Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation

Repetitive transcranial magnetic stimulation in Alzheimer's disease: effects on neural and synaptic rehabilitation

Alzheimer's disease is a neurodegenerative disease resulting from deficits in synaptic transmission and homeostasis. The Alzheimer's disease brain tends to be hyperexcitable and hypersynchronized, thereby causing neurodegeneration and ultimately disrupting the operational abilities in daily life, leaving patients incapacitated. Repetitive transcranial magnetic stimulation is a cost-effective, neuro-modulatory technique used for multiple neurological conditions. Over the past two decades, it has been widely used to predict cognitive decline; identify pathophysiological markers; promote neuroplasticity; and assess brain excitability, plasticity, and connectivity. It has also been applied to patients with dementia, because it can yield facilitatory effects on cognition and promote brain recovery after a neurological insult. However, its therapeutic effectiveness at the molecular and synaptic levels has not been elucidated because of a limited number of studies. This study aimed to characterize the neurobiological changes following repetitive transcranial magnetic stimulation treatment, evaluate its effects on synaptic plasticity, and identify the associated mechanisms. This review essentially focuses on changes in the pathology, amyloidogenesis, and clearance pathways, given that amyloid deposition is a major hypothesis in the pathogenesis of Alzheimer's disease. Apoptotic mechanisms associated with repetitive transcranial magnetic stimulation procedures and different pathways mediating gene transcription, which are closely related to the neural regeneration process, are also highlighted. Finally, we discuss the outcomes of animal studies in which neuroplasticity is modulated and assessed at the structural and functional levels by using repetitive transcranial magnetic stimulation, with the aim to highlight future directions for better clinical translations.

Similarity of brain activity patterns during learning and subsequent resting state predicts memory consolidation

Spontaneous reactivation of brain activity from learning to a subsequent off-line period has been implicated as a neural mechanism underlying memory consolidation. However, similarities in brain activity may also emerge as a result of individual, trait-like characteristics. Here, we introduced a novel approach for analyzing continuous electroencephalography (EEG) data to investigate learning-induced changes as well as trait-like characteristics in brain activity underlying memory consolidation. Thirty-one healthy young adults performed a learning task, and their performance was retested after a short (∼1 h) delay. Consolidation of two distinct types of information (serial-order and probability) embedded in the task were tested to reveal similarities in functional networks that uniquely predict the changes in the respective memory performance. EEG was recorded during learning and pre- and post-learning rest periods. To investigate brain activity associated with consolidation, we quantified similarities in EEG functional connectivity between learning and pre-learning rest (baseline similarity) and learning and post-learning rest (post-learning similarity). While comparable patterns of these two could indicate trait-like similarities, changes from baseline to post-learning similarity could indicate learning-induced changes, possibly spontaneous reactivation. Higher learning-induced changes in alpha frequency connectivity (8.5-9.5 Hz) were associated with better consolidation of serial-order information, particularly for long-range connections across central and parietal sites. The consolidation of probability information was associated with learning-induced changes in delta frequency connectivity (2.5-3 Hz) specifically for more local, short-range connections. Furthermore, there was a substantial overlap between the baseline and post-learning similarities and their associations with consolidation performance, suggesting robust (trait-like) differences in functional connectivity networks underlying memory processes.

Frontostriatal salience network expansion in individuals in depression

Decades of neuroimaging studies have shown modest differences in brain structure and connectivity in depression, hindering mechanistic insights or the identification of risk factors for disease onset1. Furthermore, whereas depression is episodic, few longitudinal neuroimaging studies exist, limiting understanding of mechanisms that drive mood-state transitions. The emerging field of precision functional mapping has used densely sampled longitudinal neuroimaging data to show behaviourally meaningful differences in brain network topography and connectivity between and in healthy individuals2-4, but this approach has not been applied in depression. Here, using precision functional mapping and several samples of deeply sampled individuals, we found that the frontostriatal salience network is expanded nearly twofold in the cortex of most individuals with depression. This effect was replicable in several samples and caused primarily by network border shifts, with three distinct modes of encroachment occurring in different individuals. Salience network expansion was stable over time, unaffected by mood state and detectable in children before the onset of depression later in adolescence. Longitudinal analyses of individuals scanned up to 62 times over 1.5 years identified connectivity changes in frontostriatal circuits that tracked fluctuations in specific symptoms and predicted future anhedonia symptoms. Together, these findings identify a trait-like brain network topology that may confer risk for depression and mood-state-dependent connectivity changes in frontostriatal circuits that predict the emergence and remission of depressive symptoms over time.

Principles of cortical areas and their implications for neuroimaging

Cortical organization should constrain the study of how the brain performs behavior and cognition. A fundamental concept in cortical organization is that of arealization: that the cortex is parceled into discrete areas. In part one of this report, we review how non-human animal studies have illuminated principles of cortical arealization by revealing: (1) what defines a cortical area, (2) how cortical areas are formed, (3) how cortical areas interact with one another, and (4) what "computations" or "functions" areas perform. In part two, we discuss how these principles apply to neuroimaging research. In doing so, we highlight several examples where the commonly accepted interpretation of neuroimaging observations requires assumptions that violate the principles of arealization, including nonstationary areas that move on short time scales, large-scale gradients as organizing features, and cortical areas with singular functionality that perfectly map psychological constructs. Our belief is that principles of neurobiology should strongly guide the nature of computational explanations.

A machine-learning approach for differentiating borderline personality disorder from community participants with brain-wide functional connectivity

BACKGROUND

Functional connectivity has garnered interest as a potential biomarker of psychiatric disorders including borderline personality disorder (BPD). However, small sample sizes and lack of within-study replications have led to divergent findings with no clear spatial foci.

AIMS

Evaluate discriminative performance and generalizability of functional connectivity markers for BPD.

METHOD

Whole-brain fMRI resting state functional connectivity in matched subsamples of 116 BPD and 72 control individuals defined by three grouping strategies. We predicted BPD status using classifiers with repeated cross-validation based on multiscale functional connectivity within and between regions of interest (ROIs) covering the whole brain-global ROI-based network, seed-based ROI-connectivity, functional consistency, and voxel-to-voxel connectivity-and evaluated the generalizability of the classification in the left-out portion of non-matched data.

RESULTS

Full-brain connectivity allowed classification (∼70 %) of BPD patients vs. controls in matched inner cross-validation. The classification remained significant when applied to unmatched out-of-sample data (∼61-70 %). Highest seed-based accuracies were in a similar range to global accuracies (∼70-75 %), but spatially more specific. The most discriminative seed regions included midline, temporal and somatomotor regions. Univariate connectivity values were not predictive of BPD after multiple comparison corrections, but weak local effects coincided with the most discriminative seed-ROIs. Highest accuracies were achieved with a full clinical interview while self-report results remained at chance level.

LIMITATIONS

The accuracies vary considerably between random sub-samples of the population, global signal and covariates limiting the practical applicability.

CONCLUSIONS

Spatially distributed functional connectivity patterns are moderately predictive of BPD despite heterogeneity of the patient population.

High-level cognition is supported by information-rich but compressible brain activity patterns

To efficiently yet reliably represent and process information, our brains need to produce information-rich signals that differentiate between moments or cognitive states, while also being robust to noise or corruption. For many, though not all, natural systems, these two properties are often inversely related: More information-rich signals are less robust, and vice versa. Here, we examined how these properties change with ongoing cognitive demands. To this end, we applied dimensionality reduction algorithms and pattern classifiers to functional neuroimaging data collected as participants listened to a story, temporally scrambled versions of the story, or underwent a resting state scanning session. We considered two primary aspects of the neural data recorded in these different experimental conditions. First, we treated the maximum achievable decoding accuracy across participants as an indicator of the "informativeness" of the recorded patterns. Second, we treated the number of features (components) required to achieve a threshold decoding accuracy as a proxy for the "compressibility" of the neural patterns (where fewer components indicate greater compression). Overall, we found that the peak decoding accuracy (achievable without restricting the numbers of features) was highest in the intact (unscrambled) story listening condition. However, the number of features required to achieve comparable classification accuracy was also lowest in the intact story listening condition. Taken together, our work suggests that our brain networks flexibly reconfigure according to ongoing task demands and that the activity patterns associated with higher-order cognition and high engagement are both more informative and more compressible than the activity patterns associated with lower-order tasks and lower engagement.

