A mechanistic model of connector hubs, modularity and cognition

Brain Network Interconnectivity Dynamics Explain Metacognitive Differences in Listening Behavior

Complex auditory scenes pose a challenge to attentive listening, rendering listeners slower and more uncertain in their perceptual decisions. How can we explain such behaviors from the dynamics of cortical networks that pertain to the control of listening behavior? We here follow up on the hypothesis that human adaptive perception in challenging listening situations is supported by modular reconfiguration of auditory-control networks in a sample of N = 40 participants (13 males) who underwent resting-state and task functional magnetic resonance imaging (fMRI). Individual titration of a spatial selective auditory attention task maintained an average accuracy of ∼70% but yielded considerable interindividual differences in listeners' response speed and reported confidence in their own perceptual decisions. Whole-brain network modularity increased from rest to task by reconfiguring auditory, cinguloopercular, and dorsal attention networks. Specifically, interconnectivity between the auditory network and cinguloopercular network decreased during the task relative to the resting state. Additionally, interconnectivity between the dorsal attention network and cinguloopercular network increased. These interconnectivity dynamics were predictive of individual differences in response confidence, the degree of which was more pronounced after incorrect judgments. Our findings uncover the behavioral relevance of functional cross talk between auditory and attentional-control networks during metacognitive assessment of one's own perception in challenging listening situations and suggest two functionally dissociable cortical networked systems that shape the considerable metacognitive differences between individuals in adaptive listening behavior.

A network control theory pipeline for studying the dynamics of the structural connectome

Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains the dynamics of a system. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter the dynamics of a system in a desired way. An interesting development for NCT in the neuroscience field is its application to study behavior and mental health symptoms. To date, NCT has been validated to study different aspects of the human structural connectome. NCT outputs can be monitored throughout developmental stages to study the effects of connectome topology on neural dynamics and, separately, to test the coherence of empirical datasets with brain function and stimulation. Here, we provide a comprehensive pipeline for applying NCT to structural connectomes by following two procedures. The main procedure focuses on computing the control energy associated with the transitions between specific neural activity states. The second procedure focuses on computing average controllability, which indexes nodes' general capacity to control the dynamics of the system. We provide recommendations for comparing NCT outputs against null network models, and we further support this approach with a Python-based software package called 'network control theory for python'. The procedures in this protocol are appropriate for users with a background in network neuroscience and experience in dynamical systems theory.

Sex differences of signal complexity at resting-state functional magnetic resonance imaging and their associations with the estrogen-signaling pathway in the brain

Sex differences in the brain have been widely reported and may hold the key to elucidating sex differences in many medical conditions and drug response. However, the molecular correlates of these sex differences in structural and functional brain measures in the human brain remain unclear. Herein, we used sample entropy (SampEn) to quantify the signal complexity of resting-state functional magnetic resonance imaging (rsfMRI) in a large neuroimaging cohort (N = 1,642). The frontoparietal control network and the cingulo-opercular network had high signal complexity while the cerebellar and sensory motor networks had low signal complexity in both men and women. Compared with those in male brains, we found greater signal complexity in all functional brain networks in female brains with the default mode network exhibiting the largest sex difference. Using the gene expression data in brain tissues, we identified genes that were significantly associated with sex differences in brain signal complexity. The significant genes were enriched in the gene sets that were differentially expressed between the brain cortex and other tissues, the estrogen-signaling pathway, and the biological function of neural plasticity. In particular, the G-protein-coupled estrogen receptor 1 gene in the estrogen-signaling pathway was expressed more in brain regions with greater sex differences in SampEn. In conclusion, greater complexity in female brains may reflect the interactions between sex hormone fluctuations and neuromodulation of estrogen in women.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-023-09954-y.

Structural-functional brain network coupling predicts human cognitive ability

Individual differences in general cognitive ability (GCA) have a biological basis within the structure and function of the human brain. Network neuroscience investigations revealed neural correlates of GCA in structural as well as in functional brain networks. However, whether the relationship between structural and functional networks, the structural-functional brain network coupling (SC-FC coupling), is related to individual differences in GCA remains an open question. We used data from 1030 adults of the Human Connectome Project, derived structural connectivity from diffusion weighted imaging, functional connectivity from resting-state fMRI, and assessed GCA as a latent g-factor from 12 cognitive tasks. Two similarity measures and six communication measures were used to model possible functional interactions arising from structural brain networks. SC-FC coupling was estimated as the degree to which these measures align with the actual functional connectivity, providing insights into different neural communication strategies. At the whole-brain level, higher GCA was associated with higher SC-FC coupling, but only when considering path transitivity as neural communication strategy. Taking region-specific variations in the SC-FC coupling strategy into account and differentiating between positive and negative associations with GCA, allows for prediction of individual cognitive ability scores in a cross-validated prediction framework (correlation between predicted and observed scores: r = 0.25, p < .001). The same model also predicts GCA scores in a completely independent sample (N = 567, r = 0.19, p < .001). Our results propose structural-functional brain network coupling as a neurobiological correlate of GCA and suggest brain region-specific coupling strategies as neural basis of efficient information processing predictive of cognitive ability.

Precise detection of awareness in disorders of consciousness using deep learning framework

Diagnosis of disorders of consciousness (DOC) remains a formidable challenge. Deep learning methods have been widely applied in general neurological and psychiatry disorders, while limited in DOC domain. Considering the successful use of resting-state functional MRI (rs-fMRI) for evaluating patients with DOC, this study seeks to explore the conjunction of deep learning techniques and rs-fMRI in precisely detecting awareness in DOC. We initiated our research with a benchmark dataset comprising 140 participants, including 76 unresponsive wakefulness syndrome (UWS), 25 minimally conscious state (MCS), and 39 Controls, from three independent sites. We developed a cascade 3D EfficientNet-B3-based deep learning framework tailored for discriminating MCS from UWS patients, referred to as "DeepDOC", and compared its performance against five state-of-the-art machine learning models. We also included an independent dataset consists of 11 DOC patients to test whether our model could identify patients with cognitive motor dissociation (CMD), in which DOC patients were behaviorally diagnosed unconscious but could be detected conscious by brain computer interface (BCI) method. Our results demonstrate that DeepDOC outperforms the five machine learning models, achieving an area under curve (AUC) value of 0.927 and accuracy of 0.861 for distinguishing MCS from UWS patients. More importantly, DeepDOC excels in CMD identification, achieving an AUC of 1 and accuracy of 0.909. Using gradient-weighted class activation mapping algorithm, we found that the posterior cortex, encompassing the visual cortex, posterior middle temporal gyrus, posterior cingulate cortex, precuneus, and cerebellum, as making a more substantial contribution to classification compared to other brain regions. This research offers a convenient and accurate method for detecting covert awareness in patients with MCS and CMD using rs-fMRI data.

Modeling the neurocognitive dynamics of language across the lifespan

Healthy aging is associated with a heterogeneous decline across cognitive functions, typically observed between language comprehension and language production (LP). Examining resting-state fMRI and neuropsychological data from 628 healthy adults (age 18-88) from the CamCAN cohort, we performed state-of-the-art graph theoretical analysis to uncover the neural mechanisms underlying this variability. At the cognitive level, our findings suggest that LP is not an isolated function but is modulated throughout the lifespan by the extent of inter-cognitive synergy between semantic and domain-general processes. At the cerebral level, we show that default mode network (DMN) suppression coupled with fronto-parietal network (FPN) integration is the way for the brain to compensate for the effects of dedifferentiation at a minimal cost, efficiently mitigating the age-related decline in LP. Relatedly, reduced DMN suppression in midlife could compromise the ability to manage the cost of FPN integration. This may prompt older adults to adopt a more cost-efficient compensatory strategy that maintains global homeostasis at the expense of LP performances. Taken together, we propose that midlife represents a critical neurocognitive juncture that signifies the onset of LP decline, as older adults gradually lose control over semantic representations. We summarize our findings in a novel synergistic, economical, nonlinear, emergent, cognitive aging model, integrating connectomic and cognitive dimensions within a complex system perspective.

