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Discriminating cognitive status in Parkinson's disease through functional connectomics and machine learning.

ABSTRACT: There is growing interest in the potential of neuroimaging to help develop non-invasive biomarkers in neurodegenerative diseases. In this study, connection-wise patterns of functional connectivity were used to distinguish Parkinson's disease patients according to cognitive status using machine learning. Two independent subject samples were assessed with resting-state fMRI. The first (training) sample comprised 38 healthy controls and 70 Parkinson's disease patients (27 with mild cognitive impairment). The second (validation) sample included 25 patients (8 with mild cognitive impairment). The Brainnetome atlas was used to reconstruct the functional connectomes. Using a support vector machine trained on features selected through randomized logistic regression with leave-one-out cross-validation, a mean accuracy of 82.6% (p < 0.002) was achieved in separating patients with mild cognitive impairment from those without it in the training sample. The model trained on the whole training sample achieved an accuracy of 80.0% when used to classify the validation sample (p = 0.006). Correlation analyses showed that the connectivity level in the edges most consistently selected as features was associated with memory and executive function performance in the patient group. Our results demonstrate that connection-wise patterns of functional connectivity may be useful for discriminating Parkinson's disease patients according to the presence of cognitive deficits.

SUBMITTER: Abos A

PROVIDER: S-EPMC5368610 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Discriminating cognitive status in Parkinson's disease through functional connectomics and machine learning.

Abós Alexandra A Baggio Hugo C HC Segura Bàrbara B García-Díaz Anna I AI Compta Yaroslau Y Martí Maria José MJ Valldeoriola Francesc F Junqué Carme C

Scientific reports 20170328

There is growing interest in the potential of neuroimaging to help develop non-invasive biomarkers in neurodegenerative diseases. In this study, connection-wise patterns of functional connectivity were used to distinguish Parkinson's disease patients according to cognitive status using machine learning. Two independent subject samples were assessed with resting-state fMRI. The first (training) sample comprised 38 healthy controls and 70 Parkinson's disease patients (27 with mild cognitive impair ...[more]

PMID: 28349948

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Project description:BackgroundClinical markers of cognitive decline in Parkinson's disease (PD) encompass several mental non-motor symptoms such as hallucinations, apathy, anxiety, and depression. Furthermore, freezing of gait (FOG) and specific gait alterations have been associated with cognitive dysfunction in PD. Finally, although low cerebrospinal fluid levels of amyloid-β42 have been found to predict cognitive decline in PD, hitherto PET imaging of amyloid-β (Aβ) failed to consistently demonstrate the association between Aβ plaques deposition and mild cognitive impairment in PD (PD-MCI).AimFinding significant features associated with PD-MCI through a machine learning approach.Patients and methodsPatients were assessed with an extensive clinical and neuropsychological examination. Clinical evaluation included the assessment of mental non-motor symptoms and FOG using the specific items of the MDS-UPDRS I and II. Based on the neuropsychological examination, patients were classified as subjects without and with MCI (noPD-MCI, PD-MCI). All patients were evaluated using a motion analysis system. A subgroup of PD patients also underwent amyloid PET imaging. PD-MCI and noPD-MCI subjects were compared with a univariate statistical analysis on demographic data, clinical features, gait analysis variables, and amyloid PET data. Then, machine learning analysis was performed two times: Model 1 was implemented with age, clinical variables (hallucinations/psychosis, depression, anxiety, apathy, sleep problems, FOG), and gait features, while Model 2, including only the subgroup performing PET, was implemented with PET variables combined with the top five features of the former model.ResultsSeventy-five PD patients were enrolled (33 PD-MCI and 42 noPD-MCI). PD-MCI vs. noPD-MCI resulted in older and showed worse gait patterns, mainly characterized by increased dynamic instability and reduced step length; when comparing amyloid PET data, the two groups did not differ. Regarding the machine learning analyses, evaluation metrics were satisfactory for Model 1 overcoming 80% for accuracy and specificity, whereas they were disappointing for Model 2.ConclusionsThis study demonstrates that machine learning implemented with specific clinical features and gait variables exhibits high accuracy in predicting PD-MCI, whereas amyloid PET imaging is not able to increase prediction. Additionally, our results prompt that a data mining approach on certain gait parameters might represent a reliable surrogate biomarker of PD-MCI.

