Journal of Psychiatry & Neuroscience最新文献

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.

Background: Deficient causality perception and attribution may underlie key symptoms of schizophrenia spectrum disorder (SSD), such as delusions and ideas of reference. Although transcranial direct current stimulation (tDCS) can increase the influence of spatial information on perceptual causality judgments among healthy participants, its effect among patients with SSD remains unknown. We sought to determine whether tDCS modulates the contribution of stimulus characteristics to perceptual causality judgments among patients with SSD; we predicted that right parietal tDCS would increase the influence of spatial stimulus characteristics on patients' causality perception.

Methods: Patients with SSD received frontal, parietal, frontoparietal and sham tDCS in 4 separate sessions. Pre- and post-tDCS, patients viewed video clips of ball A colliding with ball B. Spatial linearity (ball B's angle of egress) and temporal contiguity (delay between collision and ball B's movement) varied parametrically. After each launching event, patients rated perceived causality.

Results: Among 19 patients with SSD, we found a brain region-dependent effect of tDCS regarding sensitivity to violations of spatial linearity. After right parietal anodal tDCS, the influence of angle variations on patients' perceptual causality judgments increased, reflected by a higher probability of perceived causality for stimuli with small angles and a lower probability of perceived causality for stimuli with high angles.

Conclusion: Transcranial direct current stimulation increased the influence of spatial stimulus characteristics on causality perception among patients with SSD. Future research should explore potential links between tDCS-induced changes in basic perceptual processes and clinical symptoms, such as delusions and ideas of reference.

Background: Increasing evidence suggests that heroin addiction may be related to the dysfunction among the triple brain network (default mode network [DMN], salience network [SN] and executive control network [ECN]). However, the characteristics of glucose metabolism and metabolic connectivity among core regions of the triple brain network remain unknown. Therefore, we hypothesized that individuals with heroin dependence would show abnormal glucose metabolism and accompanied abnormal metabolic connectivity within the triple brain network.

Methods: Individuals with heroin dependence and healthy controls matched for age and sex underwent integrated positron emission tomography/magnetic resonance imaging (PET/MRI). Differences in glucose metabolism and metabolic connectivity among the DMN, SN and ECN were analyzed based on ¹⁸F-fluorodeoxyglucose PET and resting-state fMRI data.

Results: We included 36 individuals with heroin dependence and 30 matched healthy controls in our study. The heroin dependence group showed a significant reduction of glucose metabolism in the bilateral anterior insula (AI) and inferior parietal lobule (IPL), and a significantly decreased metabolic connectivity between the right AI and the left dorsolateral prefrontal cortex (DLPFC). The daily dose of methadone was negatively correlated with glucose metabolism of the right AI and right IPL.

Limitations: The results revealed the glucose metabolism alterations and metabolic connectivity only within the triple brain network in individuals with heroin dependence; additional brain networks should be investigated in future studies. Although methadone is an opioid with a similar neurophysiological mechanism as heroin, the specific chronic effects of methadone on cerebral metabolism and metabolic connectivity should also be investigated in future studies.

Conclusion: Our findings suggest that long-term opioid use might, to some extent, be associated with reduced synergistic ability between the SN and ECN, which may be associated with the dysfunction of cognitive control. In particular, the right AI, which showed hypometabolism and related reduction in SN-ECN metabolic connectivity, should receive increasing attention in future studies.