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

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

Connectome-based fingerprinting: reproducibility, precision, and behavioral prediction

Functional magnetic resonance imaging-based functional connectivity enables the non-invasive mapping of individual differences in brain functional organization to individual differences in a vast array of behavioral phenotypes. This flexibility has renewed the search for neuroimaging-based biomarkers that exhibit reproducibility, prediction, and precision. Functional connectivity-based measures that share these three characteristics are key to achieving this goal. Here, we review the functional connectome fingerprinting approach and discuss its value, not only as a simple and intuitive conceptualization of the "functional connectome" that provides new insights into how the connectome is altered in association with psychiatric symptoms, but also as a straightforward and interpretable method for indexing the reproducibility of functional connectivity-based measures. We discuss how these advantages provide new avenues for strengthening reproducibility, precision, and behavioral prediction for functional connectomics and we consider new directions toward discovering better biomarkers for neuropsychiatric conditions.

Psychiatric neuroimaging designs for individualised, cohort, and population studies

Psychiatric neuroimaging faces challenges to rigour and reproducibility that prompt reconsideration of the relative strengths and limitations of study designs. Owing to high resource demands and varying inferential goals, current designs differentially emphasise sample size, measurement breadth, and longitudinal assessments. In this overview and perspective, we provide a guide to the current landscape of psychiatric neuroimaging study designs with respect to this balance of scientific goals and resource constraints. Through a heuristic data cube contrasting key design features, we discuss a resulting trade-off among small sample, precision longitudinal studies (e.g., individualised studies and cohorts) and large sample, minimally longitudinal, population studies. Precision studies support tests of within-person mechanisms, via intervention and tracking of longitudinal course. Population studies support tests of generalisation across multifaceted individual differences. A proposed reciprocal validation model (RVM) aims to recursively leverage these complementary designs in sequence to accumulate evidence, optimise relative strengths, and build towards improved long-term clinical utility.

Longitudinal stability of individual brain plasticity patterns in blindness

The primary visual cortex (V1) in blindness is engaged in a wide spectrum of tasks and sensory modalities, including audition, touch, language, and memory. This widespread involvement raises questions regarding the constancy of its role and whether it might exhibit flexibility in its function over time, connecting to diverse network functions specific to task demands. This would suggest that reorganized V1 assumes a role like multiple-demand system regions. Alternatively, varying patterns of plasticity in blind V1 may be attributed to individual factors, with different blind individuals recruiting V1 preferentially for different functions. In support of this, we recently showed that V1 functional connectivity (FC) varies greatly across blind individuals. But do these represent stable individual patterns of plasticity, or are they driven more by instantaneous changes, like a multiple-demand system now inhabiting V1? Here, we tested whether individual FC patterns from the V1 of blind individuals are stable over time. We show that over two years, FC from the V1 is unique and highly stable in a small sample of repeatedly sampled congenitally blind individuals. Further, using multivoxel pattern analysis, we demonstrate that the unique reorganization patterns of these individuals allow decoding of participant identity. Together with recent evidence for substantial individual differences in V1 connectivity, this indicates that there may be a consistent role for V1 in blindness, which may differ for each individual. Further, it suggests that the variability in visual reorganization in blindness across individuals could be used to seek stable neuromarkers for sight rehabilitation and assistive approaches.

Psilocybin desynchronizes the human brain

A single dose of psilocybin, a psychedelic that acutely causes distortions of space-time perception and ego dissolution, produces rapid and persistent therapeutic effects in human clinical trials1-4. In animal models, psilocybin induces neuroplasticity in cortex and hippocampus5-8. It remains unclear how human brain network changes relate to subjective and lasting effects of psychedelics. Here we tracked individual-specific brain changes with longitudinal precision functional mapping (roughly 18 magnetic resonance imaging visits per participant). Healthy adults were tracked before, during and for 3 weeks after high-dose psilocybin (25 mg) and methylphenidate (40 mg), and brought back for an additional psilocybin dose 6-12 months later. Psilocybin massively disrupted functional connectivity (FC) in cortex and subcortex, acutely causing more than threefold greater change than methylphenidate. These FC changes were driven by brain desynchronization across spatial scales (areal, global), which dissolved network distinctions by reducing correlations within and anticorrelations between networks. Psilocybin-driven FC changes were strongest in the default mode network, which is connected to the anterior hippocampus and is thought to create our sense of space, time and self. Individual differences in FC changes were strongly linked to the subjective psychedelic experience. Performing a perceptual task reduced psilocybin-driven FC changes. Psilocybin caused persistent decrease in FC between the anterior hippocampus and default mode network, lasting for weeks. Persistent reduction of hippocampal-default mode network connectivity may represent a neuroanatomical and mechanistic correlate of the proplasticity and therapeutic effects of psychedelics.

The promise of precision functional mapping for neuroimaging in psychiatry

Precision functional mapping (PFM) is a neuroimaging approach to reliably estimate metrics of brain function from individual people via the collection of large amounts of fMRI data (hours per person). This method has revealed much about the inter-individual variation of functional brain networks. While standard group-level studies, in which we average brain measures across groups of people, are important in understanding the generalizable neural underpinnings of neuropsychiatric disorders, many disorders are heterogeneous in nature. This heterogeneity often complicates clinical care, leading to patient uncertainty when considering prognosis or treatment options. We posit that PFM methods may help streamline clinical care in the future, fast-tracking the choice of personalized treatment that is most compatible with the individual. In this review, we provide a history of PFM studies, foundational results highlighting the benefits of PFM methods in the pursuit of an advanced understanding of individual differences in functional network organization, and possible avenues where PFM can contribute to clinical translation of neuroimaging research results in the way of personalized treatment in psychiatry.

A subset of brain regions within adult functional connectivity networks demonstrate high reliability across early development

The human cerebral cortex contains groups of areas that support sensory, motor, cognitive, and affective functions, often categorized as functional networks. These areas show stronger internal and weaker external functional connectivity (FC) and exhibit similar FC profiles within rather than between networks. Previous studies have demonstrated the development of these networks from nascent forms present before birth to their mature, adult-like topography in childhood. However, analyses often still use definitions based on adult functional networks. We aim to assess how this might lead to the misidentification of functional networks and explore potential consequences and solutions. Our findings suggest that even though adult networks provide only a marginally better than-chance description of the infant FC organization, misidentification was largely driven by specific areas. By restricting functional networks to areas showing adult-like network clustering, we observed consistent within-network FC both within and across scans and throughout development. Additionally, these areas were spatially closer to locations with low variability in network identity among adults. Our analysis aids in understanding the potential consequences of using adult networks "as is" and provides guidance for future research on selecting and utilizing functional network models based on the research question and scenario.

