Connectivity Patterns Research Articles

Larval dispersal and physical connectivity of Pheronema carpenteri populations in the Azores

The study of larval dispersal and connectivity between deep-sea populations is essential for the effective conservation and management of deep-sea environments and the design and implementation of Marine Protected Areas. Dense sponge aggregations, known as “sponge grounds”, are a key component of marine benthic ecosystems, by increasing the structural complexity of the sea floor and providing structure and habitat for many other species. These aggregations are characteristic of the Azores deep-sea environment. These sessile organisms rely primarily on larval dispersal for their reproduction. Connectivity between specific Pheronema carpenteri sponge aggregations in the Azores was studied using a 3-D biophysical dispersal model. Different biological trait scenarios were analyzed, considering spawning seasonality and pelagic larval duration. Model results indicate that regional circulation patterns drive larval dispersion, shaping population connectivity of P. carpenteri sponge aggregations in the Azores, particularly among aggregations in the Central Group of Azorean islands. Some areas present high retention rates, receiving larvae from several sponge aggregations while also being important larval source aggregations. In contrast, aggregations from the Eastern Group may be isolated from the others. Larval dispersal and connectivity patterns were analyzed concerning the current configuration of Marine Protected Areas (MPAs) in the Azores. The results underscored the importance of maintaining protection efforts in existing MPAs and identified stepping-stone locations and specific sites where additional measures could enhance species connectivity in the Azores.

Exploring low- and high-order functional connectivity in chronic ankle instability through resting-state fMRI.

The functional connectivity (FC) has emerged as a valuable tool for comprehending the cerebral operational mechanism. Understanding the FC changes in patients with chronic ankle instability (CAI) helps reveal the underlying central nervous system mechanisms of the disease and provides clues for developing personalized treatment plans. To explore differences between low- and high-order FC in patients with CAI and healthy controls, as well as the correlation among the feature connections and clinical data. In our study, we recruited 40 patients with CAI and 42 healthy individuals who had not experienced ankle injuries. All participants underwent clinical assessments of ankle joints, collected the number of ankle sprains within the past 6 months, and performed resting-state functional magnetic resonance imaging (rs-fMRI) scans. Pearson correlation and matrix variate normal distribution (MVND) were used to construct low-order and high-order FC networks, respectively. Feature selections between groups were performed by two-sample t-tests, and a multi-kernel support vector machine (MK-SVM) was subsequently applied to combine the multiple connection patterns for the classification. Using leave-one-out cross-validation (LOOCV) to assess classification performance and identify the consensus connections contributing most to classification. FC was reduced in certain brain regions of CAI patients. More consensus connections were recognized in low-order FC network than in high-order FC network. The highest classification accuracy of 91.30% was achieved by combining three connection patterns. The most discriminating functional connections were primarily centered on the default mode network and spanned the visual network, sensorimotor network, ventral attention network, and central executive network. In addition, FC strength in the left cingulate and paracingulate gyrus (DCG.L) and right superior temporal gyrus (STG.R) was negatively correlated with the number of ankle sprains in the past 6 months in all FC networks (p<0.05). Abnormalities in connectivity in patients with CAI were observed in both low- and high-order FC networks. The adaptive changes in the brain related to CAI may extend beyond the sensorimotor networks, primarily involving higher-level default mode networks associated with attention. Moreover, the FC strength between DCG.L and STG.R may predict the risk of ankle re-sprains and help clinicians develop personalized treatment plans.

Characteristics of Functional Connections and Topographical Properties Distinguished the Healthy State and Obsessive-Compulsive Disorder

