Segregation Of Brain Networks Research Articles

The Age of Reason: Functional Brain Network Development during Childhood.

Human childhood is characterized by dramatic changes in the mind and brain. However, little is known about the large-scale intrinsic cortical network changes that occur during childhood because of methodological challenges in scanning young children. Here, we overcome this barrier by using sophisticated acquisition and analysis tools to investigate functional network development in children between the ages of 4 and 10 years ([Formula: see text]; 50 female, 42 male). At multiple spatial scales, age is positively associated with brain network segregation. At the system level, age was associated with segregation of systems involved in attention from those involved in abstract cognition, and with integration among attentional and perceptual systems. Associations between age and functional connectivity are most pronounced in visual and medial prefrontal cortex, the two ends of a gradient from perceptual, externally oriented cortex to abstract, internally oriented cortex. These findings suggest that both ends of the sensory-association gradient may develop early, in contrast to the classical theories that cortical maturation proceeds from back to front, with sensory areas developing first and association areas developing last. More mature patterns of brain network architecture, controlling for age, were associated with better visuospatial reasoning abilities. Our results suggest that as cortical architecture becomes more specialized, children become more able to reason about the world and their place in it.SIGNIFICANCE STATEMENT Anthropologists have called the transition from early to middle childhood the "age of reason", when children across cultures become more independent. We employ cutting-edge neuroimaging acquisition and analysis approaches to investigate associations between age and functional brain architecture in childhood. Age was positively associated with segregation between cortical systems that process the external world and those that process abstract phenomena like the past, future, and minds of others. Surprisingly, we observed pronounced development at both ends of the sensory-association gradient, challenging the theory that sensory areas develop first and association areas develop last. Our results open new directions for research into how brains reorganize to support rapid gains in cognitive and socioemotional skills as children reach the age of reason.

Multiple spatial scale mapping of time-resolved brain network reconfiguration during evoked pain in patients with rheumatoid arthritis.

Functional brain networks and the perception of pain can fluctuate over time. However, how the time-dependent reconfiguration of functional brain networks contributes to chronic pain remains largely unexplained. Here, we explored time-varying changes in brain network integration and segregation during pain over a disease-affected area (joint) compared to a neutral site (thumbnail) in 28 patients with rheumatoid arthritis (RA) in comparison with 22 healthy controls (HC). During functional magnetic resonance imaging, all subjects received individually calibrated pain pressures corresponding to visual analog scale 50 mm at joint and thumbnail. We implemented a novel approach to track changes of task-based network connectivity over time. Within this framework, we quantified measures of integration (participation coefficient, PC) and segregation (within-module degree z-score). Using these network measures at multiple spatial scales, both at the level of single nodes (brain regions) and communities (clusters of nodes), we found that PC at the community level was generally higher in RA patients compared to HC during and after painful pressure over the inflamed joint and corresponding site in HC. This shows that all brain communities integrate more in RA patients than in HC for time points following painful stimulation to a disease-relevant body site. However, the elevated community-related integration seen in patients appeared to not pertain uniquely to painful stimulation at the inflamed joint, but also at the neutral thumbnail, as integration and segregation at the community level did not differ across body sites in patients. Moreover, there was no specific nodal contribution to brain network integration or segregation. Altogether, our findings indicate widespread and persistent changes in network interaction in RA patients compared to HC in response to painful stimulation.

Decreased modular segregation of the frontal-parietal network in major depressive disorder.

Major depressive disorder (MDD) is a common psychiatric condition associated with aberrant large-scale distributed brain networks. However, it is unclear how the network dysfunction in MDD patients is characterized by imbalance or derangement of network modular segregation. Fifty-one MDD patients and forty-three matched healthy controls (HC) were recruited in the present study. We analyzed intrinsic brain activity derived from resting-state functional magnetic resonance imaging (R-fMRI) and then examined brain network segregation by computing the participation coefficient (PC). Further intra- and inter-modular connections analysis were preformed to explain atypical PC. Besides, we explored the potential relationship between the above graph theory measures and symptom severity in MDD. Lower modular segregation of the frontal–parietal network (FPN) was found in MDD compared with the HC group. The MDD group exhibited increased inter-module connections between the FPN and cingulo-opercular network (CON), between the FPN and cerebellum (Cere), between the CON and Cere. At the nodal level, the PC of the anterior prefrontal cortex, anterior cingulate cortex, inferior parietal lobule (IPL), and intraparietal sulcus showed larger in MDD. Additionally, the inter-module connections between the FPN and CON and the PC values of the IPL were negatively correlated with depression symptom in the MDD group. These findings might give evidence about abnormal FPN in MDD from the perspective of modular segregation in brain networks.

