Functional Connectivity Patterns Research Articles

Common and distinct neural underpinnings of the association between childhood maltreatment and depression and aggressive behavior

BackgroundAlthough childhood maltreatment (CM) is widely recognized as a transdiagnostic risk factor for various internalizing and externalizing psychological disorders, the neural basis underlying this association remain unclear. The potential reasons for the inconsistent findings may be attributed to the involvement of both common and specific neural pathways that mediate the influence of childhood maltreatment on the emergence of psychopathological conditions.MethodsThis study aimed to delineate both the common and distinct neural pathways linking childhood maltreatment to depression and aggression. First, we employed Network-Based Statistics (NBS) on resting-state functional magnetic resonance imaging (fMRI) data to identify functional connectivity (FC) patterns associated with depression and aggression. Mediation analyses were then conducted to assess the role of these FC patterns in the relationship between childhood maltreatment and each outcome.ResultsThe results demonstrated that FC within the default mode network (DMN) and between the cingulo-opercular network (CON) and dorsal attention network (DAN) mediated the association between childhood maltreatment and aggression, whereas FC within the reward system and between the CON and the reward system mediated the link between childhood maltreatment and depression.ConclusionsWe speculate that the control system may serve as a transdiagnostic neural basis accounting for the sequela of childhood maltreatment, and the attention network and the reward network may act as specific neural basis linking childhood maltreatment to depression and aggression, respectively.

Should you hold onto the treadmill handrails or not? Cortical evidence at different walking speeds

BackgroundTreadmill-based gait training is part of rehabilitation programs focused on walking abilities. The use of handrails embedded in treadmill systems is debated, and current literature only explores the issue from a behavioral perspective.MethodsWe examined the cortical correlates of treadmill walking in healthy participants using functional near-infrared spectroscopy. We investigated whether the utilization of treadmill handrails at varying walking speeds could affect cortical activation associated with the task, and we evaluated potential differences in task-based functional connectivity across the various walking conditions.ResultsSignificant differences in cortical activation were found between the two walking speeds (3 and 5 km/h) in the unsupported condition; these differences were reduced when using the handrails. Specifically, cortical activation was significantly higher when the participants swung their arms freely while walking at a speed of 5 compared to 3 km/h in several Brodmann’s Areas (BA): left BA10, BA3 and BA39, and right BA10, BA9, BA8, BA3, and BA40. No significant differences were found when participants were holding onto the handrails. A significant difference was found in the left BA40 between the two speeds, regardless of whether the participants were holding onto the handrails. Furthermore, at the higher speed and without the use of handrails, a wider pattern of task-based functional connectivity was observed, with significantly stronger connectivity between the left BA10 and BA40.ConclusionsWe suggest that speed and handrails use play a role in walking cortical activity patterns, therefore they are key ingredients to take into account when planning a rehabilitation program.

EEG-based functional and effective connectivity patterns during emotional episodes using graph theoretical analysis

Understanding the neural mechanisms underlying emotional processing is critical for advancing neuroscience and mental health interventions. This study examined these mechanisms by analyzing EEG connectivity patterns across different brain regions while participants evoked various emotions. After applying independent component analysis (ICA) to eliminate non-cortical activity, we assessed frequency-specific connectivity patterns using coherence, Granger causality, and graph theoretical measures to evaluate both functional and effective connectivity. Graph theoretical analysis revealed significant differences in connectivity between emotions across multiple frequency bands, particularly in the delta and beta bands. These results indicated modulations in key brain regions, such as the precentral, superior frontal, and temporal areas, suggesting that these regions play a crucial role in emotional processing. Coherence analysis demonstrated predominant alpha band activity across all emotions, with specific emotional states, such as fear, grief, and jealousy, exhibiting enhanced beta band activity. In terms of coherence strength, we observed that the gamma band was largely inactive, except for the emotion of sadness, which displayed significant activity in the right lobe, particularly in regions such as the supplementary motor area and the lingual gyrus. Additionally, Granger causality analysis highlighted that the beta and gamma bands were dominant across all emotional states, with minimal modulation observed in the theta band. Clustering coefficients from the graph analysis further revealed distinct patterns of connectivity in the delta and beta bands, with significant variations across different emotions, particularly in the temporal and frontal regions. These findings enhance our understanding of emotional processing and have practical applications in mental health, biomarker identification, and human-computer interaction.

