Building a Science of Individual Differences from fMRI

Functional neuroimaging of resilience to trauma: convergent evidence and challenges for future research

Resilience is broadly defined as the ability to adapt successfully following stressful life events. Here, we review functional MRI studies that investigated key psychological factors that have been consistently linked to resilience to severe adversity and trauma exposure. These domains include emotion regulation (including cognitive reappraisal), reward responsivity, and cognitive control. Further, we briefly review functional imaging evidence related to emerging areas of study that may potentially facilitate resilience: namely social cognition, active coping, and successful fear extinction. Finally, we also touch upon ongoing issues in neuroimaging study design that will need to be addressed to enable us to harness insight from such studies to improve treatments for - or, ideally, guard against the development of - debilitating post-traumatic stress syndromes.

Hierarchical overlapping modular structure in the human cerebral cortex improves individual identification

The idea that brain networks have a hierarchical modular organization is pervasive. Increasing evidence suggests that brain modules overlap. However, little is known about the hierarchical overlapping modular structure in the brain. In this study, we developed a framework to uncover brain hierarchical overlapping modular structures based on a nested-spectral partition algorithm and an edge-centric network model. Overlap degree between brain modules is symmetrical across hemispheres, with highest overlap observed in the control and salience/ventral attention networks. Furthermore, brain edges are clustered into two groups: intrasystem and intersystem edges, to form hierarchical overlapping modules. At different levels, modules are self-similar in the degree of overlap. Additionally, the brain's hierarchical structure contains more individual identifiable information than a single-level structure, particularly in the control and salience/ventral attention networks. Our results offer pathways for future studies aimed at relating the organization of hierarchical overlapping modules to brain cognitive behavior and disorders.

Multivariate associations between dopamine receptor availability and risky investment decision-making across adulthood

Enhancing dopamine increases financial risk taking across adulthood but it is unclear whether baseline individual differences in dopamine function are related to risky financial decisions. Here, thirty-five healthy adults completed an incentive-compatible risky investment decision task and a PET scan at rest using [11C]FLB457 to assess dopamine D2-like receptor availability. Participants made choices between a safe asset (bond) and a risky asset (stock) with either an expected value less than the bond ("bad stock") or expected value greater than the bond ("good stock"). Five measures of behavior (choice inflexibility, risk seeking, suboptimal investment) and beliefs (absolute error, optimism) were computed and D2-like binding potential was extracted from four brain regions of interest (midbrain, amygdala, anterior cingulate, insula). We used canonical correlation analysis to evaluate multivariate associations between decision-making and dopamine function controlling for age. Decomposition of the first dimension (r = 0.76) revealed that the strongest associations were between measures of choice inflexibility, incorrect choice, optimism, amygdala binding potential, and age. Follow-up univariate analyses revealed that amygdala binding potential and age were both independently associated with choice inflexibility. The findings suggest that individual differences in dopamine function may be associated with financial risk taking in healthy adults.

Graph propagation network captures individual specificity of the relationship between functional and structural connectivity

Functional connectivity (FC) network characterizes the functional interactions between brain regions and is considered to root in the underlying structural connectivity (SC) network. If this is the case, individual variations in SC should cause corresponding individual variations in FC. However, divergences exist in the correspondence between direct SC and FC and researchers still cannot capture individual differences in FC via direct SC. As brain regions may interact through multi-hop indirect SC pathways, we conceived that one can capture the individual specific SC-FC relationship via incorporating indirect SC pathways appropriately. In this study, we designed graph propagation network (GPN) that models the information propagation between brain regions based on the SC network. Effects of interactions through multi-hop SC pathways naturally emerge from the multilayer information propagation in GPN. We predicted the individual differences in FC network based on SC network via multilayer GPN and results indicate that multilayer GPN incorporating effects of multi-hop indirect SCs greatly enhances the ability to predict individual FC network. Furthermore, the SC-FC relationship evaluated via the prediction accuracy is negatively correlated with the functional gradient, suggesting that the SC-FC relationship gradually uncouples along the functional hierarchy spanning from unimodal to transmodal cortex. We also revealed important intermediate brain regions along multi-hop SC pathways involving in the individual SC-FC relationship. These results suggest that multilayer GPN can serve as a method to establish individual SC-FC relationship at the macroneuroimaging level.

Similar functional networks predict performance in both perceptual and value-based decision tasks

There are numerous commonalities between perceptual and preferential decision processes. For instance, previous studies have shown that both of these decision types are influenced by context. Also, the same computational models can explain both. However, the neural processes and functional connections that underlie these similarities between perceptual and value-based decisions are still unclear. Hence, in the current study, we examine whether perceptual and preferential processes can be explained by similar functional networks utilizing data from the Human Connectome Project. We used resting-state functional magnetic resonance imaging data to predict performance of 2 different decision-making tasks: a value-related task (the delay discounting task) and a perceptual task (the flanker task). We then examined the existence of shared predictive-network features across these 2 decision tasks. Interestingly, we found a significant positive correlation between the functional networks, which predicted the value-based and perceptual tasks. In addition, a larger functional connectivity between visual and frontal decision brain areas was a critical feature in the prediction of both tasks. These results demonstrate that functional connections between perceptual and value-related areas in the brain are inherently related to decision-making processes across domains.

Explainable machine learning approach to predict and explain the relationship between task-based fMRI and individual differences in cognition

Despite decades of costly research, we still cannot accurately predict individual differences in cognition from task-based functional magnetic resonance imaging (fMRI). Moreover, aiming for methods with higher prediction is not sufficient. To understand brain-cognition relationships, we need to explain how these methods draw brain information to make the prediction. Here we applied an explainable machine-learning (ML) framework to predict cognition from task-based fMRI during the n-back working-memory task, using data from the Adolescent Brain Cognitive Development (n = 3,989). We compared 9 predictive algorithms in their ability to predict 12 cognitive abilities. We found better out-of-sample prediction from ML algorithms over the mass-univariate and ordinary least squares (OLS) multiple regression. Among ML algorithms, Elastic Net, a linear and additive algorithm, performed either similar to or better than nonlinear and interactive algorithms. We explained how these algorithms drew information, using SHapley Additive explanation, eNetXplorer, Accumulated Local Effects, and Friedman's H-statistic. These explainers demonstrated benefits of ML over the OLS multiple regression. For example, ML provided some consistency in variable importance with a previous study and consistency with the mass-univariate approach in the directionality of brain-cognition relationships at different regions. Accordingly, our explainable-ML framework predicted cognition from task-based fMRI with boosted prediction and explainability over standard methodologies.