Background: Obsessive-compulsive disorder (OCD) is characterized by alterations in brain connectivity, particularly within the default mode network (DMN) and the salience network (SN). Investigating these connectivity differences can provide a deeper understanding of the neural mechanisms underlying OCD. Methods: This cross-sectional study involved 58 patients diagnosed with OCD and 38 healthy control subjects, totaling 96 participants. Resting-state functional MRI (fMRI) data were acquired and analyzed using the CONN toolbox to examine functional connectivity within intrinsic resting-state networks. Graph theory metrics were applied to evaluate node connections and the overall network topology. Clinical symptoms were assessed using the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS), and their correlations with connectivity patterns and graph-theory parameters were analyzed. Results: The OCD patients and healthy controls were matched in terms of age, gender, marital status, socioeconomic status, and handedness. However, OCD patients had significantly worse general health, quality of life, and higher levels of depression and anxiety. Network analyses revealed altered whole-brain connectivity in OCD patients, particularly within the DMN and the frontoparietal network. The most significant between-group connectivity differences were observed between the posterior parietal cortex (PPC) and the precuneus. Disruptions in the DMN, specifically altered connectivity between the medial prefrontal cortex and posterior cingulate cortex, and changes in the SN involving the anterior insula and anterior cingulate cortex, were significantly correlated with the severity of OCD symptoms. Conclusions: The findings suggest that OCD is associated with distinct alterations in DMN connectivity, which may play a critical role in the disorder's pathophysiology. These disruptions offer potential targets for therapeutic intervention. Further research is needed to explore these connectivity changes in larger cohorts and at various stages of OCD to better understand their clinical significance.

Identification of dominant drivers of streamflow spatiotemporal variations in typical mountainous areas in the Hexi Corridor, China

Study regionTypical mountain areas in the Hexi Corridor, China. Study focusWater security and ecosystem sustainability of arid inland river basins are highly dependent on upstream streamflow. However, due to the complex geographical environment and limited observation data in the study region, the attribution of spatiotemporal variations in streamflow influenced by climate change and/or human activities remains unclear. Here, we used partial least squares regression (PLSR) and the Budyko framework to unravel the dominant drivers of spatiotemporal variation in streamflow over the past 30 yr. New hydrological insight for the regionPrecipitation, topographic wetness index, slope, forest land, gross primary productivity, hydrological connectivity, soil organic carbon content, silt content, relative relief, NDVI and gravel content dominated spatial variation in streamflow. Temporal variation of streamflow was sensitive to precipitation and land surface. Specifically, increased precipitation and land surface alteration dominated the increase in streamflow in 50 % of the watersheds and the decrease in streamflow in 33 % of them, respectively. Further, land surface alteration was dominated by expansion of agricultural and built-up areas, weakened hydrological connectivity, increased landscape aggregation and forest cover. Controlling agricultural and built-up areas and the scale of afforestation, and focusing on the dynamics of hydrological connectivity and landscape patterns in the upstream reaches are imperative to maintain the security and sustainability of water resources in the arid inland river basins.

Connectome-based Predictive Models of General and Specific Cognitive Control.

Cognitive control, the ability to adapt thoughts and actions to shifting contexts and goals, is composed primarily of three distinct yet interrelated components: Inhibition, Shifting, and Updating. While prior research has examined the nature of different cognitive components as well as their inter-relationships, fewer studies examined whole-brain connectivity to predict individual differences for the three cognitive components and associated tasks. Here, using the Connectome-based Predictive Modelling (CPM) approach and open-access data from the Human Connectome Project, we built brain network models to successfully predict individual performance differences on the Flanker task, the Dimensional Change Card Sort task, and the 2-Back task, each putatively corresponding to Inhibition, Shifting, and Updating. We focused on grayordinate fMRI data collected during the 2-Back tasks after confirming superior predictive performance over resting-state and volumetric data. High cross-task prediction accuracy as well as joint recruitment of canonical networks, such as the frontoparietal and default-mode networks, suggest the existence of a common cognitive control factor. To directly investigate the relationships among the three cognitive control components, we developed new measures to disentangle their shared and unique aspects. Our analysis confirmed that a shared control component can be well predicted from functional connectivity patterns densely located around the frontoparietal, default-mode and dorsal attention networks. In contrast, the Shifting-specific and Inhibition-specific components exhibited lower cross-prediction performance, indicating their distinct and specialized roles. Notably, the Updating-specific component showed significant cross-prediction with the general control factor, suggesting its central role in cognitive control. Given the limitation that individual behavioral measures do not purely reflect the intended cognitive constructs, our study demonstrates the need to distinguish between common and specific components of cognitive control.

Taxonomic Diversity, Predicted Metabolic Pathway, and Interaction Pattern of Bacterial Community in Sea Urchin Anthocidaris crassispina.

