Psilocybin therapy increases cognitive and neural flexibility in patients with major depressive disorder

Translational Psychiatry, Journal Year: 2021, Volume and Issue: 11(1)

Published: Nov. 8, 2021

Psilocybin has shown promise for the treatment of mood disorders, which are often accompanied by cognitive dysfunction including rigidity. Recent studies have proposed neuropsychoplastogenic effects as mechanisms underlying enduring therapeutic psilocybin. In an open-label study 24 patients with major depressive disorder, we tested psilocybin therapy on flexibility (perseverative errors a set-shifting task), neural (dynamics functional connectivity or dFC via magnetic resonance imaging), and neurometabolite concentrations (via spectroscopy) in brain regions supporting implicated acute (e.g., anterior cingulate cortex, ACC). increased at least 4 weeks post-treatment, though these improvements were not correlated previously reported antidepressant effects. One week after therapy, glutamate N-acetylaspartate decreased ACC, was between ACC posterior cortex (PCC). Surprisingly, greater increases PCC associated less improvement therapy. Connectome-based predictive modeling demonstrated that baseline emanating from predicted flexibility. models, better but These findings suggest nuanced relationship Whereas some dynamics may allow shifting out maladaptively rigid state, larger persisting be benefit to

Language: Английский

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Movie-watching outperforms rest for functional connectivity-based prediction of behavior

NeuroImage, Journal Year: 2021, Volume and Issue: 235, P. 117963 - 117963

Published: April 2, 2021

A major goal of human neuroscience is to relate differences in brain function behavior across people. Recent work has established that whole-brain functional connectivity patterns are relatively stable within individuals and unique individuals, features these predict various traits. However, while most often measured at rest, certain tasks may enhance individual signals improve sensitivity differences. Here, we show compared the resting state, during naturalistic viewing—i.e., movie watching—yields more accurate predictions trait-like phenotypes domains both cognition emotion. Traits could be predicted using less than three minutes data from single video clips, clips with highly social content gave predictions. Results suggest stimuli amplify behaviorally relevant networks.

Language: Английский

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The OpenNeuro resource for sharing of neuroscience data

eLife, Journal Year: 2021, Volume and Issue: 10

Published: Oct. 18, 2021

The sharing of research data is essential to ensure reproducibility and maximize the impact public investments in scientific research. Here, we describe OpenNeuro, a BRAIN Initiative archive that provides ability openly share from broad range brain imaging types following FAIR principles for sharing. We highlight importance Brain Imaging Data Structure standard enabling effective curation, sharing, reuse data. presently shares more than 600 datasets including 20,000 participants, comprising multiple species measurement modalities phenotypes. shared evident growing number published reuses, currently totalling 150 publications. conclude by describing plans future development integration with other ongoing open science efforts.

Language: Английский

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How to establish robust brain–behavior relationships without thousands of individuals

Nature Neuroscience, Journal Year: 2022, Volume and Issue: 25(7), P. 835 - 837

Published: June 16, 2022

Language: Английский

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The normative modeling framework for computational psychiatry

Nature Protocols, Journal Year: 2022, Volume and Issue: 17(7), P. 1711 - 1734

Published: June 1, 2022

Language: Английский

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Why is everyone talking about brain state?

Trends in Neurosciences, Journal Year: 2023, Volume and Issue: 46(7), P. 508 - 524

Published: May 8, 2023

The rapid and coordinated propagation of neural activity across the brain provides foundation for complex behavior cognition. Technical advances neuroscience subfields have advanced understanding these dynamics, but points convergence are often obscured by semantic differences, creating silos subfield-specific findings. In this review we describe how a parsimonious conceptualization state as fundamental building block whole-brain offers common framework to relate findings scales species. We present examples diverse techniques commonly used study states associated with physiology higher-order cognitive processes, discuss integration them will enable more comprehensive mechanistic characterization dynamics that crucial survival disrupted in disease.

