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Classification of Parkinson's disease with and without dopaminergic deficiency based on non-motor symptoms and structural neuroimaging.

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Title: Classification of Parkinson's disease with and without dopaminergic deficiency based on non-motor symptoms and structural neuroimaging.
Authors: Ronat, Lucas (AUTHOR), Rainville, Pierre (AUTHOR), Monchi, Oury (AUTHOR), Hanganu, Alexandru (AUTHOR)
Source: Neurological Sciences. Jun2025, Vol. 46 Issue 6, p2611-2625. 15p.
Subjects: Parkinson's disease, Medical sciences, Clinical neurosciences, Logistic regression analysis, Magnetic resonance imaging
Abstract: The presence of non-motor symptoms (NMS) such as olfactive deficit or neuropsychiatric symptoms has been associated with the diagnosis of Parkinson's Disease (PD). NMS are also associated with different brain structural features underlying distinctive processes in PD. NMS has been poorly studied in patients with a PD-like clinical profile, showing Scans Without Evidence of Dopaminergic Deficit (SWEDD). This study proposes to compare classification models differentiating PD, SWEDD and Healthy Controls (HC) based on NMS and neurostructural factors. 683 participants (382 PD diagnosed in the last 2 years, 48 with SWEDD, 170 HC) from the PPMI dataset were compared based on available assessments. Each participant underwent an olfactive, neuropsychiatric and sleep assessment, and a 3T MRI. Brain volumes were extracted and standardized from each MRI. Classifications were based on logistic regressions using 5-fold cross-validation models combining different NMS and MRI data and determining their involvement in differentiation between patient subgroups (PD vs. SWEDD) or between patients and HC. NMS were significant factors in PD vs. SWEDD, PD vs. HC and SWEDD vs. HC classifiers, when considered alone or in combination with MRI data. No classification models were significantly different from chance based-on MRI, nor more accurate combining NMS and MRI when compared with models based on NMS only. These results highlight the importance of NMS in differentiating between PD and SWEDD, PD and HC, SWEDD and HC. However, classical imaging data such as cortical and subcortical volumetry seems insufficient to improve these classifications. Other imaging features such as connectivity could also be studied. [ABSTRACT FROM AUTHOR]
Copyright of Neurological Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Classification of Parkinson's disease with and without dopaminergic deficiency based on non-motor symptoms and structural neuroimaging.
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  Data: <searchLink fieldCode="AR" term="%22Ronat%2C+Lucas%22">Ronat, Lucas</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rainville%2C+Pierre%22">Rainville, Pierre</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Monchi%2C+Oury%22">Monchi, Oury</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hanganu%2C+Alexandru%22">Hanganu, Alexandru</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Neurological+Sciences%22">Neurological Sciences</searchLink>. Jun2025, Vol. 46 Issue 6, p2611-2625. 15p.
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  Data: <searchLink fieldCode="DE" term="%22Parkinson's+disease%22">Parkinson's disease</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+sciences%22">Medical sciences</searchLink><br /><searchLink fieldCode="DE" term="%22Clinical+neurosciences%22">Clinical neurosciences</searchLink><br /><searchLink fieldCode="DE" term="%22Logistic+regression+analysis%22">Logistic regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging%22">Magnetic resonance imaging</searchLink>
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  Data: The presence of non-motor symptoms (NMS) such as olfactive deficit or neuropsychiatric symptoms has been associated with the diagnosis of Parkinson's Disease (PD). NMS are also associated with different brain structural features underlying distinctive processes in PD. NMS has been poorly studied in patients with a PD-like clinical profile, showing Scans Without Evidence of Dopaminergic Deficit (SWEDD). This study proposes to compare classification models differentiating PD, SWEDD and Healthy Controls (HC) based on NMS and neurostructural factors. 683 participants (382 PD diagnosed in the last 2 years, 48 with SWEDD, 170 HC) from the PPMI dataset were compared based on available assessments. Each participant underwent an olfactive, neuropsychiatric and sleep assessment, and a 3T MRI. Brain volumes were extracted and standardized from each MRI. Classifications were based on logistic regressions using 5-fold cross-validation models combining different NMS and MRI data and determining their involvement in differentiation between patient subgroups (PD vs. SWEDD) or between patients and HC. NMS were significant factors in PD vs. SWEDD, PD vs. HC and SWEDD vs. HC classifiers, when considered alone or in combination with MRI data. No classification models were significantly different from chance based-on MRI, nor more accurate combining NMS and MRI when compared with models based on NMS only. These results highlight the importance of NMS in differentiating between PD and SWEDD, PD and HC, SWEDD and HC. However, classical imaging data such as cortical and subcortical volumetry seems insufficient to improve these classifications. Other imaging features such as connectivity could also be studied. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Neurological Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10072-025-08045-6
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              Text: Jun2025
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