Objective: Contingency management (CM) is often criticized for limited long-term impact. This meta-analysis focused on objective indices of drug use (i.e., urine toxicology) to examine the effects of CM on illicit substance use up to 1 year following treatment. Method: Analyses included randomized trials (k = 23) of CM for stimulant, opioid, or polysubstance use disorders that reported outcomes up to 1 year after the incentive delivery had ended. Using random effects models, odds ratios (OR) were calculated for the likelihood of abstinence. Metaregressions and subgroup analyses explored how parameters of CM treatment, namely escalation, frequency, immediacy, and magnitude of reinforcers, moderated outcomes. Results: The overall likelihood of abstinence at the long-term follow-up among participants who received CM versus a comparison treatment (nearly half of which were community-based comprehensive therapies or protocol-based specific therapies) was OR = 1.22, 95% confidence interval [1.01, 1.44], with low to moderate heterogeneity (I² = 36.68). Among 18 moderators, longer length of active treatment was found to significantly improve long-term abstinence. Conclusions: CM showed long-term benefit in reducing objective indices of drug use, above and beyond other active, evidence-based treatments (e.g., cognitive–behavioral therapy, 12-step facilitation) and community-based intensive outpatient treatment. These data suggest that policymakers and insurers should support and cover costs for CM, which is the focus of hundreds of studies demonstrating its short-term efficacy and, now, additional data supporting its long-term efficacy. (PsycInfo Database Record (c) 2021 APA, all rights reserved) What is the public health significance of this article?—This meta-analysis provides a summary of long-term outcomes of contingency management treatment using objective indices of drug use. Contingency management was found to be more efficacious than either standard care or other evidence-based approaches up to 1 year following the discontinuation of incentives. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

A growing literature supports the efficacy of cognitive-behavioral therapies, motivational interventions, and personalized feedback to treat problem and disordered gambling. However, there is currently debate as to how much treatment is necessary. Some studies indicate that attending a greater number of sessions is related to enhanced therapeutic outcomes, while other studies indicate that one session produces equivalent therapeutic outcomes to multiple sessions. To contribute to this debate, meta-analysis was used to examine the relation between dose and outcome in studies of cognitive-behavioral, motivational, and personalized feedback interventions (both individual and group treatment formats were included). Fourteen studies of randomized controlled trials representing 1,203 participants across 19 treatmentcontrol comparisons were identified. The intended treatment dose (i.e., the number of sessions prescribed to participants) across the 14 studies ranged from 1 to 30 sessions. Of the 10 studies reporting the received treatment dose (i.e., the number of sessions that participants attended), the weighted mean dose was 6.8 sessions (SD = 2.7). Both intended treatment dose and received treatment dose were positively related to outcome at posttreatment-as the number of sessions increased, so too did the magnitude of the between group effect size. There were an insufficient number of studies reporting outcome at long-term follow up to evaluate the relation between intended dose, received dose, and outcome. Discussion centers on several areas for future research on psychological treatments for problem and disordered gambling. Specific recommendations are made for researchers and practicing clinicians.

<div><div><div><p>Impacting individual’s social and physical well-being, psychiatric disorders have been a substantial burden on public health. As such disorders are frequently observed aggregating in families, we can expect a large involvement of heritable components underlying their etiologies. Therefore, studying the genetic architecture and basis is one of the most important aims toward developing effective treatments for psychiatric disorders. The overall objective of this dissertation is to contribute to understanding the genetics of psychiatric disorders. Analyzing summary statistics from genomewide association studies (GWAS) of psychiatric disorders, we mainly present results of two projects. In the first one, we evaluated commonalities and distinctions in genetic risk of four highly comorbid childhood onset neuropsychiatric disorders: attention deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD) and Tourette’s syndrome (TS). Through systematic analysis of genetic architecture and correlation, we confirmed exitance of genetic components shared across ADHD, ASD and TS, as well as OCD and TS. Subsequently, we identified those components at variant, gene, and tissue specificity levels through meta-analyses. Our results pointed toward possible involvement of hypothalamus-pituitary-adrenal (HPA) axis, a human stress response system, in the etiology of these childhood onset disorders. The second project includes the proposition of a novel framework for general GWAS summary statistics-based analyses. Instead of regular odds ratio and standard errors archived in the summary statistics, we proposed a recounstruction approach to rewrite the results in terms of single nucleotide polymorphisms (SNP) allelic and genotypic frequencies. We also put forward three applications built-upon the proposed framework, and evaluated the performance on both synthetic data and real GWAS results of psychiatric disorders for each of them. Through these three applications, we demonstrated that this framework can broaden the scope of GWAS summary statistics-based analyses and unify various of analyses pipelines. We hope our work can serve as a stepping-stone for future researchers aiming at understanding and utilizing GWAS results of complex psychiatric disorders.</p></div></div></div>

