Molecular Psychiatry最新文献

Cortical hypometabolism on FDG-PET is a well-established neuroimaging biomarker of cognitive impairment in Parkinson’s disease (PD), but its pathophysiologic origins are incompletely understood. Cholinergic basal forebrain (cBF) degeneration is a prominent pathological feature of PD-related cognitive impairment and may contribute to cortical hypometabolism through cholinergic denervation of cortical projection areas. Here, we investigated in-vivo associations between subregional cBF volumes on 3T-MRI, cortical hypometabolism on [¹⁸F]FDG-PET, and cognitive deficits in a cohort of 95 PD participants with varying degrees of cognitive impairment. We further assessed the spatial correspondence of the cortical pattern of cBF-associated hypometabolism with the pattern of cholinergic denervation in PD as assessed by [¹⁸F]FEOBV-PET imaging of presynaptic cholinergic terminal density in a second cohort. Lower volume of the cortically-projecting posterior cBF, but not of the anterior cBF, was significantly associated with extensive neocortical hypometabolism [p(FDR) < 0.05], which mediated the association between cBF atrophy and cognitive impairment (mediated proportion: 43%, p < 0.001). In combined models, posterior cBF atrophy explained more variance in cortical hypometabolism (R² = 0.26, p < 0.001) than local atrophy in the cortical areas themselves (R² = 0.16, p = 0.01). Topographic correspondence analysis with the [¹⁸F]FEOBV-PET pattern revealed that cortical areas showing most pronounced cBF-associated hypometabolism correspond to those showing most severe cholinergic denervation in PD (Spearman’s ρ = 0.57, p < 0.001). In conclusion, posterior cBF atrophy in PD is selectively associated with hypometabolism in denervated cortical target areas, which mediates the effect of cBF atrophy on cognitive impairment. These data provide first-time in-vivo evidence that cholinergic degeneration represents a principle pathological correlate of cortical hypometabolism underlying cognitive impairment in PD.

Dysfunctional glial cells play a pre-eminent role in schizophrenia pathophysiology. Post-mortem studies have provided evidence for significantly decreased glial cell numbers in different brain regions of individuals with schizophrenia. Reduced glial cell numbers are most pronounced in oligodendroglia, but reduced astrocyte cell densities have also been reported. This review highlights that oligo- and astroglial deficits are a key histopathological feature in schizophrenia, distinct from typical changes seen in neurodegenerative disorders. Significant deficits of oligodendrocytes in schizophrenia may arise in two ways: (i) demise of mature functionally compromised oligodendrocytes; and (ii) lack of mature oligodendrocytes due to failed maturation of progenitor cells. We also analyse in detail the controversy regarding deficits of astrocytes. Regardless of their origin, glial cell deficits have several pathophysiological consequences. Among these, myelination deficits due to a reduced number of oligodendrocytes may be the most important factor, resulting in the disconnectivity between neurons and different brain regions observed in schizophrenia. When glial cells die, it appears to be through degeneration, a process which is basically reversible. Thus, therapeutic interventions that (i) help rescue glial cells (ii) or improve their maturation might be a viable option. Since antipsychotic treatment alone does not seem to prevent glial cell loss or maturation deficits, there is intense search for new therapeutic options. Current proposals range from the application of antidepressants and other chemical agents as well as physical exercise to engrafting healthy glial cells into brains of schizophrenia patients.

Background

Depression in pregnancy can increase vulnerability for psychiatric disorders in the offspring, likely via the transfer of heightened maternal cortisol and cytokines to the in-utero environment. However, the precise cellular and molecular mechanisms, are largely unclear. Animal studies can represent this complex pathophysiology at a systemic level but are expensive and ethically challenging. While simpler, in vitro models offer high-throughput opportunities. Therefore, this systematic review integrates findings of in vitro models relevant to depression in pregnancy, to generate novel hypotheses and targets for intervention.

Methods

The systematic analysis covered studies investigating glucocorticoid or cytokine challenges on placental or foetal neural progenitor cells (NPCs), with or without co-treatment with sex hormones.

Results

Of the 50 included studies, 11 used placental cells and 39 NPCs; surprisingly, only one used a combination of oestrogen and cortisol, and no study combined placental cells and NPCs. In placental cells, cortisol or cytokines decreased nutrient transporter expression and steroidogenic enzyme activity, and increased cytokine production. NPCs exhibited decreases in proliferation and differentiation, via specific molecular pathways, namely, inhibition of hedgehog signalling and activation of kynurenine pathway. In these cells, studies also highlighted epigenetic priming of stress and inflammatory pathways.

