Connectome-based individualized prediction of loneliness

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

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

Task functional networks predict individual differences in the speed of emotional facial discrimination

Every individual experiences negative emotions, such as fear and anger, significantly influencing how external information is perceived and processed. With the gradual rise in brain-behavior relationship studies, analyses investigating individual differences in negative emotion processing and a more objective measure such as the response time (RT) remain unexplored. This study aims to address this gap by establishing that the individual differences in the speed of negative facial emotion discrimination can be predicted from whole-brain functional connectivity when participants were performing a face discrimination task. Employing the connectome predictive modeling (CPM) framework, we demonstrated this in the young healthy adult group from the Human Connectome Project-Young Adults (HCP-YA) dataset and the healthy group of the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA) dataset. We identified distinct network contributions in the adult and adolescent predictive models. The highest represented brain networks involved in the adult model predictions included representations from the motor, visual association, salience, and medial frontal networks. Conversely, the adolescent predictive models showed substantial contributions from the cerebellum-frontoparietal network interactions. Finally, we observed that despite the successful within-dataset prediction in healthy adults and adolescents, the predictive models failed in the cross-dataset generalization. In conclusion, our study shows that individual differences in the speed of emotional facial discrimination can be predicted in healthy adults and adolescent samples using their functional connectivity during negative facial emotion processing. Future research is needed in the derivation of more generalizable models.

Loneliness and brain rhythmic activity in resting state: an exploratory report

Recent studies using resting-state functional magnetic resonance imaging have shown that loneliness is associated with altered blood oxygenation in several brain regions. However, the relationship between loneliness and changes in neuronal rhythm activity in the brain remains unclear. To evaluate brain rhythm, we conducted an exploratory resting-state electroencephalogram (EEG) study of loneliness. We recorded resting-state EEG signals from 139 participants (94 women; mean age = 19.96 years) and analyzed power spectrum density (PSD) and functional connectivity (FC) in both the electrode and source spaces. The PSD analysis revealed significant correlations between loneliness scores and decreased beta-band powers, which may indicate negative emotion, attention, reward, and/or sensorimotor processing. The FC analysis revealed a trend of alpha-band FC associated with individuals' loneliness scores. These findings provide new insights into the neural basis of loneliness, which will facilitate the development of neurobiologically informed interventions for loneliness.

Predicting creative behavior using resting-state electroencephalography

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

Connectome-based prediction of decreased trust propensity in older adults with mild cognitive impairment: A resting-state functional magnetic resonance imaging study

Trust propensity (TP) relies more on social than economic rationality to transform the perceived probability of betrayal into positive reciprocity expectations in older adults with normal cognition. While deficits in social rationality have been observed in older adults with mild cognitive impairment (MCI), there is limited research on TP and its associated resting-state functional connectivity (RSFC) mechanisms in this population. To measure TP and related psychological functions (affect, motivation, executive cognition, and social cognition), MCI (n = 42) and normal healthy control (NHC, n = 115) groups completed a one-shot trust game and additional assessments of related psychological functions. RSFC associated with TP was analyzed using connectome-based predictive modeling (CPM) and lesion simulations. Our behavioral results showed that the MCI group trusted less (i.e., had lower TP) than the NHC group, with lower TP associated with higher sensitivity to the probability of betrayal in the MCI group. In the MCI group, only negative CPM models (RSFC negatively correlated with TP) significantly predicted TP, with a high salience network (SN) contribution. In contrast, in the NHC group, positive CPM models (RSFC positively correlated with TP) significantly predicted TP, with a high contribution from the default mode network (DMN). In addition, the total network strength of the NHC-specific positive network was lower in the MCI group than in the NHC group. Our findings demonstrated a decrease in TP in the MCI group compared to the NHC group, which is associated with deficits in social rationality (social cognition, associated with DMN) and increased sensitivity to betrayal (affect, associated with SN) in a trust dilemma. In conclusion, our study contributes to understanding MCI-related alterations in trust and their underlying neural mechanisms.

Social Isolation, Brain Food Cue Processing, Eating Behaviors, and Mental Health Symptoms

Importance

Perceived social isolation is associated with negative health outcomes, including increased risk for altered eating behaviors, obesity, and psychological symptoms. However, the underlying neural mechanisms of these pathways are unknown.

Objective

To investigate the association of perceived social isolation with brain reactivity to food cues, altered eating behaviors, obesity, and mental health symptoms.

Design, Setting, and Participants

This cross-sectional, single-center study recruited healthy, premenopausal female participants from the Los Angeles, California, community from September 7, 2021, through February 27, 2023.

Exposure

Participants underwent functional magnetic resonance imaging while performing a food cue viewing task.

Main Outcomes and Measures

The main outcomes included brain reactivity to food cues, body composition, self-reported eating behaviors (food cravings, reward-based eating, food addiction, and maladaptive eating behaviors), and mental health symptoms (anxiety, depression, positive and negative affect, and psychological resilience).

Results

The study included 93 participants (mean [SD] age, 25.38 [7.07] years). Participants with higher perceived social isolation reported higher fat mass percentage, lower diet quality, increased maladaptive eating behaviors (cravings, reward-based eating, uncontrolled eating, and food addiction), and poor mental health (anxiety, depression, and psychological resilience). In whole-brain comparisons, the higher social isolation group showed altered brain reactivity to food cues in regions of the default mode, executive control, and visual attention networks. Isolation-related neural changes in response to sweet foods correlated with various altered eating behaviors and psychological symptoms. These altered brain responses mediated the connection between social isolation and maladaptive eating behaviors (β for indirect effect, 0.111; 95% CI, 0.013-0.210; P = .03), increased body fat composition (β, -0.141; 95% CI, -0.260 to -0.021; P = .02), and diminished positive affect (β, -0.089; 95% CI, -0.188 to 0.011; P = .09).

Conclusions and Relevance

These findings suggest that social isolation is associated with altered neural reactivity to food cues within specific brain regions responsible for processing internal appetite-related states and compromised executive control and attentional bias and motivation toward external food cues. These neural responses toward specific foods were associated with an increased risk for higher body fat composition, worsened maladaptive eating behaviors, and compromised mental health. These findings underscore the need for holistic mind-body-directed interventions that may mitigate the adverse health consequences of social isolation.

Identifying individual's distractor suppression using functional connectivity between anatomical large-scale brain regions

Distractor suppression (DS) is crucial in goal-oriented behaviors, referring to the ability to suppress irrelevant information. Current evidence points to the prefrontal cortex as an origin region of DS, while subcortical, occipital, and temporal regions are also implicated. The present study aimed to examine the contribution of communications between these brain regions to visual DS. To do it, we recruited two independent cohorts of participants for the study. One cohort participated in a visual search experiment where a salient distractor triggering distractor suppression to measure their DS and the other cohort filled out a Cognitive Failure Questionnaire to assess distractibility in daily life. Both cohorts collected resting-state functional magnetic resonance imaging (rs-fMRI) data to investigate function connectivity (FC) underlying DS. First, we generated predictive models of the DS measured in visual search task using resting-state functional connectivity between large anatomical regions. It turned out that the models could successfully predict individual's DS, indicated by a significant correlation between the actual and predicted DS (r = 0.32, p < 0.01). Importantly, Prefrontal-Temporal, Insula-Limbic and Parietal-Occipital connections contributed to the prediction model. Furthermore, the model could also predict individual's daily distractibility in the other independent cohort (r = -0.34, p < 0.05). Our findings showed the efficiency of the predictive models of distractor suppression encompassing connections between large anatomical regions and highlighted the importance of the communications between attention-related and visual information processing regions in distractor suppression. Current findings may potentially provide neurobiological markers of visual distractor suppression.

