Connectome Analysis Research Articles

Tobacco Smoking Functional Networks: A Whole-Brain Connectome Analysis in 24,539 Individuals.

Nicotine addiction, a multifaceted neuropsychiatric disorder, profoundly impacts brain functions through interactions with neural pathways. Despite its significance, the impact of tobacco smoking on the whole-brain functional connectome remains largely unexplored. We conducted a whole-brain analysis on 24,539 adults aged 40 and above from the UK Biobank cohort. Subjects were categorized into individuals who use nicotine and who do not use nicotine based on current and chronic tobacco smoking information. Functional connectivity was assessed using resting-state functional magnetic resonance imaging (rfMRI). We employed a network analysis method to assess the systematic effects of tobacco smoking on brain connectome by identifying subnetworks that show nicotine-use-related differences. Our analyses revealed two nicotine-use-related subnetworks with distinct network structure (permutation p-value < 0.001). In the first network, there is a significant decrease in resting-state functional connectivity (rsFC) between the basal ganglia regions (e.g., nucleus accumbens) and 73% of the remaining brain regions, emphasizing the central hub role of basal ganglia in addictive smoking behaviors. Additionally, a data-driven subnetwork mainly involving regions from frontal and occipital lobes, showed reduced rsFC among individuals who use nicotine. The results suggest significant alterations in the communication and coordination among the basal ganglia and the broader network of brain regions. The observed changes in rsFC indicate a widespread disruption in the connectivity patterns associated with nicotine use. This study identifies rsFC subnetworks related to chronic nicotine use through whole-brain connectome analysis. The findings confirm that widespread alterations in rsFC are centered around hub nodes within the basal ganglia, including bilateral nucleus accumbens, putamen, caudate, and globus pallidus. In addition, our analysis found a clique-forming subnetwork vulnerable to tobacco smoking consisting of regions from the visual, dorsal/ventral attention, and frontoparietal networks.

The relationship between white matter architecture and language lateralisation in the healthy brain.

Interhemispheric anatomical differences have long been thought to be related to language lateralisation. Previous studies have explored whether asymmetries in the diffusion characteristics of white matter language tracts are consistent with language lateralisation. These studies, typically with smaller cohorts, yielded mixed results. This study investigated whether connectomic analysis of quantitative anisotropy (QA) and shape features of white matter tracts across the whole brain are associated with language lateralisation. We analysed 1040 healthy individuals (562 females) from the Human Connectome Project database. Hemispheric language dominance for each participant was quantified using a laterality quotient (LQ) derived from fMRI activation in regions of interest (ROIs) associated with a language comprehension task compared against a math task. A linear regression model was used to examine the relationship between structural asymmetry and functional lateralisation. Connectometry revealed a significant negative correlation between LQs and QA of corpus callosum tracts, indicating that higher QA in these regions is associated with bilateral and right-hemisphere language representation in frontal and temporal regions, respectively. Left language laterality in temporal lobe was significantly associated with longer right inferior fronto-occipital fasciculus (IFOF) and forceps minor tracts. These results suggest that diffusion measures of microstructural architecture as well as geometrical features of reconstructed white matter tracts play a role in language lateralisation. People with increased dependence on right or both frontal hemispheres for language processing may have more developed commissural fibres, which may support more efficient interhemispheric communication.Significance statement The left cerebral hemisphere is dominant for language functions in most people. In some healthy people, language functions are lateralised to the right hemisphere or distributed across both hemispheres. The anatomy underlying patterns of hemispheric language dominance are not well established. Emerging evidence suggests that white matter connectivity and architecture is an important feature of cortical functional organisation. In this work, we report that people who have language functions distributed across both hemispheres have greater inter-hemispheric connectivity compared to lateralised people. Our findings provide further insights into the anatomical basis of language function and may have wider clinical implications.

