EEG-Alapú agyi hálozatok 14 neurológiai betegségben

Johanna Dömötör, B. Clemens, Tünde Csépány, M. Emri, A. Fogarasi, Katalin Hollódy, Szilvia Puskás, Klára Fekete, Attila Kovács, I. Fekete

Research output: Article

1 Citation (Scopus)

Abstract

Background - Brain networks have not been systematically investigated yet in most neurological disorders. Purpose - To investigate EEG functional connectivity (EEGfC) networks in 14 neurological disorders. Patients - Potentially eligible patients were collected from clinical and EEG databases. All the available clinical data and EEG records were critically revised. All the patients who suffered of a single neurological disorder (out of the 14) and had a good quality EEG recording entered the study. Confoundig factors as comorbidity and CNS-adive drug effects were eliminated as far as possible. EEG analysis - Three minutes of resting-state, waking EEG activity were selected for analysis. Current source density (CSD) values were computed for 2394 cortical voxels by Low Resolution Electromagnetic Tomography (LORETA). Thereafter, Pearson correlation coefficients were computed between all pairs of 23 cortical regions of interest (ROI) in each hemisphere (LORETA Source Correlation, LSC software). Computation was carried out for conventional EEG broad bands and very narrow bands (1 Hz bandwidth) between 1 and 25 Hz as well. Correlation coefficients of each group were statistically compared to our normative EEG (LSC) database by two-talied t-tests. Bonferroni-corrected p<0.05 values were accepted as statistically significant, and were graphically displayed as topographical networks. Results and conclusion - Group-specific networks were demonstrated. However, non-specific networks, charasteristic for most groups, were detected as well. Common finding were: decreased connectivity in the alpha band and increased connectivity in the delta, theta bands and upperbeta band. Decreased alpha-band connectivity presumably reflected primary lesional effects and on the other hand, non-specific vulnerability of "rich club connections". Increased connectivity in the slow bands presumably indicated adaptive-compensatory activity of brain homeostasis.

Original languageHungarian
Pages (from-to)159-178
Number of pages20
JournalIdeggyogyaszati Szemle
Volume70
Issue number5-6
DOIs
Publication statusPublished - máj. 30 2017

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Nervous System Diseases
Electroencephalography
Electromagnetic Phenomena
Tomography
Databases
Brain
Comorbidity
Homeostasis
Software
Pharmaceutical Preparations

Keywords

  • EEG
  • Network
  • Neurological disorders

ASJC Scopus subject areas

  • Neurology
  • Clinical Neurology

Cite this

EEG-Alapú agyi hálozatok 14 neurológiai betegségben. / Dömötör, Johanna; Clemens, B.; Csépány, Tünde; Emri, M.; Fogarasi, A.; Hollódy, Katalin; Puskás, Szilvia; Fekete, Klára; Kovács, Attila; Fekete, I.

In: Ideggyogyaszati Szemle, Vol. 70, No. 5-6, 30.05.2017, p. 159-178.

Research output: Article

Dömötör, J, Clemens, B, Csépány, T, Emri, M, Fogarasi, A, Hollódy, K, Puskás, S, Fekete, K, Kovács, A & Fekete, I 2017, 'EEG-Alapú agyi hálozatok 14 neurológiai betegségben', Ideggyogyaszati Szemle, vol. 70, no. 5-6, pp. 159-178. https://doi.org/10.18071/isz.70.0159
Dömötör, Johanna ; Clemens, B. ; Csépány, Tünde ; Emri, M. ; Fogarasi, A. ; Hollódy, Katalin ; Puskás, Szilvia ; Fekete, Klára ; Kovács, Attila ; Fekete, I. / EEG-Alapú agyi hálozatok 14 neurológiai betegségben. In: Ideggyogyaszati Szemle. 2017 ; Vol. 70, No. 5-6. pp. 159-178.
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abstract = "Background - Brain networks have not been systematically investigated yet in most neurological disorders. Purpose - To investigate EEG functional connectivity (EEGfC) networks in 14 neurological disorders. Patients - Potentially eligible patients were collected from clinical and EEG databases. All the available clinical data and EEG records were critically revised. All the patients who suffered of a single neurological disorder (out of the 14) and had a good quality EEG recording entered the study. Confoundig factors as comorbidity and CNS-adive drug effects were eliminated as far as possible. EEG analysis - Three minutes of resting-state, waking EEG activity were selected for analysis. Current source density (CSD) values were computed for 2394 cortical voxels by Low Resolution Electromagnetic Tomography (LORETA). Thereafter, Pearson correlation coefficients were computed between all pairs of 23 cortical regions of interest (ROI) in each hemisphere (LORETA Source Correlation, LSC software). Computation was carried out for conventional EEG broad bands and very narrow bands (1 Hz bandwidth) between 1 and 25 Hz as well. Correlation coefficients of each group were statistically compared to our normative EEG (LSC) database by two-talied t-tests. Bonferroni-corrected p<0.05 values were accepted as statistically significant, and were graphically displayed as topographical networks. Results and conclusion - Group-specific networks were demonstrated. However, non-specific networks, charasteristic for most groups, were detected as well. Common finding were: decreased connectivity in the alpha band and increased connectivity in the delta, theta bands and upperbeta band. Decreased alpha-band connectivity presumably reflected primary lesional effects and on the other hand, non-specific vulnerability of {"}rich club connections{"}. Increased connectivity in the slow bands presumably indicated adaptive-compensatory activity of brain homeostasis.",
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AU - Dömötör, Johanna

AU - Clemens, B.

AU - Csépány, Tünde

AU - Emri, M.

AU - Fogarasi, A.

AU - Hollódy, Katalin

AU - Puskás, Szilvia

AU - Fekete, Klára

AU - Kovács, Attila

AU - Fekete, I.

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N2 - Background - Brain networks have not been systematically investigated yet in most neurological disorders. Purpose - To investigate EEG functional connectivity (EEGfC) networks in 14 neurological disorders. Patients - Potentially eligible patients were collected from clinical and EEG databases. All the available clinical data and EEG records were critically revised. All the patients who suffered of a single neurological disorder (out of the 14) and had a good quality EEG recording entered the study. Confoundig factors as comorbidity and CNS-adive drug effects were eliminated as far as possible. EEG analysis - Three minutes of resting-state, waking EEG activity were selected for analysis. Current source density (CSD) values were computed for 2394 cortical voxels by Low Resolution Electromagnetic Tomography (LORETA). Thereafter, Pearson correlation coefficients were computed between all pairs of 23 cortical regions of interest (ROI) in each hemisphere (LORETA Source Correlation, LSC software). Computation was carried out for conventional EEG broad bands and very narrow bands (1 Hz bandwidth) between 1 and 25 Hz as well. Correlation coefficients of each group were statistically compared to our normative EEG (LSC) database by two-talied t-tests. Bonferroni-corrected p<0.05 values were accepted as statistically significant, and were graphically displayed as topographical networks. Results and conclusion - Group-specific networks were demonstrated. However, non-specific networks, charasteristic for most groups, were detected as well. Common finding were: decreased connectivity in the alpha band and increased connectivity in the delta, theta bands and upperbeta band. Decreased alpha-band connectivity presumably reflected primary lesional effects and on the other hand, non-specific vulnerability of "rich club connections". Increased connectivity in the slow bands presumably indicated adaptive-compensatory activity of brain homeostasis.

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