Effective connectivity during face processing in major depression - distinguishing markers of pathology, risk, and resilience.

BACKGROUND: Aberrant brain connectivity during emotional processing, especially within the fronto-limbic pathway, is one of the hallmarks of major depressive disorder (MDD). However, the methodological heterogeneity of previous studies made it difficult to determine the functional and etiological implications of specific alterations in brain connectivity. We previously reported alterations in psychophysiological interaction measures during emotional face processing, distinguishing depressive pathology from at-risk/resilient and healthy states. Here, we extended these findings by effective connectivity analyses in the same sample to establish a refined neural model of emotion processing in depression. METHODS: Thirty-seven patients with MDD, 45 first-degree relatives of patients with MDD and 97 healthy controls performed a face-matching task during functional magnetic resonance imaging. We used dynamic causal modeling to estimate task-dependent effective connectivity at the subject level. Parametric empirical Bayes was performed to quantify group differences in effective connectivity. RESULTS: MDD patients showed decreased effective connectivity from the left amygdala and left lateral prefrontal cortex to the fusiform gyrus compared to relatives and controls, whereas patients and relatives showed decreased connectivity from the right orbitofrontal cortex to the left insula and from the left orbitofrontal cortex to the right fusiform gyrus compared to controls. Relatives showed increased connectivity from the anterior cingulate cortex to the left dorsolateral prefrontal cortex compared to patients and controls. CONCLUSIONS: Our results suggest that the depressive state alters top-down control of higher visual regions during face processing. Alterations in connectivity within the cognitive control network present potential risk or resilience mechanisms.

Behavioural and functional evidence revealing the role of RBFOX1 variation in multiple psychiatric disorders and traits.

Common variation in the gene encoding the neuron-specific RNA splicing factor RNA Binding Fox-1 Homolog 1 (RBFOX1) has been identified as a risk factor for several psychiatric conditions, and rare genetic variants have been found causal for autism spectrum disorder (ASD). Here, we explored the genetic landscape of RBFOX1 more deeply, integrating evidence from existing and new human studies as well as studies in Rbfox1 knockout mice. Mining existing data from large-scale studies of human common genetic variants, we confirmed gene-based and genome-wide association of RBFOX1 with risk tolerance, major depressive disorder and schizophrenia. Data on six mental disorders revealed copy number losses and gains to be more frequent in ASD cases than in controls. Consistently, RBFOX1 expression appeared decreased in post-mortem frontal and temporal cortices of individuals with ASD and prefrontal cortex of individuals with schizophrenia. Brain-functional MRI studies demonstrated that carriers of a common RBFOX1 variant, rs6500744, displayed increased neural reactivity to emotional stimuli, reduced prefrontal processing during cognitive control, and enhanced fear expression after fear conditioning, going along with increased avoidance behaviour. Investigating Rbfox1 neuron-specific knockout mice allowed us to further specify the role of this gene in behaviour. The model was characterised by pronounced hyperactivity, stereotyped behaviour, impairments in fear acquisition and extinction, reduced social interest, and lack of aggression; it provides excellent construct and face validity as an animal model of ASD. In conclusion, convergent translational evidence shows that common variants in RBFOX1 are associated with a broad spectrum of psychiatric traits and disorders, while rare genetic variation seems to expose to early-onset neurodevelopmental psychiatric disorders with and without developmental delay like ASD, in particular. Studying the pleiotropic nature of RBFOX1 can profoundly enhance our understanding of mental disorder vulnerability.

Brain structural correlates of upward social mobility in ethnic minority individuals.

