bioRxiv ScienceSearch

Biology subjects

Grosse-Wentrup, M.

Publications and source records attributed to Grosse-Wentrup, M..

2 recordsLinked to original sources

Intrinsic neural timescales related to sensory processing: Evidence from abnormal behavioural states

The brain exhibits a complex temporal structure which translates into a hierarchy of distinct neural timescales. An open question is how these intrinsic timescales are related to sensory or motor information processing and whether these dynamics have common patterns in different behavioural states. We address these questions by investigating the brains intrinsic timescales in healthy controls, motor (amyotrophic lateral sclerosis, locked-in syndrome), sensory (anaesthesia, unresponsive wakefulness syndrome), and progressive reduction of sensory processing (from awake states over N1, N2, N3). We employed a combination of measures from EEG resting-state data: auto-correlation window (ACW), power spectral density (PSD), and power-law exponent (PLE). Prolonged neural timescales accompanied by a shift towards slower frequencies were observed in the conditions with sensory deficits, but not in conditions with motor deficits. Our results establish that the spontaneous activitys intrinsic neural timescale is related to specifically sensory rather than motor information processing in the healthy brain. HighlightsO_LIEEG resting-state shows a hierarchy of intrinsic neural timescales. C_LIO_LISensory deficits as in disorders of consciousness lead to prolonged intrinsic neuraltimescales. C_LIO_LIClinical conditions with motor deficits do not show changes in intrinsic neural timescales.20 C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=156 SRC="FIGDIR/small/229161v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@1893c45org.highwire.dtl.DTLVardef@d4176dorg.highwire.dtl.DTLVardef@4dfaa8org.highwire.dtl.DTLVardef@183621e_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience

Stratification of behavioral response to transcranial current stimulation by resting-state electrophysiology

Transcranial alternating current stimulation (tACS) enables the non-invasive stimulation of brain areas in desired frequencies, intensities and spatial configurations. These attributes have raised tACS to a widely used tool in cognitive neuroscience and a promising treatment in the field of motor rehabilitation. Nevertheless, considerable heterogeneity of its behavioral effects has been reported across individuals. We present a machine learning pipeline for predicting the behavioral response to 70 Hz contralateral motor cortex-tACS from Electroencephalographic resting-state activity preceding the stimulation. Specifically, we show in a cross-over study design that high-gamma (90-160 Hz) resting-state activity predicts arm-speed response to the stimulation in a concurrent reaching task. Moreover, we show in a prospective stimulation study that the behavioral effect size of stimulation significantly increases after the stratification of subjects with our prediction method. Finally, we discuss a plausible neurophysiological mechanism that links high resting-state gamma power in motor areas to stimulation response. As such, we provide a method that can distinguish responders from non-responders to tACS, prior to the stimulation treatment. This contribution could eventually bring us a step closer towards translating tACS into a safe and effective clinical treatment tool.

neuroscience