Longitudinal maturation of resting state networks: Relevance to sustained attention and attention deficit/hyperactivity disorder

The transition from childhood to adolescence involves important neural function, cognition, and behavior changes. However, the links between maturing brain function and sustained attention over this period could be better understood. This study examined typical changes in network functional connectivity over childhood to adolescence, developmental differences in attention deficit/hyperactivity disorder (ADHD), and how functional connectivity might underpin variability in sustained attention development in a longitudinal sample. A total of 398 resting state scans were collected from 173 children and adolescents (88 ADHD, 85 control) at up to three timepoints across ages 9-14 years. The effects of age, sex, and diagnostic group on changes in network functional connectivity were assessed, followed by relationships between functional connectivity and sustained attention development using linear mixed effects modelling. The ADHD group displayed greater decreases in functional connectivity between salience and visual networks compared with controls. Lower childhood functional connectivity between the frontoparietal and several brain networks was associated with more rapid sustained attention development, whereas frontoparietal to dorsal attention network connectivity related to attention trajectories in children with ADHD alone. Brain network segregation may increase into adolescence as predicted by key developmental theories; however, participants with ADHD demonstrated altered developmental trajectories between salience and visual networks. The segregation of the frontoparietal network from other brain networks may be a mechanism supporting sustained attention development. Frontoparietal to dorsal attention connectivity can be a focus for further work in ADHD.

RONC-12. Evaluation of brain network segregation using resting state functional MRI in pediatric brain tumor patients treated with proton beam therapy

Abstract INTRODUCTION: Mechanisms of cognitive injury in pediatric brain tumor (BT) patients are poorly understood and biomarkers predicting cognitive deficits are lacking. Resting state functional MRI (rsfMRI) can evaluate large-scale brain networks to provide insight into neural changes that follow diagnosis and treatment. This project evaluated functional brain network segregation in pediatric BT patients treated with proton beam radiation therapy (PBRT). METHODS: rsfMRI scans were acquired from 21 pediatric patients (5 female, 16 male) with any brain tumor diagnosis treated with PBRT. Data were processed to reduce contamination, and correlation matrices were generated using all gray matter voxels. Brain network segregation (BNS) was measured and compared to 139 age-matched controls. Analysis included comparison of BNS in association systems (comprised of default mode, frontoparietal, dorsal attention, language, salience, and cingulo-opercular networks) and somatosensory systems (including auditory, visual, and somatomotor networks). Patient and clinical factors were evaluated for relationships with BNS. Patients underwent cognitive testing utilizing the NIH Toolbox Cognitive Domain (NIH-TCD). RESULTS: Median age at PBRT was 10 years (range 2.2-17.9). PBRT volume was whole brain in 10 and conformal in 11. rsfMRI occurred at a median of 2.9 years (range 1.0-5.6) after PBRT. Average BNS of association systems was significantly lower in patients than in controls (t= -8.1, p<10-10), but there was no significant difference in BNS of somatosensory systems (p=0.4). There was a trend towards worse BNS in association systems in patients <10 years old at PBRT (p=0.07). Patients demonstrated deficits in cognitive testing as assessed by the NIH-TCD; however, there were no significant relationships between score and network segregation. CONCLUSION: Pediatric BT patients treated with PBRT demonstrated normal BNS in somatosensory systems. However, BNS of association systems, which control high-level cognitive functions including social cognition, attention, and memory formation, was significantly decreased in BT patients treated with PBRT.

