Fragility and volatility of structural hubs in the human connectome

Fiber length distribution characterizes the brain network maturation during early school-age

Environmental and social changes during early school age have a profound impact on brain development. However, it remains unclear how the brains of typically-developing children adjust white matter to optimize network topology during this period. This study proposes fiber length distribution as a novel nodal metric to capture the continuous maturation of brain network. We acquired dMRI data from N = 30 typically developing children in their first year of primary school and a one-year follow-up. We assessed the longitudinal changes in fiber length distribution, characterized by the median length of connected fibers for each brain region. The length median was positively correlated with degree and betweenness centrality, while negatively correlated with clustering coefficient and local efficiency. From ages 7 to 8, we observed significant decreases in length median in the temporal, superior parietal, anterior cingulate, and medial prefrontal cortices, accompanied by a reduction in long-range connections and an increase in short-range connections. Meta-analytic decoding revealed that the widespread decrease in length median occurred in regions responsible for sensory processing, whereas a more localized increase in length median was observed in regions involved in memory and cognitive control. Finally, simulation tests on healthy adults further supported that the decrease in long-range connections and increase in short-range connections contributed to enhanced network segregation and integration, respectively. Our results suggest that the dual process of short- and long-range fiber changes reflects a cost-efficient strategy for optimizing network organization during this critical developmental stage.

Mapping and modeling age-related changes in intrinsic neural timescales

Intrinsic timescales of brain regions exhibit heterogeneity, escalating with hierarchical levels, and are crucial for the temporal integration of external stimuli. Aging, often associated with cognitive decline, involves progressive neuronal and synaptic loss, reshaping brain structure and dynamics. However, the impact of these structural changes on temporal coding in the aging brain remains unclear. We mapped intrinsic timescales and gray matter volume (GMV) using magnetic resonance imaging (MRI) in young and elderly adults. We found shorter intrinsic timescales across multiple large-scale functional networks in the elderly cohort, and a significant positive association between intrinsic timescales and GMV. Additionally, age-related decline in performance on visual discrimination tasks was linked to a reduction in intrinsic timescales in the cuneus. To explain these age-related shifts, we developed an age-dependent spiking neuron network model. In younger subjects, brain regions were near a critical branching regime, while regions in elderly subjects had fewer neurons and synapses, pushing the dynamics toward a subcritical regime. The model accurately reproduced the empirical results, showing longer intrinsic timescales in young adults due to critical slowing down. Our findings reveal how age-related structural brain changes may drive alterations in brain dynamics, offering testable predictions and informing possible interventions targeting cognitive decline.

A simulated annealing algorithm for randomizing weighted networks

Scientific discovery in connectomics relies on network null models. The prominence of network features is conventionally evaluated against null distributions estimated using randomized networks. Modern imaging technologies provide an increasingly rich array of biologically meaningful edge weights. Despite the prevalence of weighted graph analysis in connectomics, randomization models that only preserve binary node degree remain most widely used. Here we propose a simulated annealing procedure for generating randomized networks that preserve weighted degree (strength) sequences. We show that the procedure outperforms other rewiring algorithms and generalizes to multiple network formats, including directed and signed networks, as well as diverse real-world networks. Throughout, we use morphospace representation to assess the sampling behavior of the algorithm and the variability of the resulting ensemble. Finally, we show that accurate strength preservation yields different inferences about brain network organization. Collectively, this work provides a simple but powerful method to analyze richly detailed next-generation connectomics datasets.

Flexibility of intrinsic neural timescales during distinct behavioral states

Recent neuroimaging studies demonstrate a heterogeneity of timescales prevalent in the brain's ongoing spontaneous activity, labeled intrinsic neural timescales (INT). At the same time, neural timescales also reflect stimulus- or task-related activity. The relationship of the INT during the brain's spontaneous activity with their involvement in task states including behavior remains unclear. To address this question, we combined calcium imaging data of spontaneously behaving mice and human electroencephalography (EEG) during rest and task states with computational modeling. We obtained four primary findings: (i) the distinct behavioral states can be accurately predicted from INT, (ii) INT become longer during behavioral states compared to rest, (iii) INT change from rest to task is correlated negatively with the variability of INT during rest, (iv) neural mass modeling shows a key role of recurrent connections in mediating the rest-task change of INT. Extending current findings, our results show the dynamic nature of the brain's INT in reflecting continuous behavior through their flexible rest-task modulation possibly mediated by recurrent connections.

Multimodal gradients of basal forebrain connectivity across the neocortex

Cortical cholinergic projections originate from subregions of the basal forebrain (BF). To examine its organization in humans, we computed multimodal gradients of BF connectivity by combining 7 T diffusion and resting state functional MRI. Moving from anteromedial to posterolateral BF, we observe reduced tethering between structural and functional connectivity gradients, with the lowest tethering in the nucleus basalis of Meynert. In the neocortex, this gradient is expressed by progressively reduced tethering from unimodal sensory to transmodal cortex, with the lowest tethering in the midcingulo-insular network, and is also spatially correlated with the molecular concentration of VAChT, measured by [18F]fluoroethoxy-benzovesamicol (FEOBV) PET. In mice, viral tracing of BF cholinergic projections and [18F]FEOBV PET confirm a gradient of axonal arborization. Altogether, our findings reveal that BF cholinergic neurons vary in their branch complexity, with certain subpopulations exhibiting greater modularity and others greater diffusivity in the functional integration with their cortical targets.

Interhemispheric functional connectivity: an fMRI study in callosotomized patients

Introduction

Functional connectivity (FC) is defined in terms of temporal correlations between physiological signals, which mainly depend upon structural (axonal) connectivity; it is commonly studied using functional magnetic resonance imaging (fMRI). Interhemispheric FC appears mostly supported by the corpus callosum (CC), although several studies investigating this aspect have not provided conclusive evidence. In this context, patients in whom the CC was resected for therapeutic reasons (split-brain patients) provide a unique opportunity for research into this issue. The present study was aimed at investigating with resting-state fMRI the interhemispheric FC in six epileptic patients who have undergone surgical resection of the CC.

Methods

The analysis was performed using fMRI of the Brain Software Library; the evaluation of interhemispheric FC and the recognition of the resting-state networks (RSNs) were performed using probabilistic independent component analysis.

Results

Generally, bilateral brain activation was often observed in primary sensory RSNs, while in the associative areas, such as those composing the default mode and fronto-parietal networks, the activation was often unilateral.

Discussion

These results suggest that even in the absence of the CC, some interhemispheric communication is still present. This residual FC might be supported through extra-callosal pathways that are likely subcortical, making it possible for some interhemispheric integration. Further studies are needed to confirm these conclusions.

Can hubs of the human connectome be identified consistently with diffusion MRI?

Recent years have seen a surge in the use of diffusion MRI to map connectomes in humans, paralleled by a similar increase in processing and analysis choices. Yet these different steps and their effects are rarely compared systematically. Here, in a healthy young adult population (n = 294), we characterized the impact of a range of analysis pipelines on one widely studied property of the human connectome: its degree distribution. We evaluated the effects of 40 pipelines (comparing common choices of parcellation, streamline seeding, tractography algorithm, and streamline propagation constraint) and 44 group-representative connectome reconstruction schemes on highly connected hub regions. We found that hub location is highly variable between pipelines. The choice of parcellation has a major influence on hub architecture, and hub connectivity is highly correlated with regional surface area in most of the assessed pipelines (ρ > 0.70 in 69% of the pipelines), particularly when using weighted networks. Overall, our results demonstrate the need for prudent decision-making when processing diffusion MRI data, and for carefully considering how different processing choices can influence connectome organization.