Community detection in the human connectome: Method types, differences and their impact on inference

Community structure is a fundamental topological characteristic of optimally organized brain networks. Currently, there is no clear standard or systematic approach for selecting the most appropriate community detection method. Furthermore, the impact of method choice on the accuracy and robustness of estimated communities (and network modularity), as well as method-dependent relationships between network communities and cognitive and other individual measures, are not well understood. This study analyzed large datasets of real brain networks (estimated from resting-state fMRI fromn $$ n $$ = 5251 pre/early adolescents in the adolescent brain cognitive development [ABCD] study), andn $$ n $$ = 5338 synthetic networks with heterogeneous, data-inspired topologies, with the goal to investigate and compare three classes of community detection methods: (i) modularity maximization-based (Newman and Louvain), (ii) probabilistic (Bayesian inference within the framework of stochastic block modeling (SBM)), and (iii) geometric (based on graph Ricci flow). Extensive comparisons between methods and their individual accuracy (relative to the ground truth in synthetic networks), and reliability (when applied to multiple fMRI runs from the same brains) suggest that the underlying brain network topology plays a critical role in the accuracy, reliability and agreement of community detection methods. Consistent method (dis)similarities, and their correlations with topological properties, were estimated across fMRI runs. Based on synthetic graphs, most methods performed similarly and had comparable high accuracy only in some topological regimes, specifically those corresponding to developed connectomes with at least quasi-optimal community organization. In contrast, in densely and/or weakly connected networks with difficult to detect communities, the methods yielded highly dissimilar results, with Bayesian inference within SBM having significantly higher accuracy compared to all others. Associations between method-specific modularity and demographic, anthropometric, physiological and cognitive parameters showed mostly method invariance but some method dependence as well. Although method sensitivity to different levels of community structure may in part explain method-dependent associations between modularity estimates and parameters of interest, method dependence also highlights potential issues of reliability and reproducibility. These findings suggest that a probabilistic approach, such as Bayesian inference in the framework of SBM, may provide consistently reliable estimates of community structure across network topologies. In addition, to maximize robustness of biological inferences, identified network communities and their cognitive, behavioral and other correlates should be confirmed with multiple reliable detection methods.

Integrated sentence-level speech perception evokes strengthened language networks and facilitates early speech development

Natural poetic speeches (i.e., proverbs, nursery rhymes, and commercial ads) with strong prosodic regularities are easily memorized by children and the harmonious acoustic patterns are suggested to facilitate their integrated sentence processing. Do children have specific neural pathways for perceiving such poetic utterances, and does their speech development benefit from it? We recorded the task-induced hemodynamic changes of 94 children aged 2 to 12 years using functional near-infrared spectroscopy (fNIRS) while they listened to poetic and non-poetic natural sentences. Seventy-three adult as controls were recruited to investigate the developmental specificity of children group. The results indicated that poetic sentences perceiving is a highly integrated process featured by a lower brain workload in both groups. However, an early activated large-scale network was induced only in the child group, coordinated by hubs for connectivity diversity. Additionally, poetic speeches evoked activation in the phonological encoding regions in the children's group rather than adult controls which decreases with children's ages. The neural responses to poetic speeches were positively linked to children's speech communication performance, especially the fluency and semantic aspects. These results reveal children's neural sensitivity to integrated speech perception which facilitate early speech development by strengthening more sophisticated language networks and the perception-production circuit.

Hierarchical consciousness: the Nested Observer Windows model

Foremost in our experience is the intuition that we possess a unified conscious experience. However, many observations run counter to this intuition: we experience paralyzing indecision when faced with two appealing behavioral choices, we simultaneously hold contradictory beliefs, and the content of our thought is often characterized by an internal debate. Here, we propose the Nested Observer Windows (NOW) Model, a framework for hierarchical consciousness wherein information processed across many spatiotemporal scales of the brain feeds into subjective experience. The model likens the mind to a hierarchy of nested mosaic tiles-where an image is composed of mosaic tiles, and each of these tiles is itself an image composed of mosaic tiles. Unitary consciousness exists at the apex of this nested hierarchy where perceptual constructs become fully integrated and complex behaviors are initiated via abstract commands. We define an observer window as a spatially and temporally constrained system within which information is integrated, e.g. in functional brain regions and neurons. Three principles from the signal analysis of electrical activity describe the nested hierarchy and generate testable predictions. First, nested observer windows disseminate information across spatiotemporal scales with cross-frequency coupling. Second, observer windows are characterized by a high degree of internal synchrony (with zero phase lag). Third, observer windows at the same spatiotemporal level share information with each other through coherence (with non-zero phase lag). The theoretical framework of the NOW Model accounts for a wide range of subjective experiences and a novel approach for integrating prominent theories of consciousness.

Brain-inspired modular echo state network for EEG-based emotion recognition

Previous studies have successfully applied a lightweight recurrent neural network (RNN) called Echo State Network (ESN) for EEG-based emotion recognition. These studies use intrinsic plasticity (IP) and synaptic plasticity (SP) to tune the hidden reservoir layer of ESN, yet they require extra training procedures and are often computationally complex. Recent neuroscientific research reveals that the brain is modular, consisting of internally dense and externally sparse subnetworks. Furthermore, it has been proved that this modular topology facilitates information processing efficiency in both biological and artificial neural networks (ANNs). Motivated by these findings, we propose Modular Echo State Network (M-ESN), where the hidden layer of ESN is directly initialized to a more efficient modular structure. In this paper, we first describe our novel implementation method, which enables us to find the optimal module numbers, local and global connectivity. Then, the M-ESN is benchmarked on the DEAP dataset. Lastly, we explain why network modularity improves model performance. We demonstrate that modular organization leads to a more diverse distribution of node degrees, which increases network heterogeneity and subsequently improves classification accuracy. On the emotion arousal, valence, and stress/calm classification tasks, our M-ESN outperforms regular ESN by 5.44, 5.90, and 5.42%, respectively, while this difference when comparing with adaptation rules tuned ESNs are 0.77, 5.49, and 0.95%. Notably, our results are obtained using M-ESN with a much smaller reservoir size and simpler training process.

Cortical timescales and the modular organization of structural and functional brain networks

Recent years have seen growing interest in characterizing the properties of regional brain dynamics and their relationship to other features of brain structure and function. In particular, multiple studies have observed regional differences in the "timescale" over which activity fluctuates during periods of quiet rest. In the cerebral cortex, these timescales have been associated with both local circuit properties as well as patterns of inter-regional connectivity, including the extent to which each region exhibits widespread connectivity to other brain areas. In the current study, we build on prior observations of an association between connectivity and dynamics in the cerebral cortex by investigating the relationship between BOLD fMRI timescales and the modular organization of structural and functional brain networks. We characterize network community structure across multiple scales and find that longer timescales are associated with greater within-community functional connectivity and diverse structural connectivity. We also replicate prior observations of a positive correlation between timescales and structural connectivity degree. Finally, we find evidence for preferential functional connectivity between cortical areas with similar timescales. We replicate these findings in an independent dataset. These results contribute to our understanding of functional brain organization and structure-function relationships in the human brain, and support the notion that regional differences in cortical dynamics may in part reflect the topological role of each region within macroscale brain networks.

Depth- and curvature-based quantitative susceptibility mapping analyses of cortical iron in Alzheimer's disease

In addition to amyloid beta plaques and neurofibrillary tangles, Alzheimer's disease (AD) has been associated with elevated iron in deep gray matter nuclei using quantitative susceptibility mapping (QSM). However, only a few studies have examined cortical iron, using more macroscopic approaches that cannot assess layer-specific differences. Here, we conducted column-based QSM analyses to assess whether AD-related increases in cortical iron vary in relation to layer-specific differences in the type and density of neurons. We obtained global and regional measures of positive (iron) and negative (myelin, protein aggregation) susceptibility from 22 adults with AD and 22 demographically matched healthy controls. Depth-wise analyses indicated that global susceptibility increased from the pial surface to the gray/white matter boundary, with a larger slope for positive susceptibility in the left hemisphere for adults with AD than controls. Curvature-based analyses indicated larger global susceptibility for adults with AD versus controls; the right hemisphere versus left; and gyri versus sulci. Region-of-interest analyses identified similar depth- and curvature-specific group differences, especially for temporo-parietal regions. Finding that iron accumulates in a topographically heterogenous manner across the cortical mantle may help explain the profound cognitive deterioration that differentiates AD from the slowing of general motor processes in healthy aging.

