DOI: 10.5061/dryad.d51c5b06x
title: Wavelet variance coefficients of children and adolescents with and without ADHD

description: Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental
 disorder that often persists into adulthood. One hallmark in the
 characterization of pathological processing in ADHD is that attention
 skills are not impaired per se but more inconsistent and with higher
 variability compared to typically developing children (TDC). Increased
 variability in ADHD patients has been found in reaction times, as well as
 resting-state fMRI (rs-fMRI) brain signals. High variability has been
 assumed to reflect occasional lapses in attention, linked to intrusions of
 distracting activity during task performance and/or reduced
 anti-correlation between regions in the DMN and attention networks.
 Therefore, Dajani et al. (2019) concluded that it is more likely the
 dynamics between and within neural networks [i.e., the variability of
 network processing across time- and frequency-scales], that are affected
 in ADHD, than functional connectivity [in terms of one coefficient
 describing the (averaged) correlation between two regions over
 time].  We determined wavelet variance to quantify these
 dynamics. We determined wVar at rest and under task in fMRI timeseries of
 regions of the DMN and the FPN in three different frequency bands: 0.02 to
 0.04Hz, 0.04 to 0.08Hz, and 0.08-0.16Hz. We found that wVar differed group
 specifically between rest and task (significant group X condition
 interaction: whereas wVar was higher at rest compared to task in TDC, wVar
 was comparable or even decreased at rest in ADHD. For an external
 validation of group comparisons in wVar at rest, we determined wVar in
 rs-fMRI timeseries of a subsample of the Child Mind Institute data set
 (Functional Connectomes Project International Neuroimaging Data-Sharing
 Initiative http://dx.doi.org/10.15387/CMI_HBN (2017)). Results replicated
 our findings in terms of no significant group differences in wVar at rest
 in combination with similar or lower absolute values in ADHD patients
 compared to control subjects.  In normal processing, high wVar at
 rest was interpreted as reflecting free fluctuating brain signaling, in
 comparison to small wVar under task indicating focussed processing. Thus,
 we conclude that wVar is a sensitive measure of cognitive processing and
 is even capable of detecting deviant processing in pathological brain
 function.
description: MRI data acquisition:
 Scanning was performed on a 3 Tesla TIM Trio Scanner at the
 Institute for Diagnostical and Interventional Neuroradiology at the
 University Hospital Wuerzburg and on a 3 Tesla PRISMA Scanner (Siemens,
 Erlangen, Germany) at the Department of Diagnostic and Interventional
 Radiology at the University Hospital Duesseldorf. Whole-brain T2*-weighted
 BOLD images were recorded with a simultaneous multi-slice echo-planar
 imaging sequence (repetition time=800ms, echo time=37ms, 72 slices, 2mm
 thickness, flip angle=52°, rs-fMRI: 6:58min, 512volumes, task-fMRI:
 14.5min, 1069 volumes). We determined wavelet variance (wVar) at rest
 and under task in fMRI timeseries of the DMN and the FPN in three
 different frequency bands: 0.02 to 0.04Hz, 0.04 to 0.08Hz, and 0.08-0.16Hz
 in children and adolescents with and without ADHD. WVar was
 determined from fMRI timeseries in regions defined from CONN’s ICA analyses of the HCP
 dataset (497 subjects) including the default mode network (DMN) and the
 fronto-parietal attention network (FPN) using wavelet transform from a signal filtering
 perspective, and the definition of wVar quantity as introduced by Percival
 (1995).<br><br>Additionally,
 for an external validation, wVar in rs-fMRI timeseries of a subsample of
 the Child Mind Institute data set (Functional Connectomes Project International
 Neuroimaging Data-Sharing Initiative http://dx.doi.org/10.15387/CMI_HBN
 (2017)) was
 determined.