Precision psychiatry: a neural circuit taxonomy for depression and anxiety

Should rTMS be considered a first-line treatment for major depressive episodes in adults?

Treatment-resistant depression (TRD) is an epidemic with rising social, economic, and political costs. In a patient whose major depressive episode (MDE) persists through an adequate antidepressant trial, insurance companies often cover alternative treatments which may include repetitive transcranial magnetic stimulation (rTMS). RTMS is an FDA-cleared neuromodulation technique for TRD which is safe, efficacious, noninvasive, and well-tolerated. Recent developments in the optimization of rTMS algorithms and targeting have increased the efficacy of rTMS in treating depression, improved the clinical convenience of these treatments, and decreased the cost of a course of rTMS. In this opinion paper, we make a case for why conventional FDA-cleared rTMS should be considered as a first-line treatment for all adult MDEs. RTMS is compared to other first-line treatments including psychotherapy and SSRIs. These observations suggest that rTMS has similar efficacy, fewer side-effects, lower risk of serious adverse events, comparable compliance, the potential for more rapid relief, and cost-effectiveness. This suggestion, however, would be strengthened by further research with an emphasis on treatment-naive subjects in their first depressive episode, and trials directly contrasting rTMS with SSRIs or psychotherapy.

Psilocybin desynchronizes the human brain

A single dose of psilocybin, a psychedelic that acutely causes distortions of space-time perception and ego dissolution, produces rapid and persistent therapeutic effects in human clinical trials1-4. In animal models, psilocybin induces neuroplasticity in cortex and hippocampus5-8. It remains unclear how human brain network changes relate to subjective and lasting effects of psychedelics. Here we tracked individual-specific brain changes with longitudinal precision functional mapping (roughly 18 magnetic resonance imaging visits per participant). Healthy adults were tracked before, during and for 3 weeks after high-dose psilocybin (25 mg) and methylphenidate (40 mg), and brought back for an additional psilocybin dose 6-12 months later. Psilocybin massively disrupted functional connectivity (FC) in cortex and subcortex, acutely causing more than threefold greater change than methylphenidate. These FC changes were driven by brain desynchronization across spatial scales (areal, global), which dissolved network distinctions by reducing correlations within and anticorrelations between networks. Psilocybin-driven FC changes were strongest in the default mode network, which is connected to the anterior hippocampus and is thought to create our sense of space, time and self. Individual differences in FC changes were strongly linked to the subjective psychedelic experience. Performing a perceptual task reduced psilocybin-driven FC changes. Psilocybin caused persistent decrease in FC between the anterior hippocampus and default mode network, lasting for weeks. Persistent reduction of hippocampal-default mode network connectivity may represent a neuroanatomical and mechanistic correlate of the proplasticity and therapeutic effects of psychedelics.

Neuronal HIPK2-HDAC3 axis regulates mitochondrial fragmentation to participate in stroke injury and post-stroke anxiety like behavior

Post-stroke anxiety (PSA) seriously affects the prognosis of patients, which is an urgent clinical problem to be addressed. However, the pathological mechanism of PSA is largely unclear. Here, we found that neuronal HIPK2 expression was upregulated in the ischemic lesion after stroke. The upregulation of HIPK2 promotes Drp1 oligomerization through the HDAC3-dependent pathway, leading to excessive mitochondrial damage. This subsequently triggers the release of cellular cytokines such as IL-18 from neurons under ischemic stress. Microglia are capable of responding to IL-18, which promotes their activation and enhances their phagocytosis, ultimately resulting in the loss of synapses and neurons, thereby exacerbating the pathological progression of PSA. HIPK2 knockdown or inhibition suppresses excessive pruning of neuronal synapses by activated microglia in the contralateral vCA1 region to compromise inactivated anxiolytic pBLA-vCA1Calb1+ circuit, relieving anxiety-like behavior after stroke. Furthermore, we discovered that early remimazolam administration can remodel HIPK2-HDAC3 axis, ameliorating the progression of PSA. In conclusion, our study revealed that the neuronal HIPK2-HDAC3 axis in the ischemic focus regulates mitochondrial fragmentation to balance inflammation stress reservoir to participate in anxiety susceptibility after stroke.

Neuroimaging for precision medicine in psychiatry

Although the lifetime burden due to mental disorders is increasing, we lack tools for more precise diagnosing and treating prevalent and disabling disorders such as major depressive disorder. We lack strategies for selecting among available treatments or expediting access to new treatment options. This critical review concentrates on functional neuroimaging as a modality of measurement for precision psychiatry, focusing on major depressive and anxiety disorders. We begin by outlining evidence for the use of functional neuroimaging to stratify the heterogeneity of these disorders, based on underlying circuit dysfunction. We then review the current landscape of how functional neuroimaging-derived circuit predictors can predict treatment outcomes and clinical trajectories in depression and anxiety. Future directions for advancing clinically appliable neuroimaging measures are considered. We conclude by considering the opportunities and challenges of translating neuroimaging measures into practice. As an illustration, we highlight one approach for quantifying brain circuit function at an individual level, which could serve as a model for clinical translation.

Lack of evidence for predictive utility from resting state fMRI data for individual exposure-based cognitive behavioral therapy outcomes: A machine learning study in two large multi-site samples in anxiety disorders

Data-based predictions of individual Cognitive Behavioral Therapy (CBT) treatment response are a fundamental step towards precision medicine. Past studies demonstrated only moderate prediction accuracy (i.e. ability to discriminate between responders and non-responders of a given treatment) when using clinical routine data such as demographic and questionnaire data, while neuroimaging data achieved superior prediction accuracy. However, these studies may be considerably biased due to very limited sample sizes and bias-prone methodology. Adequately powered and cross-validated samples are a prerequisite to evaluate predictive performance and to identify the most promising predictors. We therefore analyzed resting state functional magnet resonance imaging (rs-fMRI) data from two large clinical trials to test whether functional neuroimaging data continues to provide good prediction accuracy in much larger samples. Data came from two distinct German multicenter studies on exposure-based CBT for anxiety disorders, the Protect-AD and SpiderVR studies. We separately and independently preprocessed baseline rs-fMRI data from n = 220 patients (Protect-AD) and n = 190 patients (SpiderVR) and extracted a variety of features, including ROI-to-ROI and edge-functional connectivity, sliding-windows, and graph measures. Including these features in sophisticated machine learning pipelines, we found that predictions of individual outcomes never significantly differed from chance level, even when conducting a range of exploratory post-hoc analyses. Moreover, resting state data never provided prediction accuracy beyond the sociodemographic and clinical data. The analyses were independent of each other in terms of selecting methods to process resting state data for prediction input as well as in the used parameters of the machine learning pipelines, corroborating the external validity of the results. These similar findings in two independent studies, analyzed separately, urge caution regarding the interpretation of promising prediction results based on neuroimaging data from small samples and emphasizes that some of the prediction accuracies from previous studies may result from overestimation due to homogeneous data and weak cross-validation schemes. The promise of resting-state neuroimaging data to play an important role in the prediction of CBT treatment outcomes in patients with anxiety disorders remains yet to be delivered.

Beta activity in human anterior cingulate cortex mediates reward biases

The rewards that we get from our choices and actions can have a major influence on our future behavior. Understanding how reward biasing of behavior is implemented in the brain is important for many reasons, including the fact that diminution in reward biasing is a hallmark of clinical depression. We hypothesized that reward biasing is mediated by the anterior cingulate cortex (ACC), a cortical hub region associated with the integration of reward and executive control and with the etiology of depression. To test this hypothesis, we recorded neural activity during a biased judgment task in patients undergoing intracranial monitoring for either epilepsy or major depressive disorder. We found that beta (12-30 Hz) oscillations in the ACC predicted both associated reward and the size of the choice bias, and also tracked reward receipt, thereby predicting bias on future trials. We found reduced magnitude of bias in depressed patients, in whom the beta-specific effects were correspondingly reduced. Our findings suggest that ACC beta oscillations may orchestrate the learning of reward information to guide adaptive choice, and, more broadly, suggest a potential biomarker for anhedonia and point to future development of interventions to enhance reward impact for therapeutic benefit.

