Applied Psychophysiology and Biofeedback, Vol. 31, No. 1, March 2006 (C ?2006)
Functional Magnetic Resonance Imaging Investigation
of the Effects of Neurofeedback Training on the Neural
Bases of Selective Attention and Response Inhibition
in Children with Attention-Deficit/Hyperactivity Disorder
Mario Beauregard1,2,3,4,5and Johanne L´ evesque1,4
Published online: 22 March 2006
Two functional magnetic resonance imaging (fMRI) experiments were undertaken to mea-
of selective attention and response inhibition. Twenty unmedicated AD/HD children partic-
ipated to these experiments. Fifteen children were randomly assigned to the Experimental
(EXP) group whereas the other five children were randomly assigned to the Control (CON)
group. Only subjects in the EXP group underwent NFT. EXP subjects were trained to en-
hance the amplitude of the SMR (12–15 Hz) and beta 1 activity (15–18 Hz), and decrease
the amplitude of theta activity (4–7 Hz). Subjects from both groups were scanned one week
before the beginning of NFT (Time 1) and 1 week after the end of NFT (Time 2), while they
performed a “Counting Stroop” task (Experiment 1) and a Go/No-Go task (Experiment
2). At Time 1, in both groups, the Counting Stroop task was associated with significant
activation in the left superior parietal lobule. For the Go/No-Go task, no significant activity
was detected in the EXP and CON groups. At Time 2, in both groups, the Counting Stroop
task was associated with significant activation of the left superior parietal lobule. This
time, however, there were significant loci of activation, in the EXP group, in the right ACC,
left caudate nucleus, and left substantia nigra. No such activation loci were seen in CON
in the right ventrolateral prefrontal cortex, right ACcd, left thalamus, left caudate nucleus,
and left substantia nigra. No significant activation of these brain regions was measured in
CON subjects. These results suggest that NFT has the capacity to functionally normalize
the brain systems mediating selective attention and response inhibition in AD/HD children.
KEY WORDS: selective attention; response inhibition; AD/HD children; neurofeedback; functional magnetic
resonance imaging; prefrontal cortex; anterior cingulate; striatum.
1Centre de Recherche en Neuropsychologie et Cognition (CERNEC), D´ epartement de Psychologie, Universit´ e
de Montr´ eal.
2D´ epartement de Radiologie, Universit´ e de Montr´ eal.
3Centre de recherche en sciences neurologiques (CRSN), Universit´ e de Montr´ eal.
4Centre de Recherche, Institut universitaire de g´ eriatrie de Montr´ eal (CRIUGM).
5Address all correspondence to Mario Beauregard, D´ epartement de Psychologie, Universit´ e de Montr´ eal, C.P.
6128, succursale Centre-Ville, Montr´ eal, Qu´ ebec, Canada H3C 3J7; e-mail: firstname.lastname@example.org.
1090-0586/06/0300-0003/1 C ?2006 Springer Science+Business Media, Inc.
4Beauregard and L´ evesque
of childhood, affects 3–7% of children and frequently continues into adulthood (Barkley,
1996). AD/HD in childhood negatively affects academic performance and leads to in-
Malloy, & Hynes, 1997). This syndrome is mainly characterized by deficits in selective
attention and response inhibition (Barkley, 1997). These symptoms reflect impairments in
cognitive executive functions. These functions refer to the dynamic regulatory capacities
for the initiation and maintenance of efficient attainment of goals (Lezak, 1990), as well as
1988). This type of behavioral regulation is essential to successfully adapt one’s behavior
to changing environmental demands.
