Predicting personality from network-based resting-state functional connectivity

Multi-modal EEG NEO-FFI with Trained Attention Layer (MENTAL) for mental disorder prediction

Early detection and accurate diagnosis of mental disorders is difficult due to the complexity of the diagnostic process, resulting in conditions being left undiagnosed or misdiagnosed. Previous studies have demonstrated that features of Electroencephalogram (EEG) data, such as Power Spectral Density (PSD), are useful biomarkers for indicating the onset of various mental disorders. Existing models using EEG data are typically trained to distinguish between healthy and afflicted individuals, and they are unable to distinguish between individuals with different disorders. We propose MENTAL (Multi-modal EEG NEO-FFI with Trained Attention Layer) to predict an individual's mental state using both EEG and Neo-Five Factor Inventory (NEO-FFI) personality data. We include an attention layer that captures the interactions between personality traits and PSD features, and emphasizes the important PSD features. MENTAL features a Recurrent Neural Network (RNN) to model the temporal nature of EEG data. We train our model with the Two Decades Brainclinics Research Archive for Insights in Neuroscience (TDBRAIN) dataset, which consists of 1274 healthy and psychiatric individuals including over 30 different diagnoses. MENTAL is able to achieve 93.3% accuracy when trained to classify between healthy individuals and those with ADHD. When trained to identify individuals with ADHD from among 33 disorder classes, MENTAL improves accuracy from 70.5 to 81.7%. MENTAL also demonstrates over 20% improvement in predictive accuracy when trained for MDD prediction. For the multi-class classification task of three classes, MENTAL improves accuracy by 4%, and for five classes, by nearly 8%. MENTAL is the first multi-modal model that utilizes both EEG and NEO-FFI data for the task of mental disorder prediction. We are one of the first groups to utilize TDBRAIN for automated disorder classification. MENTAL is accessible and cost-effective, as EEG is more affordable than other neuroimaging methods such as MRI, and the NEO-FFI is a self- reported survey. Our model can lead to acceptance and support for individuals living with mental health challenges and improve quality of life in our society.

Network-level enrichment provides a framework for biological interpretation of machine learning results

Machine learning algorithms are increasingly being utilized to identify brain connectivity biomarkers linked to behavioral and clinical outcomes. However, research often prioritizes prediction accuracy at the expense of biological interpretability, and inconsistent implementation of ML methods may hinder model accuracy. To address this, our paper introduces a network-level enrichment approach, which integrates brain system organization in the context of connectome-wide statistical analysis to reveal network-level links between brain connectivity and behavior. To demonstrate the efficacy of this approach, we used linear support vector regression (LSVR) models to examine the relationship between resting-state functional connectivity networks and chronological age. We compared network-level associations based on raw LSVR weights to those produced from the forward and inverse models. Results indicated that not accounting for shared family variance inflated prediction performance, the k-best feature selection via Pearson correlation reduced accuracy and reliability, and raw LSVR model weights produced network-level associations that deviated from the significant brain systems identified by forward and inverse models. Our findings offer crucial insights for applying machine learning to neuroimaging data, emphasizing the value of network enrichment for biological interpretation.

D-MHGCN: An End-to-End Individual Behavioral Prediction Model Using Dual Multi-Hop Graph Convolutional Network

Predicting individual behavior is a crucial area of research in neuroscience. Graph Neural Networks (GNNs), as powerful tools for extracting graph-structured features, are increasingly being utilized in various functional connectivity (FC) based behavioral prediction tasks. However, current predictive models primarily focus on enhancing GNNs' ability to extract features from FC networks while neglecting the importance of upstream individual network construction quality. This oversight results in constructed functional networks that fail to adequately represent individual behavioral capacity, thereby affecting the subsequent prediction accuracy. To address this issue, we proposed a new GNN-based behavioral prediction framework, named Dual Multi-Hop Graph Convolutional Network (D-MHGCN). Through the joint training of two GCNs, this framework integrates individual functional network construction and behavioral prediction into a unified optimization model. It allows the model to dynamically adjust the individual functional cortical parcellation according to the downstream tasks, thus creating task-aware, individual-specific FCNs that largely enhance its ability to predict behavior scores. Additionally, we employed multi-hop graph convolution layers instead of traditional single-hop methods in GCN to capture complex hierarchical connectivity patterns in brain networks. Our experimental evaluations, conducted on the large, public Human Connectome Project dataset, demonstrate that our proposed method outperforms existing methods in various behavioral prediction tasks. Moreover, it produces more functionally homogeneous cortical parcellation, showcasing its practical utility and effectiveness. Our work not only enhances the accuracy of individual behavioral prediction but also provides deeper insights into the neural mechanisms underlying individual differences in behavior.

Motor impulsivity and spicy food craving: A mediation analysis of insula-based resting state functional connectivity

In China, the rate of spicy food consumption is rising, and chili pepper is among the most popular spicy foods consumed nationwide. However, little effort has been made to understand the mechanism behind spicy food craving. This exploratory study aimed to investigate differences in insula-based resting state functional connectivity (rsFC) between spicy food cravers and non-cravers, and the association between rsFC, impulsivity and spicy food craving. A group of extreme cravers (n = 49) and a group of age- and sex-matched non-cravers (n = 46) completed a resting-state fMRI scan, during which participants were instructed to keep their eyes closed, to not think of anything in particular, and to remain awake. Participants completed the Spicy Food Craving Questionnaire, Barratt Impulsiveness Scale, Sensation Seeking Scale and Positive and Negative Affect Schedule, and rated the frequency of spicy food intake. Results revealed increased insula-occipital lobe resting-state functional connectivity in individuals with spicy food cravings, and the positive correlations between insula-middle occipital gyrus rsFC, impulsivity and spicy food craving. Specifically, the insula-middle occipital gyrus rsFC strength mediated the relationship between the motor impulsivity and spicy food craving. It is hoped that our exploratory findings may shed new insights into the neural mechanisms of spicy food craving and motivate further exploration of spicy food craving in diverse contexts and cultures.

Neuroticism and openness exhibit an anti-correlation pattern to dissociable default mode network: using resting connectivity and structural equation modeling analysis

The default mode network (DMN) can be subdivided into ventral and dorsal subsystems, which serve affective cognition and mental sense construction, respectively. An internally dissociated pattern of anti-correlations was observed between these two subsystems. Although numerous studies on neuroticism and openness have demonstrated the neurological functions of the DMN, little is known about whether different subsystems and hubs regions within the network are engaged in different functions in response to the two traits. We recruited 223 healthy volunteers in this study and collected their resting-state functional magnetic resonance imaging (fMRI) and NEO Five-Factor Inventory scores. We used independent component analysis (ICA) to obtain the DMN, before further decomposing it into the ventral and dorsal subsystems. Then, the network coherence of hubs regions within subsystems was extracted to construct two structural equation models (SEM) to explore the relationship between neuroticism and openness traits and DMN. We observed that the ventral DMN could significantly predict positive openness and negative neuroticism. The dorsal DMN was diametrically opposed. Additionally, the medial prefrontal cortex (mPFC) and middle temporal gyrus (MTG), both of which are core hubs of the subnetworks within the DMN, are significantly positively correlated with neuroticism and openness. These findings may point to a biological basis that neuroticism and openness are engaged in opposite mechanisms and support the hypothesis about the functional dissociation of the DMN.