Structural-functional coupling abnormalities in temporal lobe epilepsy

Background

Nowadays, researchers are using advanced multimodal neuroimaging techniques to construct the brain network connectome to elucidate the complex relationship among the networks of brain functions and structure. The objective of this study was to evaluate the coupling of structural connectivity (SC) and functional connectivity (FC) in the entire brain of healthy controls (HCs), and to investigate modifications in SC-FC coupling in individuals suffering from temporal lobe epilepsy (TLE).

Methods

We evaluated 65 patients with TLE matched for age and gender with 48 healthy controls. The SC-FC coupling between regions was determined, based on which whole-brain nodes were clustered. Differences in the coupling among the three groups of nodes were compared. To further validate the results obtained, the within-cluster coupling indices of the three groups were compared to determine the inter-group differences.

Results

Nodes were divided into five clusters. Cluster 1 was primarily located in the limbic system (n = 9/27), whereas cluster 5 was mainly within the visual network (n = 12/29). By comparing average cluster SC-FC coupling in each cluster of the three groups, we identified marked discrepancies within the three cohorts in Cluster 3 (p = 0.001), Cluster 4 (p < 0.001), and Cluster 5 (p < 0.001). Post-hoc analysis revealed that the SC-FC coupling strengths in LTLE and RTLE were significantly lower than that in HCs in Cluster 3 (PL = 0.001/PR = 0.003), Cluster 4 (PL = 0.001/PR < 0.001), and Cluster 5 (PL < 0.001/PR < 0.001). We also observed that the within-cluster SC-FC coupling in cluster 5 of left- and right TLE was significantly lower than in HCs (PL = 0.0001, PR = 0.0005).

Conclusion

The SC and FC are inconsistently coupled across the brain with spatial heterogeneity. In the fifth cluster with the highest degree of coupling in HCs, the average SC-FC coupling index of individuals with TLE was notably less than that of HCs, manifesting that brain regions with high coupling may be more delicate and prone to pathological disruption.

Trade-offs among cost, integration, and segregation in the human connectome

The human brain structural network is thought to be shaped by the optimal trade-off between cost and efficiency. However, most studies on this problem have focused on only the trade-off between cost and global efficiency (i.e., integration) and have overlooked the efficiency of segregated processing (i.e., segregation), which is essential for specialized information processing. Direct evidence on how trade-offs among cost, integration, and segregation shape the human brain network remains lacking. Here, adopting local efficiency and modularity as segregation factors, we used a multiobjective evolutionary algorithm to investigate this problem. We defined three trade-off models, which represented trade-offs between cost and integration (Dual-factor model), and trade-offs among cost, integration, and segregation (local efficiency or modularity; Tri-factor model), respectively. Among these, synthetic networks with optimal trade-off among cost, integration, and modularity (Tri-factor model [Q]) showed the best performance. They had a high recovery rate of structural connections and optimal performance in most network features, especially in segregated processing capacity and network robustness. Morphospace of this trade-off model could further capture the variation of individual behavioral/demographic characteristics in a domain-specific manner. Overall, our results highlight the importance of modularity in the formation of the human brain structural network and provide new insights into the original cost-efficiency trade-off hypothesis.

Rich-club connectivity and structural connectome organization in youth at clinical high-risk for psychosis and individuals with early illness schizophrenia

Brain dysconnectivity has been posited as a biological marker of schizophrenia. Emerging schizophrenia connectome research has focused on rich-club organization, a tendency for brain hubs to be highly-interconnected but disproportionately vulnerable to dysconnectivity. However, less is known about rich-club organization in individuals at clinical high-risk for psychosis (CHR-P) and how it compares with abnormalities early in schizophrenia (ESZ). Combining diffusion tensor imaging (DTI) and magnetic resonance imaging (MRI), we examined rich-club and global network organization in CHR-P (n = 41) and ESZ (n = 70) relative to healthy controls (HC; n = 74) after accounting for normal aging. To characterize rich-club regions, we examined rich-club MRI morphometry (thickness, surface area). We also examined connectome metric associations with symptom severity, antipsychotic dosage, and in CHR-P specifically, transition to a full-blown psychotic disorder. ESZ had fewer connections among rich-club regions (ps < .024) relative to HC and CHR-P, with this reduction specific to the rich-club even after accounting for other connections in ESZ relative to HC (ps < .048). There was also cortical thinning of rich-club regions in ESZ (ps < .013). In contrast, there was no strong evidence of global network organization differences among the three groups. Although connectome abnormalities were not present in CHR-P overall, CHR-P converters to psychosis (n = 9) had fewer connections among rich-club regions (ps < .037) and greater modularity (ps < .037) compared to CHR-P non-converters (n = 19). Lastly, symptom severity and antipsychotic dosage were not significantly associated with connectome metrics (ps < .012). Findings suggest that rich-club and connectome organization abnormalities are present early in schizophrenia and in CHR-P individuals who subsequently transition to psychosis.

Parameter estimation for connectome generative models: Accuracy, reliability, and a fast parameter fitting method

Generative models of the human connectome enable in silico generation of brain networks based on probabilistic wiring rules. These wiring rules are governed by a small number of parameters that are typically fitted to individual connectomes and quantify the extent to which geometry and topology shape the generative process. A significant shortcoming of generative modeling in large cohort studies is that parameter estimation is computationally burdensome, and the accuracy and reliability of current estimation methods remain untested. Here, we propose a fast, reliable, and accurate parameter estimation method for connectome generative models that is scalable to large sample sizes. Our method achieves improved estimation accuracy and reliability and reduces computational cost by orders of magnitude, compared to established methods. We demonstrate an inherent tradeoff between accuracy, reliability, and computational expense in parameter estimation and provide recommendations for leveraging this tradeoff. To enable power analyses in future studies, we empirically approximate the minimum sample size required to detect between-group differences in generative model parameters. While we focus on the classic two-parameter generative model based on connection length and the topological matching index, our method can be generalized to other growth-based generative models. Our work provides a statistical and practical guide to parameter estimation for connectome generative models.

The whole-brain connectome landscape in patients with schizophrenia: a systematic review and meta-analysis of graph theoretical characteristics

The alterations of connectome in schizophrenia have been reported, but the results remain inconsistent. We conducted a systematic review and random-effects meta-analysis on structural or functional connectome MRI studies comparing global graph theoretical characteristics between schizophrenia and healthy controls. Meta-regression and subgroup analyses were performed to examine confounding effects. Based on the included 48 studies, Structural connectome in schizophrenia showed a significant decrease in segregation (lower clustering coefficient and local efficiency, Hedge's g= -0.352 and -0.864, respectively) and integration (higher characteristic path length and lower global efficiency, Hedge's g= 0.532 and -0.577 respectively). The functional connectome showed no difference between groups except γ. Moderator analysis indicated that clinical and methodological factors exerted a potential effect on the graph theoretical characteristics. Our analysis revealed a weaker small-worldization trend in structural connectome of schizophrenia. For the relatively unchanged functional connectome, more homogenous and high-quality studies are warranted to elucidate whether the change was blurred by heterogeneity or the presentation of pathophysiological reconfiguration.