PURPOSE: Perigenual anterior cingulate cortex (pACC) is a neural convergence site for social stress-related risk factors for mental health, including ethnic minority status. Current social status, a strong predictor of mental and somatic health, has been related to gray matter volume in this region, but the effects of social mobility over the lifespan are unknown and may differ in minorities. Recent studies suggest a diminished health return of upward social mobility for ethnic minority individuals, potentially due to sustained stress-associated experiences and subsequent activation of the neural stress response system. METHODS: To address this issue, we studied an ethnic minority sample with strong upward social mobility. In a cross-sectional design, we examined 64 young adult native German and 76 ethnic minority individuals with comparable sociodemographic attributes using whole-brain structural magnetic resonance imaging. RESULTS: Results showed a significant group-dependent interaction between perceived upward social mobility and pACC gray matter volume, with a significant negative association in the ethnic minority individuals. Post-hoc analysis showed a significant mediation of the relationship between perceived upward social mobility and pACC volume by perceived chronic stress, a variable that was significantly correlated with perceived discrimination in our ethnic minority group. CONCLUSION: Our findings extend prior work by pointing to a biological signature of the "allostatic costs" of socioeconomic attainment in socially disadvantaged upwardly mobile individuals in a key neural node implicated in the regulation of stress and negative affect.

[Cognitive disorders and sleep disturbances in long COVID]. / Kognitive Störungen und Schlafstörungen bei Long-COVID.

BACKGROUND: During the last 2 years of the coronavirus disease 2019 (COVID-19) pandemic, knowledge about the long-term effects of the disease, the so-called long COVID, has rapidly grown; however, many questions remain unanswered, especially regarding the causes of persistent symptoms and their prognosis. Cognitive disorders and sleep disturbances are among the most frequent complaints. Both are associated with severe suffering and significant impairment in everyday functioning. OBJECTIVE: What is known about the occurrence of cognitive disorders and sleep disturbances in long COVID? What are the influencing factors and what is known about the course over time and possible underlying mechanisms? What treatment options are available? MATERIAL AND METHOD: In a narrative review, the most important findings on cognitive disorders and sleep disturbances in long COVID are presented. An overview of cohort studies with data on the prevalence and influencing factors of both symptom complexes is given. Current knowledge and hypotheses on pathophysiological mechanisms are presented and an outlook on treatment approaches is given. RESULTS: About one in five of those affected report cognitive impairment more than 3 months after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and about one third report sleep disturbances. The latter comprise symptoms of insomnia as well as hypersomnia. Cognitive impairment and sleep disturbances occur in patients with all levels of initial disease severity. There are indications of an improvement of cognitive deficits over time but further longitudinal studies are needed. CONCLUSION: In addition to the prognosis, the underlying disease mechanisms are still insufficiently understood. Furthermore, there is a great need for research on the efficacy and specific effective factors of therapeutic interventions.

Generative network models of altered structural brain connectivity in schizophrenia.

Alterations in the structural connectome of schizophrenia patients have been widely characterized, but the mechanisms remain largely unknown. Generative network models have recently been introduced as a tool to test the biological underpinnings of altered brain network formation. We evaluated different generative network models in healthy controls (n=152), schizophrenia patients (n=66), and their unaffected first-degree relatives (n=32), and we identified spatial and topological factors contributing to network formation. We further investigated how these factors relate to cognition and to polygenic risk for schizophrenia. Our data show that among the four tested classes of generative network models, structural brain networks were optimally accounted for by a two-factor model combining spatial constraints and topological neighborhood structure. The same wiring model explained brain network formation across study groups. However, relatives and schizophrenia patients exhibited significantly lower spatial constraints and lower topological facilitation compared to healthy controls. Further exploratory analyses point to potential associations of the model parameter reflecting spatial constraints with the polygenic risk for schizophrenia and cognitive performance. Our results identify spatial constraints and local topological structure as two interrelated mechanisms contributing to regular brain network formation as well as altered connectomes in schizophrenia and healthy individuals at familial risk for schizophrenia. On an exploratory level, our data further point to the potential relevance of spatial constraints for the genetic risk for schizophrenia and general cognitive functioning, thereby encouraging future studies in following up on these observations to gain further insights into the biological basis and behavioral relevance of model parameters.

Amygdala functional connectivity in major depression - disentangling markers of pathology, risk and resilience.

