Connectivity Measures Research Articles

Comparison of dry and wet electroencephalography for the assessment of cognitive evoked potentials and sensor-level connectivity.

Multipin dry electrodes (dry EEG) provide faster and more convenient application than wet EEG, enabling extensive data collection. This study aims to compare task-related time-frequency representations and resting-state connectivity between wet and dry EEG methods to establish a foundation for using dry EEG in investigations of brain activity in neuropsychiatric disorders. In this counterbalanced cross-over study, we acquired wet and dry EEG in 33 healthy participants [n = 22 females, mean age (SD) = 24.3 (± 3.4) years] during resting-state and an auditory oddball paradigm. We computed mismatch negativity (MMN), theta power in task EEG, and connectivity measures from resting-state EEG using phase lag index (PLI) and minimum spanning tree (MST). Agreement between wet and dry EEG was assessed using Bland-Altman bias. MMN was detectable with both systems in time and frequency domains, but dry EEG underestimated MMN mean amplitude, peak latency, and theta power compared to wet EEG. Resting-state connectivity was reliably estimated with dry EEG using MST diameter in all except the very low frequencies (0.5-4 Hz). PLI showed larger differences between wet and dry EEG in all frequencies except theta. Dry EEG reliably detected MMN and resting-state connectivity despite a lower signal-to-noise ratio. This study provides the methodological basis for using dry EEG in studies investigating the neural processes underlying psychiatric and neurological conditions.

Forecasting Suitable Habitats of the Clouded Leopard (Neofelis nebulosa) in Asia: Insights into the Present and Future Climate Projections Within and Beyond Extant Boundaries.

Terrestrial carnivores, such as the clouded leopard (Neofelis nebulosa), are 'vulnerable' and experiencing significant population declines in mainland Asia. Considering the assessed threats, the IUCN has repetitively revised the range of this felid and now characterized it into four distinct categories (extant, possibly extant, possibly extinct, and extinct). Although several ecological works have been accomplished on this enigmatic carnivore, the overall view of its habitat suitability, fragmentation, and corridor connectivity in both present and historical ranges is lacking. Thus, achieving this ecological information under present and future climate scenarios is crucial. The model identified merely 44,033 sq. km falling within the extant range (representing 31.66%) and 20,034 sq. km (8.13%) in the possibly extant range. Fascinatingly, within the historical ranges, an additional 15,264 sq. km (6.58%) has been identified as suitable habitat in the possibly extinct range and 14,022 sq. km (2.38%) in the extinct range. Notably, a total of 25,614 sq. km of suitable habitat is found within designated protected areas across the entire range. Nevertheless, climate change is expected to drive habitat loss of up to 41% (overall IUCN range) for N. nebulosa in both present and historical extent, with habitat patches becoming increasingly fragmented. This is reflected in a projected decline in the number of viable habitat patches (NP) by up to 23.29% in the future. This study also identified 18 transboundary biological corridors for N. nebulosa, with Southeast Asian countries expected to experience the most significant declines in corridor connectivity. In contrast, the South Asian countries (Bhutan, Nepal, and India) are projected to maintain relatively higher connectivity in the future. Nevertheless, a substantial decline in overall mean corridor connectivity is projected in the near future due to the impacts of climate change. This study underscores the urgent need for a coordinated and multifaceted conservation strategy for N. nebulosa, focusing on mitigating habitat loss and fragmentation. Practical measures must be implemented to protect the species' shrinking range, considering its declining corridor networks and heightened vulnerability to inbreeding depression. Moreover, the assessment of habitat suitability both within and beyond the extant range, alongside corridor connectivity measures, provides valuable insights into potential translocation and reintroduction sites for this species. These findings provide a critical foundation for developing a strategic conservation plan tailored to the specific needs of this felid species across South and Southeast Asia, ensuring enhanced climate resilience and mitigating associated threats.

Multimodality model investigating the impact of brain atlases, connectivity measures, and dimensionality reduction techniques on Attention Deficit Hyperactivity Disorder diagnosis using resting state functional connectivity.