Cognitive executive functions are closely related with prefrontal and striatal brain
systems (Godefroy, Lhullier, & Rousseaux, 1996; Leimkuhler & Mesulam, 1985; Smith
& Jonides, 1999). In line with this, a number of structural magnetic resonance imaging
(MRI) studies have found significant volumetric reduction of prefrontal cortical areas
(Aylward et al., 1996; Castellanos et al., 1994, 1996, 2001, 2002; Durston et al., 2004;
Filipek et al., 1997; Hill et al., 2003; Garavan, Ross, Murphy, Roche, & Stein, 1993;
Kates et al., 2002; Mataro, Garcia-Sanchez, Junque, Estevez-Gonzalez, & Pujol, 1997;
Mostofsky, Cooper, Kates, Denckla, & Kaufmann, 2002; Overmeyer et al., 2001; Sowell
et al., 2003) and caudate nucleus (Castellanos et al., 1994, 1996, 2001, 2002; Filipek
et al., 1997; Hynd et al., 1993; Mataro et al., 1997) in children and adolescents with
Single photon emission computed tomography (SPECT) and positron emission to-
mography (PET) studies carried out in AD/HD children, adolescents, or adults have shown
decreased metabolism in the striatum and diverse prefrontal regions during resting state
(Amen & Carmichael, 1997; Kim, Lee, Shin, Cho, & Lee, 2002; Lou, Henriksen, Bruhn,
Borner, & Nielsen, 1989; Sieg, Gaffney, Preston, & Hellings, 1995; Zametkin et al.,
1990). Moreover, SPECT studies have demonstrated decreased perfusion in prefrontal
areas involved in the control of attentional processes in AD/HD individuals (Amen &
Carmichael, 1997; Kim et al., 2002), and a functional MRI (fMRI) study reported no
activation of the anterior cingulate cortex (ACC) in adults with AD/HD while they per-
formed a Counting Stroop task (Bush et al., 1999) (a variant of the Stroop task-Stroop,
1935). This task, which implicates selective attention and response inhibition, exploits
the conflict between a well-learned behavior (i.e., reading) and a decision rule that re-
quires this behavior to be inhibited. Converging evidence from PET and fMRI indicate
that the dorsal division of the ACC (or ACcd, Brodmann area-BA-24b?-c?and 32?) plays
a key role in the various cognitive processes involved in the Stroop task (e.g., interfer-
ence, allocation of attentional resources, response selection) (Bush et al., 1998; Bush, Luu,
& Posner, 2000).
The capacity to inhibit behaviors or responses that are inappropriate in the current
context can be studied using Go/No-Go tasks, in which the participant is required to
refrain from responding to designated items within a series of stimuli. Several studies
(Castellanos et al., 2000; Hartung, Milich, Lynam, & Martin, 2002; Iaboni, Douglas, &
Baker, 1995; Itami & Uno, 2002; Vaidya et al., 1998) have shown that AD/HD subjects
Functional Magnetic Resonance Imaging Investigation of the Effects5
exhibit more errors on Go/No-Go tasks. Furthermore, the results of a number of fMRI
studies during Go/No-Go tasks indicate that several prefrontal regions (ACC, dorsolateral
prefrontal cortex, orbitofrontal cortex, ventrolateral prefrontal cortex) (Casey, Durston,
& Fossella, 2001; Garavan, Ross, & Stein, 1999; Garavan et al., 2002; Kiehl, Liddle,
& Hopfinger, 2000; Konishi, Nakajima, Uchida, Sekihara, & Miyashita, 1998; Liddle,
Kiehl, & Smith, 2001; Menon, Adleman, White, Glover, & Reiss, 2001; Rubia, Smith,
Brammer, & Taylor, 2003) and the striatum (Menon et al., 2001) are crucially involved
in response inhibition. Underactivation of the striatum (Booth et al., 2005; Durston et al.,
2003; Rubia et al., 1999; Teicher et al., 2000; Vaidya et al., 1998) and prefrontal regions
(Booth et al., 2005; Rubia et al., 1999; Tamm, Menon, Ringel, & Reiss, 2004; Vaidya
et al., 1998) has been observed in children and adolescent with AD/HD during Go/No-Go
The results of several clinical studies carried out during the last thirty years sug-
gest that neurofeedback may be efficacious in treating children with AD/HD (Fuchs,
Birbaumer, Lutzenberger, Gruzelier, & Kaiser, 2003; Kropotov et al., 2005; Linden,
Habib, & Radojevic, 1996; Lubar & Shouse, 1976; Lubar & Lubar, 1984; Lubar,
Swartwood, Swartwood, & O’Donnell, 1995; Monastra, Monastra, & George, 2002;
Rossiter & LaVaque, 1995; Rossiter, 2004; Shouse & Lubar, 1979; Tansey, 1984, 1985;
Thompson & Thompson, 1998). In many of these studies, the operant enhancement of
sensorimotor rhythm (SMR) (12–15 Hz) and/or beta 1 (15–20 Hz) EEG activity from the
regions overlying the Rolandic area, was trained concomitantly with suppression of theta
(4–7 Hz) activity. The basic assumption guiding this approach is that SMR enhancement
reduces problems of hyperactivity, whereas increasing beta 1 activity and suppressing theta
activity diminishes attention deficits (Lubar & Shouse, 1976). In keeping with this assump-
attention deficits and hyperactivity in children with AD/HD.
