Weight-conserving characterization of complex functional brain networks

Modulation of dorsal raphe nucleus connectivity and serotonergic signalling to the insular cortex in the prosocial effects of chronic fluoxetine

Long-term exposure to fluoxetine and other selective serotonin reuptake inhibitors alters social and anxiety-related behaviours, including social withdrawal, which is a symptom of several neuropsychiatric disorders. Adaptive changes in serotonergic neurotransmission likely mediate this delayed effect, although the exact mechanisms are still unclear. Here we investigated the functional circuitry underlying the biphasic effects of fluoxetine on social approach-avoidance behaviour and explored the place of serotonergic dorsal raphe nucleus (DR) ensembles in this network, using c-Fos-immunoreactivity as a correlate of activity. Graph theory-based network analysis revealed changes in patterns of functional connectivity and identified neuronal populations in the insular cortex (IC) and serotonergic populations in the DR as central targets to the prosocial effects of chronic fluoxetine. To determine the role of serotonergic projections to the IC, a retrograde tracer was micro-injected in the IC prior to fluoxetine treatment and social behaviour testing. Chronic fluoxetine increased c-Fos immunoreactivity in insula-projecting neurons of the rostral, ventral part of the DR (DRV). Using a virally delivered Tet-Off platform for temporally-controlled marking of neuronal activation, we observed that chronic fluoxetine may affect social behaviour by influencing independent but interconnected populations of serotonergic DR ensembles. These findings suggest that sustained fluoxetine exposure causes adaptive changes in functional connectivity due to altered serotonergic neurotransmission in DR projection targets, and the increased serotonergic signalling to the IC likely mediates some of the therapeutic effects of fluoxetine on social behaviour.

Structural network measures reveal the emergence of heavy-tailed degree distributions in lottery ticket multilayer perceptrons

Artificial neural networks (ANNs) were originally modeled after their biological counterparts, but have since conceptually diverged in many ways. The resulting network architectures are not well understood, and furthermore, we lack the quantitative tools to characterize their structures. Network science provides an ideal mathematical framework with which to characterize systems of interacting components, and has transformed our understanding across many domains, including the mammalian brain. Yet, little has been done to bring network science to ANNs. In this work, we propose tools that leverage and adapt network science methods to measure both global- and local-level characteristics of ANNs. Specifically, we focus on the structures of efficient multilayer perceptrons as a case study, which are sparse and systematically pruned such that they share many characteristics with real-world networks. We use adapted network science metrics to show that the pruning process leads to the emergence of a spanning subnetwork (lottery ticket multilayer perceptrons) with complex architecture. This complex network exhibits global and local characteristics, including heavy-tailed nodal degree distributions and dominant weighted pathways, that mirror patterns observed in human neuronal connectivity. Furthermore, alterations in network metrics precede catastrophic decay in performance as the network is heavily pruned. This network science-driven approach to the analysis of artificial neural networks serves as a valuable tool to establish and improve biological fidelity, increase the interpretability, and assess the performance of artificial neural networks. Significance Statement Artificial neural network architectures have become increasingly complex, often diverging from their biological counterparts in many ways. To design plausible "brain-like" architectures, whether to advance neuroscience research or to improve explainability, it is essential that these networks optimally resemble their biological counterparts. Network science tools offer valuable information about interconnected systems, including the brain, but have not attracted much attention for analyzing artificial neural networks. Here, we present the significance of our work: •We adapt network science tools to analyze the structural characteristics of artificial neural networks. •We demonstrate that organizational patterns similar to those observed in the mammalian brain emerge through the pruning process alone. The convergence on these complex network features in both artificial neural networks and biological brain networks is compelling evidence for their optimality in information processing capabilities. •Our approach is a significant first step towards a network science-based understanding of artificial neural networks, and has the potential to shed light on the biological fidelity of artificial neural networks.

Musical Engagement of brain LObes in Alzheimer's Disease patients study (MELODY): A randomized controlled trial

OBJECTIVES

This study evaluated the effect of music exposure on global clinical impact, arousal, and brain network connectivity in subjects with moderate to severe Alzheimer's disease (AD).

DESIGN

This was a pilot, controlled, single-blind, randomized, cross-over study.

SETTING

The University Hospital Outpatient Clinic and Imaging Center and the Veterans Administration Research Facility.

PARTICIPANTS

Ten participants aged between 55 and 90 years with moderate to severe AD, a Mini-Mental State Examination (MMSE) score of 5-20, and without neuropsychiatric symptoms of dementia were enrolled in the study.

INTERVENTION

Participants were exposed to both preferred music (PM) and nature sounds (NS). A randomization program was used to determine assignment order for either PM or NS to be delivered via over-ear headphones at Visit 1. Visit 2, which occurred approximately one week later, was identical to Visit 1, but the participants who were originally exposed to PM were exposed to NS and vice versa.

MEASUREMENTS

A modified version of the Alzheimer's Disease Cooperative Study Clinical Global Impression of Change (ADCS-CGIC) was the primary outcome measure and functional magnetic resonance imaging (fMRI) scans were used to evaluate brain network connectivity.

RESULTS

There was a clinically and statistically significant positive response to music stimulation in the primary outcome measure and in the secondary outcome measure, as well as a clear differentiation in brain network connectivity when individuals were exposed to PM versus NS. There was also a strong correlation between clinical changes and brain network connectivity changes as documented by fMRI.

CONCLUSIONS

Music has the potential to be utilized as a non-invasive method to stimulate the brain of individuals with severe cognitive impairment in key functions TRIAL REGISTRATION: The trial was registered in ClinicalTrials.gov (ID: NCT05309369) by Jacobo Mintzer, MD, MBA.

Neural representations of noncentral events during narrative encoding predict subsequent story ending originality

On the basis of the confluence theories of creativity, creative ideation depends on forging links between existing memory traces. The synergy between memory and creative thought is well-established, but neural dynamics of memory integration for creativity are understudied. Here, we extended the traditional memory paradigm. Participants read, recalled narratives, and wrote endings. Computational linguistic analysis showed that those integrating more noncentral events-those less semantically connected to other events within the narrative-wrote more original endings. Analyzing fMRI data captured during narrative encoding, we discovered that story ending originality can be predicted by shared event representation across participants in the right Brodmann area 25 (BA25) and stronger hippocampal event segmentation signal during noncentral event encoding. These results held across different narrative types (i.e., crime, romance, and fantasy stories). Overall, these results offer notable insights, from the perspective of network structure into how humans encode and retrieve complex real-world experiences to enhance creativity.

Mapping global brain reconfigurations following local targeted manipulations

Understanding how localized brain interventions influence whole-brain dynamics is essential for deciphering neural function and designing therapeutic strategies. Using longitudinal functional MRI datasets collected from mice, we investigated the effects of focal interventions, such as thalamic lesions and chemogenetic silencing of cortical hubs. We found that these local manipulations disrupted the brain's ability to sustain network-wide activity, leading to global functional connectivity (FC) reconfigurations. Personalized mouse brain simulations based on experimental data revealed that alterations in local excitability modulate firing rates and frequency content across distributed brain regions, driving these FC changes. Notably, the topography of the affected brain regions depended on the intervention site, serving as distinctive signatures of localized perturbations. These findings suggest that focal interventions produce consistent yet region-specific patterns of global FC reorganization, providing an explanation for the seemingly paradoxical observations of hypo- and hyperconnectivity reported in the literature. This framework offers mechanistic insights into the systemic effects of localized neural modulation and holds potential for refining clinical diagnostics in focal brain disorders and advancing personalized neuromodulation strategies.

