Common Dimensional Reward Deficits Across Mood and Psychotic Disorders: A Connectome-Wide Association Study

Examining the role of physical activity in older adults with epilepsy

Epilepsy disproportionately affects older adults due to acquired conditions including stroke, neurodegeneration and head trauma secondary to falls. Current literature lacks adequate representation of specific therapies and considerations for this cohort. Furthermore, older adults are more susceptible to the adverse effects of anti-seizure medications necessitating increased caution when treating. Non-pharmacological interventions, including physical activity (PA), are underrecognized, particularly in older adults where they may be of greatest benefit. The following narrative review describes how older adults are uniquely impacted by epilepsy and associated comorbidities. It examines the current literature with respect to PA in epilepsy and, where available, evidence for PA in older adults. This includes how PA can affect pathogenesis and reduce the incidence of epilepsy onset through the reduction of neuroinflammation. PA may also be utilized by older adults with epilepsy to improve cardiovascular function, seizure control, prevent falls and secondary head injury, as an adjunct treatment for mood disorders and cognitive decline, and to promote general well-being. PA has a large and underappreciated role to play in older adults with epilepsy and is increasingly being recognized by healthcare providers and incorporated into practice guidelines.

Hypercomplex Graph Neural Network: Towards Deep Intersection of Multi-Modal Brain Networks

The multi-modal neuroimage study has provided insights into understanding the heteromodal relationships between brain network organization and behavioral phenotypes. Integrating data from various modalities facilitates the characterization of the interplay among anatomical, functional, and physiological brain alterations or developments. Graph Neural Networks (GNNs) have recently become popular in analyzing and fusing multi-modal, graph-structured brain networks. However, effectively learning complementary representations from other modalities remains a significant challenge due to the sophisticated and heterogeneous inter-modal dependencies. Furthermore, most existing studies often focus on specific modalities (e.g., only fMRI and DTI), which limits their scalability to other types of brain networks. To overcome these limitations, we propose a HyperComplex Graph Neural Network (HC-GNN) that models multi-modal networks as hypercomplex tensor graphs. In our approach, HC-GNN is conceptualized as a dynamic spatial graph, where the attentively learned inter-modal associations are represented as the adjacency matrix. HC-GNN leverages hypercomplex operations for inter-modal intersections through cross-embedding and cross-aggregation, enriching the deep coupling of multi-modal representations. We conduct a statistical analysis on the saliency maps to associate disease biomarkers. Extensive experiments on three datasets demonstrate the superior classification performance of our method and its strong scalability to various types of modalities. Our work presents a powerful paradigm for the study of multi-modal brain networks.

Machine learning-derived multimodal Neurobiological profiles of behavioral activation traits in adolescents

Behavioral activation (BA) traits mediate responses to positive reinforcement, and then to promote reward-seeking actions. However, few studies have investigated the neurobiological profiles of BA traits in adolescents based on multimodal neuroimaging and machine learning techniques. In this study, a total of 6626 adolescents with both valid multimodal magnetic resonance imaging (MRI) and questionnaire data were included in the Adolescent Brain Cognitive Development Study. Machine learning-based elastic net regression with 5-fold cross-validation (CV) was used to characterize the neurobiological profiles of BA traits using multimodal MRI data as predictors. Using 5-fold CV, the multi-region neurobiological profiles substantively predicted BA traits, and this finding was robust in an out-of-sample. Regarding specific regions, neurobiological profiles were enriched in the bilateral pallidum. Regarding functional networks, functional connectivity of the cingulo-opercular and the fronto-parietal networks with both the pallidum and nucleus accumbens, showed high beta weights. The relationships of the neurobiological profiles with BA traits were further supported by traditional univariate linear mixed effects models, in which many of the profiles identified as part of the neurobiological pattern showed significant univariate associations with BA traits, including the hub region pallidum. In summary, these findings revealed robust machine learning-derived neurobiological profiles of BA traits, those that comprised a key node the pallidum, which is involved in the motivational brain network. These findings suggested that the pallidum might play a vital role in developing BA traits in adolescents.

Disrupted Hierarchical Functional Brain Organization in Affective and Psychotic Disorders: Insights from Functional Brain Gradients

Patients with psychosis and depression show widespread alterations in brain resting-state functional connectivity (rs-FC), affecting both sensory and higher-order brain regions. In this study, we investigate disruptions in the hierarchical organization of brain functional networks in patients with psychotic and affective disorders. We derived functional brain gradients, low dimensional representations of rs-FC that capture cortical hierarchy, in a large patient sample including clinical high-risk for psychosis (CHR-P) patients, recent-onset psychosis (ROP) patients, recent-onset depression (ROD) patients, and healthy controls (HC). We examined regional alterations, network-level alterations and functional differentiation and their relationship to clinical symptoms. In addition, we linked case-control differences to receptor expression maps to explore underlying neurobiological mechanisms. All patient groups exhibited alterations in the visual-to-sensorimotor gradient, while only ROP patients showed alterations in the association-to-sensory gradient. CHR-P and ROP patients exhibited lower values in the ventral attention network. Additionally, patients combined showed higher values in the somatomotor network, a reduced gradient range and altered between-network dispersion. ROD showed reduced within-network dispersion in the attentional networks and a reduced range. Correlational analysis revealed weak associations of gradient measures with functioning, visual dysfunctions and cognition. Furthermore case-control differences showed associations to receptor expression maps, suggesting the involvement of neurotransmitter systems in these disruptions. Our findings reveal transdiagnostic and disease-specific alterations of hierarchical brain organization. These alterations indicate deficits in functional integration across psychiatric diseases, highlighting the role of attentional and sensory networks in disease processes.

Altered electroencephalography-based source functional connectivity in drug-free patients with major depressive disorder

BACKGROUND

Compared to functional magnetic resonance imaging (fMRI), source localization of a scalp-recorded electroencephalogram (EEG) provides higher temporal resolution and frequency synchronization to better understand the potential neurophysiological origins of disrupted functional connectivity (FC) in major depressive disorder (MDD). The present study aimed to investigate EEG-sourced measures to examine the FC in drug-free patients with MDD.

METHOD

Resting-state 32-channel EEG were recorded in 84 drug-free patients with MDD and 143 healthy controls, and the cortical source signals were estimated. Exact low-resolution brain electromagnetic tomography (eLORETA) was used to compute the intracortical activity from regions within the default mode network (DMN) and frontoparietal network (PFN). Lagged phase synchronization was used as a measure of functional connectivity.

RESULTS

Compared with control subjects, the MDD group showed greater within-DMN alpha 1 and 2 bands and within-FPN alpha 1, 2, and beta 3 bands. Furthermore, the MDD group showed hyperconnectivity between the DMN and the FPN in the alpha 1 and 2 bands. Finally, higher levels of anhedonia were associated with higher between-network DMN and FPN connectivity in the alpha-1 band.

LIMITATIONS

Due to the inherent limitations of eLORETA with predefined seeds, we could not exclude connectivity between regions of interest (ROIs), which may be related to the activity from regions adjacent to the ROIs.

CONCLUSIONS

The present findings support the importance of phase-lagged functional dysconnectivity in the neurophysiological mechanisms underlying MDD. Exploring the potential of these patterns as surrogates for treatment responses may advance targeted interventions for depression.

Robust Brain Correlates of Cognitive Performance in Psychosis and Its Prodrome

BACKGROUND

Neurocognitive impairment is a well-known phenomenon in schizophrenia that begins prior to psychosis onset. Connectome-wide association studies have inconsistently linked cognitive performance to resting-state functional magnetic resonance imaging. We hypothesized that a carefully selected cognitive instrument and refined population would allow identification of reliable brain-behavior associations with connectome-wide association studies. To test this hypothesis, we first identified brain-cognition correlations via a connectome-wide association study in early psychosis. We then asked, in an independent dataset, if these brain-cognition relationships would generalize to individuals who develop psychosis in the future.

METHODS

The Seidman Auditory Continuous Performance Task (ACPT) effectively differentiates healthy participants from those with psychosis. Our connectome-wide association study used the HCP-EP (Human Connectome Project for Early Psychosis) (n = 183) to identify links between connectivity and ACPT performance. We then analyzed data from the NAPLS2 (North American Prodrome Longitudinal Study 2) (n = 345), a multisite prospective study of individuals at risk for psychosis. We tested the connectome-wide association study-identified cognition-connectivity relationship in both individuals at risk for psychosis and control participants.

RESULTS

Our connectome-wide association study in early-course psychosis identified robust associations between better ACPT performance and higher prefrontal-somatomotor connectivity (p < .005). Prefrontal-somatomotor connectivity was also related to ACPT performance in at-risk individuals who would develop psychosis (n = 17). This finding was not observed in nonconverters (n = 196) or control participants (n = 132).

CONCLUSIONS

This connectome-wide association study identified reproducible links between connectivity and cognition in separate samples of individuals with psychosis and at-risk individuals who would later develop psychosis. A carefully selected task and population improves the ability of connectome-wide association studies to identify reliable brain-phenotype relationships.

