1
|
Sylwestrak EL, Jo Y, Vesuna S, Wang X, Holcomb B, Tien RH, Kim DK, Fenno L, Ramakrishnan C, Allen WE, Chen R, Shenoy KV, Sussillo D, Deisseroth K. Cell-type-specific population dynamics of diverse reward computations. Cell 2022; 185:3568-3587.e27. [PMID: 36113428 PMCID: PMC10387374 DOI: 10.1016/j.cell.2022.08.019] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 06/16/2022] [Accepted: 08/17/2022] [Indexed: 01/26/2023]
Abstract
Computational analysis of cellular activity has developed largely independently of modern transcriptomic cell typology, but integrating these approaches may be essential for full insight into cellular-level mechanisms underlying brain function and dysfunction. Applying this approach to the habenula (a structure with diverse, intermingled molecular, anatomical, and computational features), we identified encoding of reward-predictive cues and reward outcomes in distinct genetically defined neural populations, including TH+ cells and Tac1+ cells. Data from genetically targeted recordings were used to train an optimized nonlinear dynamical systems model and revealed activity dynamics consistent with a line attractor. High-density, cell-type-specific electrophysiological recordings and optogenetic perturbation provided supporting evidence for this model. Reverse-engineering predicted how Tac1+ cells might integrate reward history, which was complemented by in vivo experimentation. This integrated approach describes a process by which data-driven computational models of population activity can generate and frame actionable hypotheses for cell-type-specific investigation in biological systems.
Collapse
Affiliation(s)
- Emily L Sylwestrak
- Department of Biology, University of Oregon, Eugene, OR 97403, USA; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA.
| | - YoungJu Jo
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Applied Physics, Stanford University, Stanford, CA 94305, USA
| | - Sam Vesuna
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA
| | - Xiao Wang
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Blake Holcomb
- Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA
| | - Rebecca H Tien
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Doo Kyung Kim
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Lief Fenno
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA
| | - Charu Ramakrishnan
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - William E Allen
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Neurosciences Interdepartmental Program, Stanford University, Stanford, CA 94303, USA
| | - Ritchie Chen
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Krishna V Shenoy
- Department of Neurobiology, Stanford University, Stanford, CA 94303, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - David Sussillo
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Karl Deisseroth
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA.
| |
Collapse
|
2
|
Gradinaru V, Treweek J, Overton K, Deisseroth K. Hydrogel-Tissue Chemistry: Principles and Applications. Annu Rev Biophys 2019; 47:355-376. [PMID: 29792820 PMCID: PMC6359929 DOI: 10.1146/annurev-biophys-070317-032905] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Over the past five years, a rapidly developing experimental approach has enabled high-resolution and high-content information retrieval from intact multicellular animal (metazoan) systems. New chemical and physical forms are created in the hydrogel-tissue chemistry process, and the retention and retrieval of crucial phenotypic information regarding constituent cells and molecules (and their joint interrelationships) are thereby enabled. For example, rich data sets defining both single-cell-resolution gene expression and single-cell-resolution activity during behavior can now be collected while still preserving information on three-dimensional positioning and/or brain-wide wiring of those very same neurons-even within vertebrate brains. This new approach and its variants, as applied to neuroscience, are beginning to illuminate the fundamental cellular and chemical representations of sensation, cognition, and action. More generally, reimagining metazoans as metareactants-or positionally defined three-dimensional graphs of constituent chemicals made available for ongoing functionalization, transformation, and readout-is stimulating innovation across biology and medicine.
