Transdiagnostic approaches to psychiatry have significant potential in overcoming the limitations of conventional diagnostic paradigms. However, while frameworks such as the Research Domain Criteria have garnered significant enthusiasm among researchers and clinicians from a theoretical angle, examples of how such an approach might translate in practice to understand the biological mechanisms underlying complex patterns of behaviors in realistic and heterogeneous populations have been sparse. In a richly phenotyped clinical sample (n = 186) specifically designed to capture the complex nature of heterogeneity and comorbidity within- and between stress- and neurodevelopmental disorders, we use exploratory factor analysis on a wide range of clinical questionnaires to identify four stable functional domains that transcend diagnosis and relate to negative valence, cognition, social functioning and inhibition/arousal before replicating them in an independent dataset (n = 188). We then use connectopic mapping to map inter-individual variation in fine-grained topographical organization of functional connectivity in the striatum—a central hub in motor, cognitive, affective and reward-related brain circuits—and use multivariate machine learning (canonical correlation analysis) to show that these individualized topographic representations predict transdiagnostic functional domains out of sample (r = 0.20, p = 0.026). We propose that investigating psychiatric symptoms across disorders is a promising path to linking them to underlying biology, and can help bridge the gap between neuroscience and clinical psychiatry.

Objective: To uncover transdiagnostic domains of functioning across stress- and neurodevelopmental disorders, and to map these on to the topographic functional organization of cortico-striatal circuitry. Methods: In a clinical sample (n=186) of subjects with high rates of comorbidity of major depressive disorder, anxiety disorder, attention-deficit/hyperactivity disorder and/or autism spectrum disorder, we use exploratory factor analysis on a wide range of clinical questionnaires to identify consistent functional domains of symptomatology across disorders, then replicate these functional domains in an independent dataset (n=188). Then, we use canonical correlation analysis link these functional domains to the topographic organization of the striatum as represented by connectopic maps. Results: We reveal four functional domains that transcend current diagnostic categories relating to negative valence, cognition, social functioning and inhibition/arousal. These functional domains are replicated in an independent sample and are associated with the fine-grained topographical organization of functional connectivity in the striatum (out of sample r=0.20, p=0.026), a central hub in motor, cognitive, affective and reward-related brain circuits. Conclusions and relevance: Functional domains across stress- and neurodevelopmental disorders are associated with the functional organization of the striatum. We propose that investigating psychiatric symptoms across disorders is a promising path to linking them to underlying biology, and can help bridge the gap between neuroscience and clinical psychiatry.

Introduction: Major depressive disorder (MDD) is a common psychiatric disorder. Despite several treatment options, a subgroup of patients will not respond to the commonly used antidepressant treatments and thus express treatment resistance (TRD). TRD can be quantified with the Dutch Measure for Treatment Resistance in Depression (DM-TRD). Electroconvulsive therapy (ECT) is an effective treatment for MDD, also in TRD. Yet, the position of ECT as "treatment-of-lastresort" may decrease the likelihood of beneficial outcome. Our aim was to investigate the association between treatment resistance and outcome and course of ECT.Methods: We performed a retrospective, multicenter cohort study with 440 patients of which data was retrieved from patient records as collected in the Dutch ECT Cohort database. Linear and logistic regression models were used to explore the association between level of treatment resistance and outcome of ECT. Median split was used to explore the differences between high and low level of TRD and course of treatment. Results: A higher DM-TRD score was associated with significantly smaller reduction of depression symptoms (R 2 = 0.160; β = À2.968; p < 0.001) and lower chance of response (OR = 0.821 [95 CI: 0.760-0.888]; β = À0.197; p < 0.001). Low level TRD patients underwent fewer ECT sessions (mean 13 ± 6 SD vs. 16 ± 7 SD; p < 0.001) and fewer switches from right unilateral tot bifrontotemporal electrode placement (29% vs. 40%; p = 0.032). Conclusion:Reserving ECT as "treatment-of-last-resort" in the treatment algorithm for MDD seems questionable, because in our study lower level of treatment resistance predicted more beneficial ECT-outcome. Moreover, providing ECT in less treatment resistant patients showed fewer needed ECTsessions and less switches to BL electrode placement, which may decrease the risk for cognitive side-effects.

In this study we dissect the heterogeneity that underlies traditional group-level analyses, and determine how individualised patterns of predicted activation relate to age, sex, and variations in acquisition parameters and task design choices. To this end we take advantage of six large open-access/shared datasets and collate a large representative sample of over 7500 participants from which we build a normative of task-evoked activation during a widely used emotional reactivity task, the Emotional Face Matching Task. This enables us to bind heterogeneous datasets to a common reference model and enables meaningful comparisons between them. We then apply this model to the naturalistic and clinically realistic MIND-Set cohort, which is a heterogeneous and highly comorbid sample containing individuals with one or more current diagnosis (affective and anxiety disorders, autism spectrum disorders and/or attention deficit hyperactivity disorder). This enables us to determine whether, and if so how, participants with mental illness and/or neurodivergence differ from the reference cohort, both at the group level and at the level of the individual and in terms of cross-diagnostic symptom domains in addition to diagnosis. We show that patients have, on average, a higher frequency of extreme deviations, and have unique spatial distributions depending on the DSM-IV diagnosis and the number of co-occurring diagnoses when models are constructed using the face>shapes task contrast. Models built using the face>baseline task contrast, have, by comparison, greater predictive value for individuals' functioning across four transdiagnostic domains. We demonstrate the application of the normative modelling framework to task-based functional neuroimaging data, discuss its potential to further our understanding of individual differences in brain function within reference populations, and further validate the clinical relevance of these models.

IntroductionFor major depression, a one-size-fits-all treatment does not exist. Patients enter a ‘trial-and-change’ algorithm in which effective therapies are subsequently applied. Unfortunately, an empirically based order of treatments has not yet been determined. There is a magnitude of different treatment strategies while clinical trials only compare a small number of these. Network meta-analyses (NMA) might offer a solution, but so far have been limited in scope and did not account for possible differences in population characteristics that arise with increasing levels of treatment-resistance, potentially violating the transitivity assumption. We; therefore, present a protocol for a systematic review and NMA aiming at summarising and ranking treatments for treatment-resistant depression (TRD) while covering a broad range of therapeutic options and accounting for possible differences in population characteristics at increasing levels of treatment-resistance.Methods and analysisRandomised controlled trials will be included that compared next-step pharmacological, neuromodulation or psychological treatments for treatment-resistant depression (TRD; ie, failure to respond to ≥1 adequate antidepressant drug trial(s) in the current episode) to each other or to a control condition. Primary outcomes will be the proportion of patients who responded to (efficacy) and dropped out of (acceptability) the allocated treatment. A random effects NMA will be conducted, synthesising the evidence for each outcome and determining the differential efficacy of treatments. Heterogeneity in treatment nodes will be reduced by considering alternative geometries of the network structure and by conducting a meta-regression examining different levels of TRD. Local and global methods will be applied to evaluate consistency. The Cochrane Risk of Bias 2 tool, Confidence in Network Meta-Analysis and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework will be used to assess risk of bias and certainty.Ethics and disseminationThis review does not require ethical approval.