Bacterial assemblages associated with sea urchin are critical to their physiology and ecology within marine ecosystems. In this study, we characterized the bacterial communities in wild sea urchin Anthocidaris crassispina captured in Daya Bay, South China Sea. A total of 363 amplicon sequence variants belonging to nine phyla and 141 genera were classified from intestine, body surface, and surrounding seawater samples. Proteobacteria, Firmicutes, and Bacteroidetes were the dominant bacteria phyla found in this study. A network analysis of bacterial interspecies interactions revealed varying complexity, stability, connectivity, and relationship patterns across the samples, with the most intricate network observed in the surrounding seawater. Metagenomic predictions highlighted the distinct bacterial metabolic pathways, with significant differences between intestine and seawater samples. Notably, pathways associated with polysaccharide degradation, including chitin derivatives, starch, and CoM biosynthesis, were markedly abundant, underscoring the gut microbiota's key role in digesting algae. In addition, other metabolic pathways in intestine samples were linked to immune response regulation of sea urchins. Overall, this study provides a comprehensive overview of the bacterial community structure and potential functional roles in A. crassispina.

Changes in cortical manifold structure following stroke and its relation to behavioral recovery in the male macaque

Stroke, a major cause of disability, disrupts brain function and motor skills. Previous research has mainly focused on reorganization of the motor system post-stroke, but the effects on other brain areas and their influence on recovery is poorly understood. Here, we use functional neuroimaging in a nonhuman primate model (23 male Cynomolgus Macaques), we explore how ischemic stroke affects whole-brain cortical architecture and its relation to spontaneous behavioral recovery. By projecting patterns of cortical functional connectivity onto a low-dimensional manifold space, we find that several regions in both sensorimotor cortex and higher-order transmodal cortex exhibit significant shifts in their manifold embedding from pre- to post-stroke. Furthermore, we observe that changes in default mode and limbic network regions, and not preserved sensorimotor cortical regions, are associated with animal behavioral recovery post-stroke. These results establish the whole-brain functional changes associated with stroke, and suggest an important role for higher-order transmodal cortex in post-stroke outcomes.

Enhancing daily life for children with cognitive developmental delay through insights into brain development.

Cognitive developmental delay, including severe intellectual disability (IQ below 70) and borderline intellectual functioning (IQ 70-85), poses significant challenges, including high costs and emotional burden. Early diagnosis and interventions might improve adaptive behavior and daily life functioning. High-risk groups include children with neonatal complications, congenital anomalies, genetic disorders, or metabolic errors, yet over 50% of cases have unknown causes. To provide timely diagnosis and intervention for children with cognitive developmental delay, it is important to increase our understanding and ability to prognosticate their level of functioning. The pivotal role of brain development in the first few years of life presents a window of opportunity for these goals. By detailed investigation of common patterns in structural brain development and connectivity by MRI in relation to cognitive and executive functioning, this review aims to identify potential factors that might improve understanding and prognostication of children with cognitive developmental delay. Exploring similarities among diverse patient groups with childhood cognitive developmental delay, this review intends to provide a nuanced perspective. IMPACT: This review identified several MRI brain developmental markers, especially in the white matter, that might hold potential to be a prognostic marker for intellectual and executive functioning in children with cognitive developmental delay. Bringing together information on aberrant brain developmental trajectories and connectivity across different patient childhood populations with cognitive developmental delay might improve our understanding and prognostication.

Multimodal investigation of dynamic brain network alterations in autism spectrum disorder: Linking connectivity dynamics to symptoms and developmental trajectories

BackgroundAutism spectrum disorder (ASD) has been associated with disrupted brain connectivity, yet a comprehensive understanding of the dynamic neural underpinnings remains lacking. This study employed concurrent electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) techniques to investigate dynamic functional connectivity (dFC) patterns and neurovascular characteristics in children with ASD. We also explored associations between neurovascular characteristics and the developmental trajectory of adaptive behavior in individuals with ASD. MethodsResting-state EEG and fNIRS data were simultaneously recorded from 58 ASD and 63 TD children. We implemented a k-means clustering approach to extract the dFC states for each modality. In addition, a multimodal covariance network (MCN) was constructed from the EEG and fNIRS dFC features to capture the neurovascular characteristics linked to ASD. ResultsEEG analyses revealed atypical properties of dFC states in the beta and gamma bands in children with ASD compared to TD children. For fNIRS, the ASD group exhibited atypical properties of dFC states such as duration and transitions relative to the TD group. The MCN analysis revealed significantly suppressed functional covariance between right superior temporal and left Broca's areas, alongside enhanced right dorsolateral prefrontal-left Broca covariance in ASD. Notably, we found that early neurovascular characteristics can predict the developmental progress of adaptive functioning in ASD. ConclusionThe multimodal investigation revealed distinct dFC patterns and neurovascular characteristics associated with ASD, elucidating potential neural mechanisms underlying core symptoms and their developmental trajectories. Our study highlights that integrating complementary neuroimaging modalities may aid in unraveling the complex neurobiology of ASD.