Various brain atlases are available to parcellate and analyze brain connections. Most traditional machine learning and deep learning studies analyzing Attention Deficit Hyperactivity Disorder (ADHD) have used either one or two brain atlases for their analysis. However, there is a lack of comprehensive research evaluating the impact of different brain atlases and associated factors such as connectivity measures and dimension reduction techniques on ADHD diagnosis. This paper proposes an efficient and robust multimodality model that investigates various brain atlases utilizing different parcellation strategies and scales. Thirty combinations of six brain atlases and five distinct machine learning classifiers with optimized hyperparameters are implemented to identify the most promising brain atlas for ADHD diagnosis. These outcomes are validated using the statistical Friedman test. To enhance comprehensiveness, the impact of three different connectivity measures, each representing unique facets of brain connectivity, is also analyzed. Considering the extensive complexity of brain interconnections, the effect of various dimension reduction techniques on classification performance and execution time is also analyzed. The final model is integrated with phenotypic data to create an efficient multimodal ADHD classification model. Experimental results on the ADHD-200 dataset demonstrate a significant variation in classification performance introduced by each factor. The proposed model outperforms many state-of-the-art ADHD approaches and achieves an accuracy of 77.59%, an area under the curve (AUC) score of 77.25% and an -score of 75.43%. The proposed model offers clear guidance for researchers, helping to standardize atlas selection and associated factors and improve the consistency and accuracy of ADHD studies for more reliable clinical applications.

Cortical hypoactivation of frontal areas modulate resting EEG microstates in children with ADHD

BackgroundThe present study examined EEG microstate alterations and their neural generators during resting state in children with ADHD to explore a potential state biomarker. MethodsA total of seventy-six participants, thirty-eight each, combined type ADHD, and neurotypical children (NC) participated in the study. Five-minute resting (eyes open) 128 channel eeg data were acquired and two-minute clean EEG data were analyzed for microstates, its sources and connectivity in both the groups. Between groups comparisons were done for microstate parameters using modified k means clustering in Cartool software. Further, the cortical sources and functional connectivity of significant microstate maps were explored using LORETA software. Subsequently microstate parameters were correlated with the behavioral scores from Conner’s parent rating scale. ResultsAmong the microstate parameters examined, children with ADHD displayed significant difference (p<0.05) in time frames and time coverage of map B (decreased) and transition probability of map D (increased) respectively. Interestingly, source analysis of both microstate maps showed hypoactivation of frontal areas predominantly while functional connectivity showed hyperconnectivity between medial frontal gyrus and anterior cingulate gyrus (executive function area) for map B and hypoconnectivity between medial frontal gyrus and middle temporal gyrus (both are suggested to be part of DMN areas) for map D. Further CSD values of map B was found to be correlated with executive function scores of conners questionnaire. ConclusionEEG microstate features, alongside source and connectivity measures, could discern children with ADHD from neurotypical controls. The hypoactivation of predominantly frontal areas and its connectivity was found to determine microstate maps.

Learning Interpretable Brain Functional Connectivity via Self-Supervised Triplet Network With Depth-Wise Attention.

Brain functional connectivity has been widely explored to reveal the functional interaction dynamics between the brain regions. However, conventional connectivity measures rely on deterministic models demanding application-specific empirical analysis, while deep learning approaches focus on finding discriminative features for state classification, having limited capability to capture the interpretable connectivity characteristics. To address the challenges, this study proposes a self-supervised triplet network with depth-wise attention (TripletNet-DA) to generate the functional connectivity: 1) TripletNet-DA firstly utilizes channel-wise transformations for temporal data augmentation, where the correlated & uncorrelated sample pairs are constructed for self-supervised training, 2) Channel encoder is designed with a convolution network to extract the deep features, while similarity estimator is employed to generate the similarity pairs and the functional connectivity representations, 3) TripletNet-DA applies Triplet loss with anchor-negative similarity penalty for model training, where the similarities of uncorrelated sample pairs are minimized to enhance model's learning capability. Experimental results on pathological EEG datasets (Autism Spectrum Disorder, Major Depressive Disorder) indicate that 1) TripletNet-DA demonstrates superiority in both ASD discrimination and MDD classification than the state-of-the-art counterparts, where the connectivity features in beta & gamma bands have respectively achieved the accuracy of 97.05%, 98.32% for ASD discrimination, 89.88%, 91.80% for MDD classification in the eyes-closed condition and 90.90%, 92.26% in the eyes-open condition, 2) TripletNet-DA enables to uncover significant differences of functional connectivity between ASD EEG and TD ones, and the prominent connectivity links are in accordance with the empirical findings, thus providing potential biomarkers for clinical ASD analysis.