Rest-fMRI based comparison study between autism spectrum disorder and typically control using graph frequency bands

Graph signal processing (GSP) is a subset of signal processing, allowing for the analysis of functional magnetic resonance imaging (fMRI) data in the topological domain of the brain. One of the most important and popular tools of GSP is graph Fourier transform (GFT), which can analyze the brain signals in different graph frequency bands. This paper has analyzed the resting-state fMRI (rfMRI) data of two sites using the GFT tool to discover new knowledge about autism spectrum disorder (ASD) and find features discriminating between ASD subjects and typical controls (TCs). The results were reported for both structural and functional atlases with different numbers of regions of interest (ROIs). For the ASD group, the signal energy concentrations in low and somewhat high-frequency bands declined by increasing age in most well-known brain networks. The changes of signal energy levels in different graph frequency bands were less for ASD subjects in comparison to TC ones. This result seems to reflect the difficulty in dynamic switching, which in turn leads to lower behavioral flexibility in the ASD group. In the low graph frequency band, the segregation of brain ROIs and brain networks increased with the age of ASD subjects. For TCs, growing up led to the integration of brain ROIs and segregation of brain networks in low and high-frequency bands, respectively. In the low-frequency band, the growing process was accompanied by lower activation and higher isolation of ASD brain networks. In addition, the segregation of salient-ventral attention network and dorsal attention network of ASD subjects grew with age. The structural atlas results indicated the reduced segregation of ASD subjects' default mode network in the high graph frequency band. The cross-frequency functional connectivity analysis showed that high-frequency signals of the right precentral gyrus and right precuneus posterior cingulate cortex had connections with almost all the low-frequency ROIs so that all connections were dramatically different between ASD and TC. The results of different scenarios at different graph frequency bands demonstrate that the combinatorial usage of functional and structural data through GSP can open a new avenue to investigate ASD.

Reduced Modular Segregation of White Matter Brain Networks in Attention Deficit Hyperactivity Disorder

Objective: Despite studies reporting alterations in the brain networks of patients with ADHD, alterations in the modularity of white matter (WM) networks are still unclear. Method: Based on the results of module division by generalized Louvain algorithm, the modularity of ADHD was evaluated. The correlation between the modular changes of ADHD and its clinical characteristics was analyzed. Results: The participation coefficient and the connectivity between modules of ADHD increased, and the modularity coefficient decreased. Provincial hubs of ADHD did not change, and the number of connector hubs increased. All results showed that the modular segregation of WM networks of ADHD decreased. Modules with reduced modular segregation are mainly responsible for language and motor functions. Moreover, modularity showed evident correlation with the symptoms of ADHD. Conclusion: The modularity changes in WM network provided a novel insight into the understanding of brain cognitive alterations in ADHD.

Genetic Influence Underlying Brain Connectivity Phenotype: A Study on Two Age-Specific Cohorts.

Background: Human brain structural connectivity is an important imaging quantitative trait for brain development and aging. Mapping the network connectivity to the phenotypic variation provides fundamental insights in understanding the relationship between detailed brain topological architecture, function, and dysfunction. However, the underlying neurobiological mechanism from gene to brain connectome, and to phenotypic outcomes, and whether this mechanism changes over time, remain unclear. Methods: This study analyzes diffusion-weighted imaging data from two age-specific neuroimaging cohorts, extracts structural connectome topological network measures, performs genome-wide association studies of the measures, and examines the causality of genetic influences on phenotypic outcomes mediated via connectivity measures. Results: Our empirical study has yielded several significant findings: 1) It identified genetic makeup underlying structural connectivity changes in the human brain connectome for both age groups. Specifically, it revealed a novel association between the minor allele (G) of rs7937515 and the decreased network segregation measures of the left middle temporal gyrus across young and elderly adults, indicating a consistent genetic effect on brain connectivity across the lifespan. 2) It revealed rs7937515 as a genetic marker for body mass index in young adults but not in elderly adults. 3) It discovered brain network segregation alterations as a potential neuroimaging biomarker for obesity. 4) It demonstrated the hemispheric asymmetry of structural network organization in genetic association analyses and outcome-relevant studies. Discussion: These imaging genetic findings underlying brain connectome warrant further investigation for exploring their potential influences on brain-related complex diseases, given the significant involvement of altered connectivity in neurological, psychiatric and physical disorders.