Development of the whole-brain functional connectome explored via graph theory analysis

Introduction Adolescence is a period of remarkable development as children’s brains change to resemble adult brains. Resting state fMRI measures fluctuations in blood-oxygen signal from which we can infer functional connectivity (FC). Graph theory is a branch of mathematics that can quantify the complex patterns of connectivity and network architecture inherent in the functional connectome. An ideal graph theory analysis explores edges that are weighted, directional, and heterogenous (can be positive or negative). Recent developmental studies have applied graph theory to the functional connectome, yet due to the considerable complexity added by each facet, most ignore one or more aspects of an ideal graph theory analysis (directionality and heterogeneity). Methods The present cross-sectional study measured FC in typically developing children, adolescents, and young adults (age 6-24 years) using 150+ echo-planar volumes (3.6mm isotropic voxels, repetition/echo time=2000/30ms) acquired at rest. A standard pre-processing pipeline was used, and the functional connectome was quantified using a weighted, directed graph analysis, including both positive and negative connections. Five different graph theory metrics were utilized to quantify developmental trajectories: connection density, modularity, clustering coefficient, global efficiency, and betweenness centrality. Positive and negative connections were analyzed separately, and age and sex associations were explored. Results The total sample comprised 219 participants (mean age (SD) [range] = 14.1 (3.3) [6.5-24.0] years, 50% female). For positive connections, modularity and betweenness centrality increased with age (both p&lt;0.001), while connection density, clustering coefficient, and global efficiency decreased with age (all p&lt;0.001). By contrast, for negative connections, modularity and betweenness centrality decreased with age (p=0.002, p=0.003), while connection density, clustering coefficient , and global efficiency increased with age (p&lt;0.001, p&lt;0.001, p=0.003). Effects of sex, hemisphere, and their interaction were minimal, though global efficiency for negative connections was higher in the right hemisphere than the left (p&lt;0.001). Conclusion Graph theory appears to be a useful tool for quantifying the complex development of the functional connectome. The developmental changes presented here may be driven by an intrinsic pressure to balance functionality with low metabolic cost to maintain the network. The positive connection network appears to shift towards a more efficient conformation resembling “small-world” architecture. In contrast, the negative connection network seems to shift away from such efficient architecture, possibly to prioritize improving functionality before later refinement.

Enhanced ADHD classification through deep learning and dynamic resting state fMRI analysis.

Attention Deficit Hyperactivity Disorder (ADHD) is characterized by deficits in attention, hyperactivity, and/or impulsivity. Resting-state functional connectivity analysis has emerged as a promising approach for ADHD classification using resting-state functional magnetic resonance imaging (rs-fMRI), although with limited accuracy. Recent studies have highlighted dynamic changes in functional connectivity patterns among ADHD children. In this study, we introduce Skip-Vote-Net, a novel deep learning-based network designed for classifying ADHD from typically developing children (TDC) by leveraging dynamic connectivity analysis on rs-fMRI data collected from 222 participants included in the NYU dataset within the ADHD-200 database. Initially, for each subject, functional connectivity matrices were constructed from overlapping segments using Pearson's correlation between mean time series of 116 regions of interest defined by the Automated Anatomical Labeling (AAL) 116 atlas. Skip-Vote-Net was then developed, employing a majority voting mechanism to classify ADHD/TDC children, as well as distinguishing between the two main subtypes: the inattentive subtype (ADHDI) and the predominantly combined subtype (ADHDC). The proposed method was evaluated across four classification scenarios: (1) two-class classification of ADHD from TD children using balanced data, (2) two-class classification between ADHD and TD children using unbalanced data, (3) two-class classification between ADHDI and ADHDC, and (4) three-class classification among ADHDI, ADHDC, and TD children. Using Skip-Vote-Net, we achieved mean classification accuracies of 97% ± 1.87 and 97.7% ± 2.2 for the balanced and unbalanced classification cases, respectively. Furthermore, the mean classification accuracy for discriminating between ADHDI and ADHDC reached 99.4% ± 1.21. Finally, the proposed method demonstrated an average accuracy of 98.86% ± 1.03 in classifying ADHDI, ADHDC, and TD children collectively. Our findings highlight the superior performance of Skip-Vote-Net over existing methods in the classification of ADHD, showcasing its potential as an effective diagnostic tool for identifying ADHD subtypes and distinguishing ADHD from typically developing children.