Connectome-based reservoir computing with the conn2res toolbox

The connection patterns of neural circuits form a complex network. How signaling in these circuits manifests as complex cognition and adaptive behaviour remains the central question in neuroscience. Concomitant advances in connectomics and artificial intelligence open fundamentally new opportunities to understand how connection patterns shape computational capacity in biological brain networks. Reservoir computing is a versatile paradigm that uses high-dimensional, nonlinear dynamical systems to perform computations and approximate cognitive functions. Here we present conn2res: an open-source Python toolbox for implementing biological neural networks as artificial neural networks. conn2res is modular, allowing arbitrary network architecture and dynamics to be imposed. The toolbox allows researchers to input connectomes reconstructed using multiple techniques, from tract tracing to noninvasive diffusion imaging, and to impose multiple dynamical systems, from spiking neurons to memristive dynamics. The versatility of the conn2res toolbox allows us to ask new questions at the confluence of neuroscience and artificial intelligence. By reconceptualizing function as computation, conn2res sets the stage for a more mechanistic understanding of structure-function relationships in brain networks.

Investigating robust associations between functional connectivity based on graph theory and general intelligence

Previous research investigating relations between general intelligence and graph-theoretical properties of the brain's intrinsic functional network has yielded contradictory results. A promising approach to tackle such mixed findings is multi-center analysis. For this study, we analyzed data from four independent data sets (total N > 2000) to identify robust associations amongst samples between g factor scores and global as well as node-specific graph metrics. On the global level, g showed no significant associations with global efficiency or small-world propensity in any sample, but significant positive associations with global clustering coefficient in two samples. On the node-specific level, elastic-net regressions for nodal efficiency and local clustering yielded no brain areas that exhibited consistent associations amongst data sets. Using the areas identified via elastic-net regression in one sample to predict g in other samples was not successful for local clustering and only led to one significant, one-way prediction across data sets for nodal efficiency. Thus, using conventional graph theoretical measures based on resting-state imaging did not result in replicable associations between functional connectivity and general intelligence.

The balance of hippocampal and caudate network functional connectivity is associated with episodic memory performance and its decline across adulthood

The hippocampal and caudate networks interact to support episodic memory, but the relationship between hippocampal and caudate connectivity strength and episodic memory is unclear. In general, cognition is optimally supported when connectivity within a functional network dominates connectivity from other networks. For example, episodic memory may be optimally supported when the hippocampal and caudate networks express this pattern of connectivity, consistent with research showing that the two networks are organized competitively. Alternatively, episodic memory may be optimally supported when connectivity in both networks is more balanced, consistent with fMRI reports showing cooperation between networks. Using cross-sectional behavioral and resting state fMRI data from a diverse sample (N = 347; Ages 18-85), I tested the hypothesis that reduced hippocampal and caudate network dominance would be associated with reduced episodic memory across individuals and age. Consistent with this hypothesis, lower caudate network dominance in bilateral thalamic regions was associated with worse episodic memory regardless of age. Age-related differences in caudate network dominance in the pallidum and putamen were also associated with worse episodic memory performance, but through their shared variance with age. I found no evidence that network dominance was related to processing speed or executive function, or that hippocampal network dominance was relate to episodic memory performance. These results show that ongoing biological dynamics between the hippocampal and caudate networks throughout adulthood are related to episodic memory performance and support a growing literature specifying the role of the caudate network in episodic memory.

Subcortical influences on the topology of cortical networks align with functional processing hierarchies

fMRI of the human brain reveals spatiotemporal patterns of functional connectivity (FC), forming distinct cortical networks. Lately, subcortical contributions to these configurations are receiving renewed interest, but investigations rarely focus explicitly on their effects on cortico-cortical FC. Here, we employ a straightforward multivariable approach and graph-theoretic tools to assess subcortical impact on topological features of cortical networks. Given recent evidence showing that structures like the thalamus and basal ganglia integrate input from multiple networks, we expect increased segregation between cortical networks after removal of subcortical effects on their FC patterns. We analyze resting state data of young and healthy participants (male and female; N = 100) from the human connectome project. We find that overall, the cortical network architecture becomes less segregated, and more integrated, when subcortical influences are accounted for. Underlying these global effects are the following trends: 'Transmodal' systems become more integrated with the rest of the network, while 'unimodal' networks show the opposite effect. For single nodes this hierarchical organization is reflected by a close correspondence with the spatial layout of the principal gradient of FC (Margulies et al., 2016). Lastly, we show that the limbic system is significantly less coherent with subcortical influences removed. The findings are validated in a (split-sample) replication dataset. Our results provide new insight regarding the interplay between subcortex and cortical networks, by putting the integrative impact of subcortex in the context of macroscale patterns of cortical organization.

Diverse functional interaction driven by control-default network hubs supports creative thinking

Complex cognitive processes, like creative thinking, rely on interactions among multiple neurocognitive processes to generate effective and innovative behaviors on demand, for which the brain's connector hubs play a crucial role. However, the unique contribution of specific hub sets to creative thinking is unknown. Employing three functional magnetic resonance imaging datasets (total N = 1,911), we demonstrate that connector hub sets are organized in a hierarchical manner based on diversity, with "control-default hubs"-which combine regions from the frontoparietal control and default mode networks-positioned at the apex. Specifically, control-default hubs exhibit the most diverse resting-state connectivity profiles and play the most substantial role in facilitating interactions between regions with dissimilar neurocognitive functions, a phenomenon we refer to as "diverse functional interaction". Critically, we found that the involvement of control-default hubs in facilitating diverse functional interaction robustly relates to creativity, explaining both task-induced functional connectivity changes and individual creative performance. Our findings suggest that control-default hubs drive diverse functional interaction in the brain, enabling complex cognition, including creative thinking. We thus uncover a biologically plausible explanation that further elucidates the widely reported contributions of certain frontoparietal control and default mode network regions in creativity studies.

＜Editors' Choice＞ Pattern of THK 5351 retention in normal aging involves core regions of resting state networks associated with higher cognitive function

We aimed to elucidate the distribution pattern of the positron emission tomography probe [18F]THK 5351, a marker for astrogliosis and tau accumulation, in healthy aging. We also assessed the relationship between THK5351 retention and resting state networks. We enrolled 62 healthy participants in this study. All participants underwent magnetic resonance imaging/positron emission tomography scanning consisting of T1-weighted images, resting state functional magnetic resonance imaging, Pittsburgh Compound-B and THK positron emission tomography. The preprocessed THK images were entered into a scaled subprofile modeling/principal component analysis to extract THK distribution patterns. Using the most significant THK pattern, we generated regions of interest, and performed seed-based functional connectivity analyses. We also evaluated the functional connectivity overlap ratio to identify regions with high between-network connectivity. The most significant THK distributions were observed in the medial prefrontal cortex and bilateral putamen. The seed regions of interest in the medial prefrontal cortex had a functional connectivity map that significantly overlapped with regions of the dorsal default mode network. The seed regions of interest in the putamen showed strong overlap with the basal ganglia and anterior salience networks. The functional connectivity overlap ratio also showed that three peak regions had the characteristics of connector hubs. We have identified an age-related spatial distribution of THK in the medial prefrontal cortex and basal ganglia in normal aging. Interestingly, the distribution's peaks are located in regions of connector hubs that are strongly connected to large-scale resting state networks associated with higher cognitive function.

