Machine learning trained with quantitative susceptibility mapping to detect mild cognitive impairment in Parkinson's disease

Shibata, Haruto; Uchida, Yuto; Inui, Shigeru; Kan, Hirohito; Sakurai, Keita; Oishi, Naoya; Ueki, Yoshino; Oishi, Keiji; Matsukawa, Noriyuki

doi:10.1016/j.parkreldis.2021.12.004

Cited by 19 publications

(25 citation statements)

References 29 publications

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“…Moreover, the multi-atlas label-fusion method for automated segmentation of QSM images has been developed as a more accurate quantification tool for determining the magnetic susceptibilities of individuals (Li et al, 2019). Figure 3 shows a machine learning model trained with the extracted magnetic susceptibilities using the multi-atlas label-fusion method to detect early cognitive impairments (Shibata et al, 2022).…”

Section: Expectationsmentioning

confidence: 99%

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Quantitative susceptibility mapping as an imaging biomarker for Alzheimer’s disease: The expectations and limitations

et al. 2022

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Alzheimer’s disease (AD) is the most common type of dementia and a distressing diagnosis for individuals and caregivers. Researchers and clinical trials have mainly focused on β-amyloid plaques, which are hypothesized to be one of the most important factors for neurodegeneration in AD. Meanwhile, recent clinicopathological and radiological studies have shown closer associations of tau pathology rather than β-amyloid pathology with the onset and progression of Alzheimer’s symptoms. Toward a biological definition of biomarker-based research framework for AD, the 2018 National Institute on Aging–Alzheimer’s Association working group has updated the ATN classification system for stratifying disease status in accordance with relevant pathological biomarker profiles, such as cerebral β-amyloid deposition, hyperphosphorylated tau, and neurodegeneration. In addition, altered iron metabolism has been considered to interact with abnormal proteins related to AD pathology thorough generating oxidative stress, as some prior histochemical and histopathological studies supported this iron-mediated pathomechanism. Quantitative susceptibility mapping (QSM) has recently become more popular as a non-invasive magnetic resonance technique to quantify local tissue susceptibility with high spatial resolution, which is sensitive to the presence of iron. The association of cerebral susceptibility values with other pathological biomarkers for AD has been investigated using various QSM techniques; however, direct evidence of these associations remains elusive. In this review, we first briefly describe the principles of QSM. Second, we focus on a large variety of QSM applications, ranging from common applications, such as cerebral iron deposition, to more recent applications, such as the assessment of impaired myelination, quantification of venous oxygen saturation, and measurement of blood– brain barrier function in clinical settings for AD. Third, we mention the relationships among QSM, established biomarkers, and cognitive performance in AD. Finally, we discuss the role of QSM as an imaging biomarker as well as the expectations and limitations of clinically useful diagnostic and therapeutic implications for AD.

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Section: Expectationsmentioning

confidence: 99%

“…The right-hand panel shows the pipeline for developing the machine learning-based models (B). MNI, Montreal Neurologic Institute; PD-MCI, Parkinson's disease with mild cognitive impairment; PD-CN, Parkinson's disease with normal cognition; QSM, quantitative susceptibility mapping; T1WI, T1-weighted image (adapted with permission fromShibata et al, 2022).…”

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confidence: 99%

Quantitative susceptibility mapping as an imaging biomarker for Alzheimer’s disease: The expectations and limitations

et al. 2022

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“…Lee et al confirmed that an XGBoost model based on electroencephalography signals had a good effect in the diagnosis of PD [ 21 ], with the highest accuracy rate of 71.4%. Shibata et al applied the XGBoost model based on quantitative susceptibility mapping (a type of MR method reflecting iron deposition) features to classify PD-MCI and PD-NC patients, achieving an accuracy of 79.1% [ 32 ].…”

Section: Discussionmentioning

confidence: 99%

Detection of mild cognitive impairment in Parkinson’s disease using gradient boosting decision tree models based on multilevel DTI indices

