Within-Category Feature Correlations and the Curse of Dimensionality

This item is only available electronically.Within the domain of category learning, the curse of dimensionality states that as categories acquire more features, the size of the feature space and thus the number of examples necessary to adequately learn the category grows rapidly. As a result, category learning should be a highly difficult task. However, people learn to classify categories with ease. The primary aim of the current study was to address how people overcome this problem by determining if they were attuned to information about which features were correlated with each other, and whether or not they used this information as an indication that those features were relevant for categorisation. In theory, this would then allow for the detection of natural category family resemblance structure in highly dimensional environments, and subsequently, allow human learners to overcome the curse of dimensionality. In addition to this primary aim, and under the assumption that people were attuned to this correlational structure, a number of secondary hypotheses were also proposed. These hypotheses assessed both feature and correlation learning, evaluated relative category learning improvement as a function of dimensionality between conditions, as well as compared human performance to a Naïve Bayes model that was inherently incapable of detecting any correlational structure. The results of the current study suggest that people do not utilise within-category feature correlational structure as a heuristic for category predictive feature detection. However, these findings were contingent on a number of methodological shortcomings present in the current study. Directions for future research are proposed that provide clear methodological changes in category structure which may mitigate the lack of support found for the proposed hypotheses.Thesis (B.PsychSc(Hons)) -- University of Adelaide, School of Psychology, 201

Systematic Overestimation of Machine Learning Performance in Neuroimaging Studies of Depression

We currently observe a disconcerting phenomenon in machine learning studies in psychiatry: While we would expect larger samples to yield better results due to the availability of more data, larger machine learning studies consistently show much weaker performance than the numerous small-scale studies. Here, we systematically investigated this effect focusing on one of the most heavily studied questions in the field, namely the classification of patients suffering from Major Depressive Disorder (MDD) and healthy controls. Drawing upon a balanced sample of $N = 1,868$ MDD patients and healthy controls from our recent international Predictive Analytics Competition (PAC), we first trained and tested a classification model on the full dataset which yielded an accuracy of 61%. Next, we mimicked the process by which researchers would draw samples of various sizes ($N=4$ to $N=150$) from the population and showed a strong risk of overestimation. Specifically, for small sample sizes ($N=20$), we observe accuracies of up to 95%. For medium sample sizes ($N=100$) accuracies up to 75% were found. Importantly, further investigation showed that sufficiently large test sets effectively protect against performance overestimation whereas larger datasets per se do not. While these results question the validity of a substantial part of the current literature, we outline the relatively low-cost remedy of larger test sets

Combining Clinical With Cognitive or Magnetic Resonance Imaging Data for Predicting Transition to Psychosis in Ultra High-Risk Patients:Data From the PACE 400 Cohort

Background: Multimodal modeling that combines biological and clinical data shows promise in predicting transition to psychosis in individuals who are at ultra-high risk. Individuals who transition to psychosis are known to have deficits at baseline in cognitive function and reductions in gray matter volume in multiple brain regions identified by magnetic resonance imaging.Methods: In this study, we used Cox proportional hazards regression models to assess the additive predictive value of each modality—cognition, cortical structure information, and the neuroanatomical measure of brain age gap—to a previously developed clinical model using functioning and duration of symptoms prior to service entry as predictors in the Personal Assessment and Crisis Evaluation (PACE) 400 cohort. The PACE 400 study is a well-characterized cohort of Australian youths who were identified as ultra-high risk of transitioning to psychosis using the Comprehensive Assessment of At Risk Mental States (CAARMS) and followed for up to 18 years; it contains clinical data (from N = 416 participants), cognitive data (n = 213), and magnetic resonance imaging cortical parameters extracted using FreeSurfer (n = 231).Results: The results showed that neuroimaging, brain age gap, and cognition added marginal predictive information to the previously developed clinical model (fraction of new information: neuroimaging 0%–12%, brain age gap 7%, cognition 0%–16%).Conclusions: In summary, adding a second modality to a clinical risk model predicting the onset of a psychotic disorder in the PACE 400 cohort showed little improvement in the fit of the model for long-term prediction of transition to psychosis

Combining schizophrenia and depression polygenic risk scores improves the genetic prediction of lithium response in bipolar disorder patients

Lithium is the gold standard therapy for Bipolar Disorder (BD) but its effectiveness differs widely between individuals. The molecular mechanisms underlying treatment response heterogeneity are not well understood, and personalized treatment in BD remains elusive. Genetic analyses of the lithium treatment response phenotype may generate novel molecular insights into lithium's therapeutic mechanisms and lead to testable hypotheses to improve BD management and outcomes. We used fixed effect meta-analysis techniques to develop meta-analytic polygenic risk scores (MET-PRS) from combinations of highly correlated psychiatric traits, namely schizophrenia (SCZ), major depression (MD) and bipolar disorder (BD). We compared the effects of cross-disorder MET-PRS and single genetic trait PRS on lithium response. For the PRS analyses, we included clinical data on lithium treatment response and genetic information for n = 2283 BD cases from the International Consortium on Lithium Genetics (ConLi+Gen; www.ConLiGen.org). Higher SCZ and MD PRSs were associated with poorer lithium treatment response whereas BD-PRS had no association with treatment outcome. The combined MET2-PRS comprising of SCZ and MD variants (MET2-PRS) and a model using SCZ and MD-PRS sequentially improved response prediction, compared to single-disorder PRS or to a combined score using all three traits (MET3-PRS). Patients in the highest decile for MET2-PRS loading had 2.5 times higher odds of being classified as poor responders than patients with the lowest decile MET2-PRS scores. An exploratory functional pathway analysis of top MET2-PRS variants was conducted. Findings may inform the development of future testing strategies for personalized lithium prescribing in BD