Individuals with problem gambling and obsessive-compulsive disorder learn through distinct reinforcement mechanisms

Obsessive-compulsive disorder (OCD) and pathological gambling (PG) are accompanied by deficits in behavioural flexibility. In reinforcement learning, this inflexibility can reflect asymmetric learning from outcomes above and below expectations. In alternative frameworks, it reflects perseveration independent of learning. Here, we examine evidence for asymmetric reward-learning in OCD and PG by leveraging model-based functional magnetic resonance imaging (fMRI). Compared with healthy controls (HC), OCD patients exhibited a lower learning rate for worse-than-expected outcomes, which was associated with the attenuated encoding of negative reward prediction errors in the dorsomedial prefrontal cortex and the dorsal striatum. PG patients showed higher and lower learning rates for better- and worse-than-expected outcomes, respectively, accompanied by higher encoding of positive reward prediction errors in the anterior insula than HC. Perseveration did not differ considerably between the patient groups and HC. These findings elucidate the neural computations of reward-learning that are altered in OCD and PG, providing a potential account of behavioural inflexibility in those mental disorders.

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

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

Changes in task performance and frontal cortex activation within and over sessions during the n-back task

The n-back task is a popular paradigm for studying neurocognitive processing at varying working memory loads. Although much is known about the effects of load on behavior and neural activation during n-back performance, the temporal dynamics of such effects remain unclear. Here, we investigated the within- and between-session stability and consistency of task performance and frontal cortical activation during the n-back task using functional near-infrared spectroscopy (fNIRS). Forty healthy young adults performed the 1-back and 3-back conditions three times per condition. They then undertook identical retest sessions 3 weeks later (M = 21.2 days, SD = 0.9). Over the course of the task, activation in the participants' frontopolar, dorsomedial, dorsolateral, ventrolateral, and posterolateral frontal cortices was measured with fNIRS. We found significantly improved working memory performance (difference between 1-back and 3-back accuracies) over time both within and between sessions. All accuracy and reaction time measures exhibited good to excellent consistency within and across sessions. Additionally, changes in frontal oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) concentration were maintained over time across timescales, except that load-dependent (3-back > 1-back) HbO changes, particularly in the ventrolateral PFC, diminished over separate sessions. The consistency of fNIRS measures varied greatly, with changes in 3-back dorsolateral and ventrolateral HbO demonstrating fair-to-good consistency both within and between sessions. Overall, this study clarified the temporal dynamics of task performance and frontal activation during the n-back task. The findings revealed the neural mechanisms underlying the change in n-back task performance over time and have practical implications for future n-back research.

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.

High-fidelity transmission of auditory symbolic material is associated with reduced right-left neuroanatomical asymmetry between primary auditory regions

The intergenerational stability of auditory symbolic systems, such as music, is thought to rely on brain processes that allow the faithful transmission of complex sounds. Little is known about the functional and structural aspects of the human brain which support this ability, with a few studies pointing to the bilateral organization of auditory networks as a putative neural substrate. Here, we further tested this hypothesis by examining the role of left-right neuroanatomical asymmetries between auditory cortices. We collected neuroanatomical images from a large sample of participants (nonmusicians) and analyzed them with Freesurfer's surface-based morphometry method. Weeks after scanning, the same individuals participated in a laboratory experiment that simulated music transmission: the signaling games. We found that high accuracy in the intergenerational transmission of an artificial tone system was associated with reduced rightward asymmetry of cortical thickness in Heschl's sulcus. Our study suggests that the high-fidelity copying of melodic material may rely on the extent to which computational neuronal resources are distributed across hemispheres. Our data further support the role of interhemispheric brain organization in the cultural transmission and evolution of auditory symbolic systems.

Robust dynamic brain coactivation states estimated in individuals

A confluence of evidence indicates that brain functional connectivity is not static but rather dynamic. Capturing transient network interactions in the individual brain requires a technology that offers sufficient within-subject reliability. Here, we introduce an individualized network-based dynamic analysis technique and demonstrate that it is reliable in detecting subject-specific brain states during both resting state and a cognitively challenging language task. We evaluate the extent to which brain states show hemispheric asymmetries and how various phenotypic factors such as handedness and gender might influence network dynamics, discovering a right-lateralized brain state that occurred more frequently in men than in women and more frequently in right-handed versus left-handed individuals. Longitudinal brain state changes were also shown in 42 patients with subcortical stroke over 6 months. Our approach could quantify subject-specific dynamic brain states and has potential for use in both basic and clinical neuroscience research.

Alterations in working memory maintenance of fearful face distractors in depressed participants: An ERP study

Task-irrelevant threatening faces (e.g., fearful) are difficult to filter from visual working memory (VWM), but the difficulty in filtering non-threatening negative faces (e.g., sad) is not known. Depressive symptoms could also potentially affect the ability to filter different emotional faces. We tested the filtering of task-irrelevant sad and fearful faces by depressed and control participants performing a color-change detection task. The VWM storage of distractors was indicated by contralateral delay activity, a specific event-related potential index for the number of objects stored in VWM during the maintenance phase. The control group did not store sad face distractors, but they automatically stored fearful face distractors, suggesting that threatening faces are specifically difficult to filter from VWM in non-depressed individuals. By contrast, depressed participants showed no additional consumption of VWM resources for either the distractor condition or the non-distractor condition, possibly suggesting that neither fearful nor sad face distractors were maintained in VWM. Our control group results confirm previous findings of a threat-related filtering difficulty in the normal population while also suggesting that task-irrelevant non-threatening negative faces do not automatically load into VWM. The novel finding of the lack of negative distractors within VWM storage in participants with depressive symptoms may reflect a decreased overall responsiveness to negative facial stimuli. Future studies should investigate the mechanisms underlying distractor filtering in depressed populations.

Speaking in gestures: Left dorsal and ventral frontotemporal brain systems underlie communication in conducting

Conducting constitutes a well-structured system of signs anticipating information concerning the rhythm and dynamic of a musical piece. Conductors communicate the musical tempo to the orchestra, unifying the individual instrumental voices to form an expressive musical Gestalt. In a functional magnetic resonance imaging (fMRI) experiment, 12 professional conductors and 16 instrumentalists conducted real-time novel pieces with diverse complexity in orchestration and rhythm. For control, participants either listened to the stimuli or performed beat patterns, setting the time of a metronome or complex rhythms played by a drum. Activation of the left superior temporal gyrus (STG), supplementary and premotor cortex and Broca's pars opercularis (F3op) was shared in both musician groups and separated conducting from the other conditions. Compared to instrumentalists, conductors activated Broca's pars triangularis (F3tri) and the STG, which differentiated conducting from time beating and reflected the increase in complexity during conducting. In comparison to conductors, instrumentalists activated F3op and F3tri when distinguishing complex rhythm processing from simple rhythm processing. Fibre selection from a normative human connectome database, constructed using a global tractography approach, showed that the F3op and STG are connected via the arcuate fasciculus, whereas the F3tri and STG are connected via the extreme capsule. Like language, the anatomical framework characterising conducting gestures is located in the left dorsal system centred on F3op. This system reflected the sensorimotor mapping for structuring gestures to musical tempo. The ventral system centred on F3Tri may reflect the art of conductors to set this musical tempo to the individual orchestra's voices in a global, holistic way.

