 yr). Employing corrected imaginary phase-locking value, we map the evolving landscapes of connectivity within delta, theta, alpha, beta, and gamma classical frequency bands among brain areas. Our findings reveal significant shifts in functional connectivity patterns across all frequency bands, with certain networks exhibiting increased connectivity and others decreased, dependent on the frequency band and specific age groups, showcasing the dynamic reorganization of neural networks as age increases. This detailed exploration provides, to our knowledge, the first all-encompassing view of how electrophysiological functional connectivity evolves at different life stages, offering new insights into the brain's adaptability and the intricate interplay of cognitive aging and network connectivity. This work not only contributes to the body of knowledge on cognitive aging and neurological health but also emphasizes the need for further research to develop targeted interventions for maintaining cognitive function in the aging population.

A hierarchical random effects state-space model for modeling brain activities from electroencephalogram data.

Mental disorders present challenges in diagnosis and treatment due to their complex and heterogeneous nature. Electroencephalogram (EEG) has shown promise as a source of potential biomarkers for these disorders. However, existing methods for analyzing EEG signals have limitations in addressing heterogeneity and capturing complex brain activity patterns between regions. This paper proposes a novel random effects state-space model (RESSM) for analyzing large-scale multi-channel resting-state EEG signals, accounting for the heterogeneity of brain connectivities between groups and individual subjects. We incorporate multi-level random effects for temporal dynamical and spatial mapping matrices and address non-stationarity so that the brain connectivity patterns can vary over time. The model is fitted under a Bayesian hierarchical model framework coupled with a Gibbs sampler. Compared to previous mixed-effects state-space models, we directly model high-dimensional random effects matrices of interest without structural constraints and tackle the challenge of identifiability. Through extensive simulation studies, we demonstrate that our approach yields valid estimation and inference. We apply RESSM to a multi-site clinical trial of major depressive disorder (MDD). Our analysis uncovers significant differences in resting-state brain temporal dynamics among MDD patients compared to healthy individuals. In addition, we show the subject-level EEG features derived from RESSM exhibit a superior predictive value for the heterogeneous treatment effect compared to the EEG frequency band power, suggesting the potential of EEG as a valuable biomarker for MDD.

Functional connectivity compensation in Parkinson's disease with freezing of gait.

Freezing of gait (FOG) is a disabling motor symptom prevalent in patients with Parkinson's disease (PD); however, its pathophysiological mechanisms are poorly understood. This study aimed to investigate whole-brain functional connectivity (FC) pattern alterations in PD patients with FOG. A total of 18 PD patients, 10 with FOG (PD-FOG) and 8 without FOG (PD-nFOG), and 10 healthy controls were enrolled. High-resolution 3D T1-weighted and resting-state functional MRI (rs-fMRI) data were obtained from all participants. The groups' internetwork connectivity differences were explored with rs-fMRI FC using seed-based analysis and graph theory. Multiple linear regression analysis estimated the relationship between FC changes and clinical measurements. Rs-fMRI analysis demonstrated alterations in FC in various brain regions between the three groups. Freezing of Gait Questionnaire severity was correlated with decreased brain functional connection between Vermis12 and the left temporal occipital fusiform cortex (r = -0.82, P < .001). Graph theory topological metrics indicated a decreased clustering coefficient in the right superior temporal gyrus in the PD-nFOG group. PD-FOG patients exhibited a compensatory increase in connectivity between the left inferior frontal gyrus language network and the postcentral gyrus compared to PD-nFOG patients. Further, the decreased connection between Vermis 12 and the left temporal occipital fusiform cortex may serve as a potential neuroimaging biomarker for tracking PD-FOG and distinguishing between PD subtypes.