Preserved anatomical bypasses predict variance in language functions after stroke

The severity of post-stroke aphasia is related to damage to white matter connections. However, neural signaling can route not only through direct connections, but also along multi-step network paths. When brain networks are damaged by stroke, paths can bypass around the damage to restore communication. The shortest network paths between regions could be the most efficient routes for mediating bypasses. We examined how shortest-path bypasses after left hemisphere strokes were related to language performance. Regions within and outside of the canonical language network could be important in aphasia recovery. Therefore, we innovated methods to measure the influence of bypasses in the whole brain. Distinguishing bypasses from all residual shortest paths is difficult without pre-stroke imaging. We identified bypasses by finding shortest paths in subjects with stroke that were longer than the most reliably observed connections in age-matched control networks. We tested whether features of those bypasses predicted scores in four orthogonal dimensions of language performance derived from a principal components analysis of a battery of language tasks. The features were the length of each bypass in steps, and how many bypasses overlapped on each individual direct connection. We related these bypass features to language factors using support vector regression, a technique that extracts robust relationships in high-dimensional data analysis. The support vector regression parameters were tuned using grid-search cross-validation. We discovered that the length of bypasses reliably predicted variance in lexical production (R2 = .576) and auditory comprehension scores (R2 = .164). Bypass overlaps reliably predicted variance in Lexical Production scores (R2 = .247). The predictive elongation features revealed that bypass efficiency along the dorsal stream and ventral stream were most related to Lexical Production and Auditory Comprehension, respectively. Among the predictive bypass overlaps, increased bypass routing through the right hemisphere putamen was negatively related to lexical production ability.

Local molecular and global connectomic contributions to cross-disorder cortical abnormalities

Numerous brain disorders demonstrate structural brain abnormalities, which are thought to arise from molecular perturbations or connectome miswiring. The unique and shared contributions of these molecular and connectomic vulnerabilities to brain disorders remain unknown, and has yet to be studied in a single multi-disorder framework. Using MRI morphometry from the ENIGMA consortium, we construct maps of cortical abnormalities for thirteen neurodevelopmental, neurological, and psychiatric disorders from N = 21,000 participants and N = 26,000 controls, collected using a harmonised processing protocol. We systematically compare cortical maps to multiple micro-architectural measures, including gene expression, neurotransmitter density, metabolism, and myelination (molecular vulnerability), as well as global connectomic measures including number of connections, centrality, and connection diversity (connectomic vulnerability). We find a relationship between molecular vulnerability and white-matter architecture that drives cortical disorder profiles. Local attributes, particularly neurotransmitter receptor profiles, constitute the best predictors of both disorder-specific cortical morphology and cross-disorder similarity. Finally, we find that cross-disorder abnormalities are consistently subtended by a small subset of network epicentres in bilateral sensory-motor, inferior temporal lobe, precuneus, and superior parietal cortex. Collectively, our results highlight how local molecular attributes and global connectivity jointly shape cross-disorder cortical abnormalities.

The network collapse in multiple sclerosis: An overview of novel concepts to address disease dynamics

Multiple sclerosis is a neuroinflammatory and neurodegenerative disorder of the central nervous system that can be considered a network disorder. In MS, lesional pathology continuously disconnects structural pathways in the brain, forming a disconnection syndrome. Complex functional network changes then occur that are poorly understood but closely follow clinical status. Studying these structural and functional network changes has been and remains crucial to further decipher complex symptoms like cognitive impairment and physical disability. Recent insights especially implicate the importance of monitoring network hubs in MS, like the thalamus and default-mode network which seem especially hit hard. Such network insights in MS have led to the hypothesis that as the network continues to become disconnected and dysfunctional, exceeding a certain threshold of network efficiency loss leads to a "network collapse". After this collapse, crucial network hubs become rigid and overloaded, and at the same time a faster neurodegeneration and accelerated clinical (and cognitive) progression can be seen. As network neuroscience has evolved, the MS field can now move towards a clearer classification of the network collapse itself and specific milestone events leading up to it. Such an updated network-focused conceptual framework of MS could directly impact clinical decision making as well as the design of network-tailored rehabilitation strategies. This review therefore provides an overview of recent network concepts that have enhanced our understanding of clinical progression in MS, especially focusing on cognition, as well as new concepts that will likely move the field forward in the near future.

Modeling spatial, developmental, physiological, and topological constraints on human brain connectivity

The complex connectivity of nervous systems is thought to have been shaped by competitive selection pressures to minimize wiring costs and support adaptive function. Accordingly, recent modeling work indicates that stochastic processes, shaped by putative trade-offs between the cost and value of each connection, can successfully reproduce many topological properties of macroscale human connectomes measured with diffusion magnetic resonance imaging. Here, we derive a new formalism that more accurately captures the competing pressures of wiring cost minimization and topological complexity. We further show that model performance can be improved by accounting for developmental changes in brain geometry and associated wiring costs, and by using interregional transcriptional or microstructural similarity rather than topological wiring rules. However, all models struggled to capture topographical (i.e., spatial) network properties. Our findings highlight an important role for genetics in shaping macroscale brain connectivity and indicate that stochastic models offer an incomplete account of connectome organization.

Null models in network neuroscience

Recent advances in imaging and tracing technology provide increasingly detailed reconstructions of brain connectomes. Concomitant analytic advances enable rigorous identification and quantification of functionally important features of brain network architecture. Null models are a flexible tool to statistically benchmark the presence or magnitude of features of interest, by selectively preserving specific architectural properties of brain networks while systematically randomizing others. Here we describe the logic, implementation and interpretation of null models of connectomes. We introduce randomization and generative approaches to constructing null networks, and outline a taxonomy of network methods for statistical inference. We highlight the spectrum of null models - from liberal models that control few network properties, to conservative models that recapitulate multiple properties of empirical networks - that allow us to operationalize and test detailed hypotheses about the structure and function of brain networks. We review emerging scenarios for the application of null models in network neuroscience, including for spatially embedded networks, annotated networks and correlation-derived networks. Finally, we consider the limits of null models, as well as outstanding questions for the field.

Mapping cognitive deficits in cancer patients after chemotherapy: An Activation Likelihood Estimation meta-analysis of task-related fMRI studies

Recent neuroimaging studies have reported alterations in brain activation during cognitive tasks in cancer patients who have undergone chemotherapy treatment. However, the location of these altered brain activation patterns after chemotherapy varies considerably across studies. The aim of the present meta-analysis was to quantitatively synthesise this body of evidence using Activation Likelihood Estimation to identify reliable regions of altered brain activation in cancer patients treated with chemotherapy, compared to healthy controls and no chemotherapy controls. Our systematic search identified 12 studies that adopted task-related fMRI on non-central nervous system cancer patients who received chemotherapy relative to controls. All studies were included in the analyses and were grouped into four contrasts. Cancer patients treated with chemotherapy showed reduced activation in the left superior parietal lobe/precuneus (family-wise error corrected p < .05) compared to no chemotherapy controls. No significant clusters were found in three of our contrasts. The majority of studies did not support an association between altered brain activation and cognitive performance after chemotherapy. Findings point towards a possible chemotherapy-induced alteration, which could inform targeted treatment strategies. With continued work in this field using homogenous task-related protocols and cancer populations, fMRI may be used as a biomarker of cognitive deficits in the future.

