TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance.

We propose a geometric deep-learning-based framework, TractGeoNet, for performing regression using diffusion magnetic resonance imaging (dMRI) tractography and associated pointwise tissue microstructure measurements. By employing a point cloud representation, TractGeoNet can directly utilize tissue microstructure and positional information from all points within a fiber tract without the need to average or bin data along the streamline as traditionally required by dMRI tractometry methods. To improve regression performance, we propose a novel loss function, the Paired-Siamese Regression loss, which encourages the model to focus on accurately predicting the relative differences between regression label scores rather than just their absolute values. In addition, to gain insight into the brain regions that contribute most strongly to the prediction results, we propose a Critical Region Localization algorithm. This algorithm identifies highly predictive anatomical regions within the white matter fiber tracts for the regression task. We evaluate the effectiveness of the proposed method by predicting individual performance on two neuropsychological assessments of language using a dataset of 20 association white matter fiber tracts from 806 subjects from the Human Connectome Project Young Adult dataset. The results demonstrate superior prediction performance of TractGeoNet compared to several popular regression models that have been applied to predict individual cognitive performance based on neuroimaging features. Of the twenty tracts studied, we find that the left arcuate fasciculus tract is the most highly predictive of the two studied language performance assessments. Within each tract, we localize critical regions whose microstructure and point information are highly and consistently predictive of language performance across different subjects and across multiple independently trained models. These critical regions are widespread and distributed across both hemispheres and all cerebral lobes, including areas of the brain considered important for language function such as superior and anterior temporal regions, pars opercularis, and precentral gyrus. Overall, TractGeoNet demonstrates the potential of geometric deep learning to enhance the study of the brain's white matter fiber tracts and to relate their structure to human traits such as language performance.

Associations between infant amygdala functional connectivity and social engagement following a stressor: A preliminary investigation.

Functional architecture of the infant brain, especially functional connectivity (FC) within the amygdala network and between the amygdala and other networks (i.e., default-mode [DMN] and salience [SAL] networks), provides a neural basis for infant socioemotional functioning. Yet, little is known about the extent to which early within- and between-network amygdala FC are related to infant stress recovery across the first year of life. In this study, we examined associations between amygdala FC (i.e., within-network amygdala connectivity, and between-network amygdala connectivity with the DMN and SAL) at 3 months and infant recovery from a mild social stressor at 3, 6 and 9 months. At 3 months, thirty-five infants (13 girls) underwent resting-state functional magnetic resonance imaging during natural sleep. Infants and their mothers completed the still-face paradigm at 3, 6, and 9 months, and infant stress recovery was assessed at each time point as the proportion of infant social engagement during the reunion episode. Bivariate correlations indicated that greater positive within-network amygdala FC and greater positive amygdala-SAL FC, but not amygdala-DMN FC, at 3 months predicted lower levels of stress recovery at 3 and 6 months, but were nonsignificant at 9 months. These findings provide preliminary evidence that early functional synchronization within the amygdala network, as well as segregation between the amygdala and the SAL, may contribute to infant stress recovery in the context of infant-mother interaction.

The VLPFC-Engaged Voluntary Emotion Regulation: Combined TMS-fMRI Evidence for the Neural Circuit of Cognitive Reappraisal.