Network Analysis in Global Emergency Medicine: Mapping Collaborative Structures and Enhancing Connectivity

Introduction Network analysis, which draws from fields including mathematics, sociology, and public health, is essential for interpreting complex data relationships within healthcare information systems. This approach not only highlights intricate care patterns but also enhances the navigability of systems, making complex healthcare information more accessible [1]. Our study builds on previous applications of network analysis in healthcare, specifically examining its use in structuring academic health center websites in the U.S. to improve user-oriented spaces. Our research explores the digital ecosystem of emergency medicine organizations globally, focusing on the International Federation for Emergency Medicine (IFEM). By employing a systematic, automated approach to network mapping, we aim to uncover collaborative patterns and enhance the online presence of emergency resources. This effort supports greater global collaboration and information sharing among emergency medicine organizations, leveraging the strong networks and insights provided by IFEM to ensure universal access to high-quality emergency services. Methodology We utilized a four-step process for mapping and analyzing the global network of emergency medicine organizations: Web Crawling: We collected data from the IFEM website using the Screaming Frog SEO Spider tool. The crawl depth was set to three to ensure comprehensive data capture from IFEM and its connected links. Content Filtering: BeautifulSoup was used to parse HTML content. We filtered the extracted data for relevance to emergency medicine organizations. The data was further refined using the Gemini 1.0 Pro model to isolate pertinent information about the organizations' names and locations. Geolocation Resolution: Location data extracted from the web pages was converted into geographical coordinates using the Geopy library’s Nominatim tool to accurately map of each organization's position. Network Visualization: We constructed a directed network graph using the NetworkX library, with nodes representing organizations and edges representing their interconnections. This graph was visualized with Matplotlib to illustrate the relationships and distribution patterns among the organizations. Results We identified 4,775 external links on the IFEM website and refined them to 156 unique base URLs for in-depth content analysis. Out of 55 countries listed, there were 41 functional links, 10 non-functional, and 4 missing. We effectively isolated 41 relevant multilingual URLs from the functional links, achieving a 100% accuracy rate in identifying pertinent content. Additionally, our analysis uncovered 30 URLs linked to emergency medicine organizations not listed on the IFEM member page, including the American College of Osteopathic Emergency Physicians and the Swiss Society for Emergency and Rescue Medicine. Discussion Our study highlights the utility of network analysis in understanding the digital networks of emergency medicine organizations globally, revealing essential connectivity patterns and their implications for healthcare systems. The methodology used is adaptable for other healthcare areas, enhancing collaboration and addressing language barriers. Findings indicate dynamic changes in digital connectivity, pointing to evolving collaboration and resource sharing. This research provides a foundation for exploring digital connectivity's broader impacts across various sectors.

A cortical field theory – dynamics and symmetries

We characterise cortical dynamics using partial differential equations (PDEs), analysing various connectivity patterns within the cortical sheet. This exploration yields diverse dynamics, encompassing wave equations and limit cycle activity. We presume balanced equations between excitatory and inhibitory neuronal units, reflecting the ubiquitous oscillatory patterns observed in electrophysiological measurements. Our derived dynamics comprise lowest-order wave equations (i.e., the Klein-Gordon model), limit cycle waves, higher-order PDE formulations, and transitions between limit cycles and near-zero states. Furthermore, we delve into the symmetries of the models using the Lagrangian formalism, distinguishing between continuous and discontinuous symmetries. These symmetries allow for mathematical expediency in the analysis of the model and could also be useful in studying the effect of symmetrical input from distributed cortical regions. Overall, our ability to derive multiple constraints on the fields — and predictions of the model — stems largely from the underlying assumption that the brain operates at a critical state. This assumption, in turn, drives the dynamics towards oscillatory or semi-conservative behaviour. Within this critical state, we can leverage results from the physics literature, which serve as analogues for neural fields, and implicit construct validity. Comparisons between our model predictions and electrophysiological findings from the literature — such as spectral power distribution across frequencies, wave propagation speed, epileptic seizure generation, and pattern formation over the cortical surface — demonstrate a close match. This study underscores the importance of utilizing symmetry preserving PDE formulations for further mechanistic insights into cortical activity.

