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Schwitzer T, Leboyer M, Schwan R. Retinal electrophysiological markers for precision medicine in psychiatry. Encephale 2023; 49:107-108. [PMID: 36253183 DOI: 10.1016/j.encep.2022.08.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 08/01/2022] [Indexed: 03/26/2023]
Affiliation(s)
- T Schwitzer
- Pôle Hospitalo-Universitaire de Psychiatrie d'Adultes et d'Addictologie du Grand Nancy, Centre Psychothérapique de Nancy, Laxou, France; Inserm U1254, IADI, Université de Lorraine, Nancy, France; Faculté de Médecine, Université de Lorraine, Vandœuvre-lès-Nancy, France; Fondation FondaMental, Créteil, France.
| | - M Leboyer
- Fondation FondaMental, Créteil, France; Université Paris Est Creteil (UPEC), AP-HP, Hôpitaux Universitaires "H. Mondor", DMU IMPACT, FHU ADAPT, Inserm U955, IMRB, Translational Neuropsychiatry laboratory, Creteil, France
| | - R Schwan
- Pôle Hospitalo-Universitaire de Psychiatrie d'Adultes et d'Addictologie du Grand Nancy, Centre Psychothérapique de Nancy, Laxou, France; Inserm U1254, IADI, Université de Lorraine, Nancy, France; Faculté de Médecine, Université de Lorraine, Vandœuvre-lès-Nancy, France; Fondation FondaMental, Créteil, France
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Schwitzer T, Le Cam S, Cosker E, Vinsard H, Leguay A, Angioi-Duprez K, Laprevote V, Ranta R, Schwan R, Dorr VL. Retinal electroretinogram features can detect depression state and treatment response in adults: A machine learning approach. J Affect Disord 2022; 306:208-214. [PMID: 35301040 DOI: 10.1016/j.jad.2022.03.025] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 01/05/2022] [Accepted: 03/10/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND Major depressive disorder (MDD) is a major public health problem. The retina is a relevant site to indirectly study brain functioning. Alterations in retinal processing were demonstrated in MDD with the pattern electroretinogram (PERG). Here, the relevance of signal processing and machine learning tools applied on PERG was studied. METHODS PERG - whose stimulation is reversible checkerboards - was performed according to the International Society for Clinical Electrophysiology of Vision (ISCEV) standards in 24 MDD patients and 29 controls at the inclusion. PERG was recorded every 4 weeks for 3 months in patients. Amplitude and implicit time of P50 and N95 were evaluated. Then, time/frequency features were extracted from the PERG time series based on wavelet analysis. A statistical model has been learned in this feature space and a metric aiming at quantifying the state of the MDD patient has been derived, based on minimum covariance determinant (MCD) mahalanobis distance. RESULTS MDD patients showed significant increase in P50 and N95 implicit time (p = 0,006 and p = 0,0004, respectively, Mann-Whitney U test) at the inclusion. The proposed metric extracted from the raw PERG provided discrimination between patients and controls at the inclusion (p = 0,0001). At the end of the follow-up at week 12, the difference between the metrics extracted on controls and patients was not significant (p = 0,07), reflecting the efficacy of the treatment. CONCLUSIONS Signal processing and machine learning tools applied on PERG could help clinical decision in the diagnosis and the follow-up of MDD in measuring treatment response.
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Affiliation(s)
- Thomas Schwitzer
- Pôle Hospitalo-Universitaire de Psychiatrie d'Adultes et d'Addictologie du Grand Nancy, Centre Psychothérapique de Nancy, Laxou, France; INSERM U1254, IADI, Université de Lorraine, Nancy, France; Faculté de Médecine, Université de Lorraine, Vandœuvre-lès-Nancy, France.
| | - Steven Le Cam
- Université de Lorraine, CNRS, CRAN, F-54000 Nancy, France
| | - Eve Cosker
- Pôle Hospitalo-Universitaire de Psychiatrie d'Adultes et d'Addictologie du Grand Nancy, Centre Psychothérapique de Nancy, Laxou, France; Faculté de Médecine, Université de Lorraine, Vandœuvre-lès-Nancy, France
| | - Heloise Vinsard
- Pôle Hospitalo-Universitaire de Psychiatrie d'Adultes et d'Addictologie du Grand Nancy, Centre Psychothérapique de Nancy, Laxou, France
| | - Ambre Leguay
- Pôle Hospitalo-Universitaire de Psychiatrie d'Adultes et d'Addictologie du Grand Nancy, Centre Psychothérapique de Nancy, Laxou, France
| | - Karine Angioi-Duprez
- Faculté de Médecine, Université de Lorraine, Vandœuvre-lès-Nancy, France; Service d'Ophtalmologie, CHRU Nancy, Nancy, France
| | - Vincent Laprevote
- Pôle Hospitalo-Universitaire de Psychiatrie d'Adultes et d'Addictologie du Grand Nancy, Centre Psychothérapique de Nancy, Laxou, France; Faculté de Médecine, Université de Lorraine, Vandœuvre-lès-Nancy, France; INSERM U1114, Fédération de Médecine Translationnelle de Strasbourg, Département de Psychiatrie, Centre Hospitalier Régional Universitaire de Strasbourg, Strasbourg, France
| | - Radu Ranta
- Université de Lorraine, CNRS, CRAN, F-54000 Nancy, France
| | - Raymund Schwan
- Pôle Hospitalo-Universitaire de Psychiatrie d'Adultes et d'Addictologie du Grand Nancy, Centre Psychothérapique de Nancy, Laxou, France; INSERM U1254, IADI, Université de Lorraine, Nancy, France; Faculté de Médecine, Université de Lorraine, Vandœuvre-lès-Nancy, France
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