Away from the herd: loneliness as a dysfunction of social alignment

The tendency of all humans to experience loneliness at some point in their lives implies that it serves an adaptive function. Building on biological theories of herding in animals, according to which collective movement emerges from local interactions that are based on principles of attraction, repulsion and alignment, we propose an approach that synthesizes these principles with theories of loneliness in humans. We present here the 'herding model of loneliness' that extends these principles into the psychological domain. We hold that these principles serve as basic building blocks of human interactions and propose that distorted attraction and repulsion tendencies may lead to inability to align properly with others, which may be a core component in loneliness emergence and perpetuation. We describe a neural model of herding in humans and suggest that loneliness may be associated with altered interactions between the gap/error detection, reward signaling, threat and observation-execution systems. The proposed model offers a framework to predict the behavior of lonely individuals and thus may inform intervention designs for reducing loneliness intensity.

Connectome-based predictive modeling of Internet addiction symptomatology

Internet addiction symptomatology (IAS) is characterized by persistent and involuntary patterns of compulsive Internet use, leading to significant impairments in both physical and mental well-being. Here, a connectome-based predictive modeling approach was applied to decode IAS from whole-brain resting-state functional connectivity in healthy population. The findings showed that IAS could be predicted by the functional connectivity between prefrontal cortex with the cerebellum and limbic lobe and connections of the occipital lobe with the limbic lobe and insula lobe. The identified edges associated with IAS exhibit generalizability in predicting IAS within an independent sample. Furthermore, we found that the unique contributing network, which predicted IAS in contrast to the prediction networks of alcohol use disorder symptomatology (the range of symptoms and behaviors associated with alcohol use disorder), prominently comprised connections involving the occipital lobe and other lobes. The current data-driven approach provides the first evidence of the predictive brain features of IAS based on the organization of intrinsic brain networks, thus advancing our understanding of the neurobiological basis of Internet addiction disorder (IAD) susceptibility, and may have implications for the timely intervention of people potentially at risk of IAD.

Individual differences in the neural architecture in semantic processing

Neural mechanisms underlying semantic processing have been extensively studied by using functional magnetic resonance imaging, nevertheless, the individual differences of it are yet to be unveiled. To further our understanding of functional and anatomical brain organization underlying semantic processing to the level of individual humans, we used out-of-scanner language behavioral data, T1, resting-state, and story comprehension task-evoked functional image data in the Human Connectome Project, to investigate individual variability in the task-evoked semantic processing network, and attempted to predict individuals' language skills based on task and intrinsic functional connectivity of highly variable regions, by employing a machine-learning framework. Our findings first confirmed that individual variability in both functional and anatomical markers were heterogeneously distributed throughout the semantic processing network, and that the variability increased towards higher levels in the processing hierarchy. Furthermore, intrinsic functional connectivities among these highly variable regions were found to contribute to predict individual reading decoding abilities. The contributing nodes in the overall network were distributed in the left superior, inferior frontal, and temporo-parietal cortices. Our results suggested that the individual differences of neurobiological markers were heterogeneously distributed in the semantic processing network, and that neurobiological markers of highly variable areas are not only linked to individual variability in language skills, but can predict language skills at the individual level.

Individual differences in the language task-evoked and resting-state functional networks

The resting state functional network is highly variable across individuals. However, inter-individual differences in functional networks evoked by language tasks and their comparison with resting state are still unclear. To address these two questions, we used T1 anatomical data and functional brain imaging data of resting state and a story comprehension task from the Human Connectome Project (HCP) to characterize functional network variability and investigate the uniqueness of the functional network in both task and resting states. We first demonstrated that intrinsic and task-induced functional networks exhibited remarkable differences across individuals, and language tasks can constrain inter-individual variability in the functional brain network. Furthermore, we found that the inter-individual variability of functional networks in two states was broadly consistent and spatially heterogeneous, with high-level association areas manifesting more significant variability than primary visual processing areas. Our results suggested that the functional network underlying language comprehension is unique at the individual level, and the inter-individual variability architecture of the functional network is broadly consistent in language task and resting state.

Shared and distinct structural brain networks related to childhood maltreatment and social support: connectome-based predictive modeling

Childhood maltreatment (CM) has been associated with changes in structural brain connectivity even in the absence of mental illness. Social support, an important protective factor in the presence of childhood maltreatment, has been positively linked to white matter integrity. However, the shared effects of current social support and CM and their association with structural connectivity remain to be investigated. They might shed new light on the neurobiological basis of the protective mechanism of social support. Using connectome-based predictive modeling (CPM), we analyzed structural connectomes of N = 904 healthy adults derived from diffusion-weighted imaging. CPM predicts phenotypes from structural connectivity through a cross-validation scheme. Distinct and shared networks of white matter tracts predicting childhood trauma questionnaire scores and the social support questionnaire were identified. Additional analyses were applied to assess the stability of the results. CM and social support were predicted significantly from structural connectome data (all rs ≥ 0.119, all ps ≤ 0.016). Edges predicting CM and social support were inversely correlated, i.e., positively correlated with CM and negatively with social support, and vice versa, with a focus on frontal and temporal regions including the insula and superior temporal lobe. CPM reveals the predictive value of the structural connectome for CM and current social support. Both constructs are inversely associated with connectivity strength in several brain tracts. While this underlines the interconnectedness of these experiences, it suggests social support acts as a protective factor following adverse childhood experiences, compensating for brain network alterations. Future longitudinal studies should focus on putative moderating mechanisms buffering these adverse experiences.

Connectome-based modeling reveals a resting-state functional network that mediates the relationship between social rejection and rumination

Background

Rumination impedes problem solving and is one of the most important factors in the onset and maintenance of multiple psychiatric disorders. The current study aims to investigate the impact of social rejection on rumination and explore the underlying neural mechanisms involved in this process.

Methods

We utilized psychological questionnaire and resting-state brain imaging data from a sample of 560 individuals. The predictive model for rumination scores was constructed using resting-state functional connectivity data through connectome-based predictive modeling. Additionally, a mediation analysis was conducted to investigate the mediating role of the prediction network in the relationship between social rejection and rumination.

Results

A positive correlation between social rejection and rumination was found. We obtained the prediction model of rumination and found that the strongest contributions came from the intra- and internetwork connectivity within the default mode network (DMN), dorsal attention network (DAN), frontoparietal control network (FPCN), and sensorimotor networks (SMN). Analysis of node strength revealed the significance of the supramarginal gyrus (SMG) and angular gyrus (AG) as key nodes in the prediction model. In addition, mediation analysis showed that the strength of the prediction network mediated the relationship between social rejection and rumination.

Conclusion

The findings highlight the crucial role of functional connections among the DMN, DAN, FPCN, and SMN in linking social rejection and rumination, particular in brain regions implicated in social cognition and emotion, namely the SMG and AG regions. These results enhance our understanding of the consequences of social rejection and provide insights for novel intervention strategies targeting rumination.

