Biological psychiatry. Cognitive neuroscience and neuroimaging最新文献

Throughout the brief history of contemplative neuroscience, electroencephalography (EEG) has been a valuable and enduring methodology used to elucidate the neural correlates and mechanisms of mindfulness. In this review, we provide a reminder that longevity should not be conflated with obsoletion and that EEG continues to offer exceptional promise for addressing key questions and challenges that pervade the field today. Toward this end, we first outline the unique advantages of EEG from a research strategy and experimental design perspective, then highlight an array of new sophisticated data analytic approaches and translational paradigms. Along the way, we provide illustrative examples from our own work and the broader literature to showcase how these innovations can be leveraged to spark new insights and stimulate progress across both basic science and translational applications of mindfulness. Ultimately, we argue that EEG still has much to contribute to contemplative neuroscience, and we hope to solicit the interest of other investigators to make full use of its capabilities in service of maximizing its potential within the field.

Background: The neurobiological differences between women who have experienced a peripartum episode and those who have only had episodes outside of this period are not well understood.

Methods: 64 parous female patients with major depressive disorder that have either a positive (n=30) or negative (n=34) history of peripartum depression (PPD) underwent MRI acquisition to obtain structural brain images. An independent two-sample t-test comparing patients with and without a history of PPD was performed using voxel-based morphometry analysis (VBM). Additionally, polygenic risk scores (PRSs) for estradiol were calculated and a moderation analysis was conducted between 3 estradiol PRSs and PPD history status on extracted cluster volumes using IBM SPSS PROCESS macro.

Results: The VBM analysis identified larger grey matter volumes in bilateral clusters encompassing the putamen, pallidum, caudate, and thalamus in patients with PPD history compared to patients without a history. The moderation analysis identified a significant interaction of 2 estradiol PRSs and PPD history on grey matter cluster volumes with a positive effect in PPD women and a negative effect in women with no history of PPD.

Conclusions: Our findings demonstrate that women who have experienced a peripartum episode are neurobiologically distinct from women who have no history of PPD in a cluster within the basal ganglia, an area important for motivation, decision-making, and emotional processing. Furthermore, we show that the genetic load for estradiol has a differing effect in this area based on PPD status which supports the claim that PPD is associated with sensitivity to sex steroid hormones.

Background: Obsessive-compulsive disorder (OCD) is often comorbid with obsessive-compulsive personality disorder (OCPD). The relationship between OCD and OCPD is complex, and the impact of comorbid OCPD on OCD remains underexplored, necessitating further research.. This study aims to investigate the clinical correlates and brain morphometries associated with comorbid OCPD in a large sample of unmedicated OCD patients.

Methods: A total of 248 unmedicated patients diagnosed with OCD (45 comorbid with OCPD) were included in this study. All participants were assessed for OCD symptoms, OCPD traits, obsessive beliefs, depression and anxiety. Among them, 145 patients (23 comorbid with OCPD) volunteered to receive magnetic resonance imaging (MRI) brain scans.

Results: Approximately 18% (45/248) of OCD patients were comorbid with OCPD. OCD comorbid with OCPD (OCD+OCPD) exhibited more severe OCD symptoms, obsessive beliefs, depression and anxiety than OCD comorbid without OCPD. Additionally, the severity of OCPD was positively correlated with OCD symptoms and obsessive beliefs. Furthermore, OCD+OCPD patients exhibited increased cortical complexity in the left superior parietal lobule and left precuneus, which mediated the relationship between OCPD and OCD symptoms only in OCD patients comorbid without OCPD.

Conclusions: The co-occurrence of OCPD may contribute to the heightened severity of psychopathological symptoms and associated brain morphological alterations in OCD patients, indicating distinct but interrelated constructs between these two disorders.

Background: Late-life depression (LLD) has been associated cross-sectionally with lower brain structural volumes and accelerated brain aging compared with healthy control participants (HCs). There are few longitudinal studies on the neurobiological predictors of recurrence in LLD. We tested a machine learning brain age model and its prospective association with LLD recurrence risk.

Methods: We recruited individuals with LLD (n = 102) and HCs (n = 43) into a multisite, 2-year longitudinal study. Individuals with LLD were enrolled within 4 months of remission. Remitted participants with LLD underwent baseline neuroimaging and longitudinal clinical follow-up. Over 2 years, 43 participants with LLD relapsed and 59 stayed in remission. We used a previously developed machine learning brain age algorithm to compute brain age at baseline, and we evaluated brain age group differences (HC vs. LLD and HC vs. remitted LLD vs. relapsed LLD). We conducted a Cox proportional hazards model to evaluate whether baseline brain age predicted time to relapse.

Results: We found that brain age did not significantly differ between the HC and LLD groups or between the HC, remitted LLD, and relapsed LLD groups. Brain age did not significantly predict time to relapse.