Collapse
Affiliation(s)
- Viviana Gradinaru
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125, USA;
| | - Jennifer Treweek
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125, USA;
| | - Kristin Overton
- Department of Bioengineering, Stanford University, Stanford, California 94305, USA;
| | - Karl Deisseroth
- Department of Bioengineering, Stanford University, Stanford, California 94305, USA; .,Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California 94305, USA.,H oward Hughes Medical Institute, Stanford University, Stanford, California 94305, USA
| |
Collapse
|
3
|
Tretter F. From mind to molecules and back to mind-Metatheoretical limits and options for systems neuropsychiatry. CHAOS (WOODBURY, N.Y.) 2018; 28:106325. [PMID: 30384654 DOI: 10.1063/1.5040174] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 10/01/2018] [Indexed: 06/08/2023]
Abstract
Psychiatric illnesses like dementia are increasingly relevant for public health affairs. Neurobiology promises progress in diagnosis and treatment of these illnesses and exhibits a rapid increase of knowledge by new neurotechnologies. In order to find generic patterns in huge neurobiological data sets and by exploring formal brain models, non-linear science offers many examples of fruitful insights into the complex dynamics of neuronal information processing. However, it should be minded that neurobiology neither can bridge the explanatory gap between brain and mind nor can substitute psychological and psychiatric categories and knowledge. For instance, volition is impaired in many mental disorders. In experimental setups, a "preactional" brain potential was discovered that occurs 0.5 s before a consciously evoked motor action. Neglecting the specific experimental conditions, this finding was over-interpreted as the empirical falsification of the philosophical (!) concept of "free volition/will." In contrast, the psychology of volition works with models that are composed of several stage-related hierarchically nested mental process cycles that were never tested in obviously "theory-free" neurobiology. As currently neurobiology shows a network turn (or systemic turn), this is one good reason to enhance systemic approaches in theoretical psychology, independently from neurobiology that still lacks "theory." Cybernetic control loop models and system models should be integrated and elaborated and in turn could give new impulses to neuropsychology and neuropsychiatry that conceptually can more easily connect to a network-oriented neurobiology. In this program, the conceptual background of nonlinear science is essential to bridge gaps between neurobiology and psychiatry, defining a real "theoretical" field of neuropsychiatry.
Collapse
Affiliation(s)
- Felix Tretter
- Bertalanffy Center for the Study of Systems Science, Paulanergasse 13 / door 5, A 1040 Vienna, Austria
| |
Collapse
|
4
|
Dalton B, Bartholdy S, Campbell IC, Schmidt U. Neurostimulation in Clinical and Sub-clinical Eating Disorders: A Systematic Update of the Literature. Curr Neuropharmacol 2018; 16:1174-1192. [PMID: 29308739 PMCID: PMC6187753 DOI: 10.2174/1570159x16666180108111532] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Revised: 10/17/2017] [Accepted: 01/04/2017] [Indexed: 02/08/2023] Open
Abstract
INTRODUCTION Whilst psychological therapies are the main approach to treatment of eating disorders (EDs), advances in aetiological research suggest the need for the development of more targeted, brain-focused treatments. A range of neurostimulation approaches, most prominently repetitive transcranial magnetic stimulation (rTMS), transcranial direct current stimulation (tDCS) and deep brain stimulation (DBS), are rapidly emerging as potential novel interventions. We have previously reviewed these techniques as potential treatments of EDs. AIM To provide an update of the literature examining the effects of DBS, rTMS and tDCS on eating behaviours, body weight and associated symptoms in people with EDs and relevant analogue populations. METHODS Using PRISMA guidelines, we reviewed articles in PubMed, Web of Science, and PsycINFO from 1st January 2013 until 14th August 2017, to update our earlier search. Studies assessing the effects of neurostimulation techniques on eating and weight-related outcomes in people with EDs and relevant analogue populations were included. Data from both searches were combined. RESULTS We included a total of 32 studies (526 participants); of these, 18 were newly identified by our update search. Whilst findings are somewhat mixed for bulimia nervosa, neurostimulation techniques have shown potential in the treatment of other EDs, in terms of reduction of ED and associated symptoms. Studies exploring cognitive, neural, and hormonal correlates of these techniques are also beginning to appear. CONCLUSIONS Neurostimulation approaches show promise as treatments for EDs. As yet, large wellconducted randomised controlled trials are lacking. More information is needed about treatment targets, stimulation parameters and mechanisms of action.
Collapse
Affiliation(s)
- Bethan Dalton
- Section of Eating Disorders, Department of Psychological Medicine, King`s College London, London, United Kingdom
| | - Savani Bartholdy
- Section of Eating Disorders, Department of Psychological Medicine, King`s College London, London, United Kingdom
| | - Iain C Campbell
- Section of Eating Disorders, Department of Psychological Medicine, King`s College London, London, United Kingdom
| | - Ulrike Schmidt
- Section of Eating Disorders, Department of Psychological Medicine, King`s College London, London, United Kingdom
| |
Collapse
|