Gut microbiota composition links to variation in functional domains across psychiatric disorders.

OBJECTIVE: Changes in microbial composition are observed in various psychiatric disorders, but their specificity to certain symptoms or processes remains unclear. This study explores the associations between the gut microbiota composition and the Research Domain Criteria (RDoC) domains of functioning, representing symptom domains, specifically focusing on stress-related and neurodevelopmental disorders in patients with and without psychiatric comorbidity. METHODS: The gut microbiota was analyzed in 369 participants, comprising 272 individuals diagnosed with a mood disorder, anxiety disorder, attention deficit/hyperactivity disorder, autism spectrum disorder, and/or substance use disorder, as well as 97 psychiatrically unaffected individuals. The RDoC domains were estimated using principal component analysis (PCA) with oblique rotation on a range of psychiatric, psychological, and personality measures. Associations between the gut microbiota and the functional domains were assessed using multiple linear regression and permanova, adjusted for age, sex, diet, smoking, medication use and comorbidity status. RESULTS: Four functional domains, aligning with RDoC's negative valence, social processes, cognitive systems, and arousal/regulatory systems domains, were identified. Significant associations were found between these domains and eight microbial genera, including associations of negative valence with the abundance of the genera Sellimonas, CHKCI001, Clostridium sensu stricto 1, Oscillibacter, and Flavonifractor; social processes with Sellimonas; cognitive systems with Sporobacter and Hungatella; and arousal/regulatory systems with Ruminococcus torques (all pFDR < 0.05). CONCLUSION: Our findings demonstrate associations between the gut microbiota and the domains of functioning across patients and unaffected individuals, potentially mediated by immune-related processes. These results open avenues for microbiota-focused personalized interventions, considering psychiatric comorbidity. However, further research is warranted to establish causality and elucidate mechanistic pathways.

Cool and hot executive function problems in young children: linking self-regulation processes to emerging clinical symptoms.

Self-regulation (SR) difficulties are implicated in a wide range of disorders which develop in childhood, including attention deficit hyperactivity disorder (ADHD), oppositional defiance disorder (ODD), anxiety and depression. However, the integration of the existing research evidence is challenging because of varying terminology and the wide range of tasks used, as well as the heterogeneity and comorbidity within and across diagnostic categories. The current study used the Research Domain Criteria (RDoC) framework to guide the examination of different SR processes in young children showing a wide range of symptomatology. Children (aged 4-8) referred by teachers for moderate-to-high conduct, hyperactivity and/or emotional problems at school (assessed using the Strengths and Difficulties Questionnaire (SDQ) subscales; n = 212), and children in SDQ typical ranges (n = 30) completed computerised cognitive control and decision-making tasks. Parents completed questionnaires to assess ADHD, ODD, anxiety and depression symptoms (n = 191). Compared to children with no teacher-reported difficulties, those with moderate-to-high problems showed poorer visuomotor control and decision-making. A factor analysis revealed that task variables adhered to RDoC dimensions and predicted variance in specific disorders: difficulties in cognitive control predicted ADHD symptoms, low reward-seeking was associated with depression and high reward-seeking was associated with ODD. This study highlights how the assessment of cognitive processes positioned within the RDoC framework can inform our understanding of disorder-specific and transdiagnostic difficulties in SR which are associated with diverse clinical symptoms in children.

Novel genome-wide associations for effort valuation and psychopathology in children and adults.

The Research Domain Criteria (RDoC) initiative was established by the US National Institute of Mental Health as a multilevel, disorder-agnostic framework for analysis of human psychopathology through designated domains and constructs, including the "Positive Valence Systems" domain focused on reward-related behavior. This study investigates the reward valuation subconstruct of "effort" and its association with genetic markers, functional neurobiological pathways, and polygenic risk scores for psychopathology in 1215 children aged 6-12 and their parents (n = 1044). All participants completed the effort expenditure for rewards task (EEfRT), which assesses "effort" according to two quantitative measures: hard-task choice and reward sensitivity. Genetic association analyses were undertaken in MAGMA, utilizing EEfRT outcome variables as genome-wide association studies phenotypes to compute SNP and gene-level associations. Genome-wide association analyses found two distinct genetic loci that were significantly associated with measures of reward sensitivity and a separate genetic locus associated with hard task choice. Gene-set enrichment analysis yielded significant associations between "effort" and multiple gene sets involved in reward processing-related pathways, including dopamine receptor signaling, limbic system and forebrain development, and biological response to cocaine. These results serve to establish "effort" as a relevant construct for understanding reward-related behavior at the genetic level and support the RDoC framework for assessing disorder-agnostic psychopathology.

