Computational Predictions for OCD Pathophysiology and Treatment: A Review
Obsessive compulsive disorder (OCD) can manifest as a debilitating disease with high degrees of co-morbidity as well as clinical and etiological heterogenity. However, the underlying pathophysiology is not clearly understood. Computational psychiatry is an emerging field in which behavior and its ne...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-10-01
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| Series: | Frontiers in Psychiatry |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2021.687062/full |
| _version_ | 1819293368158519296 |
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| author | Krisztina Szalisznyó Krisztina Szalisznyó David N. Silverstein |
| author_facet | Krisztina Szalisznyó Krisztina Szalisznyó David N. Silverstein |
| author_sort | Krisztina Szalisznyó |
| collection | DOAJ |
| description | Obsessive compulsive disorder (OCD) can manifest as a debilitating disease with high degrees of co-morbidity as well as clinical and etiological heterogenity. However, the underlying pathophysiology is not clearly understood. Computational psychiatry is an emerging field in which behavior and its neural correlates are quantitatively analyzed and computational models are developed to improve understanding of disorders by comparing model predictions to observations. The aim is to more precisely understand psychiatric illnesses. Such computational and theoretical approaches may also enable more personalized treatments. Yet, these methodological approaches are not self-evident for clinicians with a traditional medical background. In this mini-review, we summarize a selection of computational OCD models and computational analysis frameworks, while also considering the model predictions from a perspective of possible personalized treatment. The reviewed computational approaches used dynamical systems frameworks or machine learning methods for modeling, analyzing and classifying patient data. Bayesian interpretations of probability for model selection were also included. The computational dissection of the underlying pathology is expected to narrow the explanatory gap between the phenomenological nosology and the neuropathophysiological background of this heterogeneous disorder. It may also contribute to develop biologically grounded and more informed dimensional taxonomies of psychopathology. |
| first_indexed | 2024-12-24T04:09:19Z |
| format | Article |
| id | doaj.art-dd52239067f74a86a30ecfb525965895 |
| institution | Directory Open Access Journal |
| issn | 1664-0640 |
| language | English |
| last_indexed | 2024-12-24T04:09:19Z |
| publishDate | 2021-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj.art-dd52239067f74a86a30ecfb5259658952022-12-21T17:16:07ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402021-10-011210.3389/fpsyt.2021.687062687062Computational Predictions for OCD Pathophysiology and Treatment: A ReviewKrisztina Szalisznyó0Krisztina Szalisznyó1David N. Silverstein2Department of Neuroscience and Psychiatry, Uppsala University Hospital, Uppsala, SwedenTheoretical Neuroscience Group, Wigner Research Centre for Physics, Hungarian Academy of Sciences, Budapest, HungaryAgora for Biosystems, Sigtuna Foundation, Sigtuna, SwedenObsessive compulsive disorder (OCD) can manifest as a debilitating disease with high degrees of co-morbidity as well as clinical and etiological heterogenity. However, the underlying pathophysiology is not clearly understood. Computational psychiatry is an emerging field in which behavior and its neural correlates are quantitatively analyzed and computational models are developed to improve understanding of disorders by comparing model predictions to observations. The aim is to more precisely understand psychiatric illnesses. Such computational and theoretical approaches may also enable more personalized treatments. Yet, these methodological approaches are not self-evident for clinicians with a traditional medical background. In this mini-review, we summarize a selection of computational OCD models and computational analysis frameworks, while also considering the model predictions from a perspective of possible personalized treatment. The reviewed computational approaches used dynamical systems frameworks or machine learning methods for modeling, analyzing and classifying patient data. Bayesian interpretations of probability for model selection were also included. The computational dissection of the underlying pathology is expected to narrow the explanatory gap between the phenomenological nosology and the neuropathophysiological background of this heterogeneous disorder. It may also contribute to develop biologically grounded and more informed dimensional taxonomies of psychopathology.https://www.frontiersin.org/articles/10.3389/fpsyt.2021.687062/fullOCDcomputational modelingtrans-diagnostic perspectivecomputational psychiatrypersonalized treatment |
| spellingShingle | Krisztina Szalisznyó Krisztina Szalisznyó David N. Silverstein Computational Predictions for OCD Pathophysiology and Treatment: A Review Frontiers in Psychiatry OCD computational modeling trans-diagnostic perspective computational psychiatry personalized treatment |
| title | Computational Predictions for OCD Pathophysiology and Treatment: A Review |
| title_full | Computational Predictions for OCD Pathophysiology and Treatment: A Review |
| title_fullStr | Computational Predictions for OCD Pathophysiology and Treatment: A Review |
| title_full_unstemmed | Computational Predictions for OCD Pathophysiology and Treatment: A Review |
| title_short | Computational Predictions for OCD Pathophysiology and Treatment: A Review |
| title_sort | computational predictions for ocd pathophysiology and treatment a review |
| topic | OCD computational modeling trans-diagnostic perspective computational psychiatry personalized treatment |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2021.687062/full |
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