Smartphones and the Neuroscience of Mental Health.

Improvements in understanding the neurobiological basis of mental illness have unfortunately not translated into major advances in treatment. At this point, it is clear that psychiatric disorders are exceedingly complex and that, in order to account for and leverage this complexity, we need to collect longitudinal data sets from much larger and more diverse samples than is practical using traditional methods. We discuss how smartphone-based research methods have the potential to dramatically advance our understanding of the neuroscience of mental health. This, we expect, will take the form of complementing lab-based hard neuroscience research with dense sampling of cognitive tests, clinical questionnaires, passive data from smartphone sensors, and experience-sampling data as people go about their daily lives. Theory- and data-driven approaches can help make sense of these rich data sets, and the combination of computational tools and the big data that smartphones make possible has great potential value for researchers wishing to understand how aspects of brain function give rise to, or emerge from, states of mental health and illness.

Elevated emotion network connectivity is associated with fluctuations in depression.

Elevated emotion network connectivity is thought to leave people vulnerable to become and stay depressed. The mechanism through which this arises is however unclear. Here, we test the idea that the connectivity of emotion networks is associated with more extreme fluctuations in depression over time, rather than necessarily more severe depression. We gathered data from two independent samples of N = 155 paid students and N = 194 citizen scientists who rated their positive and negative emotions on a smartphone app twice a day and completed a weekly depression questionnaire for 8 wk. We constructed thousands of personalized emotion networks for each participant and tested whether connectivity was associated with severity of depression or its variance over 8 wk. Network connectivity was positively associated with baseline depression severity in citizen scientists, but not paid students. In contrast, 8-wk variance of depression was correlated with network connectivity in both samples. When controlling for depression variance, the association between connectivity and baseline depression severity in citizen scientists was no longer significant. We replicated these findings in an independent community sample (N = 519). We conclude that elevated network connectivity is associated with greater variability in depression symptoms. This variability only translates into increased severity in samples where depression is on average low and positively skewed, causing mean and variance to be more strongly correlated. These findings, although correlational, suggest that while emotional network connectivity could predispose individuals to severe depression, it could also be leveraged to bring about therapeutic improvements.

Estimating the prognostic value of cross-sectional network connectivity for treatment response in depression.

BACKGROUND: Tightly connected symptom networks have previously been linked to treatment resistance, but most findings come from small-sample studies comparing single responder v. non-responder networks. We aimed to estimate the association between baseline network connectivity and treatment response in a large sample and benchmark its prognostic value against baseline symptom severity and variance. METHODS: N = 40 518 patients receiving treatment for depression in routine care in England from 2015-2020 were analysed. Cross-sectional networks were constructed using the Patient Health Questionnaire-9 (PHQ-9) for responders and non-responders (N = 20 259 each). To conduct parametric tests investigating the contribution of PHQ-9 sum score mean and variance to connectivity differences, networks were constructed for 160 independent subsamples of responders and non-responders (80 each, n = 250 per sample). RESULTS: The baseline non-responder network was more connected than responders (3.15 v. 2.70, S = 0.44, p < 0.001), but effects were small, requiring n = 750 per group to have 85% power. Parametric analyses revealed baseline network connectivity, PHQ-9 sum score mean, and PHQ-9 sum score variance were correlated (r = 0.20-0.58, all p < 0.001). Both PHQ-9 sum score mean (ß = -1.79, s.e. = 0.07, p < 0.001), and PHQ-9 sum score variance (ß = -1.67, s.e. = 0.09, p < 0.001) had larger effect sizes for predicting response than connectivity (ß = -1.35, s.e. = 0.12, p < 0.001). The association between connectivity and response disappeared when PHQ-9 sum score variance was accounted for (ß = -0.28, s.e. = 0.19, p = 0.14). We replicated these results in patients completing longer treatment (8-12 weeks, N = 22 952) and using anxiety symptom networks (N = 70 620). CONCLUSIONS: The association between baseline network connectivity and treatment response may be largely due to differences in baseline score variance.

Multimodal mechanisms of human socially reinforced learning across neurodegenerative diseases.