Parosmia: Pathophysiology and Management.

Parosmia is a qualitative olfactory disorder in which there is a mismatch between the memory of an odor and the actual experience triggered by an odor. There has been a surge in parosmia-related publications since the COVID-19 pandemic. This review summarizes the latest clinical findings, theories on pathophysiology and potential treatment options. Potential models of parosmia include peripheral or central hypotheses, which refer to aberrancies in olfactory neuron regeneration or information processing in central olfactory centers respectively. This leads to an incomplete or disorganized pattern of olfactory information relay. Studies using gas chromatography and functional magnetic resonance imaging have identified molecular triggers and intracranial functional connectivity patterns in parosmia respectively. Parosmia tends to occur in a delayed fashion after virus-induced anosmia. It may run a protracted course, but typically improves over time. Currently there are no generally approved, objective ways to ascertain the presence and measure the extent of parosmia. Evidence-based treatment for parosmia remains elusive. In some people, this can lead to health and quality of life issues.

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

Background and purposeAsymptomatic 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.MethodsWe 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.ResultsACS 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.ConclusionsThese 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.

Dialogue mechanisms between astrocytic and neuronal networks: A whole-brain modelling approach.

Astrocytes critically shape whole-brain structure and function by forming extensive gap junctional networks that intimately and actively interact with neurons. Despite their importance, existing computational models of whole-brain activity ignore the roles of astrocytes while primarily focusing on neurons. Addressing this oversight, we introduce a biophysical neural mass network model, designed to capture the dynamic interplay between astrocytes and neurons via glutamatergic and GABAergic transmission pathways. This network model proposes that neural dynamics are constrained by a two-layered structural network interconnecting both astrocytic and neuronal populations, allowing us to investigate astrocytes' modulatory influences on whole-brain activity and emerging functional connectivity patterns. By developing a simulation methodology, informed by bifurcation and multilayer network theories, we demonstrate that the dialogue between astrocytic and neuronal networks manifests over fast-slow fluctuation mechanisms as well as through phase-amplitude connectivity processes. The findings from our research represent a significant leap forward in the modeling of glial-neuronal collaboration, promising deeper insights into their collaborative roles across health and disease states.

Altered brain functional connectivity in patients with tension-type headache.

To evaluate whether patients with tension-type headache (TTH) exhibit abnormal brain functional connectivity compared to healthy controls. TTH is one of the most prevalent headache disorders throughout the world. The present study delves into brain functional connectivity in patients with TTH to enhance the understanding of its underlying pathophysiology. A cross-sectional study was conducted, enrolling patients with TTH diagnosed in line with the International Classification of Headache Disorders, 3rd edition beta criteria and a cohort of healthy controls (HCs). We used four metrics-global brain functional connectivity, functional connectivity, Granger causality analysis, and dynamic functional connectivity-to evaluate alterations of functional connectivity patterns in patients with TTH from both static and dynamic perspectives. Furthermore, correlational analyses were performed to explore the relationships between abnormal brain activities and clinical characteristics. A total of 33 patients with TTH (mean age = 42.3; 13 males/20 females) and 30 HCs (mean age = 37.1; 13 males/17 females) were included in the current study. Compared to HCs, patients with TTH showed altered global brain functional connectivity in the right dorsolateral superior frontal gyrus (SFGdor, t = 4.60). Abnormal functional connectivity was also detected between the right SFGdor and the right superior temporal gyrus (t = 4.56). Furthermore, the right SFGdor exhibited altered information flow with several brain regions, including the left precuneus (t = 5.16), right middle temporal gyrus (MTG, t = 4.72/-4.41), right inferior temporal gyrus (t = 4.64), right caudate nucleus (t = 4.09), and right thalamus (THA, t = -4.04). In terms of dynamic functional connectivity, disconnection was observed between the right SFGdor and the right MTG (t = -3.10), right Rolandic operculum (ROL, t = 3.60), left opercular inferior frontal gyrus (t = -3.48), and left medial superior frontal gyrus (t = -3.00). In addition, the correlation analyses revealed that activities in the MTG (r = 0.48), THA (r = -0.38), and ROL (r = 0.36) were significantly correlated with disease duration, while THA activity was associated with Visual Analogue Scale scores (r = 0.50). This study revealed alterations in both static and dynamic brain functional connectivity in patients with TTH within regions implicated in sensory perception, emotional processing, cognition, and pain regulation. These results may promote the understanding of the neural networks involved in TTH and potentially inform future therapeutic approaches for the condition.