Language: Английский

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Connectome‐Based Predictive Modeling of Trait Mindfulness

Human Brain Mapping, Journal Year: 2025, Volume and Issue: 46(1)

Published: Jan. 1, 2025

ABSTRACT Trait mindfulness refers to one's disposition or tendency pay attention their experiences in the present moment, a non‐judgmental and accepting way. has been robustly associated with positive mental health outcomes, but its neural underpinnings are poorly understood. Prior resting‐state fMRI studies have trait within‐ between‐network connectivity of default‐mode (DMN), fronto‐parietal (FPN), salience networks. However, it is unclear how generalizable findings are, they relate different components mindfulness, other networks brain areas may be involved. To address these gaps, we conducted largest study to‐date, consisting pre‐registered connectome‐based predictive modeling analysis 367 meditation‐naïve adults across three samples collected at sites. In model‐training dataset, did not find connections that predicted overall identified models two subscales, Acting Awareness Non‐judging . Models included both (sets pairwise positively increasing connectivity) negative networks, which showed inverse relationship. The network distinct representations involving FPN DMN, respectively. models, overlapped significantly involved whole prominent involvement somatomotor, visual DMN Only generalized predict subscale scores out‐of‐sample, test datasets. Predictions from were also negatively correlated predictions well‐established mind‐wandering connectome model. We preliminary evidence for based on specific affective cognitive facets. incomplete generalization all sites scanners, limited stability as well substantial overlap between underscores difficulty finding robust markers

Language: Английский

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What have we really learned from functional connectivity in clinical populations?

NeuroImage, Journal Year: 2021, Volume and Issue: 242, P. 118466 - 118466

Published: Aug. 10, 2021

Functional connectivity (FC), or the statistical interdependence of blood-oxygen dependent level (BOLD) signals between brain regions using fMRI, has emerged as a widely used tool for probing functional abnormalities in clinical populations due to promise approach across conceptual, technical, and practical levels. With an already vast steadily accumulating neuroimaging literature on neurodevelopmental, psychiatric, neurological diseases disorders which FC is primary measure, we aim here provide high-level synthesis major concepts that have arisen from findings manner cuts different conditions sheds light overarching principles. We highlight allowed us discover ubiquity intrinsic networks virtually all brains clarify typical patterns neurodevelopment over lifespan. This understanding maturation with age provided important benchmarks against evaluate divergent early life degeneration late life. turn led insight many are associated complex, distributed, network-level changes brain, opposed solely focal abnormalities. further emphasize role studies played supporting dimensional studying transdiagnostic symptoms enhancing multimodal characterization prediction trajectory symptom progression conditions. unprecedented opportunity offered by probe where function could not be easily studied otherwise, such consciousness. Lastly, suggest high priority areas future research acknowledge critical barriers use methods, particularly those related artifact removal, data denoising feasibility contexts.

Language: Английский

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The neural correlates of ongoing conscious thought

iScience, Journal Year: 2021, Volume and Issue: 24(3), P. 102132 - 102132

Published: Feb. 2, 2021

A core goal in cognitive neuroscience is identifying the physical substrates of patterns thought that occupy our daily lives. Contemporary views suggest landscape ongoing experience heterogeneous and can be influenced by features both person context. This perspective piece considers recent work explicitly accounts for heterogeneity context dependence thought. These studies reveal systems linked to attention control are important organizing response changing environmental demands. also establish a role default mode network beyond task-negative or purely episodic content, example, implicating it level vivid detail task contexts spontaneous self-generated experiential states. Together, this demonstrates reflected activity multiple neural systems, distinguish between processes contributing how unfolds from those these experiences regulated.

Language: Английский

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Prediction of stimulus-independent and task-unrelated thought from functional brain networks

Nature Communications, Journal Year: 2021, Volume and Issue: 12(1)

Published: March 19, 2021

Abstract Neural substrates of “mind wandering” have been widely reported, yet experiments varied in their contexts and definitions this psychological phenomenon, limiting generalizability. We aimed to develop test the generalizability, specificity, clinical relevance a functional brain network-based marker for well-defined feature mind wandering—stimulus-independent, task-unrelated thought (SITUT). Combining MRI (fMRI) with online experience sampling healthy adults, we defined connectome-wide model inter-regional coupling—dominated by default-frontoparietal control subnetwork interactions—that predicted trial-by-trial SITUT fluctuations within novel individuals. Model predictions generalized an independent sample adults attention-deficit/hyperactivity disorder (ADHD). In three additional resting-state fMRI studies (total n = 1115), including individuals ADHD, demonstrated further prediction (at modest effect sizes) using multiple trait-level in-scanner measures. Our findings suggest that is represented common pattern network interactions across time scales contexts.

Language: Английский

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