Inter- and intra-subject similarity in network functional connectivity across a full narrative movie

Naturalistic paradigms, such as watching movies during functional magnetic resonance imaging, are thought to prompt the emotional and cognitive processes typically elicited in real life situations. Therefore, naturalistic viewing (NV) holds great potential for studying individual differences. Previous studies have primarily focused on using shorter movie clips, geared toward eliciting specific and often isolated emotions, while the potential behind using full narratives depicted in commercial movies as a proxy for real-life experiences has barely been explored. Here, we offer preliminary evidence that a full narrative movie (FNM), that is, a movie covering a complete narrative arc, can capture complex socio-affective dynamics and their links to individual differences. Using the studyforrest dataset, we investigated inter- and intra-subject similarity in network functional connectivity (NFC) of 14 meta-analytically defined networks across a full narrative, audio-visual movie split into eight consecutive movie segments. We characterized the movie segments by valence and arousal portrayed within the sequences, before utilizing a linear mixed model to analyze which factors explain inter- and intra-subject similarity. Our results show that the model best explaining inter-subject similarity comprised network, movie segment, valence and a movie segment by valence interaction. Intra-subject similarity was influenced significantly by the same factors and an additional three-way interaction between movie segment, valence and arousal. Overall, inter- and intra-subject similarity in NFC were sensitive to the ongoing narrative and emotions in the movie. We conclude that FNMs offer complex content and dynamics that might be particularly valuable for studying individual differences. Further characterization of movie features, such as the overarching narratives, that enhance individual differences is needed for advancing the potential of NV research.

Substance use disorders are characterised by increased voxel-wise intrinsic measures in sensorimotor cortices: An ALE meta-analysis

Substance use disorders (SUDs) are severe psychiatric illnesses. Seed region and independent component analyses are currently the dominant connectivity measures but carry the risk of false negatives due to selection. They can be complemented by a data-driven and whole-brain usage of voxel-wise intrinsic measures (VIMs). We meta-analytically integrated VIMs, namely regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF), voxel-mirrored homotopy connectivity (VMHC) and degree centrality (DC) across different SUDs using the Activation Likelihood Estimation (ALE) algorithm, functionally decoded emerging clusters, and analysed their connectivity profiles. Our systematic search identified 51 studies including 1439 SUD participants. Although no overall convergent pattern of alterations across VIMs in SUDs was found, sensitivity analyses demonstrated two ALE-derived clusters of increased ReHo and ALFF in SUDs, which peaked in the left pre- and postcentral cortices. Subsequent analyses showed their involvement in action execution, somesthesis, finger tapping and vibrotactile monitoring/discrimination. Their numerous clinical correlates across included studies highlight the under-discussed role of sensorimotor cortices in SUD, urging a more attentive exploration of their clinical significance.

Network and state specificity in connectivity-based predictions of individual behavior

Predicting individual behavior from brain functional connectivity (FC) patterns can contribute to our understanding of human brain functioning. This may apply in particular if predictions are based on features derived from circumscribed, a priori defined functional networks, which improves interpretability. Furthermore, some evidence suggests that task-based FC data may yield more successful predictions of behavior than resting-state FC data. Here, we comprehensively examined to what extent the correspondence of functional network priors and task states with behavioral target domains influences the predictability of individual performance in cognitive, social, and affective tasks. To this end, we used data from the Human Connectome Project for large-scale out-of-sample predictions of individual abilities in working memory (WM), theory-of-mind cognition (SOCIAL), and emotion processing (EMO) from FC of corresponding and non-corresponding states (WM/SOCIAL/EMO/resting-state) and networks (WM/SOCIAL/EMO/whole-brain connectome). Using root mean squared error and coefficient of determination to evaluate model fit revealed that predictive performance was rather poor overall. Predictions from whole-brain FC were slightly better than those from FC in task-specific networks, and a slight benefit of predictions based on FC from task versus resting state was observed for performance in the WM domain. Beyond that, we did not find any significant effects of a correspondence of network, task state, and performance domains. Together, these results suggest that multivariate FC patterns during both task and resting states contain rather little information on individual performance levels, calling for a reconsideration of how the brain mediates individual differences in mental abilities.

Inter- and intra-subject similarity in network functional connectivity across a full narrative movie

Naturalistic paradigms, such as watching movies during functional magnetic resonance imaging (fMRI), are thought to prompt the emotional and cognitive processes typically elicited in real life situations. Therefore, naturalistic viewing (NV) holds great potential for studying individual differences. However, in how far NV elicits similarity within and between subjects on a network level, particularly depending on emotions portrayed in movies, is currently unknown. We used the studyforrest dataset to investigate the inter- and intra-subject similarity in network functional connectivity (NFC) of 14 meta-analytically defined networks across a full narrative, audio-visual movie split into 8 consecutive movie segments. We characterized the movie segments by valence and arousal portrayed within the sequences, before utilizing a linear mixed model to analyze which factors explain inter- and intra-subject similarity. Our results showed that the model best explaining inter-subject similarity comprised network, movie segment, valence and a movie segment by valence interaction. Intra-subject similarity was influenced significantly by the same factors and an additional three-way interaction between movie segment, valence and arousal. Overall, inter- and intra-subject similarity in NFC were sensitive to the ongoing narrative and emotions in the movie. Lowest similarity both within and between subjects was seen in the emotional regulation network and networks associated with long-term memory processing, which might be explained by specific features and content of the movie. We conclude that detailed characterization of movie features is crucial for NV research.

Sex classification from functional brain connectivity: Generalization to multiple datasets

Machine learning (ML) approaches are increasingly being applied to neuroimaging data. Studies in neuroscience typically have to rely on a limited set of training data which may impair the generalizability of ML models. However, it is still unclear which kind of training sample is best suited to optimize generalization performance. In the present study, we systematically investigated the generalization performance of sex classification models trained on the parcelwise connectivity profile of either single samples or compound samples of two different sizes. Generalization performance was quantified in terms of mean across-sample classification accuracy and spatial consistency of accurately classifying parcels. Our results indicate that the generalization performance of parcelwise classifiers (pwCs) trained on single dataset samples is dependent on the specific test samples. Certain datasets seem to "match" in the sense that classifiers trained on a sample from one dataset achieved a high accuracy when tested on the respected other one and vice versa. The pwCs trained on the compound samples demonstrated overall highest generalization performance for all test samples, including one derived from a dataset not included in building the training samples. Thus, our results indicate that both a large sample size and a heterogeneous data composition of a training sample have a central role in achieving generalizable results.

Cross-subject brain entropy mapping

We present a method to map the regional similarity between resting state fMRI activities of different individuals. The similarity was measured using cross-entropy. Group level patterns were displayed based on the Human Connectome Project Youth data. While we only showed the cross-subject brain entropy (BEN) mapping results in this manuscript, the same concept can be directly extended to map the cross-sessional BEN and the cross-regional cross-subject or subject-session BEN.

Sex classification from functional brain connectivity: Generalization to multiple datasets Generalizability of sex classifiers

Machine learning (ML) approaches are increasingly being applied to neuroimaging data. Studies in neuroscience typically have to rely on a limited set of training data which may impair the generalizability of ML models. However, it is still unclear which kind of training sample is best suited to optimize generalization performance. In the present study, we systematically investigated the generalization performance of sex classification models trained on the parcelwise connectivity profile of either single samples or a compound sample containing data from four different datasets. Generalization performance was quantified in terms of mean across-sample classification accuracy and spatial consistency of accurately classifying parcels. Our results indicate that generalization performance of pwCs trained on single dataset samples is dependent on the specific test samples. Certain datasets seem to "match" in the sense that classifiers trained on a sample from one dataset achieved a high accuracy when tested on the respected other one and vice versa. The pwC trained on the compound sample demonstrated overall highest generalization performance for all test samples, including one derived from a dataset not included in building the training samples. Thus, our results indicate that a big and heterogenous training sample comprising data of multiple datasets is best suited to achieve generalizable results.