BACKGROUND: Limbic-cortical imbalance is an established model for the neurobiology of major depressive disorder (MDD), but imaging genetics studies have been contradicting regarding potential risk and resilience mechanisms. Here, we re-assessed previously reported limbic-cortical alterations between MDD relatives and controls in combination with a newly acquired sample of MDD patients and controls, to disentangle pathology, risk, and resilience. METHODS: We analyzed functional magnetic resonance imaging data and negative affectivity (NA) of MDD patients (n = 48), unaffected first-degree relatives of MDD patients (n = 49) and controls (n = 109) who performed a faces matching task. Brain response and task-dependent amygdala functional connectivity (FC) were compared between groups and assessed for associations with NA. RESULTS: Groups did not differ in task-related brain activation but activation in the superior frontal gyrus (SFG) was inversely correlated with NA in patients and controls. Pathology was associated with task-independent decreases of amygdala FC with regions of the default mode network (DMN) and decreased amygdala FC with the medial frontal gyrus during faces matching, potentially reflecting a task-independent DMN predominance and a limbic-cortical disintegration during faces processing in MDD. Risk was associated with task-independent decreases of amygdala-FC with fronto-parietal regions and reduced faces-associated amygdala-fusiform gyrus FC. Resilience corresponded to task-independent increases in amygdala FC with the perigenual anterior cingulate cortex (pgACC) and increased FC between amygdala, pgACC, and SFG during faces matching. CONCLUSION: Our results encourage a refinement of the limbic-cortical imbalance model of depression. The validity of proposed risk and resilience markers needs to be tested in prospective studies. Further limitations are discussed.

MAOA-VNTR genotype affects structural and functional connectivity in distributed brain networks.

Previous studies have linked the low expression variant of a variable number of tandem repeat polymorphism in the monoamine oxidase A gene (MAOA-L) to the risk for impulsivity and aggression, brain developmental abnormalities, altered cortico-limbic circuit function, and an exaggerated neural serotonergic tone. However, the neurobiological effects of this variant on human brain network architecture are incompletely understood. We studied healthy individuals and used multimodal neuroimaging (sample size range: 219-284 across modalities) and network-based statistics (NBS) to probe the specificity of MAOA-L-related connectomic alterations to cortical-limbic circuits and the emotion processing domain. We assessed the spatial distribution of affected links across several neuroimaging tasks and data modalities to identify potential alterations in network architecture. Our results revealed a distributed network of node links with a significantly increased connectivity in MAOA-L carriers compared to the carriers of the high expression (H) variant. The hyperconnectivity phenotype primarily consisted of between-lobe ("anisocoupled") network links and showed a pronounced involvement of frontal-temporal connections. Hyperconnectivity was observed across functional magnetic resonance imaging (fMRI) of implicit emotion processing (pFWE = .037), resting-state fMRI (pFWE = .022), and diffusion tensor imaging (pFWE = .044) data, while no effects were seen in fMRI data of another cognitive domain, that is, spatial working memory (pFWE = .540). These observations are in line with prior research on the MAOA-L variant and complement these existing data by novel insights into the specificity and spatial distribution of the neurogenetic effects. Our work highlights the value of multimodal network connectomic approaches for imaging genetics.

Dynamic brain network reconfiguration as a potential schizophrenia genetic risk mechanism modulated by NMDA receptor function.

Schizophrenia is increasingly recognized as a disorder of distributed neural dynamics, but the molecular and genetic contributions are poorly understood. Recent work highlights a role for altered N-methyl-d-aspartate (NMDA) receptor signaling and related impairments in the excitation-inhibitory balance and synchrony of large-scale neural networks. Here, we combined a pharmacological intervention with novel techniques from dynamic network neuroscience applied to functional magnetic resonance imaging (fMRI) to identify alterations in the dynamic reconfiguration of brain networks related to schizophrenia genetic risk and NMDA receptor hypofunction. We quantified "network flexibility," a measure of the dynamic reconfiguration of the community structure of time-variant brain networks during working memory performance. Comparing 28 patients with schizophrenia, 37 unaffected first-degree relatives, and 139 healthy controls, we detected significant differences in network flexibility [F(2,196) = 6.541, P = 0.002] in a pattern consistent with the assumed genetic risk load of the groups (highest for patients, intermediate for relatives, and lowest for controls). In an observer-blinded, placebo-controlled, randomized, cross-over pharmacological challenge study in 37 healthy controls, we further detected a significant increase in network flexibility as a result of NMDA receptor antagonism with 120 mg dextromethorphan [F(1,34) = 5.291, P = 0.028]. Our results identify a potential dynamic network intermediate phenotype related to the genetic liability for schizophrenia that manifests as altered reconfiguration of brain networks during working memory. The phenotype appears to be influenced by NMDA receptor antagonism, consistent with a critical role for glutamate in the temporal coordination of neural networks and the pathophysiology of schizophrenia.