Neurobiological mechanisms for language, symbols and concepts: Clues from brain-constrained deep neural networks

Neural networks are successfully used to imitate and model cognitive processes. However, to provide clues about the neurobiological mechanisms enabling human cognition, these models need to mimic the structure and function of real brains. Brain-constrained networks differ from classic neural networks by implementing brain similarities at different scales, ranging from the micro- and mesoscopic levels of neuronal function, local neuronal links and circuit interaction to large-scale anatomical structure and between-area connectivity. This review shows how brain-constrained neural networks can be applied to study in silico the formation of mechanisms for symbol and concept processing and to work towards neurobiological explanations of specifically human cognitive abilities. These include verbal working memory and learning of large vocabularies of symbols, semantic binding carried by specific areas of cortex, attention focusing and modulation driven by symbol type, and the acquisition of concrete and abstract concepts partly influenced by symbols. Neuronal assembly activity in the networks is analyzed to deliver putative mechanistic correlates of higher cognitive processes and to develop candidate explanations founded in established neurobiological principles.

Functional gradients reveal altered functional segregation in patients with amnestic mild cognitive impairment and Alzheimer's disease

Alzheimer's disease and amnestic mild cognitive impairment are associated with disrupted functional organization in brain networks, involved with alteration of functional segregation. Connectome gradients are a new tool representing brain functional topological organization to smoothly capture the human macroscale hierarchy. Here, we examined altered topological organization in amnestic mild cognitive impairment and Alzheimer's disease by connectome gradient mapping. We further quantified functional segregation by gradient dispersion. Then, we systematically compared the alterations observed in amnestic mild cognitive impairment and Alzheimer's disease patients with those in normal controls in a two-dimensional functional gradient space from both the whole-brain level and module level. Compared with normal controls, the first gradient, which described the neocortical hierarchy from unimodal to transmodal regions, showed a more distributed and significant suppression in Alzheimer's disease than amnestic mild cognitive impairment patients. Furthermore, gradient dispersion showed significant decreases in Alzheimer's disease at both the global level and module level, whereas this alteration was limited only to limbic areas in amnestic mild cognitive impairment. Notably, we demonstrated that suppressed gradient dispersion in amnestic mild cognitive impairment and Alzheimer's disease was associated with cognitive scores. These findings provide new evidence for altered brain hierarchy in amnestic mild cognitive impairment and Alzheimer's disease, which strengthens our understanding of the progressive mechanism of cognitive decline.

Disruptions in modular structure and network integration of language-related network predict language performance in temporal lobe epilepsy: Evidence from graph-based analysis

OBJECTIVE

Temporal lobe epilepsy (TLE) is a network disorder that alters the total organization of the language-related network. Task-based functional magnetic resonance imaging (fMRI) aimed at functional connectivity is a direct method to investigate how the network is reorganized. However, such studies are scarce and represented mostly by the resting-state analysis of the individual connections between regions. To fill this gap, we used a graph-based analysis, which allows us to cover the total language-related network changes, such as disruptions in an integration/segregation balance, during a language task in TLE.

METHODS

We collected task-based fMRI data with sentence completion from 19 healthy controls and 28 people with left TLE. Using graph-based analysis, we estimated how the language-related network segregated into modules and tested whether they differed between groups. We evaluated the total network integration and the integration within modules. To assess intermodular integration, we considered the number and location of connector hubs-regions with high connectivity.

RESULTS

The language-related network was differently segregated during language processing in the groups. While healthy controls showed a module consisting of left perisylvian regions, people with TLE exhibited a bilateral module formed by the anterior language-related areas and a module in the left temporal lobe, reflecting hyperconnectivity within the epileptic focus. As a consequence of this reorganization, there was a statistical tendency that the dominance of the intramodular integration over the total network integration was greater in TLE, which predicted language performance. The increase in the number of connector hubs in the right hemisphere, in turn, was compensatory in TLE.

SIGNIFICANCE

Our study provides insights into the reorganization of the language-related network in TLE, revealing specific network changes in segregation and integration. It confirms reduced global connectivity and compensation across the healthy hemisphere, commonly observed in epilepsy. These findings advance the understanding of the network-based reorganizational processes underlying language processing in TLE.

Evaluating functional brain organization in individuals and identifying contributions to network overlap

Individual differences in the spatial organization of resting state networks have received increased attention in recent years. Measures of individual-specific spatial organization of brain networks and overlapping network organization have been linked to important behavioral and clinical traits and are therefore potential biomarker targets for personalized psychiatry approaches. To better understand individual-specific spatial brain organization, this paper addressed three key goals. First, we determined whether it is possible to reliably estimate weighted (non-binarized) resting state network maps using data from only a single individual, while also maintaining maximum spatial correspondence across individuals. Second, we determined the degree of spatial overlap between distinct networks, using test-retest and twin data. Third, we systematically tested multiple hypotheses (spatial mixing, temporal switching, and coupling) as candidate explanations for why networks overlap spatially. To estimate weighted network organization, we adopt the Probabilistic Functional Modes (PROFUMO) algorithm, which implements a Bayesian framework with hemodynamic and connectivity priors to supplement optimization for spatial sparsity/independence. Our findings showed that replicable individual-specific estimates of weighted resting state networks can be derived using high quality fMRI data within individual subjects. Network organization estimates using only data from each individual subject closely resembled group-informed network estimates (which was not explicitly modeled in our individual-specific analyses), suggesting that cross-subject correspondence was largely maintained. Furthermore, our results confirmed the presence of spatial overlap in network organization, which was replicable across sessions within individuals and in monozygotic twin pairs. Intriguingly, our findings provide evidence that network overlap is indicative of linear additive coupling. These results suggest that regions of network overlap concurrently process information from all contributing networks, potentially pointing to the role of overlapping network organization in the integration of information across multiple brain systems.

The role of cannabidiol in aging

Aging is usually considered a key risk factor associated with multiple diseases, such as neurodegenerative diseases, cardiovascular diseases and cancer. Furthermore, the burden of age-related diseases has become a global challenge. It is of great significance to search for drugs to extend lifespan and healthspan. Cannabidiol (CBD), a natural nontoxic phytocannabinoid, has been regarded as a potential candidate drug for antiaging. An increasing number of studies have suggested that CBD could benefit healthy longevity. Herein, we summarized the effect of CBD on aging and analyzed the possible mechanism. All these conclusions may provide a perspective for further study of CBD on aging.

Using network control theory to study the dynamics of the structural connectome

Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains dynamics. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter dynamics in a desired way. We have extensively developed and validated the application of NCT to the human structural connectome. Through these efforts, we have studied (i) how different aspects of connectome topology affect neural dynamics, (ii) whether NCT outputs cohere with empirical data on brain function and stimulation, and (iii) how NCT outputs vary across development and correlate with behavior and mental health symptoms. In this protocol, we introduce a framework for applying NCT to structural connectomes following two main pathways. Our primary pathway focuses on computing the control energy associated with transitioning between specific neural activity states. Our second pathway focuses on computing average controllability, which indexes nodes' general capacity to control dynamics. We also provide recommendations for comparing NCT outputs against null network models. Finally, we support this protocol with a Python-based software package called network control theory for python (nctpy).

Brain hubs defined in the group do not overlap with regions of high inter-individual variability

Connector 'hubs' are brain regions with links to multiple networks. These regions are hypothesized to play a critical role in brain function. While hubs are often identified based on group-average functional magnetic resonance imaging (fMRI) data, there is considerable inter-subject variation in the functional connectivity profiles of the brain, especially in association regions where hubs tend to be located. Here we investigated how group hubs are related to locations of inter-individual variability. To answer this question, we examined inter-individual variation at group-level hubs in both the Midnight Scan Club and Human Connectome Project datasets. The top group hubs defined based on the participation coefficient did not overlap strongly with the most prominent regions of inter-individual variation (termed 'variants' in prior work). These hubs have relatively strong similarity across participants and consistent cross-network profiles, similar to what was seen for many other areas of cortex. Consistency across participants was further improved when these hubs were allowed to shift slightly in local position. Thus, our results demonstrate that the top group hubs defined with the participation coefficient are generally consistent across people, suggesting they may represent conserved cross-network bridges. More caution is warranted with alternative hub measures, such as community density (which are based on spatial proximity to network borders) and intermediate hub regions which show higher correspondence to locations of individual variability.