Functional brain connectivity predictors of prospective substance use initiation and their environmental correlates

BackgroundEarly substance use initiation (SUI) places youth at substantially higher risk for later substance use disorders. Furthermore, adolescence is a critical period for the maturation of brain networks, the pace and magnitude of which are susceptible to environmental influences and may shape risk for SUI. MethodsWe examined whether patterns of functional brain connectivity during rest (rsFC), measured longitudinally in pre-and-early adolescence, can predict future SUI. Next, in an independent sub-sample, we tested whether these patterns are associated with earlier environmental exposures, specifically neighborhood pollution and socioeconomic dimensions. We utilized data from the Adolescent Brain Cognitive Development (ABCD) Study®. SUI was defined as first-time use of at least one full dose of alcohol, nicotine, cannabis, or other drugs. We created a control group (N = 228) of participants without SUI who were matched with the SUI group (N = 233) on age, sex, race/ethnicity, and parental income and education. ResultsMultivariate analysis showed that whole-brain rsFC from 9-10 to 11-12 years of age (prior to SUI) prospectively differentiated the SUI and control groups. The SUI-related rsFC pattern was also related to aging in both groups, suggesting a pattern of accelerated maturation in the years prior to SUI. This same pattern of rsFC was predicted by higher pollution, but not neighborhood disadvantage (adjusted for family socioeconomic factors) in an independent sub-sample (N = 2,854). ConclusionBrain functional connectivity patterns in early adolescence that are linked to accelerated maturation can predict SUI in youth and are associated with exposure to pollution.

Autism Spectrum Disorder DetectionUsingMachine Learning

Convolutional Neural Networks (CNNs) have made significant strides in various domains such as image classifica- tion, speech recognition, automotive software engineering and neuroscience. In this study we leverage CNNs for the automated detection of Autism Spectrum Disorder using brain imaging data,specificallywe utilize resting-state functional Magnetic Resonance Imaging (f-MRI) data from the Autism Brain Imaging Exchange (ABIDE) dataset. To identify ASD patients, our approach relies on analyzing patterns of functional connectivity to classify ASD and control subjects. Experimental results demonstrate that our proposed model achieves an accuracy of 96.40% on the ABIDEI dataset using the CC400 functional parcellation atlas of thebrain. Notably our CNN model exhibits computational efficiency by employing fewer parameters comparedtostate-of-the-art techniques. Our findings suggest that the developed model shows promise for prescreeningasdpatients and warrants further evaluation with additional datasets.

Flexibility potential quantification of electric vehicle charging clusters

A significant obstacle to providing flexibility services with electric vehicles (EVs) is the uncertainty surrounding the profitability and flexibility potential of charging clusters when utilized as a flexible load. Currently, there is a lack of comprehensive and easily applicable methods for quantifying flexibility in the literature. This paper introduces an evaluation tool and a set of flexibility indexes to assess the capability of charging clusters to deliver flexibility services. The method is designed to evaluate and quantify the flexibility potential of charging clusters in terms of short-term and long-term power adjustments and charge scheduling. Through sensitivity analysis, we examine how connection capacity, EV battery capacities, power capabilities, and the number of daily charging sessions influence the flexibility potential of charging clusters. Our findings highlight a direct relationship between the grid connection capacity of clusters and their ability to perform short-term power adjustments. Moreover, while larger batteries tend to reduce energy and time flexibility, their increased storage capability facilitates managing and scheduling a larger energy volume. Furthermore, for the days analysed, the flexibility potential showed minimal sensitivity to the number of daily charging sessions. Instead, the amount of energy requested and connection patterns emerge as key determinants of overall flexibility. In summary, this research provides valuable insights that can inform the design, monitoring, and assessment of EV charging clusters when evaluating their suitability for various flexibility services.

D-MHGCN: An End-to-End Individual Behavioral Prediction Model Using Dual Multi-Hop Graph Convolutional Network.