Charting Alzheimer's Disease and Dementia: Epidemiological Insights, Risk Factors and Prevention Pathways

Alzheimer's disease (AD), the most common cause of dementia, is a complex and multifactorial condition without cure at present. The latest treatments, based on anti-amyloid monoclonal antibodies, have only a modest effect in reducing the progression of cognitive decline in AD, whereas the possibility of preventing AD has become a crucial area of research. In fact, recent studies have observed a decrease in dementia incidence in developed regions such as the US and Europe. However, these trends have not been mirrored in non-Western countries (Japan or China), and the contributing factors of this reduction remain unclear. The Lancet Commission has delineated a constrained classification of 12 risk factors across different life stages. Nevertheless, the scientific literature has pointed to over 200 factors-including sociodemographic, medical, psychological, and sociocultural conditions-related to the development of dementia/AD. This narrative review aims to synthesize the risk/protective factors of dementia/AD. Essentially, we found that risk/protective factors vary between individuals and populations, complicating the creation of a unified prevention strategy. Moreover, dementia/AD explanatory mechanisms involve a diverse array of genetic and environmental factors that interact from the early stages of life. In the future, studies across different population-based cohorts are essential to validate risk/protective factors of dementia. This evidence would help develop public health policies to decrease the incidence of dementia.

Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression

Treatment outcomes widely vary for individuals diagnosed with major depressive disorder, implicating a need for deeper understanding of the biological mechanisms conferring a greater likelihood of response to a particular treatment. Our improved understanding of intrinsic brain networks underlying depression psychopathology via magnetic resonance imaging and other neuroimaging modalities has helped reveal novel and potentially clinically meaningful biological markers of response. And while we have made considerable progress in identifying such biomarkers over the last decade, particularly with larger, multisite trials, there are significant methodological and practical obstacles that need to be overcome to translate these markers into the clinic. The aim of this review is to review current literature on brain network structural and functional biomarkers of treatment response or selection in depression, with a specific focus on recent large, multisite trials reporting predictive accuracy of candidate biomarkers. Regarding pharmaco- and psychotherapy, we discuss candidate biomarkers, reporting that while we have identified candidate biomarkers of response to a single intervention, we need more trials that distinguish biomarkers between first-line treatments. Further, we discuss the ways prognostic neuroimaging may help to improve treatment outcomes to neuromodulation-based therapies, such as transcranial magnetic stimulation and deep brain stimulation. Lastly, we highlight obstacles and technical developments that may help to address the knowledge gaps in this area of research. Ultimately, integrating neuroimaging-derived biomarkers into clinical practice holds promise for enhancing treatment outcomes and advancing precision psychiatry strategies for depression management. By elucidating the neural predictors of treatment response and selection, we can move towards more individualized and effective depression interventions, ultimately improving patient outcomes and quality of life.

Neurophysiological and neuroimaging markers of repetitive transcranial magnetic stimulation treatment response in major depressive disorder: A systematic review and meta-analysis of predictive modeling studies

Predicting repetitive transcranial magnetic stimulation (rTMS) treatment outcomes in major depressive disorder (MDD) could reduce the financial and psychological risks of treatment failure. We systematically reviewed and meta-analyzed studies that leveraged neurophysiological and neuroimaging markers to predict rTMS response in MDD. Five databases were searched from inception to May 25, 2023. The primary meta-analytic outcome was predictive accuracy pooled from classification models. Regression models were summarized qualitatively. A promising marker was identified if it showed a sensitivity and specificity of 80% or higher in at least two independent studies. Searching yielded 36 studies. Twenty-two classification modeling studies produced an estimated area under the summary receiver operating characteristic curve of 0.87 (95% CI = 0.83-0.92), with 86.8% sensitivity (95% CI = 80.6-91.2%) and 81.9% specificity (95% CI = 76.1-86.4%). Frontal theta cordance measured by electroencephalography is closest to proof of concept. Predicting rTMS response using neurophysiological and neuroimaging markers is promising for clinical decision-making. However, replications by different research groups are needed to establish rigorous markers.

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.

Dorsal dentate gyrus mediated enriched environment-induced anxiolytic and antidepressant effects in cortical infarcted mice

Anxiety and depression are the most common mental health disorders worldwide, each affecting around 30% stroke survivors. These complications not only affect the functional recovery and quality of life in stroke patients, but also are distressing for caregivers. However, effective treatments are still lacking. Enriched environment (EE), characterized with novel and multi-dimensional stimulation, has been reported to exert therapeutic effects on physical and cognitive function. In addition, EE also had potential positive effects on emotional disorders after ischemic stroke; however, the underling mechanisms have not been well elucidated. This study aimed to explore the effectiveness of EE on emotional disorders after cerebral ischemia and its underling mechanism. Sensorimotor cortical infarction was induced by photothrombosis with stable infarct location and volume, resulting in motor dysfunction, anxiety and depression-like behaviors in mice, with decreased ALFF and ReHo values and decreased c-fos expression in the infarction area and adjacent regions. Seven days' EE treatment significantly improved motor function of contralateral forelimb and exhibited anxiolytic and antidepressant effects in infarcted mice. Compared to the mice housing in a standard environment, those subjected to acute EE stimulation had significantly increased ALFF and ReHo values in the bilateral somatosensory cortex (S1, S2), dorsal dentate gyrus (dDG), dorsal CA1 of hippocampus (dCA1), lateral habenular nucleus (LHb), periaqueductal gray (PAG), ipsilateral primary motor cortex (M1), retrosplenial cortex (RSC), parietal association cortex (PtA), dorsal CA3 of hippocampus (dCA3), claustrum (Cl), ventral pallidum (VP), amygdala (Amy), and contralateral auditory cortex (Au). Some of, but not all, the ipsilateral brain regions mentioned above showed accompanying increases in c-fos expression with the most significant changes in the dDG. The number of FosB positive cells in the dDG, decreased in infarcted mice, was significantly increased after chronic EE treatment. Chemogenetic activation of dDG neurons reduced anxiety and depressive-like behaviors in infarcted mice, while neuronal inhibition resulted in void of the anxiolytic and antidepressant effects of EE. Altogether, these findings indicated that dDG neurons may mediate EE-triggered anxiolytic and antidepressant effects in cortical infarcted mice.

Personalized brain circuit scores identify clinically distinct biotypes in depression and anxiety

There is an urgent need to derive quantitative measures based on coherent neurobiological dysfunctions or 'biotypes' to enable stratification of patients with depression and anxiety. We used task-free and task-evoked data from a standardized functional magnetic resonance imaging protocol conducted across multiple studies in patients with depression and anxiety when treatment free (n = 801) and after randomization to pharmacotherapy or behavioral therapy (n = 250). From these patients, we derived personalized and interpretable scores of brain circuit dysfunction grounded in a theoretical taxonomy. Participants were subdivided into six biotypes defined by distinct profiles of intrinsic task-free functional connectivity within the default mode, salience and frontoparietal attention circuits, and of activation and connectivity within frontal and subcortical regions elicited by emotional and cognitive tasks. The six biotypes showed consistency with our theoretical taxonomy and were distinguished by symptoms, behavioral performance on general and emotional cognitive computerized tests, and response to pharmacotherapy as well as behavioral therapy. Our results provide a new, theory-driven, clinically validated and interpretable quantitative method to parse the biological heterogeneity of depression and anxiety. Thus, they represent a promising approach to advance precision clinical care in psychiatry.

Potential role of gut microbiota in major depressive disorder: A review

Interactions between the gut microbiota and host immunity are sophisticated, dynamic, and host-dependent. Scientists have recently conducted research showing that disturbances in the gut bacterial community can lead to a decrease in some metabolites and, consequently, to behaviors such as depression. Exposure to stressors dropped the relative abundance of bacteria in the genus Bacteroides while soaring the relative abundance of bacteria in the genus Clostridium, Coprococcus, Dialister, and Oscillibacter, which were also reduced in people with depression. Microbiota and innate immunity are in a bilateral relationship. The gut microbiota has been shown to induce the synthesis of antimicrobial proteins such as catalysidins, type C lectins, and defensins. Probiotic bacteria can modulate depressive behavior through GABA signaling. The gut microbiome produces essential metabolites such as neurotransmitters, tryptophan metabolites, and short-chain fatty acids (SCFAs) that can act on the CNS. In the case of dysbiosis, due to mucin changes, the ratio of intestinal-derived molecules may change and contribute to depression. Psychotropics, including Bifidobacterium longum NCC3001, Clostridium butyricum CBM588, and Lactobacillus acidophilus, have mental health benefits, and can have a positive effect on the host-brain relationship, and have antidepressant effects. This article reviews current studies on the association between gut microbiota dysbiosis and depression. Comprehensively, these findings could potentially lead to novel approaches to improving depressive symptoms via gut microbiota alterations, including probiotics, prebiotics, and fecal microbiota transplantation.