A study of brain functional network and alertness changes in temporal lobe epilepsy with and without focal to bilateral tonic\u2013clonic seizures

BackgroundTemporal lobe epilepsy (TLE) is commonly refractory. Epilepsy surgery is an effective treatment strategy for refractory epilepsy, but patients with a history of focal to bilateral tonic-clonic seizures (FBTCS) have poor outcomes. Previous network studies on epilepsy have found that TLE and idiopathic generalized epilepsy with generalized tonic-clonic seizures (IGE-GTCS) showed altered global and nodal topological properties. Alertness deficits also were found in TLE. However, FBTCS is a common type of seizure in TLE, and the implications for alertness as well as the topological rearrangements associated with this seizure type are not well understood.MethodsWe obtained rs-fMRI data and collected the neuropsychological assessment data from 21 TLE patients with FBTCS (TLE- FBTCS), 18 TLE patients without FBTCS (TLE-non- FBTCS) and 22 controls, and constructed their respective functional brain networks. The topological properties were analyzed using the graph theoretical approach and correlations between altered topological properties and alertness were analyzed.ResultsWe found that TLE-FBTCS patients showed more serious impairment in alertness effect, intrinsic alertness and phasic alertness than the patients with TLE-non-FBTCS. They also showed significantly higher small-worldness, normalized clustering coefficient (γ) and a trend of higher global network efficiency (gE) compared to TLE-non-FBTCS patients. The gE showed a significant negative correlation with intrinsic alertness for TLE-non-FBTCS patients.ConclusionOur findings show different impairments in brain network information integration, segregation and alertness between the patients with TLE-FBTCS and TLE-non-FBTCS, demonstrating that impairments of the brain network may underlie the disruptions in alertness functions.

Brain network segregation and integration during painful thermal stimulation.

The present study aimed to determine changes in brain network integration/segregation during thermal pain using methods optimized for network connectivity events with high temporal resolution. Participants (n = 33) actively judged whether thermal stimuli applied to the volar forearm were painful or not and then rated the warmth/pain intensity after each trial. We show that the temporal evolution of integration/segregation within trials correlates with the subjective ratings of pain. Specifically, the brain shifts from a segregated state to an integrated state when processing painful stimuli. The association with subjective pain ratings occurred at different time points for all networks. However, the degree of association between ratings and integration/segregation vanished for several brain networks when time-varying functional connectivity was measured at lower temporal resolution. Moreover, the increased integration associated with pain is explained to some degree by relative increases in between-network connectivity. Our results highlight the importance of investigating the relationship between pain and brain network connectivity at a single time point scale, since commonly used temporal aggregations of connectivity data may result in that fine-scale changes in network connectivity may go unnoticed. The interplay between integration/segregation reflects shifting demands of information processing between brain networks and this adaptation occurs both for cognitive tasks and nociceptive processing.

Aberrant Whole-Brain Resting-State Functional Connectivity Architecture in Obsessive-Compulsive Disorder: An EEG Study.

Obsessive-compulsive disorder (OCD) is a common neuropsychiatric disorder characterized by intrusive thoughts (obsessions) and repetitive behaviors (compulsions), and few studies have assessed the whole-brain functional connectivity architecture of OCD with electroencephalogram (EEG) during different resting states. Graph theory and network-based statistics (NBS) were employed to examine the neural synchronization and the whole-brain functional connectivity (FC) based on the phase-locking value (PLV) of OCD patients and healthy controls (HCs) during eyes-closed (EC) and eyes-open (EO) states. Compared with HCs, OCD patients exhibited not only decreased global synchronization in terms of phase synchrony but also aberrant global topological properties (decreased average shortest path lengths and normalized shortest path lengths together with increased global efficiencies and normalized clustering coefficients) together with inhibited intra-hemispheric and interhemispheric FCs during rest, which suggested an imbalance between functional integration and segregation of brain networks for OCD patients. Meanwhile, OCD patients had increased global efficiencies and normalized clustering coefficients, but decreased average clustering coefficients and normalized shortest path lengths together with significantly decreased FCs in the alpha band from EC to EO states, which suggested a dynamic switch between highly integrated (EC state) and highly specialized (EO state) modes of information processing. Moreover, the decreased FCs of OCD patients showed obvious hemispheric asymmetry within or between groups during EC and EO states, which might serve as a potential biomarker to classify OCD patients from HCs.