Examining the presence and nature of delusions in Alzheimer's disease and frontotemporal dementia syndromes.

OBJECTIVES: Abnormal beliefs and delusions have been reported in some people with dementia, however, the prevalence of delusions, and their neurocognitive basis has been underexplored. This study aimed to examine the presence, severity, content and neural correlates of delusions in a large, well-characterised cohort of dementia patients using a transdiagnostic, cross-sectional approach. METHODS: Four-hundred and eighty-seven people with dementia were recruited: 102 Alzheimer's disease, 136 behavioural-variant frontotemporal dementia, 154 primary progressive aphasia, 29 motor neurone disease, 46 corticobasal syndrome, 20 progressive supranuclear palsy. All patients underwent neuropsychological assessment and brain magnetic resonance imaging, and the Neuropsychiatric Inventory was conducted with an informant, by an experienced clinician. RESULTS: In our cohort, 48/487 patients (10.8%) had delusions. A diagnosis of behavioural-variant frontotemporal dementia (18.4%) and Alzheimer's disease (11.8%) were associated with increased risk of delusions. A positive gene mutation was observed in 11/27 people with delusions. Individuals with frequent delusions performed worse on the Addenbrooke's Cognitive Examination (p = 0.035), particularly on the orientation/attention (p = 0.022) and memory (p = 0.013) subtests. Voxel-based morphometry analyses found that increased delusional psychopathology was associated with reduced integrity of the right middle frontal gyrus, right planum temporale and left anterior temporal pole. CONCLUSION: Our results demonstrate that delusions are relatively common in dementia and uncover a unique cognitive and neural profile associated with the manifestation of delusions. Clinically, delusions may lead to delayed or misdiagnosis. Our results shed light on how to identify individuals at risk of neuropsychiatric features of dementia, a crucial first step to enable targeted symptom management.

Anxiety and the Neurobiology of Temporally Uncertain Threat Anticipation.

When extreme, anxiety-a state of distress and arousal prototypically evoked by uncertain danger-can be debilitating. Uncertain anticipation is a shared feature of situations that elicit signs and symptoms of anxiety across psychiatric disorders, species, and assays. Despite the profound significance of anxiety for human health and wellbeing, the neurobiology of uncertain-threat anticipation remains unsettled. Leveraging a paradigm adapted from animal research and optimized for fMRI signal decomposition, we examined the neural circuits engaged during the anticipation of temporally uncertain and certain threat in 99 men and women. Results revealed that the neural systems recruited by uncertain and certain threat anticipation are anatomically colocalized in frontocortical regions, extended amygdala, and periaqueductal gray. Comparison of the threat conditions demonstrated that this circuitry can be fractionated, with frontocortical regions showing relatively stronger engagement during the anticipation of uncertain threat, and the extended amygdala showing the reverse pattern. Although there is widespread agreement that the bed nucleus of the stria terminalis and dorsal amygdala-the two major subdivisions of the extended amygdala-play a critical role in orchestrating adaptive responses to potential danger, their precise contributions to human anxiety have remained contentious. Follow-up analyses demonstrated that these regions show statistically indistinguishable responses to temporally uncertain and certain threat anticipation. These observations provide a framework for conceptualizing anxiety and fear, for understanding the functional neuroanatomy of threat anticipation in humans, and for accelerating the development of more effective intervention strategies for pathological anxiety.SIGNIFICANCE STATEMENT Anxiety-an emotion prototypically associated with the anticipation of uncertain harm-has profound significance for public health, yet the underlying neurobiology remains unclear. Leveraging a novel neuroimaging paradigm in a relatively large sample, we identify a core circuit responsive to both uncertain and certain threat anticipation, and show that this circuitry can be fractionated into subdivisions with a bias for one kind of threat or the other. The extended amygdala occupies center stage in neuropsychiatric models of anxiety, but its functional architecture has remained contentious. Here we demonstrate that its major subdivisions show statistically indistinguishable responses to temporally uncertain and certain threat. Collectively, these observations indicate the need to revise how we think about the neurobiology of anxiety and fear.

