Segregation, integration, and balance of large-scale resting brain networks configure different cognitive abilities

Aberrant regional neural fluctuations and functional connectivity in insomnia comorbid depression revealed by resting-state functional magnetic resonance imaging

Insomnia is a common mental illness seriously affecting people lives, that might progress to major depression. However, the neural mechanism of patients with CID comorbid MDD remain unclear. Combining fractional amplitude of low-frequency fluctuation (fALFF) and seed-based functional connectivity (FC), this study investigated abnormality in local and long-range neural activity of patients with CID comorbid MDD. Here, we acquired resting-state blood oxygenation level dependent (BOLD) data from 57 patients with CID comorbid MDD and 57 healthy controls (HC). Compared with the controls, patients with CID comorbid MDD exhibited abnormal functional activity in posterior cerebral cortex related to the visual cortex, including the middle occipital gyrus (MOG), the cuneus and the lingual gyrus, specifically, lower fALFF values in the right MOG, left cuneus, and right postcentral gyrus, increased FC between the right MOG and the left cerebellum, and decreased FC between the right MOG and the right lingual gyrus. Neuropsychological correlation analysis revealed that the decreased fALFF in the right MOG was negatively correlated with all the neuropsychological scores of insomnia and depression, reflecting common relationships with symptoms of CID and MDD. While the decreased fALFF of the left cuneus was distinctly correlated with the scores of depression related scales. The decreased FC between the right MOG and the right lingual gyrus was distinctly correlated with the scores of insomnia related scales. This study not only widened neuroimaging evidence that associated with insomnia and depressive symptoms of patients with CID comorbid MDD, but also provided new potential targets for clinical treatment.

Development of brain metastable dynamics during the equivalent of the third gestational trimester

Metastability, a concept from dynamical systems theory, provides a framework for understanding how the brain shifts between various functional states and underpins essential cognitive, behavioural, and social function. While studied in adults, metastability in early brain development has only received recent attention. As the brain undergoes dramatic functional and structural changes over the third gestational trimester, here we review how these are reflected in changes in brain metastable dynamics in preterm, preterm at term-equivalent and full-term neonates. We synthesize findings from EEG, fMRI, fUS, and computational models, focusing on the spatial distribution and temporal dynamics of metastable states, which include functional integration and segregation, signal predictability and complexity. Despite fragmented evidence, studies suggest that neonatal metastability develops over the equivalent of the third gestational trimester, with increasing ability for integration-segregation, broader range of metastable states, faster metastable state transitions and greater signal complexity. Preterms at term-equivalent age exhibit immature metastability features compared to full-terms. We explain and interpret these changes in terms of maturation of the brain in a free energy landscape and establishment of cognitive functions.

Functional Connectivity Patterns Following Mild Traumatic Brain Injury and the Association With Longitudinal Cognitive Function

Functional magnetic resonance imaging (fMRI) has revealed subtle neuroplastic changes in brain networks following mild traumatic brain injury (mTBI), even when standard clinical imaging fails to detect abnormalities. However, prior findings have been inconsistent, in part due to methodological differences and high researcher degrees of freedom in region-based analyses, which often rely on predefined hypotheses and overlook complex, distributed connectivity patterns. Here, we apply an unbiased, data-driven multi-voxel pattern analysis (MVPA) to examine whole-brain functional connectivity differences in a large cohort of individuals with acute mTBI. Unlike conventional statistical approaches, MVPA enables a data-driven analysis of brain-wide connectivity patterns without requiring prior assumptions about the location or nature of abnormalities, allowing for the identification of the most informative features. This approach provides an exploratory characterization of whole-brain functional connectivity patterns and their relationship with cognitive recovery, offering new insights into the neural mechanisms underlying post-injury outcomes. A total of 265 adults (87 women) between 18 and 83 years old with Glasgow Coma Scale (GCS) scores of 13-15 were included in this analysis. Two replicate samples (n = 165, n = 155), with similar demographic characteristics, were also included. Data were collected as part of the prospective multi-center Transforming Research and Clinical Knowledge in TBI (TRACK-TBI). The goal of this study was to assess whole-brain functional connectivity patterns using fc-MVPA and post hoc seed-to-voxel analyses in a large, well-characterized sample to determine if changes in functional connectivity can differentiate subacute mTBI (within 2 weeks of injury) from a matched group of orthopedic control subjects (n = 49). Additionally, we aimed to investigate whether these connectivity patterns were linked to cognitive performance at 2 weeks, 6 months, and 12 months post-injury to better understand cognitive trajectories and recovery over time in individuals with mTBI. Voxel-to-voxel functional connectivity across the entire connectome revealed significant differences between TBI and no TBI in the functional connectivity patterns of 8 clusters (p-voxel < 0.001, FEW cluster-level p < 0.05) (k > 40, Fmax = 15.36), including right occipital cortex, anterior cingulate gyrus, inferior and middle temporal gyrus, right thalamus, left cerebellum, and the bilateral frontal pole. These clusters belong mainly to the visual network (VIS), frontoparietal network (FPN), default mode network (DMN) and limbic network (LIM). Post hoc characterization of each significant cluster revealed by MVPA using seed-to-voxel analysis showed a mixed pattern of connectivity between relevant networks and subcortico-cortical connections. After connectivity characterization, visual-motor skills assessed with Trail Making Test (TMT) A were significantly associated with the increased anticorrelation between the inferior temporal cortex and the bilateral occipital pole (FPN-VIS connectivity), along with decreased anticorrelations between the cerebellum and extensive areas of the somatomotor network (SMN) over 12 months post injury. Additionally, hypoconnectivity between the frontal pole (LIM) and anterior cingulate gyrus (salience network [SAL]) was associated with better executive functions performance measured by TMT-B over 12 months post-mTBI. In our study examining neuroplastic changes following TBI across the entire voxel-to-voxel functional connectome, we identified significant differences in the functional connectivity patterns of several regions known to be particularly vulnerable to injury mechanisms. Our findings highlight the complex and compensatory nature of brain network alterations after mTBI, suggesting both detrimental and adaptive changes in connectivity that affect cognitive functions. Consequently, our study provides novel evidence that specific brain networks and regions are particularly susceptible to functional connectivity changes during the acute stages of mTBI, which are related to cognitive recovery post-injury.

The balance between integration and segregation drives network dynamics maximizing multistability and metastability

The brain's ability to switch between functional states while maintaining both flexibility and stability is shaped by its structural connectivity. Understanding the relationship between brain structure and neural dynamics is a central challenge in neuroscience. Prior studies link neural dynamics to local noisy activity and mesoscale coupling mechanisms, but causal links at the whole-brain scale remain elusive. This study investigates how the balance between integration and segregation in brain networks influences their dynamical properties, focusing on multistability (switching between stable states) and metastability (transient stability over time). We analyzed a spectrum of network models, from highly segregated to highly integrated, using structural metrics like modularity, efficiency, and small-worldness. By simulating neural activity with a neural mass model, and analyzing Functional Connectivity Dynamics (FCD), we found that segregated networks sustain dynamic synchronization patterns, while small-world networks, which balance local clustering and global efficiency, exhibit the richest dynamical behavior. Networks with intermediate small-worldness (ω) values showed peak dynamical richness, measured by variance in FCD and metastability. Using Mutual Information (MI), we quantified the structure-dynamics relationship, revealing that modularity is the strongest predictor of network dynamics, as modular architectures support transitions between dynamical states. These findings underscore the importance of the balance between local specialization, global integration, and network's modularity, which fosters the dynamic complexity necessary for cognitive functions. Our study enhances the understanding of how structural features shape neural dynamics.

Association Between White Matter Hyperintensity and Cognitive Impairment in Cerebral Small Vessel Disease: The Frequency-dependent Role of Brain Functional Activity

BACKGROUND

Cognitive dysfunction in cerebral small vessel disease (CSVD) patients is associated with white matter hyperintensity (WMH), which demonstrates frequency-dependent correlations with brain functional activities. However, the neural mechanisms underlying the relationship between these structural and functional abnormalities and cognitive impairment remain unclear.

METHODS

We recruited 34 CSVD patients (mean age 63.74 ± 4.85 years, 19 males) and 45 age-matched healthy controls (mean age 63.69 ± 6.15 years, 15 males). All participants underwent magnetic resonance imaging (MRI) scanning and comprehensive cognitive assessments, including three behavioral tasks and a cognitive questionnaire battery. Regional brain activity and network topological properties were separately compared between the two groups for each of the three frequency bands (slow-4, slow-5, and typical band) using two-sample t-tests. Simple and multiple mediation analyses were performed to examine the relationships among WMH, functional brain measures, and global cognition.

RESULTS

CSVD patients exhibited frequency-specific alterations in regional activity and reduced global functional organization in the slow-4 band. Frequency-dependent functional measures in the slow-4 band significantly mediated the relationship between deep WMH and cognitive performance.