Combined effects of neuroticism and negative emotional context on spontaneous EEG dynamics

Neuroticism is a personality trait with great clinical relevance, defined as a tendency to experience negative affect, sustained self-generated negative thoughts and impaired emotion regulation. Here, we investigated spontaneous brain dynamics in the aftermath of negative emotional events and their links with neuroticism in order to shed light on the prolonged activity of large-scale brain networks associated with the control of affect. We recorded electroencephalography (EEG) from 36 participants who were asked to rest after watching neutral or fearful video clips. Four topographic maps (i.e. microstates classes A, B, C and D) explained the majority of the variance in spontaneous EEG. Participants showed greater presence of microstate D and lesser presence of microstate C following exposure to fearful stimuli, pointing to changes in attention- and introspection-related networks previously associated with these microstates. These emotional effects were more pronounced for participants with low neuroticism. Moreover, neuroticism scores were positively correlated with microstate C and negatively correlated with microstate D, regardless of previous emotional stimulation. Our results reveal distinctive effects of emotional context on resting-state EEG, consistent with a prolonged impact of negative affect on the brain, and suggest a possible link with neuroticism.

Impact of inhibition level on field-dependent and field-independent individuals in attentional blink

The attentional blink (AB) phenomenon is a cognitive limitation that refers to the failure in identifying the second target if it follows the first one in close temporal proximity (200-500 ms). However, more recent studies have demonstrated that AB task performance greatly differs among individuals. This behavioral heterogeneity in AB has promoted research on exploring the predictive value of individual differences. The present study examined how AB magnitudes were related to personal cognitive styles. The Embedded Figures Test was carried out to classify participants' cognitive styles, along with the manipulation of the physical characteristics of distractors in the rapid serial visual presentation paradigm (RSVP) as two levels of inhibition (target-distractor similarity). Results from two experiments of varying difficulty revealed that the AB effect varied between field-dependent (FD) and field-independent (FI) individuals. The AB magnitude in FD individuals was more easily influenced by different inhibition levels of distractors, compared to the FI individuals. Results are interpreted in terms of the contemporary theories of AB that highlighted the inhibitory control over attention.

Test-retest reliability and predictive utility of a macroscale principal functional connectivity gradient

Mapping individual differences in brain function has been hampered by poor reliability as well as limited interpretability. Leveraging patterns of brain-wide functional connectivity (FC) offers some promise in this endeavor. In particular, a macroscale principal FC gradient that recapitulates a hierarchical organization spanning molecular, cellular, and circuit level features along a sensory-to-association cortical axis has emerged as both a parsimonious and interpretable measure of individual differences in behavior. However, the measurement reliabilities of this FC gradient have not been fully evaluated. Here, we assess the reliabilities of both global and regional principal FC gradient measures using test-retest data from the young adult Human Connectome Project (HCP-YA) and the Dunedin Study. Analyses revealed that the reliabilities of principal FC gradient measures were (1) consistently higher than those for traditional edge-wise FC measures, (2) higher for FC measures derived from general FC (GFC) in comparison with resting-state FC, and (3) higher for longer scan lengths. We additionally examined the relative utility of these principal FC gradient measures in predicting cognition and aging in both datasets as well as the HCP-aging dataset. These analyses revealed that regional FC gradient measures and global gradient range were significantly associated with aging in all three datasets, and moderately associated with cognition in the HCP-YA and Dunedin Study datasets, reflecting contractions and expansions of the cortical hierarchy, respectively. Collectively, these results demonstrate that measures of the principal FC gradient, especially derived using GFC, effectively capture a reliable feature of the human brain subject to interpretable and biologically meaningful individual variation, offering some advantages over traditional edge-wise FC measures in the search for brain-behavior associations.

Network and State Specificity in Connectivity-Based Predictions of Individual Behavior

Predicting individual behavior from brain functional connectivity (FC) patterns can contribute to our understanding of human brain functioning. This may apply in particular if predictions are based on features derived from circumscribed, a priori defined functional networks, which improves interpretability. Furthermore, some evidence suggests that task-based FC data may yield more successful predictions of behavior than resting-state FC data. Here, we comprehensively examined to what extent the correspondence of functional network priors and task states with behavioral target domains influences the predictability of individual performance in cognitive, social, and affective tasks. To this end, we used data from the Human Connectome Project for large-scale out-of-sample predictions of individual abilities in working memory (WM), theory-of-mind cognition (SOCIAL), and emotion processing (EMO) from FC of corresponding and non-corresponding states (WM/SOCIAL/EMO/resting-state) and networks (WM/SOCIAL/EMO/whole-brain connectome). Using root mean squared error and coefficient of determination to evaluate model fit revealed that predictive performance was rather poor overall. Predictions from whole-brain FC were slightly better than those from FC in task-specific networks, and a slight benefit of predictions based on FC from task versus resting state was observed for performance in the WM domain. Beyond that, we did not find any significant effects of a correspondence of network, task state, and performance domains. Together, these results suggest that multivariate FC patterns during both task and resting states contain rather little information on individual performance levels, calling for a reconsideration of how the brain mediates individual differences in mental abilities.

A neural mechanism of cognitive reserve: The case of bilingualism

Cognitive Reserve (CR) refers to the preservation of cognitive function in the face of age- or disease-related neuroanatomical decline. While bilingualism has been shown to contribute to CR, the extent to which, and what particular aspect of, second language experience contributes to CR are debated, and the underlying neural mechanism(s) unknown. Intrinsic functional connectivity reflects experience-dependent neuroplasticity that occurs across timescales ranging from minutes to decades, and may be a neural mechanism underlying CR. To test this hypothesis, we used voxel-based morphometry and resting-state functional connectivity analyses of MRI data to compare structural and functional brain integrity between monolingual and bilingual older adults, matched on cognitive performance, and across levels of second language proficiency measured as a continuous variable. Bilingualism, and degree of second language proficiency specifically, were associated with lower gray matter integrity in a hub of the default mode network - a region that is particularly vulnerable to decline in aging and dementia - but preserved intrinsic functional network organization. Bilingualism moderated the association between neuroanatomical differences and cognitive decline, such that lower gray matter integrity was associated with lower executive function in monolinguals, but not bilinguals. Intrinsic functional network integrity predicted executive function when controlling for group differences in gray matter integrity and language status. Our findings confirm that lifelong bilingualism is a CR factor, as bilingual older adults performed just as well as their monolingual peers on tasks of executive function, despite showing signs of more advanced neuroanatomical aging, and that this is a consequence of preserved intrinsic functional network organization.

Neural Representation of Collective Self-esteem in Resting-state Functional Connectivity and its Validation in Task-dependent Modality

INTRODUCTION

Collective self-esteem (CSE) is an important personality variable, defined as self-worth derived from membership in social groups. A study explored the neural basis of CSE using a task-based functional magnetic resonance imaging (fMRI) paradigm; however, task-independent neural basis of CSE remains to be explored, and whether the CSE neural basis of resting-state fMRI is consistent with that of task-based fMRI is unclear.

