Increasing the Construct Validity of Computational Phenotypes of Mental Illness Through Active Inference and Brain Imaging
After more than 30 years since its inception, the utility of brain imaging for understanding and diagnosing mental illnesses is in doubt, receiving well-grounded criticisms from clinical practitioners. Symptom-based correlational approaches have struggled to provide psychiatry with reliable brain-im...
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MDPI AG
2024-12-01
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| Series: | Brain Sciences |
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| Online Access: | https://www.mdpi.com/2076-3425/14/12/1278 |
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| author | Roberto Limongi Alexandra B. Skelton Lydia H. Tzianas Angelica M. Silva |
| author_facet | Roberto Limongi Alexandra B. Skelton Lydia H. Tzianas Angelica M. Silva |
| author_sort | Roberto Limongi |
| collection | DOAJ |
| description | After more than 30 years since its inception, the utility of brain imaging for understanding and diagnosing mental illnesses is in doubt, receiving well-grounded criticisms from clinical practitioners. Symptom-based correlational approaches have struggled to provide psychiatry with reliable brain-imaging metrics. However, the emergence of computational psychiatry has paved a new path not only for understanding the psychopathology of mental illness but also to provide practical tools for clinical practice in terms of computational metrics, specifically computational phenotypes. However, these phenotypes still lack sufficient test–retest reliability. In this review, we describe recent works revealing that mind and brain-related computational phenotypes show structural (not random) variation over time, longitudinal changes. Furthermore, we show that these findings suggest that understanding the causes of these changes will improve the construct validity of the phenotypes with an ensuing increase in test–retest reliability. We propose that the active inference framework offers a general-purpose approach for causally understanding these longitudinal changes by incorporating brain imaging as observations within partially observable Markov decision processes. |
| format | Article |
| id | doaj-art-481d80e999c7481b8d9919302e9eba98 |
| institution | DOAJ |
| issn | 2076-3425 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj-art-481d80e999c7481b8d9919302e9eba982025-08-20T02:53:27ZengMDPI AGBrain Sciences2076-34252024-12-011412127810.3390/brainsci14121278Increasing the Construct Validity of Computational Phenotypes of Mental Illness Through Active Inference and Brain ImagingRoberto Limongi0Alexandra B. Skelton1Lydia H. Tzianas2Angelica M. Silva3Department of Psychology, Brandon University, Brandon, MB R7A 6A9, CanadaDepartment of Psychology, Brandon University, Brandon, MB R7A 6A9, CanadaDepartment of Psychology, University of Western Ontario, London, ON N6A 3K7, CanadaDepartment of French and Francophone Studies, Brandon University, Brandon, MB R7A 6A9, CanadaAfter more than 30 years since its inception, the utility of brain imaging for understanding and diagnosing mental illnesses is in doubt, receiving well-grounded criticisms from clinical practitioners. Symptom-based correlational approaches have struggled to provide psychiatry with reliable brain-imaging metrics. However, the emergence of computational psychiatry has paved a new path not only for understanding the psychopathology of mental illness but also to provide practical tools for clinical practice in terms of computational metrics, specifically computational phenotypes. However, these phenotypes still lack sufficient test–retest reliability. In this review, we describe recent works revealing that mind and brain-related computational phenotypes show structural (not random) variation over time, longitudinal changes. Furthermore, we show that these findings suggest that understanding the causes of these changes will improve the construct validity of the phenotypes with an ensuing increase in test–retest reliability. We propose that the active inference framework offers a general-purpose approach for causally understanding these longitudinal changes by incorporating brain imaging as observations within partially observable Markov decision processes.https://www.mdpi.com/2076-3425/14/12/1278computational phenotypescomputational psychiatryactive inferencecomputational psychopathologylinguistic phenotypesfree energy principle |
| spellingShingle | Roberto Limongi Alexandra B. Skelton Lydia H. Tzianas Angelica M. Silva Increasing the Construct Validity of Computational Phenotypes of Mental Illness Through Active Inference and Brain Imaging Brain Sciences computational phenotypes computational psychiatry active inference computational psychopathology linguistic phenotypes free energy principle |
| title | Increasing the Construct Validity of Computational Phenotypes of Mental Illness Through Active Inference and Brain Imaging |
| title_full | Increasing the Construct Validity of Computational Phenotypes of Mental Illness Through Active Inference and Brain Imaging |
| title_fullStr | Increasing the Construct Validity of Computational Phenotypes of Mental Illness Through Active Inference and Brain Imaging |
| title_full_unstemmed | Increasing the Construct Validity of Computational Phenotypes of Mental Illness Through Active Inference and Brain Imaging |
| title_short | Increasing the Construct Validity of Computational Phenotypes of Mental Illness Through Active Inference and Brain Imaging |
| title_sort | increasing the construct validity of computational phenotypes of mental illness through active inference and brain imaging |
| topic | computational phenotypes computational psychiatry active inference computational psychopathology linguistic phenotypes free energy principle |
| url | https://www.mdpi.com/2076-3425/14/12/1278 |
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