Tracking EEG network dynamics through transitions between eyes-closed, eyes-open, and task states

Our study aimed to verify the possibilities of effectively applying chronnectomics methods to reconstruct the dynamic processes of network transition between three types of brain states, namely, eyes-closed rest, eyes-open rest, and a task state. The study involved dense EEG recordings and reconstruction of the source-level time-courses of the signals. Functional connectivity was measured using the phase lag index, and dynamic analyses concerned coupling strength and variability in alpha and beta frequencies. The results showed significant and dynamically specific transitions regarding processes of eyes opening and closing and during the eyes-closed-to-task transition in the alpha band. These observations considered a global dimension, default mode network, and central executive network. The decrease of connectivity strength and variability that accompanied eye-opening was a faster process than the synchronization increase during eye-opening, suggesting that these two transitions exhibit different reorganization times. While referring the obtained results to network studies, it was indicated that the scope of potential similarities and differences between rest and task-related networks depends on whether the resting state was recorded in eyes closed or open condition.

Brain fingerprinting and cognitive behavior predicting using functional connectome of high inter-subject variability

The functional connectivity (FC) graph of the brain has been widely recognized as a ``fingerprint'' that can be used to identify individuals from a group of subjects. Research has indicated that individual identification accuracy can be improved by eliminating the impact of shared information among individuals. However, current research extracts not only shared information of inter-subject but also individual-specific information from FC graphs, resulting in incomplete separation of shared information and fingerprint information among individuals, leading to lower individual identification accuracy across all functional magnetic resonance imaging (fMRI) states session pairs and poor cognitive behavior prediction performance. In this paper, we propose a method to enhance inter-subject variability combining conditional variational autoencoder (CVAE) network and sparse dictionary learning (SDL) module. By embedding fMRI state information in the encoding and decoding processes, the CVAE network can better capture and represent the common features among individuals and enhance inter-subject variability by residual. Our experimental results on Human Connectome Project (HCP) data show that the refined connectomes obtained by using CVAE with SDL can accurately distinguish an individual from the remaining participants. The success accuracies reached 99.7 % and 99.6 % in the session pair rest1-rest2 and reverse rest2-rest1, respectively. In the identification experiment involving task-task combinations carried out on the same day, the identification accuracies ranged from 94.2 % to 98.8 %. Furthermore, we showed the Frontoparietal and Default networks make the most significant contributions to individual identification and the edges that significantly contribute to individual identification are found within and between the Frontoparietal and Default networks. Additionally, high-level cognitive behaviors can also be better predicted with the obtained refined connectomes, suggesting that higher fingerprinting can be useful for resulting in higher behavioral associations. In summary, our proposed framework provides a promising approach to use functional connectivity networks for studying cognition and behavior, promoting a deeper understanding of brain functions.

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

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

Spectral-switching analysis reveals real-time neuronal network representations of concurrent spontaneous naturalistic behaviors in human brain

Despite abundant evidence of functional networks in the human brain, their neuronal underpinnings, and relationships to real-time behavior have been challenging to resolve. Analyzing brain-wide intracranial-EEG recordings with video monitoring, acquired in awake subjects during clinical epilepsy evaluation, we discovered the tendency of each brain region to switch back and forth between 2 distinct power spectral densities (PSDs 2-55Hz). We further recognized that this 'spectral switching' occurs synchronously between distant sites, even between regions with differing baseline PSDs, revealing long-range functional networks that would be obscured in analysis of individual frequency bands. Moreover, the real-time PSD-switching dynamics of specific networks exhibited striking alignment with activities such as conversation and hand movements, revealing a multi-threaded functional network representation of concurrent naturalistic behaviors. Network structures and their relationships to behaviors were stable across days, but were altered during N3 sleep. Our results provide a new framework for understanding real-time, brain-wide neural-network dynamics.

Network mechanisms of ongoing brain activity's influence on conscious visual perception

Sensory inputs enter a constantly active brain, whose state is always changing from one moment to the next. Currently, little is known about how ongoing, spontaneous brain activity participates in online task processing. We employed 7 Tesla fMRI and a threshold-level visual perception task to probe the effects of prestimulus ongoing brain activity on perceptual decision-making and conscious recognition. Prestimulus activity originating from distributed brain regions, including visual cortices and regions of the default-mode and cingulo-opercular networks, exerted a diverse set of effects on the sensitivity and criterion of conscious recognition, and categorization performance. We further elucidate the mechanisms underlying these behavioral effects, revealing how prestimulus activity modulates multiple aspects of stimulus processing in highly specific and network-dependent manners. These findings reveal heretofore unknown network mechanisms underlying ongoing brain activity's influence on conscious perception, and may hold implications for understanding the precise roles of spontaneous activity in other brain functions.

Spatiotemporal patterns in cortical development: Age, puberty, and individual variability from 9 to 13 years of age

Humans and nonhuman primate studies suggest that timing and tempo of cortical development varies neuroanatomically along a sensorimotor-to-association (S-A) axis. Prior human studies have reported a principal S-A axis across various modalities, but largely rely on cross-sectional samples with wide age-ranges. Here, we investigate developmental changes and individual variability in cortical organization along the S-A axis between the ages of 9-13 years using a large, longitudinal sample (N = 2487-3747, 46-50% female) from the Adolescent Brain Cognitive Development Study (ABCD Study®). This work assesses multiple aspects of neurodevelopment indexed by changes in cortical thickness, cortical microarchitecture, and resting-state functional fluctuations. First, we evaluated S-A organization in age-related changes and, then, computed individual-level S-A alignment in brain changes and assessing differences therein due to age, sex, and puberty. Varying degrees of linear and quadratic age-related brain changes were identified along the S-A axis. Yet, these patterns of cortical development were overshadowed by considerable individual variability in S-A alignment. Even within individuals, there was little correspondence between S-A patterning across the different aspects of neurodevelopment investigated (i.e., cortical morphology, microarchitecture, function). Some of the individual variation in developmental patterning of cortical morphology and microarchitecture was explained by age, sex, and pubertal development. Altogether, this work contextualizes prior findings that regional age differences do progress along an S-A axis at a group level, while highlighting broad variation in developmental change between individuals and between aspects of cortical development, in part due to sex and puberty.

Significance Statement

Understanding normative patterns of adolescent brain change, and individual variability therein, is crucial for disentangling healthy and abnormal development. We used longitudinal human neuroimaging data to study several aspects of neurodevelopment during early adolescence and assessed their organization along a sensorimotor-to-association (S-A) axis across the cerebral cortex. Age differences in brain changes were linear and curvilinear along this S-A axis. However, individual-level sensorimotor-association alignment varied considerably, driven in part by differences in age, sex, and pubertal development.

Multi-hierarchy Network Configuration Can Predict Brain States and Performance

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

Predicting creative behavior using resting-state electroencephalography

Neuroscience research has shown that specific brain patterns can relate to creativity during multiple tasks but also at rest. Nevertheless, the electrophysiological correlates of a highly creative brain remain largely unexplored. This study aims to uncover resting-state networks related to creative behavior using high-density electroencephalography (HD-EEG) and to test whether the strength of functional connectivity within these networks could predict individual creativity in novel subjects. We acquired resting state HD-EEG data from 90 healthy participants who completed a creative behavior inventory. We then employed connectome-based predictive modeling; a machine-learning technique that predicts behavioral measures from brain connectivity features. Using a support vector regression, our results reveal functional connectivity patterns related to high and low creativity, in the gamma frequency band (30-45 Hz). In leave-one-out cross-validation, the combined model of high and low networks predicts individual creativity with very good accuracy (r = 0.36, p = 0.00045). Furthermore, the model's predictive power is established through external validation on an independent dataset (N = 41), showing a statistically significant correlation between observed and predicted creativity scores (r = 0.35, p = 0.02). These findings reveal large-scale networks that could predict creative behavior at rest, providing a crucial foundation for developing HD-EEG-network-based markers of creativity.

