Identifying a predictive model of cognitive impairment in bipolar disorder patients: a machine learning study
Introduction Bipolar patients (BP) frequently have cognitive deficits, that impact on prognosis and quality of life. Finding biomarkers for this condition is essential to improve patients’ healthcare. Given the association between cognitive dysfunctions and structural brain abnormalities, we used a...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
2023-03-01
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| Series: | European Psychiatry |
| Online Access: | https://www.cambridge.org/core/product/identifier/S0924933823012762/type/journal_article |
| _version_ | 1797616445864017920 |
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| author | C. Monopoli L. Fortaner-Uyà F. Calesella F. Colombo B. Bravi E. Maggioni E. Tassi I. Bollettini S. Poletti F. Benedetti B. Vai |
| author_facet | C. Monopoli L. Fortaner-Uyà F. Calesella F. Colombo B. Bravi E. Maggioni E. Tassi I. Bollettini S. Poletti F. Benedetti B. Vai |
| author_sort | C. Monopoli |
| collection | DOAJ |
| description |
Introduction
Bipolar patients (BP) frequently have cognitive deficits, that impact on prognosis and quality of life. Finding biomarkers for this condition is essential to improve patients’ healthcare. Given the association between cognitive dysfunctions and structural brain abnormalities, we used a machine learning approach to identify patients with cognitive deficits.
Objectives
The aim of this study was to assess if structural neuroimaging data could identify patients with cognitive impairments in several domains using a machine learning framework.
Methods
Diffusion tensor imaging and T1-weighted images of 150 BP were acquired and both grey matter voxel-based morphometry (VBM) and tract-based white matter fractional anisotropy (FA) measures were extracted. Support vector machine (SVM) models were trained through a 10-fold nested cross-validation with subsampling. VBM and FA maps were entered separately and in combination as input features to discriminate BP with and without deficits in six cognitive domains, assessed through the Brief Assessment of Cognition in Schizophrenia.
Results
The best classification performance for each cognitive domain is illustrated in Table 1. FA was the most relevant neuroimaging modality for the prediction of verbal memory, verbal fluency, and executive functions deficits, whereas VBM was more predictive for working memory and motor speed domains.Table 1.
Performance of best classification models.
Input feature
Balance Accuracy (%)
Specificity (%)
Sensitivity (%)
Verbal Memory
FA
60.17
51.31
43
Verbal Fluency
FA
57.67
62
53.33
Executive functions
FA
60
63.33
56.67
Working Memory
VBM
56.50
56
57
Motor speed
VBM
53.50
47.67
59.33
Attention and processing speed
VBM + FA
58.33
49.17
67.5
Conclusions
Overall, the tested SVM models showed a good predictive performance. Although only partially, our results suggest that different structural neuroimaging data can predict cognitive deficits in BP with accuracy higher than chance level. Unexpectedly, only for the attention and processing speed domain the best model was obtained combining the structural features. Future research may promote data fusion methods to develop better predictive models.