Social feedback can selectively enhance learning in diverse domains. Relevant neurocognitive mechanisms have been studied mainly in healthy persons, yielding correlational findings. Neurodegenerative lesion models, coupled with multimodal brain measures, can complement standard approaches by revealing direct multidimensional correlates of the phenomenon. To this end, we assessed socially reinforced and non-socially reinforced learning in 40 healthy participants as well as persons with behavioural variant frontotemporal dementia (n = 21), Parkinson's disease (n = 31) and Alzheimer's disease (n = 20). These conditions are typified by predominant deficits in social cognition, feedback-based learning and associative learning, respectively, although all three domains may be partly compromised in the other conditions. We combined a validated behavioural task with ongoing EEG signatures of implicit learning (medial frontal negativity) and offline MRI measures (voxel-based morphometry). In healthy participants, learning was facilitated by social feedback relative to non-social feedback. In comparison with controls, this effect was specifically impaired in behavioural variant frontotemporal dementia and Parkinson's disease, while unspecific learning deficits (across social and non-social conditions) were observed in Alzheimer's disease. EEG results showed increased medial frontal negativity in healthy controls during social feedback and learning. Such a modulation was selectively disrupted in behavioural variant frontotemporal dementia. Neuroanatomical results revealed extended temporo-parietal and fronto-limbic correlates of socially reinforced learning, with specific temporo-parietal associations in behavioural variant frontotemporal dementia and predominantly fronto-limbic regions in Alzheimer's disease. In contrast, non-socially reinforced learning was consistently linked to medial temporal/hippocampal regions. No associations with cortical volume were found in Parkinson's disease. Results are consistent with core social deficits in behavioural variant frontotemporal dementia, subtle disruptions in ongoing feedback-mechanisms and social processes in Parkinson's disease and generalized learning alterations in Alzheimer's disease. This multimodal approach highlights the impact of different neurodegenerative profiles on learning and social feedback. Our findings inform a promising theoretical and clinical agenda in the fields of social learning, socially reinforced learning and neurodegeneration.

The Precision in Psychiatry (PIP) study: Testing an internet-based methodology for accelerating research in treatment prediction and personalisation.

BACKGROUND: Evidence-based treatments for depression exist but not all patients benefit from them. Efforts to develop predictive models that can assist clinicians in allocating treatments are ongoing, but there are major issues with acquiring the volume and breadth of data needed to train these models. We examined the feasibility, tolerability, patient characteristics, and data quality of a novel protocol for internet-based treatment research in psychiatry that may help advance this field. METHODS: A fully internet-based protocol was used to gather repeated observational data from patient cohorts receiving internet-based cognitive behavioural therapy (iCBT) (N = 600) or antidepressant medication treatment (N = 110). At baseline, participants provided > 600 data points of self-report data, spanning socio-demographics, lifestyle, physical health, clinical and other psychological variables and completed 4 cognitive tests. They were followed weekly and completed another detailed clinical and cognitive assessment at week 4. In this paper, we describe our study design, the demographic and clinical characteristics of participants, their treatment adherence, study retention and compliance, the quality of the data gathered, and qualitative feedback from patients on study design and implementation. RESULTS: Participant retention was 92% at week 3 and 84% for the final assessment. The relatively short study duration of 4 weeks was sufficient to reveal early treatment effects; there were significant reductions in 11 transdiagnostic psychiatric symptoms assessed, with the largest improvement seen for depression. Most participants (66%) reported being distracted at some point during the study, 11% failed 1 or more attention checks and 3% consumed an intoxicating substance. Data quality was nonetheless high, with near perfect 4-week test retest reliability for self-reported height (ICC = 0.97). CONCLUSIONS: An internet-based methodology can be used efficiently to gather large amounts of detailed patient data during iCBT and antidepressant treatment. Recruitment was rapid, retention was relatively high and data quality was good. This paper provides a template methodology for future internet-based treatment studies, showing that such an approach facilitates data collection at a scale required for machine learning and other data-intensive methods that hope to deliver algorithmic tools that can aid clinical decision-making in psychiatry.