Association of loneliness and grey matter volume in the dorsolateral prefrontal cortex: the mediating role of interpersonal self-support traits

As a social and public health concern, loneliness is associated with an abundance of negative life outcomes such as depressive symptomatology, mortality, and sleep disturbance. Nevertheless, the neural basis underlying loneliness remains elusive; in addition, previous neuroimaging studies about loneliness mainly focused on the elderly and were limited by small sample sizes. Here, utilizing the voxel-based morphometry (VBM) approach via structural magnetic resonance imaging, we investigated the association between brain GMV and loneliness in 462 young adults (67.7% females, age = 18.59 ± 1.14 years). Results from whole-brain VBM analyses revealed that individuals with higher loneliness tended to have greater GMV in the right dorsolateral prefrontal cortex (DLPFC), which was thought to be associated with emotional regulation deficits and executive dysfunction. Importantly, the GMV-based predictive models (a machine-learning method) demonstrated that the correlation between loneliness and GMV in the DLPFC was robust. Further, interpersonal self-support traits (ISS), a Chinese indigenous personality construct and pivotal personality factor for resisting negative life outcomes, mediated the relationship between the GMV in the right DLPFC and loneliness. Taken together, the present study reveals that the GMV in right DLPFC acts as an underlying neurostructural correlate of loneliness in the healthy brain, and further provides a brain-personality-symptom pathway for protection against loneliness in which GMV of DLPFC affects loneliness through ISS traits. Future intervention procedures aiming to decrease loneliness and enhance mental health levels among young adults should be developed through improving interpersonal relationships such as social skills training.

Connectome-based prediction of eating disorder-associated symptomatology

BACKGROUND

Despite increasing knowledge on the neuroimaging patterns of eating disorder (ED) symptoms in non-clinical populations, studies using whole-brain machine learning to identify connectome-based neuromarkers of ED symptomatology are absent. This study examined the association of connectivity within and between large-scale functional networks with specific symptomatic behaviors and cognitions using connectome-based predictive modeling (CPM).

METHODS

CPM with ten-fold cross-validation was carried out to probe functional networks that were predictive of ED-associated symptomatology, including body image concerns, binge eating, and compensatory behaviors, within the discovery sample of 660 participants. The predictive ability of the identified networks was validated using an independent sample of 821 participants.

RESULTS

The connectivity predictive of body image concerns was identified within and between networks implicated in cognitive control (frontoparietal and medial frontal), reward sensitivity (subcortical), and visual perception (visual). Crucially, the set of connections in the positive network related to body image concerns identified in one sample was generalized to predict body image concerns in an independent sample, suggesting the replicability of this effect.

CONCLUSIONS

These findings point to the feasibility of using the functional connectome to predict ED symptomatology in the general population and provide the first evidence that functional interplay among distributed networks predicts body shape/weight concerns.

The neurocognitive impact of loneliness and social networks on social adaptation

Social adaptation arises from the interaction between the individual and the social environment. However, little empirical evidence exists regarding the relationship between social contact and social adaptation. We propose that loneliness and social networks are key factors explaining social adaptation. Sixty-four healthy subjects with no history of psychiatric conditions participated in this study. All participants completed self-report questionnaires about loneliness, social network, and social adaptation. On a separate day, subjects underwent a resting state fMRI recording session. A hierarchical regression model on self-report data revealed that loneliness and social network were negatively and positively associated with social adaptation. Functional connectivity (FC) analysis showed that loneliness was associated with decreased FC between the fronto-amygdalar and fronto-parietal regions. In contrast, the social network was positively associated with FC between the fronto-temporo-parietal network. Finally, an integrative path model examined the combined effects of behavioral and brain predictors of social adaptation. The model revealed that social networks mediated the effects of loneliness on social adaptation. Further, loneliness-related abnormal brain FC (previously shown to be associated with difficulties in cognitive control, emotion regulation, and sociocognitive processes) emerged as the strongest predictor of poor social adaptation. Findings offer insights into the brain indicators of social adaptation and highlight the role of social networks as a buffer against the maladaptive effects of loneliness. These findings can inform interventions aimed at minimizing loneliness and promoting social adaptation and are especially relevant due to the high prevalence of loneliness around the globe. These findings also serve the study of social adaptation since they provide potential neurocognitive factors that could influence social adaptation.

Age differences in functional brain networks associated with loneliness and empathy

Loneliness is associated with differences in resting-state functional connectivity (RSFC) within and between large-scale networks in early- and middle-aged adult cohorts. However, age-related changes in associations between sociality and brain function into late adulthood are not well understood. Here, we examined age differences in the association between two dimensions of sociality-loneliness and empathic responding-and RSFC of the cerebral cortex. Self-report measures of loneliness and empathy were inversely related across the entire sample of younger (mean age = 22.6y, n = 128) and older (mean age = 69.0y, n = 92) adults. Using multivariate analyses of multi-echo fMRI RSFC, we identified distinct functional connectivity patterns for individual and age group differences associated with loneliness and empathic responding. Loneliness in young and empathy in both age groups was related to greater visual network integration with association networks (e.g., default, fronto-parietal control). In contrast, loneliness was positively related to within- and between-network integration of association networks for older adults. These results extend our previous findings in early- and middle-aged cohorts, demonstrating that brain systems associated with loneliness, as well as empathy, differ in older age. Further, the findings suggest that these two aspects of social experience engage different neurocognitive processes across human life-span development.

Multivariate resting-state functional connectomes predict and characterize obesity phenotypes

The univariate obesity-brain associations have been extensively explored, while little is known about the multivariate associations between obesity and resting-state functional connectivity. We therefore utilized machine learning and resting-state functional connectivity to develop and validate predictive models of 4 obesity phenotypes (i.e. body fat percentage, body mass index, waist circumference, and waist-height ratio) in 3 large neuroimaging datasets (n = 2,992). Preliminary evidence suggested that the resting-state functional connectomes effectively predicted obesity/weight status defined by each obesity phenotype with good generalizability to longitudinal and independent datasets. However, the differences between resting-state functional connectivity patterns characterizing different obesity phenotypes indicated that the obesity-brain associations varied according to the type of measure of obesity. The shared structure among resting-state functional connectivity patterns revealed reproducible neuroimaging biomarkers of obesity, primarily comprising the connectomes within the visual cortex and between the visual cortex and inferior parietal lobule, visual cortex and orbital gyrus, and amygdala and orbital gyrus, which further suggested that the dysfunctions in the perception, attention and value encoding of visual information (e.g. visual food cues) and abnormalities in the reward circuit may act as crucial neurobiological bases of obesity. The recruitment of multiple obesity phenotypes is indispensable in future studies seeking reproducible obesity-brain associations.

Behavioral and histochemical characterization of sexually dimorphic responses to acute social isolation and reunion in mice

In many mammalian species, females exhibit higher sociability and gregariousness than males, presumably due to the benefit of group living for maternal care. We have previously reported that adult female mice exhibit contact-seeking behaviors upon acute social isolation via amylin-calcitonin receptor (Calcr) signaling in the medial preoptic area (MPOA). In this study, we examined the sex differences in the behavioral responses to acute social isolation and reunion, and the levels of amylin and Calcr expression in the MPOA. We found that male mice exhibited significantly less contact-seeking upon social isolation. Upon reunion, male mice contacted each other to a similar extent as females, but their interactions were more aggressive and less affiliative compared with females. While Calcr-expressing neurons were activated during social contacts in males as in females, the amylin and Calcr expression were significantly lower in males than in females. Together with our previous findings, these findings suggested that the lower expression of both amylin and Calcr may explain the lower contact-seeking and social affiliation of male mice.

