P 136 Brain network analysis of EEG data in the service of clinical assessment--utilizing big data and prior theoretical knowledge to identify a biomarker for mTBI in adolscents

A Reches; H Or-ly; M Weiss; Y Stern; JC Baumeister; KB Foss; J Ellis; B Laish; O Laufer; B Sadeh; M. Ettinger; T.  Arthur; G.  Shaham; G. Myer; O. Kehat; R.   Shani-Hershkovich; Z. Peremen; Amir Geva

doi:10.1016/j.clinph.2017.06.209

P 136 Brain network analysis of EEG data in the service of clinical assessment--utilizing big data and prior theoretical knowledge to identify a biomarker for mTBI in adolscents

A Reches, H Or-ly, M Weiss, Y Stern, JC Baumeister, KB Foss, J Ellis, B Laish, O Laufer, B Sadeh, M. Ettinger, T. Arthur, G. Shaham, G. Myer, O. Kehat, R. Shani-Hershkovich, Z. Peremen, Amir Geva

Research output: Contribution to journal › Meeting Abstract

Abstract

Mild traumatic brain injury (mTBI) in adolecents has gained
increased attention in recent years amongst parents, clinicians, and
researchers due to their growing rate and hazardous outcomes.
Electroencephalography (EEG) and Event-Related Potential (ERP)
have been encouraged as a diagnostic tool for mTBI due to its objectivity and cost-effectiveness. However, extracting clinically meaningful neuro-cognitive information from human EEG/ERP is
challenging, particularly in highly variable groups like adolescents.
Therefore, it is not surprising that despite them being especially susceptible to the effects of mTBI, no sensitive and specific application
of EEG has yet been determined for this age group.

Original language	English
Pages (from-to)	e395
Number of pages	1
Journal	Clinical Neurophysiology
Volume	128
Issue number	10
DOIs	https://doi.org/10.1016/j.clinph.2017.06.209
State	Published - Oct 2017

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Reches, A., Or-ly, H., Weiss, M., Stern, Y., Baumeister, JC., Foss, KB., Ellis, J., Laish, B., Laufer, O., Sadeh, B., Ettinger, M., Arthur, T., Shaham, G., Myer, G., Kehat, O., Shani-Hershkovich, R., Peremen, Z., & Geva, A. (2017). P 136 Brain network analysis of EEG data in the service of clinical assessment--utilizing big data and prior theoretical knowledge to identify a biomarker for mTBI in adolscents. Clinical Neurophysiology, 128(10), e395. https://doi.org/10.1016/j.clinph.2017.06.209

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title = "P 136 Brain network analysis of EEG data in the service of clinical assessment--utilizing big data and prior theoretical knowledge to identify a biomarker for mTBI in adolscents",

abstract = "Mild traumatic brain injury (mTBI) in adolecents has gainedincreased attention in recent years amongst parents, clinicians, andresearchers due to their growing rate and hazardous outcomes.Electroencephalography (EEG) and Event-Related Potential (ERP)have been encouraged as a diagnostic tool for mTBI due to its objectivity and cost-effectiveness. However, extracting clinically meaningful neuro-cognitive information from human EEG/ERP ischallenging, particularly in highly variable groups like adolescents.Therefore, it is not surprising that despite them being especially susceptible to the effects of mTBI, no sensitive and specific applicationof EEG has yet been determined for this age group.",

author = "A Reches and H Or-ly and M Weiss and Y Stern and JC Baumeister and KB Foss and J Ellis and B Laish and O Laufer and B Sadeh and M. Ettinger and T. Arthur and G. Shaham and G. Myer and O. Kehat and R. Shani-Hershkovich and Z. Peremen and Amir Geva",

year = "2017",

month = oct,

doi = "10.1016/j.clinph.2017.06.209",

language = "English",

volume = "128",

pages = "e395",

journal = "Clinical Neurophysiology",

issn = "1388-2457",

publisher = "Elsevier Ireland Ltd",

number = "10",

}

Reches, A, Or-ly, H, Weiss, M, Stern, Y, Baumeister, JC, Foss, KB, Ellis, J, Laish, B, Laufer, O, Sadeh, B, Ettinger, M, Arthur, T, Shaham, G, Myer, G, Kehat, O, Shani-Hershkovich, R, Peremen, Z & Geva, A 2017, 'P 136 Brain network analysis of EEG data in the service of clinical assessment--utilizing big data and prior theoretical knowledge to identify a biomarker for mTBI in adolscents', Clinical Neurophysiology, vol. 128, no. 10, pp. e395. https://doi.org/10.1016/j.clinph.2017.06.209

TY - JOUR

T1 - P 136 Brain network analysis of EEG data in the service of clinical assessment--utilizing big data and prior theoretical knowledge to identify a biomarker for mTBI in adolscents

AU - Reches, A

AU - Or-ly, H

AU - Weiss, M

AU - Stern, Y

AU - Baumeister, JC

AU - Foss, KB

AU - Ellis, J

AU - Laish, B

AU - Laufer, O

AU - Sadeh, B

AU - Ettinger, M.

AU - Arthur, T.

AU - Shaham, G.

AU - Myer, G.

AU - Kehat, O.

AU - Shani-Hershkovich, R.

AU - Peremen, Z.

AU - Geva, Amir

PY - 2017/10

Y1 - 2017/10

N2 - Mild traumatic brain injury (mTBI) in adolecents has gainedincreased attention in recent years amongst parents, clinicians, andresearchers due to their growing rate and hazardous outcomes.Electroencephalography (EEG) and Event-Related Potential (ERP)have been encouraged as a diagnostic tool for mTBI due to its objectivity and cost-effectiveness. However, extracting clinically meaningful neuro-cognitive information from human EEG/ERP ischallenging, particularly in highly variable groups like adolescents.Therefore, it is not surprising that despite them being especially susceptible to the effects of mTBI, no sensitive and specific applicationof EEG has yet been determined for this age group.

AB - Mild traumatic brain injury (mTBI) in adolecents has gainedincreased attention in recent years amongst parents, clinicians, andresearchers due to their growing rate and hazardous outcomes.Electroencephalography (EEG) and Event-Related Potential (ERP)have been encouraged as a diagnostic tool for mTBI due to its objectivity and cost-effectiveness. However, extracting clinically meaningful neuro-cognitive information from human EEG/ERP ischallenging, particularly in highly variable groups like adolescents.Therefore, it is not surprising that despite them being especially susceptible to the effects of mTBI, no sensitive and specific applicationof EEG has yet been determined for this age group.

U2 - 10.1016/j.clinph.2017.06.209

DO - 10.1016/j.clinph.2017.06.209

M3 - Meeting Abstract

SN - 1388-2457

VL - 128

SP - e395

JO - Clinical Neurophysiology

JF - Clinical Neurophysiology

IS - 10

ER -

P 136 Brain network analysis of EEG data in the service of clinical assessment--utilizing big data and prior theoretical knowledge to identify a biomarker for mTBI in adolscents

Abstract

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