Neural circuit mechanisms of acupuncture effect: where are we now?

Recently, there has been increasing attention on the impact of acupuncture on the dysregulated neural circuits in different disease. This has led to new understandings of how acupuncture works. This review presents a comprehensive analysis of research that have examined the impact of acupuncture on abnormal neural circuits associated with pain, anxiety, Parkinson's disease, addiction disorders, cognitive problems, and gastrointestinal disorders. These studies have shown that acupuncture's therapeutic effects are mediated by specific brain areas and neurons involved in neural circuit mechanisms, emphasising its wide-ranging influence. The positive impacts of acupuncture can be ascribed to its ability to modify the functioning of neurocircuits in various physiological conditions. Nevertheless, contemporary studies on acupuncture neural circuits frequently overlook the comprehensive circuit mechanism including the periphery, central nervous system, and target organ. Additionally, the scope of diseases studied is restricted. Future study should focus on broadening the range of diseases studied and exploring the neural circuit mechanisms of these diseases in depth in order to enhance our understanding of acupuncture's neurobiological impacts.

Impact of an internet-based insomnia intervention on suicidal ideation and associated correlates in veterans at elevated suicide risk

Improving public health approaches to suicide prevention requires scalable evidence-based interventions that can be easily disseminated. Given empirical data supporting the association between insomnia and suicide risk, internet-delivered insomnia interventions are promising candidates to meet this need. The purpose of this study was to examine whether an unguided internet-delivered cognitive-behavioral therapy for insomnia (iCBT-I) improved insomnia severity, suicidal ideation (SI), and suicide risk correlates (depression, post-traumatic stress disorder, anxiety, hostility, belongingness, hopelessness, agitation, irritability, concentration) in a sample of veterans. Secondary data analysis of Operation Enduring Freedom, Operation Iraqi Freedom, and Operation New Dawn veterans (n = 50) with clinically significant insomnia and elevated SI drawn from a larger randomized controlled trial (RCT) of an iCBT-I, Sleep Healthy Using the Internet (SHUTi). Two-sample t-tests or Wilcoxon rank sum tests were used to evaluate between-group differences (SHUTi vs. Insomnia Education Website control) in symptom improvement from baseline to post-intervention. SHUTi participants experienced a significant improvement in insomnia severity (P < .001; d = -1.08) and a non-significant with small (subthreshold medium) effect size reduction of SI (P = .17, d = 0.40), compared to control participants. Significant improvement in hopelessness was observed (medium effect size), with non-significant small to medium effect size reductions in most remaining suicide risk correlates. Self-administered iCBT-I was associated with improvements in insomnia severity in veterans at elevated risk for suicide. These preliminary findings suggest that SI and suicide risk correlates may improve following an iCBT-I intervention, demonstrating the need for future well-powered iCBT-I RCTs targeted for populations at elevated suicide risk.

Sex differences in aberrant functional connectivity of three core networks and subcortical networks in medication-free adolescent-onset major depressive disorder

Major depressive disorder demonstrated sex differences in prevalence and symptoms, which were more pronounced during adolescence. Yet, research on sex-specific brain network characteristics in adolescent-onset major depressive disorder remains limited. This study investigated sex-specific and nonspecific alterations in resting-state functional connectivity of three core networks (frontoparietal network, salience network, and default mode network) and subcortical networks in adolescent-onset major depressive disorder, using seed-based resting-state functional connectivity in 50 medication-free patients with adolescent-onset major depressive disorder and 56 healthy controls. Irrespective of sex, compared with healthy controls, adolescent-onset major depressive disorder patients showed hypoconnectivity between bilateral hippocampus and right superior temporal gyrus (default mode network). More importantly, we further found that females with adolescent-onset major depressive disorder exhibited hypoconnectivity within the default mode network (medial prefrontal cortex), and between the subcortical regions (i.e. amygdala, striatum, and thalamus) with the default mode network (angular gyrus and posterior cingulate cortex) and the frontoparietal network (dorsal prefrontal cortex), while the opposite patterns of resting-state functional connectivity alterations were observed in males with adolescent-onset major depressive disorder, relative to their sex-matched healthy controls. Moreover, several sex-specific resting-state functional connectivity changes were correlated with age of onset, sleep disturbance, and anxiety in adolescent-onset major depressive disorder with different sex. These findings suggested that these sex-specific resting-state functional connectivity alterations may reflect the differences in brain development or processes related to early illness onset, underscoring the necessity for sex-tailored diagnostic and therapeutic approaches in adolescent-onset major depressive disorder.

Ketamine vs Electroconvulsive Therapy for Treatment-Resistant Depression: A Secondary Analysis of a Randomized Clinical Trial

Importance

The ELEKT-D: Electroconvulsive Therapy (ECT) vs Ketamine in Patients With Treatment Resistant Depression (TRD) (ELEKT-D) trial demonstrated noninferiority of intravenous ketamine vs ECT for nonpsychotic TRD. Clinical features that can guide selection of ketamine vs ECT may inform shared decision-making for patients with TRD.

Objective

To evaluate whether selected clinical features were associated with differential improvement with ketamine vs ECT.

Design, Setting, and Participants

This secondary analysis of an open-label noninferiority randomized clinical trial was a multicenter study conducted at 5 US academic medical centers from April 7, 2017, to November 11, 2022. Analyses for this study, which were not prespecified in the trial protocol, were conducted from May 10 to Oct 31, 2023. The study cohort included patients with TRD, aged 21 to 75 years, who were in a current nonpsychotic depressive episode of at least moderate severity and were referred for ECT by their clinicians.

Exposures

Eligible participants were randomized 1:1 to receive either 6 infusions of ketamine or 9 treatments with ECT over 3 weeks.

Main Outcomes and Measures

Association between baseline factors (including 16-item Quick Inventory of Depressive Symptomatology Self-Report [QIDS-SR16], Montgomery-Asberg Depression Rating Scale [MADRS], premorbid intelligence, cognitive function, history of attempted suicide, and inpatient vs outpatient status) and treatment response were assessed with repeated measures mixed-effects model analyses.

Results

Among the 365 participants included in this study (mean [SD] age, 46.0 [14.5] years; 191 [52.3%] female), 195 were randomized to the ketamine group and 170 to the ECT group. In repeated measures mixed-effects models using depression levels over 3 weeks and after false discovery rate adjustment, participants with a baseline QIDS-SR16 score of 20 or less (-7.7 vs -5.6 points) and those starting treatment as outpatients (-8.4 vs -6.2 points) reported greater reduction in the QIDS-SR16 with ketamine vs ECT. Conversely, those with a baseline QIDS-SR16 score of more than 20 (ie, very severe depression) and starting treatment as inpatients reported greater reduction in the QIDS-SR16 earlier in course of treatment (-8.4 vs -6.7 points) with ECT, but scores were similar in both groups at the end-of-treatment visit (-9.0 vs -9.9 points). In the ECT group only, participants with higher scores on measures of premorbid intelligence (-14.0 vs -11.2 points) and with a comorbid posttraumatic stress disorder diagnosis (-16.6 vs -12.0 points) reported greater reduction in the MADRS score. Those with impaired memory recall had greater reduction in MADRS during the second week of treatment (-13.4 vs -9.6 points), but the levels of MADRS were similar to those with unimpaired recall at the end-of-treatment visit (-14.3 vs -12.2 points). Other results were not significant after false discovery rate adjustment.

Conclusions and Relevance

In this secondary analysis of the ELEKT-D randomized clinical trial of ECT vs ketamine, greater improvement in depression was observed with intravenous ketamine among outpatients with nonpsychotic TRD who had moderately severe or severe depression, suggesting that these patients may consider ketamine over ECT for TRD.

Precision psychiatry: predicting predictability

Precision psychiatry is an emerging field that aims to provide individualized approaches to mental health care. An important strategy to achieve this precision is to reduce uncertainty about prognosis and treatment response. Multivariate analysis and machine learning are used to create outcome prediction models based on clinical data such as demographics, symptom assessments, genetic information, and brain imaging. While much emphasis has been placed on technical innovation, the complex and varied nature of mental health presents significant challenges to the successful implementation of these models. From this perspective, I review ten challenges in the field of precision psychiatry, including the need for studies on real-world populations and realistic clinical outcome definitions, and consideration of treatment-related factors such as placebo effects and non-adherence to prescriptions. Fairness, prospective validation in comparison to current practice and implementation studies of prediction models are other key issues that are currently understudied. A shift is proposed from retrospective studies based on linear and static concepts of disease towards prospective research that considers the importance of contextual factors and the dynamic and complex nature of mental health.