CONCLUSION

Our findings demonstrate the frequency-specific mediating role of abnormal brain functions in the pathophysiological pathway linking WMHs to cognitive impairment. This study provides new insight into the pathological mechanisms underlying WMH-related cognitive dysfunction.

CLINICAL TRIAL REGISTRATION

ChiCTR2100043346, 02 November 2021, https://www.chictr.org.cn/showproj.html?proj=52285.

Segregation and integration of resting-state brain networks in a longitudinal long COVID cohort

Long COVID is characterized by debilitating fatigue, likely stemming from abnormal interactions among brain regions, but the neural mechanisms remain unclear. Here, we utilized a nested-spectral partition (NSP) approach to study the segregation and integration of resting-state brain functional networks in 34 patients with long COVID from acute to chronic phase post infection. Compared to healthy controls, patients with long COVID exhibited significantly higher fatigue scores and shifted the brain into a less segregated state at both 1 month and 3 months post infection. During the recovery of fatigue severity, there was no significant difference of segregation/integration. A positive correlation between network integration and fatigue was observed at 1 month, shifting to a negative correlation by 3 months. Gene Ontology analysis revealed that both acute and long-term effects of fatigue were associated with abnormal social behavior. Our findings reveal the brain network reconfiguration trajectories during post-viral fatigue progression that serve as functional biomarkers for tracking neurocognitive sequelae.

Evolution of the Rich Club Properties in Mouse, Macaque, and Human Brain Networks: A Study of Functional Integration, Segregation, and Balance

The rich club, as a community of highly interconnected nodes, serves as the topological center of the network. However, the similarities and differences in how the rich club supports functional integration and segregation in the brain across different species remain unknown. In this study, we first detected and validated the rich club in the structural networks of mouse, monkey, and human brains using neuronal tracing or diffusion magnetic resonance imaging data. Further, we assessed the role of rich clubs in functional integration, segregation, and balance using quantitative metrics. Our results indicate that the presence of a rich club facilitates whole-brain functional integration in all three species, with the functional networks of higher species exhibiting greater integration. These findings are expected to help to understand the relationship between brain structure and function from the perspective of brain evolution.

High-Density Electroencephalography Detects Spatiotemporal Abnormalities in Brain Networks in Patients With Glioma-Related Epilepsy

AIMS

The current study aimed to investigate brain network abnormalities in glioma-related epilepsy (gre) patients through high-density electroencephalography (eeg) data analysis.

METHODS

The study included 35 patients with newly diagnosed frontal gliomas. All participants underwent 128-channel resting-state EEG recordings before surgery. Afterward, graph theory and microstate analyses were performed, and the resulting metrics were compared between patients with GRE and those without GRE.

RESULTS

The network topology analysis demonstrated that the GRE group had a higher clustering coefficient, global efficiency, and local efficiency; a lower characteristic path length; and a higher small-worldness coefficient than the non-GRE group (adjusted p < 0.05 for all). Additionally, the microstate analysis indicated that the GRE group had lower occurrence and global explained variance of microstate E and higher global explained variance of microstate D (adjusted p < 0.05 for all). Moreover, the occurrence of microstate D was significantly negatively correlated with the maximum tumor diameter in the non-GRE group (r = -0.542, p = 0.009).

CONCLUSION

The current study revealed specific brain network abnormalities in GRE patients based on graph theory and microstate analyses of resting-state high-density EEG data. These findings can enhance our comprehension of the mechanisms behind GRE and offer potential biomarkers for improving individualized management of glioma patients.

Targeted Time-Varying Functional Connectivity

To elucidate the neurobiological basis of cognition, which is dynamic and evolving, various methods have emerged to characterise time-varying functional connectivity (FC) and track the temporal evolution of functional networks. However, given a selection of regions, many of these methods are based on modelling all possible pairwise connections, diluting a potential focus of interest on individual connections. This is the case with the hidden Markov model (HMM), which relies on region-by-region covariance matrices across all pairs of selected regions, assuming that fluctuations in FC occur across all investigated connections; that is, that all connections are locked to the same temporal pattern. To address this limitation, we introduce Targeted Time-Varying FC (T-TVFC), a variant of the HMM that explicitly models the temporal fluctuations between two sets of regions in a targeted fashion, rather than across the entire connectivity matrix. In this study, we apply T-TVFC to both simulated and real-world data. Specifically, we investigate thalamocortical connectivity, hypothesising distinct temporal signatures compared to corticocortical networks. Given the thalamus's role as a critical hub, thalamocortical connections might contain unique information about cognitive processing that could be overlooked in a coarser representation. We tested these hypotheses on high-field functional magnetic resonance data from 60 participants engaged in a reasoning task with varying complexity levels. Our findings demonstrate that the time-varying interactions captured by T-TVFC contain task-related information not detected by more traditional decompositions.

Exploring the transmission of cognitive task information through optimal brain pathways

Understanding the large-scale information processing that underlies complex human cognition is the central goal of cognitive neuroscience. While emerging activity flow models demonstrate that cognitive task information is transferred by interregional functional or structural connectivity, graph-theory-based models typically assume that neural communication occurs via the shortest path of brain networks. However, whether the shortest path is the optimal route for empirical cognitive information transmission remains unclear. Based on a large-scale activity flow mapping framework, we found that the performance of activity flow prediction with the shortest path was significantly lower than that with the direct path. The shortest path routing was superior to other network communication strategies, including search information, path ensembles, and navigation. Intriguingly, the shortest path outperformed the direct path in activity flow prediction when the physical distance constraint and asymmetric routing contribution were simultaneously considered. This study not only challenges the shortest path assumption through empirical network models but also suggests that cognitive task information routing is constrained by the spatial and functional embedding of the brain network.

Functional Hierarchy of the Human Neocortex from Cradle to Grave

Recent evidence indicates that the organization of the human neocortex is underpinned by smooth spatial gradients of functional connectivity (FC). These gradients provide crucial insight into the relationship between the brain's topographic organization and the texture of human cognition. However, no studies to date have charted how intrinsic FC gradient architecture develops across the entire human lifespan. In this work, we model developmental trajectories of the three primary gradients of FC using a large, high-quality, and temporally-dense functional MRI dataset spanning from birth to 100 years of age. The gradient axes, denoted as sensorimotor-association (SA), visual-somatosensory (VS), and modulation-representation (MR), encode crucial hierarchical organizing principles of the brain in development and aging. By tracking their development throughout the human lifespan, we provide the first ever comprehensive low-dimensional normative reference of global FC hierarchical architecture. We observe significant age-related changes in global network features, with global markers of hierarchical organization increasing from birth to early adulthood and decreasing thereafter. During infancy and early childhood, FC organization is shaped by primary sensory processing, dense short-range connectivity, and immature association and control hierarchies. Functional differentiation of transmodal systems supported by long-range coupling drives a convergence toward adult-like FC organization during late childhood, while adolescence and early adulthood are marked by the expansion and refinement of SA and MR hierarchies. While gradient topographies remain stable during late adulthood and aging, we observe decreases in global gradient measures of FC differentiation and complexity from 30 to 100 years. Examining cortical microstructure gradients alongside our functional gradients, we observed that structure-function gradient coupling undergoes differential lifespan trajectories across multiple gradient axes.

Unraveling Integration-Segregation Imbalances in Schizophrenia Through Topological High-Order Functional Connectivity

BACKGROUND

The occurrence of brain disorders correlates with detectable dysfunctions in the specialization of brain connectomics. While extensive research has explored this relationship, there is a lack of studies specifically examining the statistical correlation between the integration and segregation of psychotic brain networks using high-order networks, given the limitations of low-order networks. Moreover, these dysfunctions are believed to be linked to information imbalances in brain functions. However, our understanding of how these imbalances give rise to specific psychotic symptoms remains limited.

METHODS

This study aims to address this gap by investigating variations at the topological high-order level of the system with regard to specialization in both healthy individuals and those diagnosed with schizophrenia. By employing graph-theoretic brain network analysis, we systematically examine information integration and segregation to delineate system-level differences in the connectivity patterns of brain networks.

RESULTS

The findings indicate that topological high-order functional connectomics highlight differences in the connectome between healthy controls and schizophrenia, demonstrating increased cingulo-opercular task control and salience interactions, while the interaction between subcortical and default mode networks, dorsal attention and sensory/somatomotor mouth decreases in schizophrenia. Furthermore, we observed a reduction in the segregation of brain systems in healthy controls compared to individuals with schizophrenia, which means the balance between segregation and integration of brain networks is disrupted in schizophrenia, suggesting that restoring this balance may aid in the treatment of the disorder. Additionally, the increased segregation and decreased integration of brain systems in schizophrenia patients compared to healthy controls may serve as a novel indicator for early schizophrenia diagnosis.

