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Pinotsis DA, Fitzgerald S, See C, Sementsova A, Widge AS. Toward biophysical markers of depression vulnerability. Front Psychiatry 2022; 13:938694. [PMID: 36329919 PMCID: PMC9622949 DOI: 10.3389/fpsyt.2022.938694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 09/22/2022] [Indexed: 11/13/2022] Open
Abstract
A major difficulty with treating psychiatric disorders is their heterogeneity: different neural causes can lead to the same phenotype. To address this, we propose describing the underlying pathophysiology in terms of interpretable, biophysical parameters of a neural model derived from the electroencephalogram. We analyzed data from a small patient cohort of patients with depression and controls. Using DCM, we constructed biophysical models that describe neural dynamics in a cortical network activated during a task that is used to assess depression state. We show that biophysical model parameters are biomarkers, that is, variables that allow subtyping of depression at a biological level. They yield a low dimensional, interpretable feature space that allowed description of differences between individual patients with depressive symptoms. They could capture internal heterogeneity/variance of depression state and achieve significantly better classification than commonly used EEG features. Our work is a proof of concept that a combination of biophysical models and machine learning may outperform earlier approaches based on classical statistics and raw brain data.
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Affiliation(s)
- D. A. Pinotsis
- Centre for Mathematical Neuroscience and Psychology, Department of Psychology, City, University of London, London, United Kingdom
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - S. Fitzgerald
- Centre for Mathematical Neuroscience and Psychology, Department of Psychology, City, University of London, London, United Kingdom
| | - C. See
- Department of Computer Science, City, University of London, London, United Kingdom
| | - A. Sementsova
- Department of Computer Science, City, University of London, London, United Kingdom
| | - A. S. Widge
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States
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Uscătescu LC, Kronbichler L, Stelzig-Schöler R, Pearce BG, Said-Yürekli S, Reich LA, Weber S, Aichhorn W, Kronbichler M. Effective Connectivity of the Hippocampus Can Differentiate Patients with Schizophrenia from Healthy Controls: A Spectral DCM Approach. Brain Topogr 2021; 34:762-778. [PMID: 34482503 PMCID: PMC8556208 DOI: 10.1007/s10548-021-00868-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 08/22/2021] [Indexed: 12/01/2022]
Abstract
We applied spectral dynamic causal modelling (Friston et al. in Neuroimage 94:396–407. 10.1016/j.neuroimage.2013.12.009, 2014) to analyze the effective connectivity differences between the nodes of three resting state networks (i.e. default mode network, salience network and dorsal attention network) in a dataset of 31 male healthy controls (HC) and 25 male patients with a diagnosis of schizophrenia (SZ). Patients showed increased directed connectivity from the left hippocampus (LHC) to the: dorsal anterior cingulate cortex (DACC), right anterior insula (RAI), left frontal eye fields and the bilateral inferior parietal sulcus (LIPS & RIPS), as well as increased connectivity from the right hippocampus (RHC) to the: bilateral anterior insula (LAI & RAI), right frontal eye fields and RIPS. In SZ, negative symptoms predicted the connectivity strengths from the LHC to: the DACC, the left inferior parietal sulcus (LIPAR) and the RHC, while positive symptoms predicted the connectivity strengths from the LHC to the LIPAR and from the RHC to the LHC. These results reinforce the crucial role of hippocampus dysconnectivity in SZ pathology and its potential as a biomarker of disease severity.
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Affiliation(s)
- Lavinia Carmen Uscătescu
- Centre for Cognitive Neuroscience and Department of Psychology, University of Salzburg, Salzburg, Austria
| | - Lisa Kronbichler
- Centre for Cognitive Neuroscience and Department of Psychology, University of Salzburg, Salzburg, Austria
- Neuroscience Institute, Christian-Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria
| | - Renate Stelzig-Schöler
- Department of Psychiatry, Psychotherapy and Psychosomatics, Christian-Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria
| | - Brandy-Gale Pearce
- Department of Psychiatry, Psychotherapy and Psychosomatics, Christian-Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria
| | - Sarah Said-Yürekli
- Centre for Cognitive Neuroscience and Department of Psychology, University of Salzburg, Salzburg, Austria
- Neuroscience Institute, Christian-Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria
| | | | - Stefanie Weber
- Department of Psychiatry, Psychotherapy and Psychosomatics, Christian-Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria
| | - Wolfgang Aichhorn
- Department of Psychiatry, Psychotherapy and Psychosomatics, Christian-Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria
| | - Martin Kronbichler
- Centre for Cognitive Neuroscience and Department of Psychology, University of Salzburg, Salzburg, Austria
- Neuroscience Institute, Christian-Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria
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