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Session: Brain
Session starts: Friday 27 January, 14:00
Presentation starts: 14:15
Room: Room 558

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Stefan Dukic (University Medical Centre Utrecht)
Boudewijn T.H.M. Sleutjes (University Medical Centre Utrecht)
Leonard H. van den Berg (University Medical Centre Utrecht)

Abstract:
Source reconstruction of brain activity using EEG/MEG data is becoming an established tool in neuroscientific research. Recently, using source analysis on EEG data we have shown multiple networks that are affected in amyotrophic lateral sclerosis (ALS)[1], some of which we have not observed using sensor analysis[2]. This approach, however, requires a volume conductor model of the human head that mimics the electromagnetic properties of the investigated subject as accurately as possible. These models often assume isotropic conductivity tensors across different tissues, which is known to be incorrect in particular for the white matter and skull. Here, we investigate the influence of white matter anisotropy derived from diffusion tensor imaging (DTI) data on the dipole estimation error. To construct an anisotropic head model, a T1-weighted and a DTI dataset were acquired from a healthy volunteer (female, 60 years). We segmented the MRI scan into six compartments (i.e. grey and white matter, cerebrospinal fluid, skull, scalp and air cavities). For the finite element mesh generation hexahedral elements were used. Based on this geometry, two models were made: 1) a simple model with isotropic conductivity tensors assigned to elements belonging to the same tissue type and 2) an advanced model with anisotropic conductivity tensors assigned to elements belonging to the white matter. Anisotropic conductivity tensors were estimated using DTI-derived diffusion tensors and the linear relationship between the two estimates[3]. The dipole location error is determined by applying dipole fitting 125 times on each model using simulated EEG data (15 Hz sinusoid originating from the right putamen). Using Euclidean distance, the localisation error was on average 6.28 mm (range: 0.13 - 44.43 mm) for the simple model and 6.07 mm (range: 0.09 - 32.71 mm) for the advanced model indicating slight advantages for the advanced model. This study shows evidence of the importance of white matter anisotropy modelling in healthy individuals. Accounting for white matter anisotropy is likely to have an even greater impact in diseases that affect white matter, such as ALS. Additional analyses that use more repetitions (>125) and that assess other dipole locations (beyond the putamen) are warrant. [1] Dukic, S. et al. Patterned functional network disruption in amyotrophic lateral sclerosis. Hum. Brain Mapp 40, 4827–4842 (2019). [2] Nasseroleslami, B. et al. Characteristic increases in EEG connectivity correlate with changes of structural MRI in amyotrophic lateral sclerosis. Cereb. Cortex 29, 27–41 (2019). [3] Rullmann, M. et al. EEG source analysis of epileptiform activity using a 1 mm anisotropic hexahedra finite element head model. Neuroimage 44, 399–410 (2009).