Deficient salience and default mode functional integration in high worry-proneness subject: a connectome-wide association study

Worry has been conceptualized as a relatively uncontrollable chain of thought that increases the risk of mental problems, such as anxiety disorders. Here, we examined the link between individual variation in the functional connectome and worry proneness, which remains unclear. A total of 32 high worry-proneness (HWP) subjects and 25 low worry-proneness (LWP) subjects were recruited. We conducted multivariate distance-based matrix regression to identify phenotypic relationships in high-dimensional brain resting-state functional connectivity data from HWP subjects. Multiple hub regions, including key brain nodes of the salience network (SN) and default mode network (DMN), were identified in HWP subjects. Follow-up analyses revealed that a high worry-proneness score was dominated by functional connectivity between the SN and the DMN. Moreover, HWP subjects showed hypoconnectivity between the cerebellum and the SN and DMN compared with LWP subjects. This cross-sectional study could not fully measure the causal relationships between changes in functional networks and worry proneness in healthy subjects. Functional changes in the cerebellum-cortical region might affect the modulation of external stimuli processing. Together, our results provide new insight into the role of key networks, including the SN, DMN and cerebellum, in understanding the potential mechanism underlying the high worry dimension in healthy subjects.

Anhedonia is associated with higher functional connectivity between the nucleus accumbens and paraventricular nucleus of thalamus

BACKGROUND

Anhedonia stands as a life-threatening transdiagnostic feature of many mental illnesses, most notably major depression and involves neural circuits for processing reward information. The paraventricular nucleus of the thalamus (PVT) is associated with reward-seeking behavior, however, links between the PVT circuit and anhedonia have not been investigated in humans.

METHODS

In a sample of adults with and without psychiatric symptoms (n = 75, 18-41 years, 55 female), we generated an anhedonia factor score for each participant using a latent factor analysis, utilizing data from depression and anxiety assessments. Functional connectivity between the PVT and the nucleus accumbens (NAc) was calculated from high-resolution (1.5 mm) resting state fMRI.

RESULTS

Anhedonia factor scores showed a positive relationship with functional connectivity between the PVT and the NAc, principally in males and in those with psychiatric symptoms. In males, connectivity between other midline thalamic nuclei and the NAc did not show these relationships, suggesting that this link may be specific to PVT.

LIMITATIONS

This cohort was originally recruited to study depression and not anhedonia per se. The distribution of male and female participants in our cohort was not equal. Partial acquisition in high-resolution fMRI scans restricted regions of interest outside of the thalamus and reward networks.

CONCLUSIONS

We report evidence that anhedonia is associated with enhanced functional connectivity between the PVT and the NAc, regions that are relevant to reward processing. These results offer clues as to the potential prevention and prevention and treatment of anhedonia.

Regional neural functional efficiency across schizophrenia, bipolar disorder, and major depressive disorder: a transdiagnostic resting-state fMRI study

BACKGROUND

Major psychiatric disorders (MPDs) are delineated by distinct clinical features. However, overlapping symptoms and transdiagnostic effectiveness of medications have challenged the traditional diagnostic categorisation. We investigate if there are shared and illness-specific disruptions in the regional functional efficiency (RFE) of the brain across these disorders.

METHODS

We included 364 participants (118 schizophrenia [SCZ], 80 bipolar disorder [BD], 91 major depressive disorder [MDD], and 75 healthy controls [HCs]). Resting-state fMRI was used to caclulate the RFE based on the static amplitude of low-frequency fluctuation, regional homogeneity, and degree centrality and corresponding dynamic measures indicating variability over time. We used principal component analysis to obtain static and dynamic RFE values. We conducted functional and genetic annotation and enrichment analysis based on abnormal RFE profiles.

RESULTS

SCZ showed higher static RFE in the cortico-striatal regions and excessive variability in the cortico-limbic regions. SCZ and MDD shared lower static RFE with higher dynamic RFE in sensorimotor regions than BD and HCs. We observed association between static RFE abnormalities with reward and sensorimotor functions and dynamic RFE abnormalities with sensorimotor functions. Differential spatial expression of genes related to glutamatergic synapse and calcium/cAMP signaling was more likely in the regions with aberrant RFE.

CONCLUSIONS

SCZ shares more regions with disrupted functional integrity, especially in sensorimotor regions, with MDD rather than BD. The neural patterns of these transdiagnostic changes appear to be potentially driven by gene expression variations relating to glutamatergic synapses and calcium/cAMP signaling. The aberrant sensorimotor, cortico-striatal, and cortico-limbic integrity may collectively underlie neurobiological mechanisms of MPDs.

Prospective associations between early adolescent reward functioning and later dimensions of psychopathology

Individual differences in reward functioning have been associated with numerous disorders in adolescence. Given relations with multiple forms of psychopathology, it is unclear whether these associations are disorder specific or reflective of shared variance across multiple disorders. In a sample of adolescents (N = 418), we examined associations between neural and self-reported indices of early reward functioning (age 12) with different levels of a hierarchical psychopathology model assessed later in adolescence (age 18). We examined whether prospective relationships between reward functioning are specific to individual disorders or better explained by transdiagnostic dimensions. We found modest results for prospective associations between reward indices and different dimensions of psychopathology, with most significant associations not surviving correction for multiple comparisons. We discuss the benefits and limitations of the modeling approach used to examine dimension-specific associations that future work can build on. Overall, more work is needed to better understand how reward functioning is specifically associated with different forms of and hierarchical levels of psychopathology. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Brain structural associations of syntactic complexity and diversity across schizophrenia spectrum and major depressive disorders, and healthy controls

Deviations in syntax production have been well documented in schizophrenia spectrum disorders (SSD). Recently, we have shown evidence for transdiagnostic subtypes of syntactic complexity and diversity. However, there is a lack of studies exploring brain structural correlates of syntax across diagnoses. We assessed syntactic complexity and diversity of oral language production using four Thematic Apperception Test pictures in a sample of N = 87 subjects (n = 24 major depressive disorder (MDD), n = 30 SSD patients both diagnosed according to DSM-IV-TR, and n = 33 healthy controls (HC)). General linear models were used to investigate the association of syntax with gray matter volume (GMV), fractional anisotropy (FA), axial (AD), radial (RD), and mean diffusivity (MD). Age, sex, total intracranial volume, group, interaction of group and syntax were covariates of no interest. Syntactic diversity was positively correlated with the GMV of the right medial pre- and postcentral gyri and with the FA of the left superior-longitudinal fasciculus (temporal part). Conversely, the AD of the left cingulum bundle and the forceps minor were negatively correlated with syntactic diversity. The AD of the right inferior-longitudinal fasciculus was positively correlated with syntactic complexity. Negative associations were observed between syntactic complexity and the FA of the left cingulum bundle, the right superior-longitudinal fasciculus, and the AD of the forceps minor and the left uncinate fasciculus. Our study showed brain structural correlates of syntactic complexity and diversity across diagnoses and HC. This contributes to a comprehensive understanding of the interplay between linguistic and neural substrates in syntax production in psychiatric disorders and HC.

Exploring mechanisms of anhedonia in depression through neuroimaging and data-driven approaches

BACKGROUND

Anhedonia is a core symptom of depression that is closely related to prognosis and treatment outcomes. However, accurate and efficient treatments for anhedonia are lacking, mandating a deeper understanding of the underlying mechanisms.

METHODS

A total of 303 patients diagnosed with depression and anhedonia were assessed by the Snaith-Hamilton Pleasure Scale (SHAPS) and magnetic resonance imaging (MRI). The patients were categorized into a low-anhedonia group and a high-anhedonia group using the K-means algorithm. A data-driven approach was used to explore the differences in brain structure and function with different degrees of anhedonia based on MATLAB. A random forest model was used exploratorily to test the predictive ability of differences in brain structure and function on anhedonia in depression.

RESULTS

Structural and functional differences were apparent in several brain regions of patients with depression and high-level anhedonia, including in the temporal lobe, paracingulate gyrus, superior frontal gyrus, inferior occipital gyrus, right insular gyrus, and superior parietal lobule. And changes in these brain regions were significantly correlated with scores of SHAPS.

CONCLUSIONS

These brain regions may be useful as biomarkers that provide a more objective assessment of anhedonia in depression, laying the foundation for precision medicine in this treatment-resistant, relatively poor prognosis group.

Dimensional and Categorical Solutions to Parsing Depression Heterogeneity in a Large Single-Site Sample

BACKGROUND

Recent studies have reported significant advances in modeling the biological basis of heterogeneity in major depressive disorder, but investigators have also identified important technical challenges, including scanner-related artifacts, a propensity for multivariate models to overfit, and a need for larger samples with more extensive clinical phenotyping. The goals of the current study were to evaluate dimensional and categorical solutions to parsing heterogeneity in depression that are stable and generalizable in a large, single-site sample.

METHODS

We used regularized canonical correlation analysis to identify data-driven brain-behavior dimensions that explain individual differences in depression symptom domains in a large, single-site dataset comprising clinical assessments and resting-state functional magnetic resonance imaging data for 328 patients with major depressive disorder and 461 healthy control participants. We examined the stability of clinical loadings and model performance in held-out data. Finally, hierarchical clustering on these dimensions was used to identify categorical depression subtypes.

RESULTS

The optimal regularized canonical correlation analysis model yielded 3 robust and generalizable brain-behavior dimensions that explained individual differences in depressed mood and anxiety, anhedonia, and insomnia. Hierarchical clustering identified 4 depression subtypes, each with distinct clinical symptom profiles, abnormal resting-state functional connectivity patterns, and antidepressant responsiveness to repetitive transcranial magnetic stimulation.

CONCLUSIONS

Our results define dimensional and categorical solutions to parsing neurobiological heterogeneity in major depressive disorder that are stable, generalizable, and capable of predicting treatment outcomes, each with distinct advantages in different contexts. They also provide additional evidence that regularized canonical correlation analysis and hierarchical clustering are effective tools for investigating associations between functional connectivity and clinical symptoms.