Efficient coding in the economics of human brain connectomics

In systems neuroscience, most models posit that brain regions communicate information under constraints of efficiency. Yet, evidence for efficient communication in structural brain networks characterized by hierarchical organization and highly connected hubs remains sparse. The principle of efficient coding proposes that the brain transmits maximal information in a metabolically economical or compressed form to improve future behavior. To determine how structural connectivity supports efficient coding, we develop a theory specifying minimum rates of message transmission between brain regions to achieve an expected fidelity, and we test five predictions from the theory based on random walk communication dynamics. In doing so, we introduce the metric of compression efficiency, which quantifies the trade-off between lossy compression and transmission fidelity in structural networks. In a large sample of youth (n = 1,042; age 8-23 years), we analyze structural networks derived from diffusion-weighted imaging and metabolic expenditure operationalized using cerebral blood flow. We show that structural networks strike compression efficiency trade-offs consistent with theoretical predictions. We find that compression efficiency prioritizes fidelity with development, heightens when metabolic resources and myelination guide communication, explains advantages of hierarchical organization, links higher input fidelity to disproportionate areal expansion, and shows that hubs integrate information by lossy compression. Lastly, compression efficiency is predictive of behavior-beyond the conventional network efficiency metric-for cognitive domains including executive function, memory, complex reasoning, and social cognition. Our findings elucidate how macroscale connectivity supports efficient coding and serve to foreground communication processes that utilize random walk dynamics constrained by network connectivity.

Integrative omics of schizophrenia: from genetic determinants to clinical classification and risk prediction

Schizophrenia (SCZ) is a debilitating neuropsychiatric disorder with high heritability and complex inheritance. In the past decade, successful identification of numerous susceptibility loci has provided useful insights into the molecular etiology of SCZ. However, applications of these findings to clinical classification and diagnosis, risk prediction, or intervention for SCZ have been limited, and elucidating the underlying genomic and molecular mechanisms of SCZ is still challenging. More recently, multiple Omics technologies - genomics, transcriptomics, epigenomics, proteomics, metabolomics, connectomics, and gut microbiomics - have all been applied to examine different aspects of SCZ pathogenesis. Integration of multi-Omics data has thus emerged as an approach to provide a more comprehensive view of biological complexity, which is vital to enable translation into assessments and interventions of clinical benefit to individuals with SCZ. In this review, we provide a broad survey of the single-omics studies of SCZ, summarize the advantages and challenges of different Omics technologies, and then focus on studies in which multiple omics data are integrated to unravel the complex pathophysiology of SCZ. We believe that integration of multi-Omics technologies would provide a roadmap to create a more comprehensive picture of interactions involved in the complex pathogenesis of SCZ, constitute a rich resource for elucidating the potential molecular mechanisms of the illness, and eventually improve clinical assessments and interventions of SCZ to address clinical translational questions from bench to bedside.

Alcohol use in emerging adults associated with lower rich-club connectivity and greater connectome network disorganization

BACKGROUND

Emerging adulthood is a critical neurodevelopmental stage, with alcohol use during this period consistently associated with brain abnormalities and damage in anatomical structure and white matter integrity. However, it is less clear how alcohol use is associated with the brain's structural organization (i.e., white matter connections between anatomical regions). Recent connectome research has focused on rich-club regions, a collection of highly-interconnected hubs that are critical in brain communication and global network organization and disproportionately vulnerable to insults.

METHODS

For the first time, we examined alcohol use associations with structural rich-club and connectome organization in emerging adults (N = 66).

RESULTS

Greater lifetime drinks and current monthly drinks were significantly associated with lower rich-club organization (rs =-0.38, ps < 0.003) and lower rich-club connectivity (rs <-0.34, ps < 0.007). Additionally, rich-club connectivity was significantly more negatively correlated with alcohol use than connectivity among non-rich-club regions (ps < 0.035). Examining overall structural organization, greater lifetime drinks and current monthly drinks were significantly associated with lower network density (i.e., lower network resilience; rs <-0.36, ps = 0.004). Additionally, greater lifetime drinks and current monthly drinks were significantly associated with higher network segregation (i.e., network's tendency to divide into subnetworks; rs >0.33, ps<0.008). Alcohol use was not significantly associated with network integration (i.e., network's efficiency in combining information across the brain; ps > 0.064).

CONCLUSIONS

Results provide novel evidence that alcohol use is associated with decreased rich-club connectivity and structural network disorganization. Given that both are critical in global brain communication, these results highlight the importance of examining alcohol use and brain relationships in emerging adulthood.

Modularity maximization as a flexible and generic framework for brain network exploratory analysis

The modular structure of brain networks supports specialized information processing, complex dynamics, and cost-efficient spatial embedding. Inter-individual variation in modular structure has been linked to differences in performance, disease, and development. There exist many data-driven methods for detecting and comparing modular structure, the most popular of which is modularity maximization. Although modularity maximization is a general framework that can be modified and reparamaterized to address domain-specific research questions, its application to neuroscientific datasets has, thus far, been narrow. Here, we highlight several strategies in which the "out-of-the-box" version of modularity maximization can be extended to address questions specific to neuroscience. First, we present approaches for detecting "space-independent" modules and for applying modularity maximization to signed matrices. Next, we show that the modularity maximization frame is well-suited for detecting task- and condition-specific modules. Finally, we highlight the role of multi-layer models in detecting and tracking modules across time, tasks, subjects, and modalities. In summary, modularity maximization is a flexible and general framework that can be adapted to detect modular structure resulting from a wide range of hypotheses. This article highlights multiple frontiers for future research and applications.

Where the genome meets the connectome: Understanding how genes shape human brain connectivity

The integration of modern neuroimaging methods with genetically informative designs and data can shed light on the molecular mechanisms underlying the structural and functional organization of the human connectome. Here, we review studies that have investigated the genetic basis of human brain network structure and function through three complementary frameworks: (1) the quantification of phenotypic heritability through classical twin designs; (2) the identification of specific DNA variants linked to phenotypic variation through association and related studies; and (3) the analysis of correlations between spatial variations in imaging phenotypes and gene expression profiles through the integration of neuroimaging and transcriptional atlas data. We consider the basic foundations, strengths, limitations, and discoveries associated with each approach. We present converging evidence to indicate that anatomical connectivity is under stronger genetic influence than functional connectivity and that genetic influences are not uniformly distributed throughout the brain, with phenotypic variation in certain regions and connections being under stronger genetic control than others. We also consider how the combination of imaging and genetics can be used to understand the ways in which genes may drive brain dysfunction in different clinical disorders.

Profiling Transcription Initiation in Peripheral Leukocytes Reveals Severity-Associated Cis-Regulatory Elements in Critical COVID-19

The contribution of transcription factors (TFs) and gene regulatory programs in the immune response to COVID-19 and their relationship to disease outcome is not fully understood. Analysis of genome-wide changes in transcription at both promoter-proximal and distal cis-regulatory DNA elements, collectively termed the 'active cistrome,' offers an unbiased assessment of TF activity identifying key pathways regulated in homeostasis or disease. Here, we profiled the active cistrome from peripheral leukocytes of critically ill COVID-19 patients to identify major regulatory programs and their dynamics during SARS-CoV-2 associated acute respiratory distress syndrome (ARDS). We identified TF motifs that track the severity of COVID- 19 lung injury, disease resolution, and outcome. We used unbiased clustering to reveal distinct cistrome subsets delineating the regulation of pathways, cell types, and the combinatorial activity of TFs. We found critical roles for regulatory networks driven by stimulus and lineage determining TFs, showing that STAT and E2F/MYB regulatory programs targeting myeloid cells are activated in patients with poor disease outcomes and associated with single nucleotide genetic variants implicated in COVID-19 susceptibility. Integration with single-cell RNA-seq found that STAT and E2F/MYB activation converged in specific neutrophils subset found in patients with severe disease. Collectively we demonstrate that cistrome analysis facilitates insight into disease mechanisms and provides an unbiased approach to evaluate global changes in transcription factor activity and stratify patient disease severity.

