Journal of Psychiatry & Neuroscience最新文献

Background: Among healthy participants, the interindividual variability of brain response to facial emotions is associated with genetic variation, including common risk variants for schizophrenia, a heritable brain disorder characterized by anomalies in emotion processing. We aimed to identify genetic variants associated with heritable brain activity during processing of facial emotions among healthy participants and to explore the impact of these identified variants among patients with schizophrenia.

Methods: We conducted a data-driven stepwise study including samples of healthy twins, unrelated healthy participants and patients with schizophrenia. Participants approached or avoided pictures of faces with negative emotional valence during functional magnetic resonance imaging (fMRI).

Results: We investigated 3 samples of healthy participants - including 28 healthy twin pairs, 289 unrelated healthy participants (genome-wide association study [GWAS] discovery sample) and 90 unrelated healthy participants (replication sample) - and 1 sample of 48 patients with schizophrenia. Among healthy twins, we identified the amygdala as the brain region with the highest heritability during processing of angry faces (heritability estimate 0.54, p < 0.001). Subsequent GWAS in both discovery and replication samples of healthy non-twins indicated that amygdala activity was associated with a polymorphism in the miR-137 locus (rs1198575), a micro-RNA strongly involved in risk for schizophrenia. A significant effect in the same direction was found among patients with schizophrenia (p = 0.03).

Limitations: The limited sample size available for GWAS analyses may require further replication of results.

Conclusion: Our data-driven approach shows preliminary evidence that amygdala activity, as evaluated with our task, is heritable. Our genetic associations preliminarily suggest a role for miR-137 in brain activity during explicit processing of facial emotions among healthy participants and patients with schizophrenia, pointing to the amygdala as a brain region whose activity is related to miR-137.

Background: Among its pleiotropic properties, gender-affirming hormone therapy (GHT) affects regional brain volumes. The hypothalamus, which regulates neuroendocrine function and associated emotional and cognitive processes, is an intuitive target for probing GHT effects. We sought to assess changes to hypothalamus and hypothalamic subunit volumes after GHT, thereby honouring the region's anatomical and functional heterogeneity.

Methods: Individuals with gender dysphoria and cisgender controls underwent 2 MRI measurements, with a median interval of 145 days (interquartile range [IQR] 128.25-169.75 d, mean 164.94 d) between the first and second MRI. Transgender women (TW) and transgender men (TM) underwent the first MRI before GHT and the second MRI after approximately 4.5 months of GHT, which comprised estrogen and anti-androgen therapy in TW or testosterone therapy in TM. Hypothalamic volumes were segmented using FreeSurfer, and effects of GHT were tested using repeated-measures analysis of covariance.

Results: The final sample included 106 participants: 38 TM, 15 TW, 32 cisgender women (CW) and 21 cisgender men (CM). Our analyses revealed group × time interaction effects for total, left and right hypothalamus volume, and for several subunits (left and right inferior tubular, left superior tubular, right anterior inferior, right anterior superior, all p _corr < 0.01). In TW, volumes decreased between the first and second MRI in these regions (all p _corr ≤ 0.01), and the change from the first to second MRI in TW differed significantly from that in CM and CW in several subunits (p _corr < 0.05).

Limitations: We did not address the influence of transition-related psychological and behavioural changes.

Conclusion: Our results suggest a subunit-specific effect of GHT on hypothalamus volumes in TW. This finding is in accordance with previous reports of positive and negative effects of androgens and estrogens, respectively, on cerebral volumes.

Background: Over recent decades, autism spectrum disorder (ASD) has been of increasing epidemiological importance, given the substantial increase in its prevalence; at present, clinical diagnosis is possible only after 2 years of age. In this study, we sought to develop a potential predictive model for ASD screening.

Methods: We conducted a longitudinal follow-up study of newborns over 3 years. We measured transcript levels of 4 genes (superoxide dismutase-2 [SOD2], retinoic acid-related orphan receptor-α [RORA], G protein-coupled estrogen receptor-1 [GPER], progesterone receptor [PGR]), 2 oxidative stress markers and epigenetic marks at the RORA promoter in case-control umbilical cord blood mononuclear cell (UCBMC) samples.

