Event-related potentials and cognitive performance in multiple sclerosis patients with fatigue

Pokryszko−Dragan, Anna; Zagrajek, Mieszko; Słotwiński, Krzysztof; Bilińska, Małgorzata; Gruszka, Ewa; Podemski, Ryszard

doi:10.1007/s10072-016-2622-x

Cited by 45 publications

(24 citation statements)

References 34 publications

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“…In a similar manner, cognitive measures were the subject of some studies. While some studies have found a correlation between self-reported fatigue and each of attention [80,165], memory [165] and information processing speed [3,142]; others failed to detect such a relationship [5,158,194]. The discrepancy in these findings might lie in the difference among the trials in terms of chosen cohorts and/or employed motor/cognitive tasks (e.g.…”

Section: Objective Measuresmentioning

confidence: 84%

Fatigue in multiple sclerosis – Insights into evaluation and management

Ayache

Chalah

2017

Neurophysiologie Clinique/Clinical Neurophysiology

126

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Section: Objective Measuresmentioning

confidence: 84%

Fatigue in multiple sclerosis – Insights into evaluation and management

Ayache

Chalah

2017

Neurophysiologie Clinique/Clinical Neurophysiology

126

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“…[29][30][31] Relationships between ERP parameters and the duration of the disease or the degree of disability were more disputable. 27,28,32 Sundgren et al pointed to the prognostic importance of the P300 parameters. 33 They noted that the progression of cognitive disorders was accompanied by an increased latency and decreased amplitude of the P300 wave.…”

Section: Discussionmentioning

confidence: 99%

The relationship between event-related potentials, stress perception and personality type in patients with multiple sclerosis without cognitive impairment: A pilot study

Waliszewska‐Prosół¹,

Nowakowska-Kotas²,

Kotas³

et al. 2018

Adv Clin Exp Med

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“…ERPs also provide non-invasive neurophysiological measurements with high temporal resolution, allowing to assess dysfunctional brain dynamics, including cognitive processes that may not be apparent at the behavioral level (Woodman, 2010;Sanei and Chambers, 2013). Indeed, ERPs are commonly used clinically in neurophysiological diagnostic units to support the assessment of neuropsychiatric disorders [e.g., multiple sclerosis (Pokryszko-Dragan et al, 2016)] and sensory disorders [e.g., screening of neonates for hearing impairments (Paulraj et al, 2015)].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Convolutional Neural Networks Discriminate Adult ADHD From Healthy Individuals on the Basis of Event-Related Spectral EEG

Dubreuil-Vall

Ruffini²,

Camprodon

2020

Front. Neurosci.

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Attention deficit hyperactivity disorder (ADHD) is a heterogeneous neurodevelopmental disorder that affects 5% of the pediatric and adult population worldwide. The diagnosis remains essentially clinical, based on history and exam, with no available biomarkers. In this paper, we describe a convolutional neural network (CNN) with a four-layer architecture combining filtering and pooling, which we train using stacked multi-channel EEG time-frequency decompositions (spectrograms) of electroencephalography data (EEG), particularly of event-related potentials (ERP) from ADHD patients (n = 20) and healthy controls (n = 20) collected during the Flanker Task, with 2800 samples for each group. We treat the data as in audio or image classification approaches, where deep networks have proven successful by exploiting invariances and compositional features in the data. The model reaches a classification accuracy of 88% ± 1.12%, outperforming the Recurrent Neural Network and the Shallow Neural Network used for comparison, and with the key advantage, compared with other machine learning approaches, of avoiding the need for manual selection of EEG spectral or channel features. The event-related spectrograms also provide greater accuracy compared to resting state EEG spectrograms. Finally, through the use of feature visualization techniques such as DeepDream, we show that the main features exciting the CNN nodes are a decreased power in the alpha band and an increased power in the delta-theta band around 100 ms for ADHD patients compared to healthy controls, suggestive of attentional and inhibition deficits, which have been previously suggested as pathophyisiological signatures of ADHD. While confirmation with larger clinical samples is necessary, these results suggest that deep networks may provide a useful tool for the analysis of EEG dynamics even from relatively small datasets, highlighting the potential of this methodology to develop biomarkers of practical clinical utility.

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Event-related potentials and cognitive performance in multiple sclerosis patients with fatigue

Cited by 45 publications

References 34 publications

Fatigue in multiple sclerosis – Insights into evaluation and management

Fatigue in multiple sclerosis – Insights into evaluation and management

The relationship between event-related potentials, stress perception and personality type in patients with multiple sclerosis without cognitive impairment: A pilot study

Deep Learning Convolutional Neural Networks Discriminate Adult ADHD From Healthy Individuals on the Basis of Event-Related Spectral EEG

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