Individualized prediction of trait self-control from whole-brain functional connectivity

Self-control is a core psychological construct for human beings and it plays a crucial role in the adaptation to society and achievement of success and happiness for individuals. Although progress has been made in behavioral studies examining self-control, its neural mechanisms remain unclear. In this study, we employed a machine-learning approach-relevance vector regression (RVR) to explore the potential predictive power of intrinsic functional connections to trait self-control in a large sample (N = 390). We used resting-state functional MRI (fMRI) to explore whole-brain functional connectivity patterns characteristic of 390 healthy adults and to confirm the effectiveness of RVR in predicting individual trait self-control scores. A set of connections across multiple neural networks that significantly predicted individual differences were identified, including the classic control network (e.g., fronto-parietal network (FPN), salience network (SAL)), the sensorimotor network (Mot), and the medial frontal network (MF). Key nodes that contributed to the predictive model included the dorsolateral prefrontal cortex (dlPFC), middle frontal gyrus (MFG), anterior cingulate and paracingulate gyri, inferior temporal gyrus (ITG) that have been associated with trait self-control. Our findings further assert that self-control is a multidimensional construct rooted in the interactions between multiple neural networks.

Connectome-based predictive modeling for functional recovery of acute ischemic stroke

Patients of acute ischemic stroke possess considerable chance of recovery of various levels in the first several weeks after stroke onset. Prognosis of functional recovery is important for decision-making in poststroke patient care and placement. Poststroke functional recovery has conventionally been based on demographic and clinical variables such as age, gender, and severity of stroke impairment. On the other hand, the concept of connectome has become a basis of interpreting the functional impairment and recovery of stroke patients. In this research, the connectome-based predictive modeling was used to provide predictive models for prognosing poststroke functional recovery. Predictive models were developed to use the brain connectivity at stroke onset to predict functional assessment scores at one or three months later, or to use the brain connectivity one-month poststroke to predict functional assessment scores at three months after stroke onset. The brain connectivity was computed from the resting-state fMRI signals. The functional assessment scores used in this research included modified Rankin Scale (mRS) and Barthel Index (BI). This research found significant models that used the brain connectivity at onset to predict the mRS one-month poststroke and to predict the BI three-month poststroke for patients with supratentorial infarction, as well as predictive models that used the brain connectivity one-month poststroke to predict the mRS three-month poststroke for patients with supratentorial infarction in the right hemisphere. The connectome-based predictive modeling could provide clinical value in prognosis of acute ischemic stroke.

Connectome-based predictive modeling of trait forgiveness

Forgiveness is a positive, prosocial manner of reacting to transgressions and is strongly associated with mental health and well-being. Despite recent studies exploring the neural mechanisms underlying forgiveness, a model capable of predicting trait forgiveness at the individual level has not been developed. Herein, we applied a machine-learning approach, connectome-based predictive modeling (CPM), with whole-brain resting-state functional connectivity (rsFC) to predict individual differences in trait forgiveness in a training set (dataset 1, N = 100, 35 men, 17-24 years). As a result, CPM successfully predicted individual trait forgiveness based on whole-brain rsFC, especially via the functional connectivity of the limbic, prefrontal and temporal areas, which are key contributors to the prediction model comprising regions previously implicated in forgiveness. These regions include the retrosplenial cortex, temporal pole, dorsolateral prefrontal cortex (PFC), dorsal anterior cingulate cortex, precuneus and dorsal posterior cingulate cortex. Importantly, this predictive model could be successfully generalized to an independent sample (dataset 2, N = 71, 17 men, 16-25 years). These findings highlight the important roles of the limbic system, PFC and temporal region in trait forgiveness prediction and represent the initial steps toward establishing an individualized prediction model of forgiveness.

Connectome-based predictive modeling of compulsion in obsessive-compulsive disorder

Compulsion is one of core symptoms of obsessive-compulsive disorder (OCD). Although many studies have investigated the neural mechanism of compulsion, no study has used brain-based measures to predict compulsion. Here, we used connectome-based predictive modeling (CPM) to identify networks that could predict the levels of compulsion based on whole-brain functional connectivity in 57 OCD patients. We then applied a computational lesion version of CPM to examine the importance of specific brain areas. We also compared the predictive network strength in OCD with unaffected first-degree relatives (UFDR) of patients and healthy controls. CPM successfully predicted individual level of compulsion and identified networks positively (primarily subcortical areas of the striatum and limbic regions of the hippocampus) and negatively (primarily frontoparietal regions) correlated with compulsion. The prediction power of the negative model significantly decreased when simulating lesions to the prefrontal cortex and cerebellum, supporting the importance of these regions for compulsion prediction. We found a similar pattern of network strength in the negative predictive network for OCD patients and their UFDR, demonstrating the potential of CPM to identify vulnerability markers for psychopathology.

Predicting individual differences in behavioral activation and behavioral inhibition from functional networks in the resting EEG

The behavioral activation system (BAS) and behavioral inhibition system (BIS) are thought to underly affective dispositions and self-regulatory processes. The BAS is sensitive to reward and involved in approach behaviors, and the BIS is sensitive to punishment and involved in avoidance behaviors. Trait BAS and BIS relate to distinct behavioral profiles and neural activity, but little is known about how trait BAS and BIS relate to functional networks in EEG. We applied a data-driven method called connectome predictive modeling (CPM) to identify networks relating to trait BAS and BIS and tested whether the strength of those networks predicted trait BAS and BIS in novel subjects using a leave-one-out cross-validation procedure. Adult participants (N = 107) completed a resting state task with eyes closed and eyes open, and trait BAS and BIS were measured via Carver and White's (1994) BIS and BAS scales. We hypothesized distinct positive (more synchronization) and negative (less synchronization) networks would relate to trait BAS and BIS. For eyes closed, we identified two negative networks, one in theta and one in alpha predicted BIS. We identified three positive networks, one in theta and one in beta predicted Fun Seeking and one in theta predicted Drive. For eyes open, negative theta and alpha networks predicted BIS, a positive theta network predicted Fun Seeking, and a negative gamma network predicted mean BAS. Visualization of the networks are presented. Discussion centers on the observed networks and how to advance application of CPM to EEG, including with clinical implications.

On the relationship between the social brain, social connectedness, and wellbeing

The emergence of social neuroscience in the past two decades has offered a useful neurocognitive framework for understanding human social behavior. Of importance, social neuroscience research aimed to provide mechanistic explanations for the established link between wellbeing and social behavioral phenomena-particularly those reflective of social connectedness. Here, we provide an overview of the relevant literature focusing on recent work using functional magnetic resonance imaging (fMRI). In general, fMRI research demonstrated that aspects of social connectedness that are known to either positively (e.g., social acceptance) or negatively (e.g., social isolation) impact wellbeing also modulated the activity of subcortical reward system accordingly. Similar modulatory influence was found for the activity of other brain regions such as the medial prefrontal cortex, which are typically regarded as components of the "social brain" that support a wide range of functions related to social cognition and behavior. Elucidating such individual differences in brain activity may shed light onto the neural underpinnings of the link between social connectedness and wellbeing.