Cortical timescales and the modular organization of structural and functional brain networks

Recent years have seen growing interest in characterizing the properties of regional brain dynamics and their relationship to other features of brain structure and function. In particular, multiple studies have observed regional differences in the "timescale" over which activity fluctuates during periods of quiet rest. In the cerebral cortex, these timescales have been associated with both local circuit properties as well as patterns of inter-regional connectivity, including the extent to which each region exhibits widespread connectivity to other brain areas. In the current study, we build on prior observations of an association between connectivity and dynamics in the cerebral cortex by investigating the relationship between BOLD fMRI timescales and the modular organization of structural and functional brain networks. We characterize network community structure across multiple scales and find that longer timescales are associated with greater within-community functional connectivity and diverse structural connectivity. We also replicate prior observations of a positive correlation between timescales and structural connectivity degree. Finally, we find evidence for preferential functional connectivity between cortical areas with similar timescales. We replicate these findings in an independent dataset. These results contribute to our understanding of functional brain organization and structure-function relationships in the human brain, and support the notion that regional differences in cortical dynamics may in part reflect the topological role of each region within macroscale brain networks.

Improving comparability across cognitive training trials for brain aging: A focus on interoperability

Cognitive training may promote healthy brain aging and prevent dementia, but results from individual studies are inconsistent. There are disagreements on how to evaluate cognitive training interventions between clinical and basic scientists. Individual labs typically create their own assessment and training materials, leading to difficulties reproducing methods. Here, we advocate for improved interoperability: the exchange and cooperative development of a consensus for cognitive training design, analysis, and result interpretation. We outline five guiding principles for improving interoperability: (i) design interoperability, developing standard design and analysis models; (ii) material interoperability, promoting sharing of materials; (iii) interoperability incentives; (iv) privacy and security norms, ensuring adherence to accepted ethical standards; and (v) interpretability prioritization, encouraging a shared focus on neurobiological mechanisms to improve clinical relevance. Improving interoperability will allow us to develop scientifically optimized, clinically useful cognitive training programs to slow/prevent brain aging.

HIGHLIGHTS

Interoperability facilitates progress via resource sharing and comparability.Better interoperability is needed in cognitive training for brain aging research.We adapt an interoperability framework to cognitive training research.We suggest five guiding principles for improved interoperability.We propose an open-source pipeline to facilitate interoperability.

Atypical Functional Network Properties and Associated Dimensions of Child Psychopathology During Rest and Task Performance

Background

When brain networks deviate from typical development, this is thought to contribute to varying forms of psychopathology. However, research has been limited by the reliance on discrete diagnostic categories that overlook the potential for psychological comorbidity and the dimensional nature of symptoms.

Methods

This study examined the topology of functional networks in association with 4 bifactor-defined psychopathology dimensions-general psychopathology, internalizing symptoms, conduct problems, and attention-deficit/hyperactivity disorder symptoms-via the Child Behavior Checklist in a sample of 3568 children from the ABCD (Adolescent Brain Cognitive Development) Study. Local and global graph theory metrics were calculated at rest and during tasks of reward processing, inhibition, and working memory.

Results

Greater attention-deficit/hyperactivity disorder symptoms were associated with reduced modularity across rest and tasks as well as reduced local efficiency in motor networks at rest. Results survived sensitivity analyses for medication and socioeconomic status. Greater conduct problem symptoms were associated with reduced modularity on working memory and reward processing tasks; however, these results did not persist after sensitivity analyses. General psychopathology and internalizing symptoms showed no significant network associations.

Conclusions

Our findings suggest reduced efficiency in topology in those with greater attention-deficit/hyperactivity disorder symptoms across 4 critical cognitive states, with conduct problems also showing network deficits, although less consistently. This may suggest that modularity deficits are a neurobiological marker of externalizing behavior in children. Such specificity has not been demonstrated before using graph theory metrics and has the potential to redefine our understanding of network deficits in children with psychopathology symptoms.

Network targets for therapeutic brain stimulation: towards personalized therapy for pain

Precision neuromodulation of central brain circuits is a promising emerging therapeutic modality for a variety of neuropsychiatric disorders. Reliably identifying in whom, where, and in what context to provide brain stimulation for optimal pain relief are fundamental challenges limiting the widespread implementation of central neuromodulation treatments for chronic pain. Current approaches to brain stimulation target empirically derived regions of interest to the disorder or targets with strong connections to these regions. However, complex, multidimensional experiences like chronic pain are more closely linked to patterns of coordinated activity across distributed large-scale functional networks. Recent advances in precision network neuroscience indicate that these networks are highly variable in their neuroanatomical organization across individuals. Here we review accumulating evidence that variable central representations of pain will likely pose a major barrier to implementation of population-derived analgesic brain stimulation targets. We propose network-level estimates as a more valid, robust, and reliable way to stratify personalized candidate regions. Finally, we review key background, methods, and implications for developing network topology-informed brain stimulation targets for chronic pain.

Amyloid-β Accumulation in Relation to Functional Connectivity in Aging: a Longitudinal Study

The brain undergoes many changes at pathological and functional levels in healthy aging. This study employed a longitudinal and multimodal imaging dataset from the OASIS-3 study (n=300) and explored possible relationships between amyloid beta (Aβ) accumulation and functional brain organization over time in healthy aging. We used positron emission tomography (PET) with Pittsburgh compound-B (PIB) to quantify the Aβ accumulation in the brain and resting-state functional MRI (rs-fMRI) to measure functional connectivity (FC) among brain regions. Each participant had at least 2 to 3 follow-up visits. A linear mixed-effect model was used to examine longitudinal changes of Aβ accumulation and FC throughout the whole brain. We found that the limbic and frontoparietal networks had a greater annual Aβ accumulation and a slower decline in FC in aging. Additionally, the amount of the Aβ deposition in the amygdala network at baseline slowed down the decline in its FC in aging. Furthermore, the functional connectivity of the limbic, default mode network (DMN), and frontoparietal networks accelerated the Aβ propagation across their functionally highly connected regions. The functional connectivity of the somatomotor and visual networks accelerated the Aβ propagation across the brain regions in the limbic, frontoparietal, and DMN networks. These findings suggested that the slower decline in the functional connectivity of the functional hubs may compensate for their greater Aβ accumulation in aging. The Aβ propagation from one brain region to the other may depend on their functional connectivity strength.

Disrupted longitudinal restoration of brain connectivity during weight normalization in severe anorexia nervosa

Altered intrinsic brain connectivity of patients with anorexia nervosa has been observed in the acute phase of the disorder, but it remains unclear to what extent these alterations recover during weight normalization. In this study, we used functional imaging data from three time points to probe longitudinal changes in intrinsic connectivity patterns in patients with severe anorexia nervosa (BMI ≤ 15.5 kg/m²) over the course of weight normalization. At three distinct stages of inpatient treatment, we examined resting-state functional connectivity in 27 women with severe anorexia nervosa and 40 closely matched healthy controls. Using network-based statistics and graph-theoretic measures, we examined differences in global network strength, subnetworks with altered intrinsic connectivity, and global network topology. Patients with severe anorexia nervosa showed weakened intrinsic connectivity and altered network topology which did not recover during treatment. The persistent disruption of brain networks suggests sustained alterations of information processing in weight-recovered severe anorexia nervosa.

Cognitive and functional connectivity impairment in post-COVID-19 olfactory dysfunction

OBJECTIVES

To explore the neuropsychological profile and the integrity of the olfactory network in patients with COVID-19-related persistent olfactory dysfunction (OD).

METHODS

Patients with persistent COVID-19-related OD underwent olfactory assessment with Sniffin' Sticks and neuropsychological evaluation. Additionally, both patients and a control group underwent brain MRI, including T1-weighted and resting-state functional MRI (rs-fMRI) sequences on a 3 T scanner. Morphometrical properties were evaluated in olfaction-associated regions; the rs-fMRI data were analysed using graph theory at the whole-brain level and within a standard parcellation of the olfactory functional network. All the MR-derived quantities were compared between the two groups and their correlation with clinical scores in patients were explored.