Authors' reply to the commentary on "Establishing norms for error-related brain activity during the arrow Flanker task among young adults".

In their commentary on our article, "Establishing norms for error-related brain activity during the arrow Flanker task among young adults" (Imburgio et al., 2020), Clayson and colleagues (2021) voiced their concerns about our development of norms for an event-related potential measure of error monitoring, the error-related negativity (ERN). The central flaw in their commentary is the idea that because we don't know all the factors that can affect the ERN, it should not be normed. We respond to this idea, while also reiterating points made in our original manuscript: a) at present, the reported norms are not intended to be used for individual clinical assessment and b) our norms should be considered specific to the procedures (i.e., recording and processing parameters) and task used (i.e., arrow Flanker). Contrary to Clayson and colleagues' claims, we believe that information about the distribution of the ERN (i.e., our norms) in a large sample representative of those used in much of the ERN literature (i.e., unselected young adults) will be useful to the field and that this information stands to increase, not decrease, understanding of the ERN.

Distinct neural networks associated with obsession and delusion: a connectome-wide association study.

BACKGROUND: Obsession and delusion are theoretically distinct from each other in terms of reality testing. Despite such phenomenological distinction, no extant studies have examined the identification of common and distinct neural correlates of obsession and delusion by employing biologically grounded methods. Here, we investigated dimensional effects of obsession and delusion spanning across the traditional diagnostic boundaries reflected upon the resting-state functional connectivity (RSFC) using connectome-wide association studies (CWAS). METHODS: Our study sample comprised of 96 patients with obsessive-compulsive disorder, 75 patients with schizophrenia, and 65 healthy controls. A connectome-wide analysis was conducted to examine the relationship between obsession and delusion severity and RFSC using multivariate distance-based matrix regression. RESULTS: Obsession was associated with the supplementary motor area, precentral gyrus, and superior parietal lobule, while delusion was associated with the precuneus. Follow-up seed-based RSFC and modularity analyses revealed that obsession was related to aberrant inter-network connectivity strength. Additional inter-network analyses demonstrated the association between obsession severity and inter-network connectivity between the frontoparietal control network and the dorsal attention network. CONCLUSIONS: Our CWAS study based on the Research Domain Criteria (RDoC) provides novel evidence for the circuit-level functional dysconnectivity associated with obsession and delusion severity across diagnostic boundaries. Further refinement and accumulation of biomarkers from studies embedded within the RDoC framework would provide useful information in treating individuals who have some obsession or delusion symptoms but cannot be identified by the category of clinical symptoms alone.

The centrality of temperament to the research domain criteria (RDoC): The earliest building blocks of psychopathology.

The research domain criteria (RDoC) is an innovative approach designed to explore dimensions of human behavior. The aim of this approach is to move beyond the limits of psychiatric categories in the hope of aligning the identification of psychological health and dysfunction with clinical neuroscience. Despite its contributions to adult psychopathology research, RDoC undervalues ontogenetic development, which circumscribes our understanding of the etiologies, trajectories, and maintaining mechanisms of psychopathology risk. In this paper, we argue that integrating temperament research into the RDoC framework will advance our understanding of the mechanistic origins of psychopathology beginning in infancy. In illustrating this approach, we propose the incorporation of core principles of temperament theories into a new "life span considerations" subsection as one option for infusing development into the RDoC matrix. In doing so, researchers and clinicians may ultimately have the tools necessary to support emotional development and reduce a young child's likelihood of psychological dysfunction beginning in the first years of life.

Prenatal substance exposure and maternal hostility from pregnancy to toddlerhood: Associations with temperament profiles at 16 months of age.

We investigated whether infant temperament was predicted by level of and change in maternal hostility, a putative transdiagnostic vulnerability for psychopathology, substance use, and insensitive parenting. A sample of women (N = 247) who were primarily young, low-income, and had varying levels of substance use prenatally (69 nonsmokers, 81 tobacco-only smokers, and 97 tobacco and marijuana smokers) reported their hostility in the third trimester of pregnancy and at 2, 9, and 16 months postpartum, and their toddler's temperament and behavior problems at 16 months. Maternal hostility decreased from late pregnancy to 16 months postpartum. Relative to pregnant women who did not use substances, women who used both marijuana and tobacco prenatally reported higher levels of hostility while pregnant and exhibited less change in hostility over time. Toddlers who were exposed to higher levels of prenatal maternal hostility were more likely to be classified in temperament profiles that resemble either irritability or inhibition, identified via latent profile analysis. These two profiles were each associated with more behavior problems concurrently, though differed in their association with competence. Our results underscore the utility of transdiagnostic vulnerabilities in understanding the intergenerational transmission of psychopathology risk and are discussed in regards to the Research Domain Criteria (RDoC) framework.