The Effectiveness of Low-Intensity Psychological Interventions for Comorbid Depression and Anxiety in Patients with Long-Term Conditions: A Real-World Naturalistic Observational Study in IAPT Integrated Care.

BACKGROUND: Low-intensity psychological interventions may be a cost-effective, accessible solution for treating depression and anxiety in patients with long-term conditions, but evidence from real-world service settings is lacking. This study examined the effectiveness of low-intensity psychological interventions provided in the Improving Access to Psychological Therapies programme in England for patients with and without long-term conditions. METHODS: A retrospective analysis was conducted on patients (total N = 21,051, long-term conditions n = 4024) enrolled in three low-intensity psychological interventions, i.e. Internet-delivered cognitive behavioural therapy (iCBT), guided self-help (GSH), and psychoeducational group therapy (PGT) within a Talking Therapies service from 2016 to 2020. Primary outcomes included pre-post-treatment changes in depression (Patient Health Questionnaire-9) and anxiety (Generalised Anxiety Disorder-7). RESULTS: Overall, both cohorts significantly improved on all outcomes post-treatment, with large effect sizes. Patients with long-term conditions experienced a greater reduction in depression while those without experienced a greater reduction in anxiety, but these differences were marginal (< 1 score difference on both measures). No difference between the cohorts was shown when comparing the differential effectiveness across interventions, but those engaging in iCBT showed greater reduction in depression and anxiety than those in GSH and PGT, while those in GSH improved more than PGT. CONCLUSIONS: Low-intensity psychological interventions, particularly iCBT, were effective in treating depression and anxiety in patients with long-term conditions in a real-world service setting. Our large-scale study supports the continued and increased implementation of low-intensity psychological interventions for this subpopulation via integrated care.

Model-Based Planning Deficits in Compulsivity Are Linked to Faulty Neural Representations of Task Structure.

Compulsive individuals have deficits in model-based planning, but the mechanisms that drive this have not been established. We examined two candidates-that compulsivity is linked to (1) an impaired model of the task environment and/or (2) an inability to engage cognitive control when making choices. To test this, 192 participants performed a two-step reinforcement learning task with concurrent EEG recordings, and we related the neural and behavioral data to their scores on a self-reported transdiagnostic dimension of compulsivity. To examine subjects' internal model of the task, we used established behavioral and neural responses to unexpected events [reaction time (RT) slowing, P300 wave, and parietal-occipital alpha band power] measured when an unexpected transition occurred. To assess cognitive control, we probed theta power at the time of initial choice. As expected, model-based planning was linked to greater behavioral (RT) and neural (alpha power, but not P300) sensitivity to rare transitions. Critically, the sensitivities of both RT and alpha to task structure were weaker in those high in compulsivity. This RT-compulsivity effect was tested and replicated in an independent pre-existing dataset (N = 1413). We also found that mid-frontal theta power at the time of choice was reduced in highly compulsive individuals though its relation to model-based planning was less pronounced. These data suggest that model-based planning deficits in compulsive individuals may arise, at least in part, from having an impaired representation of the environment, specifically how actions lead to future states.SIGNIFICANCE STATEMENT Compulsivity is linked to poorer performance on tasks that require model-based planning, but it is unclear what precise mechanisms underlie this deficit. Do compulsive individuals fail to engage cognitive control at the time of choice? Or do they have difficulty in building and maintaining an accurate representation of their environment, the foundation needed to behave in a goal-directed manner? With reaction time and EEG measures in 192 individuals who performed a two-step decision-making task, we found that compulsive individuals are less sensitive to surprising action-state transitions, where they slow down less and show less alpha band suppression following a rare transition. These findings implicate failures in maintaining an accurate model of the world in model-based planning deficits in compulsivity.

New principles and new paths needed for online research in mental health: Commentary on Burnette et al. (2021).