Genetic factors associated with higher segregation of brain networks and cognition mediated by cardiovascular health: GWAS and Mendelian randomization analyses in the UK Biobank and Rotterdam Study.

Higher segregation of functional networks in the brain has been associated with better cognitive abilities in aging (Chan, PNAS, 2014) and higher cognitive resilience against Alzheimer's disease (Ewers, Brain, in press). Here, we elucidated for the first time the genetic and environmental (i.e. cardiovascular) determinants of system segregation (SyS) in two large population-based cohorts. We included 16,635 UK Biobank (UKB) participants (45-81y, discovery-sample) and 2,414 Rotterdam-Study participants (52-90y, replication-sample). Resting-state-fMRI SyS was computed as the ratio of between-network to within-network connectivity, where networks (N=55) were defined by independent component analysis. Genome-wide association study (GWAS) of SyS was performed in UKB, controlling for twenty principal components, age, sex, genotype-array and assessment center. For out-of-sample prediction, a polygenic risk score (PRS) of SyS was tested in Rotterdam-Study participants. In both cohorts, we determined the effect of cardiovascular health (adherence to Life's simple 7) on SyS. We estimated age-dependent and -independent effects of SyS on cognition (multi-domain factor score) in both cohorts and, in a subsample of 2,113 UKB participants, on cognitive decline over time. To explore causal effects, Mendelian Randomization (MR) analyses were performed. GWAS of SyS in UKB yielded 659 genome-wide significant single-nucleotide polymorphisms (SNPs; P<5e-08, h2 =0.144, Fig. 1). Nine independent risk loci were detected implicated 59 genes. Lead SNPs with highest likelihood to have deleterious consequences were located near the INPP5A and PLCE1 genes. In Rotterdam-Study participants, PRS explained 1.4% of variance in SyS. We found overall better cardiovascular health to be associated with higher SyS, while higher blood pressure alone was a significant predictor of lower SyS (UKB:βstd =-0.059(0.007), P<0.001; Rotterdam-Study:βstd (SE)=-0.060(0.020), P=0.003; Fig. 2). MR analysis in UKB confirmed that genetically elevated levels of blood pressure were associated with lower SyS (β(CI95%)=-0.003(-0.002,-0.001), P=0.002). We found higher SyS to be associated with better cognition across all ages in UKB (βstd (SE)=-0.031(0.012), P=0.005) and in older, but not younger Rotterdam-Study participants (βstd (SE)=0.038(0.016), P=0.0026, Fig. 3). In MR analysis, genetically elevated levels of SyS were associated with better cognition (β(CI95)=0.104(0.03-0.18), P=0.008). The current study highlights the importance of cardiovascular health for maintaining segregated brain systems what in turn was shown to benefit cognitive functions during aging.

Association between brain structural network efficiency at term-equivalent age and early development of cerebral palsy in very preterm infants

Very preterm infants (born at less than 32 weeks gestational age) are at high risk for serious motor impairments, including cerebral palsy (CP). The brain network changes that antecede the early development of CP in infants are not well characterized, and a better understanding may suggest new strategies for risk-stratification at term, which could lead to earlier access to therapies. Graph theoretical methods applied to diffusion MRI-derived brain connectomes may help quantify the organization and information transfer capacity of the preterm brain with greater nuance than overt structural or regional microstructural changes. Our aim was to shed light on the pathophysiology of early CP development, before the occurrence of early intervention therapies and other environmental confounders, to help identify the best early biomarkers of CP risk in VPT infants. In a cohort of 395 very preterm infants, we extracted cortical morphometrics and brain volumes from structural MRI and also applied graph theoretical methods to diffusion MRI connectomes, both acquired at term-equivalent age. Metrics from graph network analysis, especially global efficiency, strength values of the major sensorimotor tracts, and local efficiency of the motor nodes and novel non-motor regions were strongly inversely related to early CP diagnosis. These measures remained significantly associated with CP after correction for common risk factors of motor development, suggesting that metrics of brain network efficiency at term may be sensitive biomarkers for early CP detection. We demonstrate for the first time that in VPT infants, early CP diagnosis is anteceded by decreased brain network segregation in numerous nodes, including motor regions commonly-associated with CP and also novel regions that may partially explain the high rate of cognitive impairments concomitant with CP diagnosis. These advanced MRI biomarkers may help identify the highest risk infants by term-equivalent age, facilitating earlier interventions that are informed by early pathophysiological changes.