A clear understanding of the neural circuit underlying emotion regulation (ER) is important for both basic and translational research. However, a lack of evidence based on combined neuroimaging and neuromodulation techniques calls into question (1) whether the change of prefrontal-subcortical activity intrinsically and causally contributes to the ER effect; and (2) whether the prefrontal control system directly modulates the subcortical affective system. Accordingly, we combined fMRI recordings with transcranial magnetic stimulation (TMS) to map the causal connections between the PFC and subcortical affective structures (amygdala and insula). A total of 117 human adult participants (57 males and 60 females) were included in the study. The results revealed that TMS-induced ventrolateral PFC (VLPFC) facilitation led to enhanced activity in the VLPFC and ventromedial PFC (VMPFC) as well as attenuated activity in the amygdala and insula during reappraisal but not during nonreappraisal (i.e., baseline). Moreover, the activated VLPFC intensified the prefrontal-subcortical couplings via the VMPFC during reappraisal only. This study provides combined TMS-fMRI evidence that downregulating negative emotion involves the prefrontal control system suppressing the subcortical affective system, with the VMPFC serving as a crucial hub within the VLPFC-subcortical network, suggesting an indirect pathway model of the ER circuit. Our findings outline potential protocols for improving ER ability by intensifying the VLPFC-VMPFC coupling in patients with mood and anxiety disorders.SIGNIFICANCE STATEMENT Using fMRI to examine the TMS effect, we uncovered that the opposite neural changes in prefrontal (enhanced) and subcortical (attenuated) regions are not a byproduct of emotion regulation (ER); instead, this prefrontal-subcortical activity per se causally contributes to the ER effect. Furthermore, using TMS to amplify the neural changes within the ER circuit, the "bridge" role of the VMPFC is highlighted under the reappraisal versus nonreappraisal contrast. This "perturb-and-measure" approach overcomes the correlational nature of fMRI data, helping us to identify brain regions that causally support reappraisal (the VLPFC and VMPFC) and those that are modulated by reappraisal (the amygdala and insula). The uncovered ER circuit is important for understanding the neural systems underlying reappraisal and valuable for translational research.

Edge-centric functional network predicts risk propensity in economic decision-making: evidence from a resting-state fMRI study.

Despite node-centric studies revealing an association between resting-state functional connectivity and individual risk propensity, the prediction of future risk decisions remains undetermined. Herein, we applied a recently emerging edge-centric method, the edge community similarity network (ECSN), to alternatively describe the community structure of resting-state brain activity and to probe its contribution to predicting risk propensity during gambling. Results demonstrated that inter-individual variability of risk decisions correlates with the inter-subnetwork couplings spanning the visual network (VN) and default mode network (DMN), cingulo-opercular task control network, and sensory/somatomotor hand network (SSHN). Particularly, participants who have higher community similarity of these subnetworks during the resting state tend to choose riskier and higher yielding bets. And in contrast to low-risk propensity participants, those who behave high-risky show stronger couplings spanning the VN and SSHN/DMN. Eventually, based on the resting-state ECSN properties, the risk rate during the gambling task is effectively predicted by the multivariable linear regression model at the individual level. These findings provide new insights into the neural substrates of the inter-individual variability in risk propensity and new neuroimaging metrics to predict individual risk decisions in advance.

Functional connectivity of sub-cortical brain regions: disparities and similarities.

Sub-cortical grey matter structures, such as the putamen, pallidum, caudate, thalamus, amygdala and hippocampus, play substantial roles in both simple and complex brain functions, including regulation of pleasure and emotions; control of movements; learning; decision-making; language development; and sensory, cognitive, social and other higher-order functions. Most of these regions act as information hubs for the nervous system, relaying and controlling the flow of information to various portions of the brain. To further understand the complex neurophysiological characteristics of sub-cortical areas, the aim of this study was to investigate the functional integrations of six sub-cortical areas to different major functional brain networks. One hundred ninety-eight healthy individuals were examined using resting-state functional MRI. The seeds identified in this study were six sub-cortical deep grey matter regions, namely putamen, pallidum, caudate, thalamus, amygdala and hippocampus. The analysis indicated that the link between the sub-cortical regions and some functional brain networks was similar in some aspects, but there were disparities in the mechanism underlying such a link and in the existence of functional connections between these regions and networks. Despite the substantial functional connectivity linkages between the sub-cortical regions, discrepancies were still noted. On the basis of the connections to the majority of the major brain networks, this study demonstrated the essential functional roles and involvements of the sub-cortical regions. This finding is consistent with an earlier report that revealed a substantial role of the sub-cortical regions in several brain functions.

Sex/Gender Differences in Brain Lateralisation and Connectivity.