Chen

et al. 2023

J Transl Med

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Background Cognitive dysfunction is the most common non-motor symptom in Parkinson’s disease (PD), and timely detection of a slight cognitive decline is crucial for early treatment and prevention of dementia. This study aimed to build a machine learning model based on intra- and/or intervoxel metrics extracted from diffusion tensor imaging (DTI) to automatically classify PD patients without dementia into mild cognitive impairment (PD-MCI) and normal cognition (PD-NC) groups. Methods We enrolled PD patients without dementia (52 PD-NC and 68 PD-MCI subtypes) who were assigned to the training and test datasets in an 8:2 ratio. Four intravoxel metrics, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD), and two novel intervoxel metrics, local diffusion homogeneity (LDH) using Spearman’s rank correlation coefficient (LDHs) and Kendall’s coefficient concordance (LDHk), were extracted from the DTI data. Decision tree, random forest, and eXtreme gradient boosting (XGBoost) models based on individual and combined indices were built for classification, and model performance was assessed and compared via the area under the receiver operating characteristic curve (AUC). Finally, feature importance was evaluated using SHapley Additive exPlanation (SHAP) values. Results The XGBoost model based on a combination of the intra- and intervoxel indices achieved the best classification performance, with an accuracy of 91.67%, sensitivity of 92.86%, and AUC of 0.94 in the test dataset. SHAP analysis showed that the LDH of the brainstem and MD of the right cingulum (hippocampus) were important features. Conclusions More comprehensive information on white matter changes can be obtained by combining intra- and intervoxel DTI indices, improving classification accuracy. Furthermore, machine learning methods based on DTI indices can be used as alternatives for the automatic identification of PD-MCI at the individual level.

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“…The authors in [9] have focused on Mini-Mental State Examination with classical machine learning methods to extract long-term predictions. Different studies [10,11] have used specific tools to extract the cerebellar and subcortical features from MRI, before applying some classical machine learning algorithms to obtain multiple indicators to assist the clinical diagnosis.…”

Section: Parkinson Dedicated Studiesmentioning

confidence: 99%

Long-term cognitive decline prediction based on multi-modal data using Multimodal3DSiameseNet: transfer learning from Alzheimer’s disease to Parkinson’s disease

et al. 2023

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Purpose Monitoring and predicting the cognitive state of subjects with neurodegenerative disorders is crucial to provide appropriate treatment as soon as possible. In this work, we present a machine learning approach using multimodal data (brain MRI and clinical) from two early medical visits, to predict the longer-term cognitive decline of patients. Using transfer learning, our model can be successfully transferred from one neurodegenerative disease (Alzheimer’s) to another (Parkinson’s). Methods Our model is a Deep Neural Network with siamese sub-modules dedicated to extracting features from each modality. We pre-train it with data from ADNI (Alzheimer’s disease), then transfer it on the smaller PPMI dataset (Parkinson’s disease). We show that, even when we do not fine-tune the filters learnt from the ADNI MRIs, the transferred model’s results are satisfying on PPMI. Results The first main result is that our model provides satisfying long-term predictions of cognitive decline from any pair of early visits, with no fixed time delay between these visits (provided the potential decline has started at the second visit). The second main result is that the prediction performance on Parkinson’s dataset (PPMI) reaches an AUC of 0.81 on PPMI after transfer learning from Alzheimer’s dataset (ADNI), without even having to re-train the image filters, versus an AUC of 0.72 for the model trained from scratch on PPMI. Conclusions First, our model is effective for predicting long-term cognitive decline from only two visits, even with irregular intervals of time. When dealing with neurodegenerative diseases, where patients often miss some control visits, this is an important finding. Second, our model is able to transfer the knowledge learnt from one neurodegenerative disease (Alzheimer’s) to another (Parkinson’s), when using the same imaging modalities (brain MRI) and different clinical variables. This makes it usable even for diseases that are rare or under-studied.

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Machine learning trained with quantitative susceptibility mapping to detect mild cognitive impairment in Parkinson's disease

Cited by 19 publications

References 29 publications

Quantitative susceptibility mapping as an imaging biomarker for Alzheimer’s disease: The expectations and limitations

Quantitative susceptibility mapping as an imaging biomarker for Alzheimer’s disease: The expectations and limitations

Detection of mild cognitive impairment in Parkinson’s disease using gradient boosting decision tree models based on multilevel DTI indices

Long-term cognitive decline prediction based on multi-modal data using Multimodal3DSiameseNet: transfer learning from Alzheimer’s disease to Parkinson’s disease

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