METHODS

We built support vector regression (SVR) models to predict CSE scores using topological metrics measured in the resting-state functional connectivity network (RSFC) as features. Then, to test the reliability of the SVR analysis, the activation pattern of the identified brain regions from SVR analysis was used as features to distinguish collective self-worth from other conditions by multivariate pattern classification in task-based fMRI dataset.

RESULTS

SVR analysis results showed that leverage centrality successfully decoded the individual differences in CSE. The ventromedial prefrontal cortex, anterior cingulate cortex, posterior cingulate gyrus, precuneus, orbitofrontal cortex, posterior insula, postcentral gyrus, inferior parietal lobule, temporoparietal junction, and inferior frontal gyrus, which are involved in self-referential processing, affective processing, and social cognition networks, participated in this prediction. Multivariate pattern classification analysis found that the activation pattern of the identified regions from the SVR analysis successfully distinguished collective self-worth from relational self-worth, personal self-worth and semantic control.

CONCLUSION

Our findings revealed CSE neural basis in the whole-brain RSFC network, and established the concordance between leverage centrality and the activation pattern (evoked during collective self-worth task) of the identified regions in terms of representing CSE.

Brain-Based Predictions of Psychiatric Illness-Linked Behaviors Across the Sexes

BACKGROUND

Individual differences in functional brain connectivity can be used to predict both the presence of psychiatric illness and variability in associated behaviors. However, despite evidence for sex differences in functional network connectivity and in the prevalence, presentation, and trajectory of psychiatric illnesses, the extent to which disorder-relevant aspects of network connectivity are shared or unique across the sexes remains to be determined.

METHODS

In this work, we used predictive modeling approaches to evaluate whether shared or unique functional connectivity correlates underlie the expression of psychiatric illness-linked behaviors in males and females in data from the Adolescent Brain Cognitive Development Study (N = 5260; 2571 females).

RESULTS

We demonstrate that functional connectivity profiles predict individual differences in externalizing behaviors in males and females but predict internalizing behaviors only in females. Furthermore, models trained to predict externalizing behaviors in males generalize to predict internalizing behaviors in females, and models trained to predict internalizing behaviors in females generalize to predict externalizing behaviors in males. Finally, the neurobiological correlates of many behaviors are largely shared within and across sexes: functional connections within and between heteromodal association networks, including default, limbic, control, and dorsal attention networks, are associated with internalizing and externalizing behaviors.

CONCLUSIONS

Taken together, these findings suggest that shared neurobiological patterns may manifest as distinct behaviors across the sexes. Based on these results, we recommend that both clinicians and researchers carefully consider how sex may influence the presentation of psychiatric illnesses, especially those along the internalizing-externalizing spectrum.

Task and Resting-State Functional Connectivity Predict Driving Violations

Aberrant driving behaviors cause accidents; however, there is a lack of understanding of the neural mechanisms underlying these behaviors. To address this issue, a task and resting-state functional connectivity was used to predict aberrant driving behavior and associated personality traits. The study included 29 right-handed participants with driving licenses issued for more than 1 year. During the functional magnetic resonance imaging experiment, participants first recorded their resting state and then watched a driving video while continuously rating the risk and speed on each block. Functional connectome-based predictive modeling was employed for whole brain tasks and resting-state functional connectivity to predict driving behavior (violation, error, and lapses), sensation-seeking, and impulsivity. Resting state and task-based functional connectivity were found to significantly predict driving violations, with resting state significantly predicting lapses and task-based functional connectivity showing a tendency to predict errors. Conversely, neither impulsivity nor sensation-seeking was associated with functional connectivity. The results suggest a significant association between aberrant driving behavior, but a nonsignificant association between impulsivity and sensation-seeking, and task-based or resting state functional connectivity. This could provide a deeper understanding of the neural processing underlying reckless driving that may ultimately be used to prevent accidents.

The challenges and prospects of brain-based prediction of behaviour

Relating individual brain patterns to behaviour is fundamental in system neuroscience. Recently, the predictive modelling approach has become increasingly popular, largely due to the recent availability of large open datasets and access to computational resources. This means that we can use machine learning models and interindividual differences at the brain level represented by neuroimaging features to predict interindividual differences in behavioural measures. By doing so, we could identify biomarkers and neural correlates in a data-driven fashion. Nevertheless, this budding field of neuroimaging-based predictive modelling is facing issues that may limit its potential applications. Here we review these existing challenges, as well as those that we anticipate as the field develops. We focus on the impacts of these challenges on brain-based predictions. We suggest potential solutions to address the resolvable challenges, while keeping in mind that some general and conceptual limitations may also underlie the predictive modelling approach.

Functional connectome fingerprinting and stability in multiple sclerosis

Background

Functional connectome fingerprinting can identify individuals based on their functional connectome. Previous studies relied mostly on short intervals between fMRI acquisitions.

Objective

This cohort study aimed to determine the stability of connectome-based identification and their underlying signatures in patients with multiple sclerosis and healthy individuals with long follow-up intervals.

Methods

We acquired resting-state fMRI in 70 patients with multiple sclerosis and 273 healthy individuals with long follow-up times (up to 4 and 9 years, respectively). Using functional connectome fingerprinting, we examined the stability of the connectome and additionally investigated which regions, connections and networks supported individual identification. Finally, we predicted cognitive and behavioural outcome based on functional connectivity.

Results

Multiple sclerosis patients showed connectome stability and identification accuracies similar to healthy individuals, with longer time delays between imaging sessions being associated with accuracies dropping from 89% to 76%. Lesion load, brain atrophy or cognitive impairment did not affect identification accuracies within the range of disease severity studied. Connections from the fronto-parietal and default mode network were consistently most distinctive, i.e., informative of identity. The functional connectivity also allowed the prediction of individual cognitive performances.

Conclusion

Our results demonstrate that discriminatory signatures in the functional connectome are stable over extended periods of time in multiple sclerosis, resulting in similar identification accuracies and distinctive long-lasting functional connectome fingerprinting signatures in patients and healthy individuals.

A topography-based predictive framework for naturalistic viewing fMRI

Functional magnetic resonance imaging (fMRI) during naturalistic viewing (NV) provides exciting opportunities for studying brain functions in more ecologically valid settings. Understanding individual differences in brain functions during NV and their behavioural relevance has recently become an important goal. However, methods specifically designed for this purpose remain limited. Here, we propose a topography-based predictive framework (TOPF) to fill this methodological gap. TOPF identifies individual-specific evoked activity topographies in a data-driven manner and examines their behavioural relevance using a machine learning-based predictive framework. We validate TOPF on both NV and task-based fMRI data from multiple conditions. Our results show that TOPF effectively and stably captures individual differences in evoked brain activity and successfully predicts phenotypes across cognition, emotion and personality on unseen subjects from their activity topographies. Moreover, TOPF compares favourably with functional connectivity-based approaches in prediction performance, with the identified predictive brain regions being neurobiologically interpretable. Crucially, we highlight the importance of examining individual evoked brain activity topographies in advancing our understanding of the brain-behaviour relationship. We believe that the TOPF approach provides a simple but powerful tool for understanding brain-behaviour relationships on an individual level with a strong potential for clinical applications.