Combining graph theory and spatially-explicit, individual-based models to improve invasive species control strategies at a regional scale

ContextInvasive species cause widespread species extinction and economic loss. There is an increasing need to identify ways to efficiently target control efforts from local to regional scales.ObjectivesOur goal was to test whether prioritizing managed habitat using different treatments based on spatial measures of connectivity, including graph-theoretic measures, can improve management of invasive species and whether the level of control effort affects treatment performance. We also explored how uncertainty in biological variables, such as dispersal ability, affects measures performance.MethodsWe used a spatially-explicit, individual-based model (sIBM) based on the American bullfrog (Lithobates catesbeianus), a globally pervasive invasive species. Simulations were informed by geographic data from part of the American bullfrog’s non-native range in southeastern Arizona, USA where they are known to pose a threat to native species.ResultsWe found that total bullfrog populations and occupancy declined in response to all treatments regardless of effort level or patch prioritization methods. The most effective spatial prioritization was effort-dependent and varied depending on spatial context, but frequently a buffer strategy was most effective. Treatments were also sensitive to dispersal ability. Performance of treatments prioritizing habitat patches using betweenness centrality improved with increasing dispersal ability, while performance of eigenvalue centrality improved as dispersal ability decreased.ConclusionsWith the careful application of connectivity measures to prioritize control efforts, similar reductions in invasive species population size and occupancy could be achieved with less than half the effort of sub-optimal connectivity measures at higher effort rates. More work is needed to determine if trait-based generalities may define appropriate connectivity measures for specific suites of dispersal abilities, demographic traits, and population dynamics.

The language network ages well: Preserved selectivity, lateralization, and within-network functional synchronization in older brains.

Healthy aging is associated with structural and functional brain changes. However, cognitive abilities differ from one another in how they change with age: whereas executive functions, like working memory, show age-related decline, aspects of linguistic processing remain relatively preserved (Hartshorne et al., 2015). This heterogeneity of the cognitive-behavioral landscape in aging predicts differences among brain networks in whether and how they should change with age. To evaluate this prediction, we used individual-subject fMRI analyses ('precision fMRI') to examine the language-selective network (Fedorenko et al., 2024) and the Multiple Demand (MD) network, which supports executive functions (Duncan et al., 2020), in older adults (n=77) relative to young controls (n=470). In line with past claims, relative to young adults, the MD network of older adults shows weaker and less spatially extensive activations during an executive function task and reduced within-network functional synchronization. However, in stark contrast to the MD network, we find remarkable preservation of the language network in older adults. Their language network responds to language as strongly and selectively as in younger adults, and is similarly lateralized and internally synchronized. In other words, the language network of older adults looks indistinguishable from that of younger adults. Our findings align with behavioral preservation of language skills in aging and suggest that some networks remain young-like, at least on standard measures of function and connectivity.

Altered electroencephalography-based source functional connectivity in drug-free patients with major depressive disorder