A general framework for characterizing optimal communication in brain networks

Efficient communication in brain networks is foundational for cognitive function and behavior. However, how communication efficiency is defined depends on the assumed model of signaling dynamics, e.g., shortest path signaling, random walker navigation, broadcasting, and diffusive processes. Thus, a general and model-agnostic framework for characterizing optimal neural communication is needed. We address this challenge by assigning communication efficiency through a virtual multi-site lesioning regime combined with game theory, applied to large-scale models of human brain dynamics. Our framework quantifies the exact influence each node exerts over every other, generating optimal influence maps given the underlying model of neural dynamics. These descriptions reveal how communication patterns unfold if regions are set to maximize their influence over one another. Comparing these maps with a variety of brain communication models showed that optimal communication closely resembles a broadcasting regime in which regions leverage multiple parallel channels for information dissemination. Moreover, we found that the brain's most influential regions are its rich-club, exploiting their topological vantage point by broadcasting across numerous pathways that enhance their reach even if the underlying connections are weak. Altogether, our work provides a rigorous and versatile framework for characterizing optimal brain communication, and uncovers the most influential brain regions, and the topological features underlying their influence.

Tell me why: A scoping review on the fundamental building blocks of fMRI-based network analysis

Understanding complex brain-behaviour relationships in psychiatric and neurological conditions is crucial for advancing clinical insights. This review explores the current landscape of network estimation methods in the context of functional MRI (fMRI) based network neuroscience, focusing on static undirected network analysis. We focused on papers published in a single year (2022) and characterised what we consider the fundamental building blocks of network analysis: sample size, network size, association type, edge inclusion strategy, edge weights, modelling level, and confounding factors. We found that the most common methods across all included studies (n = 191) were the use of pairwise correlations to estimate the associations between brain regions (79.6 %), estimation of weighted networks (95.3 %), and estimation of the network at the individual level (86.9 %). Importantly, a substantial number of studies lacked comprehensive reporting on their methodological choices, hindering the synthesis of research findings within the field. This review underscores the critical need for careful consideration and transparent reporting of fMRI network estimation methodologies to advance our understanding of complex brain-behaviour relationships. By facilitating the integration between network neuroscience and network psychometrics, we aim to significantly enhance our clinical understanding of these intricate connections.

Altered integrated and segregated states in cocaine use disorder

Introduction

Cocaine use disorder (CUD) is a chronic brain condition that severely impairs cognitive function and behavioral control. The neural mechanisms underlying CUD, particularly its impact on brain integration-segregation dynamics, remain unclear.

Methods

In this study, we integrate dynamic functional connectivity and graph theory to compare the brain state properties of healthy controls and CUD patients.

Results

We find that CUD influences both integrated and segregated states, leading to distinct alterations in connectivity patterns and network properties. CUD disrupts connectivity involving the default mode network, frontoparietal network, and subcortical structures. In addition, integrated states show distinct sensorimotor connectivity alterations, while segregated states exhibit significant alterations in frontoparietal-subcortical connectivity. Regional connectivity alterations among both states are significantly associated with MOR and H3 receptor distributions, with integrated states showing more receptor-connectivity couplings. Furthermore, CUD alters the positive-negative correlation balance, increases functional complexity at threshold 0, and reduces mean betweenness centrality and modularity in the critical subnetworks. Segregated states in CUD exhibit lower normalized clustering coefficients and functional complexity at a threshold of 0.3. We also identify network properties in integrated states that are reliably correlated with cocaine consumption patterns.

Discussion

Our findings reveal temporal effects of CUD on brain integration and segregation, providing novel insights into the dynamic neural mechanisms underlying cocaine addiction.

Brain overlapping system-level architecture influenced by external magnetic stimulation and internal gene expression in AD-spectrum patients

The brain overlapping system-level architecture is associated with functional information integration in the multiple roles of the same region, and it has been developed as an underlying novel biomarker of brain disease and may characterise the indicators for the treatment of Alzheimer's disease (AD). However, it remains uncertain whether these changes are influenced by external magnetic stimulation and internal gene expression. A total of 73 AD-spectrum patients (52 with true stimulation and 21 with sham stimulation) were underwent four-week neuronavigated transcranial magnetic stimulation (rTMS). Shannon-entropy diversity coefficient analysis was used to explore the brain overlapping system of the neuroimaging data in these pre- and posttreatment patients. Transcription-neuroimaging association analysis was further performed via gene expression data from the Allen Human Brain Atlas. Compared with the rTMS_sham stimulation group, the rTMS_true stimulation group achieved the goal of cognitive improvement through the reconstruction of functional information integration in the multiple roles of 27 regions associated with the brain overlapping system, involving the attentional network, sensorimotor network, default mode network and limbic network. Furthermore, these overlapping regions were closely linked to gene expression on cellular homeostasis and immune inflammation, and support vector regression analysis revealed that the baseline diversity coefficients of the attentional and sensorimotor networks can effectively predict memory improvement after rTMS treatment. These findings highlight the brain overlapping system associated with cognitive improvement, and provide the first evidence that external magnetic stimulation and internal gene expression could influence these overlapping regions in AD-spectrum patients.

Network segregation during episodic memory shows age-invariant relations with memory performance from 7 to 82 years

Lower episodic memory capability, as seen in development and aging compared with younger adulthood, may partly depend on lower brain network segregation. Here, our objective was twofold: (1) test this hypothesis using within- and between-network functional connectivity (FC) during episodic memory encoding and retrieval, in two independent samples (n = 734, age 7-82 years). (2) Assess associations with age and the ability to predict memory comparing task-general FC and memory-modulated FC. In a multiverse-inspired approach, we performed tests across multiple analytic choices. Results showed that relationships differed based on these analytic choices and were mainly present in the largest dataset,. Significant relationships indicated that (i) memory-modulated FC predicted memory performance and associated with memory in an age-invariant manner. (ii) In line with the so-called neural dedifferentiation view, task-general FC showed lower segregation with higher age in adults which was associated with worse memory performance. In development, although there were only weak signs of a neural differentiation, that is, gradually higher segregation with higher age, we observed similar lower segregation-worse memory relationships. This age-invariant relationships between FC and episodic memory suggest that network segregation is pivotal for memory across the healthy lifespan.

A simple clustering approach to map the human brain's cortical semantic network organization during task

Constructing task-state large-scale brain networks can enhance our understanding of the organization of brain functions during cognitive tasks. The primary goal of brain network partitioning is to cluster functionally homogeneous brain regions. However, a brain region often serves multiple cognitive functions, complicating the partitioning process. This study proposes a novel clustering method for partitioning large-scale brain networks based on specific cognitive functions, selecting semantic representation as the target cognitive function to evaluate the validity of the proposed method. Specifically, we analyzed functional magnetic resonance imaging (fMRI) data from 11 subjects, each exposed to 672 concepts, and correlated this with semantic rating data related to these concepts. We identified distinct semantic networks based on the concept comprehension task and validated the robustness of our network partitioning through multiple methods. We found that the semantic networks derived from multidimensional semantic activation clustering exhibit high reliability and cross-semantic model consistency (semantic ratings and word embeddings extracted from GPT-2), particularly in networks associated with high semantic functions. Moreover, these semantic networks exhibits significant differences from the resting-state and task-based brain networks obtained using traditional methods. Further analysis revealed functional differences between semantic networks, including disparities in their multidimensional semantic representation capabilities, differences in the information modalities they rely on to acquire semantic information, and varying associations with general cognitive domains. This study introduces a novel approach for analyzing brain networks tailored to specific cognitive functions, establishing a standard semantic parcellation with seven networks for future research, potentially enriching our understanding of complex cognitive processes and their neural bases.

Abnormal Structural-Functional Coupling and MRI Alterations of Brain Network Topology in Progressive Supranuclear Palsy

BACKGROUND

Progressive supranuclear palsy (PSP) can cause structural and functional brain reconstruction. There is a lack of knowledge about the consistency between structural-functional (S-F) connection networks in PSP, despite growing evidence of anomalies in various single brain network parameters.