Bilingual language entropy influences executive functions through functional connectivity and signal variability

How bilinguals’ experience in managing multiple languages in different communicative contexts influences the cognitive and neural aspects of executive functions remains unclear. Therefore, we examined whether variations in language experience in young adult bilinguals were associated with data-driven brain functional network patterns (connectivity and signal variability) defined by performance during executive control tasks (Stroop and task-switching). Multiple aspects of language experience, such as the extent of balanced bilingualism in language proficiency and usage, and language diversity across social contexts (i.e., language entropy) were assessed. We found that greater language diversity, rather than balanced bilingualism per se, was related to higher brain network specialization and segregation concentrated on the default mode and executive control networks, and lower signal variability, a pattern linked to smaller RT-indices related to executive functioning of shifting, goal-maintenance, and conflict-monitoring. Our findings underscore the important role of language diversity in influencing bilinguals’ neurocognitive characteristics of executive functioning.

Segregation, Integration and Balance of Large-Scale Resting Brain Networks Configure Different Cognitive Abilities Across Individuals

The Ultraviolet Imaging Telescope (UVIT) was launched as part of the multi-wavelength Indian AstroSat mission on 28 September, 2015 into a low Earth orbit. A 6-month performance verification (PV) phase ended in March 2016, and the instrument is now in the general observing phase. UVIT operates in three channels: visible, near-ultraviolet (NUV) and far-ultraviolet (FUV), each with a choice of broad and narrow band filters, and has NUV and FUV gratings for low-resolution spectroscopy. We have written a software package (JUDE) to convert the Level 1 data from UVIT into scientifically useful photon lists and images. The routines are written in the GNU Data Language (GDL) and are compatible with the IDL software package. We use these programs in our own scientific work, and will continue to update the programs as we gain better understanding of the UVIT instrument and its performance. We have released JUDE under an Apache License.

Graph-theoretical analysis of EEG functional connectivity during balance perturbation in traumatic brain injury: A pilot study.

Traumatic brain injury (TBI) often results in balance impairment, increasing the risk of falls, and the chances of further injuries. However, the underlying neural mechanisms of postural control after TBI are not well understood. To this end, we conducted a pilot study to explore the neural mechanisms of unpredictable balance perturbations in 17 chronic TBI participants and 15 matched healthy controls (HC) using the EEG, MRI, and diffusion tensor imaging (DTI) data. As quantitative measures of the functional integration and segregation of the brain networks during the postural task, we computed the global graph‐theoretic network measures (global efficiency and modularity) of brain functional connectivity derived from source‐space EEG in different frequency bands. We observed that the TBI group showed a lower balance performance as measured by the center of pressure displacement during the task, and the Berg Balance Scale (BBS). They also showed reduced brain activation and connectivity during the balance task. Furthermore, the decrease in brain network segregation in alpha‐band from baseline to task was smaller in TBI than HC. The DTI findings revealed widespread structural damage. In terms of the neural correlates, we observed a distinct role played by different frequency bands: theta‐band modularity during the task was negatively correlated with the BBS in the TBI group; lower beta‐band network connectivity was associated with the reduction in white matter structural integrity. Our future studies will focus on how postural training will modulate the functional brain networks in TBI.