Connectome-based prediction of marital quality in husbands' processing of spousal interactions

Marital quality may decrease during the early years of marriage. Establishing models predicting individualized marital quality may help develop timely and effective interventions to maintain or improve marital quality. Given that marital interactions have an important impact on marital well-being cross-sectionally and prospectively, neural responses during marital interactions may provide insight into neural bases underlying marital well-being. The current study applies connectome-based predictive modeling, a recently developed machine-learning approach, to functional magnetic resonance imaging (fMRI) data from both partners of 25 early-stage Chinese couples to examine whether an individual's unique pattern of brain functional connectivity (FC) when responding to spousal interactive behaviors can reliably predict their own and their partners' marital quality after 13 months. Results revealed that husbands' FC involving multiple large networks, when responding to their spousal interactive behaviors, significantly predicted their own and their wives' marital quality, and this predictability showed gender specificity. Brain connectivity patterns responding to general emotional stimuli and during the resting state were not significantly predictive. This study demonstrates that husbands' differences in large-scale neural networks during marital interactions may contribute to their variability in marital quality and highlights gender-related differences. The findings lay a foundation for identifying reliable neuroimaging biomarkers for developing interventions for marital quality early in marriages.

Individualized morphometric similarity predicts body mass index and food approach behavior in school-age children

Childhood obesity is associated with alterations in brain structure. Previous studies generally used a single structural index to characterize the relationship between body mass index(BMI) and brain structure, which could not describe the alterations of structural covariance between brain regions. To cover this research gap, this study utilized two independent datasets with brain structure profiles and BMI of 155 school-aged children. Connectome-based predictive modeling(CPM) was used to explore whether children’s BMI is reliably predictable by the novel individualized morphometric similarity network(MSN). We revealed the MSN can predict the BMI in school-age children with good generalizability to unseen dataset. Moreover, these revealed significant brain structure covariant networks can further predict children’s food approach behavior. The positive predictive networks mainly incorporated connections between the frontoparietal network(FPN) and the visual network(VN), between the FPN and the limbic network(LN), between the default mode network(DMN) and the LN. The negative predictive network primarily incorporated connections between the FPN and DMN. These results suggested that the incomplete integration of the high-order brain networks and the decreased dedifferentiation of the high-order networks to the primary reward networks can be considered as a core structural basis of the imbalance between inhibitory control and reward processing in childhood obesity.

Predictive models for social functioning in healthy young adults: A machine learning study integrating neuroanatomical, cognitive, and behavioral data

Poor social functioning is an emerging public health problem associated with physical and mental health consequences. Developing prognostic tools is critical to identify individuals at risk for poor social functioning and guide interventions. We aimed to inform prediction models of social functioning by evaluating models relying on bio-behavioral data using machine learning. With data from the Human Connectome Project Healthy Young Adult sample (age 22-35, N = 1,101), we built Support Vector Regression models to estimate social functioning from variable sets of brain morphology to behavior with increasing complexity: 1) brain-only model, 2) brain-cognition model, 3) cognition-behavioral model, and 4) combined brain-cognition-behavioral model. Predictive accuracy of each model was assessed and the importance of individual variables for model performance was determined. The combined and cognition-behavioral models significantly predicted social functioning, whereas the brain-only and brain-cognition models did not. Negative affect, psychological wellbeing, extraversion, withdrawal, and cortical thickness of the rostral middle-frontal and superior-temporal regions were the most important predictors in the combined model. Results demonstrate that social functioning can be accurately predicted using machine learning methods. Behavioral markers may be more significant predictors of social functioning than brain measures for healthy young adults and may represent important leverage points for preventative intervention.

Resting-state occipito-frontal alpha connectome is linked to differential word learning ability in adult learners

Adult language learners show distinct abilities in acquiring a new language, yet the underlying neural mechanisms remain elusive. Previous studies suggested that resting-state brain connectome may contribute to individual differences in learning ability. Here, we recorded electroencephalography (EEG) in a large cohort of 106 healthy young adults (50 males) and examined the associations between resting-state alpha band (8-12 Hz) connectome and individual learning ability during novel word learning, a key component of new language acquisition. Behavioral data revealed robust individual differences in the performance of the novel word learning task, which correlated with their performance in the language aptitude test. EEG data showed that individual resting-state alpha band coherence between occipital and frontal regions positively correlated with differential word learning performance (p = 0.001). The significant positive correlations between resting-state occipito-frontal alpha connectome and differential world learning ability were replicated in an independent cohort of 35 healthy adults. These findings support the key role of occipito-frontal network in novel word learning and suggest that resting-state EEG connectome may be a reliable marker for individual ability during new language learning.

Altered brain regional homogeneity is associated with depressive symptoms in COVID-19

BACKGROUND

COVID-19 is an infectious disease that has spread worldwide in 2020, causing a severe pandemic. In addition to respiratory symptoms, neuropsychiatric manifestations are commonly observed, including chronic fatigue, depression, and anxiety. The neural correlates of neuropsychiatric symptoms in COVID-19 are still largely unknown.

METHODS

A total of 79 patients with COVID-19 (COV) and 17 healthy controls (HC) underwent 3 T functional magnetic resonance imaging at rest, as well as structural imaging. Regional homogeneity (ReHo) was calculated. We also measured depressive symptoms with the Patient Health Questionnaire (PHQ-9), anxiety using the General Anxiety Disorder 7-item scale, and fatigue with the Multidimension Fatigue Inventory.

RESULTS

In comparison with HC, COV showed significantly higher depressive scores. Moreover, COV presented reduced ReHo in the left angular gyrus, the right superior/middle temporal gyrus and the left inferior temporal gyrus, and higher ReHo in the right hippocampus. No differences in gray matter were detected in these areas. Furthermore, we observed a negative correlation between ReHo in the left angular gyrus and PHQ-9 scores and a trend toward a positive correlation between ReHo in the right hippocampus and PHQ-9 scores.

LIMITATIONS

Heterogeneity in the clinical presentation in COV, the different timing from the first positive molecular swab test to the MRI, and the cross-sectional design of the study limit the generalizability of our findings.

CONCLUSIONS

Our results suggest that COVID-19 infection may contribute to depressive symptoms via a modulation of local functional connectivity in cortico-limbic circuits.

Considering hearing loss as a modifiable risk factor for dementia

INTRODUCTION

Accumulating evidence links hearing loss to impaired cognitive performance and increased risk for dementia. Hearing loss can lead to deafferentation-induced atrophy of frontotemporal brain regions and dysregulation of cognitive control networks from increased listening effort. Hearing loss is also associated with reduced social engagement, loneliness, and depression, which are independently associated with poor cognitive function.

AREAS COVERED

We summarize the evidence and postulated mechanisms linking hearing loss to dementia in older adults and synthesize the available literature demonstrating beneficial effects of hearing remediation on brain structure and function.

EXPERT OPINION

: Further research is needed to evaluate whether treatment of hearing loss may reduce risk of cognitive decline and improve neural consequences of hearing loss. Studies may investigate the pathologic mechanisms linking these late-life disorders and identify individuals vulnerable to dementia, and future clinical trials may evaluate whether hearing treatment may reduce the risk for dementia.

Multi-modality connectome-based predictive modeling of individualized compulsions in obsessive-compulsive disorder

BACKGROUND

While previous neuroimaging studies are mainly focused on dichotomous classification of obsessive-compulsive disorder (OCD) from controls, predicting continuous severity of specific symptom is also pivotal to clinical diagnosis and treatment.

METHODS

We applied a machine-learning approach, connectome-based predictive modeling, on functional and structural brain networks constructed from resting-state functional magnetic resonance imaging and diffusion tensor imaging data to decode compulsions and obsessions of fifty-four patients with OCD.