Reconfigurations of cortical manifold structure during reward-based motor learning

Adaptive motor behavior depends on the coordinated activity of multiple neural systems distributed across the brain. While the role of sensorimotor cortex in motor learning has been well established, how higher-order brain systems interact with sensorimotor cortex to guide learning is less well understood. Using functional MRI, we examined human brain activity during a reward-based motor task where subjects learned to shape their hand trajectories through reinforcement feedback. We projected patterns of cortical and striatal functional connectivity onto a low-dimensional manifold space and examined how regions expanded and contracted along the manifold during learning. During early learning, we found that several sensorimotor areas in the dorsal attention network exhibited increased covariance with areas of the salience/ventral attention network and reduced covariance with areas of the default mode network (DMN). During late learning, these effects reversed, with sensorimotor areas now exhibiting increased covariance with DMN areas. However, areas in posteromedial cortex showed the opposite pattern across learning phases, with its connectivity suggesting a role in coordinating activity across different networks over time. Our results establish the neural changes that support reward-based motor learning and identify distinct transitions in the functional coupling of sensorimotor to transmodal cortex when adapting behavior.

The Transdiagnostic Connectome Project: a richly phenotyped open dataset for advancing the study of brain-behavior relationships in psychiatry

An important aim in psychiatry is the establishment of valid and reliable associations linking profiles of brain functioning to clinically relevant symptoms and behaviors across patient populations. To advance progress in this area, we introduce an open dataset containing behavioral and neuroimaging data from 241 individuals aged 18 to 70, comprising 148 individuals meeting diagnostic criteria for a broad range of psychiatric illnesses and a healthy comparison group of 93 individuals. These data include high-resolution anatomical scans, multiple resting-state, and task-based functional MRI runs. Additionally, participants completed over 50 psychological and cognitive assessments. Here, we detail available behavioral data as well as raw and processed MRI derivatives. Associations between data processing and quality metrics, such as head motion, are reported. Processed data exhibit classic task activation effects and canonical functional network organization. Overall, we provide a comprehensive and analysis-ready transdiagnostic dataset, which we hope will accelerate the identification of illness-relevant features of brain functioning, enabling future discoveries in basic and clinical neuroscience.

Individual Variability in Brain Connectivity Patterns and Driving-Fatigue Dynamics

Mental fatigue during driving poses significant risks to road safety, necessitating accurate assessment methods to mitigate potential hazards. This study explores the impact of individual variability in brain networks on driving fatigue assessment, hypothesizing that subject-specific connectivity patterns play a pivotal role in understanding fatigue dynamics. By conducting a linear regression analysis of subject-specific brain networks in different frequency bands, this research aims to elucidate the relationships between frequency-specific connectivity patterns and driving fatigue. As such, an EEG sustained driving simulation experiment was carried out, estimating individuals' brain networks using the Phase Lag Index (PLI) to capture shared connectivity patterns. The results unveiled notable variability in connectivity patterns across frequency bands, with the alpha band exhibiting heightened sensitivity to driving fatigue. Individualized connectivity analysis underscored the complexity of fatigue assessment and the potential for personalized approaches. These findings emphasize the importance of subject-specific brain networks in comprehending fatigue dynamics, while providing sensor space minimization, advocating for the development of efficient mobile sensor applications for real-time fatigue detection in driving scenarios.

Functional dissociation of the language network and other cognition in early childhood

Is language distinct from other cognition during development? Does neural machinery for language emerge from general-purpose neural mechanisms, becoming tuned for language after years of experience and maturation? Answering these questions will shed light on the origins of domain-specificity in the brain. We address these questions using precision fMRI, scanning young children (35 months to 9 years of age) on an auditory language localizer, spatial working memory localizer (engaging the domain-general multiple demand [MD] network), and a resting-state scan. We create subject-specific functional regions of interest for each network and examine their selectivity, specificity, and functional connectivity. We find young children show domain-specific, left-lateralized language activation, and that the language network is not responsive to domain-general cognitive load. Additionally, the cortically adjacent MD network is selective to cognitive load, but not to language. These networks show higher within versus between-network functional connectivity. This connectivity is stable across ages (examined cross-sectionally and longitudinally), whereas language responses increase with age and across time within subject, reflecting a domain-specific developmental change. Overall, we provide evidence for a double dissociation of the language and MD network throughout development, in both their function and connectivity. These findings suggest that domain-specificity, even for uniquely human cognition like language, develops early and distinctly from mechanisms that presumably support other human cognition.

Neuroimaging epicenters as potential sites of onset of the neuroanatomical pathology in schizophrenia

Schizophrenia lacks a clear definition at the neuroanatomical level, capturing the sites of origin and progress of this disorder. Using a network-theory approach called epicenter mapping on cross-sectional magnetic resonance imaging from 1124 individuals with schizophrenia, we identified the most likely "source of origin" of the structural pathology. Our results suggest that the Broca's area and adjacent frontoinsular cortex may be the epicenters of neuroanatomical pathophysiology in schizophrenia. These epicenters can predict an individual's response to treatment for psychosis. In addition, cross-diagnostic similarities based on epicenter mapping over of 4000 individuals diagnosed with neurological, neurodevelopmental, or psychiatric disorders appear to be limited. When present, these similarities are restricted to bipolar disorder, major depressive disorder, and obsessive-compulsive disorder. We provide a comprehensive framework linking schizophrenia-specific epicenters to multiple levels of neurobiology, including cognitive processes, neurotransmitter receptors and transporters, and human brain gene expression. Epicenter mapping may be a reliable tool for identifying the potential onset sites of neural pathophysiology in schizophrenia.

Brain age has limited utility as a biomarker for capturing fluid cognition in older individuals

One well-known biomarker candidate that supposedly helps capture fluid cognition is Brain Age, or a predicted value based on machine-learning models built to predict chronological age from brain MRI. To formally evaluate the utility of Brain Age for capturing fluid cognition, we built 26 age-prediction models for Brain Age based on different combinations of MRI modalities, using the Human Connectome Project in Aging (n=504, 36-100 years old). First, based on commonality analyses, we found a large overlap between Brain Age and chronological age: Brain Age could uniquely add only around 1.6% in explaining variation in fluid cognition over and above chronological age. Second, the age-prediction models that performed better at predicting chronological age did NOT necessarily create better Brain Age for capturing fluid cognition over and above chronological age. Instead, better-performing age-prediction models created Brain Age that overlapped larger with chronological age, up to around 29% out of 32%, in explaining fluid cognition. Third, Brain Age missed around 11% of the total variation in fluid cognition that could have been explained by the brain variation. That is, directly predicting fluid cognition from brain MRI data (instead of relying on Brain Age and chronological age) could lead to around a 1/3-time improvement of the total variation explained. Accordingly, we demonstrated the limited utility of Brain Age as a biomarker for fluid cognition and made some suggestions to ensure the utility of Brain Age in explaining fluid cognition and other phenotypes of interest.

Heterogeneity in functional connectivity: Dimensional predictors of individual variability during rest and task fMRI in psychosis

BACKGROUND

Individuals with schizophrenia spectrum disorders (SSD) often demonstrate cognitive impairments, associated with poor functional outcomes. While neurobiological heterogeneity has posed challenges when examining social cognition in SSD, it provides a unique opportunity to explore brain-behavior relationships. The aim of this study was to investigate the relationship between individual variability in functional connectivity during resting state and the performance of a social task and social and non-social cognition in a large sample of controls and individuals diagnosed with SSD.

METHODS

Neuroimaging and behavioral data were analyzed for 193 individuals with SSD and 155 controls (total n = 348). Individual variability was quantified through mean correlational distance (MCD) of functional connectivity between participants; MCD was defined as a global 'variability score'. Pairwise correlational distance was calculated as 1 - the correlation coefficient between a given pair of participants, and averaging distance from one participant to all other participants provided the mean correlational distance metric. Hierarchical regressions were performed on variability scores derived from resting state and Empathic Accuracy (EA) task functional connectivity data to determine potential predictors (e.g., age, sex, neurocognitive and social cognitive scores) of individual variability.