Whole-brain annotation and multi-connectome cell typing of Drosophila

The fruit fly Drosophila melanogaster has emerged as a key model organism in neuroscience, in large part due to the concentration of collaboratively generated molecular, genetic and digital resources available for it. Here we complement the approximately 140,000 neuron FlyWire whole-brain connectome1 with a systematic and hierarchical annotation of neuronal classes, cell types and developmental units (hemilineages). Of 8,453 annotated cell types, 3,643 were previously proposed in the partial hemibrain connectome2, and 4,581 are new types, mostly from brain regions outside the hemibrain subvolume. Although nearly all hemibrain neurons could be matched morphologically in FlyWire, about one-third of cell types proposed for the hemibrain could not be reliably reidentified. We therefore propose a new definition of cell type as groups of cells that are each quantitatively more similar to cells in a different brain than to any other cell in the same brain, and we validate this definition through joint analysis of FlyWire and hemibrain connectomes. Further analysis defined simple heuristics for the reliability of connections between brains, revealed broad stereotypy and occasional variability in neuron count and connectivity, and provided evidence for functional homeostasis in the mushroom body through adjustments of the absolute amount of excitatory input while maintaining the excitation/inhibition ratio. Our work defines a consensus cell type atlas for the fly brain and provides both an intellectual framework and open-source toolchain for brain-scale comparative connectomics.

Self-guided Knowledge-Injected Graph Neural Network for Alzheimer's Diseases.

Graph neural networks (GNNs) are proficient machine learning models in handling irregularly structured data. Nevertheless, their generic formulation falls short when applied to the analysis of brain connectomes in Alzheimer's Disease (AD), necessitating the incorporation of domain-specific knowledge to achieve optimal model performance. The integration of AD-related expertise into GNNs presents a significant challenge. Current methodologies reliant on manual design often demand substantial expertise from external domain specialists to guide the development of novel models, thereby consuming considerable time and resources. To mitigate the need for manual curation, this paper introduces a novel self-guided knowledge-infused multimodal GNN to autonomously integrate domain knowledge into the model development process. We propose to conceptualize existing domain knowledge as natural language, and devise a specialized multimodal GNN framework tailored to leverage this uncurated knowledge to direct the learning of the GNN submodule, thereby enhancing its efficacy and improving prediction interpretability. To assess the effectiveness of our framework, we compile a comprehensive literature dataset comprising recent peer-reviewed publications on AD. By integrating this literature dataset with several real-world AD datasets, our experimental results illustrate the effectiveness of the proposed method in extracting curated knowledge and offering explanations on graphs for domain-specific applications. Furthermore, our approach successfully utilizes the extracted information to enhance the performance of the GNN.

Neurons underlying aggression-like actions that are shared by both males and females in Drosophila.

Aggression involves both sexually monomorphic and dimorphic actions. How the brain implements these two types of actions is poorly understood. We found that in Drosophila melanogaster a set of neurons, which we call CL062, previously shown to mediate male aggression also mediate female aggression. These neurons elicit aggression acutely and without the presence of a target. Although the same set of actions is elicited in males and females, the overall behavior is sexually dimorphic. The CL062 neurons do not express fruitless, a gene required for sexual dimorphism in flies, and expressed by most other neurons important for controlling fly aggression. Connectomic analysis in a female electron microscopy dataset suggests that these neurons have limited connections with fruitless expressing neurons that have been shown to be important for aggression, and signal to different descending neurons. Thus, CL062 is part of a monomorphic circuit for aggression that functions parallel to the known dimorphic circuits.Significance Statement Aggression is an important component of social interaction in most animals. Aggressive behavior serve a critical purpose by helping an animal secure territory, mates or food. Aggressive behaviors are very diverse in both their goals and their target. However, most studies aimed at uncovering neural circuits important for aggression have found circuits that are sexually dimorphic and are either only present in either male or female or only produce aggression in one. In this study, using Drosophila as a model, we report that a small set of neurons, when activated, produce aggressive behaviors in both males and females. We also show that these neurons are not strongly connected to other aggression promoting neurons implying that many parallel pathways mediate aggression.

A Principled Framework to Assess the Information-Theoretic Fitness of Brain Functional Sub-Circuits

In systems and network neuroscience, many common practices in brain connectomic analysis are often not properly scrutinized. One such practice is mapping a predetermined set of sub-circuits, like functional networks (FNs), onto subjects’ functional connectomes (FCs) without adequately assessing the information-theoretic appropriateness of the partition. Another practice that goes unchallenged is thresholding weighted FCs to remove spurious connections without justifying the chosen threshold. This paper leverages recent theoretical advances in Stochastic Block Models (SBMs) to formally define and quantify the information-theoretic fitness (e.g., prominence) of a predetermined set of FNs when mapped to individual FCs under different fMRI task conditions. Our framework allows for evaluating any combination of FC granularity, FN partition, and thresholding strategy, thereby optimizing these choices to preserve the important topological features of the human brain connectomes. By applying to the Human Connectome Project with Schaefer parcellations at multiple levels of granularity, the framework showed that the common thresholding value of 0.25 was indeed information-theoretically valid for group-average FCs, despite its previous lack of justification. Our results pave the way for the proper use of FNs and thresholding methods, and provide insights for future research in individualized parcellations.

