Multiplex Networks to Characterize Seizure Development in Traumatic Brain Injury Patients

Rocca, Marianna La; Garner, Rachael; Amoroso, Nicola; Lutkenhoff, Evan S.; Monti, Martin M.; Vespa, Paul; Toga, Arthur W.; Duncan, Dominique

doi:10.3389/fnins.2020.591662

Cited by 15 publications

(13 citation statements)

References 35 publications

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“…Magnetic resonance imaging (MRI) methods that probe structural and functional alterations after TBI are promising for biomarker detection [14]. Diffusionweighted MRI (dMRI) is one method that researchers have used to identify changes in white matter (WM) connectivity that relate to seizure development [10].…”

Section: Introductionmentioning

confidence: 99%

Lesion Normalization and Supervised Learning in Post-Traumatic Seizure Classification with Diffusion MRI

Akbar

Ruf

Rocca

et al. 2021

Preprint

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Traumatic brain injury (TBI) is a serious condition, potentially causing seizures and other lifelong disabilities. Patients who experience at least one seizure one week after TBI (late seizure) are at high risk for lifelong complications of TBI, such as post-traumatic epilepsy (PTE). Identifying which TBI patients are at risk of developing seizures remains a challenge. Although magnetic resonance imaging (MRI) methods that probe structural and functional alterations after TBI are promising for biomarker detection, physical deformations following moderate-severe TBI present problems for standard processing of neuroimaging data, complicating the search for biomarkers. In this work, we consider a prediction task to identify which TBI patients will develop late seizures, using fractional anisotropy (FA) features from white matter tracts in diffusion-weighted MRI (dMRI). To understand how best to account for brain lesions and deformations, four preprocessing strategies are applied to dMRI, including the novel application of a lesion normalization technique to dMRI. The pipeline involving the lesion normalization technique provides the best prediction performance, with a mean accuracy of 0.819 and a mean area under the curve of 0.785. Finally, following statistical analyses of selected features, we recommend the dMRI alterations of a certain white matter tract as a potential biomarker.

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Section: Introductionmentioning

confidence: 99%

Lesion Normalization and Supervised Learning in Post-Traumatic Seizure Classification with Diffusion MRI

Akbar

Ruf

Rocca

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Functional neuroimaging and/or network measurement are often used for this prediction (Paldino et al, 2017a(Paldino et al, ,b, 2019He et al, 2018;Zhang et al, 2018;Mazrooyisebdani et al, 2020). Other ML applications included predicting drug responsiveness (Amarreh et al, 2014;Yao et al, 2019), acquiring epileptogenicity Akeret et al, 2020;La Rocca et al, 2020;, and the differentiation of types of autoantibodies (Ernst et al, 2019).…”

Section: Prediction Of Clinical Outcomesmentioning

confidence: 99%

Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review

Sone

Beheshti

2021

Front. Neurosci.

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Epilepsy is a common neurological disorder characterized by recurrent and disabling seizures. An increasing number of clinical and experimental applications of machine learning (ML) methods for epilepsy and other neurological and psychiatric disorders are available. ML methods have the potential to provide a reliable and optimal performance for clinical diagnoses, prediction, and personalized medicine by using mathematical algorithms and computational approaches. There are now several applications of ML for epilepsy, including neuroimaging analyses. For precise and reliable clinical applications in epilepsy and neuroimaging, the diverse ML methodologies should be examined and validated. We review the clinical applications of ML models for brain imaging in epilepsy obtained from a PubMed database search in February 2021. We first present an overview of typical neuroimaging modalities and ML models used in the epilepsy studies and then focus on the existing applications of ML models for brain imaging in epilepsy based on the following clinical aspects: (i) distinguishing individuals with epilepsy from healthy controls, (ii) lateralization of the temporal lobe epilepsy focus, (iii) the identification of epileptogenic foci, (iv) the prediction of clinical outcomes, and (v) brain-age prediction. We address the practical problems and challenges described in the literature and suggest some future research directions.

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“…Because of the potential problem of overfitting ( 136 ) of the evaluation statistical model, investigators now seek to measure an “optimism corrected AUC,” which corrects for/avoids optimism by either: cross-validation with replication ( 137 – 139 ) or leave-pair-out cross-validation ( 140 ).…”

Section: Background For Understanding Seizure Generation Inhibition and Propagationmentioning

confidence: 99%

Prediction of Seizure Recurrence. A Note of Caution

2021

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Great strides have been made recently in documenting that machine-learning programs can predict seizure occurrence in people who have epilepsy. Along with this progress have come claims that appear to us to be a bit premature. We anticipate that many people will benefit from seizure prediction. We also doubt that all will benefit. Although machine learning is a useful tool for aiding discovery, we believe that the greatest progress will come from deeper understanding of seizures, epilepsy, and the EEG features that enable seizure prediction. In this essay, we lay out reasons for optimism and skepticism.

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Multiplex Networks to Characterize Seizure Development in Traumatic Brain Injury Patients

Cited by 15 publications

References 35 publications

Lesion Normalization and Supervised Learning in Post-Traumatic Seizure Classification with Diffusion MRI

Lesion Normalization and Supervised Learning in Post-Traumatic Seizure Classification with Diffusion MRI

Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review

Prediction of Seizure Recurrence. A Note of Caution

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