Mapping white matter structural covariance connectivity for single subject using wavelet transform with T1-weighted anatomical brain MRI

Introduction

Current studies of structural covariance networks were focused on the gray matter in the human brain. The structural covariance connectivity in the white matter remains largely unexplored. This paper aimed to build novel metrics that can infer white matter structural covariance connectivity, and to explore the predictive power of the proposed features.

Methods

To this end, a cohort of 315 adult subjects with the anatomical brain MRI datasets were obtained from the publicly available Dallas Lifespan Brain Study (DLBS) project. The 3D wavelet transform was applied on the individual voxel-based morphology (VBM) volume to obtain the white matter structural covariance connectivity. The predictive models for cognitive functions were built using support vector regression (SVR).

Results

The predictive models exhibited comparable performance with previous studies. The novel features successfully predicted the individual ability of digit comparison (DC) (r = 0.41 ± 0.01, p < 0.01) and digit symbol (DSYM) (r = 0.5 ± 0.01, p < 0.01). The sensorimotor-related white matter system exhibited as the most predictive network node. Furthermore, the node strengths of sensorimotor mode were significantly correlated to cognitive scores.

Discussion

The results suggested that the white matter structural covariance connectivity was informative and had potential for predictive tasks of brain-behavior research.

Measurement of burnout during the prolonged pandemic in the Chinese zero-COVID context: COVID-19 burnout views scale

Burnout is an important public health issue at times of the COVID-19 pandemic. Current measures which focus on work-based burnout have limitations in length and/or relevance. When stepping into the post-pandemic as a new Norm Era, the burnout scale for the general population is urgently needed to fill the gap. This study aimed to develop a COVID-19 Burnout Views Scale (COVID-19 BVS) to measure burnout views of the general public in a Chinese context and examine its psychometric properties. A multiphase approach including literature review, expert consultation, and pilot testing was adopted in developing the scale. The scale was administered to a sample of 1,078 of the general public in Hong Kong with an average age of 34.45 years (SD = 12.47). Exploratory and Confirmatory Factor Analyses suggested a 5-item unidimensional model of COVID-19 BVS. The CFA results indicated that the COVID-19 BVS had a good model fit, as χ2 (10.054)/5 = 2.01, SRMR = 0.010, CFI = 0.998, RMSEA = 0.031. Five items were maintained in EFA with high internal consistency in terms of Cronbach's α of 0.845 and McDonald's ω coefficient of 0.87, and the corrected item-to-total correlations of 0.512 to 0.789 are way above the acceptable range. The KMO values of 0.841 and Bartlett's Test of Sphericity (p < 0.01) verified the normal distribution of the EFA and the adequacy of the EFA sampling. The analyses suggest that the COVID-19 BVS is a promising tool for assessing burnout views on the impacts of the epidemic on the Chinese general populations.

Neural mechanisms of background and velocity effects in smooth pursuit eye movements

Smooth pursuit eye movements (SPEM) are essential to guide behaviour in complex visual environments. SPEM accuracy is known to be degraded by the presence of a structured visual background and at higher target velocities. The aim of this preregistered study was to investigate the neural mechanisms of these robust behavioural effects. N = 33 participants performed a SPEM task with two background conditions (present and absent) at two target velocities (0.4 and 0.6 Hz). Eye movement and BOLD data were collected simultaneously. Both the presence of a structured background and faster target velocity decreased pursuit gain and increased catch-up saccade rate. Faster targets additionally increased position error. Higher BOLD response with background was found in extensive clusters in visual, parietal, and frontal areas (including the medial frontal eye fields; FEF) partially overlapping with the known SPEM network. Faster targets were associated with higher BOLD response in visual cortex and left lateral FEF. Task-based functional connectivity analyses (psychophysiological interactions; PPI) largely replicated previous results in the basic SPEM network but did not yield additional information regarding the neural underpinnings of the background and velocity effects. The results show that the presentation of visual background stimuli during SPEM induces activity in a widespread visuo-parieto-frontal network including areas contributing to cognitive aspects of oculomotor control such as medial FEF, whereas the response to higher target velocity involves visual and motor areas such as lateral FEF. Therefore, we were able to propose for the first time different functions of the medial and lateral FEF during SPEM.

Interpreting mental state decoding with deep learning models

In mental state decoding, researchers aim to identify the set of mental states (e.g., experiencing happiness or fear) that can be reliably identified from the activity patterns of a brain region (or network). Deep learning (DL) models are highly promising for mental state decoding because of their unmatched ability to learn versatile representations of complex data. However, their widespread application in mental state decoding is hindered by their lack of interpretability, difficulties in applying them to small datasets, and in ensuring their reproducibility and robustness. We recommend approaching these challenges by leveraging recent advances in explainable artificial intelligence (XAI) and transfer learning, and also provide recommendations on how to improve the reproducibility and robustness of DL models in mental state decoding.

State-like changes in the salience network correlate with delusion severity in first-episode psychosis patients

BACKGROUND AND HYPOTHESIS

Delusions are characteristic of psychotic disorders; however, the brain correlates of delusions remain poorly known. Imaging studies on delusions typically compare images across individuals. Related confounding of inter-individual differences beyond delusions may be avoided by comparing delusional and non-delusional states within individuals.

STUDY DESIGN

We studied correlations of delusions using intra-subject correlation (intra-SC) and inter-subject correlation of functional magnetic resonance imaging (fMRI) signal time series, obtained during a movie stimulus at baseline and follow-up. We included 27 control subjects and 24 first-episode psychosis patients, who were free of delusions at follow-up, to calculate intra-SC between fMRI signals obtained during the two time points. In addition, we studied changes in functional connectivity at baseline and during the one-year follow-up using regions where delusion severity correlated with intra-SC as seeds.