Instantaneous effects of prefrontal transcranial magnetic stimulation on brain oxygenation: A systematic review

This systematic review investigates how prefrontal transcranial magnetic stimulation (TMS) immediately influences neuronal excitability based on oxygenation changes measured by functional magnetic resonance imaging (fMRI) or functional near-infrared spectroscopy (fNIRS). A thorough understanding of TMS-induced excitability changes may enable clinicians to adjust TMS parameters and optimize treatment plans proactively. Five databases were searched for human studies evaluating brain excitability using concurrent TMS/fMRI or TMS/fNIRS. Thirty-seven studies (13 concurrent TMS/fNIRS studies, 24 concurrent TMS/fMRI studies) were included in a qualitative synthesis. Despite methodological inconsistencies, a distinct pattern of activated nodes in the frontoparietal central executive network, the cingulo-opercular salience network, and the default-mode network emerged. The activated nodes included the prefrontal cortex (particularly dorsolateral prefrontal cortex), insula cortex, striatal regions (especially caudate, putamen), anterior cingulate cortex, and thalamus. High-frequency repetitive TMS most consistently induced expected facilitatory effects in these brain regions. However, varied stimulation parameters (e.g., intensity, coil orientation, target sites) and the inter- and intra-individual variability of brain state contribute to the observed heterogeneity of target excitability and co-activated regions. Given the considerable methodological and individual variability across the limited evidence, conclusions should be drawn with caution.

Beyond the serotonin deficit hypothesis: communicating a neuroplasticity framework of major depressive disorder

The serotonin deficit hypothesis explanation for major depressive disorder (MDD) has persisted among clinicians and the general public alike despite insufficient supporting evidence. To combat rising mental health crises and eroding public trust in science and medicine, researchers and clinicians must be able to communicate to patients and the public an updated framework of MDD: one that is (1) accessible to a general audience, (2) accurately integrates current evidence about the efficacy of conventional serotonergic antidepressants with broader and deeper understandings of pathophysiology and treatment, and (3) capable of accommodating new evidence. In this article, we summarize a framework for the pathophysiology and treatment of MDD that is informed by clinical and preclinical research in psychiatry and neuroscience. First, we discuss how MDD can be understood as inflexibility in cognitive and emotional brain circuits that involves a persistent negativity bias. Second, we discuss how effective treatments for MDD enhance mechanisms of neuroplasticity-including via serotonergic interventions-to restore synaptic, network, and behavioral function in ways that facilitate adaptive cognitive and emotional processing. These treatments include typical monoaminergic antidepressants, novel antidepressants like ketamine and psychedelics, and psychotherapy and neuromodulation techniques. At the end of the article, we discuss this framework from the perspective of effective science communication and provide useful language and metaphors for researchers, clinicians, and other professionals discussing MDD with a general or patient audience.

Optogenetic behavioral studies in depression research: A systematic review

Optogenetics has made substantial contributions to our understanding of the mechanistic underpinnings of depression. This systematic review employs quantitative analysis to investigate the impact of optogenetic stimulation in mice and rats on behavioral alterations in social interaction, sucrose consumption, and mobility. The review analyses optogenetic behavioral studies using standardized behavioral tests to detect behavioral changes induced via optogenetic stimulation in stressed or stress-naive mice and rats. Behavioral changes were evaluated as either positive, negative, or not effective. The analysis comprises the outcomes of 248 behavioral tests of 168 studies described in 37 articles, including negative and null results. Test outcomes were compared for each behavior, depending on the animal cohort, applied type of stimulation and the stimulated neuronal circuit and cell type. The presented synthesis contributes toward a comprehensive picture of optogenetic behavioral research in the context of depression.

From Connectivity to Controllability: Unraveling the Brain Biomarkers of Major Depressive Disorder

Major Depressive Disorder (MDD) is a significant neurological condition associated with aberrations in brain functional networks. Traditional studies have predominantly analyzed these from a network topology perspective. However, given the brain's dynamic and complex nature, exploring its mechanisms from a network control standpoint provides a fresh and insightful framework. This research investigates the integration of network controllability and machine learning to pinpoint essential biomarkers for MDD using functional magnetic resonance imaging (fMRI) data. By employing network controllability methods, we identify crucial brain regions that are instrumental in facilitating transitions between brain states. These regions demonstrate the brain's ability to navigate various functional states, emphasizing the utility of network controllability metrics as potential biomarkers. Furthermore, these metrics elucidate the complex dynamics of MDD and support the development of precision medicine strategies that incorporate machine learning to improve the precision of diagnostics and the efficacy of treatments. This study underscores the value of merging machine learning with network neuroscience to craft personalized interventions that align with the unique pathological profiles of individuals, ultimately enhancing the management and treatment of MDD.

Perceptual Dysfunction in Eating Disorders

Eating disorders (EDs) are characterized by abnormal responses to food and weight-related stimuli and are associated with significant distress, impairment, and poor outcomes. Because many of the cardinal symptoms of EDs involve disturbances in perception of one's body or abnormal affective or cognitive reactions to food intake and how that affects one's size, there has been longstanding interest in characterizing alterations in sensory perception among differing ED diagnostic groups. Within the current review, we aimed to critically assess the existing research on exteroceptive and interoceptive perception and how sensory perception may influence ED behavior. Overall, existing research is most consistent regarding alterations in taste, visual, tactile, and gastric-specific interoceptive processing in EDs, with emerging work indicating elevated respiratory and cardiovascular sensitivity. However, this work is far from conclusive, with most studies unable to speak to the precise etiology of observed perceptual differences in these domains and disentangle these effects from affective and cognitive processes observed within EDs. Further, existing knowledge regarding perceptual disturbances in EDs is limited by heterogeneity in methodology, lack of multimodal assessment protocols, and inconsistent attention to different ED diagnoses. We propose several new avenues for improving neurobiology-informed research on sensory processing to generate actionable knowledge that can inform the development of innovative interventions for these serious disorders.

Effects of transcranial magnetic stimulation on the human brain recorded with intracranial electrocorticography

Transcranial magnetic stimulation (TMS) is increasingly used as a noninvasive technique for neuromodulation in research and clinical applications, yet its mechanisms are not well understood. Here, we present the neurophysiological effects of TMS using intracranial electrocorticography (iEEG) in neurosurgical patients. We first evaluated safety in a gel-based phantom. We then performed TMS-iEEG in 22 neurosurgical participants with no adverse events. We next evaluated intracranial responses to single pulses of TMS to the dorsolateral prefrontal cortex (dlPFC) (N = 10, 1414 electrodes). We demonstrate that TMS is capable of inducing evoked potentials both locally within the dlPFC and in downstream regions functionally connected to the dlPFC, including the anterior cingulate and insular cortex. These downstream effects were not observed when stimulating other distant brain regions. Intracranial dlPFC electrical stimulation had similar timing and downstream effects as TMS. These findings support the safety and promise of TMS-iEEG in humans to examine local and network-level effects of TMS with higher spatiotemporal resolution than currently available methods.

Multidimensional behavioral profiles associated with resilience and susceptibility after inescapable stress

Clinical depression is characterized by multiple concurrent symptoms, manifesting as a complex heterogeneous condition. Although some well-established classical behavioral assessments are widespread in rodent models, it remains uncertain whether rats also display stress-induced depression-related phenotypes in a multidimensional manner, i.e., simultaneous alterations in multiple behavioral tests. Here, we investigated multivariate patterns and profiles of depression-related behavioral traits in male Wistar rats subjected to inescapable footshocks (IS) or no-shocks (NS), followed by a comprehensive battery of behavioral tests and ethological characterization. We observed generalized stronger intra-test but weaker inter-test correlations. However, feature clustering of behavioral measures successfully delineated variables linked to resilience and susceptibility to stress. Accordingly, a noteworthy covariation pattern emerged, characterized by increased open field locomotion, reduced time in the elevated plus maze open arms, lower sucrose preference, and increased shuttle box escape failures that consistently differentiated IS from NS. Surprisingly there is little contribution from forced swim. In addition, individual clustering revealed a diversity of behavioral profiles, naturally separating NS and IS, including subpopulations entirely characterized by resilience or susceptibility. In conclusion, our study elucidates intricate relationships among classical depression-related behavioral measures, highlighting multidimensional individual variability. Our work emphasizes the importance of a multivariate framework for behavioral assessment in animal models to understand stress-related neuropsychiatric disorders.