Multiclass classification of Autism Spectrum Disorder, attention deficit hyperactivity disorder, and typically developed individuals using fMRI functional connectivity analysis.

Neurodevelopmental conditions, such as Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD), present unique challenges due to overlapping symptoms, making an accurate diagnosis and targeted intervention difficult. Our study employs advanced machine learning techniques to analyze functional magnetic resonance imaging (fMRI) data from individuals with ASD, ADHD, and typically developed (TD) controls, totaling 120 subjects in the study. Leveraging multiclass classification (ML) algorithms, we achieve superior accuracy in distinguishing between ASD, ADHD, and TD groups, surpassing existing benchmarks with an area under the ROC curve near 98%. Our analysis reveals distinct neural signatures associated with ASD and ADHD: individuals with ADHD exhibit altered connectivity patterns of regions involved in attention and impulse control, whereas those with ASD show disruptions in brain regions critical for social and cognitive functions. The observed connectivity patterns, on which the ML classification rests, agree with established diagnostic approaches based on clinical symptoms. Furthermore, complex network analyses highlight differences in brain network integration and segregation among the three groups. Our findings pave the way for refined, ML-enhanced diagnostics in accordance with established practices, offering a promising avenue for developing trustworthy clinical decision-support systems.

Fault-tolerant double-circular connectivity pattern for quantum stabilizer codes

Recently, the circular connectivity pattern has been presented for a class of stabilizer quantum error correction codes. The circular connectivity pattern for such a class of stabilizer codes can be implemented in a resource-efficient manner using a single ancilla and native two-qubit Controlled-Not-Swap gates (CNS) gates, which may be interesting for demonstrating error-correction codes with superconducting quantum processors. However, one concern is that this scheme is not fault-tolerant. And it might not apply to the Calderbank-Shor-Steane (CSS) codes. In this paper, we present a fault-tolerant version of the circular connectivity pattern, named the double-circular connectivity pattern. This pattern is an implementation for syndrome-measurement circuits with a flagged error correction scheme for stabilizer codes. We illustrate that this pattern is available for Steane code (a CSS code), Laflamme’s five-qubit code, and Shor’s nine-qubit code. For Laflamme’s five-qubit code and Shor’s nine-qubit code, the pattern has the property that it uses only native two-qubit CNS gates, which are more efficient in the superconducting quantum platform.

Dynamic patterns of functional connectivity in the human brain underlie individual memory formation

Remembering our everyday experiences involves dynamically coordinating information distributed across different brain regions. Investigating how momentary fluctuations in connectivity in the brain are relevant for episodic memory formation, however, has been challenging. Here we leverage the high temporal precision of intracranial EEG to examine sub-second changes in functional connectivity in the human brain as 20 participants perform a paired associates verbal memory task. We first identify potential functional connections by selecting electrode pairs across the neocortex that exhibit strong correlations with a consistent time delay across random recording segments. We then find that successful memory formation during the task involves dynamic sub-second changes in functional connectivity that are specific to each word pair. These patterns of dynamic changes are reinstated when participants successfully retrieve the word pairs from memory. Therefore, our data provide direct evidence that specific patterns of dynamic changes in human brain connectivity are associated with successful memory formation.

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

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

A Deep Learning-based Pipeline for Segmenting the Cerebral Cortex Laminar Structure in Histology Images.