CONCLUSION

We discovered that topological high-order functional connectivity highlights brain network interactions compared to low-order functional connectivity. Furthermore, we observed alterations in specific brain regions associated with schizophrenia, as well as changes in brain network information integration and segregation in individuals with schizophrenia.

Local structural-functional coupling with counterfactual explanations for epilepsy prediction

The structural-functional brain connections coupling (SC-FC coupling) describes the relationship between white matter structural connections (SC) and the corresponding functional activation or functional connections (FC). It has been widely used to identify brain disorders. However, the existing research on SC-FC coupling focuses on global and regional scales, and few studies have investigated the impact of brain disorders on this relationship from the perspective of multi-brain region cooperation (i.e., local scale). Here, we propose the local SC-FC coupling pattern for brain disorders prediction. Compared with previous methods, the proposed patterns quantify the relationship between SC and FC in terms of subgraphs rather than whole connections or single brain regions. Specifically, we first construct structural and functional connections using diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) data, subsequently organizing them into a multimodal brain network. Then, we extract subgraphs from these multimodal brain networks and select them based on their frequencies to generate local SC-FC coupling patterns. Finally, we employ these patterns to identify brain disorders while refining abnormal patterns to generate counterfactual explanations. Results on a real epilepsy dataset suggest that the proposed method not only outperforms existing methods in accuracy but also provides insights into the local SC-FC coupling pattern and their changes in brain disorders. Code available at https://github.com/UAIBC-Brain/Local-SC-FC-coupling-pattern.

Network Segregation and Integration Changes in Healthy Aging: Evidence From EEG Subbands During the Visual Short-Term Memory Binding Task

Working memory, which tends to be the most vulnerable cognitive domain to aging, is thought to depend on a functional brain network for efficient communication. The dynamic communication within this network is represented by segregation and integration. This study aimed to investigate healthy aging by examining age effect on outcomes of graph theory analysis during the visual short-term memory binding (VSTMB) task. VSTMB tasks rely on the integration of visual features and are less sensitive to semantic and verbal strategies. Effects of age on neuropsychological test scores, along with the EEG graph-theoretical integration, segregation and global organization metrics in frequencies from delta to gamma band were investigated. Neuropsychological assessment showed low sensitivity as a measure of age-related changes. EEG results indicated that network architecture changed more effectively during middle age, while this effectiveness appears to vanish or show compensatory mechanisms in the elderly. These differences were further found to be related to cognitive domain scores. This study is the first to demonstrate differences in working memory network architecture across a broad age range.

Static and dynamic brain functional connectivity patterns in patients with unilateral moderate-to-severe asymptomatic carotid stenosis

Background and purpose

Asymptomatic carotid stenosis (ACS) is an independent risk factor for ischemic stroke and vascular cognitive impairment, affecting cognitive function across multiple domains. This study aimed to explore differences in static and dynamic intrinsic functional connectivity and temporal dynamics between patients with ACS and those without carotid stenosis.

Methods

We recruited 30 patients with unilateral moderate-to-severe (stenosis ≥ 50%) ACS and 30 demographically-matched healthy controls. All participants underwent neuropsychological testing and 3.0T brain MRI scans. Resting-state functional MRI (rs-fMRI) was used to calculate both static and dynamic functional connectivity. Dynamic independent component analysis (dICA) was employed to extract independent circuits/networks and to detect time-frequency modulation at the circuit level. Further imaging-behavior associations identified static and dynamic functional connectivity patterns that reflect cognitive decline.

Results

ACS patients showed altered functional connectivity in multiple brain regions and networks compared to controls. Increased connectivity was observed in the inferior parietal lobule, frontal lobe, and temporal lobe. dICA further revealed changes in the temporal frequency of connectivity in the salience network. Significant differences in the temporal variability of connectivity were found in the fronto-parietal network, dorsal attention network, sensory-motor network, language network, and visual network. The temporal parameters of these brain networks were also related to overall cognition and memory.

Conclusions

These results suggest that ACS involves not only changes in the static large-scale brain network connectivity but also dynamic temporal variations, which parallel overall cognition and memory recall.

Dynamic switching between brain networks predicts creative ability

Creativity is hypothesized to arise from a mental state which balances spontaneous thought and cognitive control, corresponding to functional connectivity between the brain's Default Mode (DMN) and Executive Control (ECN) Networks. Here, we conduct a large-scale, multi-center examination of this hypothesis. Employing a meta-analytic network neuroscience approach, we analyze resting-state fMRI and creative task performance across 10 independent samples from Austria, Canada, China, Japan, and the United States (N = 2433)-constituting the largest and most ethnically diverse creativity neuroscience study to date. Using time-resolved network analysis, we investigate the relationship between creativity (i.e., divergent thinking ability) and dynamic switching between DMN and ECN. We find that creativity, but not general intelligence, can be reliably predicted by the number of DMN-ECN switches. Importantly, we identify an inverted-U relationship between creativity and the degree of balance between DMN-ECN switching, suggesting that optimal creative performance requires balanced brain network dynamics. Furthermore, an independent task-fMRI validation study (N = 31) demonstrates higher DMN-ECN switching during creative idea generation (compared to a control condition) and replicates the inverted-U relationship. Therefore, we provide robust evidence across multi-center datasets that creativity is tied to the capacity to dynamically switch between brain networks supporting spontaneous and controlled cognition.

Abnormal intrinsic brain functional network dynamics in stroke and correlation with neuropsychiatric symptoms revealed based on lesion and cerebral blood flow

There has been a lack of clarity about the mechanisms of widespread network dysfunctions after stroke. This study aimed to reveal dynamic functional network alternations following stroke based on lesion and brain perfusion. We prospectively enrolled 125 acute ischaemic stroke patients (25 were transient ischemic attack (TIA) patients) and 49 healthy controls with assessed the severity of their depression, anxiety, fatigue, and apathy. We performed dynamic functional network connectivity (DFNC) analysis using the sliding window method. The common static FC biomarkers of stroke were used to define functional states and calculated stroke-specific changes in dynamic indicators. Next, ridge regression (RR) analyses were performed on the dynamic indicators using voxel-wise lesion maps, cerebral blood flow (CBF) difference maps (removal of voxels overlapping lesions) and a combination of both. Mediation analyses were used to characterize the effect of dynamic networks changes on the relationship between lesion, CBF, and neuropsychological scores. Our results showed that DFNC identified three functional states with three dynamic metrics extracted for subsequent analyses. RR analyses show that both CBF and lesions partially explain post-stroke dysfunction (CBF: dynamic indicator1: R2 = 0.110, p = 0.163; dynamic indicator2: R2 = 0.277, p = 0.006; dynamic indicator3: R2 = 0.125, p = 0.121; lesion: dynamic indicator1: R2 = 0.132, p = 0.109; dynamic indicator2: R2 = 0.238, p = 0.015; dynamic indicator3: R2 = 0.131, p = 0.110). In addition, combining the two can improve the efficacy of explanations. Finally, exploratory mediation analyses identified that dynamic functional network changes can mediate between CBF, lesion and neuropsychiatric disorders. Our results suggest that CBF and lesion can be combined to improve the interpretation of dynamic network dysfunction after stroke.

Atypical brain network topology of the triple network and cortico-subcortical network in autism spectrum disorder

The default mode network (DMN), salience network (SN), and central executive control network (CEN) form the well-known triple network, providing a framework for understanding various neurodevelopmental and psychiatric disorders. However, the topology of this network remains unclear in autism spectrum disorder (ASD). To gain a more profound understanding of ASD, we explored the topology of the triple network in ASD. Additionally, the striatum and thalamus are pivotal centres of information transmission within the brain, and the realization of various brain functions requires the coordination of cortical and subcortical structures. Therefore, we also investigated the topology of the cortico-subcortical network in ASD, which consists of the DMN, SN, CEN, striatum, and thalamus. Resting-state functional magnetic resonance imaging data on 208 ASD patients and 278 typically developing (TD) controls (8-18 years old) were obtained from the Autism Brain Imaging Data Exchange database. We performed graph theory analysis on the triple network and the cortico-subcortical network. The results showed that the triple network's clustering coefficient, lambda, and network local efficiency values were significantly lower in ASD, and the nodal degree and efficiency of the medial prefrontal cortex also decreased. For the cortico-subcortical network, the sigma, clustering coefficient, gamma, and network local efficiency showed the same reduction, and the altered clustering coefficient negatively correlated with ASD manifestations. In addition, the interaction between the DMN and CEN was more robust in ASD patients. These findings enhance our understanding of ASD and suggest that subcortical structures should be more considered in future ASD related studies.

Intermittent theta burst stimulation for negative symptoms in schizophrenia patients with mild cognitive impairment: a randomized controlled trail

Background

This study aims to evaluate the intervention effect of intermittent Theta burst stimulation (iTBS) on bilateral dorsomedial prefrontal cortex (DMPFC) for negative symptoms in schizophrenia using functional near-infrared spectroscopy (fNIRS) to confirm the therapeutic significance of DMPFC in treating negative symptoms and provide new evidence for schizophrenia treatment and research.