Dynamic reconfiguration of frontal brain networks during executive cognition in humans.

The brain is an inherently dynamic system, and executive cognition requires dynamically reconfiguring, highly evolving networks of brain regions that interact in complex and transient communication patterns. However, a precise characterization of these reconfiguration processes during cognitive function in humans remains elusive. Here, we use a series of techniques developed in the field of "dynamic network neuroscience" to investigate the dynamics of functional brain networks in 344 healthy subjects during a working-memory challenge (the "n-back" task). In contrast to a control condition, in which dynamic changes in cortical networks were spread evenly across systems, the effortful working-memory condition was characterized by a reconfiguration of frontoparietal and frontotemporal networks. This reconfiguration, which characterizes "network flexibility," employs transient and heterogeneous connectivity between frontal systems, which we refer to as "integration." Frontal integration predicted neuropsychological measures requiring working memory and executive cognition, suggesting that dynamic network reconfiguration between frontal systems supports those functions. Our results characterize dynamic reconfiguration of large-scale distributed neural circuits during executive cognition in humans and have implications for understanding impaired cognitive function in disorders affecting connectivity, such as schizophrenia or dementia.

Multimodal Associations of FKBP5 Methylation With Emotion-Regulatory Brain Circuits.

BACKGROUND: Understanding the biological processes that underlie individual differences in emotion regulation and stress responsivity is a key challenge for translational neuroscience. The gene FKBP5 is a core regulator in molecular stress signaling that is implicated in the development of psychiatric disorders. However, it remains unclear how FKBP5 DNA methylation in peripheral blood is related to individual differences in measures of neural structure and function and their relevance to daily-life stress responsivity. METHODS: Here, we characterized multimodal correlates of FKBP5 DNA methylation by combining epigenetic data with neuroimaging and ambulatory assessment in a sample of 395 healthy individuals. RESULTS: First, we showed that FKBP5 demethylation as a psychiatric risk factor was related to an anxiety-associated reduction of gray matter volume in the ventromedial prefrontal cortex, a brain area that is involved in emotion regulation and mental health risk and resilience. This effect of epigenetic upregulation of FKBP5 on neuronal structure is more pronounced where FKBP5 is epigenetically downregulated at baseline. Leveraging 208 functional magnetic resonance imaging scans during a well-established emotion-processing task, we found that FKBP5 DNA methylation in peripheral blood was associated with functional differences in prefrontal-limbic circuits that modulate affective responsivity to daily stressors, which we measured using ecological momentary assessment in daily life. CONCLUSIONS: Overall, we demonstrated how FKBP5 contributes to interindividual differences in neural and real-life affect regulation via structural and functional changes in prefrontal-limbic brain circuits.

Ventral Striatal-Hippocampus Coupling During Reward Processing as a Stratification Biomarker for Psychotic Disorders.

BACKGROUND: Altered ventral striatal (vST) activation to reward expectancy is a well-established intermediate phenotype for psychiatric disorders, specifically schizophrenia (SZ). Preclinical research suggests that striatal alterations are related to a reduced inhibition by the hippocampal formation, but its role in human transdiagnostic reward-network dysfunctions is not well understood. METHODS: We performed functional magnetic resonance imaging during reward processing in 728 individuals including healthy control subjects (n = 396), patients (SZ: n = 46; bipolar disorder: n = 45; major depressive disorder: n = 60), and unaffected first-degree relatives (SZ: n = 46; bipolar disorder: n = 50; major depressive disorder: n = 85). We assessed disorder-specific differences in functional vST-hippocampus coupling and transdiagnostic associations with dimensional measures of positive, negative, and cognitive symptoms. We also probed the genetic underpinning using polygenic risk scores for SZ in a subset of healthy participants (n = 295). RESULTS: Functional vST-hippocampus coupling was 1) reduced in patients with SZ and bipolar disorder (pFWE < .05, small-volume corrected [SVC]); 2) associated transdiagnostically to dimensional measures of positive (pFWE = .01, SVC) and cognitive (pFWE = .02, SVC), but not negative, (pFWE > .05, SVC) symptoms; and 3) reduced in first-degree relatives of patients with SZ (pFWE = .017, SVC) and linked to the genetic risk for SZ in healthy participants (p = .035). CONCLUSIONS: We provide evidence that reduced vST-hippocampus coupling during reward processing is an endophenotype for SZ linked to positive and cognitive symptoms, supporting current preclinical models of the emergence of psychosis. Moreover, our data indicate that vST-hippocampus coupling is familial and linked to polygenic scores for SZ, supporting the use of this measure as an intermediate phenotype for psychotic disorders.