Label-based meta-analysis of functional brain dysconnectivity across mood and psychotic disorders

BACKGROUND

Resting-state functional magnetic resonance imaging (rsfMRI) studies have revealed patterns of functional brain dysconnectivity in psychiatric disorders such as major depression disorder (MDD), bipolar disorder (BD) and schizophrenia (SZ). Although these disorders have been mostly studied in isolation, there is mounting evidence of shared neurobiological alterations across them.

METHODS

To uncover the nature of the relatedness between these psychiatric disorders, we conducted an innovative meta-analysis of dysconnectivity findings reported separately in MDD, BD and SZ. Rather than relying on a classical voxel level coordinate-based approach, our procedure extracted relevant neuroanatomical labels from text data and examined findings at the whole brain network level. Data were drawn from 428 rsfMRI studies investigating MDD (158 studies, 7429 patients/7414 controls), BD (81 studies, 3330 patients/4096 patients) and/or SZ (223 studies, 11,168 patients/11,754 controls). Permutation testing revealed commonalities and differences in hypoconnectivity and hyperconnectivity patterns across disorders.

RESULTS

Hypoconnectivity and hyperconnectivity patterns of higher-order cognitive (default-mode, fronto-parietal, cingulo-opercular) networks were similarly observed across the three disorders. By contrast, dysconnectivity of lower-order (somatomotor, visual, auditory) networks in some cases differed between disorders, notably dissociating SZ from BD and MDD.

CONCLUSIONS

Findings suggest that functional brain dysconnectivity of higher-order cognitive networks is largely transdiagnostic in nature while that of lower-order networks may best discriminate between mood and psychotic disorders, thus emphasizing the relevance of motor and sensory networks to psychiatric neuroscience.

Opaque Ontology: Neuroimaging Classification of ICD-10 Diagnostic Groups in the UK Biobank

Background

1.The use of machine learning to classify diagnostic cases versus controls defined based on diagnostic ontologies such as the ICD-10 from neuroimaging features is now commonplace across a wide range of diagnostic fields. However, transdiagnostic comparisons of such classifications are lacking. Such transdiagnostic comparisons are important to establish the specificity of classification models, set benchmarks, and assess the value of diagnostic ontologies.

Results

2.We investigated case-control classification accuracy in 17 different ICD-10 diagnostic groups from Chapter V (mental and behavioral disorders) and Chapter VI (diseases of the nervous system) using data from the UK Biobank. Classification models were trained using either neuroimaging (structural or functional brain MRI feature sets) or socio-demographic features. Random forest classification models were adopted using rigorous shuffle splits to estimate stability as well as accuracy of case-control classifications. Diagnostic classification accuracies were benchmarked against age classification (oldest versus youngest) from the same feature sets and against additional classifier types (K-nearest neighbors and linear support vector machine). In contrast to age classification accuracy, which was high for all feature sets, few ICD-10 diagnostic groups were classified significantly above chance (namely, demyelinating diseases based on structural neuroimaging features, and depression based on socio-demographic and functional neuroimaging features).

Conclusion

3.These findings highlight challenges with the current disease classification system, leading us to recommend caution with the use of ICD-10 diagnostic groups as target labels in brain-based disease prediction studies.

Sustained antidepressant effects of ketamine metabolite involve GABAergic inhibition-mediated molecular dynamics in aPVT glutamatergic neurons

Despite the rapid and sustained antidepressant effects of ketamine and its metabolites, their underlying cellular and molecular mechanisms are not fully understood. Here, we demonstrate that the sustained antidepressant-like behavioral effects of (2S,6S)-hydroxynorketamine (HNK) in repeatedly stressed animal models involve neurobiological changes in the anterior paraventricular nucleus of the thalamus (aPVT). Mechanistically, (2S,6S)-HNK induces mRNA expression of extrasynaptic GABAA receptors and subsequently enhances GABAA-receptor-mediated tonic currents, leading to the nuclear export of histone demethylase KDM6 and its replacement by histone methyltransferase EZH2. This process increases H3K27me3 levels, which in turn suppresses the transcription of genes associated with G-protein-coupled receptor signaling. Thus, our findings shed light on the comprehensive cellular and molecular mechanisms in aPVT underlying the sustained antidepressant behavioral effects of ketamine metabolites. This study may support the development of potentially effective next-generation pharmacotherapies to promote sustained remission of stress-related psychiatric disorders.

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

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

Causal network localization of brain stimulation targets for trait anxiety

Transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS) can treat some neuropsychiatric disorders, but there is no consensus approach for identifying new targets. We localized causal circuit-based targets for anxiety that converged across multiple natural experiments. Lesions (n=451) and TMS sites (n=111) that modify anxiety mapped to a common normative brain circuit (r=0.68, p=0.01). In an independent dataset (n=300), individualized TMS site connectivity to this circuit predicted anxiety change (p=0.02). Subthalamic DBS sites overlapping the circuit caused more anxiety (n=74, p=0.006), thus demonstrating a network-level effect, as the circuit was derived without any subthalamic sites. The circuit was specific to trait versus state anxiety in datasets that measured both (p=0.003). Broadly, this illustrates a pathway for discovering novel circuit-based targets across neuropsychiatric disorders.

EEG changes induced by meditative practices: State and trait effects in healthy subjects and in patients with epilepsy

The effect of meditation on brain activity has been the topic of many studies in healthy subjects and in patients suffering from chronic diseases. These effects are either explored during meditation practice (state effects) or as a longer-term result of meditation training during the resting-state (trait). The topic of this article is to first review these findings by focusing on electroencephalography (EEG) changes in healthy subjects with or without experience in meditation. Modifications in EEG baseline rhythms, functional connectivity and advanced nonlinear parameters are discussed in regard to feasibility in clinical applications. Secondly, we provide a state-of-the-art of studies that proposed meditative practices as a complementary therapy in patients with epilepsy, in whom anxiety and depressive symptoms are prevalent. In these studies, the effects of standardized meditation programs including elements of traditional meditation practices such as mindfulness, loving-kindness and compassion are explored both at the level of psychological functioning and on the occurrence of seizures. Lastly, preliminary results are given regarding our ongoing study, the aim of which is to quantify the effects of a mindfulness self-compassion (MSC) practice on interictal and ictal epileptic activity. Feasibility, difficulties, and prospects of this study are discussed.

Deep brain stimulation for refractory major depressive disorder: a comprehensive review

Deep brain stimulation (DBS) has emerged as a promising treatment for select patients with refractory major depressive disorder (MDD). The clinical effectiveness of DBS for MDD has been demonstrated in meta-analyses, open-label studies, and a few controlled studies. However, randomized controlled trials have yielded mixed outcomes, highlighting challenges that must be addressed prior to widespread adoption of DBS for MDD. These challenges include tracking MDD symptoms objectively to evaluate the clinical effectiveness of DBS with sensitivity and specificity, identifying the patient population that is most likely to benefit from DBS, selecting the optimal patient-specific surgical target and stimulation parameters, and understanding the mechanisms underpinning the therapeutic benefits of DBS in the context of MDD pathophysiology. In this review, we provide an overview of the latest clinical evidence of MDD DBS effectiveness and the recent technological advancements that could transform our understanding of MDD pathophysiology, improve the clinical outcomes for MDD DBS, and establish a path forward to develop more effective neuromodulation therapies to alleviate depressive symptoms.

Linking connectivity of deep brain stimulation of nucleus accumbens area with clinical depression improvements: a retrospective longitudinal case series

Treatment-resistant depression is a severe form of major depressive disorder and deep brain stimulation is currently an investigational treatment. The stimulation's therapeutic effect may be explained through the functional and structural connectivities between the stimulated area and other brain regions, or to depression-associated networks. In this longitudinal, retrospective study, four female patients with treatment-resistant depression were implanted for stimulation in the nucleus accumbens area at our center. We analyzed the structural and functional connectivity of the stimulation area: the structural connectivity was investigated with probabilistic tractography; the functional connectivity was estimated by combining patient-specific stimulation volumes and a normative functional connectome. These structural and functional connectivity profiles were then related to four clinical outcome scores. At 1-year follow-up, the remission rate was 66%. We observed a consistent structural connectivity to Brodmann area 25 in the patient with the longest remission phase. The functional connectivity analysis resulted in patient-specific R-maps describing brain areas significantly correlated with symptom improvement in this patient, notably the prefrontal cortex. But the connectivity analysis was mixed across patients, calling for confirmation in a larger cohort and over longer time periods.