RESULTS

The intra-SC correlated negatively with the baseline delusion severity in the bilateral anterior insula. In addition, we observed a subthreshold cluster in the anterior cingulate. These three regions constitute the cortical salience network (SN). Functional connectivity between the bilateral insula and the precuneus was weaker in the patients at baseline than in patients at follow-up or in control subjects at any time point.

CONCLUSIONS

The results suggest that intra-SC is a powerful tool to study brain correlates of symptoms and highlight the role of the SN and internetwork dysconnectivity between the SN and the default mode network in delusions.

Structural gray matter features and behavioral preliterate skills predict future literacy – A machine learning approach

When children learn to read, their neural system undergoes major changes to become responsive to print. There seem to be nuanced interindividual differences in the neurostructural anatomy of regions that later become integral parts of the reading network. These differences might affect literacy acquisition and, in some cases, might result in developmental disorders like dyslexia. Consequently, the main objective of this longitudinal study was to investigate those interindividual differences in gray matter morphology that might facilitate or hamper future reading acquisition. We used a machine learning approach to examine to what extent gray matter macrostructural features and cognitive-linguistic skills measured before formal literacy teaching could predict literacy 2 years later. Forty-two native German-speaking children underwent T1-weighted magnetic resonance imaging and psychometric testing at the end of kindergarten. They were tested again 2 years later to assess their literacy skills. A leave-one-out cross-validated machine-learning regression approach was applied to identify the best predictors of future literacy based on cognitive-linguistic preliterate behavioral skills and cortical measures in a priori selected areas of the future reading network. With surprisingly high accuracy, future literacy was predicted, predominantly based on gray matter volume in the left occipito-temporal cortex and local gyrification in the left insular, inferior frontal, and supramarginal gyri. Furthermore, phonological awareness significantly predicted future literacy. In sum, the results indicate that the brain morphology of the large-scale reading network at a preliterate age can predict how well children learn to read.

Enhanced Mirror Neuron Network Activity and Effective Connectivity during Live Interaction Among Female Subjects

Facial expressions are indispensable in daily human communication. Previous neuroimaging studies investigating facial expression processing have presented pre-recorded stimuli and lacked live face-to-face interaction. Our paradigm alternated between presentations of real-time model performance and pre-recorded videos of dynamic facial expressions to participants. Simultaneous functional magnetic resonance imaging (fMRI) and facial electromyography activity recordings, as well as post-scan valence and arousal ratings were acquired from 44 female participants. Live facial expressions enhanced the subjective valence and arousal ratings as well as facial muscular responses. Live performances showed greater engagement of the right posterior superior temporal sulcus (pSTS), right inferior frontal gyrus (IFG), right amygdala and right fusiform gyrus, and modulated the effective connectivity within the right mirror neuron system (IFG, pSTS, and right inferior parietal lobule). A support vector machine algorithm could classify multivoxel activation patterns in brain regions involved in dynamic facial expression processing in the mentalizing networks (anterior and posterior cingulate cortex). These results indicate that live social interaction modulates the activity and connectivity of the right mirror neuron system and enhances spontaneous mimicry, further facilitating emotional contagion.

Predicting children's math skills from task-based and resting-state functional brain connectivity

A critical goal of cognitive neuroscience is to predict behavior from neural structure and function, thereby providing crucial insights into who might benefit from clinical and/or educational interventions. Across development, the strength of functional connectivity among a distributed set of brain regions is associated with children's math skills. Therefore, in the present study we use connectome-based predictive modeling to investigate whether functional connectivity during numerical processing and at rest "predicts" children's math skills (N = 31, Mage = 9.21 years, 14 Female). Overall, we found that functional connectivity during symbolic number comparison and rest, but not during nonsymbolic number comparison, predicts children's math skills. Each task revealed a largely distinct set of predictive connections distributed across canonical brain networks and major brain lobes. Most of these predictive connections were negatively correlated with children's math skills so that weaker connectivity predicted better math skills. Notably, these predictive connections were largely nonoverlapping across task states, suggesting children's math abilities may depend on state-dependent patterns of network segregation and/or regional specialization. Furthermore, the current predictive modeling approach moves beyond brain-behavior correlations and toward building models of brain connectivity that may eventually aid in predicting future math skills.

Person-specific and precision neuroimaging: Current methods and future directions

Most neuroimaging studies of brain function analyze data in normalized space to identify regions of common activation across participants. These studies treat interindividual differences in brain organization as noise, but this approach can obscure important information about the brain's functional architecture. Recently, a number of studies have adopted a person-specific approach that aims to characterize these individual differences and explore their reliability and implications for behavior. A subset of these studies has taken a precision imaging approach that collects multiple hours of data from each participant to map brain function on a finer scale. In this review, we provide a broad overview of how person-specific and precision imaging techniques have used resting-state measures to examine individual differences in the brain's organization and their impact on behavior, followed by how task-based activity continues to add detail to these discoveries. We argue that person-specific and precision approaches demonstrate substantial promise in uncovering new details of the brain's functional organization and its relationship to behavior in many areas of cognitive neuroscience. We also discuss some current limitations in this new field and some new directions it may take.

Intrinsic brain activity patterns across large-scale networks predict reciprocity propensity

Reciprocity is prevalent across human societies, but individuals are heterogeneous regarding their reciprocity propensity. Although a large body of task-based brain imaging measures has shed light on the neural underpinnings of reciprocity at group level, the neural basis underlying the individual differences in reciprocity propensity remains largely unclear. Here, we combined brain imaging and machine learning techniques to individually predict reciprocity propensity from resting-state brain activity measured by fractional amplitude of low-frequency fluctuation. The brain regions contributing to the prediction were then analyzed for functional connectivity and decoding analyses, allowing for a data-driven quantitative inference on psychophysiological functions. Our results indicated that patterns of resting-state brain activity across multiple brain systems were capable of predicting individual reciprocity propensity, with the contributing regions distributed across the salience (e.g., ventrolateral prefrontal cortex), fronto-parietal (e.g., dorsolateral prefrontal cortex), default mode (e.g., ventromedial prefrontal cortex), and sensorimotor (e.g., supplementary motor area) networks. Those contributing brain networks are implicated in emotion and cognitive control, mentalizing, and motor-based processes, respectively. Collectively, these findings provide novel evidence on the neural signatures underlying the individual differences in reciprocity, and lend support the assertion that reciprocity emerges from interactions among regions embodied in multiple large-scale brain networks.