Online methods have become a powerful research tool, allowing us to conduct well-powered studies, to explore and replicate effects, and to recruit often rare and diverse samples. However, concerns about the validity and reliability of the data collected from some platforms have reached crescendo. In this issue, Burnette et al. (2021) describe how commonly employed protective measures such as captchas, response consistency requirements, and attention checks may no longer be sufficient to ensure high-quality data in survey-based studies on Amazon's Mechanical Turk. We echo and elaborate on these concerns, but believe that although imperfect, online research will continue to be incredibly important in driving progress in mental health science. Not all platforms or populations are well suited to every research question and so we posit that the future of online research will be much more varied, and in no small part supported by citizen scientists and those with lived experience. Whatever the medium, researchers cannot stand still; we must continuously reflect and adapt to technological advances, demographics, and motivational shifts of our participants. Online research is difficult but worthwhile.

Goal-Directed and Habitual Control in Smokers.

INTRODUCTION: Harmful behavior such as smoking may reflect a disturbance in the balance of goal-directed and habitual control. Animal models suggest that habitual control develops after prolonged substance use. In this study, we investigated whether smokers (N = 49) differ from controls (N = 46) in the regulation of goal-directed and habitual behavior. It was also investigated whether individual differences in nicotine dependence levels were associated with habitual responding. METHODS: We used two different multistage instrumental learning tasks that consist of an instrumental learning phase, subsequent outcome devaluation, and a testing phase to measure the balance between goal-directed and habitual responding. The testing phases of these tasks occurred after either appetitive versus avoidance instrumental learning. The appetitive versus aversive instrumental learning stages in the two different tasks modeled positive versus negative reinforcement, respectively. RESULTS: Smokers and nonsmoking controls did not differ on habitual versus goal-directed control in either task. Individual differences in nicotine dependence within the group of smokers, however, were positively associated with habitual responding after appetitive instrumental learning. This effect seems to be due to impaired stimulus-outcome learning, thereby hampering goal-directed task performance and tipping the balance to habitual responding. CONCLUSIONS: The current finding highlights the importance of individual differences within smokers. For future research, neuroimaging studies are suggested to further unravel the nature of the imbalance between goal-directed versus habitual control in severely dependent smokers by directly measuring activity in the corresponding brain systems. IMPLICATIONS: Goal-directed versus habitual behavior in substance use and addiction is highly debated. This study investigated goal-directed versus habitual control in smokers. The findings suggest that smokers do not differ from controls in goal-directed versus habitual control. Individual differences in nicotine dependence within smokers, however, were positively associated with habitual responding after appetitive instrumental learning. This effect seems to be due to impaired stimulus-outcome learning, thereby hampering goal-directed task performance and tipping the balance to habitual responding. These findings add to the ongoing debate on habitual versus goal-directed control in addiction and emphasize the importance of individual differences within smokers.

Neural basis of impaired safety signaling in Obsessive Compulsive Disorder.

The ability to assign safety to stimuli in the environment is integral to everyday functioning. A key brain region for this evaluation is the ventromedial prefrontal cortex (vmPFC). To investigate the importance of vmPFC safety signaling, we used neuroimaging of Pavlovian fear reversal, a paradigm that involves flexible updating when the contingencies for a threatening (CS+) and safe (CS-) stimulus reverse, in a prototypical disorder of inflexible behavior influenced by anxiety, Obsessive Compulsive Disorder (OCD). Skin conductance responses in OCD patients (n = 43) failed to differentiate during reversal compared with healthy controls (n = 35), although significant differentiation did occur during early conditioning and amygdala BOLD signaling was unaffected in these patients. Increased vmPFC activation (for CS+ > CS-) during early conditioning predicted the degree of generalization in OCD patients during reversal, whereas vmPFC safety signals were absent throughout learning in these patients. Regions of the salience network (dorsal anterior cingulate, insula, and thalamus) showed early learning task-related hyperconnectivity with the vmPFC in OCD, consistent with biased processing of the CS+. Our findings reveal an absence of vmPFC safety signaling in OCD, undermining flexible threat updating and explicit contingency knowledge. Although differential threat learning can occur to some extent in the absence of vmPFC safety signals, effective CS- signaling becomes crucial during conflicting threat and safety cues. These results promote further investigation of vmPFC safety signaling in other anxiety disorders, with potential implications for the development of exposure-based therapies, in which safety signaling is likely to play a key role.