There is now a significant body of literature concerning sex/gender differences in the human brain. This chapter will critically review and synthesise key findings from several studies that have investigated sex/gender differences in structural and functional lateralisation and connectivity. We argue that while small, relative sex/gender differences reliably exist in lateralisation and connectivity, there is considerable overlap between the sexes. Some inconsistencies exist, however, and this is likely due to considerable variability in the methodologies, tasks, measures, and sample compositions between studies. Moreover, research to date is limited in its consideration of sex/gender-related factors, such as sex hormones and gender roles, that can explain inter-and inter-individual differences in brain and behaviour better than sex/gender alone. We conclude that conceptualising the brain as 'sexually dimorphic' is incorrect, and the terms 'male brain' and 'female brain' should be avoided in the neuroscientific literature. However, this does not necessarily mean that sex/gender differences in the brain are trivial. Future research involving sex/gender should adopt a biopsychosocial approach whenever possible, to ensure that non-binary psychological, biological, and environmental/social factors related to sex/gender, and their interactions, are routinely accounted for.

Parental education is associated with differential engagement of neural pathways during inhibitory control.

Response inhibition and socioeconomic status (SES) are critical predictors of many important outcomes, including educational attainment and health. The current study extends our understanding of SES and cognition by examining brain activity associated with response inhibition, during the key developmental period of adolescence. Adolescent males (N = 81), aged 16-17, completed a response inhibition task while undergoing fMRI brain imaging and reported on their parents' education, one component of socioeconomic status. A region of interest analysis showed that parental education was associated with brain activation differences in the classic response inhibition network (right inferior frontal gyrus + subthalamic nucleus + globus pallidus) despite the absence of consistent parental education-performance effects. Further, although activity in our main regions of interest was not associated with performance differences, several regions that were associated with better inhibitory performance (ventromedial prefrontal cortex, middle frontal gyrus, middle temporal gyrus, amygdala/hippocampus) also differed in their levels of activation according to parental education. Taken together, these results suggest that individuals from households with higher versus lower parental education engage key brain regions involved in response inhibition to differing degrees, though these differences may not translate into performance differences.

Obesity status and obesity-associated gut dysbiosis effects on hypothalamic structural covariance.

BACKGROUND: Functional connectivity alterations in the lateral and medial hypothalamic networks have been associated with the development and maintenance of obesity, but the possible impact on the structural properties of these networks remains largely unexplored. Also, obesity-related gut dysbiosis may delineate specific hypothalamic alterations within obese conditions. We aim to assess the effects of obesity, and obesity and gut-dysbiosis on the structural covariance differences in hypothalamic networks, executive functioning, and depressive symptoms. METHODS: Medial (MH) and lateral (LH) hypothalamic structural covariance alterations were identified in 57 subjects with obesity compared to 47 subjects without obesity. Gut dysbiosis in the subjects with obesity was defined by the presence of high (n = 28) and low (n = 29) values in a BMI-associated microbial signature, and posthoc comparisons between these groups were used as a proxy to explore the role of obesity-related gut dysbiosis on the hypothalamic measurements, executive function, and depressive symptoms. RESULTS: Structural covariance alterations between the MH and the striatum, lateral prefrontal, cingulate, insula, and temporal cortices are congruent with previously functional connectivity disruptions in obesity conditions. MH structural covariance decreases encompassed postcentral parietal cortices in the subjects with obesity and gut-dysbiosis, but increases with subcortical nuclei involved in the coding food-related hedonic information in the subjects with obesity without gut-dysbiosis. Alterations for the structural covariance of the LH in the subjects with obesity and gut-dysbiosis encompassed increases with frontolimbic networks, but decreases with the lateral orbitofrontal cortex in the subjects with obesity without gut-dysbiosis. Subjects with obesity and gut dysbiosis showed higher executive dysfunction and depressive symptoms. CONCLUSIONS: Obesity-related gut dysbiosis is linked to specific structural covariance alterations in hypothalamic networks relevant to the integration of somatic-visceral information, and emotion regulation.

Economic Choices under Simultaneous or Sequential Offers Rely on the Same Neural Circuit.