Virtual navigation in healthy aging: Activation during learning and deactivation during retrieval predicts successful memory for spatial locations

Spatial navigation and spatial memory are two important skills for independent living, and are known to be compromised with age. Here, we investigate the neural correlates of successful spatial memory in healthy older adults in order to learn more about the neural underpinnings of maintenance of navigation skill into old age. Healthy older adults watched a video shot by a person navigating a route and were asked to remember objects along the route and then attempted to remember object locations by virtually pointing to the location of hidden objects from several locations along the route. Brain activity during watching and pointing was recorded with functional MRI. Larger activations in temporal and frontal regions during watching, and larger deactivations in superior parietal cortex and intraparietal sulcus during pointing, were associated with smaller location errors. These findings suggest that larger evoked responses during learning of spatial information coupled with larger deactivation of canonical spatial memory regions at retrieval are important for effective spatial memory in late life.

Computational approaches to predicting treatment response to obesity using neuroimaging

Obesity is a worldwide disease associated with multiple severe adverse consequences and comorbid conditions. While an increased body weight is the defining feature in obesity, etiologies, clinical phenotypes and treatment responses vary between patients. These variations can be observed within individual treatment options which comprise lifestyle interventions, pharmacological treatment, and bariatric surgery. Bariatric surgery can be regarded as the most effective treatment method. However, long-term weight regain is comparably frequent even for this treatment and its application is not without risk. A prognostic tool that would help predict the effectivity of the individual treatment methods in the long term would be essential in a personalized medicine approach. In line with this objective, an increasing number of studies have combined neuroimaging and computational modeling to predict treatment outcome in obesity. In our review, we begin by outlining the central nervous mechanisms measured with neuroimaging in these studies. The mechanisms are primarily related to reward-processing and include "incentive salience" and psychobehavioral control. We then present the diverse neuroimaging methods and computational prediction techniques applied. The studies included in this review provide consistent support for the importance of incentive salience and psychobehavioral control for treatment outcome in obesity. Nevertheless, further studies comprising larger sample sizes and rigorous validation processes are necessary to answer the question of whether or not the approach is sufficiently accurate for clinical real-world application.

COVID-19 vicarious traumatization links functional connectome to general distress

As characterized by repeated exposure of others' trauma, vicarious traumatization is a common negative psychological reaction during the COVID-19 pandemic and plays a crucial role in the development of general mental distress. This study aims to identify functional connectome that encodes individual variations of pandemic-related vicarious traumatization and reveal the underlying brain-vicarious traumatization mechanism in predicting general distress. The eligible subjects were 105 general university students (60 females, aged from 19 to 27 years) undergoing brain MRI scanning and baseline behavioral tests (October 2019 to January 2020), whom were re-contacted for COVID-related vicarious traumatization measurement (February to April 2020) and follow-up general distress evaluation (March to April 2021). We applied a connectome-based predictive modeling (CPM) approach to identify the functional connectome supporting vicarious traumatization based on a 268-region-parcellation assigned to network memberships. The CPM analyses showed that only the negative network model stably predicted individuals' vicarious traumatization scores (q² = -0.18, MSE = 617, r _{[predicted, actual]} = 0.18, p = 0.024), with the contributing functional connectivity primarily distributed in the fronto-parietal, default mode, medial frontal, salience, and motor network. Furthermore, mediation analysis revealed that vicarious traumatization mediated the influence of brain functional connectome on general distress. Importantly, our results were independent of baseline family socioeconomic status, other stressful life events and general mental health as well as age, sex and head motion. Our study is the first to provide evidence for the functional neural markers of vicarious traumatization and reveal an underlying neuropsychological pathway to predict distress symptoms in which brain functional connectome affects general distress via vicarious traumatization.

Interpreting Brain Biomarkers: Challenges and solutions in interpreting machine learning-based predictive neuroimaging

Predictive modeling of neuroimaging data (predictive neuroimaging) for evaluating individual differences in various behavioral phenotypes and clinical outcomes is of growing interest. However, the field is experiencing challenges regarding the interpretability of the results. Approaches to defining the specific contribution of functional connections, regions, or networks in prediction models are urgently needed, which may help explore the underlying mechanisms. In this article, we systematically review the methods and applications for interpreting brain signatures derived from predictive neuroimaging based on a survey of 326 research articles. Strengths, limitations, and the suitable conditions for major interpretation strategies are also deliberated. In-depth discussion of common issues in existing literature and the corresponding recommendations to address these pitfalls are provided. We highly recommend exhaustive validation on the reliability and interpretability of the biomarkers across multiple datasets and contexts, which thereby could translate technical advances in neuroimaging into concrete improvements in precision medicine.

Structural insight into the individual variability architecture of the functional brain connectome

Human cognition and behaviors depend upon the brain's functional connectomes, which vary remarkably across individuals. However, whether and how the functional connectome individual variability architecture is structurally constrained remains largely unknown. Using tractography- and morphometry-based network models, we observed the spatial convergence of structural and functional connectome individual variability, with higher variability in heteromodal association regions and lower variability in primary regions. We demonstrated that functional variability is significantly predicted by a unifying structural variability pattern and that this prediction follows a primary-to-heteromodal hierarchical axis, with higher accuracy in primary regions and lower accuracy in heteromodal regions. We further decomposed group-level connectome variability patterns into individual unique contributions and uncovered the structural-functional correspondence that is associated with individual cognitive traits. These results advance our understanding of the structural basis of individual functional variability and suggest the importance of integrating multimodal connectome signatures for individual differences in cognition and behaviors.

Do You See What I See? Diversity in Human Color Perception

In our tendency to discuss the objective properties of the external world, we may fail to notice that our subjective perceptions of those properties differ between individuals. Variability at all levels of the color vision system creates diversity in color perception, from discrimination to color matching, appearance, and subjective experience, such that each of us lives in a unique perceptual world. In this review, I discuss what is known about individual differences in color perception and its determinants, particularly considering genetically mediated variability in cone photopigments and the paradoxical effects of visual environments in both contributing to and counteracting individual differences. I make the case that, as well as being of interest in their own right and crucial for a complete account of color vision, individual differences can be used as a methodological tool in color science for the insights that they offer about the underlying mechanisms of perception. Expected final online publication date for the Annual Review of Vision Science, Volume 8 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Behavioral Studies Using Large-Scale Brain Networks – Methods and Validations

Mapping human behaviors to brain activity has become a key focus in modern cognitive neuroscience. As methods such as functional MRI (fMRI) advance cognitive scientists show an increasing interest in investigating neural activity in terms of functional connectivity and brain networks, rather than activation in a single brain region. Due to the noisy nature of neural activity, determining how behaviors are associated with specific neural signals is not well-established. Previous research has suggested graph theory techniques as a solution. Graph theory provides an opportunity to interpret human behaviors in terms of the topological organization of brain network architecture. Graph theory-based approaches, however, only scratch the surface of what neural connections relate to human behavior. Recently, the development of data-driven methods, e.g., machine learning and deep learning approaches, provide a new perspective to study the relationship between brain networks and human behaviors across the whole brain, expanding upon past literatures. In this review, we sought to revisit these data-driven approaches to facilitate our understanding of neural mechanisms and build models of human behaviors. We start with the popular graph theory approach and then discuss other data-driven approaches such as connectome-based predictive modeling, multivariate pattern analysis, network dynamic modeling, and deep learning techniques that quantify meaningful networks and connectivity related to cognition and behaviors. Importantly, for each topic, we discuss the pros and cons of the methods in addition to providing examples using our own data for each technique to describe how these methods can be applied to real-world neuroimaging data.