Method

Thirty-nine schizophrenia patients with negative symptoms and mild cognitive impairment were randomly divided into a treatment group (n=20) and a control group (n=19). The treatment group received iTBS in bilateral DMPFC. The control group received the sham treatment. Negative symptoms, cognitive function, emotional state, and social function were assessed at pre-treatment, post-treatment, 4-, 8-, and 12-week follow-ups. Brain activation in regions of interest (ROIs) was evaluated through verbal fluency tasks. Changes in scale scores were analyzed by repeated measures ANOVA.

Result

After 20 sessions of iTBS, the Scale for the Assessment of Negative Symptoms (SANS) total and sub-scale scores significantly improved in the treatment group, with statistically significant differences. SANS scores differed significantly between pre- and post-treatment in both groups, with post-treatment scores markedly lower than pre-treatment and better efficacy in the treatment group. However, there was no significant difference in cognitive function, emotional state, and social function. ROIs did not differ significantly between groups before intervention. After treatment, prefrontal cortex activation was significantly higher in the treatment group than in controls, with a statistically significant difference. Regarding functional connectivity, the small-world properties Sigma and Gamma were enhanced.

Conclusion

iTBS on bilateral DMPFC can effectively alleviate negative symptoms and enhance prefrontal cortex activation and the small-world properties in patients of schizophrenia.

Adverse childhood experiences, brain efficiency, and the development of pain symptoms in youth

BACKGROUND

Adverse childhood experiences (ACEs) are often reported by youths with chronic pain, and both ACEs and chronic pain disrupt how information is processed. However, it is unknown whether changes to shared neural networks underlie the relationship between ACEs and the development of pain symptoms. This study explored the relationships between ACEs, brain efficiency, and pain symptomology in youth.

METHODS

A community sample of youths aged 14-18 years underwent MRIs, answered trauma and pain questionnaires, and underwent pain sensory testing, twice, 3 months apart (Nbaseline = 44; Nfollow-up = 42). Sensory testing determined thresholds for mechanical and thermal stimuli. Global and local network efficiencies were evaluated using graph theory. Generalized estimating equations were applied to examine whether brain efficiency moderated the relationships between ACEs, pain intensity, and pain sensitivity (i.e., mechanical detection, heat pain, and temperature change thresholds).

RESULTS

There was a significant interaction between ACEs and global brain efficiency in association with pain intensity (β = -0.31, p = 0.02) and heat pain (β = -0.29, p = 0.004). Lower global brain efficiency exacerbated the relationship between ACEs and pain intensity (θX → Y|W = -1.16 = 0.37, p = 0.05), and heat pain sensitivity (θX → Y|W = -1.30 = 0.44, p = 0.05). Higher global brain efficiency ameliorated the relationship between ACEs and pain intensity (θX → Y|W = 1.75 = -0.53, p = 0.05).

CONCLUSIONS

The relationship between ACEs and pain symptomology was comparable to chronic pain phenotypes (i.e., higher pain intensity and pain thresholds) and may vary as a function of brain efficiency in youth. This stresses the importance of assessing for pain symptoms in trauma-exposed youth, as earlier identification and intervention are critical in preventing the chronification of pain.

SIGNIFICANCE

This article explores the relationship between ACEs, pain symptomology, and brain efficiency in youth. ACEs may affect how the brain processes information, including pain. Youths with lower brain efficiencies that were exposed to more ACEs have pain symptomology comparable to youths with chronic pain. Understanding this relationship is important for the earlier identification of pain symptoms, particularly in vulnerable populations such as youths exposed to trauma, and is critical for preventing the chronification of pain.

Simulated brain networks reflecting progression of Parkinson's disease

The neurodegenerative progression of Parkinson's disease affects brain structure and function and, concomitantly, alters the topological properties of brain networks. The network alteration accompanied by motor impairment and the duration of the disease has not yet been clearly demonstrated in the disease progression. In this study, we aim to resolve this problem with a modeling approach using the reduced Jansen-Rit model applied to large-scale brain networks derived from cross-sectional MRI data. Optimizing whole-brain simulation models allows us to discover brain networks showing unexplored relationships with clinical variables. We observe that the simulated brain networks exhibit significant differences between healthy controls (n = 51) and patients with Parkinson's disease (n = 60) and strongly correlate with disease severity and disease duration of the patients. Moreover, the modeling results outperform the empirical brain networks in these clinical measures. Consequently, this study demonstrates that utilizing the simulated brain networks provides an enhanced view of network alterations in the progression of motor impairment and identifies potential biomarkers for clinical indices.

Aberrant hippocampal intrinsic morphological connectivity patterns in Neuromyelitis optica spectrum disorder with cognitive impairment: Insights from an individual-based morphological brain network

BACKGROUND

Although several clinical studies have demonstrated that hippocampus volume loss in neuromyelitis optica spectrum disorder (NMOSD) may be a significant predictor of cognition, no consensus has been reached. To investigate the alterations of the intrinsic ,hippocampal morphological networks in cognitively impaired NMOSD patients and their correlations with cognitive performance.

METHODS

38 NMOSD patients and 39 healthy controls (HC) were enrolled. NMOSD patients were categorized into two groups based on neuropsychological assessment, including the cognitively impaired (CI) group (n = 21) and the cognitively preserved (CP) group (n = 17). Brain high-resolution 3D-T1WI MR images were evaluated, and individual-based intrinsic hippocampus morphological networks were constructed. The between-group differences in global and nodal network topology profiles were estimated, and correlations between the nodal network metrics and cognitive scores were further analyzed.

RESULTS

Compared to the HC and CP groups, the CI group shows significant differences in nodal network metrics of the left hippocampal tail and left hippocampal cornu ammonis (CA) 1-body. Nodal network metrics of the left hippocampal tail were significantly correlated with neurocognitive scores across the entire NMOSD group.

CONCLUSIONS

NMOSD patients with cognitive impairment exhibit abnormal intrinsic hippocampal morphological networks. Nodal network property measurements can help identify those with cognitive impairment.

Reduced brain network segregation in alcohol use disorder: Associations with neurocognition

The human brain consists of functionally segregated networks, characterized by strong connections among regions belonging to the same network and weak connections between those of different networks. Alcohol use disorder (AUD) is associated with premature brain aging and neurocognitive impairments. Given the link between decreased brain network segregation and age-related cognitive decline, we hypothesized lower brain segregation in patients with AUD than healthy controls (HCs). Thirty AUD patients (9 females, 21 males) and 61 HCs (35 females, 26 males) underwent resting-state functional MRI (rs-fMRI), whose data were processed to assess segregation within the brain sensorimotor and association networks. We found that, compared to HCs, AUD patients had significantly lower segregation in both brain networks as well as poorer performance on a spatial working memory task. In the HC group, brain network segregation correlated negatively with age and positively with spatial working memory. Our findings suggest reduced brain network segregation in individuals with AUD that may contribute to cognitive impairment and is consistent with premature brain aging in this population.

Bio-Plausible Multimodal Learning with Emerging Neuromorphic Devices

Multimodal machine learning, as a prospective advancement in artificial intelligence, endeavors to emulate the brain's multimodal learning abilities with the objective to enhance interactions with humans. However, this approach requires simultaneous processing of diverse types of data, leading to increased model complexity, longer training times, and higher energy consumption. Multimodal neuromorphic devices have the capability to preprocess spatio-temporal information from various physical signals into unified electrical signals with high information density, thereby enabling more biologically plausible multimodal learning with low complexity and high energy-efficiency. Here, this work conducts a comparison between the expression of multimodal machine learning and multimodal neuromorphic computing, followed by an overview of the key characteristics associated with multimodal neuromorphic devices. The bio-plausible operational principles and the multimodal learning abilities of emerging devices are examined, which are classified into heterogeneous and homogeneous multimodal neuromorphic devices. Subsequently, this work provides a detailed description of the multimodal learning capabilities demonstrated by neuromorphic circuits and their respective applications. Finally, this work highlights the limitations and challenges of multimodal neuromorphic computing in order to hopefully provide insight into potential future research directions.

Functional Segregation-Integration Preference Configures the Cognitive Decline Against Cerebral Small Vessel Disease: An MRI Study

INTRODUCTION

Cerebral small vessel disease (CSVD) is highly prevalent in elder individuals, and its variable cognitive outcomes indicate some cognitive reserve mechanisms. Contribution from functional network features is still unclear. Here we explore how functional segregation-integration preference influences the cognitive changes against CSVD.

MATERIALS AND METHODS

A total of, 271 CSVD patients were included, all underwent MRI scans including routine and resting-state functional MRI (rs-fMRI). Hierarchical balance index (HB) was obtained from the rs-fMRI connectivity using eigenmode-based approach. Individuals were classified into segregated and integrated groups according to negative and positive HB. A composite CSVD lesion score was calculated from imaging findings. Global and five specific cognitive functions were assessed.