A series of studies in which monkeys chose between two juices offered in variable amounts identified in the orbitofrontal cortex (OFC) different groups of neurons encoding the value of individual options (offer value), the binary choice outcome (chosen juice), and the chosen value. These variables capture both the input and the output of the choice process, suggesting that the cell groups identified in OFC constitute the building blocks of a decision circuit. Several lines of evidence support this hypothesis. However, in previous experiments offers were presented simultaneously, raising the question of whether current notions generalize to when goods are presented or are examined in sequence. Recently, Ballesta and Padoa-Schioppa (2019) examined OFC activity under sequential offers. An analysis of neuronal responses across time windows revealed that a small number of cell groups encoded specific sequences of variables. These sequences appeared analogous to the variables identified under simultaneous offers, but the correspondence remained tentative. Thus, in the present study, we examined the relation between cell groups found under sequential versus simultaneous offers. We recorded from the OFC while monkeys chose between different juices. Trials with simultaneous and sequential offers were randomly interleaved in each session. We classified cells in each choice modality, and we examined the relation between the two classifications. We found a strong correspondence; in other words, the cell groups measured under simultaneous offers and under sequential offers were one and the same. This result indicates that economic choices under simultaneous or sequential offers rely on the same neural circuit.SIGNIFICANCE STATEMENT Research in the past 20 years has shed light on the neuronal underpinnings of economic choices. A large number of results indicates that decisions between goods are formed in a neural circuit within the orbitofrontal cortex. In most previous studies, subjects chose between two goods offered simultaneously. Yet, in daily situations, goods available for choice are often presented or examined in sequence. Here we recorded neuronal activity in the primate orbitofrontal cortex alternating trials under simultaneous and under sequential offers. Our analyses demonstrate that the same neural circuit supports choices in the two modalities. Hence, current notions on the neuronal mechanisms underlying economic decisions generalize to choices under sequential offers.

Intra-individual cognitive variability in neuropsychological assessment: a sign of neural network dysfunction.

Intra-Individual Cognitive Variability (IICV) predicts progression in neurocognitive disorders . Given important clinical applications, we investigated the association between IICV and multiple brain metrics across 17 networks to better understand the brain mechanisms underlying this performance measure. Sixty-three middle-aged and older adults without dementia underwent a neuropsychological battery, resting-state fMRI, and structural MRI scans. In a linear mixed effect model, higher IICV was associated with lower functional connectivity in control C network relative to medial occipital network (the reference). A multivariate partial least squares analysis revealed that lower mean and higher variability were both associated with lower connectivity in sensorimotor and default mode networks, while higher mean and higher variability were associated with lower volume in default mode and limbic networks. This study suggests that IICV signals widespread network dysfunction across multiple brain networks. These brain abnormalities offer new insights into mechanisms of early cognitive dysfunction. Clinical implications are discussed.

Reward-specific satiety affects subjective value signals in orbitofrontal cortex during multicomponent economic choice.

Sensitivity to satiety constitutes a basic requirement for neuronal coding of subjective reward value. Satiety from natural ongoing consumption affects reward functions in learning and approach behavior. More specifically, satiety reduces the subjective economic value of individual rewards during choice between options that typically contain multiple reward components. The unconfounded assessment of economic reward value requires tests at choice indifference between two options, which is difficult to achieve with sated rewards. By conceptualizing choices between options with multiple reward components ("bundles"), Revealed Preference Theory may offer a solution. Despite satiety, choices against an unaltered reference bundle may remain indifferent when the reduced value of a sated bundle reward is compensated by larger amounts of an unsated reward of the same bundle, and then the value loss of the sated reward is indicated by the amount of the added unsated reward. Here, we show psychophysically titrated choice indifference in monkeys between bundles of differently sated rewards. Neuronal chosen value signals in the orbitofrontal cortex (OFC) followed closely the subjective value change within recording periods of individual neurons. A neuronal classifier distinguishing the bundles and predicting choice substantiated the subjective value change. The choice between conventional single rewards confirmed the neuronal changes seen with two-reward bundles. Thus, reward-specific satiety reduces subjective reward value signals in OFC. With satiety being an important factor of subjective reward value, these results extend the notion of subjective economic reward value coding in OFC neurons.