Aberrant functional connectivity patterns in the pregenual anterior cingulate cortex and anterior midcingulate cortex of patients with irritable bowel syndrome accompanied by depressive symptoms.

Irritable bowel syndrome (IBS) is a common brain-gut disorder often accompanied by depressive symptoms, with atrophy and hyperactivity of the anterior cingulate gyrus (ACC) being key drivers of both IBS and its psychiatric comorbidities. This study aimed to investigate the functional connectivity (FC) patterns of pregenual ACC (pgACC) and anterior midcingulate cortex (aMCC) in IBS patients with depressive symptoms (DEP-IBS). A whole-brain FC analysis was conducted using pgACC and aMCC as regions of interest in three groups: 28 DEP-IBS patients, 21 IBS patients without depressive symptoms (nDEP-IBS), and 36 matched healthy controls (HCs). Partial correlation and mediation analyses were performed between abnormal FC and clinical symptoms. The ability of aberrant FC to identify IBS and its psychiatric comorbidity was evaluated using receiver operating characteristic (ROC) curve. DEP-IBS patients exhibited increased pgACC-related FC in the left medial prefrontal cortex (mPFC) and aMCC-related FC in the right middle frontal gyrus, angular gyrus and cerebellum, while showing decreased aMCC-related FC in the right precentral gyrus, superior parietal gyrus and precuneus. Both patient groups demonstrated increased FC between aMCC and left dorsolateral prefrontal cortex (dlPFC), effectively distinguishing them from HCs (AUC = 0.755). The FC between pgACC and left mPFC partially mediated the relationship between gastrointestinal and depressive symptoms, effectively distinguishing DEP-IBS from nDEP-IBS patients (AUC = 0.808). Aberrant FC within the emotional arousal network may serve as a neurobiological marker for IBS with comorbid depression. Furthermore, abnormal FC between the aMCC and the dlPFC may underlie the neural mechanism of IBS.

Patterns of Cerebellar-Cortical Structural Covariance Mirror Anatomical Connectivity of Sensorimotor and Cognitive Networks.

The cortex and cerebellum are densely connected through reciprocal input/output projections that form segregated circuits. These circuits are shown to differentially connect anterior lobules of the cerebellum to sensorimotor regions, and lobules Crus I and II to prefrontal regions. This differential connectivity pattern leads to the hypothesis that individual differences in structure should be related, especially for connected regions. To test this hypothesis, we examined covariation between the volumes of anterior sensorimotor and lateral cognitive lobules of the cerebellum and measures of cortical thickness (CT) and surface area (SA) across the whole brain in a sample of 270 young adults drawn from the HCP dataset. We observed that patterns of cerebellar-cortical covariance differed between sensorimotor and cognitive networks. Anterior motor lobules of the cerebellum showed greater covariance with sensorimotor regions of the cortex, while lobules Crus I and Crus II showed greater covariance with frontal and temporal regions. Interestingly, cerebellar volume showed predominantly negative relationships with CT and predominantly positive relationships with SA. Individual differences in SA are thought to be largely under genetic control while CT is thought to be more malleable by experience. This suggests that cerebellar-cortical covariation for SA may be a more stable feature, whereas covariation for CT may be more affected by development. Additionally, similarity metrics revealed that the pattern of covariance showed a gradual transition between sensorimotor and cognitive lobules, consistent with evidence of functional gradients within the cerebellum. Taken together, these findings are consistent with known patterns of structural and functional connectivity between the cerebellum and cortex. They also shed new light on possibly differing relationships between cerebellar volume and cortical thickness and surface area. Finally, our findings are consistent with the interactive specialization framework which proposes that structurally and functionally connected brain regions develop in concert.

Sex differences in resting-state fMRI functional connectivity related to humor styles.