Data-driven, connectome-wide analysis identifies psychosis-specific brain correlates of fear and anxiety

Decades of psychosis research highlight the prevalence and the clinical significance of negative emotions, such as fear and anxiety. Translational evidence demonstrates the pivotal role of the amygdala in fear and anxiety. However, most of these approaches have used hypothesis-driven analyses with predefined regions of interest. A data-driven analysis may provide a complimentary, unbiased approach to identifying brain correlates of fear and anxiety. The aim of the current study was to identify the brain basis of fear and anxiety in early psychosis and controls using a data-driven approach. We analyzed data from the Human Connectome Project for Early Psychosis, a multi-site study of 125 people with psychosis and 58 controls with resting-state fMRI and clinical characterization. Multivariate pattern analysis of whole-connectome data was used to identify shared and psychosis-specific brain correlates of fear and anxiety using the NIH Toolbox Fear-Affect and Fear-Somatic Arousal scales. We then examined clinical correlations of Fear-Affect scores and connectivity patterns. Individuals with psychosis had higher levels of Fear-Affect scores than controls (p < 0.05). The data-driven analysis identified a cluster encompassing the amygdala and hippocampus where connectivity was correlated with Fear-Affect score (p < 0.005) in the entire sample. The strongest correlate of Fear-Affect was between this cluster and the anterior insula and stronger connectivity was associated with higher Fear-Affect scores (r = 0.31, p = 0.0003). The multivariate pattern analysis also identified a psychosis-specific correlate of Fear-Affect score between the amygdala/hippocampus cluster and a cluster in the ventromedial prefrontal cortex (VMPFC). Higher Fear-Affect scores were correlated with stronger amygdala/hippocampal-VMPFC connectivity in the early psychosis group (r = 0.33, p = 0.002), but not in controls (r = -0.15, p = 0.28). The current study provides evidence for the transdiagnostic role of the amygdala, hippocampus, and anterior insula in the neural basis of fear and anxiety and suggests a psychosis-specific relationship between fear and anxiety symptoms and amygdala/hippocampal-VMPFC connectivity. Our novel data-driven approach identifies novel, psychosis-specific treatment targets for fear and anxiety symptoms and provides complimentary evidence to decades of hypothesis-driven approaches examining the brain basis of threat processing.

Diverging functional connectivity timescales: Capturing distinct aspects of cognitive performance in early psychosis

BACKGROUND

Psychosis spectrum disorders (PSDs) are marked by cognitive impairments, the neurobiological correlates of which remain poorly understood. Here, we investigate the entropy of time-varying functional connectivity (TVFC) patterns from resting-state functional magnetic resonance imaging (rs-fMRI) as potential biomarker for cognitive performance in PSDs. By combining our results with multimodal reference data, we hope to generate new insights into the mechanisms underlying cognitive dysfunction in PSDs. We hypothesized that low-entropy TVFC patterns (LEN) would be more behaviorally informative than high-entropy TVFC patterns (HEN), especially for tasks that require extensive integration across diverse cognitive subdomains.

METHODS

rs-fMRI and behavioral data from 97 patients in the early phases of psychosis and 53 controls were analyzed. Positron emission tomography (PET) and magnetoencephalography (MEG) data were taken from a public repository (Hansen et al., 2022). Multivariate analyses were conducted to examine relationships between TVFC patterns at multiple spatial scales and cognitive performance in patients.

RESULTS

Compared to HEN, LEN explained significantly more cognitive variance on average in PSD patients, driven by superior encoding of information on psychometrically more integrated tasks. HEN better captured information in specific subdomains of executive functioning. Nodal HEN-LEN transitions were spatially aligned with neurobiological gradients reflecting monoaminergic transporter densities and MEG beta-power. Exploratory analyses revealed a close statistical relationship between LEN and positive symptom severity in patients.

CONCLUSION

Our entropy-based analysis of TVFC patterns dissociates distinct aspects of cognition in PSDs. By linking topographies of neurotransmission and oscillatory dynamics with cognitive performance, it enhances our understanding of the mechanisms underlying cognitive deficits in PSDs.

Behavioral Activation and Brain Network Changes in Depression

Behavioral activation (BA) is a well-established method of evidence-based treatment for depression. There are clear links between the neural mechanisms underlying reward processing and BA treatment for depressive symptoms, including anhedonia; however, integrated interpretations of these two domains are lacking. Here we examine brain imaging studies involving BA treatments to investigate how changes in brain networks, including the reward networks, mediate the therapeutic effects of BA, and whether brain circuits are predictors of BA treatment responses. Increased activation of the prefrontal and subcortical regions associated with reward processing has been reported after BA treatment. Activation of these regions improves anhedonia. Conversely, some studies have found decreased activation of prefrontal regions after BA treatment in response to cognitive control stimuli in sad contexts, which indicates that the therapeutic mechanism of BA may involve disengagement from negative or sad contexts. Furthermore, the decrease in resting-state functional connectivity of the default-mode network after BA treatment appears to facilitate the ability to counteract depressive rumination, thereby promoting enjoyable and valuable activities. Conflicting results suggest that an intact neural response to rewards or defective reward functioning is predictive of the efficacy of BA treatments. Increasing the benefits of BA treatments requires identification of the unique individual characteristics determining which of these conflicting findings are relevant for the personalized treatment of each individual with depression.

Brain Functional Connectome Defines a Transdiagnostic Dimension Shared by Cognitive Function and Psychopathology in Preadolescents

BACKGROUND

Cognitive function and general psychopathology are two important classes of human behavior dimensions that are individually related to mental disorders across diagnostic categories. However, whether these two transdiagnostic dimensions are linked to common or distinct brain networks that convey resilience or risk for the development of psychiatric disorders remains unclear.

METHODS

The current study is a longitudinal investigation with 11,875 youths from the Adolescent Brain Cognitive Development (ABCD) Study at ages 9 to 10 years at the onset of the study. A machine learning approach based on canonical correlation analysis was used to identify latent dimensional associations of the resting-state functional connectome with multidomain behavioral assessments including cognitive functions and psychopathological measures. For the latent resting-state functional connectivity factor showing a robust behavioral association, its ability to predict psychiatric disorders was assessed using 2-year follow-up data, and its genetic association was evaluated using twin data from the same cohort.

RESULTS

A latent functional connectome pattern was identified that showed a strong and generalizable association with the multidomain behavioral assessments (5-fold cross-validation: ρ = 0.68-0.73 for the training set [n = 5096]; ρ = 0.56-0.58 for the test set [n = 1476]). This functional connectome pattern was highly heritable (h2 = 74.42%, 95% CI: 56.76%-85.42%), exhibited a dose-response relationship with the cumulative number of psychiatric disorders assessed concurrently and at 2 years post-magnetic resonance imaging scan, and predicted the transition of diagnosis across disorders over the 2-year follow-up period.

CONCLUSIONS

These findings provide preliminary evidence for a transdiagnostic connectome-based measure that underlies individual differences in the development of psychiatric disorders during early adolescence.

Diagnostically distinct resting state fMRI energy distributions: A subject-specific maximum entropy modeling study

Objective

Existing neuroimaging studies of psychotic and mood disorders have reported brain activation differences (first-order properties) and altered pairwise correlation-based functional connectivity (second-order properties). However, both approaches have certain limitations that can be overcome by integrating them in a pairwise maximum entropy model (MEM) that better represents a comprehensive picture of fMRI signal patterns and provides a system-wide summary measure called energy. This study examines the applicability of individual-level MEM for psychiatry and identifies image-derived model coefficients related to model parameters.

Method

MEMs are fit to resting state fMRI data from each individual with schizophrenia/schizoaffective disorder, bipolar disorder, and major depression (n=132) and demographically matched healthy controls (n=132) from the UK Biobank to different subsets of the default mode network (DMN) regions.

Results

The model satisfactorily explained observed brain energy state occurrence probabilities across all participants, and model parameters were significantly correlated with image-derived coefficients for all groups. Within clinical groups, averaged energy level distributions were higher in schizophrenia/schizoaffective disorder but lower in bipolar disorder compared to controls for both bilateral and unilateral DMN. Major depression energy distributions were higher compared to controls only in the right hemisphere DMN.

Conclusions

Diagnostically distinct energy states suggest that probability distributions of temporal changes in synchronously active nodes may underlie each diagnostic entity. Subject-specific MEMs allow for factoring in the individual variations compared to traditional group-level inferences, offering an improved measure of biologically meaningful correlates of brain activity that may have potential clinical utility.

Diverging functional connectivity timescales: Capturing distinct aspects of cognitive performance in early psychosis

Background

Psychosis spectrum disorders (PSDs) are marked by cognitive impairments, the neurobiological correlates of which remain poorly understood. Here, we investigate the entropy of time-varying functional connectivity (TVFC) patterns from resting-state fMRI (rfMRI) as potential biomarker for cognitive performance in PSDs. By combining our results with multimodal reference data, we hope to generate new insights into the mechanisms underlying cognitive dysfunction in PSDs. We hypothesized that low-entropy TVFC patterns (LEN) would be more behaviorally informative than high-entropy TVFC patterns (HEN), especially for tasks that require extensive integration across diverse cognitive subdomains.

Methods

rfMRI and behavioral data from 97 patients in the early phases of psychosis and 53 controls were analyzed. Positron-Emission Tomography (PET) and magnetoencephalography (MEG) data were taken from a public repository (Hansen et al., 2022). Multivariate analyses were conducted to examine relationships between TVFC patterns at multiple spatial scales and cognitive performance in patients.