Perturbation of amygdala-cortical projections reduces ensemble coherence of palatability coding in gustatory cortex.

Taste palatability is centrally involved in consumption decisions-we ingest foods that taste good and reject those that don't. Gustatory cortex (GC) and basolateral amygdala (BLA) almost certainly work together to mediate palatability-driven behavior, but the precise nature of their interplay during taste decision-making is still unknown. To probe this issue, we discretely perturbed (with optogenetics) activity in rats' BLA→GC axons during taste deliveries. This perturbation strongly altered GC taste responses, but while the perturbation itself was tonic (2.5 s), the alterations were not-changes preferentially aligned with the onset times of previously-described taste response epochs, and reduced evidence of palatability-related activity in the 'late-epoch' of the responses without reducing the amount of taste identity information available in the 'middle epoch.' Finally, BLA→GC perturbations changed behavior-linked taste response dynamics themselves, distinctively diminishing the abruptness of ensemble transitions into the late epoch. These results suggest that BLA 'organizes' behavior-related GC taste dynamics.

Anticipation of temporally structured events in the brain.

Learning about temporal structure is adaptive because it enables the generation of expectations. We examined how the brain uses experience in structured environments to anticipate upcoming events. During fMRI (functional magnetic resonance imaging), individuals watched a 90 s movie clip six times. Using a hidden Markov model applied to searchlights across the whole brain, we identified temporal shifts between activity patterns evoked by the first vs. repeated viewings of the movie clip. In many regions throughout the cortex, neural activity patterns for repeated viewings shifted to precede those of initial viewing by up to 15 s. This anticipation varied hierarchically in a posterior (less anticipation) to anterior (more anticipation) fashion. We also identified specific regions in which the timing of the brain's event boundaries was related to those of human-labeled event boundaries, with the timing of this relationship shifting on repeated viewings. With repeated viewing, the brain's event boundaries came to precede human-annotated boundaries by 1-4 s on average. Together, these results demonstrate a hierarchy of anticipatory signals in the human brain and link them to subjective experiences of events.

Anticipating future events is essential. It allows individuals to plan and prepare what they will do seconds, minutes, or hours in the future. But how the brain can predict future events in both the short-term and long-term is not yet clear. Researchers know that the brain processes images or other sensory information in stages. For example, visual features are processed from lines to shapes to objects, and eventually scenes. This staged approach allows the brain to create representations of many parts of the world simultaneously. A similar hierarchy may be at play in anticipation. Different parts of the brain may track what is happening now, and what could happen in the next few seconds and minutes. This would provide a way for the brain to forecast upcoming events in the immediate, near, and more distant future at the same time. Now, Lee et al. show that the regions in the back of the brain anticipate the immediate future, while longer-term predictions are made in brain regions near the front. In the experiments, study participants watched a 90-second clip of the movie 'The Grand Budapest Hotel' six times while undergoing functional magnetic resonance imaging (fMRI). Then, Lee et al. used computer modeling to compare the brain activity captured by fMRI during successive viewings. This allowed the researchers to watch participants' brain activity moment-by-moment. As the participants repeatedly watched the movie clip, their brains began to anticipate what was coming next. Regions near the back of the brain like the visual cortex anticipated events in the next 1 to 4 seconds. Areas in the middle of the brain anticipated 5 to 8 seconds in the future. The front of brain anticipated 8 to 15 seconds into the future. Lee et al. show that many parts of the brain work together to predict the near and more distant future. More research is needed to understand how this information translates into actions. Learning more may help scientists understand how diseases or injuries affect people's ability to plan and respond to future events.

Socioeconomic Disparities Affect Children's Amygdala-Prefrontal Circuitry via Stress Hormone Response.