Conclusions

Overall, results suggest that stress and inflammation not only detrimentally impact placental regulation of nutrients and hormones to the foetus, but also activate downstream pathways through increased inflammation in the placenta, ultimately eliciting adverse effects on foetal neurogenesis. Future research should investigate how sex hormones regulate these mechanisms, with the aim of developing targeted therapeutic approaches for depression in pregnancy.

Dyrk1A deficiency is linked to various neurodevelopmental disorders, including developmental delays, intellectual disability (ID) and autism spectrum disorders (ASD). Haploinsufficiency of Dyrk1a in mice reportedly leads to ASD-related phenotypes. However, the key pathological mechanisms remain unclear and human DYRK1A mutations remain uncharacterized in mice. Here, we generated and studied Dyrk1a-knockin mice carrying a human ASD patient mutation (Ile48LysfsX2; Dyrk1a-I48K mice). These mice display severe microcephaly, social and cognitive deficits, dendritic shrinkage, excitatory synaptic deficits, and altered phospho-proteomic patterns enriched for multiple signaling pathways and synaptic proteins. Early chronic lithium treatment of newborn mutant mice rescues the brain volume, behavior, dendritic, synaptic, and signaling/synapse phospho-proteomic phenotypes at juvenile and adult stages. These results suggest that signaling/synaptic alterations contribute to the phenotypic alterations seen in Dyrk1a-I48K mice, and that early correction of these alterations by lithium treatment has long-lasting effects in preventing juvenile and adult-stage phenotypes.

Due to methodological reasons, the X-chromosome has not been featured in the major genome-wide association studies on Alzheimer’s Disease (AD). To address this and better characterize the genetic landscape of AD, we performed an in-depth X-Chromosome-Wide Association Study (XWAS) in 115,841 AD cases or AD proxy cases, including 52,214 clinically-diagnosed AD cases, and 613,671 controls. We considered three approaches to account for the different X-chromosome inactivation (XCI) states in females, i.e. random XCI, skewed XCI, and escape XCI. We did not detect any genome-wide significant signals (P ≤ 5 × 10⁻⁸) but identified seven X-chromosome-wide significant loci (P ≤ 1.6 × 10⁻⁶). The index variants were common for the Xp22.32, FRMPD4, DMD and Xq25 loci, and rare for the WNK3, PJA1, and DACH2 loci. Overall, this well-powered XWAS found no genetic risk factors for AD on the non-pseudoautosomal region of the X-chromosome, but it identified suggestive signals warranting further investigations.

Catatonia is a severe psychomotor disorder characterized by motor, affective and cognitive-behavioral abnormalities. Although previous magnetic resonance imaging (MRI) studies suggested white matter (WM) dysconnectivity in the pathogenesis of catatonia, it is unclear whether microstructural alterations of WM tracts connecting psychomotor regions might contribute to a better classification of catatonia patients. Here, diffusion-weighted MRI data were collected from two independent cohorts (whiteCAT/replication cohort) of patients with (n = 45/n = 13) and without (n = 56/n = 26) catatonia according to ICD-11 criteria. Catatonia severity was examined using the Northoff (NCRS) and Bush-Francis (BFCRS) Catatonia Rating Scales. We used tract-based spatial statistics (TBSS), tractometry (TractSeg) and machine-learning (ML) to classify catatonia patients from tractometry values as well as tractomics features generated by the newly developed tool RadTract. Catatonia patients showed fractional anisotropy (FA) alterations measured via TractSeg in different corpus callosum segments (CC_1, CC_3, CC_4, CC_5 and CC_6) compared to non-catatonia patients across both cohorts. Our classification results indicated a higher level of performance when trained on tractomics as opposed to traditional tractometry values. Moreover, in the CC_6, we successfully trained two classifiers using the tractomics features identified in the whiteCAT data. These classifiers were applied separately to the whiteCAT and replication cohorts, demonstrating comparable performance with Area Under the Receiver Operating Characteristics (AUROC) values of 0.79 for the whiteCAT cohort and 0.76 for the replication cohort. In contrast, training on FA tractometry resulted in lower AUROC values of 0.66 for the whiteCAT cohort and 0.51 for the replication cohort. In conclusion, these findings underscore the significance of CC WM microstructural alterations in the pathophysiology of catatonia. The successful use of an ML based classification model to identify catatonia patients has the potential to improve diagnostic precision.