ADHD symptoms map onto noise-driven structure-function decoupling between hub and peripheral brain regions

Adults with childhood-onset attention-deficit hyperactivity disorder (ADHD) show altered whole-brain connectivity. However, the relationship between structural and functional brain abnormalities, the implications for the development of life-long debilitating symptoms, and the underlying mechanisms remain uncharted. We recruited a unique sample of 80 medication-naive adults with a clinical diagnosis of childhood-onset ADHD without psychiatric comorbidities, and 123 age-, sex-, and intelligence-matched healthy controls. Structural and functional connectivity matrices were derived from diffusion spectrum imaging and multi-echo resting-state functional MRI data. Hub, feeder, and local connections were defined using diffusion data. Individual-level measures of structural connectivity and structure-function coupling were used to contrast groups and link behavior to brain abnormalities. Computational modeling was used to test possible neural mechanisms underpinning observed group differences in the structure-function coupling. Structural connectivity did not significantly differ between groups but, relative to controls, ADHD showed a reduction in structure-function coupling in feeder connections linking hubs with peripheral regions. This abnormality involved connections linking fronto-parietal control systems with sensory networks. Crucially, lower structure-function coupling was associated with higher ADHD symptoms. Results from our computational model further suggest that the observed structure-function decoupling in ADHD is driven by heterogeneity in neural noise variability across brain regions. By highlighting a neural cause of a clinically meaningful breakdown in the structure-function relationship, our work provides novel information on the nature of chronic ADHD. The current results encourage future work assessing the genetic and neurobiological underpinnings of neural noise in ADHD, particularly in brain regions encompassed by fronto-parietal systems.

Genetic influences on hub connectivity of the human connectome

Brain network hubs are both highly connected and highly inter-connected, forming a critical communication backbone for coherent neural dynamics. The mechanisms driving this organization are poorly understood. Using diffusion-weighted magnetic resonance imaging in twins, we identify a major role for genes, showing that they preferentially influence connectivity strength between network hubs of the human connectome. Using transcriptomic atlas data, we show that connected hubs demonstrate tight coupling of transcriptional activity related to metabolic and cytoarchitectonic similarity. Finally, comparing over thirteen generative models of network growth, we show that purely stochastic processes cannot explain the precise wiring patterns of hubs, and that model performance can be improved by incorporating genetic constraints. Our findings indicate that genes play a strong and preferential role in shaping the functionally valuable, metabolically costly connections between connectome hubs.

A generative network model of neurodevelopmental diversity in structural brain organization

The formation of large-scale brain networks, and their continual refinement, represent crucial developmental processes that can drive individual differences in cognition and which are associated with multiple neurodevelopmental conditions. But how does this organization arise, and what mechanisms drive diversity in organization? We use generative network modeling to provide a computational framework for understanding neurodevelopmental diversity. Within this framework macroscopic brain organization, complete with spatial embedding of its organization, is an emergent property of a generative wiring equation that optimizes its connectivity by renegotiating its biological costs and topological values continuously over time. The rules that govern these iterative wiring properties are controlled by a set of tightly framed parameters, with subtle differences in these parameters steering network growth towards different neurodiverse outcomes. Regional expression of genes associated with the simulations converge on biological processes and cellular components predominantly involved in synaptic signaling, neuronal projection, catabolic intracellular processes and protein transport. Together, this provides a unifying computational framework for conceptualizing the mechanisms and diversity in neurodevelopment, capable of integrating different levels of analysis-from genes to cognition.

Associations between long-term psychosis risk, probabilistic category learning, and attenuated psychotic symptoms with cortical surface morphometry

Neuroimaging studies have consistently found structural cortical abnormalities in individuals with schizophrenia, especially in structural hubs. However, it is unclear what abnormalities predate psychosis onset and whether abnormalities are related to behavioral performance and symptoms associated with psychosis risk. Using surface-based morphometry, we examined cortical volume, gyrification, and thickness in a psychosis risk group at long-term risk for developing a psychotic disorder (n = 18; i.e., extreme positive schizotypy plus interview-rated attenuated psychotic symptoms [APS]) and control group (n = 19). Overall, the psychosis risk group exhibited cortical abnormalities in multiple structural hub regions, with abnormalities associated with poorer probabilistic category learning, a behavioral measure strongly associated with psychosis risk. For instance, the psychosis risk group had hypogyria in a right posterior midcingulate cortical hub and left superior parietal cortical hub, as well as decreased volume in a right pericalcarine hub. Morphometric measures in all of these regions were also associated with poorer probabilistic category learning. In addition to decreased right pericalcarine volume, the psychosis risk group exhibited a number of other structural abnormalities in visual network structural hub regions, consistent with previous evidence of visual perception deficits in psychosis risk. Further, severity of APS hallucinations, delusional ideation, and suspiciousness/persecutory ideas were associated with gyrification abnormalities, with all domains associated with hypogyria of the right lateral orbitofrontal cortex. Thus, current results suggest that structural abnormalities, especially in structural hubs, are present in psychosis risk and are associated both with poor learning on a psychosis risk-related task and with APS severity.

Less is more: Wiring-economical modular networks support self-sustained firing-economical neural avalanches for efficient processing

The brain network is notably cost-efficient, while the fundamental physical and dynamic mechanisms underlying its economical optimization in network structure and activity have not been determined. In this study, we investigate the intricate cost-efficient interplay between structure and dynamics in biologically plausible spatial modular neuronal network models. We observe that critical avalanche states from excitation-inhibition balance under modular network topology with less wiring cost can also achieve lower costs in firing but with strongly enhanced response sensitivity to stimuli. We derive mean-field equations that govern the macroscopic network dynamics through a novel approximate theory. The mechanism of low firing cost and stronger response in the form of critical avalanches is explained as a proximity to a Hopf bifurcation of the modules when increasing their connection density. Our work reveals the generic mechanism underlying the cost-efficient modular organization and critical dynamics widely observed in neural systems, providing insights into brain-inspired efficient computational designs.

Interactions Between Aging and Alzheimer's Disease on Structural Brain Networks

Normative aging and Alzheimer's disease (AD) propagation alter anatomical connections among brain parcels. However, the interaction between the trajectories of age- and AD-linked alterations in the topology of the structural brain network is not well understood. In this study, diffusion-weighted magnetic resonance imaging (MRI) datasets of 139 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were used to document their structural brain networks. The 139 participants consist of 45 normal controls (NCs), 37 with early mild cognitive impairment (EMCI), 27 with late mild cognitive impairment (LMCI), and 30 AD patients. All subjects were further divided into three subgroups based on their age (56-65, 66-75, and 71-85 years). After the structural connectivity networks were built using anatomically-constrained deterministic tractography, their global and nodal topological properties were estimated, including network efficiency, characteristic path length, transitivity, modularity coefficient, clustering coefficient, and betweenness. Statistical analyses were then performed on these metrics using linear regression, and one- and two-way ANOVA testing to examine group differences and interactions between aging and AD propagation. No significant interactions were found between aging and AD propagation in the global topological metrics (network efficiency, characteristic path length, transitivity, and modularity coefficient). However, nodal metrics (clustering coefficient and betweenness centrality) of some cortical parcels exhibited significant interactions between aging and AD propagation, with affected parcels including left superior temporal, right pars triangularis, and right precentral. The results collectively confirm the age-related deterioration of structural networks in MCI and AD patients, providing novel insight into the cross effects of aging and AD disorder on brain structural networks. Some early symptoms of AD may also be due to age-associated anatomic vulnerability interacting with early anatomic changes associated with AD.