Results

Compared to HEN, LEN explained significantly more cognitive variance on average in PSD patients, driven by superior encoding of information on psychometrically more integrated tasks. HEN better captured information in specific subdomains of executive functioning. Nodal HEN-LEN transitions were spatially aligned with neurobiological gradients reflecting monoaminergic transporter densities and MEG beta power. Exploratory analyses revealed a close statistical relationship between LEN and positive PSD symptoms.

Conclusion

Our entropy-based analysis of TVFC patterns dissociates distinct aspects of cognition in PSDs. By linking topographies of neurotransmission and oscillatory dynamics with cognitive performance, it enhances our understanding of the mechanisms underlying cognitive deficits in PSDs.

CRediT Authorship Contribution Statement

Fabian Hirsch: Conceptualization, Methodology, Software, Formal analysis, Writing - Original Draft, Writing - Review & Editing, Visualization; Ângelo Bumanglag: Methodology, Software, Formal analysis, Writing - Review & Editing; Yifei Zhang: Methodology, Software, Formal analysis, Writing - Review & Editing; Afra Wohlschlaeger: Methodology, Writing - Review & Editing, Supervision, Project administration.

Active versus sham DLPFC-NAc rTMS for depressed adolescents with anhedonia using resting-state functional magnetic resonance imaging (fMRI): a study protocol for a randomized placebo-controlled trial

BACKGROUND

Anhedonia, which is defined as the inability to feel pleasure, is considered a core symptom of major depressive disorder (MDD). It can lead to several adverse outcomes in adolescents, including heightened disease severity, resistance to antidepressants, recurrence of MDD, and even suicide. Specifically, patients who suffer from anhedonia may exhibit a limited response to selective serotonin reuptake inhibitors (SSRIs) and cognitive behavioral therapy (CBT). Previous researches have revealed a link between anhedonia and abnormalities within the reward circuitry, making the nucleus accumbens (NAc) a potential target for treatment. However, since the NAc is deep within the brain, repetitive transcranial magnetic stimulation (rTMS) has the potential to modulate this specific region. Recent advances have enabled treatment technology to precisely target the left dorsolateral prefrontal cortex (DLPFC) and modify the functional connectivity (FC) between DLPFC and NAc in adolescent patients with anhedonia. Therefore, we plan to conduct a study to explore the safety and effectiveness of using resting-state functional connectivity magnetic resonance imaging (fcMRI)-guided rTMS to alleviate anhedonia in adolescents diagnosed with MDD.

METHODS

The aim of this article is to provide a study protocol for a parallel-group randomized, double-blind, placebo-controlled experiment. The study will involve 88 participants who will be randomly assigned to receive either active rTMS or sham rTMS. The primary object is to measure the percentage change in the severity of anhedonia, using the Snaith-Hamilton Pleasure Scale (SHAPS). The assessment will be conducted from the baseline to 8-week post-treatment period. The secondary outcome includes encompassing fMRI measurements, scores on the 17-item Hamilton Rating Scale for Depression (HAMD-17), the Montgomery Asberg Depression Rating Scale (MADRS), the Chinese Version of Temporal Experience of Pleasure Scale (CV-TEPS), and the Chinese Version of Beck Scale for Suicide Ideation (BSI-CV). The Clinical Global Impression (CGI) scores will also be taken into account, and adverse events will be monitored. These evaluations will be conducted at baseline, as well as at 1, 2, 4, and 8 weeks.

DISCUSSION

If the hypothesis of the current study is confirmed, (fcMRI)-guided rTMS could be a powerful tool to alleviate the core symptoms of MDD and provide essential data to explore the mechanism of anhedonia.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05544071. Registered on 16 September 2022.

Uncovering the Neural Correlates of Anhedonia Subtypes in Major Depressive Disorder: Implications for Intervention Strategies

Major depressive disorder (MDD) represents a serious public health concern, negatively affecting individuals' quality of life and making a substantial contribution to the global burden of disease. Anhedonia is a core symptom of MDD and is associated with poor treatment outcomes. Variability in anhedonia components within MDD has been observed, suggesting heterogeneity in psychopathology across subgroups. However, little is known about anhedonia subgroups in MDD and their underlying neural correlates across subgroups. To address this question, we employed a hierarchical cluster analysis based on Temporal Experience of Pleasure Scale subscales in 60 first-episode, drug-naive MDD patients and 32 healthy controls. Then we conducted a connectome-wide association study and whole-brain voxel-wise functional analyses for identified subgroups. There were three main findings: (1) three subgroups with different anhedonia profiles were identified using a data mining approach; (2) several parts of the reward network (especially pallidum and dorsal striatum) were associated with anticipatory and consummatory pleasure; (3) different patterns of within- and between-network connectivity contributed to the disparities of anhedonia profiles across three MDD subgroups. Here, we show that anhedonia in MDD is not uniform and can be categorized into distinct subgroups, and our research contributes to the understanding of neural underpinnings, offering potential treatment directions. This work emphasizes the need for tailored approaches in the complex landscape of MDD. The identification of homogeneous, stable, and neurobiologically valid MDD subtypes could significantly enhance our comprehension and management of this multifaceted condition.

Exploring the link between functional connectivity of ventral tegmental area and physical fitness in schizophrenia and healthy controls

Decreased physical fitness and being overweight are highly prevalent in schizophrenia, represent a major risk factor for comorbid cardio-vascular diseases and decrease the life expectancy of the patients. Thus, it is important to understand the underlying mechanisms that link psychopathology and weight gain. We hypothesize that the dopaminergic reward system plays an important role in this. We analyzed the seed-based functional connectivity (FC) of the ventral tegmental area (VTA) in a group of schizophrenic patients (n=32) and age-, as well as gender-, matched healthy controls (n=27). We then correlated the resting-state results with physical fitness parameters, obtained in a fitness test, and psychopathology. The FC analysis revealed decreased functional connections between the VTA and the anterior cingulate cortex (ACC), as well as the dorsolateral prefrontal cortex, which negatively correlated with psychopathology, and increased FC between the VTA and the middle temporal gyrus in patients compared to healthy controls, which positively correlated with psychopathology. The decreased FC between the VTA and the ACC of the patient group further positively correlated with total body fat (p = .018, FDR-corr.) and negatively correlated with the overall physical fitness (p = .022). This study indicates a link between decreased physical fitness and higher body fat with functional dysconnectivity between the VTA and the ACC. These findings demonstrate that a dysregulated reward system might also be involved in comorbidities and could pave the way for future lifestyle therapy interventions.

Decision-making and frontoparietal resting-state functional connectivity among impulsive-compulsive diagnoses. Insights from a Bayesian approach

The Iowa Gambling Task (IGT) is one of the most widely used paradigms for assessing decision-making. An impairment in this process may be linked to several psychopathological disorders, such as obsessive-compulsive disorder (OCD), substance abuse disorder (SUD) or attention-deficit/hyperactivity disorder (ADHD), which could make it a good candidate for being consider a transdiagnostic domain. Resting-state functional connectivity (rsFC) has been proposed as a promising biomarker of decision-making. In this study, we aimed to identify idiosyncratic decision-making profiles among healthy people and impulsive-compulsive spectrum patients during the IGT, and to investigate the role of frontoparietal network (FPN) rsFC as a possible biomarker of different decision-making patterns. Using functional near-infrared spectroscopy (fNIRS), rsFC of 114 adults (34 controls; 25 OCD; 41 SUD; 14 ADHD) was obtained. Then, they completed the IGT. Hybrid clustering methods based on individual deck choices yielded three decision-makers subgroups. Cluster 1 (n = 27) showed a long-term advantageous strategy. Cluster 2 (n = 25) presented a maladaptive decision-making strategy. Cluster 3 (n = 62) did not develop a preference for any deck during the task. Interestingly, the proportion of participants in each cluster was not different between diagnostic groups. A Bayesian general linear model showed no credible differences in the IGT performance between diagnostic groups nor credible evidence to support the role of FPN rsFC as a biomarker of decision-making under the IGT context. This study highlights the importance of exploring in depth the behavioral and neurophysiological variables that may drive decision-making in clinical and healthy populations.

Individual differences in delay discounting are associated with dorsal prefrontal cortex connectivity in children, adolescents, and adults

Delay discounting is a measure of impulsive choice relevant in adolescence as it predicts many real-life outcomes, including obesity and academic achievement. However, resting-state functional networks underlying individual differences in delay discounting during youth remain incompletely described. Here we investigate the association between multivariate patterns of functional connectivity and individual differences in impulsive choice in a large sample of children, adolescents, and adults. A total of 293 participants (9-23 years) completed a delay discounting task and underwent 3T resting-state fMRI. A connectome-wide analysis using multivariate distance-based matrix regression was used to examine whole-brain relationships between delay discounting and functional connectivity. These analyses revealed that individual differences in delay discounting were associated with patterns of connectivity emanating from the left dorsal prefrontal cortex, a default mode network hub. Greater delay discounting was associated with greater functional connectivity between the dorsal prefrontal cortex and other default mode network regions, but reduced connectivity with regions in the dorsal and ventral attention networks. These results suggest delay discounting in children, adolescents, and adults is associated with individual differences in relationships both within the default mode network and between the default mode and networks involved in attentional and cognitive control.