Results: We followed 2623 newborns; we identified 41 children with ASD, 63 with delayed development and 2519 typically developing children. We matched the 41 children with ASD to 41 typically developing children for UCBMC measurements. Our results showed that children with ASD had significantly higher levels of H3K9me3 histone modifications at the RORA promoter and oxidative stress in UCBMC than typically developing children; children with delayed development showed no significant differences. Children with ASD had significantly lower expression of SOD2, RORA and GPER, but higher PGR expression than typically developing children. We established a model based on these 4 candidate genes, and achieved an area under the curve of 87.0% (standard deviation 3.9%) with a sensitivity of 1.000 and specificity of 0.854 to predict ASD in UCBMC.

Limitations: Although the gene combinations produced a good pass/fail cut-off value for ASD evaluation, relatively few children in our study sample had ASD.

Conclusion: The altered gene expression in UCBMC can predict later autism development, possibly providing a predictive model for ASD screening immediately after birth.

Background: A growing body of neuroimaging studies has reported common neural abnormalities among mental disorders in adults. However, it is unclear whether the distinct disorder-specific mechanisms operate during adolescence despite the overlap among disorders.

Methods: We studied a large cohort of more than 11 000 preadolescent (age 9-10 yr) children from the Adolescent Brain and Cognitive Development cohort. We adopted a regrouping approach to compare cortical thickness (CT) alterations and longitudinal changes between healthy controls (n = 4041) and externalizing (n = 1182), internalizing (n = 1959) and thought disorder (n = 347) groups. Genome-wide association study (GWAS) was performed on regional CT across 4468 unrelated European youth.

Results: Youth with externalizing or internalizing disorders exhibited increased regional CT compared with controls. Externalizing (p = 8 × 10^-4, Cohen d = 0.10) and internalizing disorders (p = 2 × 10^-3, Cohen d = 0.08) shared thicker CT in the left pars opercularis. The somatosensory and the primary auditory cortex were uniquely affected in externalizing disorders, whereas the primary motor cortex and higher-order visual association areas were uniquely affected in internalizing disorders. Only youth with externalizing disorders showed decelerated cortical thinning from age 10-12 years. The GWAS found 59 genome-wide significant associated genetic variants across these regions. Cortical thickness in common regions was associated with glutamatergic neurons, while internalizing-specific regional CT was associated with astrocytes, oligodendrocyte progenitor cells and GABAergic neurons.

Limitations: The sample size of the GWAS was relatively small.

Conclusion: Our study provides strong evidence for the presence of specificity in CT, developmental trajectories and underlying genetic underpinnings among externalizing and internalizing disorders during early adolescence. Our results support the neurobiological validity of the regrouping approach that could supplement the use of a dimensional approach in future clinical practice.

Background: Attention-deficit/hyperactivity disorder (ADHD) is highly prevalent among youth with or at familial risk for bipolar-I disorder (BD-I), and ADHD symptoms commonly precede and may increase the risk for BD-I; however, associated neuropathophysiological mechanisms are not known. In this cross-sectional study, we sought to investigate brain structural network topology among youth with ADHD, with and without familial risk of BD-I.

Methods: We recruited 3 groups of psychostimulant-free youth (aged 10-18 yr), namely youth with ADHD and at least 1 biological parent or sibling with BD-I (high-risk group), youth with ADHD who did not have a first- or second-degree relative with a mood or psychotic disorder (low-risk group) and healthy controls. We used graph-based network analysis of structural magnetic resonance imaging data to investigate topological properties of brain networks. We also evaluated relationships between topological metrics and mood and ADHD symptom ratings.

Results: A total of 149 youth were included in the analysis (49 healthy controls, 50 low-risk youth, 50 high-risk youth). Low-risk and high-risk ADHD groups exhibited similar differences from healthy controls, mainly in the default mode network and central executive network. We found topological alterations in the salience network of the high-risk group, relative to both low-risk and control groups. We found significant abnormalities in global network properties in the high-risk group only, compared with healthy controls. Among both low-risk and high-risk ADHD groups, nodal metrics in the right triangular inferior frontal gyrus correlated positively with ADHD total and hyperactivity/impulsivity subscale scores.

Limitations: The cross-sectional design of this study could not determine the relevance of these findings to BD-I risk progression.

Conclusion: Youth with ADHD, with and without familial risk for BD-I, exhibit common regional abnormalities in the brain connectome compared with healthy youth, whereas alterations in the salience network distinguish these groups and may represent a prodromal feature relevant to BD-I risk.