Research on how functional connectivity (FC) during resting-state relates to humor styles and sex is limited. This study aimed to address this knowledge gap by analyzing resting-state fMRI data from 56 healthy participants and measuring FC. In addition, participants completed the Humor Styles Questionnaire. We found distinct FC patterns linked to humor styles that differed by sex. Men demonstrated stronger FC between the anterior cingulate cortex (ACC) and the right anterior insula (rAI), right inferior frontal gyrus (rIFG) and right frontal pole (rFP), and between the right rostral prefrontal cortex (rRPFC) and rIFG. These patterns were associated with aggressive and self-enhancing humor. Conversely, women exhibited stronger FC between rRPFC and the posterior cingulate gyrus (PCC), left rostral prefrontal cortex (lRPFC) and right thalamus, correlating with self-defeating and less aggressive humor. These findings suggest a neurobiological basis for sex differences in humor, indicating that men's FC between the salience network (SN), particularly in cognitive monitoring regions, may be linked to aggressive humor. Their FC between the executive control network (ECN) and between the SN and ECN are related to self-enhancing humor and reflect an emphasis on executive function. Conversely, women's FC between the SN and default mode network is correlated with self-defeating humor, suggesting a stronger focus on self-reflection and social relationships.

The brain selectively allocates energy to functional brain networks under cognitive control

Abstract Network energy has been conceptualized based on structural balance theory in the physics of complex networks. We utilized this framework to assess the energy of functional brain networks under cognitive control and to understand how energy is allocated across canonical functional networks during various cognitive control tasks. We extracted network energy from functional connectivity patterns of subjects who underwent fMRI scans during cognitive tasks involving working memory, inhibitory control, and cognitive flexibility, in addition to task-free scans. We found that the energy of the whole-brain network increases when exposed to cognitive control tasks compared to the task-free resting state, which serves as a reference point. The brain selectively allocates this elevated energy to canonical functional networks; sensory networks receive more energy to support flexibility for processing sensory stimuli, while cognitive networks relevant to the task, functioning efficiently, require less energy. Furthermore, employing network energy, as a global network measure, improves the performance of predictive modeling, particularly in classifying cognitive control tasks and predicting chronological age. Our results highlight the robustness of this framework and the utility of network energy in understanding brain and cognitive mechanisms, including its promising potential as a biomarker for mental conditions and neurological disorders.

Aberrant functional connectivity between the retrosplenial cortex and hippocampal subregions in amnestic mild cognitive impairment and Alzheimer's disease.

The posterior cingulate cortex and hippocampus are the core regions involved in episodic memory, and they exhibit functional connectivity changes in the development and progression of Alzheimer's disease. Previous studies have demonstrated that the posterior cingulate cortex and hippocampus are both cytoarchitectonically heterogeneous regions. Specifically, the retrosplenial cortex, typically subsumed under the posterior cingulate cortex, is an area functionally and anatomically distinct from the posterior cingulate cortex, and the hippocampus is composed of several subregions that participate in multiple cognitive processes. However, little is known about the functional connectivity patterns of the retrosplenial cortex or other parts of the posterior cingulate cortex with hippocampal subregions and their differential vulnerability to Alzheimer's disease pathology. Demographic data, neuropsychological assessments, and resting-state functional magnetic resonance imaging data were collected from 60 Alzheimer's disease participants, 60 participants with amnestic mild cognitive impairment, and 60 sex-matched normal controls. The bilateral retrosplenial cortex, other parts of the posterior cingulate cortex, and hippocampus subregions (including the bilateral anterior hippocampus and posterior hippocampus) were selected to investigate functional connectivity alterations in amnestic mild cognitive impairment and Alzheimer's disease. Resting-state functional connectivity analysis demonstrated heterogeneity in the degree of connectivity between the hippocampus and different parts of the total posterior cingulate cortex, with considerably greater functional connectivity of the retrosplenial cortex with the hippocampus compared with other parts of the posterior cingulate cortex. Furthermore, the bilateral retrosplenial cortex exhibited widespread intrinsic functional connectivity with all anterior-posterior hippocampus subregions. Compared to the normal controls, the amnestic mild cognitive impairment and Alzheimer's disease groups showed different magnitudes of decreased functional connectivity between the retrosplenial cortex and the contralateral posterior hippocampus. Additionally, diminished functional connectivity between the left retrosplenial cortex and right posterior hippocampus was correlated with clinical disease severity in amnestic mild cognitive impairment subjects, and the combination of multiple functional connectivity indicators of the retrosplenial cortex can discriminate the three groups from each other. These findings confirm and extend previous studies suggesting that the retrosplenial cortex is extensively and functionally connected with hippocampus subregions and that these functional connections are selectively affected in the Alzheimer's disease continuum, with prominent disruptions in functional connectivity between the retrosplenial cortex and contralateral posterior hippocampus underpinning episodic memory impairment associated with the disease.