PURPOSE

To study the changes in the structural and functional networks of PSP, network's topological properties including degree, and the consistency of S-F coupling. The relationship with clinical scales was examined including the assessment of PSP severity, and so on.

STUDY TYPE

Retrospective.

SUBJECTS

A total of 51 PSP patients (70.04 ± 7.46, 25 females) and 101 healthy controls (64.58 ± 8.84, 58 females).

FIELD STRENGTH/SEQUENCE

3-T, resting-state functional MRI, diffusion tensor imaging, and T1-weighted images.

ASSESSMENT

A graph-theoretic approach was used to evaluate structural and functional network topology metrics. We used the S-F coupling changes to explore the consistency of structural and functional networks.

STATISTICAL TESTS

Independent samples t tests were employed for continuous variables, χ2 tests were used for categorical variables. For network analysis, two-sample t tests was used and implied an false discovery rate (FDR) correction. Pearson correlation analysis was used to explore the correlations. A P-value <0.05 was considered statistically significant.

RESULTS

PSP showed variations within and between modules. Specifically, PSP had decreased network properties changes (t = -2.0136; t = 2.5409; t = -2.5338; t = -2.4296; t = -2.5338; t = 2.8079). PSP showed a lower coupling in the thalamus and left putamen and a higher coupling in the visual, somatomotor, dorsal attention, and ventral attention network. S-F coupling was related to the number of network connections (r = 0.32, r = 0.22) and information transmission efficiency (r = 0.55, r = 0.28). S-F coupling was related to basic academic ability (r = 0.39) and disinhibition (r = 0.49).

DATA CONCLUSION

PSP may show abnormal S-F coupling and intramodular and intermodular connectome in the structural and functional networks.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 3.

Saccades influence functional modularity in the human cortical vision network

Visual cortex is thought to show both dorsoventral and hemispheric modularity, but it is not known if the same functional modules emerge spontaneously from an unsupervised network analysis, or how they interact when saccades necessitate increased sharing of spatial information. Here, we address these issues by applying graph theory analysis to fMRI data obtained while human participants decided whether an object's shape or orientation changed, with or without an intervening saccade across the object. BOLD activation from 50 vision-related cortical nodes was used to identify local and global network properties. Modularity analysis revealed three sub-networks during fixation: a bilateral parietofrontal network linking areas implicated in visuospatial processing and two lateralized occipitotemporal networks linking areas implicated in object feature processing. When horizontal saccades required visual comparisons between visual hemifields, functional interconnectivity and information transfer increased, and the two lateralized ventral modules became functionally integrated into a single bilateral sub-network. This network included 'between module' connectivity hubs in lateral intraparietal cortex and dorsomedial occipital areas previously implicated in transsaccadic integration. These results provide support for functional modularity in the visual system and show that the hemispheric sub-networks are modified and functionally integrated during saccades.

From initial formation to developmental refinement: GABAergic inputs shape neuronal subnetworks in the primary somatosensory cortex

Neuronal subnetworks, also known as ensembles, are functional units formed by interconnected neurons for information processing and encoding in the adult brain. Our study investigates the establishment of neuronal subnetworks in the mouse primary somatosensory (S1) cortex from postnatal days (P)11 to P21 using in vivo two-photon calcium imaging. We found that at P11, neuronal activity was highly synchronized but became sparser by P21. Clustering analyses revealed that while the number of subnetworks remained constant, their activity patterns became more distinct, with increased coherence, independent of cortical layer or sex. Furthermore, the coherence of neuronal activity within individual subnetworks significantly increased when synchrony frequencies were reduced by augmenting gamma-aminobutyric acid (GABA)ergic activity at P15/16, a period when the neuronal subnetworks were still maturing. Together, these findings indicate the early formation of subnetworks and underscore the pivotal roles of GABAergic inputs in modulating S1 neuronal subnetworks.

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

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 that may have an impact on functional outcomes after TBI.

Brain Networks Differ According to Levels of Interference in Spatiotemporal Processing

The ability to form different neural representations for similar inputs is a central process of episodic memory. Although the dorsal dentate gyrus and CA3 have been indicated as important in this phenomenon, the neuronal circuits underlying spatiotemporal memory processing with different levels of spatial similarity are still elusive. In this study, we measured the expression of the immediate early gene c-Fos to evaluate brain areas activated when rats recalled the temporal order of object locations in a task, with either high or low levels of spatial interference. Animals showed spatiotemporal memory in both conditions once they spent more time exploring the older object locations relative to the more recent ones. We found no difference in the levels of c-Fos expression between high and low spatial interference. However, the levels of c-Fos expression in CA2 positively correlated with the discrimination index in the low spatial interference condition. More importantly, functional network connectivity analysis revealed a wider and more interconnected neuronal circuit in conditions of high than in low spatial interference. Our study advances the understanding of brain networks recruited in episodic memory with different degrees of spatial similarity.

Age- and Sex-Specific Patterns in Adult Brain Network Segregation

The human brain is organized into several segregated associative and sensory functional networks, each responsible for various aspects of cognitive and sensory processing. These functional networks become less segregated over the adult lifespan, possibly contributing to cognitive decline that is observed during advanced age. To date, a comprehensive understanding of decreasing network segregation with age has been hampered by (1) small sample sizes, (2) lack of investigation at different spatial scales, (3) the limited age range of participants, and more importantly (4) an inadequate consideration of sex (biological females and males) differences. This study aimed to address these shortcomings. Resting-state functional magnetic resonance imaging data were collected from 357 cognitively intact participants (18.2-91.8 years; 49.9 ± 17.1 years; 27.70 ± 1.72 MoCA score, 203 [56.8%] females), and the segregation index (defined as one minus the ratio of between-network connectivity to within-network connectivity) was calculated at three spatial scales of brain networks: whole-brain network, intermediate sensory and associative networks, as well as core visual (VIS), sensorimotor (SMN), frontoparietal (FPN), ventral attention (VAN), dorsal attention (DAN), and default mode networks (DMN). Where applicable, secondary within-, between-, and pairwise connectivity analyses were also conducted to investigate the origin of any observed age and sex effects on network segregation. For any given functional metric, linear and quadratic age effects, sex effects, and respective age by sex interaction effects were assessed using backwards iterative linear regression modeling. Replicating previous work, brain networks were found to become less segregated across adulthood. Specifically, negative quadratic decreases in whole-brain network, intermediate associative network, VAN, and DMN segregation index were observed. Intermediate sensory networks, VIS, and SMN exhibited negative linear decreases in segregation index. Secondary analysis revealed that this process of age-related functional reorganization was preferential as functional connectivity was observed to increase either between anatomically adjacent associative networks (DMN-DAN, FPN-DAN) or between anterior associative and posterior sensory networks (VIS-DAN, VIS-DMN, VIS-FPN, SMN-DMN, and SMN-FPN). Inherent sex differences in network segregation index were also observed. Specifically, whole-brain, associative, DMN, VAN, and FPN segregation index was greater in females compared to males, irrespective of age. Secondary analysis found that females have reduced functional connectivity between associative networks (DAN-VAN, VAN-FPN) compared to males and independent of age. A notable linear age-related decrease in FPN SI was also only observed for females and not males. The observed findings support the notion that functional networks reorganize across the adult lifespan, becoming less segregated. This decline may reflect underlying neurocognitive aging mechanisms like neural dedifferentiation, inefficiency, and compensation. The aging trajectories and rates of decreasing network segregation, however, vary across associative and sensory networks. This study also provides preliminary evidence of inherent sex differences in network organization, where associative networks are more segregated in females than males. These inherent sex differences suggest that female functional networks may be more efficient and functionally specialized compared to males across adulthood. Given these findings, future studies should take a more focused approach to examining sex differences across the lifespan, incorporating multimodal methodologies.