A gut-brain-gut interoceptive circuit loop gates sugar ingestion in Drosophila.

The communication between the brain and digestive tract is critical for optimising nutrient preference and food intake, yet the underlying neural mechanisms remain poorly understood1-7. Here, we show that a gut-brain-gut circuit loop gates sugar ingestion in flies. We discovered that brain neurons regulating food ingestion, IN18, receive excitatory input from enteric sensory neurons, which innervate the oesophagus and express the sugar receptor Gr43a. These enteric sensory neurons monitor the sugar content of food within the oesophagus during ingestion and send positive feedback signals to IN1s, stimulating the consumption of high-sugar foods. Connectome analyses reveal that IN1s form a core ingestion circuit. This interoceptive circuit receives synaptic input from enteric afferents and provides synaptic output to enteric motor neurons, which modulate the activity of muscles at the entry segments of the crop, a stomach-like food storage organ. While IN1s are persistently activated upon ingestion of sugar-rich foods, enteric motor neurons are continuously inhibited, causing the crop muscles to relax and enabling flies to consume large volumes of sugar. Our findings reveal a key interoceptive mechanism that underlies the rapid sensory monitoring and motor control of sugar ingestion within the digestive tract, optimising the diet of flies across varying metabolic states.

630: Connectome analysis & cognitive outcomes post Whole-brain RT: Prospective registry's first analysis

630: Connectome analysis & cognitive outcomes post Whole-brain RT: Prospective registry's first analysis

Activity of nested neural circuits drives different courtship songs in Drosophila

Motor systems implement diverse motor programs to pattern behavioral sequences, yet how different motor actions are controlled on a moment-by-moment basis remains unclear. Here, we investigated the neural circuit mechanisms underlying the control of distinct courtship songs in Drosophila. Courting males rapidly alternate between two types of song: pulse and sine. By recording calcium signals in the ventral nerve cord in singing flies, we found that one neural population is active during both songs, whereas an expanded neural population, which includes neurons from the first population, is active during pulse song. Brain recordings showed that this nested activation pattern is present in two descending pathways required for singing. Connectomic analysis reveals that these two descending pathways provide structured input to ventral nerve cord neurons in a manner consistent with their activation patterns. These results suggest that nested premotor circuit activity, directed by distinct descending signals, enables rapid switching between motor actions.

Hub disruption in HIV disease and cocaine use: A connectomics analysis of brain function

BackgroundCocaine use (CU) is prevalent in people with HIV (PWH). Both conditions are linked to changes in cognitive functioning and neural network topology. The current study utilizes graph theory to investigate functional connectomics associated with HIV and CU, focusing on disruption of densely connected nodes called hubs. MethodsResting state functional magnetic resonance imaging (fMRI) from 206 adults (ages 22–55 years) were analyzed. A HIV x CU factorial design was implemented with participants in four groups: HIV+CU (n= 41), HIV only (n= 88), CU only (n= 36), and controls (n= 41). Functional connectomes were constructed, and thresholded graph metrics were calculated. Network centrality metrics – betweenness centrality (BC), participation coefficient (PC), and within module degree (WD) – were quantified into hub disruption indices (HDI). For each index, a 2×2 ANCOVA was performed controlling for education. ResultsParticipants were 68 % male and 74 % African-American with a mean age of 44.4 years. HIV and CU were associated with hub disruption in all three indices. Interactions were significant for HDI-PC and HDI-WD, such that HIV disease was associated with greater hub disruption among participants without CU, but not among participants with CU. Overall, lower global cognitive functioning was associated with greater hub disruption on all three indices. ConclusionsWidespread hub disruption was evident in HIV disease and CU, highlighting topological reorganization in both diseases with neurocognitive effects. Hub-related measures inform functional connectivity disruptions in HIV disease and CU, particularly with respect to changes in network topology throughout the connectome.