DFCExpert: Learning Dynamic Functional Connectivity Patterns with Modularity and State Experts.

Characterizing brain dynamic functional connectivity (dFC) patterns from functional Magnetic Resonance Imaging (fMRI) data is of paramount importance in neuroscience and medicine. Recently, many graph neural network (GNN) models, combined with transformers or recurrent neural networks (RNNs), have shown great potential for modeling the dFC patterns. However, these methods face challenges in effectively characterizing the modularity organization of brain networks and capturing varying dFC state patterns. To address these limitations, we propose dFCExpert, a novel method designed to learn robust representations of dFC patterns in fMRI data with modularity experts and state experts. Specifically, the modularity experts optimize multiple experts to characterize the brain modularity organization during graph feature learning process by combining GNN and mixture of experts (MoE), with each expert focusing on brain nodes within the same functional network module. The state experts aggregate temporal dFC features into a set of distinctive connectivity states using a soft prototype clustering method, providing insight into how these states support different brain activities or are differentially affected by brain disorders. Experiments on two large-scale fMRI datasets demonstrate the superiority of our method over existing alternatives. The learned dFC representations not only show improved interpretability but also hold promise for enhancing clinical diagnosis. The code can be accessed at MLDataAnalytics/dFCExpert on GitHub.

Local Gradients of Functional Connectivity Enable Precise Fingerprinting of Infant Brains During Dynamic Development.

Brain functional connectivity patterns exhibit distinctive, individualized characteristics capable of distinguishing one individual from others, like fingerprint. Accurate and reliable depiction of individualized functional connectivity patterns during infancy is crucial for advancing our understanding of individual uniqueness and variability of the intrinsic functional architecture during dynamic early brain development, as well as its role in neurodevelopmental disorders. However, the highly dynamic and rapidly developing nature of the infant brain presents significant challenges in capturing robust and stable functional fingerprint, resulting in low accuracy in individual identification over ages during infancy using functional connectivity. Conventional methods rely on brain parcellations for computing inter-regional functional connections, which are sensitive to the chosen parcellation scheme and completely ignore important fine-grained, spatially detailed patterns in functional connectivity that encodes developmentally-invariant, subject-specific features critical for functional fingerprinting. To solve these issues, for the first time, we propose a novel method to leverage the high-resolution, vertex-level local gradient map of functional connectivity from resting-state functional MRI, which captures sharp changes and subject-specific rich information of functional connectivity patterns, to explore infant functional fingerprint. Leveraging a longitudinal dataset comprising 591 high-resolution resting-state functional MRI scans from 103 infants, our method demonstrates superior performance in infant individual identification across ages. Our method has unprecedentedly achieved 99% individual identification rates across three age-varied sub-datasets, with consistent and robust identification rates across different phase encoding directions, significantly outperforming atlas-based approaches with only around 70% accuracy. Further vertex-wise uniqueness and differential power analyses highlighted the discriminative identifiability of higher-order functional networks. Additionally, the local gradient-based functional fingerprints demonstrated reliable predictive capabilities for cognitive performance during infancy. These findings suggest the existence of unique individualized functional fingerprints during infancy and underscore the potential of local gradients of functional connectivity in capturing neurobiologically meaningful and fine-grained features of individualized characteristics for advancing normal and abnormal early brain development.