RESULTS

Hierarchical regression analysis revealed negative contribution from lesion load to global and all cognitive domains (β = -0.22~-0.35, ∆R2 = 0.046~0.112, all p < 0.001). Inclusion of HB did not show significant contribution (all p > 0.05), but interaction between HB and lesion score was significantly associated with global (β = -0.27, ∆R2 = 0.013, p = 0.034) and execution score (β = -0.34, ∆R2 = 0.023, p = 0.002). Integrated patients show significant better global cognitive (23.9 ± 3.9 vs. 25.5 ± 3.1, p = 0.044) and executive ability (0.235 ± 0.678 vs. 0.535 ± 0.688, p = 0.049) at mild damage stage, visuospatial (-0.001 ± 0.804 vs. 0.379 ± 0.249, p = 0.034) and language ability (-0.133 ± 0.849 vs. 0.218 ± 0.704, p = 0.037) at moderate damage stage. Cross-overs of cognitive scores were observed. Significant better execution (-0.277 ± 0.717 vs. -0.675 ± 0.883, p = 0.027) was found in severe damage stage for segregated patients.

CONCLUSION

Thus, we concluded that integrated network contributes to cognitive resilience in mild and moderate but not in severe damage stages.

Hierarchical Graph Convolutional Network Built by Multiscale Atlases for Brain Disorder Diagnosis Using Functional Connectivity

Functional connectivity network (FCN) data from functional magnetic resonance imaging (fMRI) is increasingly used for the diagnosis of brain disorders. However, state-of-the-art studies used to build the FCN using a single brain parcellation atlas at a certain spatial scale, which largely neglected functional interactions across different spatial scales in hierarchical manners. In this study, we propose a novel framework to perform multiscale FCN analysis for brain disorder diagnosis. We first use a set of well-defined multiscale atlases to compute multiscale FCNs. Then, we utilize biologically meaningful brain hierarchical relationships among the regions in multiscale atlases to perform nodal pooling across multiple spatial scales, namely "Atlas-guided Pooling (AP)." Accordingly, we propose a multiscale-atlases-based hierarchical graph convolutional network (MAHGCN), built on the stacked layers of graph convolution and the AP, for a comprehensive extraction of diagnostic information from multiscale FCNs. Experiments on neuroimaging data from 1792 subjects demonstrate the effectiveness of our proposed method in the diagnoses of Alzheimer's disease (AD), the prodromal stage of AD [i.e., mild cognitive impairment (MCI)], as well as autism spectrum disorder (ASD), with the accuracy of 88.9%, 78.6%, and 72.7%, respectively. All results show significant advantages of our proposed method over other competing methods. This study not only demonstrates the feasibility of brain disorder diagnosis using resting-state fMRI empowered by deep learning but also highlights that the functional interactions in the multiscale brain hierarchy are worth being explored and integrated into deep learning network architectures for a better understanding of the neuropathology of brain disorders. The codes for MAHGCN are publicly available at "https://github.com/MianxinLiu/MAHGCN-code."

Synaptic changes in psychiatric and neurological disorders: state-of-the art of in vivo imaging

Synapses are implicated in many neuropsychiatric illnesses. Here, we provide an overview of in vivo techniques to index synaptic markers in patients. Several positron emission tomography (PET) tracers for synaptic vesicle glycoprotein 2 A (SV2A) show good reliability and selectivity. We review over 50 clinical studies including over 1700 participants, and compare findings in healthy ageing and across disorders, including addiction, schizophrenia, depression, posttraumatic stress disorder, and neurodegenerative disorders, including tauopathies, Huntington's disease and α-synucleinopathies. These show lower SV2A measures in cortical brain regions across most of these disorders relative to healthy volunteers, with the most well-replicated findings in tauopathies, whilst changes in Huntington's chorea, Parkinson's disease, corticobasal degeneration and progressive supranuclear palsy are predominantly subcortical. SV2A PET measures are correlated with functional connectivity across brain networks, and a number of other measures of brain function, including glucose metabolism. However, the majority of studies found no relationship between grey matter volume measured with magnetic resonance imaging and SV2A PET measures. Cognitive dysfunction, in domains including working memory and executive function, show replicated inverse relationships with SV2A measures across diagnoses, and initial findings also suggest transdiagnostic relationships with mood and anxiety symptoms. This suggests that synaptic abnormalities could be a common pathophysiological substrate underlying cognitive and, potentially, affective symptoms. We consider limitations of evidence and future directions; highlighting the need to develop postsynaptic imaging markers and for longitudinal studies to test causal mechanisms.

Alterations of Excitation-Inhibition Balance and Brain Network Dynamics Support Sensory Deprivation Theory in Presbycusis

Sensory deprivation theory is an important hypothesis involving potential pathways between hearing loss and cognitive impairment in patients with presbycusis. The theory suggests that prolonged auditory deprivation in presbycusis, including neural deafferentation, cortical reallocation, and atrophy, causes long-lasting changes and reorganization in brain structure and function. However, neurophysiological changes underlying the cognition-ear link have not been explored. In this study, we recruited 98 presbycusis patients and 60 healthy controls and examined the differences between the two groups in gamma-aminobutyric acid (GABA) and glutamate (Glu) levels in bilateral auditory cortex, excitation-inhibition (E/I) balance (Glu/GABA ratio), dynamic functional network connectivity (dFNC), hearing ability and cognitive performance. Then, correlations with each other were investigated and variables with statistical significance were further analyzed using the PROCESS Macro in SPSS. GABA levels in right auditory cortex and Glu levels in bilateral auditory cortex were lower but E/I balance in right auditory cortex were higher in presbycusis patients compared to healthy controls. Hearing assessments and cognitive performance were worse in presbycusis patients. Three recurring connectivity states were identified after dFNC analysis: State 1 (least frequent, middle-high dFNC strength with negative functional connectivity), State 2 (high dFNC strength), and State 3 (most frequent, low dFNC strength). The occurrence and dwell time of State 3 were higher, on the other hand, the dwell time of State 2 decreased in patients with presbycusis compared to healthy controls. In patients with presbycusis, worse hearing assessments and cognition were correlated with decreased GABA levels, increased E/I balance, and aberrant dFNC, decreased GABA levels and increased E/I balance were correlated with decreased occurrence and dwell time in State 3. In the mediation model, the fractional windows, as well as dwell time in State 3, mediated the relationship between the E/I balance in right auditory cortex and episodic memory (Auditory Verbal Learning Test, AVLT) in presbycusis. Moreover, in patients with presbycusis, we found that worse hearing loss contribute to lower GABA levels, higher E/I balance, and further impact aberrant dFNC, which caused lower AVLT scores. Overall, the results suggest that a shift in E/I balance in right auditory cortex plays an important role in cognition-ear link reorganization and provide evidence for sensory deprivation theory, enhancing our understanding the connection between neurophysiological changes and cognitive impairment in presbycusis. In presbycusis patients, E/I balance may serve as a potential neuroimaging marker for exploring and predicting cognitive impairment.

Associations between the multitrajectory neuroplasticity of neuronavigated rTMS-mediated angular gyrus networks and brain gene expression in AD spectrum patients with sleep disorders

INTRODUCTION

The multifactorial influence of repetitive transcranial magnetic stimulation (rTMS) on neuroplasticity in neural networks is associated with improvements in cognitive dysfunction and sleep disorders. The mechanisms of rTMS and the transcriptional-neuronal correlation in Alzheimer's disease (AD) patients with sleep disorders have not been fully elucidated.

METHODS

Forty-six elderly participants with cognitive impairment (23 patients with low sleep quality and 23 patients with high sleep quality) underwent 4-week periods of neuronavigated rTMS of the angular gyrus and neuroimaging tests, and gene expression data for six post mortem brains were collected from another database. Transcription-neuroimaging association analysis was used to evaluate the effects on cognitive dysfunction and the underlying biological mechanisms involved.

RESULTS

Distinct variable neuroplasticity in the anterior and posterior angular gyrus networks was detected in the low sleep quality group. These interactions were associated with multiple gene pathways, and the comprehensive effects were associated with improvements in episodic memory.

DISCUSSION

Multitrajectory neuroplasticity is associated with complex biological mechanisms in AD-spectrum patients with sleep disorders.

HIGHLIGHTS

This was the first transcription-neuroimaging study to demonstrate that multitrajectory neuroplasticity in neural circuits was induced via neuronavigated rTMS, which was associated with complex gene expression in AD-spectrum patients with sleep disorders. The interactions between sleep quality and neuronavigated rTMS were coupled with multiple gene pathways and improvements in episodic memory. The present strategy for integrating neuroimaging, rTMS intervention, and genetic data provide a new approach to comprehending the biological mechanisms involved in AD.