BackgroundCompared to functional magnetic resonance imaging (fMRI), source localization of a scalp-recorded electroencephalogram (EEG) provides higher temporal resolution and frequency synchronization to better understand the potential neurophysiological origins of disrupted functional connectivity (FC) in major depressive disorder (MDD). The present study aimed to investigate EEG-sourced measures to examine the FC in drug-free patients with MDD. MethodResting-state 32-channel EEG were recorded in 84 drug-free patients with MDD and 143 healthy controls, and the cortical source signals were estimated. Exact low-resolution brain electromagnetic tomography (eLORETA) was used to compute the intracortical activity from regions within the default mode network (DMN) and frontoparietal network (PFN). Lagged phase synchronization was used as a measure of functional connectivity. ResultsCompared with control subjects, the MDD group showed greater within-DMN alpha 1 and 2 bands and within-FPN alpha 1, 2, and beta 3 bands. Furthermore, the MDD group showed hyperconnectivity between the DMN and the FPN in the alpha 1 and 2 bands. Finally, higher levels of anhedonia were associated with higher between-network DMN and FPN connectivity in the alpha-1 band. LimitationsDue to the inherent limitations of eLORETA with predefined seeds, we could not exclude connectivity between regions of interest (ROIs), which may be related to the activity from regions adjacent to the ROIs. ConclusionsThe present findings support the importance of phase-lagged functional dysconnectivity in the neurophysiological mechanisms underlying MDD. Exploring the potential of these patterns as surrogates for treatment responses may advance targeted interventions for depression.

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.

Enhancing cognitive performance prediction by white matter hyperintensity connectivity assessment.

White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating brain health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. Lesion network mapping (LNM) enables us to infer if brain networks are connected to lesions and could be a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed LNM to test the following hypotheses: (i) LNM-informed markers surpass WMH volumes in predicting cognitive performance; and (ii) WMH contributing to cognitive impairment map to specific brain networks. We analysed cross-sectional data of 3485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in four cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity to 480 atlas-based grey and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. We compared the capacity of total and regional WMH volumes and LNM scores in predicting cognitive function using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention/executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater connectivity to WMH, in grey and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance. Measures of WMH-related brain network connectivity significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network integrity, particularly in attention-related brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders.

Functional connectomics reveals general wiring rule in mouse visual cortex.

Understanding the relationship between circuit connectivity and function is crucial for uncovering how the brain implements computation. In the mouse primary visual cortex (V1), excitatory neurons with similar response properties are more likely to be synaptically connected, but previous studies have been limited to within V1, leaving much unknown about broader connectivity rules. In this study, we leverage the millimeter-scale MICrONS dataset to analyze synaptic connectivity and functional properties of individual neurons across cortical layers and areas. Our results reveal that neurons with similar responses are preferentially connected both within and across layers and areas - including feedback connections - suggesting the universality of the 'like-to-like' connectivity across the visual hierarchy. Using a validated digital twin model, we separated neuronal tuning into feature (what neurons respond to) and spatial (receptive field location) components. We found that only the feature component predicts fine-scale synaptic connections, beyond what could be explained by the physical proximity of axons and dendrites. We also found a higher-order rule where postsynaptic neuron cohorts downstream of individual presynaptic cells show greater functional similarity than predicted by a pairwise like-to-like rule. Notably, recurrent neural networks (RNNs) trained on a simple classification task develop connectivity patterns mirroring both pairwise and higher-order rules, with magnitude similar to those in the MICrONS data. Lesion studies in these RNNs reveal that disrupting 'like-to-like' connections has a significantly greater impact on performance compared to lesions of random connections. These findings suggest that these connectivity principles may play a functional role in sensory processing and learning, highlighting shared principles between biological and artificial systems.

Conservation of structural brain connectivity in people with multiple sclerosis.

Multiple sclerosis (MS) is a neurodegenerative disease that affects the central nervous system. Structures affected in MS include the corpus callosum, connecting the hemispheres. Studies have shown that in mammalian brains, structural connectivity is organized according to a conservation principle, an inverse relationship between intra- and interhemispheric connectivity. The aim of this study was to replicate this conservation principle in subjects with MS and to explore how the disease interacts with it. A multicentric dataset has been analyzed including 513 people with MS and 208 healthy controls from seven different centers. Structural connectivity was quantified through various connectivity measures, and graph analysis was used to study the behavior of intra- and interhemispheric connectivity. The association between the intra- and the interhemispheric connectivity showed a similar strength for healthy controls (r = 0.38, p < 0.001) and people with MS (r = 0.35, p < 0.001). Intrahemispheric connectivity was associated with white matter fraction (r = 0.48, p < 0.0001), lesion volume (r = -0.44, p < 0.0001), and the Symbol Digit Modalities Test (r = 0.25, p < 0.0001). Results show that this conservation principle seems to hold for people with MS. These findings support the hypothesis that interhemispheric connectivity decreases at higher cognitive decline and disability levels, while intrahemispheric connectivity increases to maintain the balance.