Deciphering the Complexities of Pulmonary Hypertension: The Emergent Role of Single-Cell Omics.

Expanding upon the critical advancements brought forth by single-cell omics in pulmonary hypertension (PH) research, this review delves deep into how these technologies have been piloted in a new era of understanding this complex disease. By leveraging the power of single cell transcriptomics (scRNA-seq), researchers can now dissect the complicated cellular ecosystem of the lungs, examining the key players such as endothelial cells, smooth muscle cells, pericytes, and immune cells, and their unique roles in the pathogenesis of PH. This more granular view is beyond the limitations of traditional bulk analysis, allowing for the identification of novel therapeutic targets previously obscured in the aggregated data. Connectome analysis based on single-cell omics of the cells involved in pathological changes can reveal a clearer picture of the cellular interactions and transitions in the cellular subtypes. Furthermore, the review acknowledges the challenges that lie ahead, including the need for enhancing the resolution of scRNA-seq to capture even finer details of cellular changes, overcoming logistical barriers in processing human tissue samples, and the necessity of integrating diverse omics approaches to fully comprehend the molecular underpinnings of PH. The promise of these single-cell technologies is immense, offering the potential for targeted drug development and the discovery of biomarkers for early diagnosis and disease monitoring. Through these advancements, the field moves closer to realizing the goal of precision medicine for patients with PH.

Interference of default mode on attention networks in adults with attention-deficit/hyperactivity disorder and its association with genetic variants and treatment outcomes.

Altered brain functional connectivity has been proposed as the neurobiological underpinnings of attention-deficit/hyperactivity disorder (ADHD), and the default mode interference hypothesis is one of the most popular neuropsychological models. Here, we explored whether this hypothesis is supported in adults with ADHD and the association with high-risk genetic variants and treatment outcomes. Voxel-based whole-brain connectome analysis was conducted on resting-state functional MRI data from 84 adults with ADHD and 89 healthy controls to identify functional connectivity substrates corresponding to ADHD-related alterations. The candidate genetic variants and 12-week cognitive behavioral therapy data were leveraged from the same population to assess these associations. We detected breakdowns of functional connectivity in the precuneus and left middle temporal gyrus in adults with ADHD, with exact contributions from decreased connectivity within the default mode, dorsal and ventral attention networks, as well as increased connectivity among them with the middle temporal gyrus serving as a crucial 'bridge'. Additionally, significant associations between the altered functional connectivity and genetic variants in both MAOA and MAOB were detected. Treatment restored brain function, with the amelioration of connectivity of the middle temporal gyrus, accompanied by improvements in ADHD core symptoms. These findings support the interference of default mode on attention in adults with ADHD and its association with genetic risk variants and clinical management, providing insights into the underlying pathogenesis of ADHD and potential biomarkers for treatment evaluation.

Reorganization of brain connectivity across the spectrum of clinical cognitive decline.

Clinical cognitive decline, leading to Alzheimer's Disease Dementia (ADD), has long been interpreted as a disconnection syndrome, hindering the information flow capacity of the brain, hence leading to the well-known symptoms of ADD. The structural and functional brain connectome analyses play a central role in studies of brain from this perspective. However, most current research implicitly assumes that the changes accompanying the progression of cognitive decline are monotonous in time, whether measured across the entire brain or in fixed cortical regions. We investigate the structural and functional connectivity-wise reorganization of the brain without such assumptions across the entire spectrum. We utilize nodal assortativity as a local topological measure of connectivity and follow a data-centric approach to identify and verify relevant local regions, as well as to understand the nature of underlying reorganization. The analysis of our preliminary experimental data points to statistically significant, hyper and hypo-assortativity regions that depend on the disease's stage, and differ for structural and functional connectomes. Our results suggest a new perspective into the dynamic, potentially a mix of degenerative and compensatory, topological alterations that occur in the brain as cognitive decline progresses.