Challenges and Strategies for Current Classifications of Depressive Disorders: Proposal for Future Diagnostic Standards.

The Diagnostic and Statistical Manual of Mental Disorder, Fourth Edition (DSM-IV) was revised based on a combination of a categorical and a dimensional approach such that in the DSM, Fifth Edition (DSM-5), depressive disorders have been separated as a distinctive disease entity from bipolar disorders, consistent with the deconstruction of Kraepelinian dualism. Additionally, the diagnostic thresholds of depressive disorders may be reduced due to the addition of "hopelessness" to the subjective descriptors of depressed mood and the removal of the "bereavement exclusion." Manic/hypomanic, psychotic, and anxious symptoms in major depressive disorder (MDD) and other depressive disorders are described using the transdiagnostic specifiers of "with mixed features," "with psychotic features," and "with anxious distress," respectively. Additionally, due to the polythetic and operational characteristics of the DSM-5 diagnostic criteria, the heterogeneity of MDD is inevitable. Thus, 227 different symptom combinations fulfill the DSM-5 diagnostic criteria for MDD. This heterogeneity of MDD is criticized in view of the Wittgensteinian analogy of language game. Depression subtypes determined by disturbances in monoamine levels and the severity of the disease have been identified in the literature. According to a review of the Gottesman and Gould criteria, neuroticism, morning cortisol, cortisol awakening response, asymmetry in frontal cortical activity on electroencephalography (EEG), and probabilistic reward learning, among other variables, are evidenced as endophenotypes for depressive disorders. Network analysis has been proposed as a potential method to compliment the limitations of current diagnostic criteria and to explore the pathways between depressive symptoms, as well as to identify novel and interesting relationships between depressive symptoms. Based on the literature on network analysis in this field, no differences in the centrality index of the DSM and non-DSM symptoms were repeatedly present among patients with MDD. Furthermore, MDD and other depressive syndromes include two of the Research Domain Criteria (RDoC), including the Loss construct within the Negative Valence Systems domains and various Reward constructs within the Positive Valence Systems domain.

The biological classification of mental disorders (BeCOME) study: a protocol for an observational deep-phenotyping study for the identification of biological subtypes.

BACKGROUND: A major research finding in the field of Biological Psychiatry is that symptom-based categories of mental disorders map poorly onto dysfunctions in brain circuits or neurobiological pathways. Many of the identified (neuro) biological dysfunctions are "transdiagnostic", meaning that they do not reflect diagnostic boundaries but are shared by different ICD/DSM diagnoses. The compromised biological validity of the current classification system for mental disorders impedes rather than supports the development of treatments that not only target symptoms but also the underlying pathophysiological mechanisms. The Biological Classification of Mental Disorders (BeCOME) study aims to identify biology-based classes of mental disorders that improve the translation of novel biomedical findings into tailored clinical applications. METHODS: BeCOME intends to include at least 1000 individuals with a broad spectrum of affective, anxiety and stress-related mental disorders as well as 500 individuals unaffected by mental disorders. After a screening visit, all participants undergo in-depth phenotyping procedures and omics assessments on two consecutive days. Several validated paradigms (e.g., fear conditioning, reward anticipation, imaging stress test, social reward learning task) are applied to stimulate a response in a basic system of human functioning (e.g., acute threat response, reward processing, stress response or social reward learning) that plays a key role in the development of affective, anxiety and stress-related mental disorders. The response to this stimulation is then read out across multiple levels. Assessments comprise genetic, molecular, cellular, physiological, neuroimaging, neurocognitive, psychophysiological and psychometric measurements. The multilevel information collected in BeCOME will be used to identify data-driven biologically-informed categories of mental disorders using cluster analytical techniques. DISCUSSION: The novelty of BeCOME lies in the dynamic in-depth phenotyping and omics characterization of individuals with mental disorders from the depression and anxiety spectrum of varying severity. We believe that such biology-based subclasses of mental disorders will serve as better treatment targets than purely symptom-based disease entities, and help in tailoring the right treatment to the individual patient suffering from a mental disorder. BeCOME has the potential to contribute to a novel taxonomy of mental disorders that integrates the underlying pathomechanisms into diagnoses. TRIAL REGISTRATION: Retrospectively registered on June 12, 2019 on ClinicalTrials.gov (TRN: NCT03984084).