In this context, the main objective of this work was to measure the effects of NFT,
in AD/HD children, on the neural substrates of selective attention and response inhibition.
FMRI experiments were conducted during a Counting Stroop task (Experiment 1) and a
Go/No-Go task (Experiment 2). Behavioraly, we predicted that NFT would significantly
improve performance on both tasks. Neurally, we predicted that NFT would significantly
increase ACcd activity during the Counting Stroop task, and prefrontal as well as striatal
activity during the Go/No-Go task.
MATERIALS AND METHODS
The study sample was composed of 20 AD/HD children. These AD/HD children were
randomly assigned to either an Experimental (EXP) group or a control (CON) group. Fif-
teen AD/HD children comprised the EXP group (4 girls and 11 boys, mean age: 10.2, SD:
1.3, range: 8–12) and five ADHD children comprised the CON group (5 boys, mean age:
no treatment (the CON group served specifically to measure the effect of passage of time).
The parents of the subjects gave written informed consent and the study was approved
6Beauregard and L´ evesque
by the ethics research committees of the Centre hospitalier de l’Universit´ e de Montr´ eal
(CHUM), Hˆ opital Notre-Dame, and Hˆ opital Ste-Justine (a pediatric hospital affiliated with
Universit´ e de Montr´ eal). Inclusion criteria for all subjects were: (1) age 8 to 12 years;
(2) right-handedness (Edinburgh Handedness Inventory, Oldfield, 1971); (3) IQ > 85
(based on the Wechsler Intelligence Scale for Children-Revised—WISC-R); and (4) a di-
agnosis of AD/HD based on the DSM-IV criteria (DSM-IV, 1994). All were native French
chiatric diagnosis other than AD/HD; (2) a learning disability (e.g., dyslexia, dyscalculia);
(3) a neurologic disorder (e.g., epilepsia); (4) a neuropsychiatric disorder (e.g., Major
Depressive Disorder, Obsessive-Compulsive Disorder).
No subjects were taking psychostimulant drugs during the study (subjects in both
EXP and CON groups were treated with methylphenidate before the beginning of the
study-none of the subjects did undergo cognitive training before this study). Clinical and
Clinical assessment included: (1) psychiatric, medical, and neurologic evaluations by a
board certified child psychiatrist; (2) structured diagnostic interview with the Structured
Clinical Interview (Spitzer, Williams, Gibbon, & First, 1992) and an AD/HD symptom
of the Wechsler Intelligence Scale for Children—Revised (WISC-R) (Wechsler, 1981) to
assess attention span and the Integrated Visual and Auditory Continuous Performance Test
(IVA, version 4.3) to evaluate visual and auditory attention (Tinius, 2003). The Conners
Parent Rating Scale–Revised (CPRS-R) (Full Scale Attention Quotient and Full Scale
problems regarding specifically inattention and hyperactivity (Conners et al., 1997). Scaled
scores were used for data analysis.