Background: Clinical neuroimaging studies often investigate group differences between patients and controls, yet multivariate imaging features may enable individual-level classification. This study aims to classify youth with bipolar disorder (BD) versus healthy youth using grey matter cerebral blood flow (CBF) data analyzed with logistic regressions.

Methods: Using a 3 Tesla magnetic resonance imaging (MRI) system, we collected pseudo-continuous, arterial spin-labelling, resting-state functional MRI (rfMRI) and T ₁-weighted images from youth with BD and healthy controls. We used 3 logistic regression models to classify youth with BD versus controls, controlling for age and sex, using mean grey matter CBF as a single explanatory variable, quantitative CBF features based on principal component analysis (PCA) or relative (intensity-normalized) CBF features based on PCA. We also carried out a comparison analysis using rfMRI data.

Results: The study included 46 patients with BD (mean age 17 yr, standard deviation [SD] 1 yr; 25 females) and 49 healthy controls (mean age 16 yr, SD 2 yr; 24 females). Global mean CBF and multivariate quantitative CBF offered similar classification performance that was above chance. The association between CBF images and the feature map was not significantly different between groups (p = 0.13); however, the multivariate classifier identified regions with lower CBF among patients with BD (ΔCBF = -2.94 mL/100 g/min; permutation test p = 0047). Classification performance decreased when considering rfMRI data.

Limitations: We cannot comment on which CBF principal component is most relevant to the classification. Participants may have had various mood states, comorbidities, demographics and medication records.

Conclusion: Brain CBF features can classify youth with BD versus healthy controls with above-chance accuracy using logistic regression. A global CBF feature may offer similar classification performance to distinct multivariate CBF features.

Background: It has been suggested that the dorsolateral prefrontal cortex (DLPFC), especially the left DLPFC, has an important role in the pathophysiology and the treatment of major depressive disorder (MDD); furthermore, the contributory and antidepressant role of γ-aminobutyric acid (GABA) is increasingly recognized. Given that most female patients with MDD are of reproductive age, we sought to assess in vivo baseline GABA levels in the left DLPFC among unmedicated females of reproductive age with depression.

Methods: We compared healthy females and females with MDD. Both groups were of reproductive age. We confirmed absence of current or past psychiatric diagnosis among healthy controls or a current diagnosis of MDD via a structured interview. We measured GABA+ (including homocarnosine and macromolecules), referenced to creatine and phosphocreatine, via magnetic resonance spectroscopy using a 3 Tesla magnet.

Results: We included 20 healthy controls and 13 participants with MDD. All participants were unmedicated at the time of the study. All females were scanned during the early follicular phase of the menstrual cycle. Levels of GABA+ in the left DLPFC were significantly lower among participants with MDD (median 0.08) than healthy controls (median 0.10; U = 66.0, p = 0.02, r = 0.41).

Limitations: When we adjusted for fit error as a covariate, we lost statistical significance for left DLPFC GABA+. However, when we adjusted for signal-to-noise ratio, statistical significance was maintained.

Conclusion: Our results suggest that GABA+ levels in the left DLPFC may vary by depression status and should be examined as a possible treatment target.

Background: Delirium is a critically underdiagnosed syndrome of altered mental status affecting more than 50% of older adults admitted to hospital. Few studies have incorporated speech and language disturbance in delirium detection. We sought to describe speech and language disturbances in delirium, and provide a proof of concept for detecting delirium using computational speech and language features.

Methods: Participants underwent delirium assessment and completed language tasks. Speech and language disturbances were rated using standardized clinical scales. Recordings and transcripts were processed using an automated pipeline to extract acoustic and textual features. We used binomial, elastic net, machine learning models to predict delirium status.

Results: We included 33 older adults admitted to hospital, of whom 10 met criteria for delirium. The group with delirium scored higher on total language disturbances and incoherence, and lower on category fluency. Both groups scored lower on category fluency than the normative population. Cognitive dysfunction as a continuous measure was correlated with higher total language disturbance, incoherence, loss of goal and lower category fluency. Including computational language features in the model predicting delirium status increased accuracy to 78%.

Limitations: This was a proof-of-concept study with limited sample size, without a set-aside cross-validation sample. Subsequent studies are needed before establishing a generalizable model for detecting delirium.

Conclusion: Language impairments were elevated among patients with delirium and may also be used to identify subthreshold cognitive disturbances. Computational speech and language features are promising as accurate, noninvasive and efficient biomarkers of delirium.