Imaging the large-scale and cellular response to focal traumatic brain injury in mouse neocortex.

Traumatic brain injury (TBI) affects neural function at the local injury site and also at distant, connected brain areas. However, the real-time neural dynamics in response to injury and subsequent effects on sensory processing and behavior are not fully resolved, especially across a range of spatial scales. We used in vivo calcium imaging in awake, head-restrained male and female mice to measure large-scale and cellular resolution neuronal activation, respectively, in response to a mild/moderate TBI induced by focal controlled cortical impact (CCI) injury of the motor cortex (M1). Widefield imaging revealed an immediate CCI-induced activation at the injury site, followed by a massive slow wave of calcium signal activation that traveled across the majority of the dorsal cortex within approximately 30 s. Correspondingly, two-photon calcium imaging in primary somatosensory cortex (S1) found strong activation of neuropil and neuronal populations during the CCI-induced traveling wave. A depression of calcium signals followed the wave, during which we observed atypical activity of a sparse population of S1 neurons. Longitudinal imaging in the hours and days after CCI revealed increases in the area of whisker-evoked sensory maps at early time points, in parallel to decreases in cortical functional connectivity and behavioral measures. Neural and behavioral changes mostly recovered over hours to days in our M1-TBI model, with a more lasting decrease in the number of active S1 neurons. Our results in unanesthetized mice describe novel spatial and temporal neural adaptations that occur at cortical sites remote to a focal brain injury.

The association between structural connectivity and anti-seizure medication response in patients with temporal lobe epilepsy.

This study aimed to investigate the differences in structural connectivity and glymphatic system function between patients with temporal lobe epilepsy (TLE) and hippocampal sclerosis (HS) and healthy controls. Additionally, we analyzed the association between structural connectivity, glymphatic system function, and antiseizure medication (ASM) response. We retrospectively enrolled patients with TLE and HS and healthy controls who underwent diffusion tensor imaging at our hospital. We assessed structural connectivity in patients with TLE and HS and healthy controls by calculating network measures using graph theory and evaluated glymphatic system function using the diffusion tensor image analysis along the perivascular space (DTI-ALPS) index. Patients with TLE and HS were categorized into two groups: ASM poor and good responders. We enrolled 55 patients with TLE and HS and 53 healthy controls. Of the 55 patients with TLE and HS, 39 were ASM poor responders, and 16 were ASM good responders. The assortativity coefficient in patients with TLE and HS was higher than that in healthy controls (0.004 vs. -0.007, p = 0.004), and the assortativity coefficient in ASM poor responders was lower than that in ASM good responders (-0.001 vs. -0.197, p = 0.003). The DTI-ALPS index in patients with TLE and HS was lower than that in healthy controls (1.403 vs. 1.709, p < 0.001); however, the DTI-ALPS index did not differ between ASM poor and good responders (1.411 vs. 1.385, p = 0.628). The DTI-ALPS index had a significant negative correlation with age in patients with TLE and HS (r = -0.267, p = 0.049). We confirmed increased assortativity coefficient in structural connectivity and decreased DTI-ALPS index in patients with TLE and HS compared with healthy controls. Additionally, we demonstrated an association between decreased assortativity coefficient in structural connectivity and ASM poor response in patients with TLE patients and HS. This study investigates the relationship between brain connectivity changes and glymphatic system function with antiseizure medication response in patients with temporal lobe epilepsy and hippocampal sclerosis. The research reveals that these patients show altered brain connectivity and glymphatic function compared to healthy individuals. A key finding is the strong link between a specific connectivity measure (assortativity coefficient) and antiseizure medication response, providing valuable insights that could influence epilepsy treatment and future research directions.

Short Spatiotemporal Fire History Explains the Occurrence of Beetles Favoured by Fire.