Disclosure of Interest
None Declared |
| first_indexed | 2024-03-11T07:41:17Z |
| format | Article |
| id | doaj.art-85950443a4c44b12b9c9e8aeb9aad225 |
| institution | Directory Open Access Journal |
| issn | 0924-9338 1778-3585 |
| language | English |
| last_indexed | 2024-03-11T07:41:17Z |
| publishDate | 2023-03-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | European Psychiatry |
| spelling | doaj.art-85950443a4c44b12b9c9e8aeb9aad2252023-11-17T05:08:30ZengCambridge University PressEuropean Psychiatry0924-93381778-35852023-03-0166S612S61210.1192/j.eurpsy.2023.1276Identifying a predictive model of cognitive impairment in bipolar disorder patients: a machine learning studyC. Monopoli0L. Fortaner-Uyà1F. Calesella2F. Colombo3B. Bravi4E. Maggioni5E. Tassi6I. Bollettini7S. Poletti8F. Benedetti9B. Vai10IRCCS San Raffaele Hospital, Psychiatry and Clinical Psychobiology UnitIRCCS San Raffaele Hospital, Psychiatry and Clinical Psychobiology Unit University Vita-Salute San RaffaeleIRCCS San Raffaele Hospital, Psychiatry and Clinical Psychobiology Unit University Vita-Salute San RaffaeleIRCCS San Raffaele Hospital, Psychiatry and Clinical Psychobiology Unit University Vita-Salute San RaffaeleIRCCS San Raffaele Hospital, Psychiatry and Clinical Psychobiology Unit University Vita-Salute San RaffaelePolitecnico di Milano, Department of Electronics - Information and Bioengineering Department of Neurosciences and Mental Health, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, ItalyPolitecnico di Milano, Department of Electronics - Information and BioengineeringIRCCS San Raffaele Hospital, Psychiatry and Clinical Psychobiology UnitIRCCS San Raffaele Hospital, Psychiatry and Clinical Psychobiology Unit University Vita-Salute San RaffaeleIRCCS San Raffaele Hospital, Psychiatry and Clinical Psychobiology Unit University Vita-Salute San RaffaeleIRCCS San Raffaele Hospital, Psychiatry and Clinical Psychobiology Unit University Vita-Salute San Raffaele Introduction Bipolar patients (BP) frequently have cognitive deficits, that impact on prognosis and quality of life. Finding biomarkers for this condition is essential to improve patients’ healthcare. Given the association between cognitive dysfunctions and structural brain abnormalities, we used a machine learning approach to identify patients with cognitive deficits. Objectives The aim of this study was to assess if structural neuroimaging data could identify patients with cognitive impairments in several domains using a machine learning framework. Methods Diffusion tensor imaging and T1-weighted images of 150 BP were acquired and both grey matter voxel-based morphometry (VBM) and tract-based white matter fractional anisotropy (FA) measures were extracted. Support vector machine (SVM) models were trained through a 10-fold nested cross-validation with subsampling. VBM and FA maps were entered separately and in combination as input features to discriminate BP with and without deficits in six cognitive domains, assessed through the Brief Assessment of Cognition in Schizophrenia. Results The best classification performance for each cognitive domain is illustrated in Table 1. FA was the most relevant neuroimaging modality for the prediction of verbal memory, verbal fluency, and executive functions deficits, whereas VBM was more predictive for working memory and motor speed domains.Table 1. Performance of best classification models. Input feature Balance Accuracy (%) Specificity (%) Sensitivity (%) Verbal Memory FA 60.17 51.31 43 Verbal Fluency FA 57.67 62 53.33 Executive functions FA 60 63.33 56.67 Working Memory VBM 56.50 56 57 Motor speed VBM 53.50 47.67 59.33 Attention and processing speed VBM + FA 58.33 49.17 67.5 Conclusions Overall, the tested SVM models showed a good predictive performance. Although only partially, our results suggest that different structural neuroimaging data can predict cognitive deficits in BP with accuracy higher than chance level. Unexpectedly, only for the attention and processing speed domain the best model was obtained combining the structural features. Future research may promote data fusion methods to develop better predictive models. Disclosure of Interest None Declaredhttps://www.cambridge.org/core/product/identifier/S0924933823012762/type/journal_article |
| spellingShingle | C. Monopoli L. Fortaner-Uyà F. Calesella F. Colombo B. Bravi E. Maggioni E. Tassi I. Bollettini S. Poletti F. Benedetti B. Vai Identifying a predictive model of cognitive impairment in bipolar disorder patients: a machine learning study European Psychiatry |
| title | Identifying a predictive model of cognitive impairment in bipolar disorder patients: a machine learning study |
| title_full | Identifying a predictive model of cognitive impairment in bipolar disorder patients: a machine learning study |
| title_fullStr | Identifying a predictive model of cognitive impairment in bipolar disorder patients: a machine learning study |
| title_full_unstemmed | Identifying a predictive model of cognitive impairment in bipolar disorder patients: a machine learning study |
| title_short | Identifying a predictive model of cognitive impairment in bipolar disorder patients: a machine learning study |
| title_sort | identifying a predictive model of cognitive impairment in bipolar disorder patients a machine learning study |
| url | https://www.cambridge.org/core/product/identifier/S0924933823012762/type/journal_article |
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