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While major depression is a well-defined mental health condition, it's presentation can be vastly different across individuals and groups, and many factors impact one's vulnerability. A critical period of vulnerability to depression is the postpartum period. Despite the frequent assumption that postpartum conditions solely affect women, prevalence estimates of paternal postpartum depression (PPPD) are comparable with those of women. Similar to mothers, men experience significant biological and psychosocial changes following the birth of a child that contribute to the onset of postpartum depression. A growing body of research has identified many potential variables related to PPPD, yet there is no clear consensus as to which variables are the most impactful. Following PRISMA guidelines, we conducted a meta-analysis to aggregate the known findings and quantitatively determine the most salient predictors of PPPD. We hypothesized that the psychosocial factors of relationship satisfaction, social support, partner depression and history of depression would yield the strongest aggregate effect sizes. By searching databases using logical operations, as well as conducting backward and forward searches, we identified 129 articles that met inclusion/exclusion criteria with a total sample size of 114,712 participants (79,743 fathers). Trained independent coders extracted data directly from identified studies, including sample characteristics for descriptives and moderators, and demographic correlates. Correlates of primary interest include but are not limited to maternal depression, history of depression, relationship satisfaction, and social support. Effect sizes represented as Pearson correlations (or transformed to r using standard procedures) were calculated and compared using a random-effects model. In partial support of our hypothesis, fathers' reported history of depression had one of the largest effect sizes (r = .469, unbiased r = .368). The other primary psychosocial factors had relatively moderate effect sizes: social support, r = -.359 (unbiased r = -.398); relationship satisfaction, r = -.300 (unbiased r = -.266); and partner depression, r = .294 (unbiased r = .471). The correlates of PPPD with the strongest effect sizes were all factors associated with personal distress and stressful pregnancies. Demographic correlates (i.e., younger age, non-White ethnicity, lower education, unemployment, lower SES) generally had small though statistically significant effect sizes. This study helps provide a clearer picture of what predicts PPPD to more accurately screen for men at greatest risk and inform the development of diagnostic measures and treatments. It also lays the groundwork for future work in a more unified and productive direction. By researching and sharing better information, we can provide better access and better outcomes for postpartum couples.

The thesis investigated objective neuroimaging biomarkers in parkinsonian syndromes, which could be applied to increase diagnostic accuracy. To find convergence of the literature concerning disease-specific patterns in Parkinson’s disease and progressive supranuclear palsy, we conducted meta-analyses. In Parkinson’s disease glucose hypometabolism was re- vealed in bilateral inferior parietal cortex and left caudate nucleus and focal gray matter atrophy in the middle occipital gyrus. In progressive supranu- clear palsy we identified gray matter atrophy in the midbrain and white mat- ter atrophy in the cerebral/cerebellar pedunculi and midbrain. In sum, in Parkinson’s disease hypometabolism outperforms atrophy and in progres- sive supranuclear palsy we validated pathognomonic markers as disease- specific. Our studies create a novel framework to investigate disease- specific regional alterations for use in clinical routine. Further, we inves- tigated neural correlates by voxel-based morphometry and discriminated disease and clinical syndrome by multivariate pattern recognition in sin- gle patients with corticobasal syndrome and corticobasal syndrome with a unique syndrome - alien/ anarchic limb phenomenon. We found gray matter volume differences between patients and controls in asymmetric frontotem- poral/ occipital regions, motor areas, and insulae. The frontoparietal gyrus including the supplementary motor area contralateral to the side of the af- fected limb was specific for alien/ anarchic limb phenomenon. The predic- tion of the disease among controls was 79.0% accurate. The prediction of the specific syndrome within a disease reached an accuracy of 81.3%. In conclusion, we reliably classified patients and controls by objective pattern recognition. Moreover, we were able to predict a specific clinical syndrome within a disease, paving the way to individualized disease prediction.:SELBSTSTÄNDIGKEITSERKLÄRUNG I ACKNOWLEDGMENTS II SUMMARY III ZUSAMMENFASSUNG VIII BIBLIOGRAPHISCHE DARSTELLUNG XIV CONTENTS XVI 1 GENERAL INTRODUCTION 1 1.1 ParkinsonianSyndromes .................... 2 1.2 Parkinson’sDisease ....................... 2 1.2.1 DiagnosticCriteria .................... 3 1.3 ProgressiveSupranuclearPalsy ................ 4 1.3.1 DiagnosticCriteria .................... 5 1.4 CorticobasalDegeneration ................... 5 1.4.1 DiagnosticCriteria .................... 7 1.5 ImagingBiomarkers ....................... 7 1.6 CurrentThesis .......................... 9 1.6.1 MotivationandFramework ............... 9 1.6.2 ResearchQuestions................... 9 2 GENERAL MATERIALS AND METHODS 12 2.1 MagneticResonanceImaging.................. 12 2.2 AnalyticalMethods........................ 13 2.2.1 Meta-Analysis ...................... 13 2.2.2 Voxel-BasedMorphometry ............... 14 2.2.3 Support-Vector Machine Classification . . . . . . . . . 15 2.3 Multi-CentricData ........................ 16 2.4 ClinicalAssessment ....................... 17 3 Study 1 4 Study 2 5 Study 3 6 Study 4 7 Study 5 8 DISCUSSION 73 8.1 MainFindings........................... 73 8.2 Statistical Approaches to Find Imaging Biomarker . . . . . . 76 8.3 Brain Alterations and their Utility as Imaging Biomarker . . . . 77 8.4 Limitations ............................ 78 8.5 Contributions of the Current Thesis and Future Directions . . 79 9 REFERENCES APPENDIX XVIII LIST OF AUTHORSHIP XXVII CURRICULUM VITÆ XXXVIII