Specific alterations of resting-state functional connectivity in the triple network related to comorbid anxiety in major depressive disorder

The brain's default mode network (DMN) and the executive control network (ECN) switch engagement are influenced by the ventral attention network (VAN). Alterations in resting-state functional connectivity (RSFC) within this so-called triple network have been demonstrated in patients with major depressive disorder (MDD) or anxiety disorders (ADs). This study investigated alterations in the RSFC in patients with comorbid MDD and ADs to better understand the pathophysiology of this prevalent group of patients. Sixty-eight participants (52.9% male, mean age 35.3 years), consisting of 25 patients with comorbid MDD and ADs (MDD + AD), 20 patients with MDD only (MDD) and 23 healthy controls (HCs) were investigated clinically and with 3T resting-state fMRI. RSFC utilizing a seed-based approach within the three networks belonging to the triple network was compared between the groups. Compared with HC, MDD + AD showed significantly reduced RSFC between the ECN and the VAN, the DMN and the VAN and within the ECN. No differences could be found for the MDD group compared with both other groups. Furthermore, symptom severity and medication status did not affect RSFC values. The results of this study show a distinct set of alterations of RSFC for patients with comorbid MDD and AD compared with HCs. This set of dysfunctions might be related to less adequate switching between the DMN and the ECN as well as poorer functioning of the ECN. This might contribute to additional difficulties in engaging and utilizing consciously controlled emotional regulation strategies.

Brain Circuit-Derived Biotypes for Treatment Selection in Mood Disorders: A Critical Review and Illustration of a Functional Neuroimaging Tool for Clinical Translation

Although the lifetime burden due to major depressive disorder is increasing, we lack tools for selecting the most effective treatments for each patient. One-third to one-half of patients with major depressive disorder do not respond to treatment, and we lack strategies for selecting among available treatments or expediting access to new treatment options. This critical review concentrates on functional neuroimaging as a modality of measurement for precision psychiatry. We begin by summarizing the current landscape of how functional neuroimaging-derived circuit predictors can forecast treatment outcomes in depression. Then, we outline the opportunities and challenges in integrating circuit predictors into clinical practice. We highlight one standardized and reproducible approach for quantifying brain circuit function at an individual level, which could serve as a model for clinical translation. We conclude by evaluating the prospects and practicality of employing neuroimaging tools, such as the one that we propose, in routine clinical practice.

Chronometric TMS-fMRI of personalized left dorsolateral prefrontal target reveals state-dependency of subgenual anterior cingulate cortex effects

Transcranial magnetic stimulation (TMS) applied to a left dorsolateral prefrontal cortex (DLPFC) area with a specific connectivity profile to the subgenual anterior cingulate cortex (sgACC) has emerged as a highly effective non-invasive treatment option for depression. However, antidepressant outcomes demonstrate significant variability among therapy plans and individuals. One overlooked contributing factor is the individual brain state at the time of treatment. In this study we used interleaved TMS-fMRI to investigate the influence of brain state on acute TMS effects, both locally and remotely. TMS was performed during rest and during different phases of cognitive task processing. Twenty healthy participants were included in this study. In the first session, imaging data for TMS targeting were acquired, allowing for identification of individualized targets in the left DLPFC based on highest anti-correlation with the sgACC. The second session involved chronometric interleaved TMS-fMRI measurements, with 10 Hz triplets of TMS administered during rest and at distinct timings during an N-back task. Consistent with prior findings, interleaved TMS-fMRI revealed significant BOLD activation changes in the targeted network. The precise timing of TMS relative to the cognitive states during the task demonstrated distinct BOLD response in clinically relevant brain regions, including the sgACC. Employing a standardized timing approach for TMS using a task revealed more consistent modulation of the sgACC at the group level compared to stimulation during rest. In conclusion, our findings strongly suggest that acute local and remote effects of TMS are influenced by brain state during stimulation. This study establishes a basis for considering brain state as a significant factor in designing treatment protocols, possibly improving TMS treatment outcomes.

Integration of whole-exome sequencing and structural neuroimaging analysis in major depressive disorder: a joint study

Major depressive disorder (MDD) is a common mental illness worldwide and is triggered by an intricate interplay between environmental and genetic factors. Although there are several studies on common variants in MDD, studies on rare variants are relatively limited. In addition, few studies have examined the genetic contributions to neurostructural alterations in MDD using whole-exome sequencing (WES). We performed WES in 367 patients with MDD and 161 healthy controls (HCs) to detect germline and copy number variations in the Korean population. Gene-based rare variants were analyzed to investigate the association between the genes and individuals, followed by neuroimaging-genetic analysis to explore the neural mechanisms underlying the genetic impact in 234 patients with MDD and 135 HCs using diffusion tensor imaging data. We identified 40 MDD-related genes and observed 95 recurrent regions of copy number variations. We also discovered a novel gene, FRMPD3, carrying rare variants that influence MDD. In addition, the single nucleotide polymorphism rs771995197 in the MUC6 gene was significantly associated with the integrity of widespread white matter tracts. Moreover, we identified 918 rare exonic missense variants in genes associated with MDD susceptibility. We postulate that rare variants of FRMPD3 may contribute significantly to MDD, with a mild penetration effect.

Hyperactive lateral habenula mediates the comorbidity between rheumatoid arthritis and depression-like behaviors

Rheumatoid arthritis (RA) patients have a high prevalence for depression. On the other hand, comorbid with depression is associated with worse prognosis for RA. However, little is known about the underlying mechanisms for the comorbidity between RA and depression. It remains to be elucidated which brain region is critically involved in the development of depression in RA, and whether alterations in the brain may affect pathological development of RA symptoms. Here, by combining clinical and animal model studies, we show that in RA patients, the level of depression is significantly correlated with the severity of RA disease activity and affects patients' quality of life. The collagen antibody-induced arthritis (CAIA) mouse model of RA also develops depression-like behaviors, accompanied by hyperactivity and alterations in gene expression reflecting cerebrovascular disruption in the lateral habenula (LHb), a brain region critical for processing negative valence. Importantly, inhibition of the LHb not only alleviates depression-like behaviors, but also results in rapid remission of RA symptoms and amelioration of RA-related pathological changes. Together, our study highlights a critical but previously overlooked contribution of hyperactive LHb to the comorbidity between RA and depression, suggesting that targeting LHb in conjunction with RA treatments may be a promising strategy for RA patients comorbid with depression.

Precision psychiatry and Research Domain Criteria: Implications for clinical trials and future practice

Psychiatric disorders are associated with significant social and economic burdens, many of which are related to issues with current diagnosis and treatments. The coronavirus (COVID-19) pandemic is estimated to have increased the prevalence and burden of major depressive and anxiety disorders, indicating an urgent need to strengthen mental health systems globally. To date, current approaches adopted in drug discovery and development for psychiatric disorders have been relatively unsuccessful. Precision psychiatry aims to tailor healthcare more closely to the needs of individual patients and, when informed by neuroscience, can offer the opportunity to improve the accuracy of disease classification, treatment decisions, and prevention efforts. In this review, we highlight the growing global interest in precision psychiatry and the potential for the National Institute of Health-devised Research Domain Criteria (RDoC) to facilitate the implementation of transdiagnostic and improved treatment approaches. The need for current psychiatric nosology to evolve with recent scientific advancements and increase awareness in emerging investigators/clinicians of the value of this approach is essential. Finally, we examine current challenges and future opportunities of adopting the RDoC-associated translational and transdiagnostic approaches in clinical studies, acknowledging that the strength of RDoC is that they form a dynamic framework of guiding principles that is intended to evolve continuously with scientific developments into the future. A collaborative approach that recruits expertise from multiple disciplines, while also considering the patient perspective, is needed to pave the way for precision psychiatry that can improve the prognosis and quality of life of psychiatric patients.