Cost-efficiency trade-offs of the human brain network revealed by a multiobjective evolutionary algorithm

It is widely believed that the formation of brain network architecture is under the pressure of optimal trade-off between reducing wiring cost and promoting communication efficiency. However, the questions of whether this trade-off exists in empirical human brain structural networks and, if so, how it takes effect are still not well understood. Here, we employed a multiobjective evolutionary algorithm to directly and quantitatively explore the cost-efficiency trade-off in human brain structural networks. Using this algorithm, we generated a population of synthetic networks with optimal but diverse cost-efficiency trade-offs. It was found that these synthetic networks could not only reproduce a large portion of connections in the empirical brain structural networks but also embed a resembling small-world organization. Moreover, the synthetic and empirical brain networks were found similar in terms of the spatial arrangement of hub regions and the modular structure, which are two important topological features widely assumed to be outcomes of cost-efficiency trade-offs. The synthetic networks had high robustness against random attacks as the empirical brain networks did. Additionally, we also revealed some differences between the synthetic networks and the empirical brain networks, including lower segregated processing capacity and weaker robustness against targeted attacks in the synthetic networks. These findings provide direct and quantitative evidence that the structure of human brain networks is indeed largely influenced by optimal cost-efficiency trade-offs. We also suggest that some additional factors (e.g., segregated processing capacity) might jointly determine the network organization with cost and efficiency.

Single-Cell Transcriptomics Supports a Role of CHD8 in Autism

Chromodomain helicase domain 8 (CHD8) is one of the most frequently mutated and most penetrant genes in the autism spectrum disorder (ASD). Individuals with CHD8 mutations show leading symptoms of autism, macrocephaly, and facial dysmorphisms. The molecular and cellular mechanisms underpinning the early onset and development of these symptoms are still poorly understood and prevent timely and more efficient therapies of patients. Progress in this area will require an understanding of "when, why and how cells deviate from their normal trajectories". High-throughput single-cell RNA sequencing (sc-RNAseq) directly quantifies information-bearing RNA molecules that enact each cell's biological identity. Here, we discuss recent insights from sc-RNAseq of CRISPR/Cas9-editing of Chd8/CHD8 during mouse neocorticogenesis and human cerebral organoids. Given that the deregulation of the balance between excitation and inhibition (E/I balance) in cortical and subcortical circuits is thought to represent a major etiopathogenetic mechanism in ASD, we focus on the question of whether, and to what degree, results from current sc-RNAseq studies support this hypothesis. Beyond that, we discuss the pros and cons of these approaches and further steps to be taken to harvest the full potential of these transformative techniques.

Altered brain structural connectivity in patients with longstanding gut inflammation is correlated with psychological symptoms and disease duration

OBJECTIVE

We aimed to identify differences in network properties of white matter microstructure between asymptomatic ulcerative colitis (UC) participants who had a history of chronic gut inflammation, healthy controls (HCs) and a disease control group without gut inflammation (irritable bowel syndrome; IBS).

DESIGN

Diffusion weighted imaging was conducted in age and sex-matched participants with UC, IBS, and HCs (N = 74 each), together with measures of gastrointestinal and psychological symptom severity. Using streamline connectivity matrices and graph theory, we aimed to quantify group differences in brain network connectivity. Regions showing group connectivity differences were correlated with measures showing group behavioral and clinical differences.

RESULTS

UC participants exhibited greater centrality in regions of the somatosensory network and default mode network, but lower centrality in the posterior insula and globus pallidus compared to HCs (q < 0.05). Hub analyses revealed compromised hubness of the pallidus in UC and IBS compared to HCs which was replaced by increased hubness of the postcentral sulcus. Surprisingly, few differences in network matrices between UC and IBS were identified. In UC, centrality measures in the secondary somatosensory cortex were associated with depression (q < 0.03), symptom related anxiety (q < 0.04), trait anxiety (q < 0.03), and symptom duration (q < 0.05).

CONCLUSION

A history of UC is associated with neuroplastic changes in several brain networks, which are associated with symptoms of depression, trait and symptom-related anxiety, as well as symptom duration. When viewed together with the results from IBS subjects, these findings suggest that chronic gut inflammation as well as abdominal pain have a lasting impact on brain network organization, which may play a role in symptoms reported by UC patients, even when gut inflammation has subsided.

Dependence of Connectivity on the Logarithm of Geometric Distance in Brain Networks

Physical connections between nodes in a complex network are constrained by limiting factors, such as the cost of establishing links and maintaining them, which can hinder network capability in terms of signal propagation speed and processing power. Trade-off mechanisms between cost constraints and performance requirements are reflected in the topology of a network and, ultimately, on the dependence of connectivity on geometric distance. This issue, though rarely addressed, is crucial in neuroscience, where physical links between brain regions are associated with a metabolic cost. In this work we investigate brain connectivity-estimated by means of a recently developed method that evaluates time scales of cross-correlation observability-and its dependence on geometric distance by analyzing resting state magnetoencephalographic recordings collected from a large set of healthy subjects. We identify three regimes of distance each showing a specific behavior of connectivity. This identification makes up a new tool to study the mechanisms underlying network formation and sustainment, with possible applications to the investigation of neuroscientific issues, such as aging and neurodegenerative diseases.

An Evolutionary Perspective of Dyslexia, Stress, and Brain Network Homeostasis

Evolution fuels interindividual variability in neuroplasticity, reflected in brain anatomy and functional connectivity of the expanding neocortical regions subserving reading ability. Such variability is orchestrated by an evolutionarily conserved, competitive balance between epigenetic, stress-induced, and cognitive-growth gene expression programs. An evolutionary developmental model of dyslexia, suggests that prenatal and childhood subclinical stress becomes a risk factor for dyslexia when physiological adaptations to stress promoting adaptive fitness, may attenuate neuroplasticity in the brain regions recruited for reading. Stress has the potential to blunt the cognitive-growth functions of the predominantly right hemisphere Ventral and Dorsal attention networks, which are primed with high entropic levels of synaptic plasticity, and are critical for acquiring beginning reading skills. The attentional networks, in collaboration with the stress-responsive Default Mode network, modulate the entrainment and processing of the low frequency auditory oscillations (1-8 Hz) and visuospatial orienting linked etiologically to dyslexia. Thus, dyslexia may result from positive, but costly adaptations to stress system dysregulation: protective measures that reset the stress/growth balance of processing to favor the Default Mode network, compromising development of the attentional networks. Such a normal-variability conceptualization of dyslexia is at odds with the frequent assumption that dyslexia results from a neurological abnormality. To put the normal-variability model in the broader perspective of the state of the field, a traditional evolutionary account of dyslexia is presented to stimulate discussion of the scientific merits of the two approaches.

Stochastic synchronization of dynamics on the human connectome

Synchronization is a collective mechanism by which oscillatory networks achieve their functions. Factors driving synchronization include the network's topological and dynamical properties. However, how these factors drive the emergence of synchronization in the presence of potentially disruptive external inputs like stochastic perturbations is not well understood, particularly for real-world systems such as the human brain. Here, we aim to systematically address this problem using a large-scale model of the human brain network (i.e., the human connectome). The results show that the model can produce complex synchronization patterns transitioning between incoherent and coherent states. When nodes in the network are coupled at some critical strength, a counterintuitive phenomenon emerges where the addition of noise increases the synchronization of global and local dynamics, with structural hub nodes benefiting the most. This stochastic synchronization effect is found to be driven by the intrinsic hierarchy of neural timescales of the brain and the heterogeneous complex topology of the connectome. Moreover, the effect coincides with clustering of node phases and node frequencies and strengthening of the functional connectivity of some of the connectome's subnetworks. Overall, the work provides broad theoretical insights into the emergence and mechanisms of stochastic synchronization, highlighting its putative contribution in achieving network integration underpinning brain function.