Video-evoked fMRI BOLD responses are highly consistent across different data acquisition sites

Naturalistic imaging paradigms, in which participants view complex videos in the scanner, are increasingly used in human cognitive neuroscience. Videos evoke temporally synchronized brain responses that are similar across subjects as well as within subjects, but the reproducibility of these brain responses across different data acquisition sites has not yet been quantified. Here, we characterize the consistency of brain responses across independent samples of participants viewing the same videos in functional magnetic resonance imaging (fMRI) scanners at different sites (Indiana University and Caltech). We compared brain responses collected at these different sites for two carefully matched datasets with identical scanner models, acquisition, and preprocessing details, along with a third unmatched dataset in which these details varied. Our overall conclusion is that for matched and unmatched datasets alike, video-evoked brain responses have high consistency across these different sites, both when compared across groups and across pairs of individuals. As one might expect, differences between sites were larger for unmatched datasets than matched datasets. Residual differences between datasets could in part reflect participant-level variability rather than scanner- or data- related effects. Altogether our results indicate promise for the development and, critically, generalization of video fMRI studies of individual differences in healthy and clinical populations alike.

Trajectory of rich club properties in structural brain networks

Many organizational principles of structural brain networks are established before birth and undergo considerable developmental changes afterwards. These include the topologically central hub regions and a densely connected rich club. While several studies have mapped developmental trajectories of brain connectivity and brain network organization across childhood and adolescence, comparatively little is known about subsequent development over the course of the lifespan. Here, we present a cross-sectional analysis of structural brain network development in N = 8066 participants aged 5-80 years. Across all brain regions, structural connectivity strength followed an "inverted-U"-shaped trajectory with vertex in the early 30s. Connectivity strength of hub regions showed a similar trajectory and the identity of hub regions remained stable across all age groups. While connectivity strength declined with advancing age, the organization of hub regions into a rich club did not only remain intact but became more pronounced, presumingly through a selected sparing of relevant connections from age-related connectivity loss. The stability of rich club organization in the face of overall age-related decline is consistent with a "first come, last served" model of neurodevelopment, where the first principles to develop are the last to decline with age. Rich club organization has been shown to be highly beneficial for communicability and higher cognition. A resilient rich club might thus be protective of a functional loss in late adulthood and represent a neural reserve to sustain cognitive functioning in the aging brain.

Decomposition-Based Correlation Learning for Multi-Modal MRI-Based Classification of Neuropsychiatric Disorders

Multi-modal magnetic resonance imaging (MRI) is widely used for diagnosing brain disease in clinical practice. However, the high-dimensionality of MRI images is challenging when training a convolution neural network. In addition, utilizing multiple MRI modalities jointly is even more challenging. We developed a method using decomposition-based correlation learning (DCL). To overcome the above challenges, we used a strategy to capture the complex relationship between structural MRI and functional MRI data. Under the guidance of matrix decomposition, DCL takes into account the spike magnitude of leading eigenvalues, the number of samples, and the dimensionality of the matrix. A canonical correlation analysis (CCA) was used to analyze the correlation and construct matrices. We evaluated DCL in the classification of multiple neuropsychiatric disorders listed in the Consortium for Neuropsychiatric Phenomics (CNP) dataset. In experiments, our method had a higher accuracy than several existing methods. Moreover, we found interesting feature connections from brain matrices based on DCL that can differentiate disease and normal cases and different subtypes of the disease. Furthermore, we extended experiments on a large sample size dataset and a small sample size dataset, compared with several other well-established methods that were designed for the multi neuropsychiatric disorder classification; our proposed method achieved state-of-the-art performance on all three datasets.

Generalizable predictive modeling of semantic processing ability from functional brain connectivity

Semantic processing (SP) is one of the critical abilities of humans for representing and manipulating conceptual and meaningful information. Neuroimaging studies of SP typically collapse data from many subjects, but its neural organization and behavioral performance vary between individuals. It is not yet understood whether and how the individual variabilities in neural network organizations contribute to the individual differences in SP behaviors. We aim to identify the neural signatures underlying SP variabilities by analyzing functional connectivity (FC) patterns based on a large‐sample Human Connectome Project (HCP) dataset and rigorous predictive modeling. We used a two‐stage predictive modeling approach to build an internally cross‐validated model and to test the model's generalizability with unseen data from different HCP samples and other out‐of‐sample datasets. FC patterns within a putative semantic brain network were significantly predictive of individual SP scores summarized from five SP‐related behavioral tests. This cross‐validated model can be used to predict unseen HCP data. The model generalizability was enhanced in the language task compared with other tasks used during scanning and was better for females than males. The model constructed from the HCP dataset can be partially generalized to two independent cohorts that participated in different semantic tasks. FCs connecting to the Perisylvian language network show the most reliable contributions to predictive modeling and the out‐of‐sample generalization. These findings contribute to our understanding of the neural sources of individual differences in SP, which potentially lay the foundation for personalized education for healthy individuals and intervention for SP and language deficits patients.

Neuroanatomical markers of psychotic experiences in adolescents: A machine-learning approach in a longitudinal population-based sample

It is important to identify accurate markers of psychiatric illness to aid early prediction of disease course. Subclinical psychotic experiences (PEs) are important risk factors for later mental ill-health and suicidal behaviour. This study used machine learning to investigate neuroanatomical markers of PEs in early and later stages of adolescence. Machine learning using logistic regression using Elastic Net regularization was applied to T1-weighted and diffusion MRI data to classify adolescents with subclinical psychotic experiences vs. controls across 3 timepoints (Time 1:11-13 years, n = 77; Time 2:14-16 years, n = 56; Time 3:18-20 years, n = 40). Neuroimaging data classified adolescents aged 11-13 years with current PEs vs. controls returning an AROC of 0.62, significantly better than a null model, p = 1.73^e-29. Neuroimaging data also classified those with PEs at 18-20 years (AROC = 0.59;P = 7.19^e-10) but performance was at chance level at 14-16 years (AROC = 0.50). Left hemisphere frontal regions were top discriminant classifiers for 11-13 years-old adolescents with PEs, particularly pars opercularis. Those with future PEs at 18-20 years-old were best distinguished from controls based on left frontal regions, right-hemisphere medial lemniscus, cingulum bundle, precuneus and genu of the corpus callosum (CC). Deviations from normal adolescent brain development in young people with PEs included an acceleration in the typical pattern of reduction in left frontal thickness and right parietal curvature, and accelerated progression of microstructural changes in right white matter and corpus callosum. These results emphasise the importance of multi-modal analysis for understanding adolescent PEs and provide important new insights into early phenotypes for psychotic experiences.