Regional cortical thickness recovery with extended abstinence after treatment in those with alcohol use disorder

Several cross-sectional investigations reported widespread cortical thinning in those with alcohol use disorder (AUD). The few longitudinal studies investigating cortical thickness changes during abstinence are limited to the first month of sobriety. Consequently, cortical thickness changes during extended abstinence in those with AUD is unclear. In this study, AUD participants were studied at approximately 1 week (n = 68), 1 month (n = 88), and 7.3 months (n = 40) of abstinence. Forty-five never-smoking controls (CON) completed a baseline study, and 15 were reassessed after approximately 9.6 months. Participants completed magnetic resonance imaging studies at 1.5T, and cortical thickness for 34 bilateral regions of interest (ROI) was quantitated with FreeSurfer. AUD participants demonstrated significant linear thickness increases in 25/34 ROI over 7.3 months of abstinence. The rate of change from 1 week to 1 month was greater than 1 month to 7.3 months in 19/34 ROIs. Proatherogenic conditions were associated with lower thickness recovery in anterior frontal, inferior parietal, and lateral/mesial temporal regions. After 7.3 months of abstinence, AUD participants were statistically equivalent to CON on cortical thickness in 24/34 ROIs; the cortical thickness differences between AUD and CON in the banks superior temporal gyrus, post central, posterior cingulate, superior parietal, supramarginal, and superior frontal cortices were driven by thinner cortices in AUD with proatherogenic conditions relative to CON. In actively smoking AUD, increasing pack-years was associated with decreasing thickness recovery primarily in the anterior frontal ROIs. Widespread bilateral cortical thickness recovery over 7.3 months of abstinence was the central finding for this AUD cohort. The longitudinal and cross-sectional findings for AUD with proatherogenic suggests alterations in perfusion or vascular integrity may relate to structural recovery in those with AUD. These results support the adaptive and beneficial effects of sustained sobriety on brain structural recovery in people with AUD.

Synaptotagmin-4 induces anhedonic responses to chronic stress via BDNF signaling in the medial prefrontal cortex

Stressful circumstances are significant contributors to mental illnesses such as major depressive disorder. Anhedonia, defined as loss of the ability to enjoy pleasure in pleasurable situations, including rewarding activities or social contexts, is considered a key symptom of depression. Although stress-induced depression is associated with anhedonia in humans and animals, the underlying molecular mechanisms of anhedonic responses remain poorly understood. In this study, we demonstrated that synaptotagmin-4 (SYT4), which is involved in the release of neurotransmitters and neurotrophic factors, is implicated in chronic stress-induced anhedonia. Employing chronic unpredictable stress (CUS), we evaluated two subpopulations of mice, susceptible (SUS, anhedonic) and resilient (RES, nonanhedonic), based on sucrose preference, which was strongly correlated with social reward. The FosTRAP (targeted recombination in active populations) system and optogenetic approach revealed that neural activity in the medial prefrontal cortex (mPFC) was significantly associated with CUS-induced anhedonic behavioral phenotypes. By conducting weighted gene coexpression network analysis of RNA sequencing data from the mPFC of SUS and RES mice, we identified Syt4 as a hub gene in a gene network that was unique to anhedonia. We also confirmed that Syt4 overexpression in the mPFC was pro-susceptible, while Syt4 knockdown was pro-resilient; the pro-susceptible effects of SYT4 were mediated through a reduction in brain-derived neurotrophic factor (BDNF)-tropomyosin receptor kinase B (TrkB) signaling in the mPFC. These findings suggest that SYT4-BDNF interactions in the mPFC represent a crucial regulatory mechanism of anhedonic susceptibility to chronic stress.

Regional cortical brain volumes at treatment entry relates to post treatment WHO risk drinking levels in those with alcohol use disorder

BACKGROUND

Abstinence following treatment for alcohol use disorder (AUD) is associated with significant improvements in psychiatric and physical health, however, recent studies suggest resumption of low risk levels of alcohol use can also be beneficial. The present study assessed whether post-treatment levels of alcohol use were associated with cortical brain volumedifferences at treatment entry.

METHODS

Individuals seeking treatment for AUD (n=75) and light/non-drinking controls (LN, n=51) underwent 1.5T magnetic resonance imaging. The volumes of 34 bilateral cortical regions of interest (ROIs) were quantitated via FreeSurfer. Individuals with AUD were classified according to post-treatment alcohol consumption using the WHO risk drinking levels (abstainers: AB; low risk: RL; or higher risk: RH). Regional volumes for AB, RL and RH, at treatment entry, were compared to LN.

RESULTS

Relative to LN, AB demonstrated smaller volumes in 18/68 (26%), RL in 24/68 (35%) and RH in 34/68 (50%) ROIs with the largest magnitude volume differences observed between RH and LN. RH and RL reported a higher frequency of depressive disorders than AB. Among RH and RL, level of depressive and anxiety symptomatology were associated with daily number of drinks consumed after treatment.

CONCLUSIONS

Volumetric differences, at treatment entry, in brain regions implicated in executive function and salience networks corresponded with post-treatment alcohol consumption levels suggesting that pre-existing differences in neural integrity may contribute to treatment outcomes. Depressive and anxiety symptomatology was also associated with brain morphometrics and alcohol use patterns, highlighting the importance of effectively targeting these conditions during AUD treatment.

Input-output relation of midbrain connectomics in a rodent model of depression

BACKGROUND

The symptoms associated with depression are believed to arise from disruptions in information processing across brain networks. The ventral tegmental area (VTA) influences reward-based behavior, motivation, addiction, and psychiatric disorders, including depression. Deep brain stimulation (DBS) of the medial forebrain bundle (MFB), is an emerging therapy for treatment-resistant depression. Understanding the depression associated anatomical networks crucial for comprehending its antidepressant effects.

METHODS

Flinders Sensitive Line (FSL), a rodent model of depression and Sprague-Dawley rats (n = 10 each) were used in this study. We used monosynaptic tracing to map inputs of VTA efferent neurons: VTA-to-NAc nucleus accumbens (NAc) (both core and shell) and VTA-to-prefrontal cortex (PFC). Quantitative analysis explored afferent diversity and strengths.

RESULTS

VTA efferent neurons receive a variety of afferents with varying input weights and predominant neuromodulatory representation. Notably, NAc-core projecting VTA neurons showed stronger afferents from dorsal raphe, while NAc shell-projecting VTA neurons displayed lower input strengths from cortex, thalamus, zona incerta and pretectal area in FSL rats. NAc shell-projecting VTA neurons showed the most difference in connectivity across the experimental groups.

LIMITATIONS

Lack of functional properties of the anatomical connections is the major limitation of this study. Incomplete labeling and the cytotoxicity of the rabies virus should be made aware of.

CONCLUSIONS

These findings provide the first characterization of inputs to different VTA ascending projection neurons, shedding light on critical differences in the connectome of the midbrain-forebrain system. Moreover, these differences support potential network effects of these circuits in the context of MFB DBS neuromodulation for depression.

Prefrontal network engagement by deep brain stimulation in limbic hubs

Prefrontal circuits in the human brain play an important role in cognitive and affective processing. Neuromodulation therapies delivered to certain key hubs within these circuits are being used with increasing frequency to treat a host of neuropsychiatric disorders. However, the detailed neurophysiological effects of stimulation to these hubs are largely unknown. Here, we performed intracranial recordings across prefrontal networks while delivering electrical stimulation to two well-established white matter hubs involved in cognitive regulation and depression: the subcallosal cingulate (SCC) and ventral capsule/ventral striatum (VC/VS). We demonstrate a shared frontotemporal circuit consisting of the ventromedial prefrontal cortex, amygdala, and lateral orbitofrontal cortex where gamma oscillations are differentially modulated by stimulation target. Additionally, we found participant-specific responses to stimulation in the dorsal anterior cingulate cortex and demonstrate the capacity for further tuning of neural activity using current-steered stimulation. Our findings indicate a potential neurophysiological mechanism for the dissociable therapeutic effects seen across the SCC and VC/VS targets for psychiatric neuromodulation and our results lay the groundwork for personalized, network-guided neurostimulation therapy.

