Wavelet variance coefficients of children and adolescents with and without ADHD

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.