HLA-DRB1 and HLA-DQB1 genetic diversity modulates response to lithium in bipolar affective disorders

Bipolar afective disorder (BD) is a severe psychiatric illness, for which lithium (Li) is the gold standard for acute and maintenance therapies. The therapeutic response to Li in BD is heterogeneous and reliable biomarkers allowing patients stratifcation are still needed. A GWAS performed by the International Consortium on Lithium Genetics (ConLiGen) has recently identifed genetic markers associated with treatment responses to Li in the human leukocyte antigens (HLA) region. To better understand the molecular mechanisms underlying this association, we have genetically imputed the classical alleles of the HLA region in the European patients of the ConLiGen cohort. We found our best signal for amino-acid variants belonging to the HLA-DRB1*11:01 classical allele, associated with a better response to Li (p < 1 × ­10−3; FDR< 0.09 in the recessive model). Alanine or Leucine at position 74 of the HLA-DRB1 heavy chain was associated with a good response while Arginine or Glutamic acid with a poor response. As these variants have been implicated in common infammatory/autoimmune processes, our fndings strongly suggest that HLA-mediated low infammatory background may contribute to the efcient response to Li in BD patients, while an infammatory status overriding Li anti-infammatory properties would favor a weak response

Association of polygenic score for major depression with response to lithium in patients with bipolar disorder

Lithium is a first-line medication for bipolar disorder (BD), but only one in three patients respond optimally to the drug. Since evidence shows a strong clinical and genetic overlap between depression and bipolar disorder, we investigated whether a polygenic susceptibility to major depression is associated with response to lithium treatment in patients with BD. Weighted polygenic scores (PGSs) were computed for major depression (MD) at different GWAS p value thresholds using genetic data obtained from 2586 bipolar patients who received lithium treatment and took part in the Consortium on Lithium Genetics (ConLi+Gen) study. Summary statistics from genome-wide association studies in MD (135,458 cases and 344,901 controls) from the Psychiatric Genomics Consortium (PGC) were used for PGS weighting. Response to lithium treatment was defined by continuous scores and categorical outcome (responders versus non-responders) using measurements on the Alda scale. Associations between PGSs of MD and lithium treatment response were assessed using a linear and binary logistic regression modeling for the continuous and categorical outcomes, respectively. The analysis was performed for the entire cohort, and for European and Asian sub-samples. The PGSs for MD were significantly associated with lithium treatment response in multi-ethnic, European or Asian populations, at various p value thresholds. Bipolar patients with a low polygenic load for MD were more likely to respond well to lithium, compared to those patients with high polygenic load [lowest vs highest PGS quartiles, multi-ethnic sample: OR = 1.54 (95% CI: 1.18–2.01) and European sample: OR = 1.75 (95% CI: 1.30–2.36)]. While our analysis in the Asian sample found equivalent effect size in the same direction: OR = 1.71 (95% CI: 0.61–4.90), this was not statistically significant. Using PGS decile comparison, we found a similar trend of association between a high genetic loading for MD and lower response to lithium. Our findings underscore the genetic contribution to lithium response in BD and support the emerging concept of a lithium-responsive biotype in BD

Exploring the genetics of lithium response in bipolar disorders

Background: Lithium (Li) remains the treatment of choice for bipolar disorders (BP). Its mood-stabilizing effects help reduce the long-term burden of mania, depression and suicide risk in patients with BP. It also has been shown to have beneficial effects on disease-associated conditions, including sleep and cardiovascular disorders. However, the individual responses to Li treatment vary within and between diagnostic subtypes of BP (e.g. BP-I and BP-II) according to the clinical presentation. Moreover, long-term Li treatment has been linked to adverse side-effects that are a cause of concern and non-adherence, including the risk of developing chronic medical conditions such as thyroid and renal disease. In recent years, studies by the Consortium on Lithium Genetics (ConLiGen) have uncovered a number of genetic factors that contribute to the variability in Li treatment response in patients with BP. Here, we leveraged the ConLiGen cohort (N = 2064) to investigate the genetic basis of Li effects in BP. For this, we studied how Li response and linked genes associate with the psychiatric symptoms and polygenic load for medical comorbidities, placing particular emphasis on identifying differences between BP-I and BP-II. Results: We found that clinical response to Li treatment, measured with the Alda scale, was associated with a diminished burden of mania, depression, substance and alcohol abuse, psychosis and suicidal ideation in patients with BP-I and, in patients with BP-II, of depression only. Our genetic analyses showed that a stronger clinical response to Li was modestly related to lower polygenic load for diabetes and hypertension in BP-I but not BP-II. Moreover, our results suggested that a number of genes that have been previously linked to Li response variability in BP differentially relate to the psychiatric symptomatology, particularly to the numbers of manic and depressive episodes, and to the polygenic load for comorbid conditions, including diabetes, hypertension and hypothyroidism. Conclusions: Taken together, our findings suggest that the effects of Li on symptomatology and comorbidity in BP are partially modulated by common genetic factors, with differential effects between BP-I and BP-II