Attachment disorder and attachment theory - Two sides of one medal or two different coins?

INTRODUCTION: Currently, attachment quality and attachment disorder exist in parallel, but the mutual association is still insufficiently clarified. For policy makers and clinical experts, it can be difficult to differentiate between these constructs, but the distinction is crucial to develop mental-health services and effective treatment concepts. We aimed to investigate the association between attachment representations (AR) and attachment disorders (AD), including Reactive Attachment Disorder (RAD) and Disinhibited Social Engagement Disorder (DSED) in children aged between 5 and 9. METHODS: A total of 135 children aged between 5 and 9 years (M=7.17 years, SD=1.40, 63% male) and their primary caregivers participated in the study. Children were interviewed with the story stem method to assess AR, and the primary caregiver completed diagnostic interviews and questionnaires on mental disorders, AD, emotional and behavioral problems, and intelligence and development. RESULTS: The prevalence of AR in children with AD was 28.6% for the 'secure' form of AR, 17.1% for the 'insecure-avoidant' form, 25.7% for the 'insecure-ambivalent' form, and 28.6% for the 'disorganized' form. Prevalences of the various AR forms did not differ statistically significantly, indicating that AR is conceptionally distinct from AD. Children with disorganized attachment scored significantly lower on language and intelligence skills than children with secure attachment. AD was significantly associated with a higher number of comorbidities, emotional and behavioral problems, and lower language skills. CONCLUSIONS: Longitudinal studies using standardized assessment instruments are needed to systematically provide comparable and reliable empirical findings to improve current understanding of AR and AD as well as their etiological models.

Intergenerational transmission of emotion dysregulation: Part II. Developmental origins of newborn neurobehavior.

We investigated whether neurobehavioral markers of risk for emotion dysregulation were evident among newborns, as well as whether the identified markers were associated with prenatal exposure to maternal emotion dysregulation. Pregnant women (N = 162) reported on their emotion dysregulation prior to a laboratory assessment. The women were then invited to the laboratory to assess baseline respiratory sinus arrhythmia (RSA) and RSA in response to an infant cry. Newborns were assessed after birth via the NICU Network Neurobehavioral Scale. We identified two newborn neurobehavioral factors-arousal and attention-via exploratory factor analysis. Low arousal was characterized by less irritability, excitability, and motor agitation, while low attention was related to a lower threshold for auditory and visual stimulation, less sustained attention, and poorer visual tracking abilities. Pregnant women who reported higher levels of emotion dysregulation had newborns with low arousal levels and less attention. Larger decreases in maternal RSA in response to cry were also related to lower newborn arousal. We provide the first evidence that a woman's emotion dysregulation while pregnant is associated with risks for dysregulation in her newborn. Implications for intergenerational transmission of emotion dysregulation are discussed.

Classification of Psychiatric Disorders.

Because of the poor link between psychiatric diagnosis and neurobiological findings, it is difficult to classify mental disorders. The changes made to psychiatric diagnostic systems over the years can be understood in terms of "practical conservatism." The Diagnostic and Statistical Manual of Mental Disorders (DSM)-I and DSM-II were theoretically supported by the psychoanalytic and psychodynamic approach. Subsequently, psychiatric diagnoses of this kind were opposed by the anti-psychiatry movement, as well as by the findings of the Rosenhan experiment. Thus, the DSM-III revolution contained more empiricism, aligning psychiatry with biomedicine. Psychiatric diagnoses are classified and defined in terms of Kraepelinian dualism, using a categorical approach. The empirical trend was continued in the DSM-IV. To overcome the limitations of current psychiatric diagnostic systems and integrate fundamental genetic, neurobiological, behavioral, environmental, and experimental components into psychiatry, the Research Domain Criteria (RDoC) were established. To overcome the limitations of the categorical approach, psychiatrists have considered adopting a dimensional approach. However, their efforts were frustrated in the DSM-5 revision process. Thus, the DSM-5 is characterized by the rearrangement of psychiatric diagnoses, the partial adoption of a dimensional approach, the introduction of new diagnoses, and harmonization with the International Classification of Diseases.