RESULTS

Group comparison between SSD and controls showed greater SSD MCD during rest (p = 0.00038), while no diagnostic differences were observed during task (p = 0.063). Hierarchical regression analyses demonstrated the persistence of a significant diagnostic effect during rest (p = 0.008), contrasting with its non-significance during the task (p = 0.50), after social cognition was added to the model. Notably, social cognition exhibited significance in both resting state and task conditions (both p = 0.01).

CONCLUSIONS

Diagnostic differences were more prevalent during unconstrained resting scans, whereas the task pushed participants into a more common pattern which better emphasized transdiagnostic differences in cognitive abilities. Focusing on variability may provide new opportunities for interventions targeting specific cognitive impairments to improve functional outcomes.

In-Scanner Thoughts shape Resting-state Functional Connectivity: how participants "rest" matters

Resting-state fMRI (rs-fMRI) scans-namely those lacking experimentally-controlled stimuli or cognitive demands-are often used to identify aberrant patterns of functional connectivity (FC) in clinical populations. To minimize interpretational uncertainty, researchers control for across-cohort disparities in age, gender, co-morbidities, and head motion. Yet, studies rarely, if ever, consider the possibility that systematic differences in inner experience (i.e., what subjects think and feel during the scan) may directly affect FC measures. Here we demonstrate that is the case using a rs-fMRI dataset comprising 471 scans annotated with experiential data. Wide-spread significant differences in FC are observed between scans that systematically differ in terms of reported in-scanner experience. Additionally, we show that FC can successfully predict specific aspects of in-scanner experience in a manner similar to how it predicts demographics, cognitive abilities, clinical outcomes and labels. Together, these results highlight the key role of in-scanner experience in shaping rs-fMRI estimates of FC.

Large Individual Differences in Functional Connectivity in the Context of Major Depression and Antidepressant Pharmacotherapy

Clinical studies of major depression (MD) generally focus on group effects, yet interindividual differences in brain function are increasingly recognized as important and may even impact effect sizes related to group effects. Here, we examine the magnitude of individual differences in relation to group differences that are commonly investigated (e.g., related to MD diagnosis and treatment response). Functional MRI data from 107 participants (63 female, 44 male) were collected at baseline, 2, and 8 weeks during which patients received pharmacotherapy (escitalopram, N = 68) and controls (N = 39) received no intervention. The unique contributions of different sources of variation were examined by calculating how much variance in functional connectivity was shared across all participants and sessions, within/across groups (patients vs controls, responders vs nonresponders, female vs male participants), recording sessions, and individuals. Individual differences and common connectivity across groups, sessions, and participants contributed most to the explained variance (>95% across analyses). Group differences related to MD diagnosis, treatment response, and biological sex made significant but small contributions (0.3-1.2%). High individual variation was present in cognitive control and attention areas, while low individual variation characterized primary sensorimotor regions. Group differences were much smaller than individual differences in the context of MD and its treatment. These results could be linked to the variable findings and difficulty translating research on MD to clinical practice. Future research should examine brain features with low and high individual variation in relation to psychiatric symptoms and treatment trajectories to explore the clinical relevance of the individual differences identified here.

Two common and distinct forms of variation in human functional brain networks

The cortex has a characteristic layout with specialized functional areas forming distributed large-scale networks. However, substantial work shows striking variation in this organization across people, which relates to differences in behavior. While most previous work treats individual differences as linked to boundary shifts between the borders of regions, here we show that cortical 'variants' also occur at a distance from their typical position, forming ectopic intrusions. Both 'border' and 'ectopic' variants are common across individuals, but differ in their location, network associations, properties of subgroups of individuals, activations during tasks, and prediction of behavioral phenotypes. Border variants also track significantly more with shared genetics than ectopic variants, suggesting a closer link between ectopic variants and environmental influences. This work argues that these two dissociable forms of variation-border shifts and ectopic intrusions-must be separately accounted for in the analysis of individual differences in cortical systems across people.

Impact of deprivation and preferential usage on functional connectivity between early visual cortex and category selective visual regions

Human behavior can be remarkably shaped by experience, such as the removal of sensory input. Many studies of conditions such as stroke, limb amputation, and vision loss have examined how the removal of input changes brain function. However, an important question has yet to be answered: when input is lost, does the brain change its connectivity to preferentially use some remaining inputs over others? In individuals with healthy vision, the central portion of the retina is preferentially used for everyday visual tasks, due to its ability to discriminate fine details. However, when central vision is lost in conditions like macular degeneration, peripheral vision must be relied upon for those everyday tasks, with certain portions receiving "preferential" usage over others. Using resting-state fMRI collected during total darkness, we examined how deprivation and preferential usage influence the intrinsic functional connectivity of sensory cortex by studying individuals with selective vision loss due to late stages of macular degeneration. We found that cortical regions representing spared portions of the peripheral retina, regardless of whether they are preferentially used, exhibit plasticity of intrinsic functional connectivity in macular degeneration. Cortical representations of spared peripheral retinal locations showed stronger connectivity to MT, a region involved in processing motion. These results suggest that long-term loss of central vision can produce widespread effects throughout spared representations in early visual cortex, regardless of whether those representations are preferentially used. These findings support the idea that connections to visual cortex maintain the capacity for change well after critical periods of visual development.

Highlights

Portions of early visual cortex representing central vs. peripheral vision exhibit different patterns of connectivity to category-selective visual regions.When central vision is lost, cortical representations of peripheral vision display stronger functional connections to MT than central representations.When central vision is lost, connectivity to regions selective for tasks that involve central vision (FFA and PHA) are not significantly altered.These effects do not depend on which locations of peripheral vision are used more.

Reduced lateralization of multiple functional brain networks in autistic males

BACKGROUND

Autism spectrum disorder has been linked to a variety of organizational and developmental deviations in the brain. One such organizational difference involves hemispheric lateralization, which may be localized to language-relevant regions of the brain or distributed more broadly.

METHODS

In the present study, we estimated brain hemispheric lateralization in autism based on each participant's unique functional neuroanatomy rather than relying on group-averaged data. Additionally, we explored potential relationships between the lateralization of the language network and behavioral phenotypes including verbal ability, language delay, and autism symptom severity. We hypothesized that differences in hemispheric asymmetries in autism would be limited to the language network, with the alternative hypothesis of pervasive differences in lateralization. We tested this and other hypotheses by employing a cross-sectional dataset of 118 individuals (48 autistic, 70 neurotypical). Using resting-state fMRI, we generated individual network parcellations and estimated network asymmetries using a surface area-based approach. A series of multiple regressions were then used to compare network asymmetries for eight significantly lateralized networks between groups.

RESULTS

We found significant group differences in lateralization for the left-lateralized Language (d = -0.89), right-lateralized Salience/Ventral Attention-A (d = 0.55), and right-lateralized Control-B (d = 0.51) networks, with the direction of these group differences indicating less asymmetry in autistic males. These differences were robust across different datasets from the same participants. Furthermore, we found that language delay stratified language lateralization, with the greatest group differences in language lateralization occurring between autistic males with language delay and neurotypical individuals.

CONCLUSIONS

These findings evidence a complex pattern of functional lateralization differences in autism, extending beyond the Language network to the Salience/Ventral Attention-A and Control-B networks, yet not encompassing all networks, indicating a selective divergence rather than a pervasive one. Moreover, we observed an association between Language network lateralization and language delay in autistic males.