Dissociable salience and default mode network modulation in generalized anxiety disorder: a connectome-wide association study

Generalized anxiety disorder (GAD) is a common anxiety disorder experiencing psychological and somatic symptoms. Here, we explored the link between the individual variation in functional connectome and anxiety symptoms, especially psychological and somatic dimensions, which remains unknown. In a sample of 118 GAD patients and matched 85 healthy controls (HCs), we used multivariate distance-based matrix regression to examine the relationship between resting-state functional connectivity (FC) and the severity of anxiety. We identified multiple hub regions belonging to salience network (SN) and default mode network (DMN) where dysconnectivity associated with anxiety symptoms (P < 0.05, false discovery rate [FDR]-corrected). Follow-up analyses revealed that patient's psychological anxiety was dominated by the hyper-connectivity within DMN, whereas the somatic anxiety could be modulated by hyper-connectivity within SN and DMN. Moreover, hypo-connectivity between SN and DMN were related to both anxiety dimensions. Furthermore, GAD patients showed significant network-level FC changes compared with HCs (P < 0.01, FDR-corrected). Finally, we found the connectivity of DMN could predict the individual psychological symptom in an independent GAD sample. Together, our work emphasizes the potential dissociable roles of SN and DMN in the pathophysiology of GAD's anxiety symptoms, which may be crucial in providing a promising neuroimaging biomarker for novel personalized treatment strategies.

Prediction of suicidality in bipolar disorder using variability of intrinsic brain activity and machine learning

Bipolar disorder (BD) is associated with marked suicidal susceptibility, particularly during a major depressive episode. However, the evaluation of suicidal risk remains challenging since it relies mainly on self-reported information from patients. Hence, it is necessary to complement neuroimaging features with advanced machine learning techniques in order to predict suicidal behavior in BD patients. In this study, a total of 288 participants, including 75 BD suicide attempters, 101 BD nonattempters and 112 healthy controls, underwent a resting-state functional magnetic resonance imaging (rs-fMRI). Intrinsic brain activity was measured by amplitude of low-frequency fluctuation (ALFF). We trained and tested a two-level k-nearest neighbors (k-NN) model based on resting-state variability of ALFF with fivefold cross-validation. BD suicide attempters had increased dynamic ALFF values in the right anterior cingulate cortex, left thalamus and right precuneus. Compared to other machine learning methods, our proposed framework had a promising performance with 83.52% accuracy, 78.75% sensitivity and 87.50% specificity. The trained models could also replicate and validate the results in an independent cohort with 72.72% accuracy. These findings based on a relatively large data set, provide a promising way of combining fMRI data with machine learning technique to reliably predict suicide attempt at an individual level in bipolar depression. Overall, this work might enhance our understanding of the neurobiology of suicidal behavior by detecting clinically defined disruptions in the dynamics of instinct brain activity.

A deep connectome learning network using graph convolution for connectome-disease association study

Multivariate analysis approaches provide insights into the identification of phenotype associations in brain connectome data. In recent years, deep learning methods including convolutional neural network (CNN) and graph neural network (GNN), have shifted the development of connectome-wide association studies (CWAS) and made breakthroughs for connectome representation learning by leveraging deep embedded features. However, most existing studies remain limited by potentially ignoring the exploration of region-specific features, which play a key role in distinguishing brain disorders with high intra-class variations, such as autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD). Here, we propose a multivariate distance-based connectome network (MDCN) that addresses the local specificity problem by efficient parcellation-wise learning, as well as associating population and parcellation dependencies to map individual differences. The approach incorporating an explainable method, parcellation-wise gradient and class activation map (p-GradCAM), is feasible for identifying individual patterns of interest and pinpointing connectome associations with diseases. We demonstrate the utility of our method on two largely aggregated multicenter public datasets by distinguishing ASD and ADHD from healthy controls and assessing their associations with underlying diseases. Extensive experiments have demonstrated the superiority of MDCN in classification and interpretation, where MDCN outperformed competitive state-of-the-art methods and achieved a high proportion of overlap with previous findings. As a CWAS-guided deep learning method, our proposed MDCN framework may narrow the bridge between deep learning and CWAS approaches, and provide new insights for connectome-wide association studies.

A transdiagnostic network for psychiatric illness derived from atrophy and lesions

Psychiatric disorders share neurobiology and frequently co-occur. This neurobiological and clinical overlap highlights opportunities for transdiagnostic treatments. In this study, we used coordinate and lesion network mapping to test for a shared brain network across psychiatric disorders. In our meta-analysis of 193 studies, atrophy coordinates across six psychiatric disorders mapped to a common brain network defined by positive connectivity to anterior cingulate and insula, and by negative connectivity to posterior parietal and lateral occipital cortex. This network was robust to leave-one-diagnosis-out cross-validation and specific to atrophy coordinates from psychiatric versus neurodegenerative disorders (72 studies). In 194 patients with penetrating head trauma, lesion damage to this network correlated with the number of post-lesion psychiatric diagnoses. Neurosurgical ablation targets for psychiatric illness (four targets) also aligned with the network. This convergent brain network for psychiatric illness may partially explain high rates of psychiatric comorbidity and could highlight neuromodulation targets for patients with more than one psychiatric disorder.

Individual Differences in Delay Discounting are Associated with Dorsal Prefrontal Cortex Connectivity in Youth

Delay discounting is a measure of impulsive choice relevant in adolescence as it predicts many real-life outcomes, including substance use disorders, obesity, and academic achievement. However, the functional networks underlying individual differences in delay discounting during youth remain incompletely described. Here we investigate the association between multivariate patterns of functional connectivity and individual differences in impulsive choice in a large sample of youth. A total of 293 youth (9-23 years) completed a delay discounting task and underwent resting-state fMRI at 3T. A connectome-wide analysis using multivariate distance-based matrix regression was used to examine whole-brain relationships between delay discounting and functional connectivity was then performed. These analyses revealed that individual differences in delay discounting were associated with patterns of connectivity emanating from the left dorsal prefrontal cortex, a hub of the default mode network. Delay discounting was associated with greater functional connectivity between the dorsal prefrontal cortex and other parts of the default mode network, and reduced connectivity with regions in the dorsal and ventral attention networks. These results suggest that delay discounting in youth is associated with individual differences in relationships both within the default mode network and between the default mode and networks involved in attentional and cognitive control.

Individualized prediction of consummatory anhedonia from functional connectome in major depressive disorder

BACKGROUND

Anhedonia is a key symptom of major depressive disorder (MDD) and other psychiatric diseases. The neural basis of anhedonia has been widely examined, yet the interindividual variability in neuroimaging biomarkers underlying individual-specific symptom severity is not well understood.

METHODS

To establish an individualized prediction model of anhedonia, we applied connectome-based predictive modeling (CPM) to whole-brain resting-state functional connectivity profiles of MDD patients.

RESULTS

The CPM can successfully and reliably predict individual consummatory but not anticipatory anhedonia. The predictive model mainly included salience network (SN), frontoparietal network (FPN), default mode network (DMN), and motor network. Importantly, subsequent computational lesion prediction and consummatory-specific model prediction revealed that connectivity of the SN with DMN and FPN is essential and specific for the prediction of consummatory anhedonia.

CONCLUSIONS

This study shows that brain functional connectivity, especially the connectivity of SN-FPN and SN-DMN, can specifically predict individualized consummatory anhedonia in MDD. These findings suggest the potential of functional connectomes for the diagnosis and prognosis of anhedonia in MDD and other disorders.

Individual variation in brain network topology is linked to course of illness in major depressive disorder

Major depressive disorder (MDD) is a chronic and highly recurrent disorder. The functional connectivity in depression is affected by the cumulative effect of course of illness. However, previous neuroimaging studies on abnormal functional connection have not mainly focused on the disease duration, which is seen as a secondary factor. Here, we used a data-driven analysis (multivariate distance matrix regression) to examine the relationship between the course of illness and resting-state functional dysconnectivity in MDD. This method identified a region in the anterior cingulate cortex, which is most linked to course of illness. Specifically, follow-up seed analyses show this phenomenon resulted from the individual differences in the topological distribution of three networks. In individuals with short-duration MDD, the connection to the default mode network was strong. By contrast, individuals with long-duration MDD showed hyperconnectivity to the ventral attention network and the frontoparietal network. These results emphasized the centrality of the anterior cingulate cortex in the pathophysiology of the increased course of illness and implied critical links between network topography and pathological duration. Thus, dissociable patterns of connectivity of the anterior cingulate cortex is an important dimension feature of the disease process of depression.

Aberrant interhemispheric functional connectivity in major depressive disorder with and without anhedonia

OBJECTIVE

Anhedonia is a core feature of major depressive disorder (MDD), and as a subtype of depression, MDD with anhedonia may have exceptional neurobiological mechanisms. However, the neuropathology of anhedonia in MDD remains unclear. Thus, this study aimed to investigate the brain functional differences between MDD with and without anhedonia.

METHODS

A total of 62 individuals including 22 MDD patients with anhedonia, 20 MDD patients without anhedonia, and 20 healthy controls (HCs) were recruited for this study. All participants underwent 3.0-T functional magnetic resonance imaging scan. Voxel-mirrored homotopic connectivity (VMHC) was employed to quantitatively describe bilateral functional connectivity. Analyses of variance (ANOVA) were performed to obtain brain regions with significant differences among three groups and then post hoc tests were calculated for inter-group comparisons.