Mapping Compulsivity in the DSM-5 Obsessive Compulsive and Related Disorders: Cognitive Domains, Neural Circuitry, and Treatment.

Compulsions are repetitive, stereotyped thoughts and behaviors designed to reduce harm. Growing evidence suggests that the neurocognitive mechanisms mediating behavioral inhibition (motor inhibition, cognitive inflexibility) reversal learning and habit formation (shift from goal-directed to habitual responding) contribute toward compulsive activity in a broad range of disorders. In obsessive compulsive disorder, distributed network perturbation appears focused around the prefrontal cortex, caudate, putamen, and associated neuro-circuitry. Obsessive compulsive disorder-related attentional set-shifting deficits correlated with reduced resting state functional connectivity between the dorsal caudate and the ventrolateral prefrontal cortex on neuroimaging. In contrast, experimental provocation of obsessive compulsive disorder symptoms reduced neural activation in brain regions implicated in goal-directed behavioral control (ventromedial prefrontal cortex, caudate) with concordant increased activation in regions implicated in habit learning (presupplementary motor area, putamen). The ventromedial prefrontal cortex plays a multifaceted role, integrating affective evaluative processes, flexible behavior, and fear learning. Findings from a neuroimaging study of Pavlovian fear reversal, in which obsessive compulsive disorder patients failed to flexibly update fear responses despite normal initial fear conditioning, suggest there is an absence of ventromedial prefrontal cortex safety signaling in obsessive compulsive disorder, which potentially undermines explicit contingency knowledge and may help to explain the link between cognitive inflexibility, fear, and anxiety processing in compulsive disorders such as obsessive compulsive disorder.

Model-based learning protects against forming habits.

Studies in humans and rodents have suggested that behavior can at times be "goal-directed"-that is, planned, and purposeful-and at times "habitual"-that is, inflexible and automatically evoked by stimuli. This distinction is central to conceptions of pathological compulsion, as in drug abuse and obsessive-compulsive disorder. Evidence for the distinction has primarily come from outcome devaluation studies, in which the sensitivity of a previously learned behavior to motivational change is used to assay the dominance of habits versus goal-directed actions. However, little is known about how habits and goal-directed control arise. Specifically, in the present study we sought to reveal the trial-by-trial dynamics of instrumental learning that would promote, and protect against, developing habits. In two complementary experiments with independent samples, participants completed a sequential decision task that dissociated two computational-learning mechanisms, model-based and model-free. We then tested for habits by devaluing one of the rewards that had reinforced behavior. In each case, we found that individual differences in model-based learning predicted the participants' subsequent sensitivity to outcome devaluation, suggesting that an associative mechanism underlies a bias toward habit formation in healthy individuals.

New developments in human neurocognition: clinical, genetic, and brain imaging correlates of impulsivity and compulsivity.

Impulsivity and compulsivity represent useful conceptualizations that involve dissociable cognitive functions, which are mediated by neuroanatomically and neurochemically distinct components of cortico-subcortical circuitry. The constructs were historically viewed as diametrically opposed, with impulsivity being associated with risk-seeking and compulsivity with harm-avoidance. However, they are increasingly recognized to be linked by shared neuropsychological mechanisms involving dysfunctional inhibition of thoughts and behaviors. In this article, we selectively review new developments in the investigation of the neurocognition of impulsivity and compulsivity in humans, in order to advance our understanding of the pathophysiology of impulsive, compulsive, and addictive disorders and indicate new directions for research.

Confidence biases in problem gambling.

