Translational Psychiatry最新文献

The dorsal medial prefrontal cortex (dmPFC) plays a dual role in modulating drug seeking and fear-related behaviors. Learned associations between cues and drug seeking are encoded by a specific ensemble of neurons. This study explored the stability of a dmPFC cocaine seeking ensemble over 2 weeks and its influence on persistent cocaine seeking and fear memory retrieval. In the first series of experiments, we trained TetTag c-fos-driven-EGFP mice in cocaine self-administration and tagged strongly activated neurons with EGFP during the initial day 7 cocaine seeking session. Subsequently, a follow-up seeking test was conducted 2 weeks later to examine ensemble reactivation between two seeking sessions via c-Fos immunostaining. In the second series of experiments, we co-injected viruses expressing TRE-cre and a cre-dependent inhibitory PSAM-GlyR into the dmPFC of male and female c-fos-tTA mice to enable "tagging" of cocaine seeking ensemble or cued fear ensemble neurons with inhibitory chemogenetic receptors. These c-fos-tTA mice have the c-fos promoter that drives expression of the tetracycline transactivator (tTA). The tTA can bind to the tetracycline response element (TRE) site on the viral construct, resulting in the expression of cre-recombinase, which enables the expression of cre-dependent inhibitory chemogenetic receptors and fluorescent reporters. Then we investigated ensemble contribution to subsequent cocaine seeking and fear recall during inhibition of the tagged ensemble by administering uPSEM792s (0.3 mg/kg), a selective ligand for PSAM-GlyR. In both sexes, there was a positive association between the persistence of cocaine seeking and the proportion of reactivated EGFP+ neurons within the dmPFC. More importantly, inhibition of the cocaine seeking ensemble suppressed cocaine seeking, but not recall of fear memory, while inhibition of the fear ensemble reduced conditioned freezing but not cocaine seeking. The results demonstrate that cocaine and fear recall ensembles in the dmPFC are stable, but largely exclusive from one another.

From Alzheimer's disease to amyotrophic lateral sclerosis, the molecular cascades underlying neurodegenerative disorders remain poorly understood. The clinical view of neurodegeneration is confounded by symptomatic heterogeneity and mixed pathology in almost every patient. While the underlying physiological alterations originate, proliferate, and propagate potentially decades before symptomatic onset, the complexity and inaccessibility of the living brain limit direct observation over a patient's lifespan. Consequently, there is a critical need for robust computational methods to support the search for causal mechanisms of neurodegeneration by distinguishing pathogenic processes from consequential alterations, and inter-individual variability from intra-individual progression. Recently, promising advances have been made by data-driven spatiotemporal modeling of the brain, based on in vivo neuroimaging and biospecimen markers. These methods include disease progression models comparing the temporal evolution of various biomarkers, causal models linking interacting biological processes, network propagation models reproducing the spatial spreading of pathology, and biophysical models spanning cellular- to network-scale phenomena. In this review, we discuss various computational approaches for integrating cross-sectional, longitudinal, and multi-modal data, primarily from large observational neuroimaging studies, to understand (i) the temporal ordering of physiological alterations, i(i) their spatial relationships to the brain's molecular and cellular architecture, (iii) mechanistic interactions between biological processes, and (iv) the macroscopic effects of microscopic factors. We consider the extents to which computational models can evaluate mechanistic hypotheses, explore applications such as improving treatment selection, and discuss how model-informed insights can lay the groundwork for a pathobiological redefinition of neurodegenerative disorders.

Psychiatry has traditionally focused on the study of neurons and neurotransmitter physiology in the pathophysiology and treatment of psychiatric disorders. A growing literature highlights REDOX imbalance (a state in which demand for antioxidants surpasses their bioavailability) as a common pathophysiology for a diverse array of brain conditions (e.g., trichotillomania, schizophrenia, autism, Parkinson's disease). REDOX imbalance is typically measured via plasma glutathione, as glutathione is critical to the adaptive antioxidant response in the brain. Accordingly, glutathione, its precursors, and/or metabolites serve as biomarkers of disease risk, therapeutic targets, and measures of treatment response. However, as with any emerging field, there are currently several different methods for collection, processing, storage, and calculation of summary measures of plasma glutathione metabolism, within and between preclinical and clinical research. The lack of evidence-based best-practice methodology hampers reproducibility (preclinical or clinical), and translation (between preclinical and clinical work). To address this methodological need, here we used a repeated measures within-subject design to investigate how sample preparation (type of anticoagulant used during blood collection, deproteinization status, and storage temperature) affects plasma glutathione levels. Accordingly, we collected whole blood from mice (N = 13), and then, using a commercially available kit, quantified glutathione in plasma stored in four different ways. Presuming that these preparation conditions and post-processing calculations are unimportant, we would expect to see no difference in glutathione levels and summary measures from the same sample. However, we found each of these variables to significantly alter quantified glutathione levels. Accordingly, we propose a vital, gold-standard methodology for both sample collection, processing, and storage of plasma used for glutathione quantification and for summary calculations of glutathione that can be used preclinically and clinically, thus yielding more streamlined translation.

