Peripheral Attentional Targets under Covert Attention Lead to Paradoxically Enhanced Alpha Desynchronization in Neurofibromatosis Type 1

Abstract

The limited capacity of the human brain to process the full extent of visual information reaching the visual cortex requires the recruitment of mechanisms of information selection through attention. Neurofibromatosis type-1 (NF1) is a neurodevelopmental disease often exhibiting attentional deficits and learning disabilities, and is considered to model similar impairments common in other neurodevelopmental disorders such as autism. In a previous study, we found that patients with NF1 are more prone to miss targets under overt attention conditions. This finding was interpreted as a result of increased occipito-parietal alpha oscillations. In the present study, we used electroencephalography (EEG) to study alpha power modulations and the performance of patients with NF1 in a covert attention task. Covert attention was required in order to perceive changes (target offset) of a peripherally presented stimulus. Interestingly, alpha oscillations were found to undergo greater desynchronization under this task in the NF1 group compared with control subjects. A similar pattern of desynchronization was found for beta frequencies while no changes in gamma oscillations could be identified. These results are consistent with the notion that different attentional states and task demands generate different patterns of abnormal modulation of alpha oscillatory processes in NF1. Under covert attention conditions and while target offset was reported with relatively high accuracy (over 90% correct responses), excessive desynchronization was found. These findings suggest an abnormal modulation of oscillatory activity and attentional processes in NF1. Given the known role of alpha in modulating attention, we suggest that alpha patterns can show both abnormal increases and decreases that are task and performance dependent, in a way that enhanced alpha desynchronization may reflect a compensatory mechanism to keep performance at normal levels. These results suggest that dysregulation of alpha oscillations may occur in NF1 both in terms of excessive or diminished activation patterns.

Continuous theta burst stimulation increases contralateral mu and beta rhythms with arm elevation: implications for neurorehabilitation

“…Custom MATLAB (version R2017b, The MathWorks, USA) scripts were implemented [adapted from our previous works by Castelhano et al (Castelhano et al 2013) and by Silva et al (Silva et al 2016)] for the pre-processing and power quantification. Briefly, we conducted time-frequency analysis as reported by Uhlhaas et al (2006) and other works (Lachaux et al 1999;Rodriguez et al 1999;Uhlhaas et al 2006;Melloni et al 2007;Castelhano et al 2013), by applying the pseudo Wigner-Ville transformation.…”

Discriminating between neurofibromatosis‐1 and typically developing children by means of multimodal MRI and multivariate analyses

“…As for EEG, it has been shown that NF1 children differ from matched controls during low level visual processing in term of alpha oscillation amplitude in the parieto-occipital cortex . EEG also highlighted differences between NF1 and controls during higher level attentional task as flanker task (Bluschke et al, 2017) and covert attentional task (Silva et al, 2016). As for fMRI, NF1 children showed reduced activation in a set of area within the salience network (i.e., inferior occipital gyrus, fusiform gyrus/posterior cerebellum, pre-SMA and inferior frontal gyrus) during a GO-NO GO task (Pride, Korgaonkar, North, Barton, & Payne, 2017).…”

“…EEG also highlighted differences between NF1 and controls during higher level attentional task as flanker task (Bluschke et al, 2017) and covert attentional task (Silva et al, 2016). As for EEG, it has been shown that NF1 children differ from matched controls during low level visual processing in term of alpha oscillation amplitude in the parieto-occipital cortex .…”

The Role of Continuous Theta Burst TMS in the Neurorehabilitation of Subacute Stroke Patients: A Placebo-Controlled Study

“…After running Independent Component Analysis (ICA), we removed components including blinks and eye movements. For power quantification, custom MATLAB (version R2017b, The MathWorks, U.S.) scripts were implemented [adapted from our previous studies by Castelhano et al ( 30 ) and by Silva et al ( 31 )], as described in our previous work in healthy participants ( 18 ). For quantification purposes, the baseline was defined between −2,000 ms and 0 for the eyes' closure and opening, and between −2,000 and −1,500 ms, before movement, for the motor tasks.…”