Background and aims: Subjective confidence plays an important role in guiding behaviour, especially when objective feedback is unavailable. Systematic misjudgements in confidence can foster maladaptive behaviours and have been linked to various psychiatric disorders. In this study, we adopted a transdiagnostic approach to examine confidence biases in problem gamblers across three levels: local decision confidence, global task performance confidence, and overall self-esteem. The importance of taking a transdiagnostic perspective is increasingly recognised, as it captures the dimensional nature of psychiatric symptoms that often cut across diagnostic boundaries. Accordingly, we investigated if any observed confidence biases could be explained by transdiagnostic symptom dimensions of Anxiety-Depression and Compulsive Behaviour and Intrusive Thought. This approach allows us to gain a more comprehensive understanding of the role of metacognitive processes in problem gambling, beyond the constraints of traditional diagnostic categories. Methods: Thirty-eight problem gamblers and 38 demographically matched control participants engaged in a gamified metacognition task and completed self-report questionnaires assessing transdiagnostic symptom dimensions. Results: Compared to controls, problem gamblers displayed significantly elevated confidence at the local decision and global task levels, independent of their actual task performance. This elevated confidence was observed even after controlling for the heightened symptom levels of Anxiety-Depression and Compulsive Behaviour and Intrusive Thought among the problem gamblers. Discussion: The results reveal a notable disparity in confidence levels between problem gamblers and control participants, not fully accounted for by the symptom dimensions Anxiety-Depression and Compulsive Behaviour and Intrusive Thought. This suggests the contribution of other factors, perhaps linked to gambling-specific cognitive distortions, to the observed confidence biases. Conclusion: The findings highlight the intricate link between metacognitive confidence and psychiatric symptoms in the context of problem gambling. It underscores the need for further research into metacognitive biases, which could enhance therapeutic approaches for individuals with psychiatric conditions.

Reliable, rapid, and remote measurement of metacognitive bias.

Metacognitive biases have been repeatedly associated with transdiagnostic psychiatric dimensions of 'anxious-depression' and 'compulsivity and intrusive thought', cross-sectionally. To progress our understanding of the underlying neurocognitive mechanisms, new methods are required to measure metacognition remotely, within individuals over time. We developed a gamified smartphone task designed to measure visuo-perceptual metacognitive (confidence) bias and investigated its psychometric properties across two studies (N = 3410 unpaid citizen scientists, N = 52 paid participants). We assessed convergent validity, split-half and test-retest reliability, and identified the minimum number of trials required to capture its clinical correlates. Convergent validity of metacognitive bias was moderate (r(50) = 0.64, p < 0.001) and it demonstrated excellent split-half reliability (r(50) = 0.91, p < 0.001). Anxious-depression was associated with decreased confidence (ß = - 0.23, SE = 0.02, p < 0.001), while compulsivity and intrusive thought was associated with greater confidence (ß = 0.07, SE = 0.02, p < 0.001). The associations between metacognitive biases and transdiagnostic psychiatry dimensions are evident in as few as 40 trials. Metacognitive biases in decision-making are stable within and across sessions, exhibiting very high test-retest reliability for the 100-trial (ICC = 0.86, N = 110) and 40-trial (ICC = 0.86, N = 120) versions of Meta Mind. Hybrid 'self-report cognition' tasks may be one way to bridge the recently discussed reliability gap in computational psychiatry.

Identifying Transdiagnostic Mechanisms in Mental Health Using Computational Factor Modeling.

Most psychiatric disorders do not occur in isolation, and most psychiatric symptom dimensions are not uniquely expressed within a single diagnostic category. Current treatments fail to work for around 25% to 40% of individuals, perhaps due at least in part to an overreliance on diagnostic categories in treatment development and allocation. In this review, we describe ongoing efforts in the field to surmount these challenges and precisely characterize psychiatric symptom dimensions using large-scale studies of unselected samples via remote, online, and "citizen science" efforts that take a dimensional, mechanistic approach. We discuss the importance that efforts to identify meaningful psychiatric dimensions be coupled with careful computational modeling to formally specify, test, and potentially falsify candidate mechanisms that underlie transdiagnostic symptom dimensions. We refer to this approach, i.e., where symptom dimensions are identified and validated against computationally well-defined neurocognitive processes, as computational factor modeling. We describe in detail some recent applications of this method to understand transdiagnostic cognitive processes that include model-based planning, metacognition, appetitive processing, and uncertainty estimation. In this context, we highlight how computational factor modeling has been used to identify specific associations between cognition and symptom dimensions and reveal previously obscured relationships, how findings generalize to smaller in-person clinical and nonclinical samples, and how the method is being adapted and optimized beyond its original instantiation. Crucially, we discuss next steps for this area of research, highlighting the value of more direct investigations of treatment response that bridge the gap between basic research and the clinic.