The placenta plays a crucial role in protecting the fetus from environmental harm and supports the development of its brain. In fact, compromised placental function could predispose an individual to neurodevelopmental disorders. Placental epigenetic modifications, including DNA methylation, could be considered a proxy of placental function and thus plausible mediators of the association between intrauterine environmental exposures and genetics, and childhood and adult mental health. Although neurodevelopmental disorders such as autism spectrum disorder have been investigated in relation to placenta DNA methylation, no studies have addressed the association between placenta DNA methylation and child's cognitive functions. Thus, our goal here was to investigate whether the placental DNA methylation profile measured using the Illumina EPIC array is associated with three different cognitive domains (namely verbal score, perceptive performance score, and general cognitive score) assessed by the McCarthy Scales of Children's functions in childhood at age 4. To this end, we conducted epigenome-wide association analyses, including data from 255 mother-child pairs within the INMA project, and performed a follow-up functional analysis to help the interpretation of the findings. After multiple-testing correction, we found that methylation at 4 CpGs (cg1548200, cg02986379, cg00866476, and cg14113931) was significantly associated with the general cognitive score, and 2 distinct differentially methylated regions (DMRs) (including 27 CpGs) were significantly associated with each cognitive dimension. Interestingly, the genes annotated to these CpGs, such as DAB2, CEP76, PSMG2, or MECOM, are involved in placenta, fetal, and brain development. Moreover, functional enrichment analyses of suggestive CpGs (p < 1 × 10^-4) revealed gene sets involved in placenta development, fetus formation, and brain growth. These findings suggest that placental DNA methylation could be a mechanism contributing to the alteration of important pathways in the placenta that have a consequence on the offspring's brain development and cognitive function.

Harm avoidance (HA) is a Cloninger personality trait that describes behavioural inhibition to avoid aversive stimuli. It serves as a predisposing factor that contributes to the development of mental disorders such as anxiety and major depressive disorder. Neuroimaging research has identified some brain anatomical and functional correlates of HA, but reported findings are inconsistent. We therefore conducted a multimodal meta-analysis of whole-brain structural and resting-state functional neuroimaging studies to identify the most stable neural substrate of HA. Included were a total of 10 structural voxel-based morphometry studies (11 datasets) and 13 functional positron emission tomography or single photon emission computed tomography studies (16 datasets) involving 3053 healthy participants without any psychiatric or neurological disorders evaluated for HA using the Three-Dimensional Personality Questionnaire (TPQ) or the Temperament and Character Inventory (TCI). The meta-analysis revealed brain volumetric correlates of HA in parietal and temporal cortices, and resting-state functional correlates in prefrontal, temporal and parietal gray matter. Volumetric and functional correlates co-occurred in the left superior frontal gyrus and left middle frontal gyrus, and were dissociated in the left rectus gyrus. Our meta-analysis is the first study to give a comprehensive picture of the structural and functional correlates of HA, a contribution that may help bridge the grievous gap between the neurobiology of HA and the pathogenesis, prevention and treatment of HA-related mental disorders.

Negative bias is an essential characteristic of depressive episodes leading patients to attribute more negative valence to environmental cues. This negative bias affects all levels of information processing including emotional response, attention and memory, leading to the development and maintenance of depressive symptoms. In this context, pleasant stimuli become less attractive and unpleasant ones more aversive, yet the related neural circuits underlying this bias remain largely unknown. By studying a mice model for depression chronically receiving corticosterone (CORT), we showed a negative bias in valence attribution to olfactory stimuli that responds to antidepressant drug. This result paralleled the alterations in odor value assignment we observed in bipolar depressed patients. Given the crucial role of amygdala in valence coding and its strong link with depression, we hypothesized that basolateral amygdala (BLA) circuits alterations might support negative shift associated with depressive states. Contrary to humans, where limits in spatial resolution of imaging tools impair easy amygdala segmentation, recently unravelled specific BLA circuits implicated in negative and positive valence attribution could be studied in mice. Combining CTB and rabies-based tracing with ex vivo measurements of neuronal activity, we demonstrated that negative valence bias is supported by disrupted activity of specific BLA circuits during depressive states. Chronic CORT administration induced decreased recruitment of BLA-to-NAc neurons preferentially involved in positive valence encoding, while increasing recruitment of BLA-to-CeA neurons preferentially involved in negative valence encoding. Importantly, this dysfunction was dampened by chemogenetic hyperactivation of BLA-to-NAc neurons. Moreover, altered BLA activity correlated with durable presynaptic connectivity changes coming from the paraventricular nucleus of the thalamus, recently demonstrated as orchestrating valence assignment in the amygdala. Together, our findings suggest that specific BLA circuits alterations might support negative bias in depressive states and provide new avenues for translational research to understand the mechanisms underlying depression and treatment efficacy.