Project description:There is an urgent need to identify predictive biomarkers of Parkinson's disease (PD) with cognitive impairment (PDCI) in order to individualize patient management, ensure timely intervention, and improve prognosis. The aim of this study was to screen for these biomarkers by comparing the plasma proteome and metabolome of PD patients with or without cognitive impairment. Proteomics and metabolomics analyses were performed on a discover cohort. A machine learning model was used to identify candidate protein and metabolite biomarkers of PDCI, which were validated in an independent cohort. The predictive ability of these biomarkers for PDCI was evaluated by plotting receiver operating characteristic curves and calculating the area under the curve (AUC). Moreover, we assessed the predictive ability of these proteins in combination with neuroimaging. In the discover cohort (n = 100), we identified 25 protein features with best results in the machine learning model, including top-ranked PSAP and H3C15. The two-proteins were used for model construction, achieving an Area under the curve (AUC) of 0.951 in the train set and AUC of 0.981 in the test set. Similarly, the model gives a rank list of endogenous metabolite features, Glycocholic Acid and 6-Methylnicotinamide were two top features. Combining these two markers further got the AUC of 0.969 in train set and 0.867 in the test set. To validate the performance of the protein biomarkers, we performed targeted analysis of selected proteins (H3C15 and PSAP) and proteins likely associated with PDCI (NCAM2 and LAMB2) using parallel reaction monitoring in validation cohort (n = 116). The AUC of the classifier built with H3C15 and PSAP is 0.813. Moreover, when combining H3C15, PSAP, NCAM2, and LAMB2, the model achieved AUC of 0.983 in the train set, AUC of 0.981 in the test set, and AUC of 0.839 in the validation set. Furthermore, we verified that these protein markers we discovered can improve the predictive effect of neuroimaging on PDCI: the classifier built with neuroimaging features had AUC of 0.833, which improved to 0.905 when combined with H3C15. Taken together, our integrated proteomics and metabolomics analysis successfully identified potential biomarkers for PDCI. Additionally, H3C15 showed promise in enhancing the predictive performance of neuroimaging for cognitive impairment.

Project description:OBJECTIVE:Evaluation of a data-driven, model-based classification approach to discriminate idiopathic Parkinson's disease (PD) patients from healthy controls (HC) based on between-network connectivity in whole-brain resting-state functional MRI (rs-fMRI). METHODS:Whole-brain rs-fMRI (EPI, TR = 2.2?s, TE = 30?ms, flip angle = 90°. resolution = 3.1 × 3.1 × 3.1?mm, acquisition time ? 11?min) was assessed in 42 PD patients (medical OFF) and 47 HC matched for age and gender. Between-network connectivity based on full and L2-regularized partial correlation measures were computed for each subject based on canonical functional network architectures of two cohorts at different levels of granularity (Human Connectome Project: 15/25/50/100/200 networks; 1000BRAINS: 15/25/50/70 networks). A Boosted Logistic Regression model was trained on the correlation matrices using a nested cross-validation (CV) with 10 outer and 10 inner folds for an unbiased performance estimate, treating the canonical functional network architecture and the type of correlation as hyperparameters. The number of boosting iterations was fixed at 100. The model with the highest mean accuracy over the inner folds was trained using an non-nested 10-fold 20-repeats CV over the whole dataset to determine feature importance. RESULTS:Over the outer folds the mean accuracy was found to be 76.2% (median 77.8%, SD 18.2, IQR 69.4 - 87.1%). Mean sensitivity was 81% (median 80%, SD 21.1, IQR 75 - 100%) and mean specificity was 72.7% (median 75%, SD 20.4, IQR 66.7 - 80%). The 1000BRAINS 50-network-parcellation, using full correlations, performed best over the inner folds. The top features predominantly included sensorimotor as well as sensory networks. CONCLUSION:A rs-fMRI whole-brain-connectivity, data-driven, model-based approach to discriminate PD patients from healthy controls shows a very good accuracy and a high sensitivity. Given the high sensitivity of the approach, it may be of use in a screening setting. ADVANCES IN KNOWLEDGE:Resting-state functional MRI could prove to be a valuable, non-invasive neuroimaging biomarker for neurodegenerative diseases. The current model-based, data-driven approach on whole-brain between-network connectivity to discriminate Parkinson's disease patients from healthy controls shows promising results with a very good accuracy and a very high sensitivity.

Dataset Information

Discriminating cognitive status in Parkinson's disease through functional connectomics and machine learning.

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Discriminating cognitive status in Parkinson's disease through functional connectomics and machine learning.

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