RESULTS

We included 23 patients (mean age 37 ± 14 years, 12 females) with persistent (mean duration 11 ± 5 months, range 2-19 months) COVID-19-related OD (mean score 23.63 ± 5.32/48, hyposmia cut-off: 30.75) and 26 sex- and age-matched healthy controls. Applying population-derived cut-off values, the two cognitive domains mainly impaired were visuospatial memory and executive functions (17 % and 13 % of patients). Brain MRI did not show gross morphological abnormalities. The lateral orbital cortex, hippocampus, and amygdala volumes exhibited a reduction trend in patients, not significant after the correction for multiple comparisons. The olfactory bulb volumes did not differ between patients and controls. Graph analysis of the functional olfactory network showed altered global and local properties in the patients' group (n = 19, 4 excluded due to artifacts) compared to controls. Specifically, we detected a reduction in the global modularity coefficient, positively correlated with hyposmia severity, and an increase of the degree and strength of the right thalamus functional connections, negatively correlated with short-term verbal memory scores.

DISCUSSION

Patients with persistent COVID-19-related OD showed an altered olfactory network connectivity correlated with hyposmia severity and neuropsychological performance. No significant morphological alterations were found in patients compared with controls.

Dynamic flexibility and controllability of network communities in juvenile myoclonic epilepsy

Juvenile myoclonic epilepsy (JME) is the most common syndrome within the idiopathic generalized epilepsy spectrum, manifested by myoclonic and generalized tonic-clonic seizures and spike-and-wave discharges (SWDs) on electroencephalography (EEG). Currently, the pathophysiological concepts addressing SWD generation in JME are still incomplete. In this work, we characterize the temporal and spatial organization of functional networks and their dynamic properties as derived from high-density EEG (hdEEG) recordings and MRI in 40 JME patients (25.4 ± 7.6 years, 25 females). The adopted approach allows for the construction of a precise dynamic model of ictal transformation in JME at the cortical and deep brain nuclei source levels. We implement Louvain algorithm to attribute brain regions with similar topological properties to modules during separate time windows before and during SWD generation. Afterwards, we quantify how modular assignments evolve and steer through different states towards the ictal state by measuring characteristics of flexibility and controllability. We find antagonistic dynamics of flexibility and controllability within network modules as they evolve towards and undergo ictal transformation. Prior to SWD generation, we observe concomitantly increasing flexibility (F(1,39) = 25.3, corrected p < 0.001) and decreasing controllability (F(1,39) = 55.3, p < 0.001) within the fronto-parietal module in γ-band. On a step further, during interictal SWDs as compared to preceding time windows, we notice decreasing flexibility (F(1,39) = 11.9, p < 0.001) and increasing controllability (F(1,39) = 10.1, p < 0.001) within the fronto-temporal module in γ-band. During ictal SWDs as compared to prior time windows, we demonstrate significantly decreasing flexibility (F(1,14) = 31.6; p < 0.001) and increasing controllability (F(1,14) = 44.7, p < 0.001) within the basal ganglia module. Furthermore, we show that flexibility and controllability within the fronto-temporal module of the interictal SWDs relate to seizure frequency and cognitive performance in JME patients. Our results demonstrate that detection of network modules and quantification of their dynamic properties is relevant to track the generation of SWDs. The observed flexibility and controllability dynamics reflect the reorganization of de-/synchronized connections and the ability of evolving network modules to reach a seizure-free state, respectively. These findings may advance the elaboration of network-based biomarkers and more targeted therapeutic neuromodulatory approaches in JME.

Investigating cognitive neuroscience theories of human intelligence: A connectome-based predictive modeling approach

Central to modern neuroscientific theories of human intelligence is the notion that general intelligence depends on a primary brain region or network, engaging spatially localized (rather than global) neural representations. Recent findings in network neuroscience, however, challenge this assumption, providing evidence that general intelligence may depend on system-wide network mechanisms, suggesting that local representations are necessary but not sufficient to account for the neural architecture of human intelligence. Despite the importance of this key theoretical distinction, prior research has not systematically investigated the role of local versus global neural representations in predicting general intelligence. We conducted a large-scale connectome-based predictive modeling study (N = 297), administering resting-state fMRI and a comprehensive cognitive battery to evaluate the efficacy of modern neuroscientific theories of human intelligence, including spatially localized theories (Lateral Prefrontal Cortex Theory, Parieto-Frontal Integration Theory, and Multiple Demand Theory) and recent global accounts (Process Overlap Theory and Network Neuroscience Theory). The results of our study demonstrate that general intelligence can be predicted by local functional connectivity profiles but is most robustly explained by global profiles of whole-brain connectivity. Our findings further suggest that the improved efficacy of global theories is not reducible to a greater strength or number of connections, but instead results from considering both strong and weak connections that provide the basis for intelligence (as predicted by the Network Neuroscience Theory). Our results highlight the importance of considering local neural representations in the context of a global information-processing architecture, suggesting future directions for theory-driven research on system-wide network mechanisms underlying general intelligence.

Accurate module induced brain network construction for mild cognitive impairment identification with functional MRI

Introduction

Functional brain networks (FBNs) estimated from functional magnetic resonance imaging (fMRI) data has become a potentially useful way for computer-aided diagnosis of neurological disorders, such as mild cognitive impairment (MCI), a prodromal stage of Alzheimer's Disease (AD). Currently, Pearson's correlation (PC) is the most widely-used method for constructing FBNs. Despite its popularity and simplicity, the conventional PC-based method usually results in dense networks where regions-of-interest (ROIs) are densely connected. This is not accordance with the biological prior that ROIs may be sparsely connected in the brain. To address this issue, previous studies proposed to employ a threshold or l_1-regularizer to construct sparse FBNs. However, these methods usually ignore rich topology structures, such as modularity that has been proven to be an important property for improving the information processing ability of the brain.

Methods

To this end, in this paper, we propose an accurate module induced PC (AM-PC) model to estimate FBNs with a clear modular structure, by including sparse and low-rank constraints on the Laplacian matrix of the network. Based on the property that zero eigenvalues of graph Laplacian matrix indicate the connected components, the proposed method can reduce the rank of the Laplacian matrix to a pre-defined number and obtain FBNs with an accurate number of modules.

Results

To validate the effectiveness of the proposed method, we use the estimated FBNs to classify subjects with MCI from healthy controls. Experimental results on 143 subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) with resting-state functional MRIs show that the proposed method achieves better classification performance than previous methods.

Activity flow mapping over probabilistic functional connectivity

Emerging evidence indicates that activity flow over resting-state network topology allows the prediction of task activations. However, previous studies have mainly adopted static, linear functional connectivity (FC) estimates as activity flow routes. It is unclear whether an intrinsic network topology that captures the dynamic nature of FC can be a better representation of activity flow routes. Moreover, the effects of between- versus within-network connections and tight versus loose (using rest baseline) task contrasts on the prediction of task-evoked activity across brain systems remain largely unknown. In this study, we first propose a probabilistic FC estimation derived from a dynamic framework as a new activity flow route. Subsequently, activity flow mapping was tested using between- and within-network connections separately for each region as well as using a set of tight task contrasts. Our results showed that probabilistic FC routes substantially improved individual-level activity flow prediction. Although it provided better group-level prediction, the multiple regression approach was more dependent on the length of data points at the individual-level prediction. Regardless of FC type, we consistently observed that between-network connections showed a relatively higher prediction performance in higher-order cognitive control than in primary sensorimotor systems. Furthermore, cognitive control systems exhibit a remarkable increase in prediction accuracy with tight task contrasts and a decrease in sensorimotor systems. This work demonstrates that probabilistic FC estimates are promising routes for activity flow mapping and also uncovers divergent influences of connectional topology and task contrasts on activity flow prediction across brain systems with different functional hierarchies.