Stability of specific personality network features corresponding to openness trait across different adult age periods: A machine learning analysis

The functional connectivity patterns of the brain during resting state are closely related to an individual's cognition, emotion, behavior, and social interactions, making it an important research method to measure personality traits in an unbiased way, replacing traditional paper-and-pencil tests. However, due to the dynamic nature of the brain, whether the changes in functional connectivity caused by age can stably map onto personality traits has not been previously investigated. This study focuses on whether network features that are significantly related to personality traits can effectively distinguish subjects with different personality traits, and whether these network features vary across different periods of adulthood. The study included 343 healthy adult participants, divided into early adulthood and middle adulthood groups according to the age threshold of 35. Resting-state functional magnetic resonance imaging (fMRI) and the Big Five personality questionnaire were collected. we investigated the relationship between personality traits and intrinsic whole-brain functional connectome. We then used support vector machine (SVM) to evaluate the performance of personality network features in distinguishing subjects with high and low scores in the early-adulthood sample, and cross-validated in the mid-adulthood sample. Additionally, edge-based analysis (NBS) was used to explore the stability of personality networks across the two age samples. Our results show that the network features corresponding to openness personality trait are stable and can effectively differentiate subjects with different scores in both age samples. Furthermore, this study found that these network features vary to some extent across different periods of adulthood. These findings provide new evidence and insights into the application of resting-state functional connectivity patterns in measuring personality traits and help us better understand the dynamic characteristics of the human brain.

Relationship Between Prediction Accuracy and Feature Importance Reliability: an Empirical and Theoretical Study

There is significant interest in using neuroimaging data to predict behavior. The predictive models are often interpreted by the computation of feature importance, which quantifies the predictive relevance of an imaging feature. Tian and Zalesky (2021) suggest that feature importance estimates exhibit low split-half reliability, as well as a trade-off between prediction accuracy and feature importance reliability across parcellation resolutions. However, it is unclear whether the trade-off between prediction accuracy and feature importance reliability is universal. Here, we demonstrate that, with a sufficient sample size, feature importance (operationalized as Haufe-transformed weights) can achieve fair to excellent split-half reliability. With a sample size of 2600 participants, Haufe-transformed weights achieve average intra-class correlation coefficients of 0.75, 0.57 and 0.53 for cognitive, personality and mental health measures respectively. Haufe-transformed weights are much more reliable than original regression weights and univariate FC-behavior correlations. Original regression weights are not reliable even with 2600 participants. Intriguingly, feature importance reliability is strongly positively correlated with prediction accuracy across phenotypes. Within a particular behavioral domain, there is no clear relationship between prediction performance and feature importance reliability across regression models. Furthermore, we show mathematically that feature importance reliability is necessary, but not sufficient, for low feature importance error. In the case of linear models, lower feature importance error is mathematically related to lower prediction error. Therefore, higher feature importance reliability might yield lower feature importance error and higher prediction accuracy. Finally, we discuss how our theoretical results relate with the reliability of imaging features and behavioral measures. Overall, the current study provides empirical and theoretical insights into the relationship between prediction accuracy and feature importance reliability.

One Size Does Not Fit All: Methodological Considerations for Brain-Based Predictive Modeling in Psychiatry

Psychiatric illnesses are heterogeneous in nature. No illness manifests in the same way across individuals, and no two patients with a shared diagnosis exhibit identical symptom profiles. Over the last several decades, group-level analyses of in vivo neuroimaging data have led to fundamental advances in our understanding of the neurobiology of psychiatric illnesses. More recently, access to computational resources and large, publicly available datasets alongside the rise of predictive modeling and precision medicine approaches have facilitated the study of psychiatric illnesses at an individual level. Data-driven machine learning analyses can be applied to identify disease-relevant biological subtypes, predict individual symptom profiles, and recommend personalized therapeutic interventions. However, when developing these predictive models, methodological choices must be carefully considered to ensure accurate, robust, and interpretable results. Choices pertaining to algorithms, neuroimaging modalities and states, data transformation, phenotypes, parcellations, sample sizes, and populations we are specifically studying can influence model performance. Here, we review applications of neuroimaging-based machine learning models to study psychiatric illnesses and discuss the effects of different methodological choices on model performance. An understanding of these effects is crucial for the proper implementation of predictive models in psychiatry and will facilitate more accurate diagnoses, prognoses, and therapeutics.

Uncovering individual differences in fine-scale dynamics of functional connectivity

Functional connectivity (FC) profiles contain subject-specific features that are conserved across time and have potential to capture brain-behavior relationships. Most prior work has focused on spatial features (nodes and systems) of these FC fingerprints, computed over entire imaging sessions. We propose a method for temporally filtering FC, which allows selecting specific moments in time while also maintaining the spatial pattern of node-based activity. To this end, we leverage a recently proposed decomposition of FC into edge time series (eTS). We systematically analyze functional magnetic resonance imaging frames to define features that enhance identifiability across multiple fingerprinting metrics, similarity metrics, and data sets. Results show that these metrics characteristically vary with eTS cofluctuation amplitude, similarity of frames within a run, transition velocity, and expression of functional systems. We further show that data-driven optimization of features that maximize fingerprinting metrics isolates multiple spatial patterns of system expression at specific moments in time. Selecting just 10% of the data can yield stronger fingerprints than are obtained from the full data set. Our findings support the idea that FC fingerprints are differentially expressed across time and suggest that multiple distinct fingerprints can be identified when spatial and temporal characteristics are considered simultaneously.

Seeing the future: Connectome strength and network efficiency in visual network predict individual ability of episodic future thinking

Episodic future thinking (EFT) refers to the critical ability that enables people to construct and pre-experience the vivid mental imagery about future events, which impacts on the decision-making for individuals and group. Although EFT is generally believed to have a visual nature by theorists, little neuroscience evidence has been provided to verify this assumption. Here, by employing the approach of connectome-based predictive modeling (CPM) and graph-theoretical analysis, we analyzed resting-state functional brain image from 191 participants to predict their variability of EFT ability (leave-one-out cross-validation), and validated the results by applying different parcellation schemas and feature selection thresholds. At the connectome strength level, CPM-based analysis revealed that EFT ability could be predicted by the connectome strength of visual network. Besides, at the network level, graph-theoretical analysis showed that EFT ability could be predicted by the network efficiency of visual network. Moreover, these findings were replicated using different parcellation schemas and feature selection thresholds. These results robustly and collectively supported that the visual network might be one of the neural substrates underlying EFT ability from a comprehensive perspective of resting-state functional connectivity strength and the neural network. This study provides indications on how the function of visual network supports EFT ability, and enhances our understanding of the EFT ability from a neural basis perspective.

Stressor-Specific Sex Differences in Amygdala-Frontal Cortex Networks

Females and males differ in stress reactivity, coping, and the prevalence rates of stress-related disorders. According to a neurocognitive framework of stress coping, the functional connectivity between the amygdala and frontal regions (including the dorsolateral prefrontal cortex (dlPFC), ventral anterior cingulate cortex (vACC), and medial prefrontal cortex (mPFC)) plays a key role in how people deal with stress. In the current study, we investigated the effects of sex and stressor type in a within-subject counterbalanced design on the resting-state functional connectivity (rsFC) of the amygdala and these frontal regions in 77 healthy participants (40 females). Both stressor types led to changes in subjective ratings, with decreasing positive affect and increasing negative affect and anger. Females showed higher amygdala-vACC and amygdala-mPFC rsFC for social exclusion than for achievement stress, and compared to males. Whereas a higher amygdala-vACC rsFC indicates the activation of emotion processing and coping, a higher amygdala-mPFC rsFC indicates feelings of reward and social gain, highlighting the positive effects of social affiliation. Thus, for females, feeling socially affiliated might be more fundamental than for males. Our data indicate interactions of sex and stressor in amygdala-frontal coupling, which translationally contributes to a better understanding of the sex differences in prevalence rates and stress coping.