Cortical glutamate, Glx, and total N-acetylaspartate: potential biomarkers of repetitive transcranial magnetic stimulation treatment response and outcomes in major depression

Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for individuals with major depressive disorder (MDD) who have not improved with standard therapies. However, only 30-45% of patients respond to rTMS. Predicting response to rTMS will benefit both patients and providers in terms of prescribing and targeting treatment for maximum efficacy and directing resources, as individuals with lower likelihood of response could be redirected to more suitable treatment alternatives. In this exploratory study, our goal was to use proton magnetic resonance spectroscopy to examine how glutamate (Glu), Glx, and total N-acetylaspartate (tNAA) predict post-rTMS changes in overall MDD severity and symptoms, and treatment response. Metabolites were measured in a right dorsal anterior cingulate cortex voxel prior to a standard course of 10 Hz rTMS to the left DLPFC in 25 individuals with MDD. MDD severity and symptoms were evaluated via the Inventory of Depression Symptomatology Self-Report (IDS-SR). rTMS response was defined as ≥50% change in full-scale IDS-SR scores post treatment. Percent change in IDS-SR symptom domains were evaluated using principal component analysis and established subscales. Generalized linear and logistic regression models were used to evaluate the relationship between baseline Glu, Glx, and tNAA and outcomes while controlling for age and sex. Participants with baseline Glu and Glx levels in the lower range had greater percent change in full scale IDS-SR scores post-treatment (p < 0.001), as did tNAA (p = 0.007). Low glutamatergic metabolite levels also predicted greater percent change in mood/cognition symptoms (p ≤ 0.001). Low-range Glu, Glx, and tNAA were associated with greater improvement on the immuno-metabolic subscale (p ≤ 0.003). Baseline Glu predicted rTMS responder status (p = 0.025) and had an area under the receiving operating characteristic curve of 0.81 (p = 0.009), demonstrating excellent discriminative ability. Baseline Glu, Glx, and tNAA significantly predicted MDD improvement after rTMS; preliminary evidence also demonstrates metabolite association with symptom subdomain improvement post-rTMS. This work provides feasibility for a personalized medicine approach to rTMS treatment selection, with individuals with Glu levels in the lower range potentially being the best candidates.

Closed-loop neurostimulation for the treatment of psychiatric disorders

Despite increasing prevalence and huge personal and societal burden, psychiatric diseases still lack treatments which can control symptoms for a large fraction of patients. Increasing insight into the neurobiology underlying these diseases has demonstrated wide-ranging aberrant activity and functioning in multiple brain circuits and networks. Together with varied presentation and symptoms, this makes one-size-fits-all treatment a challenge. There has been a resurgence of interest in the use of neurostimulation as a treatment for psychiatric diseases. Initial studies using continuous open-loop stimulation, in which clinicians adjusted stimulation parameters during patient visits, showed promise but also mixed results. Given the periodic nature and fluctuations of symptoms often observed in psychiatric illnesses, the use of device-driven closed-loop stimulation may provide more effective therapy. The use of a biomarker, which is correlated with specific symptoms, to deliver stimulation only during symptomatic periods allows for the personalized therapy needed for such heterogeneous disorders. Here, we provide the reader with background motivating the use of closed-loop neurostimulation for the treatment of psychiatric disorders. We review foundational studies of open- and closed-loop neurostimulation for neuropsychiatric indications, focusing on deep brain stimulation, and discuss key considerations when designing and implementing closed-loop neurostimulation.

Modelling the overlap and divergence of autistic and schizotypal traits on hippocampal subfield volumes and regional cerebral blood flow

Psychiatric disorders show high co-morbidity, including co-morbid expressions of subclinical psychopathology across multiple disease spectra. Given the limitations of classical case-control designs in elucidating this overlap, new approaches are needed to identify biological underpinnings of spectra and their interaction. We assessed autistic-like traits (using the Autism Quotient, AQ) and schizotypy - as models of subclinical expressions of disease phenotypes and examined their association with volumes and regional cerebral blood flow (rCBF) of anterior, mid- and posterior hippocampus segments from  structural MRI scans in 318 and arterial spin labelling (ASL) in 346 nonclinical subjects, which overlapped with the structural imaging sample (N = 298). We demonstrate significant interactive effects of positive schizotypy and AQ social skills as well as of positive schizotypy and AQ imagination on hippocampal subfield volume variation. Moreover, we show that AQ attention switching modulated hippocampal head rCBF, while positive schizotypy by AQ attention to detail interactions modulated hippocampal tail rCBF. In addition, we show significant correlation of hippocampal volume and rCBF in both region-of-interest and voxel-wise analyses, which were robust after removal of variance related to schizotypy and autistic traits. These findings provide empirical evidence for both the modulation of hippocampal subfield structure and function through subclinical traits, and in particular how only the interaction of phenotype facets leads to significant reductions or variations in these parameters. This makes a case for considering the synergistic impact of different (subclinical) disease spectra on transdiagnostic biological parameters in psychiatry.

Heritability of cognitive and emotion processing during functional MRI in a twin sample

Despite compelling evidence that brain structure is heritable, the evidence for the heritability of task-evoked brain function is less robust. Findings from previous studies are inconsistent possibly reflecting small samples and methodological variations. In a large national twin sample, we systematically evaluated heritability of task-evoked brain activity derived from functional magnetic resonance imaging. We used established standardised tasks to engage brain regions involved in cognitive and emotional functions. Heritability was evaluated across a conscious and nonconscious Facial Expressions of Emotion Task (FEET), selective attention Oddball Task, N-back task of working memory maintenance, and a Go-NoGo cognitive control task in a sample of Australian adult twins (N ranged from 136 to 226 participants depending on the task and pairs). Two methods for quantifying associations of heritability and brain activity were utilised; a multivariate independent component analysis (ICA) approach and a univariate brain region-of-interest (ROI) approach. Using ICA, we observed that a significant proportion of task-evoked brain activity was heritable, with estimates ranging from 23% to 26% for activity elicited by nonconscious facial emotion stimuli, 27% to 34% for N-back working memory maintenance and sustained attention, and 32% to 33% for selective attention in the Oddball task. Using the ROI approach, we found that activity of regions specifically implicated in emotion processing and selective attention showed significant heritability for three ROIs, including estimates of 33%-34% for the left and right amygdala in the nonconscious processing of sad faces and 29% in the medial superior prefrontal cortex for the Oddball task. Although both approaches show similar levels of heritability for the Nonconscious Faces and Oddball tasks, ICA results displayed a more extensive network of heritable brain function, including additional regions beyond the ROI analysis. Furthermore, multivariate twin modelling of both ICA networks and ROI activation suggested a mix of common genetic and unique environmental factors that contribute to the associations between networks/regions. Together, the results indicate a complex relationship between genetic factors and environmental interactions that ultimately give rise to neural activation underlying cognition and emotion.

Loneliness is associated with mentalizing and emotion recognition abilities in schizophrenia, but only in a cluster of patients with social cognitive deficits

OBJECTIVE

Loneliness is a concern for patients with schizophrenia. However, the correlates of loneliness in patients with schizophrenia are unclear; thus, the aim of the study is to investigate neuro- and social cognitive mechanisms associated with loneliness in individuals with schizophrenia.

METHOD

Data from clinical, neurocognitive, and social cognitive assessments were pooled from two cross-national samples (Poland/USA) to examine potential predictors of loneliness in 147 patients with schizophrenia and 103 healthy controls overall. Furthermore, the relationship between social cognition and loneliness was explored in clusters of patients with schizophrenia differing in social cognitive capacity.

RESULTS

Patients reported higher levels of loneliness than healthy controls. Loneliness was linked to increased negative and affective symptoms in patients. A negative association between loneliness and mentalizing and emotion recognition abilities was found in the patients with social-cognitive impairments, but not in those who performed at normative levels.

CONCLUSIONS

We have elucidated a novel mechanism which may explain previous inconsistent findings regarding the correlates of loneliness in individuals with schizophrenia.

Internalizing Symptoms and Adverse Childhood Experiences Associated With Functional Connectivity in a Middle Childhood Sample

BACKGROUND

Research has found overlapping associations in adults of resting-state functional connectivity (RSFC) to both internalizing disorders (e.g., depression, anxiety) and a history of traumatic events. The present study aimed to extend this previous research to a younger sample by examining RSFC associations with both internalizing symptoms and adverse childhood experiences (ACEs) in middle childhood.

METHODS

We used generalized linear mixed models to examine associations between a priori within- and between-network RSFC with child-reported internalizing symptoms and ACEs using the Adolescent Brain Cognitive Development dataset (N = 10,168, mean age = 9.95 years, SD = 0.627).

RESULTS

We found that internalizing symptoms and ACEs were associated with both multiple overlapping and unique RSFC network patterns. Both ACEs and internalizing symptoms were associated with a reduced anticorrelation between the default mode network and the dorsal attention network. However, internalizing symptoms were uniquely associated with lower within-network default mode network connectivity, while ACEs were uniquely associated with both lower between-network connectivity of the auditory network and cingulo-opercular network, and higher within-network frontoparietal network connectivity.

CONCLUSIONS

The present study points to overlap in the RSFC associations with internalizing symptoms and ACEs, as well as important areas of specificity in RSFC associations. Many of the RSFC associations found have been previously implicated in attentional control functions, including modulation of attention to sensory stimuli. This may have critical importance in understanding internalizing symptoms and outcomes of ACEs.