Mapping differential brain structures for psychiatric problems has been challenging due to a lack of regional convergence and poor replicability in previous brain-behavior association studies. By leveraging two independent large cohorts of neurodevelopment, the ABCD and Generation R Studies (total N = 11271), we implemented an unsupervised machine learning technique with a highly stringent generalizability test to identify reliable brain-behavior associations across diverse domains of child psychiatric problems. Across all psychiatric symptoms measured, one multivariate brain-behavior association was found, reflecting a widespread reduction of cortical surface area correlated with higher child attention problems. Crucially, this association showed marked generalizability across different populations and study protocols, demonstrating potential clinical utility. Moreover, the derived brain dimension score predicted child cognitive and academic functioning three years later and was also associated with polygenic scores for ADHD. Our results indicated that attention problems could be a phenotype for establishing promising multivariate neurobiological prediction models for children across populations. Future studies could extend this investigation into different development periods and examine the predictive values for assessment of functioning, diagnosis, and disease trajectory in clinical samples.

Negative experiences during adolescence, such as social isolation (SI), bullying, and abuse, increase the risk of psychiatric diseases in adulthood. However, the pathogenesis of psychiatric diseases induced by these factors remain poorly understood. In adolescents, stress affects the intestinal homeostasis in the gut-brain axis. This study determined whether adolescent SI induces behavioral abnormalities by disrupting colonic function. Adolescent mice exposed to SI exhibit spatial cognitive deficits and microglial activation in the hippocampus (HIP). SI decreased the differentiation of mucin-producing goblet cells, which was accompanied by alterations in the composition of the gut microbiota, particularly the depletion of mucin-feeding bacteria. Treatment with rebamipide, which promotes goblet cell differentiation in the colon, attenuated SI-induced spatial cognitive deficits and microglial activation in the HIP and decreased cystine, a downstream metabolite of homocysteine. Treatment with cystine ameliorated SI-induced spatial cognitive deficits and increased microglial C-C motif chemokine ligand 7 (CCL7) levels in the HIP. Inhibition of CCL7 receptors by antagonists of CC motif chemokine receptors 2 (CCR2) and 3 (CCR3) in the HIP prevented spatial cognitive deficits induced by SI. Infusion of CCL7 into the HIP following microglial ablation with clodronate liposome induced spatial cognitive deficits. These findings suggest that adolescent SI decreases serum cystine levels by damaging the colonic goblet cells, resulting in spatial cognitive deficits by triggering microglial activation in the HIP. Our results indicate that increased CCL7 expression in hippocampal microglia may contribute to spatial cognitive deficits by activating CCR2 and CCR3.

Major depressive disorder (MDD) is a debilitating condition that is associated with changes in the default-mode network (DMN). Commonly reported features include alterations in gray matter volume (GMV), cortical thickness (CoT), and gyrification. A comprehensive examination of these variables using ultra-high field strength MRI and machine learning methods may lead to novel insights into the pathophysiology of depression and help develop a more personalized therapy. Cerebral images were obtained from 41 patients with confirmed MDD and 41 healthy controls, matched for age and gender, using a 7-T-MRI. DMN parcellation followed the Schaefer 600 Atlas. Based on the results of a mixed-model repeated measures analysis, a support vector machine (SVM) calculation followed by leave-one-out cross-validation determined the predictive ability of structural features for the presence of MDD. A consecutive permutation procedure identified which areas contributed to the classification results. Correlating changes in those areas with BDI-II and AMDP scores added an explanatory aspect to this study. CoT did not delineate relevant changes in the mixed model and was excluded from further analysis. The SVM achieved a good prediction accuracy of 0.76 using gyrification data. GMV was not a viable predictor for disease presence, however, it correlated in the left parahippocampal gyrus with disease severity as measured by the BDI-II. Structural data of the DMN may therefore contain the necessary information to predict the presence of MDD. However, there may be inherent challenges with predicting disease course or treatment response due to high GMV variance and the static character of gyrification. Further improvements in data acquisition and analysis may help to overcome these difficulties.