Robustness of connectome harmonics to local gray matter and long-range white matter connectivity changes

Recently, it has been proposed that the harmonic patterns emerging from the brain's structural connectivity underlie the resting state networks of the human brain. These harmonic patterns, termed connectome harmonics, are estimated as the Laplace eigenfunctions of the combined gray and white matters connectivity matrices and yield a connectome-specific extension of the well-known Fourier basis. However, it remains unclear how topological properties of the combined connectomes constrain the precise shape of the connectome harmonics and their relationships to the resting state networks. Here, we systematically study how alterations of the local and long-range connectivity matrices affect the spatial patterns of connectome harmonics. Specifically, the proportion of local gray matter homogeneous connectivity versus long-range white-matter heterogeneous connectivity is varied by means of weight-based matrix thresholding, distance-based matrix trimming, and several types of matrix randomizations. We demonstrate that the proportion of local gray matter connections plays a crucial role for the emergence of wide-spread, functionally meaningful, and originally published connectome harmonic patterns. This finding is robust for several different cortical surface templates, mesh resolutions, or widths of the local diffusion kernel. Finally, using the connectome harmonic framework, we also provide a proof-of-concept for how targeted structural changes such as the atrophy of inter-hemispheric callosal fibers and gray matter alterations may predict functional deficits associated with neurodegenerative conditions.

Suppressing Anterior Cingulate Cortex Modulates Default Mode Network and Behavior in Awake Rats

The default mode network (DMN) is a principal brain network in the mammalian brain. Although the DMN in humans has been extensively studied with respect to network structure, function, and clinical implications, our knowledge of DMN in animals remains limited. In particular, the functional role of DMN nodes, and how DMN organization relates to DMN-relevant behavior are still elusive. Here we investigated the causal relationship of inactivating a pivotal node of DMN (i.e., dorsal anterior cingulate cortex [dACC]) on DMN function, network organization, and behavior by combining chemogenetics, resting-state functional magnetic resonance imaging (rsfMRI) and behavioral tests in awake rodents. We found that suppressing dACC activity profoundly changed the activity and connectivity of DMN, and these changes were associated with altered DMN-related behavior in animals. The chemo-rsfMRI-behavior approach opens an avenue to mechanistically dissecting the relationships between a specific node, brain network function, and behavior. Our data suggest that, like in humans, DMN in rodents is a functional network with coordinated activity that mediates behavior.

Network topology of the marmoset connectome

The brain is a complex network of interconnected and interacting neuronal populations. Global efforts to understand the emergence of behavior and the effect of perturbations depend on accurate reconstruction of white matter pathways, both in humans and in model organisms. An emerging animal model for next-generation applied neuroscience is the common marmoset (Callithrix jacchus). A recent open respository of retrograde and anterograde tract tracing presents an opportunity to systematically study the network architecture of the marmoset brain (Marmoset Brain Architecture Project; http://www.marmosetbrain.org). Here we comprehensively chart the topological organization of the mesoscale marmoset cortico-cortical connectome. The network possesses multiple nonrandom attributes that promote a balance between segregation and integration, including near-minimal path length, multiscale community structure, a connective core, a unique motif composition, and multiple cavities. Altogether, these structural attributes suggest a link between network architecture and function. Our findings are consistent with previous reports across a range of species, scales, and reconstruction technologies, suggesting a small set of organizational principles universal across phylogeny. Collectively, these results provide a foundation for future anatomical, functional, and behavioral studies in this model organism.

Signal propagation via cortical hierarchies

The wiring of the brain is organized around a putative unimodal-transmodal hierarchy. Here we investigate how this intrinsic hierarchical organization of the brain shapes the transmission of information among regions. The hierarchical positioning of individual regions was quantified by applying diffusion map embedding to resting-state functional MRI networks. Structural networks were reconstructed from diffusion spectrum imaging and topological shortest paths among all brain regions were computed. Sequences of nodes encountered along a path were then labeled by their hierarchical position, tracing out path motifs. We find that the cortical hierarchy guides communication in the network. Specifically, nodes are more likely to forward signals to nodes closer in the hierarchy and cover a range of unimodal and transmodal regions, potentially enriching or diversifying signals en route. We also find evidence of systematic detours, particularly in attention networks, where communication is rerouted. Altogether, the present work highlights how the cortical hierarchy shapes signal exchange and imparts behaviorally relevant communication patterns in brain networks.

In the present report we asked how signals travel on brain networks and what types of nodes they potentially visit en route. We traced individual path motifs to investigate the propensity of communication paths to explore the putative unimodal-transmodal cortical hierarchy. We find that the architecture of the network promotes signaling via the hierarchy, suggesting a link between the structure and function of the network. Importantly, we also find instances where detours are promoted, particularly as paths traverse attention-related networks. Finally, information about hierarchical position aids navigation in some parts of the network, over and above spatial location. Altogether, the present results touch on several emerging themes in network neuroscience, including the nature of structure-function relationships, network communication and the role of cortical hierarchies.

Structural determinants of dynamic fluctuations between segregation and integration on the human connectome

While segregation and integration of neural information in the neocortex are thought to be important for human behavior and cognition, the neural substrates enabling their dynamic fluctuations remain elusive. To tackle this problem, we aim to identify specific network features of the connectome that are responsible for the emergence of dynamic fluctuations between segregated and integrated patterns in human resting-state functional connectivity. Here we examine the contributions of network features to dynamic fluctuations by constructing rewired surrogate connectome in which network features of interest are selectively preserved, and then by assessing the magnitude of fluctuations simulated with these surrogates. Our analysis demonstrates significant contributions from global geometry and topology of the connectome, as well as from localized structural connections involving visual areas. By providing structural accounts of dynamic fluctuations in functional connectivity, this study offers new insights into generative mechanisms driving temporal changes in segregation and integration in the brain.

Geometric renormalization unravels self-similarity of the multiscale human connectome

Structural connectivity in the brain is typically studied by reducing its observation to a single spatial resolution. However, the brain possesses a rich architecture organized over multiple scales linked to one another. We explored the multiscale organization of human connectomes using datasets of healthy subjects reconstructed at five different resolutions. We found that the structure of the human brain remains self-similar when the resolution of observation is progressively decreased by hierarchical coarse-graining of the anatomical regions. Strikingly, a geometric network model, where distances are not Euclidean, predicts the multiscale properties of connectomes, including self-similarity. The model relies on the application of a geometric renormalization protocol which decreases the resolution by coarse-graining and averaging over short similarity distances. Our results suggest that simple organizing principles underlie the multiscale architecture of human structural brain networks, where the same connectivity law dictates short- and long-range connections between different brain regions over many resolutions. The implications are varied and can be substantial for fundamental debates, such as whether the brain is working near a critical point, as well as for applications including advanced tools to simplify the digital reconstruction and simulation of the brain.

Space-independent community and hub structure of functional brain networks

Coordinated brain activity reflects underlying cognitive processes and can be modeled as a network of inter-regional functional connections. The most costly connections in the network are long-distance correlations that, in the absence of underlying structural connections, are maintained by sustained energetic inputs. Here, we present a spatial modeling approach that amplifies contributions made by long-distance functional connections to whole-brain network architecture, while simultaneously suppressing contributions made by short-range connections. We use this method to characterize the long-distance architecture of functional networks and to identify aspects of community and hub structure that are driven by long-distance correlations and that, we argue, are of greater functional significance. We find that based only on patterns of long-distance connectivity, primary sensory cortices occupy increasingly central positions and appear more "hub-like". Additionally, we show that the community structure of long-distance connections spans multiple topological levels and differs from the community structure detected in networks that include both short-range and long-distance connections. In summary, these findings highlight the complex relationship between the brain's physical layout and its functional architecture. The results presented here inform future analyses of community structure and network hubs in health, across development, and in the case of neuropsychiatric disorders.

