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Abivardi A, Khemka S, Bach DR. Hippocampal Representation of Threat Features and Behavior in a Human Approach-Avoidance Conflict Anxiety Task. J Neurosci 2020; 40:6748-6758. [PMID: 32719163 PMCID: PMC7455211 DOI: 10.1523/jneurosci.2732-19.2020] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 06/15/2020] [Accepted: 06/20/2020] [Indexed: 12/13/2022] Open
Abstract
Decisions under threat are crucial to survival and require integration of distinct situational features, such as threat probability and magnitude. Recent evidence from human lesion and neuroimaging studies implicated anterior hippocampus (aHC) and amygdala in approach-avoidance decisions under threat, and linked their integrity to cautious behavior. Here we sought to elucidate how threat dimensions and behavior are represented in these structures. Twenty human participants (11 female) completed an approach-avoidance conflict task during high-resolution fMRI. Participants could gather tokens under threat of capture by a virtual predator, which would lead to token loss. Threat probability (predator wake-up rate) and magnitude (amount of token loss) varied on each trial. To disentangle effects of threat features, and ensuing behavior, we performed a multifold parametric analysis. We found that high threat probability and magnitude related to BOLD signal in left aHC/entorhinal cortex. However, BOLD signal in this region was better explained by avoidance behavior than by these threat features. A priori ROI analysis confirmed the relation of aHC BOLD response with avoidance. Exploratory subfield analysis revealed that this relation was specific to anterior CA2/3 but not CA1. Left lateral amygdala responded to low and high, but not intermediate, threat probability. Our results suggest that aHC BOLD signal is better explained by avoidance behavior than by threat features in approach-avoidance conflict. Rather than representing threat features in a monotonic manner, it appears that aHC may compute approach-avoidance decisions based on integration of situational threat features represented in other neural structures.SIGNIFICANCE STATEMENT An effective threat anticipation system is crucial to survival across species. Natural threats, however, are diverse and have distinct features. To be able to adapt to different modes of danger, the brain needs to recognize these features, integrate them, and use them to modify behavior. Our results disclose the human anterior hippocampus as a likely arbiter of approach-avoidance decisions harnessing compound environmental information while partially replicating previous findings and blending into recent efforts to illuminate the neural basis of approach-avoidance conflict in humans.
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Affiliation(s)
- Aslan Abivardi
- Computational Psychiatry Research, Department of Psychiatry Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, 8032, Switzerland
- Zurich, Neuroscience Center Zurich, University of Zurich, Zurich, 8057, Switzerland
| | - Saurabh Khemka
- Computational Psychiatry Research, Department of Psychiatry Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, 8032, Switzerland
- Zurich, Neuroscience Center Zurich, University of Zurich, Zurich, 8057, Switzerland
| | - Dominik R Bach
- Computational Psychiatry Research, Department of Psychiatry Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, 8032, Switzerland
- Zurich, Neuroscience Center Zurich, University of Zurich, Zurich, 8057, Switzerland
- Wellcome Centre for Human Neuroimaging and Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, WC1N 3BG, United Kingdom
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Castegnetti G, Tzovara A, Khemka S, Melinščak F, Barnes GR, Dolan RJ, Bach DR. Representation of probabilistic outcomes during risky decision-making. Nat Commun 2020; 11:2419. [PMID: 32415145 PMCID: PMC7229012 DOI: 10.1038/s41467-020-16202-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 04/21/2020] [Indexed: 12/19/2022] Open
Abstract
Goal-directed behaviour requires prospectively retrieving and evaluating multiple possible action outcomes. While a plethora of studies suggested sequential retrieval for deterministic choice outcomes, it remains unclear whether this is also the case when integrating multiple probabilistic outcomes of the same action. We address this question by capitalising on magnetoencephalography (MEG) in humans who made choices in a risky foraging task. We train classifiers to distinguish MEG field patterns during presentation of two probabilistic outcomes (reward, loss), and then apply these to decode such patterns during deliberation. First, decoded outcome representations have a temporal structure, suggesting alternating retrieval of the outcomes. Moreover, the probability that one or the other outcome is being represented depends on loss magnitude, but not on loss probability, and it predicts the chosen action. In summary, we demonstrate decodable outcome representations during probabilistic decision-making, which are sequentially structured, depend on task features, and predict subsequent action.