Predicting individual behavior is a crucial area of research in neuroscience. Graph Neural Networks (GNNs), as powerful tools for extracting graph-structured features, are increasingly being utilized in various functional connectivity (FC) based behavioral prediction tasks. However, current predictive models primarily focus on enhancing GNNs' ability to extract features from FC networks while neglecting the importance of upstream individual network construction quality. This oversight results in constructed functional networks that fail to adequately represent individual behavioral capacity, thereby affecting the subsequent prediction accuracy. To address this issue, we proposed a new GNN-based behavioral prediction framework, named Dual Multi-Hop Graph Convolutional Network (D-MHGCN). Through the joint training of two GCNs, this framework integrates individual functional network construction and behavioral prediction into a unified optimization model. It allows the model to dynamically adjust the individual functional cortical parcellation according to the downstream tasks, thus creating task-aware, individual-specific FCNs that largely enhance its ability to predict behavior scores. Additionally, we employed multi-hop graph convolution layers instead of traditional single-hop methods in GCN to capture complex hierarchical connectivity patterns in brain networks. Our experimental evaluations, conducted on the large, public Human Connectome Project dataset, demonstrate that our proposed method outperforms existing methods in various behavioral prediction tasks. Moreover, it produces more functionally homogeneous cortical parcellation, showcasing its practical utility and effectiveness. Our work not only enhances the accuracy of individual behavioral prediction but also provides deeper insights into the neural mechanisms underlying individual differences in behavior.

Human Claustrum Connections: Robust In Vivo Detection by DWI-Based Tractography in Two Large Samples.

Despite substantial neuroscience research in the last decade revealing the claustrum's prominent role in mammalian forebrain organization, as evidenced by its extraordinarily widespread connectivity pattern, claustrum studies in humans are rare. This is particularly true for studies focusing on claustrum connections. Two primary reasons may account for this situation: First, the intricate anatomy of the human claustrum located between the external and extreme capsule hinders straightforward and reliable structural delineation. In addition, the few studies that used diffusion-weighted-imaging (DWI)-based tractography could not clarify whether in vivo tractography consistently and reliably identifies claustrum connections in humans across different subjects, cohorts, imaging methods, and connectivity metrics. To address these issues, we combined a recently developed deep-learning-based claustrum segmentation tool with DWI-based tractography in two large adult cohorts: 81 healthy young adults from the human connectome project and 81 further healthy young participants from the Bavarian longitudinal study. Tracts between the claustrum and 13 cortical and 9 subcortical regions were reconstructed in each subject using probabilistic tractography. Probabilistic group average maps and different connectivity metrics were generated to assess the claustrum's connectivity profile as well as consistency and replicability of tractography. We found, across individuals, cohorts, DWI-protocols, and measures, consistent and replicable cortical and subcortical ipsi- and contralateral claustrum connections. This result demonstrates robust in vivo tractography of claustrum connections in humans, providing a base for further examinations of claustrum connectivity in health and disease.

Polyconnectomic scoring of functional connectivity patterns across eight neuropsychiatric and three neurodegenerative disorders

BackgroundNeuropsychiatric and neurodegenerative disorders involve diverse changes in brain functional connectivity. As an alternative to approaches searching for specific mosaic patterns of affected connections and networks, we used polyconnectomic scoring to quantify disorder-related whole-brain connectivity signatures into interpretable, personalized scores. MethodsThe polyconnectomic score (PCS) measures the extent to which an individual's functional connectivity (FC) mirrors the whole-brain circuitry characteristics of a trait. We computed PCS for eight neuropsychiatric conditions (attention-deficit/hyperactivity disorder, anxiety-related disorders, autism spectrum disorder, obsessive-compulsive disorder, bipolar disorder, major depressive disorder, schizoaffective disorder, and schizophrenia) and three neurodegenerative conditions (Alzheimer's disease, frontotemporal dementia, and Parkinson’s disease) across 22 datasets with resting-state functional MRI of 10,667 individuals (5,325 patients, 5,342 controls). We further examined PCS in 26,673 individuals from the population-based UK Biobank cohort. ResultsPCS was consistently higher in out-of-sample patients across six of the eight neuropsychiatric and across all three investigated neurodegenerative disorders ([min, max]: AUC = [0.55, 0.73], pFDR = [1.8 x 10-16, 4.5 x 10-2]). Individuals with elevated PCS levels for neuropsychiatric conditions exhibited higher neuroticism (pFDR < 9.7 x 10-5), lower cognitive performance (pFDR < 5.3 x 10-5), and lower general wellbeing (pFDR < 9.7 x 10-4). ConclusionsOur findings reveal generalizable whole-brain connectivity alterations in brain disorders. PCS effectively aggregates disorder-related signatures across the entire brain into an interpretable, subject-specific metric. A toolbox is provided for PCS computation.