Conclusions: In contrast to our hypothesis, we found that brain age did not differ between control participants without depression and individuals with remitted LLD, and brain age was not associated with subsequent recurrence. This is in contrast to existing literature which has identified baseline brain age differences in late life but consistent with work that has shown no differences between people who do and do not relapse on gross structural measures.

Background: People with schizophrenia (PSZ) show impaired accuracy in spatial working memory (sWM), which is thought to reflect abnormalities in the sustained firing of feature selective neurons that are critical for successful encoding and maintenance processes. Recent research has documented a new source of variance in the accuracy of sWM: In healthy adults, sWM representations are unconsciously biased by previous trials such that current-trial responses are attracted to previous-trial responses (serial dependence). This opens a new window to examine how schizophrenia impacts both the sustained neural firing representing the current-trial target and the longer-term synaptic plasticity that stores previous-trial information.

Methods: We examined response accuracy in a single-item sWM test with delay intervals of 0, 2, 4, or 8 seconds in 41 PSZ and 32 demographically similar healthy control participants. Our main dependent variable was the bias index, which quantifies the extent to which the current-trial responses were biased toward or away from the previous-trial target.

Results: PSZ showed opposite-direction serial dependence bias effects: Healthy control participants showed an attractive bias that increased over increasing delays whereas PSZ showed a repulsion bias that increased over delays. In PSZ, the magnitude of the repulsion bias negatively correlated with broad measures of cognitive ability and WM capacity.

Conclusions: PSZ show opposite-direction effects of previous trials on WM. Such qualitatively distinct differences in performance are extremely rare in psychopathology and may index a fundamental alteration in neural processing that could serve as a valuable biomarker for pathophysiology and treatment development research.

Background: Functional magnetic resonance imaging (fMRI) studies examining cue-reactivity in cannabis use disorder (CUD) have either had small sample sizes or involved non-treatment-seeking participants. As a secondary analysis, we administered an fMRI cue-reactivity task to CUD participants entering two separate clinical trials (varenicline or repetitive Transcranial Magnetic Stimulation-rTMS) to determine the task activation patterns for treatment-seeking participants with CUD. We aimed to determine the activation patterns for the total sample and determined behavioral correlates. We additionally compared studies to determine if patterns were consistent.

Methods: Treatment-seeking participants with moderate or severe CUD had behavioral craving measured at baseline via the short form of the Marijuana Craving Questionnaire (MCQ-SF) and completed a visual cannabis cue-reactivity task during fMRI (measuring the Blood-Oxygen-Level-Dependent-BOLD response) following 24-hours of cannabis-abstinence.

Results: Sixty-five participants were included (37-varenicline, 28-rTMS; 32% female; mean-age 30.4±9.9SD). When contrasting cannabis-images vs. matched-neutral-images, participants showed greater BOLD response in bilateral ventromedial-prefrontal, dorsolateral-prefrontal, anterior cingulate, and visual cortices, as well as the striatum. There was stronger task-based functional-connectivity (tbFC) between the medial prefrontal cortex and both the amygdala and the visual cortex. Craving negatively correlated with BOLD response in the left ventral striatum (R²=-0.32; p=0.01) in the full sample. There were no significant differences in either activation or tbFC between studies.

Discussion: Among two separate treatment-seeking groups with CUD, there was increased cannabis cue-reactivity and tbFC in regions related to executive function and reward processing. Cannabis-craving was negatively associated with cue-reactivity in the left ventral striatum.

Background: Atypical balance of excitation (E) and inhibition (I) in the brain is thought to contribute to the emergence and symptomatology of autism spectrum disorder (ASD). E/I ratio can be estimated from resting-state functional magnetic resonance imaging (fMRI) using the Hurst exponent, H. A recent study reported decreased ventromedial prefrontal cortex (vmPFC) H in male adults with ASD. Part of the default mode network (DMN), the vmPFC plays an important role in emotion regulation, decision making, and social cognition. It frequently shows altered function and connectivity in individuals with autism.

Methods: The current study presents the first fMRI evidence of altered early development of vmPFC H and its link to DMN functional connectivity and emotional control in toddlers and preschoolers with ASD. A total of 83 children (45 with ASD), ages 1.5-5 years, underwent natural sleep fMRI as part of a longitudinal study.

Results: In a cross-sectional analysis, vmPFC H decreased with age in children with ASD, reflecting increasing E/I ratio, but not in typically developing children. This effect remained significant when controlling for gestational age at birth, socioeconomic status, or ethnicity. The same pattern was also observed in a subset of children with longitudinal fMRI data acquired 2 years apart on average. Lower vmPFC H was also associated with reduced functional connectivity within the DMN as well as with higher emotional control deficits (although only significant transdiagnostically).

Conclusions: These results suggest an early onset of E/I imbalances in the vmPFC in ASD, with likely consequences for the maturation of the DMN.