RESULTS

The ANOVA revealed significant VMHC differences among three groups in the bilateral middle temporal gyrus (MTG), superior frontal gyrus (SFG), and inferior parietal lobule (IPL) (F = 10.47 ~ 15.09, p &lt; 0.05, AlphaSim corrected). Relative to HCs, MDD with anhedonia showed significantly decreased VMHC in the bilateral MTG (t = -5.368, p &lt; 0.05, AlphaSim corrected), as well as increased VMHC in the bilateral SFG (t = -4.696, p &lt; 0.05, AlphaSim corrected). Compared to MDD without anhedonia, MDD with anhedonia showed significantly decreased VMHC in the bilateral MTG and IPL (t = -5.629 ~ -4.330, p &lt; 0.05, AlphaSim corrected), while increased VMHC in the bilateral SFG (t = 3.926, p &lt; 0.05, AlphaSim corrected). However, no significant difference was found between MDD without anhedonia and HCs.

CONCLUSION

The present findings suggest that MDD with and without anhedonia exhibit different patterns of interhemispheric connectivity. Anhedonia in MDD is related to aberrant interhemispheric connectivity within brain regions involved in the frontal-temporal-parietal circuit.

Hyperconnectivity and altered interactions of a nucleus accumbens network in post-stroke depression

Post-stroke depression is a common complication of stroke. To date, no consistent locus of injury is associated with this complication. Here, we probed network dynamics and structural alterations in post-stroke depression in four functional circuits linked to major depressive disorder and a visual network, which served as a control network. Forty-four participants with recent stroke (mean age = 69.03, standard deviation age = 8.59, age range = 51–86 and gender: female = 10) and 16 healthy volunteers (mean age = 71.53, standard deviation age = 10.62, age range = 51–84 and gender: female = 11) were imaged with 3-Tesla structural, diffusion and resting-state functional MRI. The Geriatric Depression Scale was administered to measure depression severity. Associations between depression severity and functional connectivity were investigated within networks seeded from nucleus accumbens, amygdala, dorsolateral prefrontal cortex and primary visual cortex. In addition, the default mode network was identified by connectivity with medial prefrontal cortex and posterior cingulate cortex. Circuits that exhibited altered activity associated with depression severity were further investigated by extracting within-network volumetric and microstructural measures from structural images. In the stroke group, functional connectivity within the nucleus accumbens-seeded network (left hemisphere: P = 0.001; and right hemisphere: P = 0.004) and default mode network (cluster one: P &lt; 0.001; and cluster two: P &lt; 0.001) correlated positively with depressive symptoms. Normal anticorrelations between these two networks were absent in patients with post-stroke depression. Grey matter volume of the right posterior cingulate cortex (Pearson correlation coefficient = −0.286, P = 0.03), as well as microstructural measures in the posterior cingulate cortex (right: Pearson correlation coefficient = 0.4, P = 0.024; and left: Pearson correlation coefficient = 0.3, P = 0.048), right medial prefrontal cortex (Pearson correlation coefficient = 0.312, P = 0.039) and the medial forebrain bundle (Pearson correlation coefficient = 0.450, P = 0.003), a major projection pathway interconnecting the nucleus accumbens-seeded network and linking to medial prefrontal cortex, were associated with depression severity. Depression after stroke is marked by reduced mutual inhibition between functional circuits involving nucleus accumbens and default mode network as well as volumetric and microstructural changes within these networks. Aberrant network dynamics present in patients with post-stroke depression are therefore likely to be influenced by secondary, pervasive alterations in grey and white matter, remote from the site of injury.

Smaller putamen volumes are associated with greater problems in external emotional regulation in depressed adolescents with nonsuicidal self-injury

The functions of nonsuicidal self-injury (NSSI) consist of social and emotional aspects (Social influence, Sensation seeking, Internal and External emotion regulation). Previous studies have indicated that dysfunction in reward-related brain structures especially the striatum might drive this habitual behavior. However, no studies to date have investigated the associations between striatum and different functions for adolescents engaging in NSSI behaviors. Here, we recruited 35 depressed adolescents with recent NSSI behaviors and 36 healthy controls and acquired structural brain images, depressive symptoms, social, academic and family environments assessments, in addition to NSSI functions in patients only. Subcortical volumes and cortical thickness were estimated with FreeSurfer. Mixed linear regressions were performed to examine associations between striatal structures (caudate, putamen, nucleus accumbens, pallidum) and NSSI functions, with age, sex, total intracranial volume, hemisphere and depression severity included as covariates. Effect of environmental factors and potential associations with cortical thickness and other subcortical volumes were also tested. We found that, among the four functions, external emotional regulation represented the main function for NSSI engagement. Increased external emotion regulation was significantly associated with smaller putamen volume. No environmental factors biased the association with putamen. No associations with other cortical or subcortical regions were observed. Our findings suggested that smaller putamen might be a biomarker of NSSI engagement for depressed adolescents when they regulated frustrated or angry emotions. The results have potentially clinical implications in early identification and brain intervention of NSSI in youth.

Mendelian randomization analyses support causal relationships between brain imaging-derived phenotypes and risk of psychiatric disorders

Observational studies have reported the correlations between brain imaging-derived phenotypes (IDPs) and psychiatric disorders; however, whether the relationships are causal is uncertain. We conducted bidirectional two-sample Mendelian randomization (MR) analyses to explore the causalities between 587 reliable IDPs (N = 33,224 individuals) and 10 psychiatric disorders (N = 9,725 to 161,405). We identified nine IDPs for which there was evidence of a causal influence on risk of schizophrenia, anorexia nervosa and bipolar disorder. For example, 1 s.d. increase in the orientation dispersion index of the forceps major was associated with 32% lower odds of schizophrenia risk. Reverse MR indicated that only genetically predicted schizophrenia was positively associated with two IDPs, the cortical surface area and the volume of the right pars orbitalis. We established the BrainMR database ( http://www.bigc.online/BrainMR/ ) to share our results. Our findings provide potential strategies for the prediction and intervention for psychiatric disorder risk at the brain-imaging level.

Decision value signals in the ventromedial prefrontal cortex and motivational and hedonic symptoms across mood and psychotic disorders

Deficits in motivation and pleasure are common across many psychiatric disorders, and manifest as symptoms of amotivation and anhedonia, which are prominent features of both mood and psychotic disorders. Here we provide evidence for an association between neural value signals and symptoms of amotivation and anhedonia across adults with major depression, bipolar disorder, schizophrenia, or no psychiatric diagnosis. We found that value signals in the ventromedial prefrontal cortex (vmPFC) during intertemporal decision-making were dampened in individuals with greater motivational and hedonic deficits, after accounting for primary diagnosis. This relationship remained significant while controlling for diagnosis-specific symptoms of mood and psychosis, such as depression as well as positive and negative symptoms. Our results demonstrate that dysfunction in the vmPFC during value-based decision-making is specifically linked to motivational and hedonic impairments. These findings provide a quantitative neural target for the potential development of novel treatments for amotivation and anhedonia.

Pharmacological investigations of effort-based decision-making in humans: Naltrexone and nicotine

Many mental health disorders are characterized by an impaired ability, or willingness, to exert effort to obtain rewards. This impairment is modeled in effort-based decision tasks, and neuropharmacological studies implicate dopamine in this process. However, other transmitter systems such as opioidergic and cholinergic systems have received less attention. Here, in two separate studies we tested the acute effects of naltrexone and nicotine on effort-based decision-making in healthy adults. In Study 1, we compared naltrexone (50mg and 25mg) to placebo, and in Study 2, a pilot study, we compared nicotine (7mg) to placebo. In both studies, participants completed the Effort Expenditure for Rewards Task (EEfRT), which measured effort-based decision-making related to monetary rewards. Although subjects expended greater effort for larger reward magnitude and when there was a higher probability of receiving the reward, neither naltrexone nor nicotine affected willingness to exert effort for monetary rewards. Although the drugs produced significant and typical drug effects on measures of mood and behavior, they did not alter effort-based decision-making. This has implications both for the clinical use of these drugs, as well as for understanding the neuropharmacology of effort-related behavior.

Functional Connectivity of the Nucleus Accumbens and Changes in Appetite in Patients With Depression

Importance

Major depressive disorder (MDD) is characterized by a substantial burden on health, including changes in appetite and body weight. Heterogeneity of depressive symptoms has hampered the identification of biomarkers that robustly generalize to most patients, thus calling for symptom-based mapping.

Objective

To define the functional architecture of the reward circuit subserving increases vs decreases in appetite and body weight in patients with MDD by specifying their contributions and influence on disease biomarkers using resting-state functional connectivity (FC).

Design, Setting, and Participants

In this case-control study, functional magnetic resonance imaging (fMRI) data were taken from the Marburg-Münster FOR 2107 Affective Disorder Cohort Study (MACS), collected between September 2014 and November 2016. Cross-sectional data of patients with MDD (n = 407) and healthy control participants (n = 400) were analyzed from March 2018 to June 2022.

Main Outcomes and Measures

Changes in appetite during the depressive episode and their association with FC were examined using fMRI. By taking the nucleus accumbens (NAcc) as seed of the reward circuit, associations with opposing changes in appetite were mapped, and a sparse symptom-specific elastic-net model was built with 10-fold cross-validation.