RESULTS

We successfully predicted individualized compulsions with a positive model of structural brain network and with a negative model of functional brain network. The structural predictive brain network comprises the motor cortex, cerebellum and limbic lobe, which are involved in basic motor control, motor execution and emotion processing, respectively. The functional predictive brain network is composed by the prefrontal and limbic systems which are related to cognitive and affective control. Computational lesion analysis shows that functional connectivity among the salience network (SN), the frontal parietal network and the default mode network, as well as structural connectivity within the SN are vital in the individualized prediction of compulsions in OCD.

LIMITATIONS

There was no external validation of large samples to test the robustness of our predictive model.

CONCLUSIONS

These findings provide the first evidence for the predictive role of the triple network model in individualized compulsions and have important implications in diagnosis, prognosis and treatment of patients with OCD.

Structural connectome-based prediction of trait anxiety

Neurobiological research on anxiety has shown that trait-anxious individuals may be characterized by weaker structural connectivity of the amygdala-prefrontal circuitry, representing a reduced capacity for efficient communication between the two brain regions. However, comparison of available studies has been inconsistent, possibly related to factors such as aging that influences both trait anxiety and structural connectivity of the brain. To help clarify the nature of brain-anxiety relationship, we applied a connectome-based predictive modeling framework on 148 diffusion-weighted imaging data from the Leipzig Study for Mind-Body Emotion Interactions dataset and identified multivariate patterns of whole-brain structural connectivity that predicted trait anxiety. Results showed that networks predictive of trait anxiety differed across age groups. Specifically, an isolated negative network, which shared overlapping features with the amygdala-prefrontal circuitry, was found in younger adults (20-30 years of age), whereas a widespread positive network highlighted by frontotemporal and frontolimbic connectivity was identified when both younger and older adults (20-80 years of age) were examined. No predictive network was observed when only older adults (30-80 years of age) were considered. Our findings highlight an important age-dependent effect on the structural connectome-based prediction of trait anxiety, supporting ongoing efforts to develop potential neural biomarkers of anxiety.

A Narrative Review of Loneliness and Brain Health in Older Adults: Implications of COVID-19

Purpose of Review

This narrative review highlights important factors contributing to loneliness in older adults prior to and during the COVID-19 pandemic and effects on brain health.

Recent Findings

We characterize risk factors for loneliness in older adulthood and the impact of COVID-19. Furthermore, we discuss the implications of loneliness for older adults’ brain health.

Summary

Understanding the multifactorial causes of loneliness in different subpopulations of older adults both before and during the COVID-19 pandemic will provide insights for the development of interventions targeted to reduce loneliness in older adults based on their specific risk factors.

Perceived social isolation is correlated with brain structure and cognitive trajectory in Alzheimer’s disease

Both objective and perceived social isolations were associated with future cognitive decline and increase risk of Alzheimer’s disease (AD). However, the impacts of perceived social isolation depending on different clinical stages of AD have not been elucidated. The aim of this study was to investigate the influence of perceived social isolation or loneliness on brain structure and future cognitive trajectories in patients who are living with or are at risk for AD. A total of 176 elderly patients (mean age of 78 years) who had complaint of memory problems (39 subjective cognitive decline [SCD], 53 mild cognitive impairment [MCI], 84 AD) underwent structural MRI and neuropsychological testing. Loneliness was measured by one binary item question “Do you often feel lonely?.” Voxel-based morphometry was conducted to evaluate regional gray matter volume (rGMV) difference associated with loneliness in each group. To evaluate individual differences in cognitive trajectories based on loneliness, subgroup analysis was performed in 51 patients with AD (n = 23) and pre-dementia status (SCD-MCI, n = 28) using the longitudinal scores of Alzheimer’s Disease Assessment Scale-cognitive component-Japanese version (ADAS-Jcog). Whole brain VBM analysis comparing lonely to non-lonely patients revealed loneliness was associated with decreased rGMV in bilateral thalamus in SCD patients and in the left middle occipital gyrus and the cerebellar vermal lobules I − V in MCI patients. Annual change of ADAS-Jcog in patients who reported loneliness was significantly greater comparing to these non-lonely in SCD-MCI group, but not in AD group. Our results indicate that perceived social isolation, or loneliness, might be a comorbid symptom of patients with SCD or MCI, which makes them more vulnerable to the neuropathology of future AD progression.

An electroencephalography connectome predictive model of major depressive disorder severity

Emerging evidence showed that major depressive disorder (MDD) is associated with disruptions of brain structural and functional networks, rather than impairment of isolated brain region. Thus, connectome-based models capable of predicting the depression severity at the individual level can be clinically useful. Here, we applied a machine-learning approach to predict the severity of depression using resting-state networks derived from source-reconstructed Electroencephalography (EEG) signals. Using regression models and three independent EEG datasets (N = 328), we tested whether resting state functional connectivity could predict individual depression score. On the first dataset, results showed that individuals scores could be reasonably predicted (r = 0.6, p = 4 × 10-18) using intrinsic functional connectivity in the EEG alpha band (8-13 Hz). In particular, the brain regions which contributed the most to the predictive network belong to the default mode network. We further tested the predictive potential of the established model by conducting two external validations on (N1 = 53, N2 = 154). Results showed statistically significant correlations between the predicted and the measured depression scale scores (r1 = 0.52, r2 = 0.44, p < 0.001). These findings lay the foundation for developing a generalizable and scientifically interpretable EEG network-based markers that can ultimately support clinicians in a biologically-based characterization of MDD.

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.

Influence and interaction of resting state functional magnetic resonance and tryptophan hydroxylase-2 methylation on short-term antidepressant drug response

BACKGROUND

Most antidepressants have been developed on the basis of the monoamine deficiency hypothesis of depression, in which neuronal serotonin (5-HT) plays a key role. 5-HT biosynthesis is regulated by the rate-limiting enzyme tryptophan hydroxylase-2 (TPH2). TPH2 methylation is correlated with antidepressant effects. Resting-state functional MRI (rs-fMRI) is applied for detecting abnormal brain functional activity in patients with different antidepressant effects. We will investigate the effect of the interaction between rs-fMRI and TPH2 DNA methylation on the early antidepressant effects.

METHODS

A total of 300 patients with major depressive disorder (MDD) and 100 healthy controls (HCs) were enrolled, of which 60 patients with MDD were subjected to rs-fMRI. Antidepressant responses was assessed by a 50% reduction in 17-item Hamilton Rating Scale for Depression (HAMD-17) scores at baseline and after two weeks of medication. The RESTPlus software in MATLAB was used to analyze the rs-fMRI data. The amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), fractional ALFF (fALFF), and functional connectivity (FC) were used, and the above results were used as regions of interest (ROIs) to extract the average value of brain ROIs regions in the RESTPlus software. Generalized linear model analysis was performed to analyze the association between abnormal activity found in rs-fMRI and the effect of TPH2 DNA methylation on antidepressant responses.

RESULTS

Two hundred ninety-one patients with MDD and 100 HCs were included in the methylation statistical analysis, of which 57 patients were included in the further rs-fMRI analysis (3 patients were excluded due to excessive head movement). 57 patients were divided into the responder group (n = 36) and the non-responder group (n = 21). Rs-fMRI results showed that the ALFF of the left inferior frontal gyrus (IFG) was significantly different between the two groups. The results showed that TPH2-1-43 methylation interacted with ALFF of left IFG to affect the antidepressant responses (p = 0.041, false discovery rate (FDR) corrected p = 0.149).