Social cognitive network neuroscience

Over the past three decades, research from the field of social neuroscience has identified a constellation of brain regions that relate to social cognition. Although these studies have provided important insights into the specific neural regions underlying social behavior, they may overlook the broader neural context in which those regions and the interactions between them are embedded. Network neuroscience is an emerging discipline that focuses on modeling and analyzing brain networks-collections of interacting neural elements. Because human cognition requires integrating information across multiple brain regions and systems, we argue that a novel social cognitive network neuroscience approach-which leverages methods from the field of network neuroscience and graph theory-can advance our understanding of how brain systems give rise to social behavior. This review provides an overview of the field of network neuroscience, discusses studies that have leveraged this approach to advance social neuroscience research, highlights the potential contributions of social cognitive network neuroscience to understanding social behavior and provides suggested tools and resources for conducting network neuroscience research.

Impact of Music in Males and Females for Relief from Neurodegenerative Disorder Stress

Neurological imbalance sometimes resulted in stress, which is experienced by the number of people at some moment in their life. A considerable measurement scheme can quantify the stress level in an individual, in which music has always been considered as the best therapy for stress relief in healthy human being as well in severe medical conditions. In this work, the impact of four types of music interventions with the lyrics of Hindi music and varying spectral centroid has been studied for an analysis of stress relief in males and females. The self-reported data for stress using state-trait anxiety (STA) and electroencephalography (EEG) signals for 14 channels in response to music interventions have been considered. Features such as Hjorth (activity, mobility, and complexity), variance, standard deviation, skew, kurtosis, and mean have been extracted from five bands (delta, theta, alpha, beta, and gamma) of each channel of the recorded EEG signals from 9 males and 9 females of the age category between 18 and 25 years. The support vector machine classifier has been used to classify three subsets: (i) male and female, (ii) baseline and female, and (iii) baseline and male. The noteworthy accuracy of 100% was found at the delta band for the first subset, beta and gamma bands for the second subset, and beta, gamma, and delta bands for the third subset. STA score has shown more deviation in the male category than in female, which gives a clear insight into the impact of music intervention with varying spectral centroid that has a higher impact to relieve stress in the male category than the female category.

Decision neuroscience and neuroeconomics: Recent progress and ongoing challenges

In the past decade, decision neuroscience and neuroeconomics have developed many new insights in the study of decision making. This review provides an overarching update on how the field has advanced in this time period. Although our initial review a decade ago outlined several theoretical, conceptual, methodological, empirical, and practical challenges, there has only been limited progress in resolving these challenges. We summarize significant trends in decision neuroscience through the lens of the challenges outlined for the field and review examples where the field has had significant, direct, and applicable impacts across economics and psychology. First, we review progress on topics including reward learning, explore-exploit decisions, risk and ambiguity, intertemporal choice, and valuation. Next, we assess the impacts of emotion, social rewards, and social context on decision making. Then, we follow up with how individual differences impact choices and new exciting developments in the prediction and neuroforecasting of future decisions. Finally, we consider how trends in decision-neuroscience research reflect progress toward resolving past challenges, discuss new and exciting applications of recent research, and identify new challenges for the field. This article is categorized under: Psychology > Reasoning and Decision Making Psychology > Emotion and Motivation.

A Connectome-wide Functional Signature of Trait Anger

Past research on the brain correlates of trait anger has been limited by small sample sizes, a focus on relatively few regions-of-interest, and poor test-retest reliability of functional brain measures. To address these limitations, we conducted a data-driven analysis of variability in connectome-wide functional connectivity in a sample of 1,048 young adult volunteers. Multi-dimensional matrix regression analysis showed that self-reported trait anger maps onto variability in the whole-brain functional connectivity patterns of three brain regions that serve action-related functions: bilateral supplementary motor area (SMA) and the right lateral frontal pole. We then demonstrate trait anger modulates the functional connectivity of these regions with canonical brain networks supporting somatomotor, affective, self-referential, and visual information processes. Our findings offer novel neuroimaging evidence for interpreting trait anger as a greater propensity to provoked action, supporting ongoing efforts to understand its utility as a potential transdiagnostic marker for disordered states characterized by aggressive behavior.

Predicting response time variability from task and resting-state functional connectivity in the aging brain

Aging is associated with declines in a host of cognitive functions, including attentional control, inhibitory control, episodic memory, processing speed, and executive functioning. Theoretical models attribute the age-related decline in cognitive functioning to deficits in goal maintenance and attentional inhibition. Despite these well-documented declines in executive control resources, older adults endorse fewer episodes of mind-wandering when assessed using task-embedded thought probes. Furthermore, previous work on the neural basis of mind-wandering has mostly focused on young adults with studies predominantly focusing on the activity and connectivity of a select few canonical networks. However, whole-brain functional networks associated with mind-wandering in aging have not yet been characterized. In this study, using response time variability-the trial-to-trial fluctuations in behavioral responses-as an indirect marker of mind-wandering or an "out-of-the-zone" attentional state representing suboptimal behavioral performance, we show that brain-based predictive models of response time variability can be derived from whole-brain task functional connectivity. In contrast, models derived from resting-state functional connectivity alone did not predict individual response time variability. Finally, we show that despite successful within-sample prediction of response time variability, our models did not generalize to predict response time variability in independent cohorts of older adults with resting-state connectivity. Overall, our findings provide evidence for the utility of task-based functional connectivity in predicting individual response time variability in aging. Future research is needed to derive more robust and generalizable models.