Intrinsic prefrontal functional connectivity according to cognitive impairment in patients with end-stage renal disease

BACKGROUND

This study aimed to investigate differences in intrinsic prefrontal functional connectivity according to the presence of cognitive impairment in patients with end-stage renal disease (ESRD) using functional near-infrared spectroscopy (fNIRS).

METHODS

We prospectively enrolled 37 patients with ESRD who had been undergoing hemodialysis for more than 6 months and had no history of neurological or psychiatric disorders. All patients with ESRD underwent the Korean version of the Montreal Cognitive Assessment (MoCA-K) to assess cognitive function. The NIRSIT Lite device (OBELAB Inc.) was used to acquire fNIRS data, and the NIRSIT Lite Analysis Tool program was used to process the data and generate a functional connectivity matrix. We obtained functional connectivity measures by applying graph theory to the connectivity matrix using the BRAPH (brain analysis using graph theory) program.

RESULTS

Of the 37 patients with ESRD, 23 had cognitive impairment, whereas 14 patients showed no cognitive impairment. Intrinsic prefrontal functional connectivity was significantly different between groups. Network measures of strength, global efficiency, and mean clustering coefficient were lower in ESRD patients with cognitive impairment than in those without cognitive impairment (4.458 vs. 5.129, p = 0.02; 0.397 vs. 0.437, p = 0.03; and 0.316 vs. 0.421, p = 0.003; respectively). There were no significant correlations between MoCA-K scores and clinical characteristics.

CONCLUSION

We demonstrated a significant association between cognitive function and intrinsic prefrontal functional connectivity in patients with ESRD. ESRD patients with cognitive impairment have reduced connectivity and segregation in the prefrontal brain network compared to those without cognitive impairment.

Dietary omega-3 polyunsaturated fatty acids reduce cytochrome c oxidase in brain white matter and sensorimotor regions while increasing functional interactions between neural systems related to escape behavior in postpartum rats

Introduction

Previously, we showed that omega-3 polyunsaturated fatty acid n-3 (PUFA) supplementation improved the performance of postpartum rats in the shuttle box escape test (SBET).

Methods

The brains of these rats were used in the current study which examined brain cytochrome c oxidase (CCO) activity in white matter bundles and 39 regions spanning sensorimotor, limbic, and cognitive areas to determine the effects of n-3 PUFAs on neural metabolic capacity and network interactions.

Results

We found that n-3 PUFA supplementation decreased CCO activity in white matter bundles, deep and superficial areas within the inferior colliculus, the anterior and barrel field regions of the primary somatic sensorimotor cortex, the secondary somatic sensorimotor cortex, the lateral, anterior regions of the secondary visual cortex and the ventral posterior nucleus of the thalamus, and the medial nucleus of the amygdala. Structural equation modeling revealed that animals consuming diets without n-3 PUFAs exhibited fewer inter-regional interactions when compared to those fed diets with n-3 PUFAs. Without n-3 PUFAs, inter-regional interactions were observed between the posterior cingulate cortex and amygdala as well as among amygdala subregions. With n-3 PUFAs, more inter-regional interactions were observed, particularly between regions associated with fear memory processing and escape. Correlations between regional CCO activity and SBET behavior were observed in rats lacking dietary n-3 PUFAs but not in those supplemented with these nutrients.

Discussion

In conclusion, consumption of n-3 PUFAs results in reduced CCO activity in white matter bundles and sensorimotor regions, reflecting more efficient neurotransmission, and an increase in inter-regional interactions, facilitating escape from footshock.

Measuring the dynamic balance of integration and segregation underlying consciousness, anesthesia, and sleep in humans

Consciousness requires a dynamic balance of integration and segregation in brain networks. We report an fMRI-based metric, the integration-segregation difference (ISD), which captures two key network properties: network efficiency (integration) and clustering (segregation). With this metric, we quantify brain state transitions from conscious wakefulness to unresponsiveness induced by the anesthetic propofol. The observed changes in ISD suggest a profound shift towards the segregation of brain networks during anesthesia. A common unimodal-transmodal sequence of disintegration and reintegration occurs in brain networks during, respectively, loss and return of responsiveness. Machine learning models using integration and segregation data accurately identify awake vs. unresponsive states and their transitions. Metastability (dynamic recurrence of non-equilibrium transient states) is more effectively explained by integration, while complexity (diversity of neural activity) is more closely linked with segregation. A parallel analysis of sleep states produces similar findings. Our results demonstrate that the ISD reliably indexes states of consciousness.

Enhanced efficiency in the bilingual brain through the inter-hemispheric cortico-cerebellar pathway in early second language acquisition

Bilingualism has a profound impact on the structure and function of the brain, but it is not yet well understood how this experience influences brain functional organization. We examine a large sample (151 participants) of monolinguals and bilinguals with varied age of second language acquisition, who underwent resting-state functional magnetic brain imaging. Whole-brain network analyses reveal higher global efficiency in bilingual individuals than monolinguals, indicating enhanced functional integration in the bilingual brain. Moreover, the age at which the second language was acquired correlated with this increased efficiency, suggesting that earlier exposure to a second language has lasting positive effects on brain functional organization. Further investigation using the network-based statistics approach indicates that this effect is primarily driven by heightened functional connectivity between association networks and the cerebellum. These findings show that the timing of bilingual learning experience alters the brain functional organization at both global and local levels.

Time-varying EEG networks of major depressive disorder during facial emotion tasks

Depression is a mental disease involved in emotional and cognitive impairments. Neuroimaging studies have found abnormalities in the structure and functional network of brain for major depressive disorder (MDD).However, neural mechanism of the dynamic connectivity for emotional attention of MDD is currently insufficient. In this study, event-related potentials (ERP) and time-varying network were analyzed to investigate attention bias and corresponding neural mechanisms induced by emotional facial stimuli. In the ERP results, N100 components in MDD had shorter latencies and smaller amplitudes than those in healthy controls (HC) for sad and fear faces. The P200 amplitudes induced by sad faces in MDD were significantly higher than those induced by happy and fear faces in MDD, and those induced by sad faces in HC. It was indicated that MDD patients had attention bias towards sad faces. For the time-varying network analysis, adaptive directed transfer function was explored to construct dynamic network connectivity. MDD patients had stronger information outflow from the right frontal region and weaker information outflow from parieto-occipital regions for sad faces. In addition, the network properties of sad faces were significantly correlated with PHQ-9 scores for MDD group. These findings may provide further explanation for understanding the MDD's neural mechanism of attention bias during facial emotional tasks.

Genetic fingerprinting with heritable phenotypes of the resting-state brain network topology

Cognitive, behavioral, and disease traits are influenced by both genetic and environmental factors. Individual differences in these traits have been associated with graph theoretical properties of resting-state networks, indicating that variations in connectome topology may be driven by genetics. In this study, we establish the heritability of global and local graph properties of resting-state networks derived from functional MRI (fMRI) and magnetoencephalography (MEG) using a large sample of twins and non-twin siblings from the Human Connectome Project. We examine the heritability of MEG in the source space, providing a more accurate estimate of genetic influences on electrophysiological networks. Our findings show that most graph measures are more heritable for MEG compared to fMRI and the heritability for MEG is greater for amplitude compared to phase synchrony in the delta, high beta, and gamma frequency bands. This suggests that the fast neuronal dynamics in MEG offer unique insights into the genetic basis of brain network organization. Furthermore, we demonstrate that brain network features can serve as genetic fingerprints to accurately identify pairs of identical twins within a cohort. These results highlight novel opportunities to relate individual connectome signatures to genetic mechanisms underlying brain function.

Geometric Scaling Law in Real Neuronal Networks

We investigate the synapse-resolution connectomes of fruit flies across different developmental stages, revealing a consistent scaling law in neuronal connection probability relative to spatial distance. This power-law behavior significantly differs from the exponential distance rule previously observed in coarse-grained brain networks. We demonstrate that the geometric scaling law carries functional significance, aligning with the maximum entropy of information communication and the functional criticality balancing integration and segregation. Perturbing either the empirical probability model's parameters or its type results in the loss of these advantageous properties. Furthermore, we derive an explicit quantitative predictor for neuronal connectivity, incorporating only interneuronal distance and neurons' in and out degrees. Our findings establish a direct link between brain geometry and topology, shedding lights on the understanding of how the brain operates optimally within its confined space.

Subthreshold amyloid deposition, cerebral small vessel disease, and functional brain network disruption in delayed cognitive decline after stroke

Background

Although its incidence is relatively low, delayed-onset post-stroke cognitive decline (PSCD) may offer valuable insights into the "vascular contributions to cognitive impairment and dementia," particularly concerning the roles of vascular and neurodegenerative mechanisms. We postulated that the functional segregation observed during post-stroke compensation could be disrupted by underlying amyloid pathology or cerebral small vessel disease (cSVD), leading to delayed-onset PSCD.