Effects of inhaled cannabis high in Δ9-THC or CBD on the aging brain: A translational MRI and behavioral study

With the recent legalization of inhaled cannabis for medicinal and recreational use, the elderly represents one of the newest, rapidly growing cohorts of cannabis users. To understand the neurobiological effects of cannabis on the aging brain, 19-20 months old mice were divided into three groups exposed to vaporized cannabis containing ~10% Δ9-THC, ~10% CBD, or placebo for 30 min each day. Voxel based morphometry, diffusion weighted imaging, and resting state functional connectivity data were gathered after 28 days of exposure and following a two-week washout period. Tail-flick, open field, and novel object preference tests were conducted to explore analgesic, anxiolytic, and cognitive effects of cannabis, respectively. Vaporized cannabis high in Δ9-THC and CBD achieved blood levels reported in human users. Mice showed antinociceptive effects to chronic Δ9-THC without tolerance while the anxiolytic and cognitive effects of Δ9-THC waned with treatment. CBD had no effect on any of the behavioral measures. Voxel based morphometry showed a decrease in midbrain dopaminergic volume to chronic Δ9-THC followed but an increase after a two-week washout. Fractional anisotropy values were reduced in the same area by chronic Δ9-THC, suggesting a reduction in gray matter volume. Cannabis high in CBD but not THC increased network strength and efficiency, an effect that persisted after washout. These data would indicate chronic use of inhaled cannabis high in Δ9-THC can be an effective analgesic but not for treatment of anxiety or cognitive decline. The dopaminergic midbrain system was sensitive to chronic Δ9-THC but not CBD showing robust plasticity in volume and water diffusivity prior to and following drug cessation an effect possibly related to the abuse liability of Δ9-THC. Chronic inhaled CBD resulted in enhanced global network connectivity that persisted after drug cessation. The behavioral consequences of this sustained change in brain connectivity remain to be determined.

Altered global modular organization of intrinsic functional connectivity in autism arises from atypical node-level processing

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by restricted interests and repetitive behaviors as well as social-communication deficits. These traits are associated with atypicality of functional brain networks. Modular organization in the brain plays a crucial role in network stability and adaptability for neurodevelopment. Previous neuroimaging research demonstrates discrepancies in studies of functional brain modular organization in ASD. These discrepancies result from the examination of mixed age groups. Furthermore, recent findings suggest that while much attention has been given to deriving atlases and measuring the connections between nodes, within node information may also be crucial in determining altered modular organization in ASD compared with typical development (TD). However, altered modular organization originating from systematic nodal changes are yet to be explored in younger children with ASD. Here, we used graph-theoretical measures to fill this knowledge gap. To this end, we utilized multicenter resting-state fMRI data collected from 5 to 10-year-old children-34 ASD and 40 TD obtained from the Autism Brain Image Data Exchange (ABIDE) I and II. We demonstrate that alterations in topological roles and modular cohesiveness are the two key properties of brain regions anchored in default mode, sensorimotor, and salience networks, and primarily relate to social and sensory deficits in children with ASD. These results demonstrate that atypical global network organization in children with ASD arises from nodal role changes, and contribute to the growing body of literature suggesting that there is interesting information within nodes providing critical markers of functional brain networks in autistic children.

Thalamocortical contributions to cognitive task activity

Thalamocortical interaction is a ubiquitous functional motif in the mammalian brain. Previously (Hwang et al., 2021), we reported that lesions to network hubs in the human thalamus are associated with multi-domain behavioral impairments in language, memory, and executive functions. Here, we show how task-evoked thalamic activity is organized to support these broad cognitive abilities. We analyzed functional magnetic resonance imaging (MRI) data from human subjects that performed 127 tasks encompassing a broad range of cognitive representations. We first investigated the spatial organization of task-evoked activity and found a basis set of activity patterns evoked to support processing needs of each task. Specifically, the anterior, medial, and posterior-medial thalamus exhibit hub-like activity profiles that are suggestive of broad functional participation. These thalamic task hubs overlapped with network hubs interlinking cortical systems. To further determine the cognitive relevance of thalamic activity and thalamocortical functional connectivity, we built a data-driven thalamocortical model to test whether thalamic activity can be used to predict cortical task activity. The thalamocortical model predicted task-specific cortical activity patterns, and outperformed comparison models built on cortical, hippocampal, and striatal regions. Simulated lesions to low-dimensional, multi-task thalamic hub regions impaired task activity prediction. This simulation result was further supported by profiles of neuropsychological impairments in human patients with focal thalamic lesions. In summary, our results suggest a general organizational principle of how the human thalamocortical system supports cognitive task activity.

Changes in Degree Centrality and Functional Connectivity after the First Cycle of Neoadjuvant Chemotherapy in Newly Diagnosed Breast Cancer: A Longitudinal Study

Purpose

To evaluate the longitudinal changes of brain degree centrality (DC) and functional connectivity (FC) in breast cancer patients after the first cycle of neoadjuvant chemotherapy (NAC).

Methods

Thirty-five breast cancer patients were included in the NAC group. Resting-state functional magnetic resonance imaging (rs-fMRI) and neuropsychological test were performed at baseline before NAC (time point 0, TP0) and after the first cycle of NAC (time point 1, TP1). The healthy controls (HC) included 30 healthy subjects and received the same rs-fMRI scan and neuropsychological test as the above-mentioned NAC group at one time point. DC and FC analyses were conducted to assess brain connectivity of all participants. Receiver operating characteristic (ROC) curve was used to assess the ability of DC and FC in distinguishing patients before and after chemotherapy.

Results

In the NAC group, the Self-Rating Anxiety Scale scores decreased significantly over time. At TP0 and TP1, the Digital Span Test forward score of the NAC group was significantly lower than that of the HC group. In the NAC group, DC in the right middle frontal gyrus and left precentral gyrus/middle frontal gyrus decreased significantly at TP1, and FC between the left precentral gyrus/middle frontal gyrus and bilateral precuneus was significantly reduced at TP1. Through ROC analysis, we found that the area under the curve (AUC) of DC, FC, and the combined model in distinguishing patients in TP0 or TP1 was 0.7886, 0.7665, and 0.8278, respectively.

Conclusions

Brain connectivity, involving executive and motor function related brain areas, changes in the short term after NAC treatment in breast cancer patients.

Long-term learning transforms prefrontal cortex representations during working memory

The role of the lateral prefrontal cortex (lPFC) in working memory (WM) is debated. Non-human primate (NHP) electrophysiology shows that the lPFC stores WM representations, but human neuroimaging suggests that the lPFC controls WM content in sensory cortices. These accounts are confounded by differences in task training and stimulus exposure. We tested whether long-term training alters lPFC function by densely sampling WM activity using functional MRI. Over 3 months, participants trained on both a WM and serial reaction time (SRT) task, wherein fractal stimuli were embedded within sequences. WM performance improved for trained (but not novel) fractals and, neurally, delay activity increased in distributed lPFC voxels across learning. Item-level WM representations became detectable within lPFC patterns, and lPFC activity reflected sequence relationships from the SRT task. These findings demonstrate that human lPFC develops stimulus-selective responses with learning, and WM representations are shaped by long-term experience, which could reconcile competing accounts of WM functioning.

Age-related reorganization of functional network architecture in semantic cognition

Cognitive aging is associated with widespread neural reorganization processes in the human brain. However, the behavioral impact of such reorganization is not well understood. The current neuroimaging study investigated age differences in the functional network architecture during semantic word retrieval in young and older adults. Combining task-based functional connectivity, graph theory and cognitive measures of fluid and crystallized intelligence, our findings show age-accompanied large-scale network reorganization even when older adults have intact word retrieval abilities. In particular, functional networks of older adults were characterized by reduced decoupling between systems, reduced segregation and efficiency, and a larger number of hub regions relative to young adults. Exploring the predictive utility of these age-related changes in network topology revealed high, albeit less efficient, performance for older adults whose brain graphs showed stronger dedifferentiation and reduced distinctiveness. Our results extend theoretical accounts on neurocognitive aging by revealing the compensational potential of the commonly reported pattern of network dedifferentiation when older adults can rely on their prior knowledge for successful task processing. However, we also demonstrate the limitations of such compensatory reorganization and show that a youth-like network architecture in terms of balanced integration and segregation is associated with more economical processing.