Results

Among 407 patients with MDD, 249 (61.2%) were women, and the mean (SD) age was 36.79 (13.4) years. Reduced NAcc-based FC to the ventromedial prefrontal cortex (vmPFC) and the hippocampus was associated with reduced appetite (vmPFC: bootstrap r = 0.13; 95% CI, 0.02-0.23; hippocampus: bootstrap r = 0.15; 95% CI, 0.05-0.26). In contrast, reduced NAcc-based FC to the insular ingestive cortex was associated with increased appetite (bootstrap r = -0.14; 95% CI, -0.24 to -0.04). Critically, the cross-validated elastic-net model reflected changes in appetite based on NAcc FC and explained variance increased with increasing symptom severity (all patients: bootstrap r = 0.24; 95% CI, 0.16-0.31; patients with Beck Depression Inventory score of 28 or greater: bootstrap r = 0.42; 95% CI, 0.25-0.58). In contrast, NAcc FC did not classify diagnosis (MDD vs healthy control).

Conclusions and Relevance

In this study, NAcc-based FC reflected important individual differences in appetite and body weight in patients with depression that can be leveraged for personalized prediction. However, classification of diagnosis using NAcc-based FC did not exceed chance levels. Such symptom-specific associations emphasize the need to map biomarkers onto more confined facets of psychopathology to improve the classification and treatment of MDD.

Neuropsychological Performance Among Individuals at Clinical High-Risk for Psychosis vs Putatively Low-Risk Peers With Other Psychopathology: A Systematic Review and Meta-Analysis

BACKGROUND AND HYPOTHESIS

Youth at clinical high-risk (CHR) for psychosis present with neuropsychological impairments relative to healthy controls (HC), but whether these impairments are distinguishable from those seen among putatively lower risk peers with other psychopathology remains unknown. We hypothesized that any excess impairment among CHR cohorts beyond that seen in other clinical groups is minimal and accounted for by the proportion who transition to psychosis (CHR-T).

STUDY DESIGN

We performed a systematic review and meta-analysis of studies comparing cognitive performance among CHR youth to clinical comparators (CC) who either sought mental health services but did not meet CHR criteria or presented with verified nonpsychotic psychopathology.

STUDY RESULTS

Twenty-one studies were included representing nearly 4000 participants. Individuals at CHR showed substantial cognitive impairments relative to HC (eg, global cognition: g = -0.48 [-0.60, -0.34]), but minimal impairments relative to CC (eg, global cognition: g = -0.13 [-0.20, -0.06]). Any excess impairment among CHR was almost entirely attributable to CHR-T; impairment among youth at CHR without transition (CHR-NT) was typically indistinguishable from CC (eg, global cognition, CHR-T: g = -0.42 [-0.64, -0.19], CHR-NT: g = -0.09 [-0.18, 0.00]; processing speed, CHR-T: g = -0.59 [-0.82, -0.37], CHR-NT: g = -0.12 [-0.25, 0.07]; working memory, CHR-T: g = -0.42 [-0.62, -0.22], CHR-NT: g = -0.03 [-0.14, 0.08]).

CONCLUSIONS

Neurocognitive impairment in CHR cohorts should be interpreted cautiously when psychosis or even CHR status is the specific clinical syndrome of interest as these impairments most likely represent a transdiagnostic vs psychosis-specific vulnerability.

Shared and distinct reward neural mechanisms among patients with schizophrenia, major depressive disorder, and bipolar disorder: an effort-based functional imaging study

Unwillingness to exert effort for rewards has been found in patients with schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD), but the underlying shared and distinct reward neural mechanisms remain unclear. This study aimed to compare the neural correlates of such impairments across different diagnoses. The neural responses in an effort-expenditure for reward task (EEfRT) were assessed in 20 SCZ patients, 23 MDD patients, 17 BD patients, and 30 healthy controls (HC). The results found shared activation in the cingulate gyrus, the medial frontal gyrus, and the middle frontal gyrus during the EEfRT administration. Compared to HC, SCZ patients exhibited stronger variations of functional connectivity between the right caudate and the left amygdala, the left hippocampus and the left putamen, with increase in reward magnitude. In MDD patients, an enhanced activation compared to HC in the right superior temporal gyrus was found with the increase of reward magnitude. The variations of functional connectivity between the caudate and the right cingulate gyrus, the left postcentral gyrus and the left inferior parietal lobule with increase in reward magnitude were weaker than that found in HC. In BD patients, the degree of activation in the left precuneus was increased, but that in the left dorsolateral prefrontal cortex was decreased with increase in reward probability compared to HC. These findings demonstrate both shared and distinct reward neural mechanisms associated with EEfRT in patients with SCZ, MDD, and BD, implicating potential intervention targets to alleviate amotivation in these clinical disorders.

Neural substrates of motivational dysfunction across neuropsychiatric conditions: Evidence from meta-analysis and lesion network mapping

Motivational dysfunction constitutes one of the fundamental dimensions of psychopathology cutting across traditional diagnostic boundaries. However, it is unclear whether there is a common neural circuit responsible for motivational dysfunction across neuropsychiatric conditions. To address this issue, the current study combined a meta-analysis on psychiatric neuroimaging studies of reward/loss anticipation and consumption (4308 foci, 438 contrasts, 129 publications) with a lesion network mapping approach (105 lesion cases). Our meta-analysis identified transdiagnostic hypoactivation in the ventral striatum (VS) for clinical/at-risk conditions compared to controls during the anticipation of both reward and loss. Moreover, the VS subserves a key node in a distributed brain network which encompasses heterogeneous lesion locations causing motivation-related symptoms. These findings do not only provide the first meta-analytic evidence of shared neural alternations linked to anticipatory motivation-related deficits, but also shed novel light on the role of VS dysfunction in motivational impairments in terms of both network integration and psychological functions. Particularly, the current findings suggest that motivational dysfunction across neuropsychiatric conditions is rooted in disruptions of a common brain network anchored in the VS, which contributes to motivational salience processing rather than encoding positive incentive values.

Altered brain activation during reward anticipation in bipolar disorder

Although altered reward sensitivity has been observed in individuals with bipolar disorder (BD), the brain function findings related to reward processing remain unexplored and inconsistent. This meta-analysis aimed to identify brain activation alterations underlying reward anticipation in BD. A systematic literature research was conducted to identify fMRI studies of reward-relevant tasks performed by BD individuals. Using Anisotropic Effect Size Signed Differential Mapping, whole-brain and ROI of the ventral striatum (VS) coordinate-based meta-analyses were performed to explore brain regions showing anomalous activation in individuals with BD compared to healthy controls (HC), respectively. A total of 21 studies were identified in the meta-analysis, 15 of which were included in the whole-brain meta-analysis and 17 in the ROI meta-analysis. The whole-brain meta-analysis revealed hypoactivation in the bilateral angular gyrus and right inferior frontal gyrus during reward anticipation in individuals with BD compared to HC. No significant activation differences were observed in bilateral VS between two groups by whole-brain or ROI-based meta-analysis. Individuals with BD type I and individuals with euthymic BD showed altered activation in prefrontal, angular, fusiform, middle occipital gyrus, and striatum. Hypoactivation in the right angular gyrus was positively correlated with the illness duration of BD. The present study reveals the potential neural mechanism underlying impairment in reward anticipation in BD. Some clinical features such as clinical subtype, mood state, and duration of illness confound the underlying neurobiological abnormality reward anticipation in BD. These findings may have implications for identifying clinically relevant biomarkers to guide intervention strategies for BD.

Impulsivity and Emotional Dysregulation Predict Choice Behavior During a Mixed-Strategy Game in Adolescents With Borderline Personality Disorder

Impulsivity and emotional dysregulation are two core features of borderline personality disorder (BPD), and the neural mechanisms recruited during mixed-strategy interactions overlap with frontolimbic networks that have been implicated in BPD. We investigated strategic choice patterns during the classic two-player game, Matching Pennies, where the most efficient strategy is to choose each option randomly from trial-to-trial to avoid exploitation by one’s opponent. Twenty-seven female adolescents with BPD (mean age: 16 years) and twenty-seven age-matched female controls (mean age: 16 years) participated in an experiment that explored the relationship between strategic choice behavior and impulsivity in both groups and emotional dysregulation in BPD. Relative to controls, BPD participants showed marginally fewer reinforcement learning biases, particularly decreased lose-shift biases, increased variability in reaction times (coefficient of variation; CV), and a greater percentage of anticipatory decisions. A subset of BPD participants with high levels of impulsivity showed higher overall reward rates, and greater modulation of reaction times by outcome, particularly following loss trials, relative to control and BPD participants with lower levels of impulsivity. Additionally, BPD participants with higher levels of emotional dysregulation showed marginally increased reward rate and increased entropy in choice patterns. Together, our preliminary results suggest that impulsivity and emotional dysregulation may contribute to variability in mixed-strategy decision-making in female adolescents with BPD.

Obesity and Cerebral Blood Flow in the Reward Circuitry of Youth With Bipolar Disorder

BACKGROUND

Bipolar disorder (BD) is associated with elevated body mass index (BMI) and increased rates of obesity. Obesity among individuals with BD is associated with more severe course of illness. Motivated by previous research on BD and BMI in youth as well as brain findings in the reward circuit, the current study investigates differences in cerebral blood flow (CBF) in youth BD with and without comorbid overweight/obesity (OW/OB).

METHODS

Participants consisted of youth, ages 13-20 years, including BD with OW/OB (BDOW/OB; n = 25), BD with normal weight (BDNW; n = 55), and normal-weight healthy controls (HC; n = 61). High-resolution T1-weighted and pseudo-continuous arterial spin labeling images were acquired using 3 Tesla magnetic resonance imaging. CBF differences were assessed using both region of interest and whole-brain voxel-wise approaches.

