228 Chapter 8 Materials and Methods Participants Twelve healthy adults and twelve age and sex group-matched people with narcolepsy type 1 were included. People with narcolepsy type 1 were diagnosed according to the 3rd edition of the International Classification of Sleep Disorders (ICSD-3) [8]. All participants had to be 18-65 years old, right-handed and have normal or corrected-to-normal visual acuity. Patients were medicationfree for at least two weeks before MRI acquisition. Exclusion criteria were a serious comorbidity, contraindications for MRI and macroscopic structural brain abnormalities. Subjects were asked to refrain from caffeine-containing substances 24 h prior to measurement. The Dutch National Adult Reading Test [216] was administered to assess intelligence and the Epworth Sleepiness Scale (ESS) [217] was used to measure daytime sleepiness. Written informed consent was provided beforehand. The study was approved by the Medical Ethical Committee of Leiden University Medical Center (LUMC), Netherlands. Active sleep resistance paradigm The active sleep resistance paradigm lasted 5 min in total and consisted of five cycles of alternating 30-s blocks of active sleep resistance with eyes open and waking rest with eyes closed. The scanner room light was dimmed and transitions between task conditions were indicated through brief presentation of a bright white screen that was noticeable through closed eyelids. EOG and alpha activity fluctuations were checked to assess task adherence through opening and closing of the eyes. Electroencephalography acquisition and processing High-density EEG recordings were acquired and pre-processed in accordance with van der Meer J. et al. (2016), van der Meer J. N. et al. (2016) [311, 312]. In brief, five twisted CWLs were symmetrically sewn through the 256 electrode EEG cap (MicroCel Geodesic Sensor Net, Electrical Geodesics, Inc.) that captured BCG and other movement-related artefacts. The helium pump was temporarily switched off to prevent associated artefacts. EEG data were pre-processed in MATLAB using EEGlab. We removed the MRI artefacts from the EEG recordings using the Bergen EEG-fMRI toolbox [313]. CWL and EEG signals were downsampled to 500 Hz and band-pass filtered between 0 and 100 Hz using the EEGlab CWL plugin [311]. To remove BCG and other movement-related artefacts, the signals from the five CWLs were hereafter combined, aligned with the EEG signals and subtracted from the
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