INTRODUCTION: Schizophrenia (SZ) and bipolar disorder (BD) are neurodegenerative psychotic disorders hallmarked by reductions in gray and white matter volume. Limitations in neuroimaging have led to the use of OCT to study retinal layer biomarkers and their relation to brain pathology. This thesis includes a meta-analysis of current literature and an exploratory analysis of retinal layer thickness in relation to SZ and BD. METHODS: For the meta-analysis, twelve articles were identified using PubMed, Web of Science, and Cochrane database. Diagnostic groups were proband (SZ and BD combined), SZ only, BD only, and healthy control (HC) eyes. Analyses utilized fixed and random effects models, in addition to assuring that bias was adjusted for and that results were cross-validated. Statistical analyses were performed using the “meta” package in R, with results reported as standard mean differences (SMD). The exploratory analysis included a total of 38 subjects (24 probands and 14 HC). Retinal measures were co-varied for age, sex, race, body mass index (BMI), and best-corrected visual acuity (BCVA). Correlations between retinal and clinical and cortical measures were also performed. Clinical data included illness duration, symptom severity, antipsychotic dosage, and smoking status. Neuroimaging data included gray matter (GM) thickness, gray matter volume, and intracranial volume (ICV). Linear effects and mixed effects models were used to study mean eye and right/left eye measures, respectively. Statistical analysis was done in R. RESULTS: A total of 820 patient eyes (541 SZ and 279 BD) and 904 HC eyes were used for the meta-analysis. Compared to HC eyes, probands, SZ, and BD eyes showed significant thinning the peripapillary retinal nerve fiber layer (RNFL), with atrophy greatest in the nasal, temporal, and superior regions. In addition, all diagnostic groups demonstrated significant reductions in the combined ganglion cell layer and inner plexiform layer (GCL-IPL) compared to HC. No significant differences were found for choroidal and macular measures. No significant relationships were seen from meta-regression analysis for clinical measures. For the exploratory analysis, retinal measures from a total of 24 probands (18 SZ and 6 BD) and 14 HC was studied. Compared to HC, probands showed reductions in overall RNFL in mean eye measures, while increases in the inner and outer RNFL were seen in left eye measures. No significant group differences were seen in the GCL, IPL, and inner nuclear layer (INL). The outer plexiform layer (OPL) showed significant thickening in probands and SZ compared to HC for all eye measures. Probands showed trending reductions in the outer nuclear layer (ONL) in the left eye compared to HC. No significant correlations were found between retinal layers and illness duration, overall PANSS (Positive and Negative Syndrome Scale) score, PANSS negative symptom subscore, and smoking status. PANSS positive symptom subscore showed significant and trending negative correlations to the RNFL and GCL, respectively. Antipsychotic medication dosage displayed a trending negative relationship with the IPL. GM thickness showed a significant and trending negative correlation to the RNFL and ONL, respectively. Furthermore, a trending inverse relationship was observed between GM volume and the OPL. Finally, ICV demonstrated a trending and significant negative relationship with GCL and OPL thickness, respectively. CONCLUSION: The meta-analysis showed that atrophy in RNFL and GCL-IPL measures are widely associated with psychosis. Furthermore, it supports previous findings of gray and white matter reductions in SZ and BD. The exploratory analysis showed psychosis-associated reductions in the RNFL and ONL layers, consistent with previous literature. Contradictory findings, the thickening of the ONL, can be attributed to the conflicting findings, but might also be explained by neuro-inflammatory pathways related to psychotic disorders.