Dyslexia as an adaptation to cortico-limbic stress system reactivity

A new school of thought in evolutionary developmental biology, combined with research in the neurobiology of stress, suggest that early exposure to stressful circumstances may be a cause of dyslexia. A balance between epigenetic, stress-induced and cognitive-growth genetic programs modulates the brain's cellular, regional, and network homeostasis. This balance is essential for adaptability to the normative range of everyday stress. However, even mild chronic stress exposition may overactivate the hypothalmic-pituitary-adrenal stress axis, upsetting the homeostatic balance between these programs, and exposing the brain to harmful levels of stress hormones. A protective strategy to sustained disequilibrium precociously advances maturation at the cost of neuroplasticity, which blunts stress axis reactivity but also compromises learning potential in the prefrontal cortex and networks associated with dyslexia. Stress exceeding an individual's range of resilience: (1) reduces levels of TFEB and BDNF, gene regulatory factors prolonging maturation and neuroplasticity; (2) interferes with the insular cortex, amygdala and hippocampus in coordinating afferent visceral signals with cognitive performance; (3) over-recruits the brain's Default Mode network; and (4) amplifies release from the Locus coeruleus/norepinephrine system which impairs the entrainment of oscillations in the lower phonological frequencies of speech. Evidence supporting a stress-growth imbalance is preliminary, but holds promise for reconceptualizing the neurobiology of dyslexia and reducing its prevalence.

Leveraging multi-shell diffusion for studies of brain development in youth and young adulthood

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Multi-shell imaging sequences may improve sensitivity to developmental effects.

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Models that leverage multi-shell information are often less sensitive to the confounding effects of motion.

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Multi-shell sequences and models that leverage this data may be of particular utility for studying the developing brain.

Diffusion weighted imaging (DWI) has advanced our understanding of brain microstructure evolution over development. Recently, the use of multi-shell diffusion imaging sequences has coincided with advances in modeling the diffusion signal, such as Neurite Orientation Dispersion and Density Imaging (NODDI) and Laplacian-regularized Mean Apparent Propagator MRI (MAPL). However, the relative utility of recently-developed diffusion models for understanding brain maturation remains sparsely investigated. Additionally, despite evidence that motion artifact is a major confound for studies of development, the vulnerability of metrics derived from contemporary models to in-scanner motion has not been described. Accordingly, in a sample of 120 youth and young adults (ages 12–30) we evaluated metrics derived from diffusion tensor imaging (DTI), NODDI, and MAPL for associations with age and in-scanner head motion at multiple scales. Specifically, we examined mean white matter values, white matter tracts, white matter voxels, and connections in structural brain networks. Our results revealed that multi-shell diffusion imaging data can be leveraged to robustly characterize neurodevelopment, and demonstrate stronger age effects than equivalent single-shell data. Additionally, MAPL-derived metrics were less sensitive to the confounding effects of head motion. Our findings suggest that multi-shell imaging data and contemporary modeling techniques confer important advantages for studies of neurodevelopment.

Spatial Patterning of Tissue Volume Loss in Schizophrenia Reflects Brain Network Architecture

BACKGROUND

There is growing recognition that connectome architecture shapes cortical and subcortical gray matter atrophy across a spectrum of neurological and psychiatric diseases. Whether connectivity contributes to tissue volume loss in schizophrenia in the same manner remains unknown.

METHODS

Here, we relate tissue volume loss in patients with schizophrenia to patterns of structural and functional connectivity. Gray matter deformation was estimated in a sample of 133 individuals with chronic schizophrenia (48 women, mean age 34.7 ± 12.9 years) and 113 control subjects (64 women, mean age 23.5 ± 8.4 years). Deformation-based morphometry was used to estimate cortical and subcortical gray matter deformation from T1-weighted magnetic resonance images. Structural and functional connectivity patterns were derived from an independent sample of 70 healthy participants using diffusion spectrum imaging and resting-state functional magnetic resonance imaging.

RESULTS

We found that regional deformation is correlated with the deformation of structurally and functionally connected neighbors. Distributed deformation patterns are circumscribed by specific functional systems (the ventral attention network) and cytoarchitectonic classes (limbic class), with an epicenter in the anterior cingulate cortex.

CONCLUSIONS

Altogether, the present study demonstrates that brain tissue volume loss in schizophrenia is conditioned by structural and functional connectivity, accounting for 25% to 35% of regional variance in deformation.

Prediction of individual responses to electroconvulsive therapy in patients with schizophrenia: Machine learning analysis of resting-state electroencephalography

BACKGROUND

Electroconvulsive therapy (ECT) has strong efficacy in patients with treatment refractory schizophrenia. However, access to ECT has been limited by high costs, professional labor, treatment duration, and significant adverse effects. To provide support for the decision to perform ECT, we aimed to predict individual responses to ECT among patients with schizophrenia using machine learning analysis of resting-state electroencephalography (EEG).

METHODS

Forty-seven patients diagnosed with schizophrenia or schizoaffective disorder with EEG recordings before the course of ECT were treated at Seoul National University Hospital. Among these patients, 29 were responders who showed scores of 3 or less on the Clinical Global Impression Severity scale after the course of ECT. Transfer entropy (TE), which represents information flow, was extracted from baseline EEG data and used as a feature. Feature selection was performed with four methods, including Random Subset Feature Selection (RSFS). The random forest classifier was used to predict individual ECT responses.

RESULTS

The averaged TE, especially in frontal regions, was higher in ECT responders than in nonresponders. A predictive model using the RSFS method classified ECT responders and nonresponders with 85.3% balanced accuracy, 85.2% accuracy, 88.7% sensitivity, and 81.8% specificity. The positive predictive value was 82.6%, and the negative predictive value was 88.2%.

CONCLUSIONS

The results of the current study suggest that higher effective connectivity in frontal areas may be associated with a favorable ECT response. Furthermore, personalized decisions to perform ECT in clinical practice could be augmented by resting-state EEG biomarkers of the ECT response in schizophrenia patients.

Synaptic Plasticity Shapes Brain Connectivity: Implications for Network Topology

Studies of brain network connectivity improved understanding on brain changes and adaptation in response to different pathologies. Synaptic plasticity, the ability of neurons to modify their connections, is involved in brain network remodeling following different types of brain damage (e.g., vascular, neurodegenerative, inflammatory). Although synaptic plasticity mechanisms have been extensively elucidated, how neural plasticity can shape network organization is far from being completely understood. Similarities existing between synaptic plasticity and principles governing brain network organization could be helpful to define brain network properties and reorganization profiles after damage. In this review, we discuss how different forms of synaptic plasticity, including homeostatic and anti-homeostatic mechanisms, could be directly involved in generating specific brain network characteristics. We propose that long-term potentiation could represent the neurophysiological basis for the formation of highly connected nodes (hubs). Conversely, homeostatic plasticity may contribute to stabilize network activity preventing poor and excessive connectivity in the peripheral nodes. In addition, synaptic plasticity dysfunction may drive brain network disruption in neuropsychiatric conditions such as Alzheimer's disease and schizophrenia. Optimal network architecture, characterized by efficient information processing and resilience, and reorganization after damage strictly depend on the balance between these forms of plasticity.