Cerebellar output neurons can impair non-motor behaviors by altering development of extracerebellar connectivity

The capacity of the brain to compensate for insults during development depends on the type of cell loss, whereas the consequences of genetic mutations in the same neurons are difficult to predict. We reveal powerful compensation from outside the mouse cerebellum when the excitatory cerebellar output neurons are ablated embryonically and demonstrate that the main requirement for these neurons is for motor coordination and not basic learning and social behaviors. In contrast, loss of the homeobox transcription factors Engrailed1/2 (EN1/2) in the cerebellar excitatory lineage leads to additional deficits in adult learning and spatial working memory, despite half of the excitatory output neurons being intact. Diffusion MRI indicates increased thalamo-cortico-striatal connectivity in En1/2 mutants, showing that the remaining excitatory neurons lacking En1/2 exert adverse effects on extracerebellar circuits regulating motor learning and select non-motor behaviors. Thus, an absence of cerebellar output neurons is less disruptive than having cerebellar genetic mutations.

Frontostriatal connectivity dynamically modulates the adaptation to environmental volatility

Humans adjust their learning strategies in changing environments by estimating the volatility of the reinforcement conditions. Here, we examine how volatility affects learning and the underlying functional brain organizations using a probabilistic reward reversal learning task. We found that the order of states was critically important; participants adjusted learning rate going from volatile to stable, but not from stable to volatile environments. Subjective volatility of the environment was encoded in the striatal reward system and its dynamic connections with the prefrontal control system. Flexibility, which captures the dynamic changes of network modularity in the brain, was higher in the environmental transition from volatile to stable than from stable to volatile. These findings suggest that behavioral adaptations and dynamic brain organizations in transitions between stable and volatile environments are asymmetric, providing critical insights into the way that people adapt to changing environments.

Common neuroanatomical differential factors underlying heterogeneous gray matter volume variations in five common psychiatric disorders

Multifaceted evidence has shown that psychiatric disorders share common neurobiological mechanisms. However, the tremendous inter-individual heterogeneity among patients with psychiatric disorders limits trans-diagnostic studies with case-control designs, aimed at identifying clinically promising neuroimaging biomarkers. This study aims to identify neuroanatomical differential factors (ND factors) underlying gray matter volume variations in five psychiatric disorders. We leverage 4 independent datasets of 878 patients diagnosed with psychiatric disorders and 585 healthy controls (HCs) to identify shared ND factors underlying individualized gray matter volume variations. Individualized gray matter volume variations are represented with the linear weighted sum of ND factors, and each case is assigned a unique factor composition, thus preserving interindividual variation. We identify four robust ND factors that can be generalized to unseen disorders. ND factors show significant association with group-level morphological abnormalities, reconciling individual- and group-level morphological abnormalities, and are characterized by dissociable cognitive processes, molecular signatures, and connectome-informed epicenters. Moreover, using factor compositions as features, we discover two robust transdiagnostic subtypes with opposite gray matter volume variations relative to HCs. In conclusion, we identify four reproducible and shared neuroanatomical factors that underlie the highly heterogeneous morphological abnormalities in psychiatric disorders.

The Effect of Modular Degeneracy on Neuroimaging Data

Introduction: The concept of community structure, based on modularity, is widely used to address many systems-level queries. However, its algorithm, based on the maximization of the modularity index Q, suffers from degeneracy problem, which yields a set of different possible solutions. Methods: In this work, we explored the degeneracy effect of modularity principle on resting-state functional magnetic resonance imaging (rsfMRI) data, when it is used to parcellate the cingulate cortex using data from the Human Connectome Project. We proposed a new iterative approach to address this limitation. Results: Our results show that current modularity approaches furnish a variety of different solutions, when these algorithms are repeated, leading to different number of subdivisions for the cingulate cortex. Our new proposed method, however, overcomes this limitation and generates more stable solution for the final partition. Conclusion: With this new method, we were able to mitigate the degeneracy problem and offer a tool to use modularity in a more reliable manner, when applying it to rsfMRI data.

Turbulence as a framework for brain dynamics in health and disease

Turbulence is a universal principle for fast energy and information transfer. Moving beyond the turbulence of fluid dynamics, turbulence has recently been demonstrated in brain dynamics. Importantly, turbulence can be expressed as the rich variability across spacetime of the local levels of synchronisation of coupled brain signals. In fact, the optimal mixing properties of turbulence is what allows for efficient transfer of energy/information over space and time in the brain. This is especially important for survival given the need to overcome the inherent slowness in neural dynamics. Here, we review the research showing that the turbulence offers a convenient framework for describing brain dynamics and that the scale-free nature of turbulence, reflected in power-laws, provides the necessary mechanisms for time-critical information transfer in the brain. Whole-brain modelling of turbulence as coupled-oscillators has been shown to provide precise signatures of many different brain states. The levels of turbulence change in disease, and careful research of the vortex space could potentially help discover new avenues for a better understanding of this breakdown and offer better control of these highly non-linear, non-equilibrium states. Overall, the framework of the turbulent brain is a highly fertile, fast developing field with great potential.

Deciphering the Neural Effects of Emotional, Motivational, and Cognitive Challenges on Inhibitory Control Processes

Converging lines of research indicate that inhibitory control is likely to be compromised in contexts that place competing demands on emotional, motivational, and cognitive systems, potentially leading to damaging impulsive behavior. The objective of this study was to identify the neural impact of three challenging contexts that typically compromise self-regulation and weaken impulse control. Participants included 66 healthy adults (M/SDage = 29.82/10.21 years old, 63.6% female) who were free of psychiatric disorders and psychotropic medication use. Participants completed a set of novel Go/NoGo (GNG) paradigms in the scanner, which manipulated contextual factors to induce (i) aversive emotions, (ii) appetitive drive, or (iii) concurrent working memory load. Voxelwise analysis of neural activation during each of these tasks was compared to that of a neutral GNG task. Findings revealed differential inhibition-related activation in the aversive emotions and appetitive drive GNG tasks relative to the neutral task in frontal, parietal and temporal cortices, suggesting emotional and motivational contexts may suppress activation of these cortical regions during inhibitory control. In contrast, the GNG task with a concurrent working memory load showed widespread increased activation across the cortex compared to the neutral task, indicative of enhanced recruitment of executive control regions. Results suggest the neural circuitry recruited for inhibitory control varies depending on the concomitant emotional, motivational, and cognitive demands of a given context. This battery of GNG tasks can be used by researchers interested in studying unique patterns of neural activation associated with inhibitory control across three clinically relevant contexts that challenge self-regulation and confer risk for impulsive behavior.

Brain network alterations in anorexia Nervosa: A Multi-Center structural connectivity study

Anorexia nervosa (AN) is a severe eating disorder characterized by intense fear of weight gain, distorted body image, and extreme food restriction. This research employed advanced diffusion MRI techniques including single-shell 3-tissue constrained spherical deconvolution, anatomically constrained tractography, and spherical deconvolution informed filtering of tractograms to analyze brain network alterations in AN. Diffusion MRI data from 81 AN patients and 98 healthy controls were obtained. The structural brain connectome was constructed based on nodes set in 84 brain regions, and graph theory analysis was conducted. Results showed that AN patients exhibited significantly higher clustering coefficient and local efficiency in several brain regions, including the left fusiform gyrus, bilateral orbitofrontal cortex, right entorhinal cortex, right lateral occipital gyrus, right superior temporal gyrus, and right insula. A trend towards higher global efficiency and small-worldness was also observed in AN patients, although not statistically significant. These findings suggest increased local connectivity and efficiency within regions associated with behavioral rigidity, emotional regulation, and disturbed body image among AN patients. This study contributes to the understanding of the neurological basis of AN by highlighting structural connectivity alterations in specific brain regions.

