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01-05-2018 | Original Paper

Machine Learning EEG to Predict Cognitive Functioning and Processing Speed Over a 2-Year Period in Multiple Sclerosis Patients and Controls

Authors: Hanni Kiiski, Lee Jollans, Seán Ó. Donnchadha, Hugh Nolan, Róisín Lonergan, Siobhán Kelly, Marie Claire O’Brien, Katie Kinsella, Jessica Bramham, Teresa Burke, Michael Hutchinson, Niall Tubridy, Richard B. Reilly, Robert Whelan

Published in: Brain Topography | Issue 3/2018

Abstract

Event-related potentials (ERPs) show promise to be objective indicators of cognitive functioning. The aim of the study was to examine if ERPs recorded during an oddball task would predict cognitive functioning and information processing speed in Multiple Sclerosis (MS) patients and controls at the individual level. Seventy-eight participants (35 MS patients, 43 healthy age-matched controls) completed visual and auditory 2- and 3-stimulus oddball tasks with 128-channel EEG, and a neuropsychological battery, at baseline (month 0) and at Months 13 and 26. ERPs from 0 to 700 ms and across the whole scalp were transformed into 1728 individual spatio-temporal datapoints per participant. A machine learning method that included penalized linear regression used the entire spatio-temporal ERP to predict composite scores of both cognitive functioning and processing speed at baseline (month 0), and months 13 and 26. The results showed ERPs during the visual oddball tasks could predict cognitive functioning and information processing speed at baseline and a year later in a sample of MS patients and healthy controls. In contrast, ERPs during auditory tasks were not predictive of cognitive performance. These objective neurophysiological indicators of cognitive functioning and processing speed, and machine learning methods that can interrogate high-dimensional data, show promise in outcome prediction.

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Title: Machine Learning EEG to Predict Cognitive Functioning and Processing Speed Over a 2-Year Period in Multiple Sclerosis Patients and Controls
Authors: Hanni Kiiski
Lee Jollans
Seán Ó. Donnchadha
Hugh Nolan
Róisín Lonergan
Siobhán Kelly
Marie Claire O’Brien
Katie Kinsella
Jessica Bramham
Teresa Burke
Michael Hutchinson
Niall Tubridy
Richard B. Reilly
Robert Whelan
Publication date: 01-05-2018
Publisher: Springer US
Published in: Brain Topography / Issue 3/2018
Print ISSN: 0896-0267
Electronic ISSN: 1573-6792
DOI: https://doi.org/10.1007/s10548-018-0620-4

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Machine Learning EEG to Predict Cognitive Functioning and Processing Speed Over a 2-Year Period in Multiple Sclerosis Patients and Controls

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