Ambulatory assessment for precision psychiatry: Foundations, current developments and future avenues.

Precision psychiatry stands to benefit from the latest digital technologies for assessment and analyses to tailor treatment towards individuals. Insights into dynamic psychological processes as they unfold in humans' everyday life can critically add value in understanding symptomatology and environmental stressors to provide individualized treatment where and when needed. Towards this goal, ambulatory assessment encompasses methodological approaches to investigate behavioral, physiological, and biological processes in humans' everyday life. It combines repeated assessments of symptomatology over time, e.g., via Ecological Momentary Assessment (e.g., smartphone-diaries), with monitoring of physical behavior, environmental characteristics (such as geolocations, social interactions) and physiological function via sensors, e.g., mobile accelerometers, global-positioning-systems, and electrocardiography. In this review, we expand on promises of ambulatory assessment in the investigation of mental states (e.g., real-life, dynamical and contextual perspective), on chances for precision psychiatry such as the prediction of courses of psychiatric disorders, detection of tipping points and critical windows of relapse, and treatment effects as exemplified by ongoing projects, and on future avenues of how ambulatory interventions can benefit personalized care for psychiatric patients (e.g., through real-time feedback in everyday life). Ambulatory assessment is a key contributor to precision psychiatry, opening up promising avenues in research, diagnoses, prevention and treatment.

Brain network dynamics during working memory are modulated by dopamine and diminished in schizophrenia.

Dynamical brain state transitions are critical for flexible working memory but the network mechanisms are incompletely understood. Here, we show that working memory performance entails brain-wide switching between activity states using a combination of functional magnetic resonance imaging in healthy controls and individuals with schizophrenia, pharmacological fMRI, genetic analyses and network control theory. The stability of states relates to dopamine D1 receptor gene expression while state transitions are influenced by D2 receptor expression and pharmacological modulation. Individuals with schizophrenia show altered network control properties, including a more diverse energy landscape and decreased stability of working memory representations. Our results demonstrate the relevance of dopamine signaling for the steering of whole-brain network dynamics during working memory and link these processes to schizophrenia pathophysiology.

Transdiagnostic Prediction of Affective, Cognitive, and Social Function Through Brain Reward Anticipation in Schizophrenia, Bipolar Disorder, Major Depression, and Autism Spectrum Diagnoses.

The relationship between transdiagnostic, dimensional, and categorical approaches to psychiatric nosology is under intense debate. To inform this discussion, we studied neural systems linked to reward anticipation across a range of disorders and behavioral dimensions. We assessed brain responses to reward expectancy in a large sample of 221 participants, including patients with schizophrenia (SZ; n = 27), bipolar disorder (BP; n = 28), major depressive disorder (MD; n = 31), autism spectrum disorder (ASD; n = 25), and healthy controls (n = 110). We also characterized all subjects with an extensive test battery from which a cognitive, affective, and social functioning factor was constructed. These factors were subsequently related to functional responses in the ventral striatum (vST) and neural networks linked to it. We found that blunted vST responses were present in SZ, BP, and ASD but not in MD. Activation within the vST predicted individual differences in affective, cognitive, and social functioning across diagnostic boundaries. Network alterations extended beyond the reward network to include regions implicated in executive control. We further confirmed the robustness of our results in various control analyses. Our findings suggest that altered brain responses during reward anticipation show transdiagnostic alterations that can be mapped onto dimensional measures of functioning. They also highlight the role of executive control of reward and salience signaling in the disorders we study and show the power of systems-level neuroscience to account for clinically relevant behaviors.