Longitudinal surface-based spatial Bayesian GLM reveals complex trajectories of motor neurodegeneration in ALS

Longitudinal fMRI studies hold great promise for the study of neurodegenerative diseases, development and aging, but realizing their full potential depends on extracting accurate fMRI-based measures of brain function and organization in individual subjects over time. This is especially true for studies of rare, heterogeneous and/or rapidly progressing neurodegenerative diseases. These often involve small samples with heterogeneous functional features, making traditional group-difference analyses of limited utility. One such disease is amyotrophic lateral sclerosis (ALS), a severe disease resulting in extreme loss of motor function and eventual death. Here, we use an advanced individualized task fMRI analysis approach to analyze a rich longitudinal dataset containing 190 hand clench fMRI scans from 16 ALS patients (78 scans) and 22 age-matched healthy controls (112 scans) Specifically, we adopt our cortical surface-based spatial Bayesian general linear model (GLM), which has high power and precision to detect activations in individual subjects, and propose a novel longitudinal extension to leverage information shared across visits. We perform all analyses in native surface space to preserve individua anatomical and functional features. Using mixed-effects models to subsequently study the relationship between size of activation and ALS disease progression, we observe for the first time an inverted U-shaped trajectory o motor activations: at relatively mild motor disability we observe enlarging activations, while at higher levels of motor disability we observe severely diminished activation, reflecting progression toward complete loss of motor function. We further observe distinct trajectories depending on clinical progression rate, with faster progressors exhibiting more extreme changes at an earlier stage of disability. These differential trajectories suggest that initial hyper-activation is likely attributable to loss of inhibitory neurons, rather than functional compensation as earlier assumed. These findings substantially advance scientific understanding of the ALS disease process. This study also provides the first real-world example of how surface-based spatial Bayesian analysis of task fMRI can further scientific understanding of neurodegenerative disease and other phenomena. The surface-based spatial Bayesian GLM is implemented in the BayesfMRI R package

Caltech Conte Center, a multimodal data resource for exploring social cognition and decision-making

This data release of 117 healthy community-dwelling adults provides multimodal high-quality neuroimaging and behavioral data for the investigation of brain-behavior relationships. We provide structural MRI, resting-state functional MRI, movie functional MRI, together with questionnaire-based and task-based psychological variables; many of the participants have multiple datasets from retesting over the course of several years. Our dataset is distinguished by utilizing open-source data formats and processing tools (BIDS, FreeSurfer, fMRIPrep, MRIQC), providing data that is thoroughly quality checked, preprocessed to various extents and available in multiple anatomical spaces. A customizable denoising pipeline is provided as open-source code that includes tools for the generation of functional connectivity matrices and initialization of individual difference analyses. Behavioral data include a comprehensive set of psychological assessments on gold-standard instruments encompassing cognitive function, mood and personality, together with exploratory factor analyses. The dataset provides an in-depth, multimodal resource for investigating associations between individual differences, brain structure and function, with a focus on the domains of social cognition and decision-making.

Functional lateralization of the medial temporal lobe in novel associative processing during creativity evaluation

Although hemispheric lateralization of creativity has been a longstanding topic of debate, the underlying neurocognitive mechanism remains poorly understood. Here we designed 2 types of novel stimuli-"novel useful and novel useless," adapted from "familiar useful" designs taken from daily life-to demonstrate how the left and right medial temporal lobe (MTL) respond to novel designs of different usefulness. Taking the "familiar useful" design as a baseline, we found that the right MTL showed increased activation in response to "novel useful" designs, followed by "novel useless" ones, while the left MTL only showed increased activation in response to "novel useful" designs. Calculating an asymmetry index suggests that usefulness processing is predominant in the left MTL, whereas the right MTL is predominantly involved in novelty processing. Moreover, the left parahippocampal gyrus (PHG) showed stronger functional connectivity with the anterior cingulate cortex when responding to "novel useless" designs. In contrast, the right PHG showed stronger connectivity with the amygdala, midbrain, and hippocampus. Critically, multivoxel representational similarity analyses revealed that the left MTL was more effective than the right MTL at distinguishing the usefulness differences in novel stimuli, while representational patterns in the left PHG positively predicted the post-behavior evaluation of "truly creative" products. These findings suggest an apparent dissociation of the left and right MTL in integrating the novelty and usefulness information and novel associative processing during creativity evaluation, respectively. Our results provide novel insights into a longstanding and controversial question in creativity research by demonstrating functional lateralization of the MTL in processing novel associations.

EmoCodes: a Standardized Coding System for Socio-emotional Content in Complex Video Stimuli

Social information processing is vital for inferring emotional states in others, yet affective neuroscience has only begun to scratch the surface of how we represent emotional information in the brain. Most previous affective neuroscience work has used isolated stimuli such as static images of affective faces or scenes to probe affective processing. While this work has provided rich insight to the initial stages of emotion processing (encoding cues), activation to isolated stimuli provides limited insight into later phases of emotion processing such as interpretation of cues or interactions between cues and established cognitive schemas. Recent work has highlighted the potential value of using complex video stimuli to probe socio-emotional processing, highlighting the need to develop standardized video coding schemas as this exciting field expands. Toward that end, we present a standardized and open-source coding system for complex videos, two fully coded videos, and a video and code processing Python library. The EmoCodes manual coding system provides an externally validated and replicable system for coding complex cartoon stimuli, with future plans to validate the system for other video types. The emocodes Python library provides automated tools for extracting low-level features from video files as well as tools for summarizing and analyzing the manual codes for suitability of use in neuroimaging analysis. Materials can be freely accessed at https://emocodes.org/. These tools represent an important step toward replicable and standardized study of socio-emotional processing using complex video stimuli.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s42761-021-00100-7.

Effect sizes and test-retest reliability of the fMRI-based neurologic pain signature

Identifying biomarkers that predict mental states with large effect sizes and high test-retest reliability is a growing priority for fMRI research. We examined a well-established multivariate brain measure that tracks pain induced by nociceptive input, the Neurologic Pain Signature (NPS). In N = 295 participants across eight studies, NPS responses showed a very large effect size in predicting within-person single-trial pain reports (d = 1.45) and medium effect size in predicting individual differences in pain reports (d = 0.49). The NPS showed excellent short-term (within-day) test-retest reliability (ICC = 0.84, with average 69.5 trials/person). Reliability scaled with the number of trials within-person, with ≥60 trials required for excellent test-retest reliability. Reliability was tested in two additional studies across 5-day (N = 29, ICC = 0.74, 30 trials/person) and 1-month (N = 40, ICC = 0.46, 5 trials/person) test-retest intervals. The combination of strong within-person correlations and only modest between-person correlations between the NPS and pain reports indicate that the two measures have different sources of between-person variance. The NPS is not a surrogate for individual differences in pain reports but can serve as a reliable measure of pain-related physiology and mechanistic target for interventions.