Altered dynamic functional connectivity in antagonistic state in first-episode, drug-naïve patients with major depressive disorder

ObjectiveMajor depressive disorder (MDD) is known to be characterized by disrupted brain functional network connectivity (FNC) patterns, while the dynamic change mode of different functional networks is unclear. This study aimed to characterize specific dynamic alterations pattern on intrinsic FNC in MDD by combining static FNC (sFNC) and dynamic FNC (dFNC).MethodsA total of 48 first-episode drug-naïve MDD and 48 matched healthy controls (HCs) were included in this study. The sFNC and dFNC were analyzed using complete time-series and sliding window approach, respectively. Both sFNC and dFNC differences between groups were analyzed and associations between disease severity and aberrant FNC were explored.ResultsMDD patients exhibited lower sFNC within and between sensory and motor networks than HC. Four dFNC states were identified, including a globally-weakly-connected state, a cognitive-control-dominated state, a globally-positively-connected state, and an antagonistic state. The antagonistic state was marked by strong positive connections within the sensorimotor domain and their anti-correlations with the executive-motor control domain. Notably, MDD patients exhibited significantly longer dwell time in the globally-weakly-connected state, at the cost of significantly shorter dwell time in the antagonistic state. Further, only the mean dwell time of this antagonistic state was significantly anticorrelated to disease severity measures.ConclusionsOur study highlights the altered dynamics of the antagonistic state as a fundamental aspect of disrupted FNC in early MDD.

Differences in Network Functional Connectivity in Response to Sub-Symptomatic Exercise Between Elite Adult Athletes after Sport-Related Concussion and Healthy Matched Controls: A Pilot Study.

Resting-state electroencephalography (rsEEG) has developed as a method to explore functional network alterations related to sport-related concussion (SRC). Although exercise is an integral part of an athlete's return to sport (RTS) protocol, our understanding of the effects of exercise on (impaired) brain network activity in elite adult athletes is limited. However, this information may be beneficial to inform recovery and RTS progressions. Recording (128-channel) rsEEG datasets before and after a standardized moderate aerobic bike exercise test, this study aimed to explore functional connectivity patterns in whole brain and relevant functional networks in a group of elite adult athletes post-injury compared with healthy matched controls. The following networks were selected a priori: whole brain (68 regions of interest [ROIs]), default mode network (14 ROIs), central autonomic network (CAN, 24 ROIs), and visual network (8 ROIs). Twenty-one SRC athletes and 21 age-, sex-, sport type-, and skill level-matched healthy controls participated in this study. The SRC athletes were recruited during their RTS protocol (days since injury: 2-140 days). All athletes were able to achieve the exercise goal of reaching a moderate intensity (70% of their age-calculated maximum heart rate) while staying sub-symptomatic. Before and after exercise, functional connectivity was calculated by the phase locking value, in the alpha band (7-13 Hz). Mann-Whitney U and Wilcoxon signed rank tests were used to explore neurophysiological differences between and within groups, respectively. Whole-brain connectivity increased significantly from pre- to post-exercise within both groups (SRC: 0.264-0.284; p = 0.011 vs. controls: 0.253-0.257; p = 0.011). While CAN connectivity significantly increased only within the SRC group from pre-(0.298) to post-exercise (0.317; p = 0.003). Although all athletes reached their exercise goal without exacerbation of symptoms, the impact of exercise on the CAN appears to be greater for the SRC athletes, than matched healthy controls. The potential clinical significance of this finding is that it may have revealed an underlying mechanism for the cardiac autonomic alterations post-injury. This study merits further investigation into the CAN, as a network of interest more closely aligned with the clinical features (e.g., autonomic dysfunction) during athletes' RTS.

Distinct patterns of connectivity with the motor cortex reflect different components of sensorimotor learning.

Sensorimotor learning is supported by multiple competing processes that operate concurrently, making it a challenge to elucidate their neural underpinnings. Here, using human functional MRI, we identify 3 distinct axes of connectivity between the motor cortex and other brain regions during sensorimotor adaptation. These 3 axes uniquely correspond to subjects' degree of implicit learning, performance errors and explicit strategy use, and involve different brain networks situated at increasing levels of the cortical hierarchy. We test the generalizability of these neural axes to a separate form of motor learning known to rely mainly on explicit processes and show that it is only the Explicit neural axis, composed of higher-order areas in transmodal cortex, that predicts learning in this task. Together, our study uncovers multiple distinct patterns of functional connectivity with motor cortex during sensorimotor adaptation, the component processes that these patterns support, and how they generalize to other forms of motor learning.