A neural mechanism for affective well-being: Subgenual cingulate cortex mediates real-life effects of nonexercise activity on energy.

Physical activity substantially improves well-being and mental health, but the underlying brain processes remain unclear. Most research concerns exercise, although the majority of everyday human behaviors, such as walking or stair climbing, are nonexercise activities. Combining neuroimaging with ecological assessment of activity and GPS-triggered smartphone diaries, we show a specific association of nonexercise activity with energy in two independent samples mediated by the subgenual part of the anterior cingulate cortex (sgACC), a key emotion regulatory site. Furthermore, energy predicted a range of mental health metrics. sgACC volume moderated humans' emotional gain from nonexercise activity in real life: Individuals with low sgACC volume, a risk factor for depression, felt less energized when inactive but benefited more from periods of high nonexercise activity. This suggests an everyday life mechanism affecting affective well-being in the general population and, if substantiated in patient samples, a risk and resilience process for mood disorders.

Association of a Reproducible Epigenetic Risk Profile for Schizophrenia With Brain Methylation and Function.

Importance: Schizophrenia is a severe mental disorder in which epigenetic mechanisms may contribute to illness risk. Epigenetic profiles can be derived from blood cells, but to our knowledge, it is unknown whether these predict established brain alterations associated with schizophrenia. Objective: To identify an epigenetic signature (quantified as polymethylation score [PMS]) of schizophrenia using machine learning applied to genome-wide blood DNA-methylation data; evaluate whether differences in blood-derived PMS are mirrored in data from postmortem brain samples; test whether the PMS is associated with alterations of dorsolateral prefrontal cortex hippocampal (DLPFC-HC) connectivity during working memory in healthy controls (HC); explore the association between interactions between polygenic and epigenetic risk with DLPFC-HC connectivity; and test the specificity of the signature compared with other serious psychiatric disorders. Design, Setting, and Participants: In this case-control study conducted from 2008 to 2018 in sites in Germany, the United Kingdom, the United States, and Australia, blood DNA-methylation data from 2230 whole-blood samples from 6 independent cohorts comprising HC (1238 [55.5%]) and participants with schizophrenia (803 [36.0%]), bipolar disorder (39 [1.7%]), major depressive disorder 35 [1.6%]), and autism (27 [1.2%]), and first-degree relatives of all patient groups (88 [3.9%]) were analyzed. DNA-methylation data were further explored from 244 postmortem DLPFC samples from 136 HC and 108 patients with schizophrenia. Neuroimaging and genome-wide association data were available for 393 HC. The latter data was used to calculate a polygenic risk score (PRS) for schizophrenia. The data were analyzed in 2019. Main Outcomes and Measures: The accuracy of schizophrenia control classification based on machine learning using epigenetic data; association of schizophrenia PMS scores with DLPFC-HC connectivity; and association of the interaction between PRS and PMS with DLPFC-HC connectivity. Results: This study included 7488 participants (4395 men [58.7%]), of whom 3158 (2230 men [70.6%]) received a diagnosis of schizophrenia. The PMS signature was associated with schizophrenia across 3 independent data sets (area under the curve [AUC] from 0.69 to 0.78; P value from 0.049 to 1.24 × 10-7) and data from postmortem DLPFC samples (AUC = 0.63; P = 1.42 × 10-4), but not with major depressive disorder (AUC = 0.51; P = .16), autism (AUC = 0.53; P = .66), or bipolar disorder (AUC = 0.58; P = .21). Pathways contributing most to the classification included synaptic processes. Healthy controls with schizophrenia-like PMS showed significantly altered DLPFC-HC connectivity (validation methylation/magnetic resonance imaging, t < -3.81; P for familywise error, <.04; validation magnetic resonance imaging, t < -3.54; P for familywise error, <.02), mirroring the lack of functional decoupling in schizophrenia. There was no significant association of the interaction between PMS and PRS with DLPFC-HC connectivity (P > .19). Conclusions and Relevance: We identified a reproducible blood DNA-methylation signature specific for schizophrenia that was correlated with altered functional DLPFC-HC coupling during working memory and mapped to methylation differences found in DLPFC postmortem samples. This indicates a possible epigenetic contribution to a schizophrenia intermediate phenotype and suggests that PMS could be of interest to be studied in the context of multimodal biomarkers for disease stratification and treatment personalization.