The Digit Span, the IVA,and the CPRS-R were administered at Time 1 (1 week before
the beginning of the NFT) and Time 2 (1 week after the end of the NFT). At Time 1 the
EXP and CON groups did not differ cognitively and behaviorally (Table I). Within- and
between-groups comparisons were performed using two-tailed t-tests.
NFT was based on a protocol previously developed by Lubar and Lubar (1984). It
was conducted over a period of 13 weeks and a half (40 sessions, three training sessions
per-week). The training was divided in two phases (20 sessions in each phase): in the
first phase, subjects in the EXP group were trained to enhance the amplitude of the SMR
(12–15 Hz) and decrease the amplitude of theta activity (4–7 Hz); in the second phase,
EXP subjects learned to inhibit the amplitude of their theta waves (4–7 Hz) and increase
the amplitude of their beta 1 waves (15–18 Hz). NFT was provided using the Lexicor NRS-
24 Biolex program (version 2.40) (Lexicor, Boulder, CO) and the Procomp + Biograph
program (version 2.1) (Thought Technology Ltd, Montreal, Canada). For each subject,
these systems were used in an alternating manner. Each session lasted 60 min. EEG was
recorded from CZ, with reference placed on the left earlobe and ground electrode on the
right earlobe. A sampling rate of 128 Hz with 2 s epochs was used. Skin impedance was
less than 5 K?. The pertinent frequencies were extracted from EEG recordings and feed
back using an audio-visual online feedback loop in the form of a video game. Each session
Functional Magnetic Resonance Imaging Investigation of the Effects7
Table I. Neuropsychological Data
Time 1Time 2
Note. CON: Control; EXP: Experimental; IVA: Integra-
ted Visual and Auditory Continuous Performance Test;
CPRS-R: Conners Parent Rating Scale—Revised.
∗p < 0.05;∗∗∗p < 0.005;∗∗∗∗p < 0.001.
was subdivided in 2 min periods (that were gradually increased up to 10 min). During these
problems or read texts.
The behavioral protocol used was based on the protocol conceived by Bush and col-
leagues (1998) with respect to the Counting Stroop task. Subjects were instructed that
they would see sets of one to four identical words appear on the screen. They were told
also to report, through button-press, the number of words in each set, regardless of what
the words were. During ‘‘Neutral’’ blocks, the words consisted of names of common an-
imals (dog, cat, bird, or mouse) whereas during ‘‘Interference’’ blocks, the stimuli were
the number words ‘‘one,’’ ‘‘two,’’ ‘‘three,’’ or ‘‘four’’ (words were presented in French).
Subjects were told that the keypad buttons represented one, two, three, and four from left
to right, and subjects utilized the index and middle fingers of the right hand to respond.
Subjects were instructed that the sets would change every 1.5 s. In addition, they were
told to answer as quickly and accurately as possible. Immediately prior to entering the
scanner, subjects completed a 1-min computerized practice version of the task (20 Neutral
trials followed by 20 Interference trials). During the functional scan, which started with
9 s of fixation on a cross, six 30 s blocks of the Neutral words alternated with six Inter-
ference blocks. Subjects completed 20 trials during each (Neutral/Interference) block, i.e.,
120 total trials of each type during the functional scan session. The order of presentation
of the blocks was counterbalanced across subjects. Accuracy (percent correct) and reaction
times were monitored during the scan (due to space limitations, the reaction time data will
be presented and discussed in a separate article).
8 Beauregard and L´ evesque
Using the E-Prime software (version 1.1, Psychology Software Tools, Inc., Pittsburgh,
color LCD projector (Tokyo, Japan), through a collimating lens onto a rear-projection
screen that was fastened vertically in the magnet bore at neck level. Subjects viewed the
stimuli on a tilted mirror placed in front of their head. Individual words subtended about
1◦of the visual angle vertically, and sets of four words subtended a visual angle of about
Echoplanar images (EPI) were acquired on a 1.5 Tesla system (Sonata, Siemens
Electric, Erlangen, Germany). Twenty-eight slices (4 mm thick) were acquired every
2.65 sec in an inclined axial plane, aligned with the AC-PC axis. These T2∗. weighted func-
tional images were acquired using an EPI pulse sequence (echo-spacing time = 0.8 ms,
TE = 54 ms, Flip = 90◦, FOV = 215 mm, voxel size = 3.36 mm×3.36 mm×4 mm,
matrix = 64×64). Following functional scanning, high-resolution data were acquired via
a Tl-weighted three-dimensional volume acquisition obtained using a gradient echo pulse
sequence (TR = 9.7 ms, TE = 4 ms, flip = 12◦, FOV = 250 mm, matrix = 256×256).