Predicting treatment outcomes in patients with panic disorder: Cross-sectional and two-year longitudinal structural connectome analysis using machine learning methods

PurposeThis study examined the relationship between structural brain networks and long-term treatment outcomes in patients with panic disorder (PD) using machine learning methods. MethodThe study involved 80 participants (53 PD patients and 27 healthy controls) and included clinical assessments and MRI scans at baseline and after two years (160 MRIs). Patients were categorized based on their response to two-year pharmacotherapy. Brain networks were analyzed using white matter tractography and network-based statistics. ResultsResults showed structural network changes in PD patients, particularly in the extended fear network, including frontal regions, thalamus, and cingulate gyrus. Longitudinal analysis revealed that increased connections to the amygdala, hippocampus, and insula were associated with better treatment response. Conversely, overconnectivity in the amygdala and insula at baseline was associated with poor response, and similar patterns were found in the insula and parieto-occipital cortex related to non-remission. This study found that SVM and CPM could effectively predict treatment outcomes based on network pattern changes in PD. ConclusionsThese findings suggest that monitoring structural connectome changes in limbic and paralimbic regions is critical for understanding PD and tailoring treatment. The study highlights the potential of using personalized biomarkers to develop individualized treatment strategies for PD.

Literature Review on Effective Use of Modern Modalities in Management of Intra-Axial Brain Lesions

The significant health burden caused by intra-axial brain lesions and the difficulties associated with their diagnosis and treatment have prompted a large number of researchers and practitioners to investigate various diagnostic and treatment modalities for their effectiveness and safety. This study focuses on evaluating different pre-, intra- and postoperative techniques and analyzes their advantages and limitations to ultimately improve the management of this type of brain lesion. Through a critical analysis of various scientific sources, this research aimed to synthesize existing knowledge on the topic. Magnetic resonance imaging plays a crucial role in diagnosing and managing brain tumors. Functional MRI identifies functional brain areas, but tumor-induced blood flow changes can affect its reliability. Connectome analysis provides information on functional localization and brain networks, which became possible using diffusion tensor imaging, that visualizes white matter pathways, aiding in tumor boundary delineation and surgical planning. Studies suggest this method can predict tumor histology and prognosis. Intraoperative MRI improves the extent of tumor resection and potentially patient survival. But due to its limitations it has alternative intraoperative techniques, like intraoperative ultrasound, fluorescence-guided surgery, direct electrical stimulation, deep brain stimulation, but evidence for most of these methods is limited for most brain tumors. Only ultrasound showed real-time tumor visualization and residual disease analysis offering high accuracy and is relatively inexpensive compared to ioMRI. This study can be useful to neurosurgeons, neuro-oncologists, neurologists, neuro-radiologists, and will demonstrate prospects for further research.

A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity

The macroscale connectome is the network of physical, white-matter tracts between brain areas. The connections are generally weighted and their values interpreted as measures of communication efficacy. In most applications, weights are either assigned based on imaging features–e.g. diffusion parameters–or inferred using statistical models. In reality, the ground-truth weights are unknown, motivating the exploration of alternative edge weighting schemes. Here, we explore a multi-modal, regression-based model that endows reconstructed fiber tracts with directed and signed weights. We find that the model fits observed data well, outperforming a suite of null models. The estimated weights are subject-specific and highly reliable, even when fit using relatively few training samples, and the networks maintain a number of desirable features. In summary, we offer a simple framework for weighting connectome data, demonstrating both its ease of implementation while benchmarking its utility for typical connectome analyses, including graph theoretic modeling and brain-behavior associations.

Hippocampal hub failure is linked to long-term memory impairment in anti-NMDA-receptor encephalitis: insights from structural connectome graph theoretical network analysis