Innovation in Psychotherapy, Challenges, and Opportunities: An Opinion Paper.

Psychotherapy as a field tends toward conservativism, and the rate of innovation and development of new evidence-based effective treatments has been slow. The paper explores important barriers to innovation like the dodo bird verdict and the habit of starting the development of therapeutic methods from techniques. The paper looks at the opportunities for translating basic science in psychology into psychotherapeutic techniques. Metacognitive therapy stands out from other psychotherapies by its development from basic science. The paper describes the development of the techniques detached mindfulness and attention training, how they were derived from basic science and tested for their suitability in the therapy of patients with anxiety disorders. By this process, metacognitive therapy may be an important model for the innovation process in psychotherapy.

Resting-state brain network features associated with short-term skill learning ability in humans and the influence of N-methyl-d-aspartate receptor antagonism.

Graph theoretical functional magnetic resonance imaging (fMRI) studies have demonstrated that brain networks reorganize significantly during motor skill acquisition, yet the associations between motor learning ability, brain network features, and the underlying biological mechanisms remain unclear. In the current study, we applied a visually guided sequential pinch force learning task and graph theoretical analyses to investigate the associations between short-term motor learning ability and resting-state brain network metrics in 60 healthy subjects. We further probed the test-retest reliability (n = 26) and potential effects of the N-methyl-d-aspartate (NMDA) antagonist ketamine (n = 19) in independent healthy volunteers. Our results show that the improvement of motor performance after short-term training was positively correlated with small-worldness (p = 0.032) and global efficiency (p = 0.025), whereas negatively correlated with characteristic path length (p = 0.014) and transitivity (p = 0.025). In addition, using network-based statistics (NBS), we identified a learning ability-associated (p = 0.037) and ketamine-susceptible (p = 0.027) cerebellar-cortical network with fair to good reliability (intraclass correlation coefficient [ICC] > 0.7) and higher functional connectivity in better learners. Our results provide new evidence for the association of intrinsic brain network features with motor learning and suggest a role of NMDA-related glutamatergic processes in learning-associated subnetworks.

The influence of MIR137 on white matter fractional anisotropy and cortical surface area in individuals with familial risk for psychosis.

The rs1625579 variant near the microRNA-137 (MIR137) gene is one of the best-supported schizophrenia variants in genome-wide association studies (GWAS), and microRNA-137 functionally regulates other GWAS identified schizophrenia risk variants. Schizophrenia patients with the MIR137 rs1625579 risk genotype (homozygous for the schizophrenia risk variant) also have aberrant brain structure. It is unclear if the effect of MIR137 among schizophrenia patients is due to potential epistasis with genetic risk for schizophrenia or other factors of the disorder. Here, we investigated the effect of MIR137 genotype on white matter fractional anisotropy (FA), cortical thickness (CT), and surface area (SA) in a sample comprising healthy control subjects, and individuals with familial risk for psychosis (first-degree relatives of patients with schizophrenia or bipolar disorder; N=426). In voxel-wise analyses of FA, we observed a significant genotype-by-group interaction (PFWE<0.05). The familial risk group with risk genotype had lower FA (PFWE<0.05), but there was no genetic association in controls. In vertex-wise analyses of SA, we also observed a significant genotype-by-group interaction (PFWE<0.05). Relatives with MIR137 risk genotype had lower SA, however the risk genotype was associated with higher SA in the controls (all PFWE<0.05). These results show that MIR137 risk genotype is associated with lower FA in psychosis relatives that is similar to previous imaging-genetics findings in patients with schizophrenia. Furthermore, MIR137 genotype may also be a risk factor in a subclinical population with wide reductions in white matter FA and cortical SA.