FMRI Data Analysis
Data were analyzed using Statistical Parametric Mapping software (SPM2, Wellcome
Department of Cognitive Neurology, London, UK). Images for all subjects were realigned
to correct for artifacts due to small head movements. The images for all subjects were
then spatially normalized into an MRI stereotactic space, and convolved in space with a
three-dimensional isotropic Gaussian kernel (12 mm FWHM) to improve the signal-to-
noise ratio and to accommodate for residual variations in functional neuroanatomy that
usually persist between subjects after spatial normalization. Even if the subjects were
children, the Talairach and Tournoux (1988) template was used since Burgund et al. (2002)
have recently shown that even if there are some small anatomical differences between the
brain’ structures and sulci of adults (age range: 18–30) and those of children (age range:
7–8), those differences do not compromise the usefulness of an adult stereotactic space for
children’s functional images, assuming a functional resolution of 5 m in images averaged
For the statistical analysis, the time series of the images were convolved with the
hemodynamic response function which approximates the activation patterns. Effects at
each and every voxel were estimated using the general linear model. Voxel values for
the contrasts of interest yielded a statistical parametric map of the t statistic (SPM t),
subsequently transformed to the unit normal distribution, (SPM Z). To identify the brain
regions associated with the Counting Stroop task a random-effects model was implemented
to compare the brain activity associated with the Interference trials and that associated
with the Neutral trials (Interference minus Neutral). This procedure allows one to make
inferences on the population of which participants are deemed representative (Friston
& Frackowiak, 1997). At Time 1 and Time 2 this model was implemented to produce
the Interference minus Neutral contrasts for both EXP and CON groups (within-group
Functional Magnetic Resonance Imaging Investigation of the Effects9
statistical comparison). This model was implemented using a multistage approach. First,
single scan per condition per subject images were then entered into a group analysis, i.e., a
model at the between subject level using a one-sample t-test. The variance of these single
images from subject to subject consisted of contributions from both the between and within
subject components of variance, in the correct proportions. In addition, for the Interference
minus Neutral contrast, a two-sample t-test was carried out to compare the mean blood
oxygenation level-dependent (BOLD) response within each group at Time 2 v. Time 1.
Height threshold was set at p < 0.001 (z=3.09), uncorrected for multiple comparisons.
Only clusters showing a spatialextent of atleastfive contiguous voxels werekept forimage
The event-related Go/No-Go task consisted of a 3250 ms rest epoch at the beginning
and a 3250 ms second rest epoch at the end of the task, during which subjects passively
viewed the plus sign. The letters “O” (66.7% of trials) and “V” (33.3% of trials) were
presented in random order every 3500 ms for 250 ms. Subjects were instructed to respond
with a key press to the letter “O” (Go trials) and to withhold the response to letter “V”
(No-Go trials). A higher percentage of “O” stimuli allowed for the build-up of a prepotent
response. All subjects responded using the forefinger of the right hand. In total, subjects
completed 120 trials (80 Go trials and 40 No-Go trials). Accuracy (percent correct) and
reaction times were recorded during the scan (the reaction time data will be discussed in a
separate report). Stimuli were presented as in Experiment 1.
FMRI data were acquired using the same scanning parameters as in Experiment 1.