RESULTS

Voxel-wise analysis revealed significantly higher CBF in reward-associated regions in the BDNW group relative to the HC and BDOW/OB groups. CBF did not differ between the HC and BDOW/OB groups. There were no significant region of interest findings.

CONCLUSIONS

The current study identified distinct CBF levels relating to BMI in BD in the reward circuit, which may relate to underlying differences in cerebral metabolism, compensatory effects, and/or BD severity. Future neuroimaging studies are warranted to examine for changes in the CBF-OW/OB link over time and in relation to treatment.

Anhedonia in Depression and Schizophrenia: Brain Reward and Aversion Circuits

Anhedonia, which is defined as markedly diminished interest or pleasure, is a prominent symptom of psychiatric disorders, most notably major depressive disorder (MDD) and schizophrenia. Anhedonia is considered a transdiagnostic symptom that is associated with deficits in neural reward and aversion functions. Here, we review the characteristics of anhedonia in depression and schizophrenia as well as shared or disorder-specific anhedonia-related alterations in reward and aversion pathways of the brain. In particular, we highlight that anhedonia is characterized by impairments in anticipatory pleasure and integration of reward-related information in MDD, whereas anhedonia in schizophrenia is associated with neurocognitive deficits in representing the value of rewards. Dysregulation of the frontostriatal circuit and mesocortical and mesolimbic circuit systems may be the transdiagnostic neurobiological basis of reward and aversion impairments underlying anhedonia in these two disorders. Blunted aversion processing in depression and relatively strong aversion in schizophrenia are primarily attributed to the dysfunction of the habenula, insula, amygdala, and anterior cingulate cortex. Furthermore, patients with schizophrenia appear to exhibit greater abnormal activation and extended functional coupling than those with depression. From a transdiagnostic perspective, understanding the neural mechanisms underlying anhedonia in patients with psychiatric disorders may help in the development of more targeted and efficacious treatment and intervention strategies.

Different depression: motivational anhedonia governs antidepressant efficacy in Huntington's disease

Depression is more common in neurodegenerative diseases such as Huntington's disease than the general population. Antidepressant efficacy is well-established for depression within the general population: a recent meta-analysis showed serotonin norepinephrine reuptake inhibitors, tricyclic antidepressants and mirtazapine outperformed other antidepressants. Despite the severe morbidity, antidepressant choice in Huntington's disease is based on Class IV evidence. We used complementary approaches to determine treatment choice for depression in Huntington's disease: propensity score analyses of antidepressant treatment outcome using the ENROLL-HD data set, and a dissection of the cognitive mechanisms underlying depression in Huntington's disease using a cognitive battery based on the Research Domain Criteria for Depression. Study 1 included ENROLL-HD 5486 gene-positive adult patients started on an antidepressant medication for depression. Our outcome measures were depression (Hospital Anxiety and Depression Scale or Problem Behaviours Assessment 'Depressed Mood' item) at first follow-up (primary outcome) and all follow-ups (secondary outcome). The intervention was antidepressant class. We used Svyglm&Twang in R to perform propensity scoring, using known variables (disease progression, medical comorbidity, psychiatric morbidity, sedatives, number of antidepressants, demographics and antidepressant contraindications) to determine the probability of receiving different antidepressants (propensity score) and then included the propensity score in a model of treatment efficacy. Study 2 recruited 51 gene-positive adult patients and 26 controls from the South Wales Huntington's Disease Management Service. Participants completed a motor assessment, in addition to measures of depression and apathy, followed by tasks measuring consummatory anhedonia, motivational anhedonia, learning from reward and punishment and reaction to negative outcome. We used generalised linear models to determine the association between task performance and depression scores. Study 1 showed selective serotonin reuptake inhibitors outperformed serotonin norepinephrine reuptake inhibitors on the primary outcome (P = 0.048), whilst both selective serotonin reuptake inhibitors (P = 0.00069) and bupropion (P = 0.0045) were superior to serotonin norepinephrine reuptake inhibitors on the secondary outcome. Study 2 demonstrated an association between depression score and effort for reward that was not explained by apathy. No other mechanisms were associated with depression score. We found that selective serotonin reuptake inhibitors and bupropion outperform serotonin norepinephrine reuptake inhibitors at alleviating depression in Huntington's disease. Moreover, motivational anhedonia appears the most significant mechanism underlying depression in Huntington's disease. Bupropion is improves motivational anhedonia and has a synergistic effect with selective serotonin reuptake inhibitors. This work provides the first large-scale, objective evidence to determine treatment choice for depression in Huntington's disease, and provides a model for determining antidepressant efficacy in other neurodegenerative diseases.

Alterations of Static and Dynamic Functional Connectivity of the Nucleus Accumbens in Patients With Major Depressive Disorder

BACKGROUND

Major depressive disorder (MDD) is associated with dysfunction of the reward system. As an important node in the reward system, the resting-state functional connectivity of the nucleus accumbens (NAc) is related to the etiology of MDD. However, an increasing number of recent studies propose that brain activity is dynamic over time, no study to date has examined whether the NAc dynamic functional connectivity (DFC) is changed in patients with MDD. Moreover, few studies have examined the impact of the clinical characteristics of patients with MDD.

METHODS

A total of 220 MDD patients and 159 healthy controls (HCs), group-matched for age, sex, and education level, underwent resting-state functional magnetic resonance imagining (rs-fMRI) scans. Seed-based resting-state functional connectivity (RSFC) and DFC of the NAc were conducted. Two sample t-tests were performed to alter RSFC/DFC of NAc. In addition, we examined the association between altered RSFC/DFC and depressive severity using Pearson correlation. Finally, we divided patients with MDD into different subgroups according to clinical characteristics and tested whether there were differences between the subgroups.

RESULTS

Compared with the HCs, MDD patients show reduced the NAc-based RSFC with the dorsolateral prefrontal cortex (DLPFC), hippocampus, middle temporal gyrus (MTG), inferior temporal gyrus (ITG), precuneus, and insula, and patients with MDD show reduced the NAc-based DFC with the DLPFC, ventromedial prefrontal cortex (VMPFC), ventrolateral prefrontal cortex (VLPFC), MTG, ITG, and insula. MDD severity was associated with RSFC between the NAc and precentral gyrus (r = 0.288, p = 0.002, uncorrected) and insula (r = 0.272, p = 0.003, uncorrected).

CONCLUSION

This study demonstrates abnormal RSFC and DFC between the NAc and distributed cerebral regions in MDD patients, characterized by decreased RSFC and DFC of the NAc connecting with the reward, executive, default-mode, and salience network. Our results expand previous descriptions of the NAc RSFC abnormalities in MDD, and the altered RSFC/DFC may reflect the disrupted function of the NAc.

Evidence for Schizophrenia-Specific Pathophysiology of Nicotine Dependence

Tobacco use is the top preventable cause of early mortality in schizophrenia. Over 60% of people with schizophrenia smoke, three times the general prevalence. The biological basis of this increased risk is not understood, and existing interventions do not target schizophrenia-specific pathology. We therefore used a connectome-wide analysis to identify schizophrenia-specific circuits of nicotine addiction. We reanalyzed data from two studies: In Cohort 1, 35 smokers (18 schizophrenia, 17 control) underwent resting-state fMRI and clinical characterization. A multivariate pattern analysis of whole-connectome data was used to identify the strongest links between cigarette use and functional connectivity. In Cohort 2, 12 schizophrenia participants and 12 controls were enrolled in a randomized, controlled crossover study of nicotine patch with resting-state fMRI. We correlated change in network functional connectivity with nicotine dose. In Cohort 1, the strongest (p < 0.001) correlate between connectivity and cigarette use was driven by individual variation in default mode network (DMN) topography. In individuals with greater daily cigarette consumption, we observed a pathological expansion of the DMN territory into the identified parieto-occipital region, while in individuals with lower daily cigarette consumption, this region was external to the DMN. This effect was entirely driven by schizophrenia participants. Given the relationship between DMN topography and nicotine use we observed in Cohort 1, we sought to directly test the impact of nicotine on this network using an independent second cohort. In Cohort 2, nicotine reduced DMN connectivity in a dose-dependent manner (R = -0.50; 95% CI -0.75 to -0.12, p < 0.05). In the placebo condition, schizophrenia subjects had hyperconnectivity compared to controls (p < 0.05). Nicotine administration normalized DMN hyperconnectivity in schizophrenia. We here provide direct evidence that the biological basis of nicotine dependence is different in schizophrenia and in non-schizophrenia populations. Our results suggest the high prevalence of nicotine use in schizophrenia may be an attempt to correct a network deficit known to interfere with cognition.

Neural signatures of saliency-mapping in anhedonia: A narrative review

Anhedonia is the loss of pleasure or motivation to engage in previously enjoyable activities, and is a transdiagnostic symptom associated with significant clinical impairment. Anhedonia is implicated in several different psychiatric disorders, presenting a promising opportunity for transdiagnostic treatment. Thus, developing targeted treatments for anhedonia is of critical importance for population mental health. An important first step in doing so is establishing a thorough understanding of the neural correlates of anhedonia. The Triple Network Model of Psychopathology provides a frame for how brain activity may go awry in anhedonia, specifically in the context of Salience Network (SN) function (i.e., saliency-mapping). We present a narrative review examining saliency-mapping as it relates to anhedonia severity in depressed and transdiagnostic adult samples. Results revealed increased anhedonia to be associated with hyperactivity of the SN at rest and in the context of negative stimuli, as well as a global lack of SN engagement in the context of positive stimuli. Potential treatments for anhedonia are placed within this model, and future directions for research are discussed.