Improving Predictability, Test-Retest Reliability and Generalisability of Brain-Wide Associations for Cognitive Abilities via Multimodal Stacking

Brain-wide association studies (BWASs) have attempted to relate cognitive abilities with brain phenotypes, but have been challenged by issues such as predictability, test-retest reliability, and cross-cohort generalisability. To tackle these challenges, we proposed "stacking" that combines brain magnetic resonance imaging of different modalities, from task-fMRI contrasts and functional connectivity during tasks and rest to structural measures, into one prediction model. We benchmarked the benefits of stacking, using the Human Connectome Projects: Young Adults and Aging and the Dunedin Multidisciplinary Health and Development Study. For predictability, stacked models led to out-of-sample r ∼.5-.6 when predicting cognitive abilities at the time of scanning and 36 years earlier. For test-retest reliability, stacked models reached an excellent level of reliability (ICC>.75), even when we stacked only task-fMRI contrasts together. For generalisability, a stacked model with non-task MRI built from one dataset significantly predicted cognitive abilities in other datasets. Altogether, stacking is a viable approach to undertake the three challenges of BWAS for cognitive abilities.

Tracking Age-Related Topological Changes in Individual Brain Morphological Networks Across the Human Lifespan

BACKGROUND

Many studies have shown topological alterations associated with age in population-based brain morphological networks. However, it is not clear how individual brain morphological networks change with age across the lifespan.

PURPOSE

To characterize age-related topological changes in individual networks and investigate the relationships between individual- and group-based brain networks at the nodal, modular, and connectome levels.

STUDY TYPE

Retrospective analysis.

POPULATION

One hundred seventy-nine healthy subjects (108 males and 71 females), aged 6-85 years with a median age of 32 years and an inter-quartile range (IQR) of 26 years.

FIELD STRENGTH/SEQUENCE

T1-weighted images using the magnetization-prepared rapid gradient echo (MPRAGE) sequences.

ASSESSMENT

Two nodal-level indicators (nodal similarity and node matching), five modular-level indicators (modularity, intra/inter-module similarity, adjusted mutual information [AMI], and module variation), and five connectome-level indicators (global efficiency, characteristic path length, clustering coefficient, local efficiency, and individual contribution) were calculated in brain morphological networks. Regression models for different indicators were built to examine their lifetime trajectory patterns.

STATISTICAL TESTS

Single-sample t-test, Mantel's test, Pearson correlation coefficient. A P value <0.05 was considered statistically significant.

RESULTS

Among 68 nodes, 34 nodes showed significant age-related patterns (all P < 0.05, FDR-corrected) in nodal similarity, including linear decline and quadratic trends. The lifespan trajectory of the connectome-level topological attributes of the individual networks presented U-shaped or inverse U-shaped trends with age. Between the individual- and group-based brain networks, the average nodal similarity was 0.67 and the average AMI of module partitions was 0.57.

DATA CONCLUSION

The lifespan trajectories of the nodal similarity mainly followed linear decreasing and nonlinear trends, whereas the modularity and the global topological attributes exhibited nonlinear patterns. There was a high degree of consistency at both nodal similarity and modular division between the individual and group networks.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 1.

Dynamic computational phenotyping of human cognition

Computational phenotyping has emerged as a powerful tool for characterizing individual variability across a variety of cognitive domains. An individual's computational phenotype is defined as a set of mechanistically interpretable parameters obtained from fitting computational models to behavioural data. However, the interpretation of these parameters hinges critically on their psychometric properties, which are rarely studied. To identify the sources governing the temporal variability of the computational phenotype, we carried out a 12-week longitudinal study using a battery of seven tasks that measure aspects of human learning, memory, perception and decision making. To examine the influence of state effects, each week, participants provided reports tracking their mood, habits and daily activities. We developed a dynamic computational phenotyping framework, which allowed us to tease apart the time-varying effects of practice and internal states such as affective valence and arousal. Our results show that many phenotype dimensions covary with practice and affective factors, indicating that what appears to be unreliability may reflect previously unmeasured structure. These results support a fundamentally dynamic understanding of cognitive variability within an individual.

Brexanolone Treatment in a Real-World Patient Population: A Case Series and Pilot Feasibility Study of Precision Neuroimaging

PURPOSE/BACKGROUND

Brexanolone is approved for postpartum depression (PPD) by the United States Food and Drug Administration. Brexanolone has outperformed placebo in clinical trials, but less is known about the efficacy in real-world patients with complex social and medical histories. Furthermore, the impact of brexanolone on large-scale brain systems such as changes in functional connectivity (FC) is unknown.

METHODS/PROCEDURES

We tracked changes in depressive symptoms across a diverse group of patients who received brexanolone at a large medical center. Edinburgh Postnatal Depression Scale (EPDS) scores were collected through chart review for 17 patients immediately prior to infusion through approximately 1 year postinfusion. In 2 participants, we performed precision functional neuroimaging (pfMRI), including before and after treatment in 1 patient. pfMRI collects many hours of data in individuals for precision medicine applications and was performed to assess the feasibility of investigating changes in FC with brexanolone.

FINDINGS/RESULTS

The mean EPDS score immediately postinfusion was significantly lower than the mean preinfusion score (mean change [95% CI]: 10.76 [7.11-14.40], t (15) = 6.29, P < 0.0001). The mean EPDS score stayed significantly lower at 1 week (mean difference [95% CI]: 9.50 [5.23-13.76], t (11) = 4.90, P = 0.0005) and 3 months (mean difference [95% CI]: 9.99 [4.71-15.27], t (6) = 4.63, P = 0.0036) postinfusion. Widespread changes in FC followed infusion, which correlated with EPDS scores.

IMPLICATIONS/CONCLUSIONS

Brexanolone is a successful treatment for PPD in the clinical setting. In conjunction with routine clinical care, brexanolone was linked to a reduction in symptoms lasting at least 3 months. pfMRI is feasible in postpartum patients receiving brexanolone and has the potential to elucidate individual-specific mechanisms of action.

A precision functional atlas of personalized network topography and probabilities

Although the general location of functional neural networks is similar across individuals, there is vast person-to-person topographic variability. To capture this, we implemented precision brain mapping functional magnetic resonance imaging methods to establish an open-source, method-flexible set of precision functional network atlases-the Masonic Institute for the Developing Brain (MIDB) Precision Brain Atlas. This atlas is an evolving resource comprising 53,273 individual-specific network maps, from more than 9,900 individuals, across ages and cohorts, including the Adolescent Brain Cognitive Development study, the Developmental Human Connectome Project and others. We also generated probabilistic network maps across multiple ages and integration zones (using a new overlapping mapping technique, Overlapping MultiNetwork Imaging). Using regions of high network invariance improved the reproducibility of executive function statistical maps in brain-wide associations compared to group average-based parcellations. Finally, we provide a potential use case for probabilistic maps for targeted neuromodulation. The atlas is expandable to alternative datasets with an online interface encouraging the scientific community to explore and contribute to understanding the human brain function more precisely.

Causally Probing the Role of the Hippocampus in Fear Discrimination: A Precision Functional Mapping-Guided, Transcranial Magnetic Stimulation Study in Participants With Posttraumatic Stress Symptoms

Background

Fear overgeneralization is a promising pathogenic mechanism of clinical anxiety. A dominant model posits that hippocampal pattern separation failures drive overgeneralization. Hippocampal network-targeted transcranial magnetic stimulation (HNT-TMS) has been shown to strengthen hippocampal-dependent learning/memory processes. However, no study has examined whether HNT-TMS can alter fear learning/memory.

Methods

Continuous theta burst stimulation was delivered to individualized left posterior parietal stimulation sites derived via seed-based connectivity, precision functional mapping, and electric field modeling methods. A vertex control site was also stimulated in a within-participant, randomized controlled design. Continuous theta burst stimulation was delivered prior to 2 visual discrimination tasks (1 fear based, 1 neutral). Multilevel models were used to model and test data. Participants were undergraduates with posttraumatic stress symptoms (final n = 25).