The current literature on international migration is diverse, and there is an ongoing debate as to the size and magnitude of the development-migration nexus, and no consensus about this effect has been reached. In this thesis, I explore quantitatively the effect of GDP (as a measure of development) on migration using a meta-analysis approach by synthesizing the empirical findings on this effect, adjusting for the biases, and controlling for the design of the studies. To examine the phenomenon in a systematic way, I collected 179 regression coefficients from 40 different articles, where the results suggest a weak presence of publication selection. Nevertheless, when correcting for publication bias, the effect of development on migration is rather small. Additionally, to explain the inherent model uncertainty, the Bayesian model averaging (BMA) was conducted. The results suggest that studies controlling for the variables of direct foreign investment and age results in a larger effect of development on migration and that the presence of country- level differences boosts migration inflows, particularly in OECD countries.

Genetic variants identified to date by genome-wide association studies only explain a small fraction of total heritability. Gene-by-gene interaction is one important potential source of unexplained heritability. In the first part of this dissertation, a novel approach to detect such interactions is proposed. This approach utilizes penalized regression and sparse estimation principles, and incorporates outside biological knowledge through a network-based penalty. The method is tested on simulated data under various scenarios. Simulations show that with reasonable outside biological knowledge, the new method performs noticeably better than current stage-wise strategies in finding true interactions, especially when the marginal strength of main effects is weak. The proposed method is designed for single-cohort analyses. However, it is generally acknowledged that only multi-cohort analyses have sufficient power to uncover genes and gene-by-gene interactions with moderate effects on traits, such as likely underlie complex diseases. Multi-cohort, meta-analysis approaches for penalized regressions are developed and investigated in the second part of this dissertation. Specifically, I propose two different ways of utilizing data-splitting principles in multi-cohort settings and develop three procedures to conduct meta-analysis. Using the method developed in the first part of this dissertation as an example of penalized regressions, three proposed meta-analysis procedures are compared to mega-analysis using a simulation study. The results suggest that the best approach is to split the participating cohorts into two groups, to perform variable selection for each cohort in the first group, to fit regular regression model on the union of selected variables for each cohort in the second group, and lastly to conduct a meta-analysis across cohorts in the second group. In the last part of this dissertation, the novel method developed in the first part is applied to the Framingham Heart Study measures on total plasma Immunoglobulin E (IgE) concentrations, C-reactive protein levels, and Fasting Glucose. The effect of incorporating various sources of biological information on the ability to detect gene-gene interaction is explored. For IgE, for example, a number of potentially interesting interactions are identified. Some of these interactions involve pairs in human leukocyte antigen genes, which encode proteins that are the key regulators of the immune response. The remaining interactions are among genes previously found to be associated with IgE as main effects. Identification of these interactions may provide new insights into the genetic basis and mechanisms of atopic diseases.

How is it that we feel that we own our body? And how does the brain create this feeling? By manipulating the integration of multisensory signals, researchers have recently begun to probe this question. By creating the illusory experience of owning external body-parts and entire bodies, researchers have investigated the neurofunctional correlates of body ownership. Recent attempts to quantitatively synthesize the neuroimaging literature of body ownership have shown inconsistent results. A large proportion of functional magnetic resonance imaging (fMRI) findings on body ownership includes region of interest (ROI) analysis. This analysis approach produces inflated findings when results are synthesized in meta-analyses. We conducted a systematic search of the fMRI literature of ownership of body-parts and entire bodies. Two activation likelihood estimation (ALE) meta-analyses were conducted, testing the impact of including ROI-based findings. When ROI-based results were included, frontal and posterior parietal multisensory areas were associated with body ownership. However, a whole-brain meta-analysis, excluding ROI-based results, found no significant convergence of activation across the brain. These findings highlight the difficulty of quantitatively synthesizing a neuroimaging field where a large part of the literature is based on findings from ROI analysis. We discuss the difficulty of quantitatively synthesizing results based on ROI analysis and suggest future directions for the study of body ownership within the field of cognitive neuroscience.