Satiation deficits and binge eating: Probing differences between bulimia nervosa and purging disorder using an ad lib test meal.

Purging disorder (PD) has been included as a named condition within the DSM-5 category of Other Specified Feeding or Eating Disorder and differs from bulimia nervosa (BN) in the absence of binge-eating episodes. The current study evaluated satiation through behavioral and self-report measures to understand how this construct may explain distinct symptom presentations for bulimia nervosa (BN) and purging disorder (PD). Women (N = 119) were recruited from the community if they met DSM-5 criteria for BN (n = 57), PD (n = 31), or were free of eating pathology (n = 31 controls). Participants completed structured clinical interviews and questionnaires and an ad lib test meal during which they provided reports of subjective states. Significant group differences were found on self-reported symptoms, ad lib test meal intake, and subjective responses to food intake between individuals with eating disorders and controls and between BN and PD. Further, ad lib intake was associated with self-reported frequency and size of binge episodes. In a multivariable model, the amount of food consumed during binges as reported during clinical interviews predicted amount of food consumed during the ad lib test meal, controlling for other binge-related variables. Satiation deficits distinguish BN from PD and appear to be specifically linked to the size of binge episodes. Future work should expand exploration of physiological bases of these differences to contribute to novel interventions.

Critical reappraisal of mechanistic links of copy number variants to dimensional constructs of neuropsychiatric disorders in mouse models.

Copy number variants are deletions and duplications of a few thousand to million base pairs and are associated with extraordinarily high levels of autism spectrum disorder, schizophrenia, intellectual disability, or attention-deficit hyperactivity disorder. The unprecedented levels of robust and reproducible penetrance of copy number variants make them one of the most promising and reliable entry points to delve into the mechanistic bases of many mental disorders. However, the precise mechanistic bases of these associations still remain elusive in humans due to the many genes encoded in each copy number variant and the diverse associated phenotypic features. Genetically engineered mice have provided a technical means to ascertain precise genetic mechanisms of association between copy number variants and dimensional aspects of mental illnesses. Molecular, cellular, and neuronal phenotypes can be detected as potential mechanistic substrates for various behavioral constructs of mental illnesses. However, mouse models come with many technical pitfalls. Genetic background is not well controlled in many mouse models, leading to rather obvious interpretative issues. Dose alterations of many copy number variants and single genes within copy number variants result in some molecular, cellular, and neuronal phenotypes without a behavioral phenotype or with a behavioral phenotype opposite to what is seen in humans. In this review, I discuss technical and interpretative pitfalls of mouse models of copy number variants and highlight well-controlled studies to suggest potential neuronal mechanisms of dimensional aspects of mental illnesses. Mouse models of copy number variants represent toeholds to achieve a better understanding of the mechanistic bases of dimensions of neuropsychiatric disorders and thus for development of mechanism-based therapeutic options in humans.

Identification and individualized prediction of clinical phenotypes in bipolar disorders using neurocognitive data, neuroimaging scans and machine learning.

Diagnosis, clinical management and research of psychiatric disorders remain subjective - largely guided by historically developed categories which may not effectively capture underlying pathophysiological mechanisms of dysfunction. Here, we report a novel approach of identifying and validating distinct and biologically meaningful clinical phenotypes of bipolar disorders using both unsupervised and supervised machine learning techniques. First, neurocognitive data were analyzed using an unsupervised machine learning approach and two distinct clinical phenotypes identified namely; phenotype I and phenotype II. Second, diffusion weighted imaging scans were pre-processed using the tract-based spatial statistics (TBSS) method and 'skeletonized' white matter fractional anisotropy (FA) and mean diffusivity (MD) maps extracted. The 'skeletonized' white matter FA and MD maps were entered into the Elastic Net machine learning algorithm to distinguish individual subjects' phenotypic labels (e.g. phenotype I vs. phenotype II). This calculation was performed to ascertain whether the identified clinical phenotypes were biologically distinct. Original neurocognitive measurements distinguished individual subjects' phenotypic labels with 94% accuracy (sensitivity=92%, specificity=97%). TBSS derived FA and MD measurements predicted individual subjects' phenotypic labels with 76% and 65% accuracy respectively. In addition, individual subjects belonging to phenotypes I and II were distinguished from healthy controls with 57% and 92% accuracy respectively. Neurocognitive task variables identified as most relevant in distinguishing phenotypic labels included; Affective Go/No-Go (AGN), Cambridge Gambling Task (CGT) coupled with inferior fronto-occipital fasciculus and callosal white matter pathways. These results suggest that there may exist two biologically distinct clinical phenotypes in bipolar disorders which can be identified from healthy controls with high accuracy and at an individual subject level. We suggest a strong clinical utility of the proposed approach in defining and validating biologically meaningful and less heterogeneous clinical sub-phenotypes of major psychiatric disorders.