Background: Understanding the intricate relationships between symptom dimensions, clusters, and cognitive impairments is crucial for early detection and intervention in individuals at clinical high risk for psychosis. This study delves into this complex interplay in a clinical high risk sample with the aim of predicting the conversion to psychosis.

Methods: A comprehensive cognitive assessment was performed in 744 clinical high risk individuals. The study included a 3-year follow-up period to allow assessment of conversion to psychosis. Symptom profiles were determined using the Structured Interview for Prodromal Syndromes. By applying factor analysis, symptom dimensions were categorized as dominant negative symptoms (NS), positive symptoms-stressful, and positive symptoms-odd. The factor scores were used to define 3 dominant symptom groups. Latent class analysis (LCA) and the factor mixture model (FMM) were employed to identify discrete clusters based on symptom patterns. The 3-class solution was chosen for the LCA and FMM analysis.

Results: Individuals in the dominant NS group exhibited significantly higher conversion rates to psychosis than those in the other groups. Specific cognitive variables, including performance on the Brief Visuospatial Memory Test-Revised (odds ratio = 0.702, p = .001) and Neuropsychological Assessment Battery Mazes Test (odds ratio = 0.776, p = .024), significantly predicted conversion to psychosis. Notably, cognitive impairments associated with NS and positive symptoms-stressful groups affected different cognitive domains. LCA and FMM cluster 1, which was characterized by severe NS and positive symptoms-odd, exhibited more impairments in cognitive domains than other clusters. No significant difference in the conversion rate was observed among the LCA and FMM clusters.

Conclusions: These findings highlight the importance of NS in the development of psychosis and suggest specific cognitive domains that are affected by symptom dimensions.

Background: There is increasing evidence of shared genetic factors between psychiatric disorders and brain magnetic resonance imaging (MRI) phenotypes. However, deciphering the joint genetic architecture of these outcomes has proven to be challenging, and new approaches are needed to infer the genetic structures that may underlie those phenotypes. Multivariate analyses are a meaningful approach to reveal links between MRI phenotypes and psychiatric disorders missed by univariate approaches.

Methods: First, we conducted univariate and multivariate genome-wide association studies for 9 MRI-derived brain volume phenotypes in 20,000 UK Biobank participants. Next, we performed various complementary enrichment analyses to assess whether and how univariate and multitrait approaches could distinguish disorder-associated and non-disorder-associated variants from 6 psychiatric disorders: bipolar disorder, attention-deficit/hyperactivity disorder, autism, schizophrenia, obsessive-compulsive disorder, and major depressive disorder. Finally, we conducted a clustering analysis of top associated variants based on their MRI multitrait association using an optimized k-medoids approach.

Results: A univariate MRI genome-wide association study revealed only negligible genetic correlations with psychiatric disorders, while a multitrait genome-wide association study identified multiple new associations and showed significant enrichment for variants related to both attention-deficit/hyperactivity disorder and schizophrenia. Clustering analyses also detected 2 clusters that showed not only enrichment for association with attention-deficit/hyperactivity disorder and schizophrenia but also a consistent direction of effects. Functional annotation analyses of those clusters pointed to multiple potential mechanisms, suggesting in particular a role of neurotrophin pathways in both MRI phenotypes and schizophrenia.

Conclusions: Our results show that multitrait association signature can be used to infer genetically driven latent MRI variables associated with psychiatric disorders, thereby opening paths for future biomarker development.

Background: Although the clinical high risk for psychosis (CHR-P) criteria are widely used to ascertain individuals at heightened risk for imminent onset of psychosis, it remains controversial whether CHR-P status defines a diagnostic construct in its own right. In a previous study, CHR-P nonconverters were observed to follow 3 distinct trajectories in symptoms and functioning: remission, partial remission, and maintenance of symptoms and functional impairments at subthreshold levels of intensity.

Methods: Here, we utilized the NAPLS3 (North American Prodrome Longitudinal Study phase 3) sample (N = 806) to determine whether 1) the same trajectory groups can be detected when assessing symptoms at 2-month intervals over an 8-month period and 2) the resulting trajectory groups differ from each other and from healthy control participants and converting CHR-P cases in terms of risk factors, comorbidities, and functional outcomes.

Results: Three distinctive subgroups within the CHR nonconverters were identified, largely paralleling those observed previously. Importantly, these extracted groups, together with non-CHR control participants and CHR converters, differed from each other significantly on putative etiological risk factors (e.g., predicted risk scores, physiological and self-report measures of stress), affective comorbidities, and functional outcomes, thus providing converging evidence supporting the validity of the identified trajectory groups.

Conclusions: This pattern, together with the fact that even the subgroup of CHR-P nonconverters who showed a remission trajectory deviated from healthy control participants, supports treating the CHR-P syndrome not only as a status that denotes risk for onset of full psychosis but also as a marker of ongoing distress for a population that is in need of interventions.