The number and area of forest fires in northern Europe have been dramatically reduced during the past century, and several fire-favoured species are now threatened. To promote the recovery of these species, prescribed burning is often used as a conservation measure, and to optimise the use of these conservation burns, knowledge is needed on suitable fire frequency, size and placement in the landscape. The aim of this study was to analyse the effect of recent fire history (12 yrs) on beetles sampled using smoke attraction traps at 21 forest sites in a 10,000 km2 region. We analysed the odds of finding a fire-favoured beetle species or individual among the beetles in each trap using a new spatiotemporal connectivity measure and compared the results to non-fire-favoured and saproxylic species. For fire-favoured beetles, both the number of species and individuals significantly increased with connectivity to previous fires, while the other two groups did not. The spatiotemporal connectivity that best explained the patterns suggests that fire-favoured beetles mainly respond to fires within a 2 km range up to 2-3 years after the fire. Hence, to preserve fire-favoured insects, prescribed fires must be close in space and time to other fires-whether prescribed or natural.

Discovering prominent differences in structural and functional connectomes using a multinomial stochastic block model.

Understanding the differences between functional and structural human brain connectivity has been a focus of an extensive amount of neuroscience research. We employ a novel approach using the multinomial stochastic block model (MSBM) to explicitly extract components that characterize prominent differences across graphs. We analyze structural and functional connectomes derived from high-resolution diffusion-weighted MRI and fMRI scans of 250 Human Connectome Project subjects, analyzed at group connectivity level across 50 subjects. The inferred brain partitions revealed consistent, spatially homogeneous clustering patterns across inferred resolutions demonstrating the MSBM's reliability in identifying brain areas with prominent structure-function differences. Prominent differences in low-resolution brain maps (K = {3, 4} clusters) were attributed to weak functional connectivity in the bilateral anterior temporal lobes, while higher resolution results (K ≥ 25) revealed stronger interhemispheric functional than structural connectivity. Our findings emphasize significant differences in high-resolution functional and structural connectomes, revealing challenges in extracting meaningful connectivity measurements from both modalities, including tracking fibers through the corpus callosum and attenuated functional connectivity in anterior temporal lobe fMRI data, which we attribute to increased noise levels. The MSBM emerges as a valuable tool for understanding differences across graphs, with potential future applications and avenues beyond the current focus on characterizing modality-specific distinctions in connectomics data.

Hypothalamus Connectivity in Adolescent Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.

Adolescent Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a disabling illness of unknown etiology. Increasing evidence suggests hypothalamic involvement in ME/CFS pathophysiology, which has rarely been explored using magnetic resonance imaging (MRI)in the condition. This work aimed to use MRI to examine hypothalamus connectivity in adolescents with ME/CFS and explore how this relates to fatigue severity and illness duration. 25 adolescents with ME/CFS and 23 healthy controls completed a neuroimaging protocol consisting of structural and multishell diffusion-weighted imaging sequences, in addition to the PedsQL Multidimensional Fatigue Scale to assess fatigue severity. Information about illness duration was acquired at diagnosis. Preprocessing and streamlines tractography was performed using QSIPrep combined with a custom parcellation scheme to create structural networks. The number (degree) and weight (strength) of connections between lateralized hypothalamus regions and cortical and subcortical nodes were extracted, and relationships between connectivity measures, fatigue severity, and illness duration were performed using Bayesian regression models. We observed weak-to-moderate evidence of increased degree, but not strength, of connections from the bilateral anterior-inferior (left: pd [%] = 99.18, median [95% CI] = -22.68[-40.96 to 4.45]; right: pd [%] = 99.86, median [95% CI] = -23.35[-38.47 to 8.20]), left anterior-superior (pd [%] = 99.33, median [95% CI] = -18.83[-33.45 to 4.07]) and total left hypothalamus (pd [%] = 99.44, median [95% CI] = -47.18[-83.74 to 11.03]) in the ME/CFS group compared with controls. Conversely, bilateral posterior hypothalamus degree decreased with increasing ME/CFS illness duration (left: pd [%] = 98.13, median [95% CI]: -0.47[-0.89 to 0.03]; right: pd [%] = 98.50, median [95% CI]:-0.43[-0.82 to 0.05]). Finally, a weak relationship between right intermediate hypothalamus connectivity strength and fatigue severity was identified in the ME/CFS group (pd [%] = 99.35, median [95% CI] = -0.28[-0.51 to 0.06]), which was absent in controls. These findings suggest changes in hypothalamus connectivity may occur in adolescents with ME/CFS, warranting further investigation.