Results

Main analyses did not indicate that HNT-TMS strengthened discrimination. However, multilevel interaction analyses revealed that HNT-TMS strengthened fear discrimination in participants with lower fear sensitization (indexed by responses to a control stimulus with no similarity to the conditioned fear cue) across multiple indices (anxiety ratings: β = 0.10, 95% CI, 0.04 to 0.17, p = .001; risk ratings: β = 0.07, 95% CI, 0.00 to 0.13, p = .037).

Conclusions

Overgeneralization is an associative process that reflects deficient discrimination of the fear cue from similar cues. In contrast, sensitization reflects nonassociative responding unrelated to fear cue similarity. Our results suggest that HNT-TMS may selectively sharpen fear discrimination when associative response patterns, which putatively implicate the hippocampus, are more strongly engaged.

Functional connectivity development along the sensorimotor-association axis enhances the cortical hierarchy

Human cortical maturation has been posited to be organized along the sensorimotor-association axis, a hierarchical axis of brain organization that spans from unimodal sensorimotor cortices to transmodal association cortices. Here, we investigate the hypothesis that the development of functional connectivity during childhood through adolescence conforms to the cortical hierarchy defined by the sensorimotor-association axis. We tested this pre-registered hypothesis in four large-scale, independent datasets (total n = 3355; ages 5-23 years): the Philadelphia Neurodevelopmental Cohort (n = 1207), Nathan Kline Institute-Rockland Sample (n = 397), Human Connectome Project: Development (n = 625), and Healthy Brain Network (n = 1126). Across datasets, the development of functional connectivity systematically varied along the sensorimotor-association axis. Connectivity in sensorimotor regions increased, whereas connectivity in association cortices declined, refining and reinforcing the cortical hierarchy. These consistent and generalizable results establish that the sensorimotor-association axis of cortical organization encodes the dominant pattern of functional connectivity development.

Lateral frontoparietal functional connectivity based on individual sulcal morphology

A salient neuroanatomical feature of the human brain is its pronounced cortical folding, and there is mounting evidence that sulcal morphology is relevant to functional brain architecture and cognition. Recent studies have emphasized putative tertiary sulci (pTS): small, shallow, late-developing, and evolutionarily new sulci that have been posited to serve as functional landmarks in association cortices. A fruitful approach to characterizing brain architecture has been to delineate regions based on transitions in fMRI-based functional connectivity profiles; however, exact regional boundaries can change depending on the data used to generate the parcellation. As sulci are fixed neuroanatomical structures, here, we propose to anchor functional connectivity to individual-level sulcal anatomy. We characterized fine-grained patterns of functional connectivity across 42 sulci in lateral prefrontal (LPFC) and lateral parietal cortices (LPC) in a pediatric sample (N = 43; 20 female; ages 7-18). Further, we test for relationships between pTS morphology and functional network architecture, focusing on depth as a defining characteristic of these shallow sulci, and one that has been linked to variability in cognition. We find that 1) individual sulci have distinct patterns of connectivity, but nonetheless cluster together into groups with similar patterns - in some cases with distant rather than neighboring sulci, 2) there is moderate agreement in cluster assignments at the group and individual levels, underscoring the need for individual-level analyses, and 3) across individuals, greater depth was associated with higher network centrality for several pTS. These results highlight the importance of considering individual sulcal morphology for understanding functional brain organization.

Significance Statement

A salient, and functionally relevant, feature of the human brain is its pronounced cortical folding. However, the links between sulcal anatomy and brain function are still poorly understood - particularly for small, shallow, individually variable sulci in association cortices. Here, we explore functional connectivity among individually defined sulci in lateral prefrontal and parietal regions. We find that individual sulci have distinct patterns of connectivity but nonetheless cluster together into groups with similar connectivity - in some cases spanning lateral prefrontal and parietal sulci. We further show that the network centrality of specific sulci is positively associated with their depth, thereby helping to bridge the gap between individual differences in brain anatomy and functional networks leveraging the sulcal anatomy of the individual.

Brain substates induced by DMT relate to sympathetic output and meaningfulness of the experience

N,N-Dimethyltryptamine (DMT) is a serotonergic psychedelic, known to rapidly induce short-lasting alterations in conscious experience, characterized by a profound and immersive sense of physical transcendence alongside rich and vivid auditory distortions and visual imagery. Multimodal neuroimaging data paired with dynamic analysis techniques offer a valuable approach for identifying unique signatures of brain activity - and linked autonomic physiology - naturally unfolding during the altered state of consciousness induced by DMT. We leveraged simultaneous fMRI and EKG data acquired in 14 healthy volunteers prior to, during, and after intravenous administration of DMT, and, separately, placebo. fMRI data was preprocessed to derive individual dynamic activity matrices, reflecting the similarity of brain activity in time, and community detection algorithms were applied on these matrices to identify brain activity substates; EKG data was used to derive continuous heart rate. We identified a brain substate occurring immediately after DMT injection, characterized by increased superior temporal lobe activity, and hippocampal and medial parietal deactivations under DMT. Results revealed that hippocampus and medial parietal cortex hypoactivity correlated with scores of meaningfulness of the experience. During this first post-injection substate, increased heart rate under DMT correlated negatively with the meaningfulness of the experience and positively with hippocampus/medial parietal deactivation. These results suggest a chain of influence linking sympathetic regulation to hippocampal and medial parietal deactivations under DMT, which combined, may contribute to positive mental health outcomes related to self-referential processing following psychedelic administration.

An executive-functions-based reading training enhances sensory-motor systems integration during reading fluency in children with dyslexia

The Simple View of Reading model suggests that intact language processing and word decoding lead to proficient reading comprehension, with recent studies pointing at executive functions as an important component contributing to reading proficiency. Here, we aimed to determine the underlying mechanism(s) for these changes. Participants include 120 8- to 12-year-old children (n = 55 with dyslexia, n = 65 typical readers) trained on an executive functions-based reading program, including pre/postfunctional MRI and behavioral data collection. Across groups, improved word reading was related to stronger functional connections within executive functions and sensory networks. In children with dyslexia, faster and more accurate word reading was related to stronger functional connections within and between sensory networks. These results suggest greater synchronization of brain systems after the intervention, consistent with the "neural noise" hypothesis in children with dyslexia and support the consideration of including executive functions as part of the Simple View of Reading model.

Intersubject representational similarity analysis uncovers the impact of state anxiety on brain activation patterns in the human extrastriate cortex

The current study used functional magnetic resonance imaging (fMRI) and showed that state anxiety modulated extrastriate cortex activity in response to emotionally-charged visual images. State anxiety and neuroimaging data from 53 individuals were subjected to an intersubject representational similarity analysis (ISRSA), wherein the geometries between neural and behavioral data were compared. This analysis identified the extrastriate cortex (fusiform gyrus and area MT) to be the sole regions whose activity patterns covaried with state anxiety. Importantly, we show that this brain-behavior association is revealed when treating state anxiety data as a multidimensional response pattern, rather than a single composite score. This suggests that ISRSA using multivariate distances may be more sensitive in identifying the shared geometries between self-report questionnaires and brain imaging data. Overall, our findings demonstrate that a transient state of anxiety may influence how visual information - especially those relevant to the valence dimension - is processed in the extrastriate cortex.

Adaptive-to-maladaptive gradient of emotion regulation tendencies are embedded in the functional-structural hybrid connectome

BACKGROUND

Emotion regulation tendencies are well-known transdiagnostic markers of psychopathology, but their neurobiological foundations have mostly been examined within the theoretical framework of cortical-subcortical interactions.