BACKGROUND: The socioeconomic status (SES) of a family can affect almost all aspects of a child's life, including health and current and future achievement. The potential adverse effects of low SES on children's emotional development are thought to result from proximal factors such as stress. The underlying neurobiological mechanisms, however, remain elusive. METHODS: The effect of SES on children's integrative cortisol secretion and its modulations on emotion-related brain systems and connectivity were examined in children aged 6 to 12 years. In study 1, we investigated the relationship between SES and cortisol secretion in 239 children. In study 2, using resting-state and task-dependent functional magnetic resonance imaging in a subsample of 50 children, we investigated how SES affects children's amygdala-prefrontal functional organization through cortisol secretion. RESULTS: Children from lower SES exhibited lower cortisol secretion, considering basal cortisol, nocturnal cortisol activity during sleep, and cortisol awakening response, which mediated higher amygdala nuclei intrinsic functional connectivity with the medial and dorsolateral prefrontal cortex (PFC). Critically, these children also exhibited higher task-evoked ventromedial PFC activity through higher intrinsic connectivity of the centromedial amygdala with the medial PFC. They also exhibited higher functional coupling of the centromedial amygdala with the dorsolateral PFC when processing negative emotions. CONCLUSIONS: This study demonstrates that SES shapes children's amygdala-prefrontal circuitry through stress-sensitive cortisol secretion, with the most prominent effect in the centromedial amygdala's functional coordination with the ventromedial and dorsolateral PFC involved in processing negative emotions. Our findings provide important insight into the neurobiological etiology underlying how socioeconomic disparities shape children's emotional development.

Understanding Vulnerability and Adaptation in Early Brain Development using Network Neuroscience.

Early adversity influences brain development and emerging behavioral phenotypes relevant for psychiatric disorders. Understanding the effects of adversity before and after conception on brain development has implications for contextualizing current public health crises and pervasive health inequities. The use of functional magnetic resonance imaging (fMRI) to study the brain at rest has shifted understanding of brain functioning and organization in the earliest periods of life. Here we review applications of this technique to examine effects of early life stress (ELS) on neurodevelopment in infancy, and highlight targets for future research. Building on the foundation of existing work in this area will require tackling significant challenges, including greater inclusion of often marginalized segments of society, and conducting larger, properly powered studies.

An assessment of the functional effects of amphetamine-induced dendritic changes in the nucleus accumbens, medial prefrontal cortex, and hippocampus on different types of learning and memory function.

The present experiments investigated the effects of repeated amphetamine exposure on neural networks mediating different forms of learning and memory. Different components of these networks were assessed using various functional assays. The hypothesis was that abnormal dendritic changes in nucleus accumbens, medial prefrontal cortex, and hippocampus mediated by repeated amphetamine exposure would produce impairments on forms of learning and memory dependent on neural circuits relying on these brain systems, and have little or no effect on other forms of learning not dependent on these networks. Surprisingly, the results showed that many of the dendritic changes normally found in the nucleus accumbens, prefrontal cortex, and hippocampus following repeated amphetamine exposure were reversed back to control levels following extensive multi-domain cognitive training. Learning and memory functions associated with different neural networks also appeared normal except in one case. A neural network that includes, but is not limited to, the basolateral amygdala and nucleus accumbens was dysfunctional in rats repeatedly exposed to amphetamine despite the reversal of the majority of dendritic changes in the nucleus accumbens following cognitive training. Importantly, an increase in spine density that normally occurs in these brain regions following repeated amphetamine exposure remained following extensive cognitive training, particularly in the nucleus accumbens.

Quantitative Assessment of Resting-State for Mild Cognitive Impairment Detection: A Functional Near-Infrared Spectroscopy and Deep Learning Approach.