CONCLUSIONS

Our study demonstrated that the differences in the ALFF of left IFG between the two groups and its association with TPH2 methylation affect short-term antidepressant drug responses.

Altered effective connectivity from the pregenual anterior cingulate cortex to the laterobasal amygdala mediates the relationship between internet gaming disorder and loneliness

BACKGROUND

Individual with internet gaming disorder (IGD) often experience a high level of loneliness, and neuroimaging studies have demonstrated that amygdala function is associated with both IGD and loneliness. However, the neurobiological basis underlying these relationships remains unclear.

METHODS

In the current study, Granger causal analysis was performed to investigate amygdalar subdivision-based resting-state effective connectivity differences between 111 IGD subjects and 120 matched participants with recreational game use (RGUs). We further correlated neuroimaging findings with clinical measures. Mediation analysis was conducted to explore whether amygdalar subdivision-based effective connectivity mediated the relationship between IGD severity and loneliness.

RESULTS

Compared with RGUs, IGD subjects showed inhibitory effective connections from the left pregenual anterior cingulate cortex (pACC) to the left laterobasal amygdala (LBA) and from the right medial prefrontal cortex (mPFC) to the left LBA, as well as an excitatory effective connection from the left middle prefrontal gyrus (MFG) to the right superficial amygdala. Further analyses demonstrated that the left pACC-left LBA effective connection was negatively correlated with both Internet Addiction Test and UCLA Loneliness scores, and it mediated the relationship between the two.

CONCLUSION

IGD subjects and RGUs showed different connectivity patterns involving amygdalar subdivisions. These findings support a neurobiological mechanism for the relationship between IGD and loneliness, and suggest targets for therapeutic approaches that could be used to treat IGD.

Affective Neuroscience of Loneliness: Potential Mechanisms underlying the Association between Perceived Social Isolation, Health, and Well-Being

Loneliness, or the subjective feeling of social isolation, is an important social determinant of health. Loneliness is associated with poor physical health, including higher rates of cardiovascular disease and dementia, faster cognitive decline, and increased risk of mortality, as well as disruptions in mental health, including higher levels of depression, anxiety, and negative affect. Theoretical accounts suggest loneliness is a complex cognitive and emotional state characterized by increased levels of inflammation and affective disruptions. This review examines affective neuroscience research on social isolation in animals and loneliness in humans to better understand the relationship between perceptions of social isolation and the brain. Loneliness associated increases in inflammation and neural changes consistent with increased sensitivity to social threat and disrupted emotion regulation suggest interventions targeting maladaptive social cognitions may be especially effective. Work in animal models suggests the neural changes associated with social isolation may be reversible. Therefore, ameliorating loneliness may be an actionable social determinant of health target. However, more research is needed to understand how loneliness impacts healthy aging, explore the role of inflammation as a potential mechanism in humans, and determine the best time to deliver interventions to improve physical health, mental health, and well-being across a diverse array of populations.

Individual Differences in Cerebral Perfusion as a Function of Age and Loneliness

Loneliness is defined as the subjective feeling that one's social needs are not satisfied by both quantity and quality of one's social relationships. Loneliness has been linked to a broad range of adverse physical and mental health consequences. There is an interest in identifying the neural and molecular processes by which loneliness adversely affects health. Prior imaging studies reported divergent networks involved in cognitive, emotional, and social processes associated with loneliness. Although loneliness is common among both younger and older adults, it is experienced differently across the lifespan and has different antecedents and consequences. The current study measured regional cerebral blood flow (CBF) using pulsed arterial spin labeling imaging. Forty-five older (M_age = 63.4) and forty-four younger adults (M_age = 20.9) with comparable degrees of loneliness were included. Whole-brain voxel-wise analysis revealed a main effect of age (in superior temporal and supramarginal gyri), but no main effect of loneliness. Furthermore, the age effect was only observed among people who reported higher level of loneliness. These regions have previously been implicated in social- and attention-related functions. The moderation of loneliness on age and regional CBF suggests that younger and older individuals present differential neural manifestations in response to loneliness, even with comparable levels of loneliness.

Connectome-based model predicts individual psychopathic traits in college students

BACKGROUND

Psychopathic traits have been suggested to increase the risk of violations of socio-moral norms. Previous studies revealed that abnormal neural signatures are associated with elevated psychopathic traits; however, whether the intrinsic network architecture can predict psychopathic traits at the individual level remains unclear.

METHODS

The present study utilized connectome-based predictive modeling (CPM) to investigate whether whole-brain resting-state functional connectivity (RSFC) can predict psychopathic traits in the general population. Resting-state fMRI data were collected from 84 college students with varying psychopathic traits measured by the Levenson Self-Report Psychopathy Scale (LSRP).

RESULTS

Functional connections that were negatively correlated with psychopathic traits predicted individual differences in total LSRP and secondary psychopathy score but not primary score. Particularly, nodes with the most connections in the predictive connectome anchored in the prefrontal cortex (e.g., anterior prefrontal cortex and orbitofrontal cortex) and limbic system (e.g., anterior cingulate cortex and insula). In addition, the connections between the occipital network (OCCN) and cingulo-opercular network (CON) served as a significant predictive connectome for total LSRP and secondary psychopathy score.

CONCLUSION

CPM constituted by whole-brain RSFC significantly predicted psychopathic traits individually in the general population. The brain areas including the prefrontal cortex and limbic system and large-scale networks including the CON and OCCN play special roles in the predictive model-possibly reflecting atypical cognitive control and affective processing for individuals with elevated psychopathic traits. These findings may facilitate detection and potential intervention of individuals with maladaptive psychopathic tendency.

Multivariate morphological brain signatures enable individualized prediction of dispositional need for closure

Need for closure (NFC) reflects stable individual differences in the desire for a quick, definite, and stable answer to a question. A large body of research has documented the association between NFC and various cognitive, emotional and social processes. Despite considerable interest in psychology, little effort has been made to uncover the neural substrates of individual variations in NFC. Herein, we took a data-driven approach to predict NFC trait combining machine learning framework and the whole-brain grey matter volume (GMV) features, which represent a reliable brain imaging measure and have been commonly employed to explore neural basis underlying individual differences of cognition and behaviors. Brain regions contributing to the prediction were then subjected to functional connectivity and decoding analyses for a quantitative inference on their psychophysiological functions. Our results indicated that multivariate patterns of GMV derived from multiple regions across distributed brain systems predicted NFC at individual level. The contributing regions are distributed across the emotional processing network (e.g., striatum), cognitive control network (e.g., lateral prefrontal cortex), social cognition network (e.g., temporoparietal junction) and perceptual processing network (e.g., occipital cortex). The current study provided the first evidence that dispositional NFC is embodied in multiple large-scale brain networks, helping to delineate a more complete picture about the neuropsychological processes that support individual differences in NFC. Beyond these findings, the current interdisciplinary approach to constructing and interpreting neuroimaging-based prediction model of personality traits would be informative to a wide range of future studies on personality.

