Machine-learning identifies Parkinson's disease patients based on resting-state between-network functional connectivity.

Authors:
Christian Rubbert
Christian Rubbert
University Hospitals Case Medical Center
United States
Christian Mathys
Christian Mathys
University Düsseldorf
Germany
Christiane Jockwitz
Christiane Jockwitz
University of Bonn
Christian J Hartmann
Christian J Hartmann
Institute of Clinical Neuroscience and Medical Psychology
Germany
Simon B Eickhoff
Simon B Eickhoff
RWTH Aachen University
Germany
Felix Hoffstaedter
Felix Hoffstaedter
RWTH Aachen University
Germany
Svenja Caspers
Svenja Caspers
Institute of Neuroscience and Medicine (INM-1
Germany
Claudia R Eickhoff
Claudia R Eickhoff
Institute of Neuroscience and Medicine (INM-1)
Morgantown | United States

Br J Radiol 2019 May 14:20180886. Epub 2019 May 14.

1 University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology , D-40225 Dusseldorf , Germany.

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.

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Source
http://dx.doi.org/10.1259/bjr.20180886DOI Listing
May 2019
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References

(Supplied by CrossRef)
Article in Front Neurol
Tessitore A et al.
Front Neurol 2014

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