A simulated annealing algorithm for randomizing weighted networks

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

Resting-State Functional Connectivity Predicts Cochlear-Implant Speech Outcomes

OBJECTIVES

Cochlear implants (CIs) have revolutionized hearing restoration for individuals with severe or profound hearing loss. However, a substantial and unexplained variability persists in CI outcomes, even when considering subject-specific factors such as age and the duration of deafness. In a pioneering study, we use resting-state functional near-infrared spectroscopy to predict speech-understanding outcomes before and after CI implantation. Our hypothesis centers on resting-state functional connectivity (FC) reflecting brain plasticity post-hearing loss and implantation, specifically targeting the average clustering coefficient in resting FC networks to capture variation among CI users.

DESIGN

Twenty-three CI candidates participated in this study. Resting-state functional near-infrared spectroscopy data were collected preimplantation and at 1 month, 3 months, and 1 year postimplantation. Speech understanding performance was assessed using consonant-nucleus-consonant words in quiet and Bamford-Kowal-Bench sentences in noise 1-year postimplantation. Resting-state FC networks were constructed using regularized partial correlation, and the average clustering coefficient was measured in the signed weighted networks as a predictive measure for implantation outcomes.

RESULTS

Our findings demonstrate a significant correlation between the average clustering coefficient in resting-state functional networks and speech understanding outcomes, both pre- and postimplantation.

CONCLUSIONS

This approach uses an easily deployable resting-state functional brain imaging metric to predict speech-understanding outcomes in implant recipients. The results indicate that the average clustering coefficient, both pre- and postimplantation, correlates with speech understanding outcomes.

Increasing hub disruption parallels dementia severity in autosomal dominant Alzheimer's disease

Hub regions in the brain, recognized for their roles in ensuring efficient information transfer, are vulnerable to pathological alterations in neurodegenerative conditions, including Alzheimer's disease (AD). Computational simulations and animal experiments have hinted at the theory of activity-dependent degeneration as the cause of this hub vulnerability. However, two critical issues remain unresolved. First, past research has not clearly distinguished between two scenarios: hub regions facing a higher risk of connectivity disruption (targeted attack) and all regions having an equal risk (random attack). Second, human studies offering support for activity-dependent explanations remain scarce. We refined the hub disruption index to demonstrate a hub disruption pattern in functional connectivity in autosomal dominant AD that aligned with targeted attacks. This hub disruption is detectable even in preclinical stages, 12 years before the expected symptom onset and is amplified alongside symptomatic progression. Moreover, hub disruption was primarily tied to regional differences in global connectivity and sequentially followed changes observed in amyloid-beta positron emission tomography cortical markers, consistent with the activity-dependent degeneration explanation. Taken together, our findings deepen the understanding of brain network organization in neurodegenerative diseases and could be instrumental in refining diagnostic and targeted therapeutic strategies for AD in the future.

The impact of functional correlations on task information coding

State-dependent neural correlations can be understood from a neural coding framework. Noise correlations-trial-to-trial or moment-to-moment covariability-can be interpreted only if the underlying signal correlation-similarity of task selectivity between pairs of neural units-is known. Despite many investigations in local spiking circuits, it remains unclear how this coding framework applies to large-scale brain networks. Here, we investigated relationships between large-scale noise correlations and signal correlations in a multitask human fMRI dataset. We found that task-state noise correlation changes (e.g., functional connectivity) did not typically change in the same direction as their underlying signal correlation (e.g., tuning similarity of two regions). Crucially, noise correlations that changed in the opposite direction as their signal correlation (i.e., anti-aligned correlations) improved information coding of these brain regions. In contrast, noise correlations that changed in the same direction (aligned noise correlations) as their signal correlation did not. Interestingly, these aligned noise correlations were primarily correlation increases, suggesting that most functional correlation increases across fMRI networks actually degrade information coding. These findings illustrate that state-dependent noise correlations shape information coding of functional brain networks, with interpretation of correlation changes requiring knowledge of underlying signal correlations.

Identifying neural network structures explained by personality traits: combining unsupervised and supervised machine learning techniques in translational validity assessment

There have been studies previously the neurobiological underpinnings of personality traits in various paradigms such as psychobiological theory and Eysenck’s model as well as five-factor model. However, there are limited results in terms of co-clustering of the functional connectivity as measured by functional MRI, and personality profiles. In the present study, we have analyzed resting-state connectivity networks and character type with the Lowen bioenergetic test in 66 healthy subjects. There have been identified direct correspondences between network metrics such as eigenvector centrality (EC), clustering coefficient (CC), node strength (NS) and specific personality characteristics. Specifically, N Acc L and OFCmed were associated with oral and masochistic traits in terms of EC and CC, while Insula R is associated with oral traits in terms of NS and EC. It is noteworthy that we observed significant correlations between individual items and node measures in specific regions, suggesting a more targeted relationship. However, the more relevant finding is the correlation between metrics (NS, CC, and EC) and overall traits. A hierarchical clustering algorithm (agglomerative clustering, an unsupervised machine learning technique) and principal component analysis were applied, where we identified three prominent principal components that cumulatively explain 76% of the psychometric data. Furthermore, we managed to cluster the network metrics (by unsupervised clustering) to explore whether neural connectivity patterns could be grouped based on combined average network metrics and psychometric data (global and local efficiencies, node strength, eigenvector centrality, and node strength). We identified three principal components, where the cumulative amount of explained data reaches 99%. The correspondence between network measures (CC and NS) and predictors (responses to Lowen’s items) is 62% predicted with a precision of 90%.

Changes in Functional and Structural Brain Connectivity Following Bilateral Hand Transplantation

As a surgical treatment following amputation or loss of an upper limb, nearly 200 hand transplantations have been completed to date. We report here a magnetic resonance imaging (MRI) investigation of functional and structural brain connectivity for a bilateral hand transplant patient (female, 60 years of age), with a preoperative baseline and three postoperative testing sessions each separated by approximately six months. We used graph theoretical analyses to estimate connectivity within and between modules (networks of anatomical nodes), particularly a sensorimotor network (SMN), from resting-state functional MRI and structural diffusion-weighted imaging (DWI). For comparison, corresponding MRI measures of connectivity were obtained from 10 healthy, age-matched controls, at a single testing session. The patient's within-module functional connectivity (both SMN and non-SMN modules), and structural within-SMN connectivity, were higher preoperatively than that of the controls, indicating a response to amputation. Postoperatively, the patient's within-module functional connectivity decreased towards the control participants' values, across the 1.5 years postoperatively, particularly for hand-related nodes within the SMN module, suggesting a return to a more canonical functional organization. Whereas the patient's structural connectivity values remained relatively constant postoperatively, some evidence suggested that structural connectivity supported the postoperative changes in within-module functional connectivity.

The impact of insomnia on brain networks topology in depressed patients: A resting-state fMRI study

OBJECTIVE

Depression and insomnia frequently co-occur, but the neural mechanisms between patients with varying degrees of these conditions are not fully understood. The specific topological features and connectivity patterns of this co-morbidity have not been extensively studied. This study aimed to investigate the topological characteristics of topological characteristics and functional connectivity of brain networks in depressed patients with insomnia.

METHODS

Resting-state functional magnetic resonance imaging data from 32 depressed patients with a high level of insomnia (D-HI), 35 depressed patients with a low level of insomnia (D-LI), and 81 healthy controls (HC) were used to investigate alterations in brain topological organization functional networks. Nodal and global properties were analyzed using graph-theoretic techniques, and network-based statistical analysis was employed to identify changes in brain network functional connectivity.

RESULTS

Compared to the HC group, both the D-HI and D-LI groups showed an increase in the global efficiency (Eglob) values, local efficiency (Eloc) was decreased in the D-HI group, and Lambda and shortest path length (Lp) values were decreased in the D-LI group. At the nodal level, the right parietal nodal clustering coefficient (NCp) values were reduced in D-HI and D-LI groups compared to those in HC. The functional connectivity of brain networks in patients with D-HI mainly involves default mode network (DMN)-cingulo-opercular network (CON), DMN-visual network (VN), DMN-sensorimotor network (SMN), and DMN-cerebellar network (CN), while that in patients with D-LI mainly involves SMN-CON, SMN-SMN, SMN-VN, and SMN-CN. The values of the connection between the midinsula and postoccipital gyrus was negatively correlated with scores for early awakening in D-HI.

CONCLUSION

These findings may contribute to our understanding of the underlying neuropsychological mechanisms in depressed patients with insomnia.

Structural-functional connectomics in major depressive disorder following aiTBS treatment

Major depressive disorder (MDD) has been associated with changes in the structural (SC) and functional connectivity (FC) of the brain. This study investigated the effects of accelerated intermittent theta burst stimulation (aiTBS) on SC-FC coupling and graph theory measures, focusing on the association between baseline SC-FC coupling of the dorsolateral prefrontal cortex (dlPFC) and clinical improvement. In a randomized, sham-controlled, quadruple-blind, crossover study, aiTBS was delivered to the left dlPFC of depressed patients with MDD, and diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rsfMRI) data were acquired. In 77 MDD patients, significantly increased whole-brain SC-FC coupling was observed, primarily driven by default mode network (DMN) SC-FC coupling, along with increased somatomotor network FC, and decreased FC between the DMN hubs and limbic regions after active aiTBS. Furthermore, significant increases were observed in structural global and local efficiency measures that were not specific to the stimulation condition (active/sham aiTBS). However, these changes did not significantly correlate with clinical improvement. Notably, baseline SC-FC coupling of the left dlPFC was a significant predictor of clinical improvement. Our findings highlight the potential of left dlPFC SC-FC coupling as a predictor of aiTBS treatment outcomes, as well as the effect of aiTBS in enhancing SC-FC coupling.

Prefrontal multistimulus integration within a dedicated disambiguation circuit guides interleaving contingency judgment learning

Understanding how cortical network dynamics support learning is a challenge. This study investigates the role of local neural mechanisms in the prefrontal cortex during contingency judgment learning (CJL). To better understand brain network mechanisms underlying CJL, we introduce ambiguity into associative learning after fear acquisition, inducing a generalized fear response to an ambiguous stimulus sharing nontrivial similarities with the conditioned stimulus. Real-time recordings at single-neuron resolution from the prelimbic (PL) cortex show distinct PL network dynamics across CJL phases. Fear acquisition triggers PL network reorganization, led by a disambiguation circuit managing spurious and predictive relationships during cue-danger, cue-safety, and cue-neutrality contingencies. Mice with PL-targeted memory deficiency show malfunctioning disambiguation circuit function, while naive mice lacking unconditioned stimulus exposure lack the disambiguation circuit. This study shows that fear conditioning induces prefrontal cortex cognitive map reorganization and that subsequent CJL relies on the disambiguation circuit's ability to learn predictive relationships.

Stimulant medication and symptom interrelations in children, adolescents and adults with attention-deficit/hyperactivity disorder

Stimulant medication is effective in alleviating overall symptom severity of attention-deficit/hyperactivity disorder (ADHD), yet interindividual variability in treatment response and tolerability still exists. While network analysis has identified differences in ADHD symptom relations, the impact of stimulant medication remains unexplored. Increased understanding of this association could provide valuable insights for optimizing treatment approaches for individuals with ADHD. In this study, we compared and characterized ADHD symptom networks (including 18 ADHD symptoms) between stimulant-treated (n = 348) and untreated (n = 70) individuals with ADHD and non-ADHD controls (NACs; n = 444). Moreover, we compared symptom networks between subgroups defined by their stimulant treatment trajectory (early-and-intense use, late-and-moderate use). Stimulant-treated individuals with ADHD showed stronger associations between symptoms, compared with untreated individuals with ADHD and NACs. We found no differences in symptom networks between the stimulant treatment trajectory subgroups. Prospective longitudinal studies are needed to disentangle whether the identified differences stem from treatment or pre-existing factors.

Probing heterogeneity to identify individualized treatment approaches in autism: Specific clusters of executive function challenges link to distinct co-occurring mental health problems

LAY ABSTRACT

Many autistic people struggle with mental health problems like anxiety, depression, inattention, and aggression, which can be challenging to treat. Executive function challenges, which impact many autistic individuals, may serve as a risk factor for mental health problems or make treating mental health conditions more difficult. While some people respond well to medication or therapy, others do not. This study tried to understand if there are different subgroups of autistic young people who may have similar patterns of executive function strengths and challenges-like flexibility, planning, self-monitoring, and emotion regulation. Then, we investigated whether executive function subgroups were related to mental health problems in autistic youth. We found three different types of executive function subgroups in autistic youth, each with different patterns of mental health problems. This helps us identify specific profiles of executive function strengths and challenges that may be helpful with identifying personalized supports, services, and treatment strategies for mental health conditions.

Resting-state brain activation patterns and network topology distinguish human sign and goal trackers

The "Sign-tracker/Goal-tracker" (ST/GT) is an animal model of individual differences in learning and motivational processes attributable to distinctive conditioned responses to environmental cues. While GT rats value the reward-predictive cue as a mere predictor, ST rats attribute it with incentive salience, engaging in aberrant reward-seeking behaviors that mirror those of impulse control disorders. Given its potential clinical value, the present study aimed to map such model onto humans and investigated resting state functional magnetic resonance imaging correlates of individuals categorized as more disposed to sign-tracking or goal-tracking behavior. To do so, eye-tracking was used during a translationally informed Pavlovian paradigm to classify humans as STs (n = 36) GTs (n = 35) or as Intermediates (n = 33), depending on their eye-gaze towards the reward-predictive cue or the reward location. Using connectivity and network-based approach, measures of resting state functional connectivity and centrality (role of a node as a hub) replicated preclinical findings, suggesting a major involvement of subcortical areas in STs, and dominant cortical involvement in GTs. Overall, the study strengthens the translational value of the ST/GT model, with important implications for the early identification of vulnerable phenotypes for psychopathological conditions such as substance use disorder.

Sex Differences in Hierarchical and Modular Organization of Functional Brain Networks: Insights from Hierarchical Entropy and Modularity Analysis

Existing studies have demonstrated significant sex differences in the neural mechanisms of daily life and neuropsychiatric disorders. The hierarchical organization of the functional brain network is a critical feature for assessing these neural mechanisms. But the sex differences in hierarchical organization have not been fully investigated. Here, we explore whether the hierarchical structure of the brain network differs between females and males using resting-state fMRI data. We measure the hierarchical entropy and the maximum modularity of each individual, and identify a significant negative correlation between the complexity of hierarchy and modularity in brain networks. At the mean level, females show higher modularity, whereas males exhibit a more complex hierarchy. At the consensus level, we use a co-classification matrix to perform a detailed investigation of the differences in the hierarchical organization between sexes and observe that the female group and the male group exhibit different interaction patterns of brain regions in the dorsal attention network (DAN) and visual network (VIN). Our findings suggest that the brains of females and males employ different network topologies to carry out brain functions. In addition, the negative correlation between hierarchy and modularity implies a need to balance the complexity in the hierarchical organization of the brain network, which sheds light on future studies of brain functions.

The Neural Correlates of Alcohol Approach Bias - New Insights from a Whole-Brain Network Analysis Perspective

Alcohol approach bias, a tendency to approach rather than to avoid alcohol and alcohol-related cues regardless of associated negative consequences, is an emerging key characteristic of alcohol use disorder (AUD). Reaction times from the Approach-Avoidance Task (AAT) can be used to quantify alcohol approach bias. However, only a handful of studies have investigated the neural correlates of implicit alcohol approach behavior. Graph Theory Analysis (GTA) metrics, specifically, weighted global efficiency (wGE), community detection, and inter-community information integration were used to analyze functional magnetic resonance imaging (fMRI) data of an in-scanner version of the AAT from 31 heavy drinking Veterans with AUD (HDV) engaged in out-patient treatment and 19 healthy Veterans as controls (HC). We found a functional imprint of alcohol approach bias in HDVs. HDVs showed significantly higher wGE values for approaching than for avoiding alcohol, indicating that their brain was more efficiently organized or functionally set to approach alcohol in the presence to alcohol-related external cues. In contrast, Brains of HCs did not show such a processing advantage for either the approach or avoid condition. Further post-hoc analyses revealed that HDVs and HCs differed in how they implemented top-down control when approaching/avoiding alcohol and in how the fronto-parietal control network interacted with subsystems of the default mode network. These findings contribute to understanding the complex neural underpinnings of alcohol approach bias and lay the foundation for developing more potent and targeted interventions to modify these neural patterns in AUD patients.

Cochlear implant induced changes in cortical networks associated with tinnitus severity

Objective.We investigated tinnitus-related cortical networks in cochlear implant users who experience tinnitus and whose perception of tinnitus changes with use of their implant. Tinnitus, the perception of unwanted sounds which are not present externally, can be a debilitating condition. In individuals with cochlear implants, use of the implant is known to modulate tinnitus, often improving symptoms but worsening them in some cases. Little is known about underlying cortical changes with use of the implant, which lead to changes in tinnitus perception. In this study we investigated whether changes in brain networks with the cochlear implant turned on and off, were associated with changes in tinnitus perception, as rated subjectively.Approach.Using functional near-infrared spectroscopy, we recorded cortical activity at rest, from 14 cochlear implant users who experienced tinnitus. Recordings were performed with the cochlear implant turned off and on. For each condition, participants rated the loudness and annoyance of their tinnitus using a visual rating scale. Changes in neural synchrony have been reported in humans and animal models of tinnitus. To assess neural synchrony, functional connectivity networks with the implant turned on and off, were compared using two network features: node strength and diversity coefficient.Main results.Changes in subjective ratings of loudness were significantly correlated with changes in node strength, averaged across occipital channels (r=-0.65, p=0.01). Changes in both loudness and annoyance were significantly correlated with changes in diversity coefficient averaged across all channels (r=-0.79,p<0.001 and r=-0.86,p<0.001). More distributed connectivity with the implant on, compared to implant off, was associated with a reduction in tinnitus loudness and annoyance.Significance.A better understanding of neural mechanisms underlying tinnitus suppression with cochlear implant use, could lead to their application as a tinnitus treatment and pave the way for effective use of other less invasive stimulation-based treatments.

Age-related differences in resting-state, task-related, and structural brain connectivity: graph theoretical analyses and visual search performance

Previous magnetic resonance imaging (MRI) research suggests that aging is associated with a decrease in the functional interconnections within and between groups of locally organized brain regions (modules). Further, this age-related decrease in the segregation of modules appears to be more pronounced for a task, relative to a resting state, reflecting the integration of functional modules and attentional allocation necessary to support task performance. Here, using graph-theoretical analyses, we investigated age-related differences in a whole-brain measure of module connectivity, system segregation, for 68 healthy, community-dwelling individuals 18-78 years of age. We obtained resting-state, task-related (visual search), and structural (diffusion-weighted) MRI data. Using a parcellation of modules derived from the participants' resting-state functional MRI data, we demonstrated that the decrease in system segregation from rest to task (i.e., reconfiguration) increased with age, suggesting an age-related increase in the integration of modules required by the attentional demands of visual search. Structural system segregation increased with age, reflecting weaker connectivity both within and between modules. Functional and structural system segregation had qualitatively different influences on age-related decline in visual search performance. Functional system segregation (and reconfiguration) influenced age-related decline in the rate of visual evidence accumulation (drift rate), whereas structural system segregation contributed to age-related slowing of encoding and response processes (nondecision time). The age-related differences in the functional system segregation measures, however, were relatively independent of those associated with structural connectivity.

Neighborhood structure-guided brain functional networks estimation for mild cognitive impairment identification

The adoption and growth of functional magnetic resonance imaging (fMRI) technology, especially through the use of Pearson's correlation (PC) for constructing brain functional networks (BFN), has significantly advanced brain disease diagnostics by uncovering the brain's operational mechanisms and offering biomarkers for early detection. However, the PC always tends to make for a dense BFN, which violates the biological prior. Therefore, in practice, researchers use hard-threshold to remove weak connection edges or introduce l 1-norm as a regularization term to obtain sparse BFNs. However, these approaches neglect the spatial neighborhood information between regions of interest (ROIs), and ROI with closer distances has higher connectivity prospects than ROI with farther distances due to the principle of simple wiring costs in resent studies. Thus, we propose a neighborhood structure-guided BFN estimation method in this article. In detail, we figure the ROIs' Euclidean distances and sort them. Then, we apply the K-nearest neighbor (KNN) to find out the top K neighbors closest to the current ROIs, where each ROI's K neighbors are independent of each other. We establish the connection relationship between the ROIs and these K neighbors and construct the global topology adjacency matrix according to the binary network. Connect ROI nodes with k nearest neighbors using edges to generate an adjacency graph, forming an adjacency matrix. Based on adjacency matrix, PC calculates the correlation coefficient between ROIs connected by edges, and generates the BFN. With the purpose of evaluating the performance of the introduced method, we utilize the estimated BFN for distinguishing individuals with mild cognitive impairment (MCI) from the healthy ones. Experimental outcomes imply this method attains better classification performance than the baselines. Additionally, we compared it with the most commonly used time series methods in deep learning. Results of the performance of K-nearest neighbor-Pearson's correlation (K-PC) has some advantage over deep learning.

A synergistic workspace for human consciousness revealed by Integrated Information Decomposition

How is the information-processing architecture of the human brain organised, and how does its organisation support consciousness? Here, we combine network science and a rigorous information-theoretic notion of synergy to delineate a 'synergistic global workspace', comprising gateway regions that gather synergistic information from specialised modules across the human brain. This information is then integrated within the workspace and widely distributed via broadcaster regions. Through functional MRI analysis, we show that gateway regions of the synergistic workspace correspond to the human brain's default mode network, whereas broadcasters coincide with the executive control network. We find that loss of consciousness due to general anaesthesia or disorders of consciousness corresponds to diminished ability of the synergistic workspace to integrate information, which is restored upon recovery. Thus, loss of consciousness coincides with a breakdown of information integration within the synergistic workspace of the human brain. This work contributes to conceptual and empirical reconciliation between two prominent scientific theories of consciousness, the Global Neuronal Workspace and Integrated Information Theory, while also advancing our understanding of how the human brain supports consciousness through the synergistic integration of information.

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

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

Cerebellar output neurons impair non-motor behaviors by altering development of extracerebellar connectivity

The capacity of the brain to compensate for insults during development depends on the type of cell loss, whereas the consequences of genetic mutations in the same neurons are difficult to predict. We reveal powerful compensation from outside the cerebellum when the excitatory cerebellar output neurons are ablated embryonically and demonstrate that the minimum requirement for these neurons is for motor coordination and not learning and social behaviors. In contrast, loss of the homeobox transcription factors Engrailed1/2 (EN1/2) in the cerebellar excitatory lineage leads to additional deficits in adult learning and spatial working memory, despite half of the excitatory output neurons being intact. Diffusion MRI indicates increased thalamo-cortico-striatal connectivity in En1/2 mutants, showing that the remaining excitatory neurons lacking En1/2 exert adverse effects on extracerebellar circuits regulating motor learning and select non-motor behaviors. Thus, an absence of cerebellar output neurons is less disruptive than having cerebellar genetic mutations.

Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics

Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines' suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline's performance across criteria and datasets, to inform future best practices in functional connectomics.

Edge-wise analysis reveals white matter connectivity associated with focal to bilateral tonic-clonic seizures

OBJECTIVE

Focal to bilateral tonic-clonic seizures (FBTCS) represent a challenging subtype of focal temporal lobe epilepsy (TLE) in terms of both severity and treatment response. Most studies have focused on regional brain analysis that is agnostic to the distribution of white matter (WM) pathways associated with a node. We implemented a more selective, edge-wise approach that allowed for identification of the individual connections unique to FBTCS.

METHODS

T1-weighted and diffusion-weighted images were obtained from 22 patients with solely focal seizures (FS), 43 FBTCS patients, and 65 age/sex-matched healthy participants (HPs), yielding streamline (STR) connectome matrices. We used diffusion tensor-derived STRs in an edge-wise approach to determine specific structural connectivity changes associated with seizure generalization in FBTCS compared to matched FS and HPs. Graph theory metrics were computed on both node- and edge-based connectivity matrices.

RESULTS

Edge-wise analyses demonstrated that all significantly abnormal cross-hemispheric connections belonged to the FBTCS group. Abnormal connections associated with FBTCS were mostly housed in the contralateral hemisphere, with graph metric values generally decreased compared to HPs. In FBTCS, the contralateral amygdala showed selective decreases in the structural connection pathways to the contralateral frontal lobe. Abnormal connections in TLE involved the amygdala, with the ipsilateral side showing increases and the contralateral decreases. All the FS findings indicated higher graph metrics for connections involving the ipsilateral amygdala. Data also showed that some FBTCS connectivity effects are moderated by aging, recent seizure frequency, and longer illness duration.

SIGNIFICANCE

Data showed that not all STR pathways are equally affected by the seizure propagation of FBTCS. We demonstrated two key biases, one indicating a large role for the amygdala in the propagation of seizures, the other pointing to the prominent role of cross-hemispheric and contralateral hemisphere connections in FBTCS. We demonstrated topographic reorganization in FBTCS, pointing to the specific WM tracts involved.

Defining Trajectories of Linguistic, Cognitive-Communicative, and Quality of Life Outcomes in Aphasia: Longitudinal Observational Study Protocol

Objective

To describe the trajectories of linguistic, cognitive-communicative, and health-related quality of life (HRQOL) outcomes after stroke in persons with aphasia.

Design

Longitudinal observational study from inpatient rehabilitation to 18 months after stroke.

Setting

Four US mid-west inpatient rehabilitation facilities (IRFs).

Participants

We plan to recruit 400 adult (older than 21 years) English speakers who meet the following inclusion criteria: (1) Diagnosis of aphasia after a left-hemisphere infarct confirmed by CT scan or magnetic resonance imaging (MRI); (2) first admission for inpatient rehabilitation due to a neurologic event; and (3) sufficient cognitive capacity to provide informed consent and participate in testing. Exclusion criteria include any neurologic condition other than stroke that could affect language, cognition or speech, such as Parkinson's disease, Alzheimer's disease, traumatic brain injury, or the presence of right-hemisphere lesions.

Interventions

Not applicable.

Main Outcome Measures

Subjects are administered a test battery of linguistic, cognitive-communicative, and HRQOL measures. Linguistic measures include the Western Aphasia Battery-Revised and the Apraxia of Speech Rating Scale. Cognitive-communicative measures include the Communication Participation Item Bank, Connor's Continuous Performance Test-3, the Communication Confidence Rating Scale for Aphasia, the Communication Effectiveness Index, the Neurological Quality of Life measurement system (Neuro-QoL) Communication short form, and the Neuro-QoL Cognitive Function short form. HRQOL measures include the 39-item Stroke & Aphasia Quality of Life Scale, Neuro-QoL Fatigue, Sleep Disturbance, Depression, Ability to Participate in Social Roles & Activities, and Satisfaction with Social Roles & Activities tests, and the Patient-Reported Outcome Measurement and Information System 10-item Global Health short form. The test battery is administered initially during inpatient rehabilitation, and at 3-, 6-, 12-, and 18-months post-IRF discharge. Biomarker samples are collected via saliva samples at admission and a subgroup of participants also undergo resting state fMRI scans.

Results

Not applicable.

Conclusions

This longitudinal observational study will develop trajectory models for recovery of clinically relevant linguistic, cognitive-communicative, and quality of life outcomes over 18 months after inpatient rehabilitation. Models will identify individual differences in the patterns of recovery based on variations in personal, genetic, imaging, and therapy characteristics. The resulting models will provide an unparalleled representation of recovery from aphasia resulting from stroke. This improved understanding of recovery will enable clinicians to better tailor and plan rehabilitation therapies to individual patient's needs.

Glioblastoma disrupts cortical network activity at multiple spatial and temporal scales

The emergence of glioblastoma in cortical tissue initiates early and persistent neural hyperexcitability with signs ranging from mild cognitive impairment to convulsive seizures. The influence of peritumoral synaptic density, expansion dynamics, and spatial contours of excess glutamate upon higher order neuronal network modularity is unknown. We combined cellular and widefield imaging of calcium and glutamate fluorescent reporters in two glioblastoma mouse models with distinct synaptic microenvironments and infiltration profiles. Functional metrics of neural ensembles are dysregulated during tumor invasion depending on the stage of malignant progression and tumor cell proximity. Neural activity is differentially modulated during periods of accelerated and inhibited tumor expansion. Abnormal glutamate accumulation precedes and outpaces the spatial extent of baseline neuronal calcium signaling, indicating these processes are uncoupled in tumor cortex. Distinctive excitability homeostasis patterns and functional connectivity of local and remote neuronal populations support the promise of precision genetic diagnosis and management of this devastating brain disease.

Diverging functional connectivity timescales: Capturing distinct aspects of cognitive performance in early psychosis

Background

Psychosis spectrum disorders (PSDs) are marked by cognitive impairments, the neurobiological correlates of which remain poorly understood. Here, we investigate the entropy of time-varying functional connectivity (TVFC) patterns from resting-state fMRI (rfMRI) as potential biomarker for cognitive performance in PSDs. By combining our results with multimodal reference data, we hope to generate new insights into the mechanisms underlying cognitive dysfunction in PSDs. We hypothesized that low-entropy TVFC patterns (LEN) would be more behaviorally informative than high-entropy TVFC patterns (HEN), especially for tasks that require extensive integration across diverse cognitive subdomains.

Methods

rfMRI and behavioral data from 97 patients in the early phases of psychosis and 53 controls were analyzed. Positron-Emission Tomography (PET) and magnetoencephalography (MEG) data were taken from a public repository (Hansen et al., 2022). Multivariate analyses were conducted to examine relationships between TVFC patterns at multiple spatial scales and cognitive performance in patients.

Results

Compared to HEN, LEN explained significantly more cognitive variance on average in PSD patients, driven by superior encoding of information on psychometrically more integrated tasks. HEN better captured information in specific subdomains of executive functioning. Nodal HEN-LEN transitions were spatially aligned with neurobiological gradients reflecting monoaminergic transporter densities and MEG beta power. Exploratory analyses revealed a close statistical relationship between LEN and positive PSD symptoms.

Conclusion

Our entropy-based analysis of TVFC patterns dissociates distinct aspects of cognition in PSDs. By linking topographies of neurotransmission and oscillatory dynamics with cognitive performance, it enhances our understanding of the mechanisms underlying cognitive deficits in PSDs.

CRediT Authorship Contribution Statement

Fabian Hirsch: Conceptualization, Methodology, Software, Formal analysis, Writing - Original Draft, Writing - Review & Editing, Visualization; Ângelo Bumanglag: Methodology, Software, Formal analysis, Writing - Review & Editing; Yifei Zhang: Methodology, Software, Formal analysis, Writing - Review & Editing; Afra Wohlschlaeger: Methodology, Writing - Review & Editing, Supervision, Project administration.