Neurobiology of loneliness: a systematic review

Loneliness is associated with increased morbidity and mortality. Deeper understanding of neurobiological mechanisms underlying loneliness is needed to identify potential intervention targets. We did not find any systematic review of neurobiology of loneliness. Using MEDLINE and PsycINFO online databases, we conducted a search for peer-reviewed publications examining loneliness and neurobiology. We identified 41 studies (n = 16,771 participants) that had employed various methods including computer tomography (CT), structural magnetic resonance imaging (MRI), functional MRI (fMRI), electroencephalography (EEG), diffusion tensor imaging (DTI), single-photon emission computed tomography (SPECT), positron emission tomography (PET), and post-mortem brain tissue RNA analysis or pathological analysis. Our synthesis of the published findings shows abnormal structure (gray matter volume or white matter integrity) and/or activity (response to pleasant versus stressful images in social versus nonsocial contexts) in the prefrontal cortex (especially medial and dorsolateral), insula (particularly anterior), amygdala, hippocampus, and posterior superior temporal cortex. The findings related to ventral striatum and cerebellum were mixed. fMRI studies reported links between loneliness and differential activation of attentional networks, visual networks, and default mode network. Loneliness was also related to biological markers associated with Alzheimer's disease (e.g., amyloid and tau burden). Although the published investigations have limitations, this review suggests relationships of loneliness with altered structure and function in specific brain regions and networks. We found a notable overlap in the regions involved in loneliness and compassion, the two personality traits that are inversely correlated in previous studies. We have offered recommendations for future research studies of neurobiology of loneliness.

The functional and structural connectomes associated with geriatric depression and anxiety symptoms in mild cognitive impairment: Cross-syndrome overlap and generalization

Geriatric depression and anxiety disorders often manifest as neuropsychiatric symptoms among those with mild cognitive impairment. Both tend to co-occur, and overlap in symptomology and etiology. Such commonalities are likely to be reflected in the brain as common neural correlates. Using connectome-based predictive modeling (CPM), we examined the functional and structural connectomes predicting depression and anxiety symptoms, and subsequently the overlap and cross-syndrome generalization of the connectomes associated with either disorder. Ninety-one older adults completed self-reported measures of depression and anxiety, and underwent diffusion tensor imaging and resting-state functional magnetic resonance imaging. Functional connectivity (FC) and structural connectivity (SC) matrices were derived from these scans and, in various combinations, entered into CPM models to predict either type of symptoms. Leave-one-out cross-validation was performed. Predictive accuracy was assessed via the correlation between predicted and observed scores (ρ_{predicted-observed}). While FC or SC features alone significantly predicted either type of symptoms, these symptoms were best predicted by models that consisted of both FC and SC features (depression: ρ_{predicted-observed} = 0.497; anxiety: ρ_{predicted-observed} = 0.455). The features common to depression and anxiety were identified and entered into another model which was similarly accurate in predicting either type of symptoms. Moreover, cross-syndrome generalization was observed- the depression-associated features significantly predicted anxiety symptoms (ρ_{predicted-observed} = 0.403) and vice-versa (ρ_{predicted-observed} = 0.378). These FC and SC features are complementary biomarkers of geriatric depression and anxiety symptoms. Both types of symptoms are largely underpinned by common patterns of altered FC and SC, alluding to the transdiagnostic neurobiological susceptibility in both disorders.

Efficient Brain Connectivity Reconfiguration Predicts Higher Marital Quality and Lower Depression

Social-information processing is important for successful romantic relationships and protecting against depression, and depends on functional connectivity (FC) within and between large-scale networks. Functional architecture evident at rest is adaptively reconfigured during task and there were two possible associations between brain reconfiguration and behavioral performance during neurocognitive tasks (efficiency effect and distraction-based effect). This study examined relationships between brain reconfiguration during social-information processing and relationship-specific and more general social outcomes in marriage. Resting-state FC was compared with FC during social-information processing (watching relationship-specific and general emotional stimuli) of 29 heterosexual couples, and the FC similarity (reconfiguration efficiency) was examined in relation to marital quality and depression 13 months later. The results indicated wives' reconfiguration efficiency (globally and in visual association network) during relationship-specific stimuli processing was related to their own marital quality. Higher reconfiguration efficiency (globally and in medial frontal, frontal-parietal, default mode, motor/sensory and salience networks) in wives during general emotional stimuli processing was related to their lower depression. These findings suggest efficiency effects on social outcomes during social cognition, especially among married women. The efficiency effects on relationship-specific and more general outcome are respectively higher during relationship-specific stimuli or general emotional stimuli processing.

Neural circuits and behavioral pathways linking hearing loss to affective dysregulation in older adults

Substantial evidence now links age-related hearing loss to incident major depressive disorder in older adults. However, research examining the neural circuits and behavioral mechanisms by which age-related hearing loss leads to depression is at an early phase. It is known that hearing loss has adverse structural and functional brain consequences, is associated with reduced social engagement and loneliness, and often results in tinnitus, which can independently affect cognitive control and emotion processing circuits. While pathways leading from these sequelae of hearing loss to affective dysregulation and depression are intuitive to hypothesize, few studies have yet been designed to provide conclusive evidence for specific pathophysiological mechanisms. Here we review the neurobiological and behavioral consequences of age-related hearing loss, present a model linking them to increased risk for major depressive disorder and suggest how future studies may facilitate the development of rationally designed therapeutic interventions for older adults with impaired hearing to reduce risk for depression and/or ameliorate depressive symptoms.

Neuroimaging studies exploring the neural basis of social isolation

According to the social brain hypothesis, the human brain includes a network designed for the processing of social information. This network includes several brain regions that elaborate social cues, interactions and contexts, i.e. prefrontal paracingulate and parietal cortices, amygdala, temporal lobes and the posterior superior temporal sulcus. While current literature suggests the importance of this network from both a psychological and evolutionary perspective, little is known about its neurobiological bases. Specifically, only a paucity of studies explored the neural underpinnings of constructs that are ascribed to the social brain network functioning, i.e. objective social isolation and perceived loneliness. As such, this review aimed to overview neuroimaging studies that investigated social isolation in healthy subjects. Social isolation correlated with both structural and functional alterations within the social brain network and in other regions that seem to support mentalising and social processes (i.e. hippocampus, insula, ventral striatum and cerebellum). However, results are mixed possibly due to the heterogeneity of methods and study design. Future neuroimaging studies with longitudinal designs are needed to measure the effect of social isolation in experimental v. control groups and to explore its relationship with perceived loneliness, ultimately helping to clarify the neural correlates of the social brain.

Connectome-Based Predictive Modeling of Individual Anxiety

Anxiety-related illnesses are highly prevalent in human society. Being able to identify neurobiological markers signaling high trait anxiety could aid the assessment of individuals with high risk for mental illness. Here, we applied connectome-based predictive modeling (CPM) to whole-brain resting-state functional connectivity (rsFC) data to predict the degree of trait anxiety in 76 healthy participants. Using a computational “lesion” approach in CPM, we then examined the weights of the identified main brain areas as well as their connectivity. Results showed that the CPM successfully predicted individual anxiety based on whole-brain rsFC, especially the rsFC between limbic areas and prefrontal cortex. The prediction power of the model significantly decreased from simulated lesions of limbic areas, lesions of the connectivity within limbic areas, and lesions of the connectivity between limbic areas and prefrontal cortex. Importantly, this neural model generalized to an independent large sample (n = 501). These findings highlight important roles of the limbic system and prefrontal cortex in anxiety prediction. Our work provides evidence for the usefulness of connectome-based modeling in predicting individual personality differences and indicates its potential for identifying personality factors at risk for psychopathology.