Recent trends in multiple metrics and multimodal analysis for neural activity and pupillometry.

Recent studies focusing on neural activity captured by neuroimaging modalities have provided various metrics for elucidating the functional networks and dynamics of the entire brain. Functional magnetic resonance imaging (fMRI) can depict spatiotemporal functional neural networks and dynamic characteristics due to its excellent spatial resolution. However, its temporal resolution is limited. Neuroimaging modalities such as electroencephalography (EEG) and magnetoencephalography (MEG), which have higher temporal resolutions, are utilized for multi-temporal scale and multi-frequency-band analyzes. With this advantage, numerous EEG/MEG-bases studies have revealed the frequency-band specific functional networks involving dynamic functional connectivity and multiple temporal-scale time-series patterns of neural activity. In addition to analyzing neural data, the examination of behavioral data can unveil additional aspects of brain activity through unimodal and multimodal data analyzes performed using appropriate integration techniques. Among the behavioral data assessments, pupillometry can provide comprehensive spatial-temporal-specific features of neural activity. In this perspective, we summarize the recent progress in the development of metrics for analyzing neural data obtained from neuroimaging modalities such as fMRI, EEG, and MEG, as well as behavioral data, with a special focus on pupillometry data. First, we review the typical metrics of neural activity, emphasizing functional connectivity, complexity, dynamic functional connectivity, and dynamic state transitions of whole-brain activity. Second, we examine the metrics related to the time-series data of pupillary diameters and discuss the possibility of multimodal metrics that combine neural and pupillometry data. Finally, we discuss future perspectives on these multiple and multimodal metrics.

Individualized mouse brain network models produce asymmetric patterns of functional connectivity after simulated traumatic injury

Abstract The functional and cognitive effects of traumatic brain injury (TBI) are poorly understood, as even mild injuries (concussion) can lead to long-lasting, untreatable symptoms. Simplified brain dynamics models may help researchers better understand the relationship between brain injury patterns and functional outcomes. Properly developed, these computational models provide an approach to investigate the effects of both computational and in vivo injury on simulated dynamics and cognitive function, respectively, for model organisms. In this study, we apply the Kuramoto model and an existing mesoscale mouse brain structural network to develop a simplified computational model of mouse brain dynamics. We explore how to optimize our initial model to predict existing mouse brain functional connectivity collected from mice under various anesthetic protocols. Finally, to determine how strongly the changes in our optimized models’ dynamics can predict the extent of a brain injury, we investigate how our simulations respond to varying levels of structural network damage. Results predict a mixture of hypo- and hyperconnectivity after experimental TBI, similar to results in TBI survivors, and also suggest a compensatory remodeling of connections which may have an impact on functional outcome after TBI.

Inter-modality source coupling: a fully-automated whole-brain data-driven structure-function fingerprint shows replicable links to reading in a large-scale (N∼8K) analysis.

Both structural and functional brain changes have been individually associated with developing cognitive processes such as reading. However, there is limited research about the combined influence of resting-state functional and structural magnetic resonance imaging (rs-fMRI and sMRI) features in reading development, which could provide insights into the interplay between brain structure and function in shaping cognitive growth. We propose a method called inter-modality source coupling (IMSC) to study the coupling between the rs-fMRI and sMRI and its relationship to reading ability in school-age children. This approach is applied to baseline data from four thousand participants (9-11 years) and replicated in a second group. Our analysis focused on the relationship of IMSC to overall reading score. Our findings indicate that higher reading ability was linked with increased function-structure coupling among higher-level cortical regions, particularly those links between the inferior parietal lobule and inferior frontal areas, and conversely, lower reading ability was associated with enhanced function-structure coupling among the fusiform and lingual gyrus. Our study found evidence of spatial correspondence between the data indicating an interplay between brain structure and function in our participants. Our approach revealed a linked pattern of whole brain structure to the corresponding functional connectivity pattern that correlated with reading ability. This novel IMSC analysis method provides a new approach to study the multimodal relationship between brain function and structure. These findings have interesting implications for understanding the multimodal complexity underlying the development of the neural basis for reading ability in school-aged children.