Exploring the latent structure of behavior using the Human Connectome Project's data

How behavior arises from brain physiology has been one central topic of investigation in neuroscience. Considering the recent interest in predicting behavior from brain imaging using open datasets, there is the need for a principled approach to the categorization of behavioral variables. However, this is not trivial, as the definitions of psychological constructs and their relationships-their ontology-are not always clear. Here, we propose to use exploratory factor analysis (EFA) as a data-driven approach to find robust and interpretable domains of behavior in the Human Connectome Project (HCP) dataset. Additionally, we explore the clustering of behavioral variables using consensus clustering. We find that four and five factors offer the best description of the data, a result corroborated by the consensus clustering. In the four-factor solution, factors for Mental Health, Cognition, Processing Speed, and Substance Use arise. With five factors, Mental Health splits into Well-Being and Internalizing. Clustering results show a similar pattern, with clusters for Cognition, Processing Speed, Positive Affect, Negative Affect, and Substance Use. The factor structure is replicated in an independent dataset using confirmatory factor analysis (CFA). We discuss how the content of the factors fits with previous conceptualizations of general behavioral domains.

Amplitudes of resting-state functional networks - investigation into their correlates and biophysical properties

Resting-state fMRI studies have shown that multiple functional networks, which consist of distributed brain regions that share synchronised spontaneous activity, co-exist in the brain. As these resting-state networks (RSNs) have been thought to reflect the brain's intrinsic functional organization, intersubject variability in the networks' spontaneous fluctuations may be associated with individuals' clinical, physiological, cognitive, and genetic traits. Here, we investigated resting-state fMRI data along with extensive clinical, lifestyle, and genetic data collected from 37,842 UK Biobank participants, with the object of elucidating intersubject variability in the fluctuation amplitudes of RSNs. Functional properties of the RSN amplitudes were first examined by analyzing correlations with the well-established between-network functional connectivity. It was found that a network amplitude is highly correlated with the mean strength of the functional connectivity that the network has with the other networks. Intersubject clustering analysis showed the amplitudes are most strongly correlated with age, cardiovascular factors, body composition, blood cell counts, lung function, and sex, with some differences in the correlation strengths between sensory and cognitive RSNs. Genome-wide association studies (GWASs) of RSN amplitudes identified several significant genetic variants reported in previous GWASs for their implications in sleep duration. We provide insight into key factors determining RSN amplitudes and demonstrate that intersubject variability of the amplitudes primarily originates from differences in temporal synchrony between functionally linked brain regions, rather than differences in the magnitude of raw voxelwise BOLD signal changes. This finding additionally revealed intriguing differences between sensory and cognitive RSNs with respect to sex effects on temporal synchrony and provided evidence suggesting that synchronous coactivations of functionally linked brain regions, and magnitudes of BOLD signal changes, may be related to different genetic mechanisms. These results underscore that intersubject variability of the amplitudes in health and disease need to be interpreted largely as a measure of the sum of within-network temporal synchrony and amplitudes of BOLD signals, with a dominant contribution from the former.

Normative model detects abnormal functional connectivity in psychiatric disorders

INTRODUCTION

The diagnosis of psychiatric disorders is mostly based on the clinical evaluation of the patient's signs and symptoms. Deep learning binary-based classification models have been developed to improve the diagnosis but have not yet reached clinical practice, in part due to the heterogeneity of such disorders. Here, we propose a normative model based on autoencoders.

METHODS

We trained our autoencoder on resting-state functional magnetic resonance imaging (rs-fMRI) data from healthy controls. The model was then tested on schizophrenia (SCZ), bipolar disorder (BD), and attention-deficit hyperactivity disorder (ADHD) patients to estimate how each patient deviated from the norm and associate it with abnormal functional brain networks' (FBNs) connectivity. Rs-fMRI data processing was conducted within the FMRIB Software Library (FSL), which included independent component analysis and dual regression. Pearson's correlation coefficients between the extracted blood oxygen level-dependent (BOLD) time series of all FBNs were calculated, and a correlation matrix was generated for each subject.

RESULTS AND DISCUSSION

We found that the functional connectivity related to the basal ganglia network seems to play an important role in the neuropathology of BD and SCZ, whereas in ADHD, its role is less evident. Moreover, the abnormal connectivity between the basal ganglia network and the language network is more specific to BD. The connectivity between the higher visual network and the right executive control and the connectivity between the anterior salience network and the precuneus networks are the most relevant in SCZ and ADHD, respectively. The results demonstrate that the proposed model could identify functional connectivity patterns that characterize different psychiatric disorders, in agreement with the literature. The abnormal connectivity patterns from the two independent SCZ groups of patients were similar, demonstrating that the presented normative model was also generalizable. However, the group-level differences did not withstand individual-level analysis implying that psychiatric disorders are highly heterogeneous. These findings suggest that a precision-based medical approach, focusing on each patient's specific functional network changes may be more beneficial than the traditional group-based diagnostic classification.

Classification and prediction of cognitive performance differences in older age based on brain network patterns using a machine learning approach

Age-related cognitive decline varies greatly in healthy older adults, which may partly be explained by differences in the functional architecture of brain networks. Resting-state functional connectivity (RSFC) derived network parameters as widely used markers describing this architecture have even been successfully used to support diagnosis of neurodegenerative diseases. The current study aimed at examining whether these parameters may also be useful in classifying and predicting cognitive performance differences in the normally aging brain by using machine learning (ML). Classifiability and predictability of global and domain-specific cognitive performance differences from nodal and network-level RSFC strength measures were examined in healthy older adults from the 1000BRAINS study (age range: 55-85 years). ML performance was systematically evaluated across different analytic choices in a robust cross-validation scheme. Across these analyses, classification performance did not exceed 60% accuracy for global and domain-specific cognition. Prediction performance was equally low with high mean absolute errors (MAEs ≥ 0.75) and low to none explained variance (R2 ≤ 0.07) for different cognitive targets, feature sets, and pipeline configurations. Current results highlight limited potential of functional network parameters to serve as sole biomarker for cognitive aging and emphasize that predicting cognition from functional network patterns may be challenging.

Functional connectivity of the central autonomic and default mode networks represent neural correlates and predictors of individual personality

There is solid evidence for the prominent involvement of the central autonomic and default mode systems in shaping personality. However, whether functional connectivity of these systems can represent neural correlates and predictors of individual variation in personality traits is largely unknown. Resting-state functional magnetic resonance imaging data of 215 healthy young adults were used to construct the sympathetic (SN), parasympathetic (PN), and default mode (DMN) networks, with intra- and internetwork functional connectivity measured. Personality factors were assessed using the five-factor model. We examined the associations between personality factors and functional network connectivity, followed by performance of personality prediction based on functional connectivity using connectome-based predictive modeling (CPM), a recently developed machine learning approach. All personality factors (neuroticism, extraversion, conscientiousness, and agreeableness) other than openness were significantly correlated with intra- and internetwork functional connectivity of the SN, PN, and DMN. Moreover, the CPM models successfully predicted conscientiousness and agreeableness at the individual level using functional network connectivity. Our findings may expand existing knowledge regarding the neural substrates underlying personality.