Characterizing the anatomical structure and connectivity between cortical regions is a critical step towards understanding the information processing properties of the brain and will help provide insight into the nature of neurological disorders. A key feature of the mammalian cerebral cortex is its laminar structure. Identifying these layers in neuroimaging data is important for understanding their global structure and to help understand the connectivity patterns of neurons in the brain. We studied Nissl-stained and myelin-stained slice images of the brain of the common marmoset (Callithrix jacchus), which is a new world monkey that is becoming increasingly popular in the neuroscience community as an object of study. We present a novel computational framework that first acquired the cortical labels using AI-based tools followed by a trained deep learning model to segment cerebral cortical layers. We obtained a Euclidean distance of for the cortical labels acquisition, which was in the acceptable range by computing the half Euclidean distance of the average cortex thickness ( ). We compared our cortical layer segmentation pipeline with the pipeline proposed by Wagstyl et al. (PLoS biology, 18(4), e3000678 2020) adapted to 2D data. We obtained a better mean percentile Hausdorff distance (95HD) of . Whereas a mean 95HD of was obtained from Wagstyl et al. We also compared our pipeline's performance against theirs using their dataset (the BigBrain dataset). The results also showed better segmentation quality, Jaccard Index acquired from our pipeline, while was stated in their paper.

Integrating brainstem and cortical functional architectures.

The brainstem is a fundamental component of the central nervous system, yet it is typically excluded from in vivo human brain mapping efforts, precluding a complete understanding of how the brainstem influences cortical function. In this study, we used high-resolution 7-Tesla functional magnetic resonance imaging to derive a functional connectome encompassing cortex and 58 brainstem nuclei spanning the midbrain, pons and medulla. We identified a compact set of integrative hubs in the brainstem with widespread connectivity with cerebral cortex. Patterns of connectivity between brainstem and cerebral cortex manifest as neurophysiological oscillatory rhythms, patterns of cognitive functional specialization and the unimodal-transmodal functional hierarchy. This persistent alignment between cortical functional topographies and brainstem nuclei is shaped by the spatial arrangement of multiple neurotransmitter receptors and transporters. We replicated all findings using 3-Tesla data from the same participants. Collectively, this work demonstrates that multiple organizational features of cortical activity can be traced back to the brainstem.

Cross border population movements across three East African states: Implications for disease surveillance and response.

The frequent population movement across the five East African Countries poses risk of disease spread in the region. A clear understanding of population movement patterns is critical for informing cross-border disease control interventions. We assessed population mobility patterns across the borders of the East African states of Kenya, Uganda, and Rwanda. In November 2022, we conducted Focus Group Discussions (FGDs), Key Informant Interviews (KIIs), and participatory mapping. Participants were selected using purposive sampling and a topic guide used during interviews. Key informants included border districts (Uganda and Rwanda) and county health officials (Kenya). FGD participants were identified from border communities and travellers and these included truck drivers, commercial motorcyclists, and businesspersons. During KIIs and FGDs, we conducted participatory mapping using Population Connectivity Across Borders toolkits. Data were analysed using thematic analysis approach using Atlas ti 7 software. Different age groups travelled across borders for various reasons. Younger age groups travelled across the border for education, trade, social reasons, employment opportunities, agriculture and mining. While older age groups mainly travelled for healthcare and social reasons. Other common reasons for crossing the borders included religious and cultural matters. Respondents reported seasonal variations in the volume of travellers. Respondents reported using both official (4 Kenya-Uganda, 5 Rwanda-Uganda borders) and unofficial Points of Entry (PoEs) (14 Kenya-Uganda, 20 Uganda-Rwanda) for exit and entry movements on borders. Unofficial PoEs were preferred because they had fewer restrictions like the absence of health screening, and immigration and customs checks. Key destination points (points of interest) included: markets, health facilities, places of worship, education institutions, recreational facilities and business towns. Twenty-eight health facilities (10- Lwakhakha, Uganda, 10- Lwakhakha, Kenya, and 8- Cyanika, Uganda) along the borders were the most commonly visited by the travellers and border communities. Complex population movement and connectivity patterns were identified along the borders. These were used to guide cross-border disease surveillance and other border health strategies in the three countries. Findings were used to revise district response and preparedness plans by strengthening community-based surveillance in border communities.

The national network of US state legislators on Twitter

Abstract Networks among legislators shape politics and policymaking within legislative institutions. In past work on legislative networks, the ties between legislators have been defined on those who serve in the same legislature or chamber. Online information networks, which have been found to play important roles in legislative communication at the national level, are not bounded by individual legislative bodies. We collect original data for over four thousand US state legislators and study patterns of connection among them on Twitter. We look at three types of Twitter networks—follower, retweets, and mentions. We describe these networks and estimate the relationships between ties and salient attributes of legislators. We find that networks are organized largely along geographic and partisan lines and that identity attributes—namely gender and race—exhibit strong associations with the formation of ties.

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.