Characterising the covariance pattern between lifestyle factors and structural brain measures: a multivariable replication study of two independent ageing cohorts.

Modifiable lifestyle factors have been shown to promote healthy brain ageing. However, studies have typically focused on a single factor at a time. Given that lifestyle factors do not occur in isolation, multivariable analyses provide a more realistic model of the lifestyle-brain relationship. Here, canonical correlation analyses (CCA) examined the relationship between nine lifestyle factors and seven MRI-derived indices of brain structure. The resulting covariance pattern was further explored with Bayesian regressions. CCA analyses were first conducted on a Danish cohort of older adults (n = 251) and then replicated in a British cohort (n = 668). In both cohorts, the latent factors of lifestyle and brain structure were positively correlated (UK: r = .37, p < 0.001; Denmark: r = .27, p < 0.001). In the cross-validation study, the correlation between lifestyle-brain latent factors was r = .10, p = 0.008. However, the pattern of associations differed between datasets. These findings suggest that baseline characterisation and tailoring towards the study sample may be beneficial for achieving targeted lifestyle interventions.

Resting-State Functional Connectivity Differences Following Experimental Manipulation of the Orbitofrontal Cortex in Two Directions via Theta-Burst Stimulation.

Compulsive behaviors (CBs) have been linked to orbitofrontal cortex (OFC) function in animal and human studies. However, brain regions function not in isolation but as components of widely distributed brain networks-such as those indexed via resting-state functional connectivity (RSFC). Sixty-nine individuals with CB disorders were randomized to receive a single session of neuromodulation targeting the left OFC-intermittent theta-burst stimulation (iTBS) or continuous TBS (cTBS)-followed immediately by computer-based behavioral "habit override" training. OFC seeds were used to quantify RSFC following iTBS and following cTBS. Relative to cTBS, iTBS showed increased RSFC between right OFC (Brodmann's area 47) and other areas, including dorsomedial prefrontal cortex (dmPFC), occipital cortex, and a priori dorsal and ventral striatal regions. RSFC connectivity effects were correlated with OFC/frontopolar target engagement and with subjective difficulty during habit-override training. Findings help reveal neural network-level impacts of neuromodulation paired with a specific behavioral context, informing mechanistic intervention development.

An observational treatment study of metacognition in anxious-depression.

Prior studies have found metacognitive biases are linked to a transdiagnostic dimension of anxious-depression, manifesting as reduced confidence in performance. However, previous work has been cross-sectional and so it is unclear if under-confidence is a trait-like marker of anxious-depression vulnerability, or if it resolves when anxious-depression improves. Data were collected as part of a large-scale transdiagnostic, four-week observational study of individuals initiating internet-based cognitive behavioural therapy (iCBT) or antidepressant medication. Self-reported clinical questionnaires and perceptual task performance were gathered to assess anxious-depression and metacognitive bias at baseline and 4-week follow-up. Primary analyses were conducted for individuals who received iCBT (n=649), with comparisons between smaller samples that received antidepressant medication (n=82) and a control group receiving no intervention (n=88). Prior to receiving treatment, anxious-depression severity was associated with under-confidence in performance in the iCBT arm, replicating previous work. From baseline to follow-up, levels of anxious-depression were significantly reduced, and this was accompanied by a significant increase in metacognitive confidence in the iCBT arm (ß=0.17, SE=0.02, p<0.001). These changes were correlated (r(647)=-0.12, p=0.002); those with the greatest reductions in anxious-depression levels had the largest increase in confidence. While the three-way interaction effect of group and time on confidence was not significant (F(2, 1632)=0.60, p=0.550), confidence increased in the antidepressant group (ß=0.31, SE = 0.08, p<0.001), but not among controls (ß=0.11, SE = 0.07, p=0.103). Metacognitive biases in anxious-depression are state-dependent; when symptoms improve with treatment, so does confidence in performance. Our results suggest this is not specific to the type of intervention.