The authors regret an error in a data processing script used for the manuscript titled,
“Behavioral and cognitive correlates of the aperiodic (1/f-like) exponent of the EEG
power spectrum in adolescents with and without ADHD,”. The authors discovered a typo
that binded aperiodic exponent values to the main data frame without accounting for
the 7 participants who did not provide EEG data. Reanalysis of corrected data did
not substantively affect the primary findings, but does lead to adolescent age being
significantly associated with aperiodic exponents. All other changes were minor and
did not affect interpretations or conclusions of our results. Corrections to values
reported in the main text are as follows:
1.
In Section 3.2, the correlation between the eyes-open and eyes-closed conditions should
be: r = 0.79. The association between aperiodic exponents and adolescent sex should
be: t(175) = 1.62, p = .11.
2.
In Table 1, the aperiodic exponent values should be M = 1.71 (SD = 0.30) for children
with ADHD and M = 1.80 (SD = 0.27) for children in the non-ADHD control group (p =
.04, 95 % CI [0.003, 0.173]).
3.
In Table 2, aperiodic exponents are associated with other variables as follows: age
(r = − 0.29, p < .001); mean RT (r = − 0.01, p = .94); log-transformed reaction time
variability (r = 0.15, p = .05); drift rate (r = − 0.13, p = .08); boundary separation
(r = 0.05, p = .56); and non-decision time (r < 0.01, p = .97).
4.
Age and aperiodic exponents are positively correlated (r = − 0.29, p < .001), such
that older adolescents had smaller exponents (flatter PSDs). This association is stronger
for adolescents in the non-ADHD control group (r = − 0.39, p < .001) than for adolescents
with ADHD (r = − 0.21, p = .06). Below, we present a plot of the association between
aperiodic exponents and age by ADHD status.
fx1
5.
Section 3.3 should read:
“Adolescents with ADHD had smaller exponents relative to the control group (β = − .15,
t(175) = − 2.04, p = .04), indicative of a flattened PSD (Fig. 1F). Power spectral
densities for each condition by ADHD status (in log-log and semi-log) are presented
in Fig. 1G–H. Exponents did not differ by stimulant medication history, t(172) = 0.18,
p = .86.
Regression analyses showed that, controlling for ADHD status, exponents were positively
associated with SDRT (β = 0.20, p < .01), indicating that less intraindividual variability
was related to a flattened PSD. Exponents were also associated with drift rate (β = − 0.19,
p < .01); faster drift rate was related to a flattened PSD. These effects are plotted
by ADHD status in Fig. 2. Exponents did not predict mean RT (β = − 0.02, p = .83),
boundary separation (β = 0.04, p = .65), or non-decision time (β = − 0.03, p = .72).
There was no significant interaction between ADHD status and aperiodic exponents on
any of the reaction time or drift diffusion parameters (ps > 0.26). Results remained
the same when adolescent sex was included in the model.
Finally, results were similar when accounting for missingness via MICE (van Buuren
and Groothuis-Oudshoorn, 2011). Adolescents with ADHD had smaller exponents relative
to the control group, t(172.57) = − 1.99, p = .05). Controlling for ADHD status, exponents
were significantly associated with SDRT (p < .01) and drift rate (p < .01). Exponents
and did not predict mean RT (p = .98), boundary separation (p = .71), or non-decision
time (p = .84). Posthoc analyses showed that results were similar when the 17 adolescents
in the ADHD group at Year 1 who transitioned to the control group were excluded from
the primary analyses.”
6.
In the main text, Fig. 1 should appear as follows:
Fig. 1
Distribution and group comparison of mean reaction time (
A
), reaction time variability (
B
), drift rate (
C
), boundary separation (
D
), non-decision time (
E
), and aperiodic exponents (
F
). Power spectral densities in semi-log and log-log space for both eyes-closed (solid
lines) and eyes-open (dotted lines) averaged across adolescents in the ADHD (orange)
and control (blue) groups are presented in subplots
G
and
H
, respectively. FOOOF (Donoghue et al., 2020a) removes periodic activity (putative
oscillations) that rise above the aperiodic component of the neural signal, disentangling
power spectral features that are thought to have distinct physiological mechanisms.
We did not expect a knee in the PSD across the examined frequency range, nor did we
observe one when visually inspecting each PSD after spectral parameterization via
FOOOF. On average, we did observe an alpha ”bump” around ∼ 10 Hz as well as a smaller
beta “bump” around ∼ 20 Hz, each of which is more prominent in the eyes closed relative
to eyes open conditions, as would be expected.
Fig. 1
7.
In the main text, Fig. 1 should appear as follows:
fx2
We have rechecked our analyses in detail to ensure there are no other errors. The
authors would like to apologize for any confusion or inconvenience this error may
have caused readers, other researchers, and the editors.