Methods

Using a prospective stroke registry, we identified patients who displayed normal cognitive function at baseline evaluation within a year post-stroke and received at least one subsequent assessment. Patients suspected of pre-stroke cognitive decline were excluded. Decliners [defined by a decrease of ≥3 Mini-Mental State Examination (MMSE) points annually or an absolute drop of ≥5 points between evaluations, confirmed with detailed neuropsychological tests] were compared with age- and stroke severity-matched non-decliners. Index-stroke MRI, resting-state functional MRI, and 18F-florbetaben PET were used to identify cSVD, functional network attributes, and amyloid deposits, respectively. PET data from age-, sex-, education-, and apolipoprotein E-matched stroke-free controls within a community-dwelling cohort were used to benchmark amyloid deposition.

Results

Among 208 eligible patients, 11 decliners and 10 matched non-decliners were identified over an average follow-up of 5.7 years. No significant differences in cSVD markers were noted between the groups, except for white matter hyperintensities (WMHs), which were strongly linked with MMSE scores among decliners (rho = -0.85, p < 0.01). Only one decliner was amyloid-positive, yet subthreshold PET standardized uptake value ratios (SUVR) in amyloid-negative decliners inversely correlated with final MMSE scores (rho = -0.67, p = 0.04). Decliners exhibited disrupted modular structures and more intermingled canonical networks compared to non-decliners. Notably, the somato-motor network's system segregation corresponded with the decliners' final MMSE (rho = 0.67, p = 0.03) and was associated with WMH volume and amyloid SUVR.

Conclusion

Disruptions in modular structures, system segregation, and inter-network communication in the brain may be the pathophysiological underpinnings of delayed-onset PSCD. WMHs and subthreshold amyloid deposition could contribute to these disruptions in functional brain networks. Given the limited number of patients and potential residual confounding, our results should be considered hypothesis-generating and need replication in larger cohorts in the future.

Stable individual differences from dynamic patterns of function: brain network flexibility predicts openness/intellect, intelligence, and psychoticism

A growing understanding of the nature of brain function has led to increased interest in interpreting the properties of large-scale brain networks. Methodological advances in network neuroscience provide means to decompose these networks into smaller functional communities and measure how they reconfigure over time as an index of their dynamic and flexible properties. Recent evidence has identified associations between flexibility and a variety of traits pertaining to complex cognition including creativity and working memory. The present study used measures of dynamic resting-state functional connectivity in data from the Human Connectome Project (n = 994) to test associations with Openness/Intellect, general intelligence, and psychoticism, three traits that involve flexible cognition. Using a machine-learning cross-validation approach, we identified reliable associations of intelligence with cohesive flexibility of parcels in large communities across the cortex, of psychoticism with disjoint flexibility, and of Openness/Intellect with overall flexibility among parcels in smaller communities. These findings are reasonably consistent with previous theories of the neural correlates of these traits and help to expand on previous associations of behavior with dynamic functional connectivity, in the context of broad personality dimensions.

Task-specific topology of brain networks supporting working memory and inhibition

Network neuroscience explores the brain's connectome, demonstrating that dynamic neural networks support cognitive functions. This study investigates how distinct cognitive abilities-working memory and cognitive inhibitory control-are supported by unique brain network configurations constructed by estimating whole-brain networks using mutual information. The study involved 195 participants who completed the Sternberg Item Recognition task and Flanker tasks while undergoing electroencephalography recording. A mixed-effects linear model analyzed the influence of network metrics on cognitive performance, considering individual differences and task-specific dynamics. The findings indicate that working memory and cognitive inhibitory control are associated with different network attributes, with working memory relying on distributed networks and cognitive inhibitory control on more segregated ones. Our analysis suggests that both strong and weak connections contribute to cognitive processes, with weak connections potentially leading to a more stable and support networks of memory and cognitive inhibitory control. The findings indirectly support the network neuroscience theory of intelligence, suggesting different functional topology of networks inherent to various cognitive functions. Nevertheless, we propose that understanding individual variations in cognitive abilities requires recognizing both shared and unique processes within the brain's network dynamics.

The long-term intensive gymnastic training influences functional stability and integration: A resting-state fMRI study

INTRODUCTION

Long-term motor skill training has been shown to induce anatomical and functional neuroplasticity. World class gymnasts (WCGs) provide a unique opportunity to investigate the effect of long-term intensive training on neuroplasticity. Previous resting-state fMRI studies have demonstrated a high efficient information processing related to motor and cognitive functions in gymnasts compared with healthy controls (HCs). However, most research treated brain signals as static, overlooking the fact that the brain is a complex and dynamic system. In this study, we employed functional stability, a new metric based on dynamic functional connectivity (FC), to examine the impact of long-term intensive training on the functional architecture in the WCGs.

METHODS

We first conducted a voxel-wise analysis of functional stability between the WCGs and HCs. Then, we applied FC density (FCD) to explore whether regions with modified functional stability were also accompanied by changes in connection patterns in the WCGs. We identified overlapping regions showing significant differences in both functional stability and FCD. Finally, we applied seed-based correlation analysis (SCA) to determine the detailed changes in connection patterns between the WCGs and HCs within these overlapping regions.

RESULTS

Compared with the HCs, the WCGs exhibited higher functional stability in the bilateral angular gyrus (AG), bilateral inferior temporal gyrus (ITG), bilateral precentral gyrus, and right superior frontal gyrus and lower functional stability in the bilateral hippocampus, bilateral caudate, right rolandic operculum, left superior temporal gyrus, right middle frontal gyrus, right middle cingular cortex, and right precuneus than the HCs. We found that the bilateral AG and ITG not only showed higher functional stability but also increased global and long-range FCD in the WCGs relative to the HCs. The right precuneus displayed lower functional stability as well as decreased local, long-range, and global FCD in the WCGs. Both AG and ITG showed higher FC with regions in the default mode network (DMN) in the WCGs than in the HCs.

CONCLUSIONS

The increased functional stability in the AG and ITG might be associated with enhanced functional integration within the DMN in the WCGs. These findings may offer new spatiotemporal evidence for the impact of long-term intensive training on neuroplasticity.

Emergence of metastability in frustrated oscillatory networks: the key role of hierarchical modularity

Oscillatory complex networks in the metastable regime have been used to study the emergence of integrated and segregated activity in the brain, which are hypothesised to be fundamental for cognition. Yet, the parameters and the underlying mechanisms necessary to achieve the metastable regime are hard to identify, often relying on maximising the correlation with empirical functional connectivity dynamics. Here, we propose and show that the brain's hierarchically modular mesoscale structure alone can give rise to robust metastable dynamics and (metastable) chimera states in the presence of phase frustration. We construct unweighted 3-layer hierarchical networks of identical Kuramoto-Sakaguchi oscillators, parameterized by the average degree of the network and a structural parameter determining the ratio of connections between and within blocks in the upper two layers. Together, these parameters affect the characteristic timescales of the system. Away from the critical synchronization point, we detect the emergence of metastable states in the lowest hierarchical layer coexisting with chimera and metastable states in the upper layers. Using the Laplacian renormalization group flow approach, we uncover two distinct pathways towards achieving the metastable regimes detected in these distinct layers. In the upper layers, we show how the symmetry-breaking states depend on the slow eigenmodes of the system. In the lowest layer instead, metastable dynamics can be achieved as the separation of timescales between layers reaches a critical threshold. Our results show an explicit relationship between metastability, chimera states, and the eigenmodes of the system, bridging the gap between harmonic based studies of empirical data and oscillatory models.

Cortical network reconfiguration aligns with shifts of basal ganglia and cerebellar influence

Mammalian functional architecture flexibly adapts, transitioning from integration where information is distributed across the cortex, to segregation where information is focal in densely connected communities of brain regions. This flexibility in cortical brain networks is hypothesized to be driven by control signals originating from subcortical pathways, with the basal ganglia shifting the cortex towards integrated processing states and the cerebellum towards segregated states. In a sample of healthy human participants (N=242), we used fMRI to measure temporal variation in global brain networks while participants performed two tasks with similar cognitive demands (Stroop and Multi-Source Inference Task (MSIT)). Using the modularity index, we determined cortical networks shifted from integration (low modularity) at rest to high modularity during easier i.e. congruent (segregation). Increased task difficulty (incongruent) resulted in lower modularity in comparison to the easier counterpart indicating more integration of the cortical network. Influence of basal ganglia and cerebellum was measured using eigenvector centrality. Results correlated with decreases and increases in cortical modularity respectively, with only the basal ganglia influence preceding cortical integration. Our results support the theory the basal ganglia shifts cortical networks to integrated states due to environmental demand. Cerebellar influence correlates with shifts to segregated cortical states, though may not play a causal role.