BACKGROUND: Mild cognitive impairment (MCI) is considered a prodromal stage of Alzheimer's disease. Early diagnosis of MCI can allow for treatment to improve cognitive function and reduce modifiable risk factors. OBJECTIVE: This study aims to investigate the feasibility of individual MCI detection from healthy control (HC) using a minimum duration of resting-state functional near-infrared spectroscopy (fNIRS) signals. METHODS: In this study, nine different measurement durations (i.e., 30, 60, 90, 120, 150, 180, 210, 240, and 270 s) were evaluated for MCI detection via the graph theory analysis and traditional machine learning approach, such as linear discriminant analysis, support vector machine, and K-nearest neighbor algorithms. Moreover, feature representation- and classification-based transfer learning (TL) methods were applied to identify MCI from HC through the input of connectivity maps with 30 and 90 s duration. RESULTS: There was no significant difference among the nine various time windows in the machine learning and graph theory analysis. The feature representation-based TL showed improved accuracy in both 30 and 90 s cases (i.e., 30 s: 81.27% and 90 s: 76.73%). Notably, the classification-based TL method achieved the highest accuracy of 95.81% using the pre-trained convolutional neural network (CNN) model with the 30 s interval functional connectivity map input. CONCLUSION: The results indicate that a 30 s measurement of the resting-state with fNIRS could be used to detect MCI. Moreover, the combination of neuroimaging (e.g., functional connectivity maps) and deep learning methods (e.g., CNN and TL) can be considered as novel biomarkers for clinical computer-assisted MCI diagnosis.

Increased wiring cost during development is driven by long-range cortical, but not subcortical connections.

The brain undergoes a protracted, metabolically expensive maturation process from childhood to adulthood. Therefore, it is crucial to understand how network cost is distributed among different brain systems as the brain matures. To address this issue, here we examined developmental changes in wiring cost and brain network topology using resting-state functional magnetic resonance imaging (rsfMRI) data longitudinally collected in awake rats from the juvenile age to adulthood. We found that the wiring cost increased in the vast majority of cortical connections but decreased in most subcortico-subcortical connections. Importantly, the developmental increase in wiring cost was dominantly driven by long-range cortical, but not subcortical connections, which was consistent with more pronounced increase in network integration in the cortical network. These results collectively indicate that there is a non-uniform distribution of network cost as the brain matures, and network resource is dominantly consumed for the development of the cortex, but not subcortex from the juvenile age to adulthood.

Signal diffusion along connectome gradients and inter-hub routing differentially contribute to dynamic human brain function.

Human cognition is dynamic, alternating over time between externally-focused states and more abstract, often self-generated, patterns of thought. Although cognitive neuroscience has documented how networks anchor particular modes of brain function, mechanisms that describe transitions between distinct functional states remain poorly understood. Here, we examined how time-varying changes in brain function emerge within the constraints imposed by macroscale structural network organization. Studying a large cohort of healthy adults (n = 326), we capitalized on manifold learning techniques that identify low dimensional representations of structural connectome organization and we decomposed neurophysiological activity into distinct functional states and their transition patterns using Hidden Markov Models. Structural connectome organization predicted dynamic transitions anchored in sensorimotor systems and those between sensorimotor and transmodal states. Connectome topology analyses revealed that transitions involving sensorimotor states traversed short and intermediary distances and adhered strongly to communication mechanisms of network diffusion. Conversely, transitions between transmodal states involved spatially distributed hubs and increasingly engaged long-range routing. These findings establish that the structure of the cortex is optimized to allow neural states the freedom to vary between distinct modes of processing, and so provides a key insight into the neural mechanisms that give rise to the flexibility of human cognition.

A framework to quantify controlled directed interactions in network physiology applied to cognitive function assessment.

The complex nature of physiological systems where multiple organs interact to form a network is complicated by direct and indirect interactions, with varying strength and direction of influence. This study proposes a novel framework which quantifies directional and pairwise couplings, while controlling for the effect of indirect interactions. Simulation results confirm the superiority of this framework in uncovering directional primary links compared to previous published methods. In a practical application of cognitive attention and alertness tasks, the method was used to assess controlled directed interactions between the cardiac, respiratory and brain activities (prefrontal cortex). It revealed increased interactions during the alertness task between brain wave activity on the left side of the brain with heart rate and respiration compared to resting phases. During the attention task, an increased number of right brain wave interactions involving respiration was also observed compared to rest, in addition to left brain wave activity with heart rate. The proposed framework potentially assesses directional interactions in complex network physiology and may detect cognitive dysfunctions associated with altered network physiology.