Actigraphy-estimated physical activity is associated with functional and structural brain connectivity among older adults

Higher physical activity levels are associated with reduced cognitive decline among older adults; however, current understanding of underlying brain mechanisms is limited. This cross-sectional study investigated the relationship between actigraphy-estimated total volume of physical activity (TVPA) and magnetic resonance imaging (MRI) measures of white matter hyperintensities (WMH), and functional and structural brain connectivity, measured by resting-state functional MRI and diffusion tensor imaging. Study participants (N = 156, mean age = 71 years) included 136 with normal cognition and 20 with Mild Cognitive Impairment. Higher TVPA was associated with greater functional connectivity within the default-mode network and greater network modularity (a measure of network specialization), as well as with greater anisotropy and lower radial diffusion in white matter, suggesting better structural connectivity. These associations with functional and structural connectivity were independent of one another and independent of the level of vascular risk, APOE-ε4 status, cognitive reserve, and WMH volume, which were not associated with TVPA. Findings suggest that physical activity is beneficial for brain connectivity among older individuals with varying levels of risk for cognitive decline.

The resting-state causal human connectome is characterized by hub connectivity of executive and attentional networks

We demonstrate a data-driven approach for calculating a "causal connectome" of directed connectivity from resting-state fMRI data using a greedy adjacency search and pairwise non-Gaussian edge orientations. We used this approach to construct n = 442 causal connectomes. These connectomes were very sparse in comparison to typical Pearson correlation-based graphs (roughly 2.25% edge density) yet were fully connected in nearly all cases. Prominent highly connected hubs of the causal connectome were situated in attentional (dorsal attention) and executive (frontoparietal and cingulo-opercular) networks. These hub networks had distinctly different connectivity profiles: attentional networks shared incoming connections with sensory regions and outgoing connections with higher cognitive networks, while executive networks primarily connected to other higher cognitive networks and had a high degree of bidirected connectivity. Virtual lesion analyses accentuated these findings, demonstrating that attentional and executive hub networks are points of critical vulnerability in the human causal connectome. These data highlight the central role of attention and executive control networks in the human cortical connectome and set the stage for future applications of data-driven causal connectivity analysis in psychiatry.

Interindividual variability in functional connectivity discovers differential development of cognition and transdiagnostic dimensions of psychopathology in youth

Cognitive and psychological development during adolescence is different from one another, which is rooted in individual differences in maturational changes in the adolescent brain. This study employed multi-modal MRI data and characterized interindividual variability in functional connectivity (IVFC) and its associations with cognition and psychopathology using the Philadelphia Neurodevelopmental Cohort (PNC) of 755 youth. We employed resting state functional MRI (rs-fMRI) and diffusion weighted images (DWIs) to estimate brain structural and functional networks. We computed the IVFC of individuals and examined its relation with structural and functional organizations. We further employed sparse partial least squares (sparse-PLS) and meta-analysis to examine the developmental associations of the IVFC with cognition and transdiagnostic dimensions of psychopathology in early, middle, and late adolescence. Our results revealed that the IVFC spatial topography reflects the brain functional integration and structure-function decoupling. Age effects on the IVFC of association networks were mediated by the FC among the triple networks, including frontoparietal, salience, and default mode networks (DMN), while those of primary and cerebellar networks were mediated by the cerebello-cortical FC. The IVFC of the triple and cerebellar networks explained the variance of executive functions and externalizing behaviors in early adolescence and then the variance of emotion and internalizing and psychosis in middle and late adolescence. We further evaluated this finding via meta-analysis on task-based studies on cognition and psychopathology. These findings implicate the emerging importance of the IVFC of the triple and cerebellar networks in cognitive, emotional, and psychopathological development during adolescence.

Circular inference predicts nonuniform overactivation and dysconnectivity in brain-wide connectomes

Schizophrenia is a severe mental disorder whose neural basis remains difficult to ascertain. Among the available pathophysiological theories, recent work has pointed towards subtle perturbations in the excitation-inhibition (E/I) balance within different neural circuits. Computational approaches have suggested interesting mechanisms that can account for both E/I imbalances and psychotic symptoms. Based on hierarchical neural networks propagating information through a message-passing algorithm, it was hypothesized that changes in the E/I ratio could cause a "circular belief propagation" in which bottom-up and top-down information reverberate. This circular inference (CI) was proposed to account for the clinical features of schizophrenia. Under this assumption, this paper examined the impact of CI on network dynamics in light of brain imaging findings related to psychosis. Using brain-inspired graphical models, we show that CI causes overconfidence and overactivation most specifically at the level of connector hubs (e.g., nodes with many connections allowing integration across networks). By also measuring functional connectivity in these graphs, we provide evidence that CI is able to predict specific changes in modularity known to be associated with schizophrenia. Altogether, these findings suggest that the CI framework may facilitate behavioral and neural research on the multifaceted nature of psychosis.

Getting Fit to Counteract Cognitive Aging: Evidence and Future Directions

Physical activity has shown tremendous promise for counteracting cognitive aging, but also tremendous variability in cognitive benefits. We describe evidence for how exercise affects cognitive and brain aging, and whether cardiorespiratory fitness is a key factor. We highlight a brain network framework as a valuable paradigm for the mechanistic insight needed to tailor physical activity for cognitive benefits.

Functional coupling between frontoparietal control subnetworks bridges the default and dorsal attention networks

The frontoparietal control network (FPCN) plays a central role in tuning connectivity between brain networks to achieve integrated cognitive processes. It has been proposed that two subnetworks within the FPCN separately regulate two antagonistic networks: the FPCNa is connected to the default network (DN) that deals with internally oriented introspective processes, whereas the FPCNb is connected to the dorsal attention network (DAN) that deals with externally oriented perceptual attention. However, cooperation between the DN and DAN induced by distinct task demands has not been well-studied. Here, we characterized the dynamic cooperation among the DN, DAN, and two FPCN subnetworks in a task in which internally oriented self-referential processing could facilitate externally oriented visual working memory. Functional connectivity analysis showed enhanced coupling of a circuit from the DN to the FPCNa, then to the FPCNb, and finally to the DAN when the self-referential processing improved memory recognition in high self-referential conditions. The direct connection between the DN and DAN was not enhanced. This circuit could be reflected by an increased chain-mediating effect of the FPCNa and the FPCNb between the DN and DAN in high self-referential conditions. Graph analysis revealed that high self-referential conditions were accompanied by increased global and local efficiencies, and the increases were mainly driven by the increased efficiency of FPCN nodes. Together, our findings extend prior observations and indicate that the coupling between the two FPCN subnetworks serves as a bridge between the DN and DAN, supporting the interaction between internally oriented and externally oriented processes.

Latent functional connectivity underlying multiple brain states

Functional connectivity (FC) studies have predominantly focused on resting state, where ongoing dynamics are thought to reflect the brain's intrinsic network architecture, which is thought to be broadly relevant because it persists across brain states (i.e., is state-general). However, it is unknown whether resting state is the optimal state for measuring intrinsic FC. We propose that latent FC, reflecting shared connectivity patterns across many brain states, better captures state-general intrinsic FC relative to measures derived from resting state alone. We estimated latent FC independently for each connection using leave-one-task-out factor analysis in seven highly distinct task states (24 conditions) and resting state using fMRI data from the Human Connectome Project. Compared with resting-state connectivity, latent FC improves generalization to held-out brain states, better explaining patterns of connectivity and task-evoked activation. We also found that latent connectivity improved prediction of behavior outside the scanner, indexed by the general intelligence factor (g). Our results suggest that FC patterns shared across many brain states, rather than just resting state, better reflect state-general connectivity. This affirms the notion of "intrinsic" brain network architecture as a set of connectivity properties persistent across brain states, providing an updated conceptual and mathematical framework of intrinsic connectivity as a latent factor.

Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study

How individual differences in brain network organization track behavioral variability is a fundamental question in systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the individual level. However, most studies focus on single behavioral traits, thus not capturing broader relationships across behaviors. In a large sample of 1858 typically developing children from the Adolescent Brain Cognitive Development (ABCD) study, we show that predictive network features are distinct across the domains of cognitive performance, personality scores and mental health assessments. On the other hand, traits within each behavioral domain are predicted by similar network features. Predictive network features and models generalize to other behavioral measures within the same behavioral domain. Although tasks are known to modulate the functional connectome, predictive network features are similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood.