Biological psychiatry. Cognitive neuroscience and neuroimaging最新文献

Network neuroscience is an interdisciplinary field, which can be used to understand the brain by examining the connections between its constituent elements. In recent years, the application of network neuroscience approaches to study the intricate nature of the structural and functional relationships within the human brain has yielded unique insights into its organization. In this review, we begin by defining network neuroscience and providing an overview of the common metrics that describe the topology of human structural and functional brain networks. We then present a detailed overview of a limited but growing body of literature that leverages network neuroscience metrics to demonstrate the impact of mindfulness meditation on modulating the fundamental structural and functional network properties of segregation, integration, and influence. Although preliminary, results across studies suggest that mindfulness meditation results in a shift in connector hubs, such as the anterior cingulate cortex, the thalamus, and the mid-insula. Although there is mixed evidence regarding the impact of mindfulness training on global metrics of connectivity, the default mode network exhibits reduced intra-connectivity following mindfulness training. Our review also underscores essential directions for future research, including a more comprehensive examination of mindfulness training and its potential to influence structural and functional connections at the nodal, network, and whole-brain levels. Furthermore, we emphasize the importance of open science, adoption of rigorous study designs to improve the internal validity of studies, and the inclusion of diverse samples in neuroimaging studies to comprehensively characterize the impact of mindfulness on brain organization.

Background: Repetitive transcranial magnetic stimulation (rTMS) combined with exposure and response prevention is a promising treatment modality for treatment-refractory obsessive-compulsive disorder (OCD). However, not all patients respond sufficiently to this treatment. We investigated whether brain activation during a symptom provocation task could predict treatment response.

Methods: Sixty-one adults with OCD (39 female/22 male) underwent symptom provocation with OCD- and fear-related visual stimuli during functional magnetic resonance imaging prior to an 8-week combined rTMS and exposure and response prevention treatment regimen. Participants received one of the following 3 rTMS treatments as part of a randomized controlled trial: 1) 10-Hz rTMS (110% resting motor threshold) to the left dorsolateral prefrontal cortex, 2) 10-Hz rTMS (110% resting motor threshold) to the left presupplementary motor area, or 3) 10-Hz control rTMS (60% resting motor threshold) to the vertex. Multiple regression and correlation were used to examine the predictive value of task-related brain activation for treatment response in the following regions of interest: the dorsomedial prefrontal cortex, amygdala, dorsolateral prefrontal cortex, and left presupplementary motor area.

Results: The different treatment groups responded equally to treatment. Higher pretreatment task-related activation of the right amygdala to OCD-related stimuli showed a positive association with treatment response in all groups. Exploratory whole-brain analyses showed positive associations between activation in multiple task-relevant regions and treatment response. Only dorsal anterior cingulate cortex activation to fear-related stimuli showed a negative association with treatment outcome.

Conclusions: Higher pretreatment right amygdala activation during symptom provocation predicts better treatment response to combined rTMS and exposure and response prevention in OCD.

Background: A reliable physiological biomarker for Major Depressive Disorder is essential for developing and optimizing neuromodulatory treatment paradigms. This study investigates a passive electrophysiologic biomarker that tracks changes in depressive symptom severity on the order of minutes to hours.

Methods: We analyze brief recordings from intracranial electrodes implanted deep in the brain during a clinical trial of deep brain stimulation for treatment-resistant depression in 5 human subjects (n_female= 3, n_male = 2). This surgical setting allows for precise temporal and spatial sensitivity in the ventromedial prefrontal cortex, a challenging area to measure. We focused on the aperiodic slope of the power spectral density, a metric reflecting the balance of activity across all frequency bands and serving as a proxy for excitatory/inhibitory balance in the brain.

Results: Our findings demonstrate that shifts in aperiodic slope correlate with depression severity, with flatter (less negative) slopes indicating reduced depression severity. This significant correlation was observed in all N=5 subjects, particularly in the ventromedial prefrontal cortex.

Conclusions: This biomarker offers a new way to track patient responses to Major Depressive Disorder treatment, paving the way for individualized therapies in both intracranial and non-invasive monitoring contexts.

Identifying the brain mechanisms that underlie the salutary effects of mindfulness meditation and related practices is a critical goal of contemplative neuroscience. Here, we suggest that the use of multivariate predictive models represents a promising and powerful methodology that could be better leveraged to pursue this goal. This approach incorporates key principles of multivariate decoding, predictive classification, and model-based analyses, all of which represent a strong departure from conventional brain mapping approaches. We highlight 2 such research strategies-state induction and neuromarker identification-and provide illustrative examples of how these approaches have been used to examine central questions in mindfulness, such as the distinction between internally directed focused attention and mind wandering and the effects of mindfulness interventions on somatic pain and drug-related cravings. We conclude by discussing important issues to be addressed with future research, including key tradeoffs between using a personalized versus population-based approach to predictive modeling.

Background: Reduced reward positivity (RewP), an electroencephalography marker elicited by feedback indicating reward, has been associated with an increased risk for depression during adolescence. However, the ability of the RewP to predict the first-lifetime onset of depressive disorders, as opposed to anxiety and suicidal ideation in high-risk populations, has not been thoroughly investigated. In this study, we examined whether the RewP predicts the first-lifetime onset of depression, anxiety, and suicidal ideation over 18 months in familial high-risk adolescents.

Methods: The sample included 145 adolescents (64.8% female), ages 11 to 17 years, who had at least 1 parent with a history of mood or anxiety disorders and completed baseline and at least 1 follow-up measurement. At baseline, the RewP was measured using a simple gambling task; current internalizing symptoms were assessed using self-report questionnaires; and the adolescent's psychiatric diagnoses were evaluated with diagnostic interviews. The same interview was administered to the adolescents again 9 months and 18 months later.

Results: Logistic regression models showed that higher RewP scores significantly predicted a lower likelihood of developing a first onset of major depressive disorder over 18 months, even after controlling for sex, age, and baseline internalizing symptoms. In contrast, the RewP did not significantly predict the first onset of anxiety disorders or suicidal ideation.

Conclusions: A reduced RewP precedes the first onset of depression in high-risk adolescents, highlighting the RewP's predictive capability for depression risk in predisposed populations. A blunted RewP could complement self-reported symptoms in screening and prevention.

The number of mindfulness-based wellness promotion programs offered by institutions, by governments, and through mobile apps has grown exponentially in the last decade. However, the scientific understanding of what mindfulness is and how it works is still evolving. Here, I focus on 2 common mindfulness practices: focused attention (FA) and open monitoring (OM). First, I summarize what is known about FA and OM meditation at the psychological level. While they share similar emotion regulation goals, they differ in terms of some of their attention regulation goals. Second, I turn to the neuroscientific literature, showing that FA meditation is associated with consistent activations of cortical control network regions and deactivations of cortical default network regions. In contrast, OM meditation seems to be most consistently associated with changes in the functional connectivity patterns of subcortical structures, including the basal ganglia and cerebellum. Finally, I present a novel account of the mental changes that occur during FA and OM meditation as understood from within the Dynamic Framework of Thought-a conceptual framework that distinguishes between deliberate and automatic constraints on thought. Although deliberate self-regulation processes are often emphasized in scientific and public discourse on mindfulness, here I argue that mindfulness may primarily involve changes in automatic constraints on thought. In particular, I argue that mindfulness reduces the occurrence of automatized sequences of mental states or habits of thought. In this way, mindfulness may increase the spontaneity of thought and reduce automatically constrained forms of thought such as rumination and obsessive thought.

Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualized meditation practices and designed various meditation interventions that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has been used to examine the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and nonclinical populations. However, the generalizability and replicability of current neuroscientific models of meditation have not yet been established, because they are largely based on small datasets entrenched with heterogeneity along several domains of meditation (e.g., practice types, meditation experience, clinical disorder targeted), experimental design, and neuroimaging methods (e.g., preprocessing, analysis, task-based, resting-state, structural magnetic resonance imaging). These limitations have precluded a nuanced and rigorous neuroscientific phenotyping of meditation practices and their potential benefits. Here, we present ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)-Meditation, the first worldwide collaborative consortium for neuroscientific investigations of meditation practices. ENIGMA-Meditation will enable systematic meta- and mega-analyses of globally distributed neuroimaging datasets of meditation using shared, standardized neuroimaging methods and tools to improve statistical power and generalizability. Through this powerful collaborative framework, existing neuroscientific accounts of meditation practices can be extended to generate novel and rigorous neuroscientific insights that account for multidomain heterogeneity. ENIGMA-Meditation will inform neuroscientific mechanisms that underlie therapeutic action of meditation practices on psychological and cognitive attributes, thereby advancing the field of meditation and contemplative neuroscience.

Background: Magnetic seizure therapy (MST) is under investigation as a treatment for adults with major depression. Previous research has suggested that MST has antidepressant efficacy comparable to that of electroconvulsive therapy (ECT), but with greater cognitive safety. The objective of the study was to compare the neurocognitive outcomes of patients receiving an acute course of MST with the outcomes of those receiving ECT for the treatment of major depressive episode.

Methods: This was a between-subjects, double-masked, randomized, multicenter clinical trial. Seventy-three participants with a severe major depressive episode were enrolled and randomly assigned to treatment with MST (n = 35) or ultra-brief pulse right unilateral ECT (n = 38). The main outcome was change in performance from baseline to the end of acute treatment on multiple neurocognitive measures.

Results: Compared with patients who received ECT, patients who received MST had superior cognitive outcomes up to 72 hours posttreatment. Specifically, following MST treatment, there was significant improvement in fine motor dexterity (p = .017) and no significant change in cognitive domains of attention, verbal fluency, executive function, or verbal learning and memory. In contrast, following treatment with ECT, patients demonstrated significantly worse performance on measures of verbal fluency (p < .001), executive function (p = .038), and verbal memory retention (p < .001). Autobiographical memory consistency decreased significantly following treatment with both ECT (p < .001) and MST, although the magnitude of change was greater for ECT.

Conclusions: The study findings confirm previous work and provide new evidence supporting the enhanced cognitive safety of MST relative to ECT. Future research on MST is warranted to optimize its application to individuals with neuropsychiatric illnesses across the life span.

Background: Atypical sensory processing is a prevalent feature of autism spectrum disorder (ASD) and constitutes a core diagnostic criterion in DSM-5. However, the neurocognitive underpinnings of atypical unimodal and multimodal sensory processing and their relationships with autism symptoms remain unclear.

Methods: In this study, we examined intrinsic functional connectivity (FC) patterns among 5 unimodal sensory and multisensory integration (MSI) networks in ASD using a large multisite dataset (N = 646) and investigated the relationships between altered FC, atypical sensory processing, social communicative deficits, and overall autism symptoms using correlation and mediation analyses.

Results: Compared with typically developing control participants, participants in the ASD group demonstrated increased FC of the olfactory network, decreased FC within the MSI network, and decreased FC of the MSI-unimodal sensory networks. Furthermore, altered FC was positively associated with autism symptom severity, and such associations were completely mediated by atypical sensory processing and social communicative deficits.

Conclusions: ASD-specific olfactory overconnectivity and MSI-unimodal sensory underconnectivity lend support to the intense world theory and weak central coherence theory, suggesting olfactory hypersensitivity at the expense of MSI as a potential neural mechanism underlying atypical sensory processing in ASD. These atypical FC patterns suggest potential targets for psychological and neuromodulatory interventions.

Background: Cognitive behavioral therapy (CBT) is a gold-standard approach for treating major depressive disorder in adolescents. However, nearly half of adolescents receiving CBT do not improve. To personalize treatment, it is essential to identify objective markers that predict treatment responsiveness. To address this aim, we investigated neurophysiological processes related to self-referential processing that predicted CBT response among female adolescents with depression.

Methods: At baseline, female adolescents ages 13 to 18 years (N = 80) completed a comprehensive clinical assessment, and a self-referential encoding task was administered while electroencephalographic data were recorded. Baseline electroencephalographic data were utilized to identify oscillatory differences between healthy adolescents (n = 42) and adolescents with depression (n = 38). Following the baseline assessment, adolescents with depression received up to 12 weeks of CBT. Baseline differences in electroencephalographic oscillations between healthy adolescents and those with depression were used to guide CBT prediction analysis. Cluster-based event-related spectral perturbation analysis was used to probe theta and alpha event-related synchronization (ERS)/event-related desynchronization (ERD) response to negative and positive words.

Results: Baseline analyses showed that, relative to the healthy adolescents, adolescents with depression exhibited higher levels of frontal theta ERS and greater posterior alpha ERD. Multilevel modeling identified primary neural pretreatment predictors of treatment response: greater theta ERS in the right prefrontal cortex after the onset of negative words and lower alpha ERD in both the right prefrontal cortex and posterior cingulate cortex. ERS and ERD associations with treatment response remained significant, with baseline depressive and anxiety symptoms included as covariates in all analyses.

Conclusions: Consistent with prior research, results highlighted that relative to healthy adolescents, adolescents with depression are characterized by prominent theta synchronization and alpha desynchronization over the prefrontal cortex and posterior cingulate cortex, respectively. Cluster-based event-related spectral perturbation analysis also identified key mechanisms underlying depression-related self-referential processing that predicted improved symptoms during the course of CBT. Ultimately, a better characterization of the neural underpinnings of adolescent depression and its treatment may lead to more personalized interventions.