METHODS

We explored the connectome-wide neural correlates of emotion regulation tendencies using functional and diffusion magnetic resonance images of healthy young adults (N = 99; age 20-30; 28 females). We first tested the importance of considering both the functional and structural connectome through intersubject representational similarity analyses. Then, we employed a canonical correlation analysis between the functional-structural hybrid connectome and 23 emotion regulation strategies. Lastly, we sought to externally validate the results on a transdiagnostic adolescent sample (N = 93; age 11-19; 34 females).

RESULTS

First, interindividual similarity of emotion regulation profiles was significantly correlated with interindividual similarity of the functional-structural hybrid connectome, more so than either the functional or structural connectome. Canonical correlation analysis revealed that an adaptive-to-maladaptive gradient of emotion regulation tendencies mapped onto a specific configuration of covariance within the functional-structural hybrid connectome, which primarily involved functional connections in the motor network and the visual networks as well as structural connections in the default mode network and the subcortical-cerebellar network. In the transdiagnostic adolescent dataset, stronger functional signatures of the found network were associated with higher general positive affect through more frequent use of adaptive coping strategies.

CONCLUSIONS

Taken together, our study illustrates a gradient of emotion regulation tendencies that is best captured when simultaneously considering the functional and structural connections across the whole brain.

Co-representation of Functional Brain Networks Is Shaped by Cortical Myeloarchitecture and Reveals Individual Behavioral Ability

Large-scale functional networks are spatially distributed in the human brain. Despite recent progress in differentiating their functional roles, how the brain navigates the spatial coordination among them and the biological relevance of this coordination is still not fully understood. Capitalizing on canonical individualized networks derived from functional MRI data, we proposed a new concept, that is, co-representation of functional brain networks, to delineate the spatial coordination among them. To further quantify the co-representation pattern, we defined two indexes, that is, the co-representation specificity (CoRS) and intensity (CoRI), for separately measuring the extent of specific and average expression of functional networks at each brain location by using the data from both sexes. We found that the identified pattern of co-representation was anchored by cortical regions with three types of cytoarchitectural classes along a sensory-fugal axis, including, at the first end, primary (idiotypic) regions showing high CoRS, at the second end, heteromodal regions showing low CoRS and high CoRI, at the third end, paralimbic regions showing low CoRI. Importantly, we demonstrated the critical role of myeloarchitecture in sculpting the spatial distribution of co-representation by assessing the association with the myelin-related neuroanatomical and transcriptomic profiles. Furthermore, the significance of manifesting the co-representation was revealed in its prediction of individual behavioral ability. Our findings indicated that the spatial coordination among functional networks was built upon an anatomically configured blueprint to facilitate neural information processing, while advancing our understanding of the topographical organization of the brain by emphasizing the assembly of functional networks.

Stationary stable cross-correlation pattern and task specific deviations in unresponsive wakefulness syndrome as well as clinically healthy subjects

Brain dynamics is highly non-stationary, permanently subject to ever-changing external conditions and continuously monitoring and adjusting internal control mechanisms. Finding stationary structures in this system, as has been done recently, is therefore of great importance for understanding fundamental dynamic trade relationships. Here we analyse electroencephalographic recordings (EEG) of 13 subjects with unresponsive wakefulness syndrome (UWS) during rest and while being influenced by different acoustic stimuli. We compare the results with a control group under the same experimental conditions and with clinically healthy subjects during overnight sleep. The main objective of this study is to investigate whether a stationary correlation pattern is also present in the UWS group, and if so, to what extent this structure resembles the one found in healthy subjects. Furthermore, we extract transient dynamical features via specific deviations from the stationary interrelation pattern. We find that (i) the UWS group is more heterogeneous than the two groups of healthy subjects, (ii) also the EEGs of the UWS group contain a stationary cross-correlation pattern, although it is less pronounced and shows less similarity to that found for healthy subjects and (iii) deviations from the stationary pattern are notably larger for the UWS than for the two groups of healthy subjects. The results suggest that the nervous system of subjects with UWS receive external stimuli but show an overreaching reaction to them, which may disturb opportune information processing.

Group-common and individual-specific effects of structure-function coupling in human brain networks with graph neural networks

The human cerebral cortex is organized into functionally segregated but synchronized regions bridged by the structural connectivity of white matter pathways. While structure-function coupling has been implicated in cognitive development and neuropsychiatric disorders, studies yield inconsistent findings. The extent to which the structure-function coupling reflects reliable individual differences or primarily group-common characteristics remains unclear, at both the global and regional brain levels. By leveraging two independent, high-quality datasets, we found that the graph neural network accurately predicted unseen individuals' functional connectivity from structural connectivity, reflecting a strong structure-function coupling. This coupling was primarily driven by network topology and was substantially stronger than that of the linear models. Moreover, we observed that structure-function coupling was dominated by group-common effects, with subtle yet significant individual-specific effects. The regional group and individual effects of coupling were hierarchically organized across the cortex along a sensorimotor-association axis, with lower group and higher individual effects in association cortices. These findings emphasize the importance of considering both group and individual effects in understanding cortical structure-function coupling, suggesting insights into interpreting individual differences of the coupling and informing connectivity-guided therapeutics.

Behavior can be decoded across the cortex when individual differences are considered

Group-level analyses have typically associated behavioral signatures with a constrained set of brain areas. Here we show that two behavioral metrics - reaction time (RT) and confidence - can be decoded across the cortex when each individual is considered separately. Subjects (N=50) completed a perceptual decision-making task with confidence. We built models decoding trial-level RT and confidence separately for each subject using the activation patterns in one brain area at a time after splitting the entire cortex into 200 regions of interest (ROIs). At the group level, we replicated previous results by showing that both RT and confidence could be decoded from a small number of ROIs (12.0% and 3.5%, respectively). Critically, at the level of the individual, both RT and confidence could be decoded from most brain regions even after Bonferroni correction (90.0% and 72.5%, respectively). Surprisingly, we observed that many brain regions exhibited opposite brain-behavior relationships across individuals, such that, for example, higher activations predicted fast RTs in some subjects but slow RTs in others. These results were further replicated in a second dataset. Lastly, we developed a simple test to determine the robustness of decoding performance, which showed that several hundred trials per subject are required for robust decoding. These results show that behavioral signatures can be decoded from a much broader range of cortical areas than previously recognized and suggest the need to study the brain-behavior relationship at both the group and the individual level.

Intrinsic functional connectivity among memory networks does not predict individual differences in narrative recall

Individuals differ greatly in their ability to remember the details of past events, yet little is known about the brain processes that explain such individual differences in a healthy young population. Previous research suggests that episodic memory relies on functional communication among ventral regions of the default mode network ("DMN-C") that are strongly interconnected with the medial temporal lobes. In this study, we investigated whether the intrinsic functional connectivity of the DMN-C subnetwork is related to individual differences in memory ability, examining this relationship across 243 individuals (ages 18-50 years) from the openly available Cambridge Center for Aging and Neuroscience (Cam-CAN) dataset. We first estimated each participant's whole-brain intrinsic functional brain connectivity by combining data from resting-state, movie-watching, and sensorimotor task scans to increase statistical power. We then examined whether intrinsic functional connectivity predicted performance on a narrative recall task. We found no evidence that functional connectivity of the DMN-C, with itself, with other related DMN subnetworks, or with the rest of the brain, was related to narrative recall. Exploratory connectome-based predictive modeling (CBPM) analyses of the entire connectome revealed a whole-brain multivariate pattern that predicted performance, although these changes were largely outside of known memory networks. These results add to emerging evidence suggesting that individual differences in memory cannot be easily explained by brain differences in areas typically associated with episodic memory function.