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Environmental neuroscience linking exposome to brain structure and function underlying cognition and behavior. Mol Psychiatry 2023; 28:17-27. [PMID: 35790874 DOI: 10.1038/s41380-022-01669-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 06/02/2022] [Accepted: 06/09/2022] [Indexed: 01/07/2023]
Abstract
Individual differences in human brain structure, function, and behavior can be attributed to genetic variations, environmental exposures, and their interactions. Although genome-wide association studies have identified many genetic variants associated with brain imaging phenotypes, environmental exposures associated with these phenotypes remain largely unknown. Here, we propose that environmental neuroscience should pay more attention on exploring the associations between lifetime environmental exposures (exposome) and brain imaging phenotypes and identifying both cumulative environmental effects and their vulnerable age windows during the life course. Exposome-neuroimaging association studies face several challenges including the accurate measurement of the totality of environmental exposures varied in space and time, the highly correlated structure of the exposome, and the lack of standardized approaches for exposome-wide association studies. By agnostically scanning the effects of environmental exposures on brain imaging phenotypes and their interactions with genomic variations, exposome-neuroimaging association analyses will improve our understanding of causal factors associated with individual differences in brain structure and function as well as their relations with cognitive abilities and neuropsychiatric disorders.
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Moujaes F, Preller KH, Ji JL, Murray JD, Berkovitch L, Vollenweider FX, Anticevic A. Towards mapping neuro-behavioral heterogeneity of psychedelic neurobiology in humans. Biol Psychiatry 2022:S0006-3223(22)01805-4. [PMID: 36715317 DOI: 10.1016/j.biopsych.2022.10.021] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 10/10/2022] [Accepted: 10/31/2022] [Indexed: 12/12/2022]
Abstract
Precision psychiatry aims to identify markers of inter-individual variability that allow predicting the right treatment for each patient. However, bridging the gap between molecular-level manipulations and neural systems-level functional alterations remains an unsolved problem in psychiatry. After decades of low success rates in pharmaceutical R&D for psychiatric drugs, multiple studies now point to the potential of psychedelics as a promising fast-acting and long-lasting treatment for some psychiatric symptoms. Yet, given the highly psychoactive nature of these substances, a precision medicine approach is essential to map the neural signals related to clinical efficacy in order to identify patients who can maximally benefit from this treatment. Recent studies have shown that bridging the gap between pharmacology, systems-level neural response in humans and individual experience is possible for psychedelic substances, therefore paving the way for a precision neuropsychiatric therapeutic development. Specifically, it has been shown that the integration of brain-wide PET or transcriptomic data, i.e. receptor distribution for the serotonin 2A receptor, with computational neuroimaging methods can simulate the effect of psychedelics on the human brain. These novel 'computational psychiatry' approaches allow for modeling inter-individual differences in neural as well as subjective effects of psychedelic substances. Collectively, this review provides a deep dive into psychedelic pharmaco-neuroimaging studies with a core focus on how recent computational psychiatry advances in biophysically based circuit modeling can be leveraged to predict individual responses. Finally, we emphasize the importance of human pharmacological neuroimaging for the continued precision therapeutic development of psychedelics.
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Affiliation(s)
- Flora Moujaes
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry Zurich, Lenggstr. 31, 8032 Zurich, Switzerland; Department of Psychiatry, Yale University School of Medicine, 40 Temple Street, New Haven, CT, 06511, United States
| | - Katrin H Preller
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry Zurich, Lenggstr. 31, 8032 Zurich, Switzerland; Department of Psychiatry, Yale University School of Medicine, 40 Temple Street, New Haven, CT, 06511, United States
| | - Jie Lisa Ji
- Department of Psychiatry, Yale University School of Medicine, 40 Temple Street, New Haven, CT, 06511, United States
| | - John D Murray
- Department of Psychiatry, Yale University School of Medicine, 40 Temple Street, New Haven, CT, 06511, United States; Department of Physics, Yale University, New Haven, CT, 06511, United States; Interdepartmental Neuroscience Program, Yale University, New Haven, CT, 06511, United States
| | - Lucie Berkovitch
- Department of Psychiatry, Yale University School of Medicine, 40 Temple Street, New Haven, CT, 06511, United States; Université de Paris, 15 Rue de l'École de Médecine, F-75006 Paris, France; Department of Psychiatry, Service Hospitalo-Universitaire, GHU Paris Psychiatrie & Neurosciences, 1 rue Cabanis, F-75014, Paris, France
| | - Franz X Vollenweider
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry Zurich, Lenggstr. 31, 8032 Zurich, Switzerland
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, 40 Temple Street, New Haven, CT, 06511, United States; Interdepartmental Neuroscience Program, Yale University, New Haven, CT, 06511, United States.
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Nord CL. Predicting Response to Brain Stimulation in Depression: a Roadmap for Biomarker Discovery. Curr Behav Neurosci Rep 2021; 8:11-19. [PMID: 33708470 PMCID: PMC7904553 DOI: 10.1007/s40473-021-00226-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/28/2021] [Indexed: 11/22/2022]
Abstract
PURPOSE OF REVIEW Clinical response to brain stimulation treatments for depression is highly variable. A major challenge for the field is predicting an individual patient's likelihood of response. This review synthesises recent developments in neural predictors of response to targeted brain stimulation in depression. It then proposes a framework to evaluate the clinical potential of putative 'biomarkers'. RECENT FINDINGS Largely, developments in identifying putative predictors emerge from two approaches: data-driven, including machine learning algorithms applied to resting state or structural neuroimaging data, and theory-driven, including task-based neuroimaging. Theory-driven approaches can also yield mechanistic insight into the cognitive processes altered by the intervention. SUMMARY A pragmatic framework for discovery and testing of biomarkers of brain stimulation response in depression is proposed, involving (1) identification of a cognitive-neural phenotype; (2) confirming its validity as putative biomarker, including out-of-sample replicability and within-subject reliability; (3) establishing the association between this phenotype and treatment response and/or its modifiability with particular brain stimulation interventions via an early-phase randomised controlled trial RCT; and (4) multi-site RCTs of one or more treatment types measuring the generalisability of the biomarker and confirming the superiority of biomarker-selected patients over randomly allocated groups.
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Affiliation(s)
- Camilla L. Nord
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge, CB2 7EF UK
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