ABSTRACT
We present a new study design aiming to enhance the understanding of the mechanism by which continuous theta burst stimulation (cTBS) or intermittent theta burst stimulation (iTBS) paradigms elicit cortical modulation. Using near-infrared spectroscopy (NIRS), we compared the cortical hemodynamics of the previously inhibited (after cTBS) or excited (after iTBS) left primary motor cortex (M1) as elicited by single-pulse TMS (spTMS) in a cross-over design. Mean relative changes in hemodynamics within 6 s of the stimulus were compared using a two-sample t-test (p < 0.05) and linear mixed model between real and sham stimuli and between stimuli after cTBS and iTBS. Only spTMS after cTBS resulted in a significant increase (p = 0.04) in blood volume (BV) compared to baseline. There were no significant changes in other hemodynamic parameters (oxygenated/deoxygenated hemoglobin). spTMS after cTBS induced a larger increase in BV than spTMS after iTBS (p = 0.021) and sham stimulus after cTBS (p = 0.009). BV showed no significant difference between real and sham stimuli after iTBS (p = 0.37). The greater hemodynamic changes suggest increased vasomotor reactivity after cTBS compared to iTBS. In addition, cTBS could decrease lateral inhibition, allowing activation of surrounding areas after cTBS.
ABSTRACT
Narcolepsy is a rare life-long disease that exists in two forms, narcolepsy type-1 (NT1) or type-2 (NT2), but only NT1 is accepted as clearly defined entity. Both types of narcolepsies belong to the group of central hypersomnias (CH), a spectrum of poorly defined diseases with excessive daytime sleepiness as a core feature. Due to the considerable overlap of symptoms and the rarity of the diseases, it is difficult to identify distinct phenotypes of CH. Machine learning (ML) can help to identify phenotypes as it learns to recognize clinical features invisible for humans. Here we apply ML to data from the huge European Narcolepsy Network (EU-NN) that contains hundreds of mixed features of narcolepsy making it difficult to analyze with classical statistics. Stochastic gradient boosting, a supervised learning model with built-in feature selection, results in high performances in testing set. While cataplexy features are recognized as the most influential predictors, machine find additional features, e.g. mean rapid-eye-movement sleep latency of multiple sleep latency test contributes to classify NT1 and NT2 as confirmed by classical statistical analysis. Our results suggest ML can identify features of CH on machine scale from complex databases, thus providing 'ideas' and promising candidates for future diagnostic classifications.
Subject(s)
Models, Biological , Narcolepsy/diagnosis , Rare Diseases/diagnosis , Supervised Machine Learning , Adult , Data Interpretation, Statistical , Databases, Factual/statistics & numerical data , Datasets as Topic , Female , Humans , Male , Narcolepsy/classification , Narcolepsy/physiopathology , Polysomnography/statistics & numerical data , ROC Curve , Rare Diseases/classification , Rare Diseases/physiopathology , Sleep Latency/physiology , Sleep, REM/physiology , Stochastic Processes , Young AdultABSTRACT
Repetitive transcranial magnetic stimulation (TMS) has become a popular tool to modulate neuronal networks and associated brain functions in both clinical and basic research. Yet few studies have examined the potential effects of cortical stimulation on general levels of vigilance. In this exploratory study, we used theta-burst protocols, both continuous (cTBS) and intermittent (iTBS) patterns, to examine whether inhibition or excitation of the left dorso-lateral prefrontal cortex (dlPFC) was able to induce reliable and acute changes to vigilance measures, compared to the left dorso-lateral associative visual cortex (dlAVC) as a control site in line with previous work. Partially sleep restricted participants underwent four separate sessions in a single day, in a between subjects design for TBS stimulation type and within subjects for locaton, each consisting of maintenance of wakefulness test (MWT), a sleep latency test, and a psychomotor vigilance task (PVT). TBS significantly affected measures of sleep consolidation, namely latency to sleep stage 2 and sleep efficiency, but had no effects on sleep drive or psychomotor vigilance levels for either TBS type or location. Contrary to our initial hypothesis of the dlAVC as a control site, stimulation to this region resulted in the largest differential effects between stimulation types. Moreover, the effect of TBS was found to be consistent throughout the day. These data may provide the basis for further investigation into therapeutic applications of TBS in sleep disorders.