Alcohol use disorder (AUD) is a profound psychiatric condition marked by disrupted connectivity among distributed brain regions, indicating impaired functional integration. Previous connectome studies utilizing functional magnetic resonance imaging (fMRI) have predominantly focused on undirected functional connectivity, while the specific alterations in directed effective connectivity (EC) associated with AUD remain unclear. To address this issue, this study utilized multivariate pattern analysis (MVPA) and spectral dynamic causal modeling (DCM). We recruited 32 abstinent men with AUD and 30 healthy controls (HCs) men, and collected their resting-state fMRI data. A regional homogeneity (ReHo)-based MVPA method was employed to classify AUD and HC groups, as well as predict the severity of addiction in AUD individuals. The most informative brain regions identified by the MVPA were further investigated using spectral DCM. Our results indicated that the ReHo-based support vector classification (SVC) exhibits the highest accuracy in distinguishing individuals with AUD from HCs (classification accuracy: 98.57%). Additionally, our results demonstrated that ReHo-based support vector regression (SVR) could be utilized to predict the addiction severity (alcohol use disorders identification test, AUDIT, R² = 0.38; Michigan alcoholism screening test, MAST, R² = 0.29) of patients with AUD. The most informative brain regions for the prediction include left pre-SMA, right dACC, right LOFC, right putamen, and right NACC. These findings were validated in an independent data set (35 patients with AUD and 36 HCs, Classification accuracy: 91.67%; AUDIT, R² = 0.17; MAST, R² = 0.20). The results of spectral DCM analysis indicated that individuals with AUD exhibited decreased EC from the left pre-SMA to the right putamen, from the right dACC to the right putamen, and from the right LOFC to the right NACC compared to HCs. Moreover, the EC strength from the right NACC to left pre-SMA and from the right dACC to right putamen mediated the relationship between addiction severity (MAST scores) and behavioral measures (impulsive and compulsive scores). These findings provide crucial evidence for the underlying mechanism of impaired self-control, risk assessment, and impulsive and compulsive alcohol consumption in individuals with AUD, providing novel causal insights into both diagnosis and treatment.

Social isolation (SI) is a common phenomenon in the modern world, especially during the coronavirus disease 2019 pandemic, and causes lasting cognitive impairments and mental disorders. However, it is still unclear how SI alters molecules in the brain and induces behavioural dysfunctions. Here, we report that SI impairs cognitive function and induces depressive-like behaviours in C57BL/6 J mice, in addition to impairing synaptic plasticity and increasing the levels of APP cleavage-related enzymes, thereby promoting Aβ production. Moreover, we show that in APP/PS1 transgenic mice, SI accelerates pathological changes and behavioural deficits. Interestingly, downregulation of the expression of the BACE1 attenuates SI-induced Aβ toxicity and synaptic dysfunction. Furthermore, early intervention with BACE1 shRNA blocks SI-induced cognitive impairments. Together, our data strongly suggest that SI-induced upregulation of BACE1 expression mediates Aβ toxicity and induces behavioural deficits. Down-regulation of BACE1 may be a promising strategy for preventing SI-induced cognitive impairments.

Social bonding, essential for health and survival in all social species, depends on mu-opioid signalling in non-human mammals. A growing neuroimaging and psychopharmacology literature also implicates mu-opioids in human social connectedness. To determine the role of mu-opioids for social connectedness in healthy humans, we conducted a preregistered (https://osf.io/x5wmq) multilevel random-effects meta-analysis of randomised double-blind placebo-controlled opioid antagonist studies. We included data from 8 publications and 2 unpublished projects, totalling 17 outcomes (N = 455) sourced from a final literature search in Web of Science, Scopus, PubMed and EMBASE on October 12, 2023, and through community contributions. All studies used naltrexone (25–100 mg) to block the mu-opioid system and measured social connectedness by self-report. Opioid antagonism slightly reduced feelings of social connectedness (Hedges’ g [95% CI) = −0.20] [−0.32, −0.07]. Results were highly consistent within and between studies (I² = 23%). However, there was some indication of bias in favour of larger effects among smaller studies (Egger’s test: B = −2.16, SE = 0.93, z = −2.33, p = 0.02), and publication bias analysis indicated that the effect of naltrexone might be overestimated. The results clearly demonstrate that intact mu-opioid signalling is not essential for experiencing social connectedness, as robust feelings of connectedness are evident even during full pharmacological mu-opioid blockade. Nevertheless, antagonism reduced measures of social connection, consistent with a modulatory role of mu-opioids for human social connectedness. The modest effect size relative to findings in non-human animals, could be related to differences in measurement (subjective human responses versus behavioural/motivation indices in animals), species specific neural mechanisms, or naltrexone effects on other opioid receptor subtypes. In sum, these results help explain how mu-opioid dysregulation and social disconnection can contribute to disability, and conversely—how social connection can buffer risk of ill health.