Psilocybin-induced changes in brain network integrity and segregation correlate with plasma psilocin level and psychedelic experience

The emerging novel therapeutic psilocybin produces psychedelic effects via engagement of cerebral serotonergic targets by psilocin (active metabolite). The serotonin 2A receptor critically mediates these effects by altering distributed neural processes that manifest as increased entropy, reduced functional connectivity (FC) within discrete brain networks (i.e., reduced integrity) and increased FC between networks (i.e., reduced segregation). Reduced integrity of the default mode network (DMN) is proposed to play a particularly prominent role in psychedelic phenomenology, including perceived ego-dissolution. Here, we investigate the effects of a psychoactive peroral dose of psilocybin (0.2–0.3 mg/kg) on plasma psilocin level (PPL), subjective drug intensity (SDI) and their association in fifteen healthy individuals. We further evaluate associations between these measures and resting-state FC, measured with functional magnetic resonance imaging, acquired over the course of five hours after psilocybin administration. We show that PPL and SDI correlate negatively with measures of network integrity (including DMN) and segregation, both spatially constrained and unconstrained. We also find that the executive control network and dorsal attention network desegregate, increasing connectivity with other networks and throughout the brain as a function of PPL and SDI. These findings provide direct evidence that psilocin critically shapes the time course and magnitude of changes in the cerebral functional architecture and subjective experience following psilocybin administration. Our findings provide novel insight into the neurobiological mechanisms underlying profound perceptual experiences evoked by this emerging transnosological therapeutic and implicate the expression of network integrity and segregation in the psychedelic experience and consciousness.

The maturation and cognitive relevance of structural brain network organization from early infancy to childhood

The interactions of brain regions with other regions at the network level likely provide the infrastructure necessary for cognitive processes to develop. Specifically, it has been theorized that in infancy brain networks become more modular, or segregated, to support early cognitive specialization, before integration across networks increases to support the emergence of higher-order cognition. The present study examined the maturation of structural covariance networks (SCNs) derived from longitudinal cortical thickness data collected between infancy and childhood (0–6 years). We assessed modularity as a measure of network segregation and global efficiency as a measure of network integration. At the group level, we observed trajectories of increasing modularity and decreasing global efficiency between early infancy and six years. We further examined subject-based maturational coupling networks (sbMCNs) in a subset of this cohort with cognitive outcome data at 8–10 years, which allowed us to relate the network organization of longitudinal cortical thickness maturation to cognitive outcomes in middle childhood. We found that lower global efficiency of sbMCNs throughout early development (across the first year) related to greater motor learning at 8–10 years. Together, these results provide novel evidence characterizing the maturation of brain network segregation and integration across the first six years of life, and suggest that specific trajectories of brain network maturation contribute to later cognitive outcomes.

Increased segregation of structural brain networks underpins enhanced broad cognitive abilities of cognitive training.

A major challenge in the cognitive training field is inducing broad, far‐transfer training effects. Thus far, little is known about the neural mechanisms underlying broad training effects. Here, we tested a set of competitive hypotheses regarding the role of brain integration versus segregation underlying the broad training effect. We retrospectively analyzed data from a randomized controlled trial comparing neurocognitive effects of vision‐based speed of processing training (VSOP) and an active control consisting of mental leisure activities (MLA) in older adults with MCI. We classified a subset of participants in the VSOP as learners, who showed improvement in executive function and episodic memory. The other participants in the VSOP (i.e., VSOP non‐learners) and a subset of participants in the MLA (i.e., MLA non‐learners) served as controls. Structural brain networks were constructed from diffusion tensor imaging. Clustering coefficients (CCs) and characteristic path lengths were computed as measures of segregation and integration, respectively. Learners showed significantly greater global CCs after intervention than controls. Nodal CCs were selectively enhanced in cingulate cortex, parietal regions, striatum, and thalamus. Among VSOP learners, those with more severe baseline neurodegeneration had greater improvement in segregation after training. Our findings suggest broad training effects are related to enhanced segregation in selective brain networks, providing insight into cognitive training related neuroplasticity.

Large-Scale Replication of Bipolar Disorder Dysconnectivity: A Diffusion MRI Analysis of 959 Individuals From the ENIGMA Bipolar Disorder Working Group

Bipolar disorder (BD) involves disrupted mood regulation and cognition potentially associated with altered brain connectivity patterns, including altered integration and segregation of anatomical brain networks. Establishing this as a robust finding has been hindered by limited sample sizes, and by variation in methodological pipelines. We present the largest graph-theoretical analysis to date of structural brain networks in BD.