Cross-cohort replicability and generalizability of connectivity-based psychometric prediction patterns

An increasing number of studies have investigated the relationships between inter-individual variability in brain regions' connectivity and behavioral phenotypes, making use of large population neuroimaging datasets. However, the replicability of brain-behavior associations identified by these approaches remains an open question. In this study, we examined the cross-dataset replicability of brain-behavior association patterns for fluid cognition and openness predictions using a previously developed region-wise approach, as well as using a standard whole-brain approach. Overall, we found moderate similarity in patterns for fluid cognition predictions across cohorts, especially in the Human Connectome Project Young Adult, Human Connectome Project Aging, and Enhanced Nathan Kline Institute Rockland Sample cohorts, but low similarity in patterns for openness predictions. In addition, we assessed the generalizability of prediction models in cross-dataset predictions, by training the model in one dataset and testing in another. Making use of the region-wise prediction approach, we showed that first, a moderate extent of generalizability could be achieved with fluid cognition prediction, and that, second, a set of common brain regions related to fluid cognition across cohorts could be identified. Nevertheless, the moderate replicability and generalizability could only be achieved in specific contexts. Thus, we argue that replicability and generalizability in connectivity-based prediction remain limited and deserve greater attention in future studies.

Personality similarity predicts synchronous neural responses in fMRI and EEG data

Successful communication and cooperation among different members of society depends, in part, on a consistent understanding of the physical and social world. What drives this alignment in perspectives? We present evidence from two neuroimaging studies using functional magnetic resonance imaging (fMRI; N = 66 with 2145 dyadic comparisons) and electroencephalography (EEG; N = 225 with 25,200 dyadic comparisons) to show that: (1) the extent to which people’s neural responses are synchronized when viewing naturalistic stimuli is related to their personality profiles, and (2) that this effect is stronger than that of similarity in gender, ethnicity and political affiliation. The localization of the fMRI results in combination with the additional eye tracking analyses suggest that the relationship between personality similarity and neural synchrony likely reflects alignment in the interpretation of stimuli and not alignment in overt visual attention. Together, the findings suggest that similarity in psychological dispositions aligns people’s reality via shared interpretations of the external world.

Cognitive control and dishonesty

Dishonesty is ubiquitous and imposes substantial financial and social burdens on society. Intuitively, dishonesty results from a failure of willpower to control selfish behavior. However, recent research suggests that the role of cognitive control in dishonesty is more complex. We review evidence that cognitive control is not needed to be honest or dishonest per se, but that it depends on individual differences in what we call one's 'moral default': for those who are prone to dishonesty, cognitive control indeed aids in being honest, but for those who are already generally honest, cognitive control may help them cheat to occasionally profit from small acts of dishonesty. Thus, the role of cognitive control in (dis)honesty is to override the moral default.

Reward enhances connectivity between the ventral striatum and the default mode network

The default mode network (DMN) has been theorized to participate in a range of social, cognitive, and affective functions. Yet, previous accounts do not consider how the DMN contributes to other brain regions depending on psychological context, thus rendering our understanding of DMN function incomplete. We addressed this gap by applying a novel network-based psychophysiological interaction (nPPI) analysis to the reward task within the Human Connectome Project. We first focused on the task-evoked responses of the DMN and other networks involving the prefrontal cortex, including the executive control network (salience network) and the left and right frontoparietal networks. Consistent with a host of prior studies, the DMN exhibited a relative decrease in activation during the task, while the other networks exhibited a relative increase during the task. Next, we used nPPI analyses to assess whether these networks exhibit task-dependent changes in connectivity with other brain regions. Strikingly, we found that the experience of reward enhances task-dependent connectivity between the DMN and the ventral striatum, an effect that was specific to the DMN. Surprisingly, the strength of DMN-VS connectivity was correlated with personality characteristics relating to openness. Taken together, these results advance models of DMN by demonstrating how it contributes to other brain systems during task performance and how those contributions relate to individual differences.

Act natural: Functional connectivity from naturalistic stimuli fMRI outperforms resting-state in predicting brain activity

The search for an 'ideal' approach to investigate the functional connections in the human brain is an ongoing challenge for the neuroscience community. While resting-state functional magnetic resonance imaging (fMRI) has been widely used to study individual functional connectivity patterns, recent work has highlighted the benefits of collecting functional connectivity data while participants are exposed to naturalistic stimuli, such as watching a movie or listening to a story. For example, functional connectivity data collected during movie-watching were shown to predict cognitive and emotional scores more accurately than resting-state-derived functional connectivity. We have previously reported a tight link between resting-state functional connectivity and task-derived neural activity, such that the former successfully predicts the latter. In the current work we use data from the Human Connectome Project to demonstrate that naturalistic-stimulus-derived functional connectivity predicts task-induced brain activation maps more accurately than resting-state-derived functional connectivity. We then show that activation maps predicted using naturalistic stimuli are better predictors of individual intelligence scores than activation maps predicted using resting-state. We additionally examine the influence of naturalistic-stimulus type on prediction accuracy. Our findings emphasize the potential of naturalistic stimuli as a promising alternative to resting-state fMRI for connectome-based predictive modelling of individual brain activity and cognitive traits.

Correlates of individual voice and face preferential responses during resting state

Human nonverbal social signals are transmitted to a large extent by vocal and facial cues. The prominent importance of these cues is reflected in specialized cerebral regions which preferentially respond to these stimuli, e.g. the temporal voice area (TVA) for human voices and the fusiform face area (FFA) for human faces. But it remained up to date unknown whether there are respective specializations during resting state, i.e. in the absence of any cues, and if so, whether these representations share neural substrates across sensory modalities. In the present study, resting state functional connectivity (RSFC) as well as voice- and face-preferential activations were analysed from functional magnetic resonance imaging (fMRI) data sets of 60 healthy individuals. Data analysis comprised seed-based analyses using the TVA and FFA as regions of interest (ROIs) as well as multi voxel pattern analyses (MVPA). Using the face- and voice-preferential responses of the FFA and TVA as regressors, we identified several correlating clusters during resting state spread across frontal, temporal, parietal and occipital regions. Using these regions as seeds, characteristic and distinct network patterns were apparent with a predominantly convergent pattern for the bilateral TVAs whereas a largely divergent pattern was observed for the bilateral FFAs. One region in the anterior medial frontal cortex displayed a maximum of supramodal convergence of informative connectivity patterns reflecting voice- and face-preferential responses of both TVAs and the right FFA, pointing to shared neural resources in supramodal voice and face processing. The association of individual voice- and face-preferential neural activity with resting state connectivity patterns may support the perspective of a network function of the brain beyond an activation of specialized regions.

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

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

Brain connectivity fingerprinting and behavioural prediction rest on distinct functional systems of the human connectome

The prediction of inter-individual behavioural differences from neuroimaging data is a rapidly evolving field of research focusing on individualised methods to describe human brain organisation on the single-subject level. One method that harnesses such individual signatures is functional connectome fingerprinting, which can reliably identify individuals from large study populations. However, the precise relationship between functional signatures underlying fingerprinting and behavioural prediction remains unclear. Expanding on previous reports, here we systematically investigate the link between discrimination and prediction on different levels of brain network organisation (individual connections, network interactions, topographical organisation, and connection variability). Our analysis revealed a substantial divergence between discriminatory and predictive connectivity signatures on all levels of network organisation. Across different brain parcellations, thresholds, and prediction algorithms, we find discriminatory connections in higher-order multimodal association cortices, while neural correlates of behaviour display more variable distributions. Furthermore, we find the standard deviation of connections between participants to be significantly higher in fingerprinting than in prediction, making inter-individual connection variability a possible separating marker. These results demonstrate that participant identification and behavioural prediction involve highly distinct functional systems of the human connectome. The present study thus calls into question the direct functional relevance of connectome fingerprints.