SiC@NiO Core-Shell Nanowire Networks-Based Optoelectronic Synapses for Neuromorphic Computing and Visual Systems at High Temperature

1D nanowire networks, sharing similarities of structure, information transfer, and computation with biological neural networks, have emerged as a promising platform for neuromorphic systems. Based on brain-like structures of 1D nanowire networks, neuromorphic synaptic devices can overcome the von Neumann bottleneck, achieving intelligent high-efficient sensing and computing function with high information processing rates and low power consumption. Here, high-temperature neuromorphic synaptic devices based on SiC@NiO core-shell nanowire networks optoelectronic memristors (NNOMs) are developed. Experimental results demonstrate that NNOMs attain synaptic short/long-term plasticity and modulation plasticity under both electrical and optical stimulation, and exhibit advanced functions such as short/long-term memory and "learning-forgetting-relearning" under optical stimulation at both room temperature and 200 °C. Based on the advanced functions under light stimulus, the constructed 5 × 3 optoelectronic synaptic array devices exhibit a stable visual memory function up to 200 °C, which can be utilized to develop artificial visual systems. Additionally, when exposed to multiple electronic or optical stimuli, the NNOMs effectively replicate the principles of Pavlovian classical conditioning, achieving visual heterologous synaptic functionality and refining neural networks. Overall, with abundant synaptic characteristics and high-temperature thermal stability, these neuromorphic synaptic devices offer a promising route for advancing neuromorphic computing and visual systems.

Emergence and reconfiguration of modular structure for artificial neural networks during continual familiarity detection

Advances in artificial intelligence enable neural networks to learn a wide variety of tasks, yet our understanding of the learning dynamics of these networks remains limited. Here, we study the temporal dynamics during learning of Hebbian feedforward neural networks in tasks of continual familiarity detection. Drawing inspiration from network neuroscience, we examine the network's dynamic reconfiguration, focusing on how network modules evolve throughout learning. Through a comprehensive assessment involving metrics like network accuracy, modular flexibility, and distribution entropy across diverse learning modes, our approach reveals various previously unknown patterns of network reconfiguration. We find that the emergence of network modularity is a salient predictor of performance and that modularization strengthens with increasing flexibility throughout learning. These insights not only elucidate the nuanced interplay of network modularity, accuracy, and learning dynamics but also bridge our understanding of learning in artificial and biological agents.

Distinct global brain connectivity alterations in depressed adolescents with subthreshold mania and the relationship with processing speed: Evidence from sBEAD Cohort

BACKGROUND

Bipolar disorder (BD) is a progressive condition. Investigating the neuroimaging mechanisms in depressed adolescents with subthreshold mania (SubMD) facilitates the early identification of BD. However, the global brain connectivity (GBC) patterns in SubMD patients, as well as the relationship with processing speed before the onset of full-blown BD, remain unclear.

METHODS

The study involved 72 SubMD, 77 depressed adolescents without subthreshold mania (nSubMD), and 69 gender- and age-matched healthy adolescents (HCs). All patients underwent a clinical follow-up ranging from six to twelve months. We calculated the voxel-based graph theory analysis of the GBC map and conducted the TMT-A test to measure the processing speed.

RESULTS

Compared to HCs and nSubMD, SubMD patients displayed distinctive GBC index patterns: GBC index decreased in the right Medial Superior Frontal Gyrus (SFGmed.R)/Superior Frontal Gyrus (SFG) while increased in the right Precuneus and left Postcentral Gyrus. Both patient groups showed increased GBC index in the right Inferior Temporal Gyrus. An increased GBC value in the right Supplementary Motor Area was exclusively observed in the nSubMD-group. There were opposite changes in the GBC index in SFGmed.R/SFG between two patient groups, with an AUC of 0.727. Additionally, GBC values in SFGmed.R/SFG exhibited a positive correlation with TMT-A scores in SubMD-group.

LIMITATIONS

Relatively shorter follow-up duration, medications confounding, and modest sample size.

CONCLUSION

These findings suggest that adolescents with subthreshold BD have specific impairments patterns at the whole brain connectivity level associated with processing speed impairments, providing insights into early identification and intervention strategies for BD.

Multi-hierarchy Network Configuration Can Predict Brain States and Performance

The brain is a hierarchical modular organization that varies across functional states. Network configuration can better reveal network organization patterns. However, the multi-hierarchy network configuration remains unknown. Here, we propose an eigenmodal decomposition approach to detect modules at multi-hierarchy, which can identify higher-layer potential submodules and is consistent with the brain hierarchical structure. We defined three metrics: node configuration matrix, combinability, and separability. Node configuration matrix represents network configuration changes between layers. Separability reflects network configuration from global to local, whereas combinability shows network configuration from local to global. First, we created a random network to verify the feasibility of the method. Results show that separability of real networks is larger than that of random networks, whereas combinability is smaller than random networks. Then, we analyzed a large data set incorporating fMRI data from resting and seven distinct tasking conditions. Experiment results demonstrates the high similarity in node configuration matrices for different task conditions, whereas the tasking states have less separability and greater combinability between modules compared with the resting state. Furthermore, the ability of brain network configuration can predict brain states and cognition performance. Crucially, derived from tasks are highlighted with greater power than resting, showing that task-induced attributes have a greater ability to reveal individual differences. Together, our study provides novel perspectives for analyzing the organization structure of complex brain networks at multi-hierarchy, gives new insights to further unravel the working mechanisms of the brain, and adds new evidence for tasking states to better characterize and predict behavioral traits.

Neural interactions in the human frontal cortex dissociate reward and punishment learning

How human prefrontal and insular regions interact while maximizing rewards and minimizing punishments is unknown. Capitalizing on human intracranial recordings, we demonstrate that the functional specificity toward reward or punishment learning is better disentangled by interactions compared to local representations. Prefrontal and insular cortices display non-selective neural populations to rewards and punishments. Non-selective responses, however, give rise to context-specific interareal interactions. We identify a reward subsystem with redundant interactions between the orbitofrontal and ventromedial prefrontal cortices, with a driving role of the latter. In addition, we find a punishment subsystem with redundant interactions between the insular and dorsolateral cortices, with a driving role of the insula. Finally, switching between reward and punishment learning is mediated by synergistic interactions between the two subsystems. These results provide a unifying explanation of distributed cortical representations and interactions supporting reward and punishment learning.

Large-Scale Cortical Network Analysis and Classification of MI-BCI Tasks Based on Bayesian Nonnegative Matrix Factorization

Motor imagery (MI) is a high-level cognitive process that has been widely applied to clinical rehabilitation and brain-computer interfaces (BCIs). However, the decoding of MI tasks still faces challenges, and the neural mechanisms underlying its application are unclear, which seriously hinders the development of MI-based clinical applications and BCIs. Here, we combined EEG source reconstruction and Bayesian nonnegative matrix factorization (NMF) methods to construct large-scale cortical networks of left-hand and right-hand MI tasks. Compared to right-hand MI, the results showed that the significantly increased functional network connectivities (FNCs) mainly located among the visual network (VN), sensorimotor network (SMN), right temporal network, right central executive network, and right parietal network in the left-hand MI at the β (13-30Hz) and all (8-30Hz) frequency bands. For the network properties analysis, we found that the clustering coefficient, global efficiency, and local efficiency were significantly increased and characteristic path length was significantly decreased in left-hand MI compared to right-hand MI at the β and all frequency bands. These network pattern differences indicated that the left-hand MI may need more modulation of multiple large-scale networks (i.e., VN and SMN) mainly located in the right hemisphere. Finally, based on the spatial pattern network of FNC and network properties, we propose a classification model. The proposed model achieves a top classification accuracy of 78.2% in cross-subject two-class MI-BCI tasks. Overall, our findings provide new insights into the neural mechanisms of MI and a potential network biomarker to identify MI-BCI tasks.

Verbal semantic expertise is associated with reduced functional connectivity between left and right anterior temporal lobes

The left and right anterior temporal lobes (ATLs) encode semantic representations. They show graded hemispheric specialization in function, with the left ATL contributing preferentially to verbal semantic processing. We investigated the cognitive correlates of this organization, using resting-state functional connectivity as a measure of functional segregation between ATLs. We analyzed two independent resting-state fMRI datasets (n = 86 and n = 642) in which participants' verbal semantic expertise was measured using vocabulary tests. In both datasets, people with more advanced verbal semantic knowledge showed weaker functional connectivity between left and right ventral ATLs. This effect was highly specific. It was not observed for within-hemisphere connections between semantic regions (ventral ATL and inferior frontal gyrus (IFG), though it was found for left-right IFG connectivity in one dataset). Effects were not found for tasks probing semantic control, nonsemantic cognition, or face recognition. Our results suggest that hemispheric specialization in the ATLs is not an innate property but rather emerges as people develop highly detailed verbal semantic representations. We speculate that this effect is a consequence of the left ATL's greater connectivity with left-lateralized written word recognition regions, which causes it to preferentially represent meaning for advanced vocabulary acquired primarily through reading.