Psychiatry - Research Publications
Permanent URI for this collection
Search Results
1 - 10 of 169
-
ItemBiological sex classification with structural MRI data shows increased misclassification in transgender women(SPRINGERNATURE, 2020-09)Transgender individuals (TIs) show brain-structural alterations that differ from their biological sex as well as their perceived gender. To substantiate evidence that the brain structure of TIs differs from male and female, we use a combined multivariate and univariate approach. Gray matter segments resulting from voxel-based morphometry preprocessing of N = 1753 cisgender (CG) healthy participants were used to train (N = 1402) and validate (20% holdout N = 351) a support-vector machine classifying the biological sex. As a second validation, we classified N = 1104 patients with depression. A third validation was performed using the matched CG sample of the transgender women (TW) application sample. Subsequently, the classifier was applied to N = 26 TW. Finally, we compared brain volumes of CG-men, women, and TW-pre/post treatment cross-sex hormone treatment (CHT) in a univariate analysis controlling for sexual orientation, age, and total brain volume. The application of our biological sex classifier to the transgender sample resulted in a significantly lower true positive rate (TPR-male = 56.0%). The TPR did not differ between CG-individuals with (TPR-male = 86.9%) and without depression (TPR-male = 88.5%). The univariate analysis of the transgender application-sample revealed that TW-pre/post treatment show brain-structural differences from CG-women and CG-men in the putamen and insula, as well as the whole-brain analysis. Our results support the hypothesis that brain structure in TW differs from brain structure of their biological sex (male) as well as their perceived gender (female). This finding substantiates evidence that TIs show specific brain-structural alterations leading to a different pattern of brain structure than CG-individuals.
-
ItemGenome-wide gene-environment interaction in depression: A systematic evaluation of candidate genes: The childhood trauma working-group of PGC-MDD(WILEY, 2018-01)Gene by environment (GxE) interaction studies have investigated the influence of a number of candidate genes and variants for major depressive disorder (MDD) on the association between childhood trauma and MDD. Most of these studies are hypothesis driven and investigate only a limited number of SNPs in relevant pathways using differing methodological approaches. Here (1) we identified 27 genes and 268 SNPs previously associated with MDD or with GxE interaction in MDD and (2) analyzed their impact on GxE in MDD using a common approach in 3944 subjects of European ancestry from the Psychiatric Genomics Consortium who had completed the Childhood Trauma Questionnaire. (3) We subsequently used the genome-wide SNP data for a genome-wide case-control GxE model and GxE case-only analyses testing for an enrichment of associated SNPs. No genome-wide significant hits and no consistency among the signals of the different analytic approaches could be observed. This is the largest study for systematic GxE interaction analysis in MDD in subjects of European ancestry to date. Most of the known candidate genes/variants could not be supported. Thus, their impact on GxE interaction in MDD may be questionable. Our results underscore the need for larger samples, more extensive assessment of environmental exposures, and greater efforts to investigate new methodological approaches in GxE models for MDD.
-
Item
-
ItemMixed mood: The not so united states?(WILEY, 2017-06)
-
ItemDefining melancholia: A core mood disorder(WILEY, 2017-05)
-
ItemNeural correlates of apathy in late-life depression: a pilot [18F]FDDNP positron emission tomography study(WILEY, 2017-05)BACKGROUND: Neurotoxicity associated with amyloid and tau protein aggregation could represent a pathophysiological cascade that, along with vascular compromise, may predispose individuals to late-life depression (LLD). In LLD, apathy is common, leads to worsening of functioning, and responds poorly to antidepressant treatment. Better understanding of the pathophysiological mechanisms of apathy in LLD would facilitate development of more effective diagnostic and treatment approaches. In this cross-sectional pilot study, we performed positron emission tomography scans after injection of 2-(1-{6-[(2-[18 F]fluoroethyl)(methyl)-amino]-2-naphthyl}ethylidene) malononitrile ([18 F]FDDNP), an in vivo amyloid and tau neuroimaging study, in patients with LLD to explore neural correlates of apathy. METHODS: Sixteen depressed elderly volunteers received clinical assessments and [18 F]FDDNP positron emission tomography scans. The cross-sectional relationship of [18 F]FDDNP binding levels with depression (Hamilton Depression Rating Scale) and apathy (Apathy Evaluation Scale) were studied using Spearman's correlation analyses because of the relatively small sample size. Age, sex, and years of education were partialed out. Significance levels were set at P ≤ 0.05. RESULTS: [18 F]FDDNP binding in the anterior cingulate cortex was negatively associated with the Apathy Evaluation Scale total (r = -0.62, P = 0.02; where low Apathy Evaluation Scale score equals greater severity of apathy). This suggests that apathy in LLD is associated with higher amyloid and/or tau levels in the anterior cingulate cortex. None of the regional [18 F]FDDNP binding levels was significantly associated with the Hamilton Depression Rating Scale total. CONCLUSION: This pilot study suggests that increased apathy in subjects with LLD may be associated with greater amyloid and/or tau burden in certain brain regions. Future studies in larger samples would elucidate the generalizability of these results, which eventually could lead to improved diagnostic and treatment methods in LLD.