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Affiliation(s)
- Giuseppe Castegnetti
- Computational Psychiatry Research, Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland.
- Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland.
- Institute of Cognitive Neuroscience, University College London, London, UK.
| | - Athina Tzovara
- Computational Psychiatry Research, Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland
- Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
- Department of Computer Science & Faculty of Medicine, University of Bern, Bern, Switzerland
- Helen Wills Neuroscience Institute, University of California, Berkeley, USA
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Saurabh Khemka
- Computational Psychiatry Research, Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland
- Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
| | - Filip Melinščak
- Computational Psychiatry Research, Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland
- Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
| | - Gareth R Barnes
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Raymond J Dolan
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Max Planck UCL Centre for Computational Psychiatry and Ageing, University College London, London, UK
| | - Dominik R Bach
- Computational Psychiatry Research, Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland
- Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Max Planck UCL Centre for Computational Psychiatry and Ageing, University College London, London, UK
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Khemka S, Tzovara A, Gerster S, Quednow BB, Bach DR. Modeling startle eyeblink electromyogram to assess fear learning. Psychophysiology 2016; 54:204-214. [PMID: 27753123 PMCID: PMC5298047 DOI: 10.1111/psyp.12775] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2016] [Accepted: 09/13/2016] [Indexed: 11/29/2022]
Abstract
Pavlovian fear conditioning is widely used as a laboratory model of associative learning in human and nonhuman species. In this model, an organism is trained to predict an aversive unconditioned stimulus from initially neutral events (conditioned stimuli, CS). In humans, fear memory is typically measured via conditioned autonomic responses or fear‐potentiated startle. For the latter, various analysis approaches have been developed, but a systematic comparison of competing methodologies is lacking. Here, we investigate the suitability of a model‐based approach to startle eyeblink analysis for assessment of fear memory, and compare this to extant analysis strategies. First, we build a psychophysiological model (PsPM) on a generic startle response. Then, we optimize and validate this PsPM on three independent fear‐conditioning data sets. We demonstrate that our model can robustly distinguish aversive (CS+) from nonaversive stimuli (CS‐, i.e., has high predictive validity). Importantly, our model‐based approach captures fear‐potentiated startle during fear retention as well as fear acquisition. Our results establish a PsPM‐based approach to assessment of fear‐potentiated startle, and qualify previous peak‐scoring methods. Our proposed model represents a generic startle response and can potentially be used beyond fear conditioning, for example, to quantify affective startle modulation or prepulse inhibition of the acoustic startle response.
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Affiliation(s)
- Saurabh Khemka
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zurich, Zurich, Switzerland.,Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
| | - Athina Tzovara
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zurich, Zurich, Switzerland.,Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland.,Wellcome Trust Centre for Neuroimaging, University College London, London, UK
| | - Samuel Gerster
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zurich, Zurich, Switzerland
| | - Boris B Quednow
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zurich, Zurich, Switzerland.,Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
| | - Dominik R Bach
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zurich, Zurich, Switzerland.,Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland.,Wellcome Trust Centre for Neuroimaging, University College London, London, UK
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Abstract
We report the case of a 43-year-old gentleman who presented with an isolated left sixth nerve palsy in association with postural headache. Magnetic resonance imaging showed dural enhancement with downward displacement of the brainstem. This, in association with the signs, symptoms and findings on lumbar puncture, confirmed the diagnosis of spontaneous intracranial hypotension. Treatment was successful with epidural blood patching. The case is discussed and the relevant literature reviewed.
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Affiliation(s)
- S Khemka
- The Royal Eye Unit, Kingston Hospital, Kingston-Upon-Thames, UK.
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Javle M, Khemka S, Donohue K, Demmy T, Yang G, Smith J, Nava H, Douglass HO. The role of surgery in the management of loco-regional esophageal cancer. Ann Surg Oncol 2004. [DOI: 10.1007/bf02524178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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