Mantwill et al. analysed the relationship between functional connectivity markers that are specific to individuals (i.e. brain fingerprints) and those predictive of behaviour. They reveal that these markers are unrelated and suggest an alternative perspective on the basis of individual signatures.

Towards an efficient validation of dynamical whole-brain models

Simulating the resting-state brain dynamics via mathematical whole-brain models requires an optimal selection of parameters, which determine the model’s capability to replicate empirical data. Since the parameter optimization via a grid search (GS) becomes unfeasible for high-dimensional models, we evaluate several alternative approaches to maximize the correspondence between simulated and empirical functional connectivity. A dense GS serves as a benchmark to assess the performance of four optimization schemes: Nelder-Mead Algorithm (NMA), Particle Swarm Optimization (PSO), Covariance Matrix Adaptation Evolution Strategy (CMAES) and Bayesian Optimization (BO). To compare them, we employ an ensemble of coupled phase oscillators built upon individual empirical structural connectivity of 105 healthy subjects. We determine optimal model parameters from two- and three-dimensional parameter spaces and show that the overall fitting quality of the tested methods can compete with the GS. There are, however, marked differences in the required computational resources and stability properties, which we also investigate before proposing CMAES and BO as efficient alternatives to a high-dimensional GS. For the three-dimensional case, these methods generated similar results as the GS, but within less than 6% of the computation time. Our results contribute to an efficient validation of models for personalized simulations of brain dynamics.

Need for cognitive closure is associated with different intra-network functional connectivity patterns: A resting state EEG study

Need for Cognitive Closure (NCC) is a construct referring to the desire for predictability, unambiguity and firm answers to issues. Neuroscientific literature about NCC processes has mainly focused on task-related brain activity. According to the Triple Network model (TN), the main aim of the current study was to investigate resting state (RS) electroencephalographic (EEG) intra-network dynamics associated with NCC. Fifty-two young adults (39 females) were enrolled and underwent EEG recordings during RS. Functional connectivity analysis was computed through exact Low-Resolution Electromagnetic Tomography (eLORETA) software. Our results showed that higher levels of NCC were associated with both i) decreased alpha EEG connectivity within the Central Executive Network (CEN), and ii) increased delta connectivity within the Default Mode Network (DMN). No significant correlations were observed between NCC and functional connectivity in the Salience Network (SN). Our data would seem to suggest that high levels of NCC are characterized by a specific communication pattern within the CEN and the DMN during RS. These neurophysiological patterns might reflect several typical NCC-related cognitive characteristics (e.g., lower flexibility and preference for habitual and rigid response schemas).

Resting-State Functional Connectivity Associated With Extraversion and Agreeableness in Adolescence

Although adolescence is a period in which developmental changes occur in brain connectivity, personality formation, and peer interaction, few studies have examined the neural correlates of personality dimensions related to social behavior within adolescent samples. The current study aims to investigate whether adolescents’ brain functional connectivity is associated with extraversion and agreeableness, personality dimensions linked to peer acceptance, social network size, and friendship quality. Considering sex-variant neural maturation in adolescence, we also examined sex-specific associations between personality and functional connectivity. Using resting-state functional magnetic resonance imaging (fMRI) data from a community sample of 70 adolescents aged 12–15, we examined associations between self-reported extraversion and agreeableness and seed-to-whole brain connectivity with the amygdala as a seed region of interest. Then, using 415 brain regions that correspond to 8 major brain networks and subcortex, we explored neural connectivity within brain networks and across the whole-brain. We conducted group-level multiple regression analyses with the regressors of extraversion, agreeableness, and their interactions with sex. Results demonstrated that amygdala connectivity with the postcentral gyrus, middle temporal gyrus, and the temporal pole is positively associated with extraversion in girls and negatively associated with extraversion in boys. Agreeableness was positively associated with amygdala connectivity with the middle occipital cortex and superior parietal cortex, in the same direction for boys and girls. Results of the whole-brain connectivity analysis revealed that the connectivity of the postcentral gyrus, located in the dorsal attention network, with regions in default mode network (DMN), salience/ventral attention network, and control network (CON) was associated with extraversion, with most connections showing positive associations in girls and negative associations in boys. For agreeableness, results of the within-network connectivity analysis showed that connections within the limbic network were positively associated with agreeableness in boys while negatively associated with or not associated with agreeableness in girls. Results suggest that intrinsic functional connectivity may contribute to adolescents’ individual differences in extraversion and agreeableness and highlights sex-specific neural connectivity patterns associated with the two personality dimensions. This study deepens our understanding of the neurobiological correlates of adolescent personality that may lead to different developmental trajectories of social experience.

Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain

We are now in a time of readily available brain imaging data. Not only are researchers now sharing data more than ever before, but additionally large-scale data collecting initiatives are underway with the vision that many future researchers will use the data for secondary analyses. Here I provide an overview of available datasets and some example use cases. Example use cases include examining individual differences, more robust findings, reproducibility-both in public input data and availability as a replication sample, and methods development. I further discuss a variety of considerations associated with using existing data and the opportunities associated with large datasets. Suggestions for further readings on general neuroimaging and topic-specific discussions are also provided.

The Brain Understands Social Relationships: The Emerging Field of Functional-Connectome-Based Interpersonal Research

Human functional brain imaging research over the last 2 decades has shown that data from resting-state brain activity can help predict various psychological and pathological variables and brain function during tasks. However, most variables have been attributed to the individual brain. Recently, several studies have aimed to understand interpersonal relationships based on inter-individual similarity or dissimilarity of functional connectome. In this commentary, we introduce the studies that have opened up a new era of interpersonal research using human brain imaging.

Comparative connectomics of the primate social brain

Social interaction is thought to provide a selection pressure for human intelligence, yet little is known about its neurobiological basis and evolution throughout the primate lineage. Recent advances in neuroimaging have enabled whole brain investigation of brain structure, function, and connectivity in humans and non-human primates (NHPs), leading to a nascent field of comparative connectomics. However, linking social behavior to brain organization across the primates remains challenging. Here, we review the current understanding of the macroscale neural mechanisms of social behaviors from the viewpoint of system neuroscience. We first demonstrate an association between the number of cortical neurons and the size of social groups across primates, suggesting a link between neural information-processing capacity and social capabilities. Moreover, by capitalizing on recent advances in species-harmonized functional MRI, we demonstrate that portions of the mirror neuron system and default-mode networks, which are thought to be important for representation of the other's actions and sense of self, respectively, exhibit similarities in functional organization in macaque monkeys and humans, suggesting possible homologies. With respect to these two networks, we describe recent developments in the neurobiology of social perception, joint attention, personality and social complexity. Together, the Human Connectome Project (HCP)-style comparative neuroimaging, hyperscanning, behavioral, and other multi-modal investigations are expected to yield important insights into the evolutionary foundations of human social behavior.