-
ItemMediation of Cognitive Function Improvements by Strength Gains After Resistance Training in Older Adults with Mild Cognitive Impairment: Outcomes of the Study of Mental and Resistance Training(WILEY, 2017-03)OBJECTIVES: To determine whether improvements in aerobic capacity (VO2peak ) and strength after progressive resistance training (PRT) mediate improvements in cognitive function. DESIGN: Randomized, double-blind, double-sham, controlled trial. SETTING: University research facility. PARTICIPANTS: Community-dwelling older adults (aged ≥55) with mild cognitive impairment (MCI) (N = 100). INTERVENTION: PRT and cognitive training (CT), 2 to 3 days per week for 6 months. MEASUREMENTS: Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-Cog); global, executive, and memory domains; peak strength (1 repetition maximum); and VO2peak . RESULTS: PRT increased upper (standardized mean difference (SMD) = 0.69, 95% confidence interval = 0.47, 0.91), lower (SMD = 0.94, 95% CI = 0.69-1.20) and whole-body (SMD = 0.84, 95% CI = 0.62-1.05) strength and percentage change in VO2peak (8.0%, 95% CI = 2.2-13.8) significantly more than sham exercise. Higher strength scores, but not greater VO2peak , were significantly associated with improvements in cognition (P < .05). Greater lower body strength significantly mediated the effect of PRT on ADAS-Cog improvements (indirect effect: β = -0.64, 95% CI = -1.38 to -0.004; direct effect: β = -0.37, 95% CI = -1.51-0.78) and global domain (indirect effect: β = 0.12, 95% CI = 0.02-0.22; direct effect: β = -0.003, 95% CI = -0.17-0.16) but not for executive domain (indirect effect: β = 0.11, 95% CI = -0.04-0.26; direct effect: β = 0.03, 95% CI = -0.17-0.23). CONCLUSION: High-intensity PRT results in significant improvements in cognitive function, muscle strength, and aerobic capacity in older adults with MCI. Strength gains, but not aerobic capacity changes, mediate the cognitive benefits of PRT. Future investigations are warranted to determine the physiological mechanisms linking strength gains and cognitive benefits.
-
ItemTNF receptors 1 and 2 exert distinct region-specific effects on striatal and hippocampal grey matter volumes (VBM) in healthy adults(WILEY, 2017-03)Tumour necrosis factor alpha (TNFα) has been implicated in the pathophysiology of neurodegenerative and neuropsychiatric disease, with research highlighting a role for TNFα in hippocampal and striatal regulation. TNFα signals are primarily transduced by TNF receptors 1 and 2 (TNFR1 and TNFR2), encoded by TNFRSF1A and TNFRSF1B, which exert opposing effects on cell survival (TNFR1, neurodegenerative; TNFR2, neuroprotective). We therefore sought to explore the respective roles of TNFR1 and TNFR2 in the regulation of hippocampal and striatal morphology in an imaging genetics study. Voxel-based morphometry was used to analyse the associations between TNFRSF1A (rs4149576 and rs4149577) and TNFRSF1B (rs1061624) genotypes and grey matter structure. The final samples comprised a total of 505 subjects (mean age = 33.29, SD = 11.55 years; 285 females and 220 males) for morphometric analyses of rs1061624 and rs4149576, and 493 subjects for rs4149577 (mean age = 33.20, SD = 11.56 years; 281 females and 212 males). Analyses of TNFRSF1A single nucleotide polymorphisms (SNPs) rs4149576 and rs4149577 showed highly significant genotypic associations with striatal volume but not the hippocampus. Specifically, for rs4149576, G homozygotes were associated with reduced caudate nucleus volumes relative to A homozygotes and heterozygotes, whereas for rs4149577, reduced caudate volumes were observed in C homozygotes relative to T homozygotes and heterozygotes. Analysis of the TNFRSF1B SNP rs1061624 yielded a significant association with hippocampal but not with striatal volume, whereby G homozygotes were associated with increased volumes relative to A homozygotes and heterozygotes. Our findings indicate a role for TNFR1 in regulating striatal but not hippocampal morphology, as well as a complementary role for TNFR2 in hippocampal but not in striatal morphology.
-
ItemAnti-inflammatory agents in the treatment of bipolar depression: a systematic review and meta-analysis(WILEY, 2016-03)OBJECTIVE: Inflammation has been implicated in the risk, pathophysiology, and progression of mood disorders and, as such, has become a target of interest in the treatment of bipolar disorder (BD). Therefore, the objective of the current qualitative and quantitative review was to determine the overall antidepressant effect of adjunctive anti-inflammatory agents in the treatment of bipolar depression. METHODS: Completed and ongoing clinical trials of anti-inflammatory agents for BD published prior to 15 May 15 2015 were identified through searching the PubMed, Embase, PsychINFO, and Clinicaltrials.gov databases. Data from randomized controlled trials (RCTs) assessing the antidepressant effect of adjunctive mechanistically diverse anti-inflammatory agents were pooled to determine standard mean differences (SMDs) compared with standard therapy alone. RESULTS: Ten RCTs were identified for qualitative review. Eight RCTs (n = 312) assessing adjunctive nonsteroidal anti-inflammatory drugs (n = 53), omega-3 polyunsaturated fatty acids (n = 140), N-acetylcysteine (n = 76), and pioglitazone (n = 44) in the treatment of BD met the inclusion criteria for quantitative analysis. The overall effect size of adjunctive anti-inflammatory agents on depressive symptoms was -0.40 (95% confidence interval -0.14 to -0.65, p = 0.002), indicative of a moderate and statistically significant antidepressant effect. The heterogeneity of the pooled sample was low (I² = 14%, p = 0.32). No manic/hypomanic induction or significant treatment-emergent adverse events were reported. CONCLUSIONS: Overall, a moderate antidepressant effect was observed for adjunctive anti-inflammatory agents compared with conventional therapy alone in the treatment of bipolar depression. The small number of studies, diversity of agents, and small sample sizes limited interpretation of the current analysis.
-
ItemMachine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging(SPRINGER INTERNATIONAL PUBLISHING AG, 2017)As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.