The Effects of Dose, Practice Habits, and Objects of Focus on Digital Meditation Effectiveness and Adherence: Longitudinal Study of 280,000 Digital Meditation Sessions Across 103 Countries

BackgroundThe efficacy of digital meditation is well established. However, the extent to which the benefits remain after 12 weeks in real-world settings remains unknown. Additionally, findings related to dosage and practice habits have been mixed, and the studies were conducted on small and homogeneous samples and used a limited range of analytical procedures and meditation techniques. Findings related to the predictors of adherence are also lacking and may help inform future meditators and meditation programs on how to best structure healthy sustainable practices. ObjectiveThis study aimed to measure outcome change across a large and globally diverse population of meditators and meditations in their naturalistic practice environments, assess the dose-response relationships between practice habits and outcome change, and identify predictors of adherence. MethodsWe used ecological momentary assessment to assess participants’ well-being over a 14-month period. We engineered outcomes related to the variability of change over time (equanimity) and recovery following a drop in mood (resilience) and established the convergent and divergent validity of these outcomes using a validated scale. Using linear mixed-effects and generalized additive mixed-effects models, we modeled outcome changes and patterns of dose-response across outcomes. We then used logistic regression to study the practice habits of participants in their first 30 sessions to derive odds ratios of long-term adherence. ResultsSignificant improvements were observed in all outcomes (P<.001). Generalized additive mixed models revealed rapid improvements over the first 50-100 sessions, with further improvements observed until the end of the study period. Outcome change corresponded to 1 extra day of improved mood for every 5 days meditated and half-a-day-faster mood recovery compared with baseline. Overall, consistency of practice was associated with the largest outcome change (4-7 d/wk). No significant differences were observed across session lengths in linear models (mood: P=.19; equanimity: P=.10; resilience: P=.29); however, generalized additive models revealed significant differences over time (P<.001). Longer sessions (21-30 min) were associated with the largest magnitude of change in mood from the 20th session onward and fewer sessions to recovery (increased resilience); midlength sessions (11-20 min) were associated with the largest decreases in recovery; and mood stability was similar across session lengths (equanimity). Completing a greater variety of practice types was associated with significantly greater improvements across all outcomes. Adhering to a long-term practice was best predicted by practice consistency (4-7 d/wk), a morning routine, and maintaining an equal balance between interoceptive and exteroceptive meditations. ConclusionsLong-term real-world digital meditation practice is effective and associated with improvements in mood, equanimity, and resilience. Practice consistency and variety rather than length best predict improvement. Long-term sustainable practices are best predicted by consistency, a morning routine, and a practice balanced across objects of focus that are internal and external to the body

From multivariate methods to an AI ecosystem

Depression constitutes a leading cause of disability worldwide. Despite extensive research on its interaction with psychobiological factors, associated pathways are far from being elucidated. Metabolomics, assessing the final products of complex biochemical reactions, has emerged as a valuable tool for exploring molecular pathways. We conducted a metabolome-wide association analysis to investigate the link between the serum metabolome and depressed mood (DM) in 1411 participants of the KORA (Cooperative Health Research in the Augsburg Region) F4 study (discovery cohort). Serum metabolomics data comprised 353 unique metabolites measured by Metabolon. We identified 72 (5.1%) KORA participants with DM. Linear regression tests were conducted modeling each metabolite value by DM status, adjusted for age, sex, body-mass index, antihypertensive, cardiovascular, antidiabetic, and thyroid gland hormone drugs, corticoids and antidepressants. Sensitivity analyses were performed in subcohorts stratified for sex, suicidal ideation, and use of antidepressants. We replicated our results in an independent sample of 968 participants of the SHIP-Trend (Study of Health in Pomerania) study including 52 (5.4%) individuals with DM (replication cohort). We found significantly lower laurylcarnitine levels in KORA F4 participants with DM after multiple testing correction according to Benjamini/Hochberg. This finding was replicated in the independent SHIP-Trend study. Laurylcarnitine remained significantly associated (p value &amp;lt; 0.05) with depression in samples stratified for sex, suicidal ideation, and antidepressant medication. Decreased blood laurylcarnitine levels in depressed individuals may point to impaired fatty acid oxidation and/or mitochondrial function in depressive disorders, possibly representing a novel therapeutic target