Predictive modeling for classification of positive valence system symptom severity from initial psychiatric evaluation records.

In response to the challenges set forth by the CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing, we describe a framework to automatically classify initial psychiatric evaluation records to one of four positive valence system severities: absent, mild, moderate, or severe. We used a dataset provided by the event organizers to develop a framework comprised of natural language processing (NLP) modules and 3 predictive models (two decision tree models and one Bayesian network model) used in the competition. We also developed two additional predictive models for comparison purpose. To evaluate our framework, we employed a blind test dataset provided by the 2016 CEGS N-GRID. The predictive scores, measured by the macro averaged-inverse normalized mean absolute error score, from the two decision trees and Naïve Bayes models were 82.56%, 82.18%, and 80.56%, respectively. The proposed framework in this paper can potentially be applied to other predictive tasks for processing initial psychiatric evaluation records, such as predicting 30-day psychiatric readmissions.

Automatic classification of RDoC positive valence severity with a neural network.

OBJECTIVE: Our objective was to develop a machine learning-based system to determine the severity of Positive Valance symptoms for a patient, based on information included in their initial psychiatric evaluation. Severity was rated on an ordinal scale of 0-3 as follows: 0 (absent=no symptoms), 1 (mild=modest significance), 2 (moderate=requires treatment), 3 (severe=causes substantial impairment) by experts. MATERIALS AND METHODS: We treated the task of assigning Positive Valence severity as a text classification problem. During development, we experimented with regularized multinomial logistic regression classifiers, gradient boosted trees, and feedforward, fully-connected neural networks. We found both regularization and feature selection via mutual information to be very important in preventing models from overfitting the data. Our best configuration was a neural network with three fully connected hidden layers with rectified linear unit activations. RESULTS: Our best performing system achieved a score of 77.86%. The evaluation metric is an inverse normalization of the Mean Absolute Error presented as a percentage number between 0 and 100, where 100 means the highest performance. Error analysis showed that 90% of the system errors involved neighboring severity categories. CONCLUSION: Machine learning text classification techniques with feature selection can be trained to recognize broad differences in Positive Valence symptom severity with a modest amount of training data (in this case 600 documents, 167 of which were unannotated). An increase in the amount of annotated data can increase accuracy of symptom severity classification by several percentage points. Additional features and/or a larger training corpus may further improve accuracy.

Research Domain Criteria as Psychiatric Nosology.

Diagnostic classification systems in psychiatry have continued to rely on clinical phenomenology, despite limitations inherent in that approach. In view of these limitations and recent progress in neuroscience, the National Institute of Mental Health (NIMH) has initiated the Research Domain Criteria (RDoC) project to develop a more neuroscientifically based system of characterizing and classifying psychiatric disorders. The RDoC initiative aims to transform psychiatry into an integrative science of psychopathology in which mental illnesses will be defined as involving putative dysfunctions in neural nodes and networks. However, conceptual, methodological, neuroethical, and social issues inherent in and/or derived from the use of RDoC need to be addressed before any attempt is made to implement their use in clinical psychiatry. This article describes current progress in RDoC; defines key technical, neuroethical, and social issues generated by RDoC adoption and use; and posits key questions that must be addressed and resolved if RDoC are to be employed for psychiatric diagnoses and therapeutics. Specifically, we posit that objectivization of complex mental phenomena may raise ethical questions about autonomy, the value of subjective experience, what constitutes normality, what constitutes a disorder, and what represents a treatment, enablement, and/or enhancement. Ethical issues may also arise from the (mis)use of biomarkers and phenotypes in predicting and treating mental disorders, and what such definitions, predictions, and interventions portend for concepts and views of sickness, criminality, professional competency, and social functioning. Given these issues, we offer that a preparatory neuroethical framework is required to define and guide the ways in which RDoC-oriented research can-and arguably should-be utilized in clinical psychiatry, and perhaps more broadly, in the social sphere.