Disruptions in segregation mechanisms in fMRI-based brain functional network predict the major depressive disorder condition

This study investigates the functional brain network in major depressive disorder using network theory and a consensus network approach. At the macroscopic level, we found significant differences in connectivity measures such as node strength and clustering coefficient, with healthy controls exhibiting higher values. This is consistent with disruptions in functional brain network segregation in patients with major depressive disorder. Consensus network analysis revealed that the central executive and salience networks were predominant in healthy controls, whereas depressed patients showed greater overlap with the default mode network. No differences were found in network efficiency measures, indicating comparable brain network integration between healthy controls and major depressive disorder groups. Importantly, the clustering coefficient emerged as an effective diagnostic biomarker for depression, achieving high sensitivity (90%), specificity (92%), and overall precision (90%). Further analysis at the mesoscale level uncovered unique functional connections distinguishing healthy controls and major depressive disorder groups. Our findings underscore the utility of analyzing functional networks from the macroscale to the mesoscale, and provide insight into overcoming the challenges associated with intersubject variability and the multiple comparisons problem in network analysis.

Recruitment of a U.S. Nation-Wide Sample of Transgender and Gender Diverse Youth

PurposeTransgender and gender diverse youth (TGDY) can be a challenging population to reach because of limitations related to parental/guardian consent and concerns about privacy. This pilot study's purpose was to assess the feasibility of recruiting a US nation-wide longitudinal sample of TGDY through social media, and to compare participants who completed the Wave 1 survey only to TGDY who completed both Wave 1 and Wave 2 surveys. MethodsAdolescents (aged 14–17 years) who identified as TGDY were recruited through social media and invited to complete two online surveys across a 3-month period. Surveys included measures of mental health, substance use, and community connection. ResultsThe Wave 1 sample included 252 TGDY, with 183 (73%) retained at Wave 2. There were few differences (i.e., gender identity; sex assigned at birth) between Wave 1–only participants and those retained at Wave 2. DiscussionFindings demonstrate the feasibility of recruiting a U.S. nation-wide longitudinal sample of TGDY through social media, providing an important platform for conducting research to improve well-being of TGDY.

Macroinvertebrate Community Responses to Disturbance in a Fragmented River With Contrasting Legacies of Alteration

ABSTRACTFlow is a critical factor determining the riverine ecosystem structure and function. Widespread hydrologic alteration, however, has impacted the ecological integrity of rivers in ways that are not well understood, including responses of biological communities to increasingly frequent and severe climatic disturbances. Our study compared the responses of invertebrate communities on woody debris to large flooding and extreme drought in two highly contrasting segments of an impaired low‐gradient river. The upstream segment, which according to previous research has higher α‐diversity and production of large‐bodied and sensitive invertebrates, maintained higher flows and longitudinal connectivity throughout the 4‐year study. Communities in this upper segment resembled one another among sites (lower spatial turnover) but experienced greater temporal shifts in composition associated with hydrological disturbances. Conversely, invertebrate communities in the highly altered downstream segment, which is impaired by reduced flow, sedimentation, and hypoxia, were composed of smaller‐bodied and pollution‐tolerant taxa with lower α‐diversity. Unlike the upper segment, communities were patchily distributed among sites (higher spatial turnover), which made it more difficult to detect system‐wide temporal variation in composition throughout the study. Our study underscores the benefit of including measures of connectivity and spatial heterogeneity when assessing the ecological integrity of lotic systems. Understanding the system‐wide response to disturbances across longer time frames can help better predict and mitigate the impacts of climate change on ecosystem integrity in degraded rivers.