BackgroundAnti-N-methyl-d-aspartate receptor (NMDAR) encephalitis is characterized by distinct structural and functional brain alterations, predominantly affecting the medial temporal lobes and the hippocampus. Structural connectome analysis with graph-based investigations of network properties allows for an in-depth characterization of global and local network changes and their relationship with clinical deficits in NMDAR encephalitis.MethodsStructural networks from 61 NMDAR encephalitis patients in the post-acute stage (median time from acute hospital discharge: 18 months) and 61 age- and sex-matched healthy controls (HC) were analyzed using diffusion-weighted imaging (DWI)-based probabilistic anatomically constrained tractography and volumetry of a selection of subcortical and white matter brain volumes was performed. We calculated global, modular, and nodal graph measures with special focus on default-mode network, medial temporal lobe, and hippocampus. Pathologically altered metrics were investigated regarding their potential association with clinical course, disease severity, and cognitive outcome.ResultsPatients with NMDAR encephalitis showed regular global graph metrics, but bilateral reductions of hippocampal node strength (left: p = 0.049; right: p = 0.013) and increased node strength of right precuneus (p = 0.013) compared to HC. Betweenness centrality was decreased for left-sided entorhinal cortex (p = 0.042) and left caudal middle frontal gyrus (p = 0.037). Correlation analyses showed a significant association between reduced left hippocampal node strength and verbal long-term memory impairment (p = 0.021). We found decreased left (p = 0.013) and right (p = 0.001) hippocampal volumes that were associated with hippocampal node strength (left p = 0.009; right p < 0.001).ConclusionsFocal network property changes of the medial temporal lobes indicate hippocampal hub failure that is associated with memory impairment in NMDAR encephalitis at the post-acute stage, while global structural network properties remain unaltered. Graph theory analysis provides new pathophysiological insight into structural network changes and their association with persistent cognitive deficits in NMDAR encephalitis.

MCMC sampling of directed flag complexes with fixed undirected graphs

Constructing null models to test the significance of extracted information is a crucial step in data analysis. In this work, we provide a uniformly sampleable null model of directed graphs with the same (or similar) number of simplices in the flag complex, with the restriction of retaining the underlying undirected graph. We describe an MCMC-based algorithm to sample from this null model and statistically investigate the mixing behaviour. This is paired with a high-performance, Rust-based, publicly available implementation. The motivation comes from topological data analysis of connectomes in neuroscience. In particular, we answer the fundamental question: are the high Betti numbers observed in the investigated graphs evidence of an interesting topology, or are they merely a byproduct of the high numbers of simplices? Indeed, by applying our new tool on the connectome of C. elegans and parts of the statistical reconstructions of the Blue Brain Project, we find that the Betti numbers observed are considerable statistical outliers with respect to this new null model. We thus, for the first time, statistically confirm that topological data analysis in microscale connectome research is extracting statistically meaningful information.

Fast connectivity gradient approximation: maintaining spatially fine-grained connectivity gradients while reducing computational costs

Brain connectome analysis suffers from the high dimensionality of connectivity data, often forcing a reduced representation of the brain at a lower spatial resolution or parcellation. This is particularly true for graph-based representations, which are increasingly used to characterize connectivity gradients, capturing patterns of systematic spatial variation in the functional connectivity structure. However, maintaining a high spatial resolution is crucial for enabling fine-grained topographical analysis and preserving subtle individual differences that might otherwise be lost. Here we introduce a computationally efficient approach to establish spatially fine-grained connectivity gradients. At its core, it leverages a set of landmarks to approximate the underlying connectivity structure at the full spatial resolution without requiring a full-scale vertex-by-vertex connectivity matrix. We show that this approach reduces computational time and memory usage while preserving informative individual features and demonstrate its application in improving brain-behavior predictions. Overall, its efficiency can remove computational barriers and enable the widespread application of connectivity gradients to capture spatial signatures of the connectome. Importantly, maintaining a spatially fine-grained resolution facilitates to characterize the spatial transitions inherent in the core concept of gradients of brain organization.

Descending networks transform command signals into population motor control

To convert intentions into actions, movement instructions must pass from the brain to downstream motor circuits through descending neurons (DNs). These include small sets of command-like neurons that are sufficient to drive behaviours1—the circuit mechanisms for which remain unclear. Here we show that command-like DNs in Drosophila directly recruit networks of additional DNs to orchestrate behaviours that require the active control of numerous body parts. Specifically, we found that command-like DNs previously thought to drive behaviours alone2–4 in fact co-activate larger populations of DNs. Connectome analyses and experimental manipulations revealed that this functional recruitment can be explained by direct excitatory connections between command-like DNs and networks of interconnected DNs in the brain. Descending population recruitment is necessary for behavioural control: DNs with many downstream descending partners require network co-activation to drive complete behaviours and drive only simple stereotyped movements in their absence. These DN networks reside within behaviour-specific clusters that inhibit one another. These results support a mechanism for command-like descending control in which behaviours are generated through the recruitment of increasingly large DN networks that compose behaviours by combining multiple motor subroutines.