The task was programmed using the E-Prime software (version 1.1, Psychology Software
Tools, Inc., Pittsburgh, PA) on an IBM Aptiva P3 600 MHz. Initiation of the scan and task
was synchronized using a TTL pulse delivered to the scanner timing microprocessor board
from a button box connected to the IBM.
FMRI Data Analysis
Data were analyzed using SPM2. Image preprocessing was identical to that in Experi-
ment 1. General linear modeling was carried out for the functional scans from each subject
by modeling the measured event-related BOLD signals and regressors to determinate the
relationship between the experimental parameters and the hemodynamic response. Event-
related analyses were performed by using the default statistical parametric mapping basis
function, a synthetic hemodynamic response function consisting of two gamma functions
10Beauregard and L´ evesque
senting the sequence of individual trials) with the base function. The linear combination of
all the regressors was used to model the hemodynamic response to two conditions: No-Go
and Go trials. The six realignment parameters produced during motion correction served as
covariates. The images for each participant were concatenated into a single image for each
of the two conditions. The specific effects of response inhibition were tested by applying
appropriate linear contrasts to the parameter estimates for the No-Go minus correct Go
contrast, resulting in a contrast map for each participant. The contrast images of all par-
ticipants were entered into second-level group analyses conducted with a random-effects
At Time 1, there was no significant difference between CON and EXP subjects with
regard to the average scores on the Digit Span, the IVA, and the CPRS-R (Table I). This
suggests that before EXP subjects started the NFT, inattention and hyperactivity were
equivalent in both groups. At Time 2, the scores of the CON subjects on the three tests were
not significantly different than those at Time 1 (Table I). For the EXP group, however, the
scores on the Digit Span and the IVA significantly increased at Time 2, compared to Time 1
(Digit Span: p < 0.05; IVA: p < 0.005) (Table I). Furthermore, at Time 2 the scores on the
Inattention and Hyperactivity components of the CPRS-R significantly decreased, relative
to Time 1 (Inattention: p < 0.001; Hyperactivity: p < 0.05) (Table I).
For the Neutral trials at Time 1, the average accuracy scores (percentage of correct
responses) were not statistically different between the CON (58.4%, SD=24) and EXP
(48.1%, SD=25.5) groups (Table II). At Time 2, the average accuracy score of the CON
subjects (59.6%, SD=24.3) was comparable to that of Time 1. For the EXP group, this
Table II. Counting Stroop Task (Percentage of Correct
Time 1 Time 2
Note. CON: Control; EXP: Experimental.
∗p < 0.05.
Functional Magnetic Resonance Imaging Investigation of the Effects11
For the Interference trials, the pattern was very similar, that is, at Time 1 the
average accuracy scores of the CON (55.8%, SD=24.1) and EXP (48.2%, SD=23.8)
groups were comparable (Table II). At Time 2, the average accuracy score of the CON
subjects (56.8%, SD=24.3) was not different than that of Time 1. For the EXP group, this
Functional MRI Data
At Time 1, the Interference minus Neutral contrast generated a significant locus of
activation in the left superior parietal lobule (BA 7) (Table III, Fig. 1). At Time 2, this
contrast was associated with another locus of activation in the left superior parietal lobule
(BA 7) (Table III, Fig. 1). The two-sample t-test performed to compare BOLD responses
at Time 1 and Time 2 did not reveal anything significant.
At Time 1, the Interference minus Neutral contrast produced a significant lo-
cus of activation in the left superior parietal lobule (BA 7) (Table III, Fig. 1). At
Time 2, this contrast was associated with another locus of activation in the left su-
perior parietal lobule (BA 7) (Table III, Fig. 1). Significant loci of activation were
also detected in the right ACcd (BA 32), left caudate nucleus and left substantia nigra
(Table III, Fig. 1). Moreover, a two-sample t-test revealed that BOLD activation in the right
Table III. Interference Minus Neutral Contrast at Time 1 and Time 2
GroupRegion Brodmann area
EXP R ACcd
L Caudate nucleus
L Substantia nigra
(1988) and refer to medial-lateral position (x) relative to medline (positive = right),
anterior-posterior position (y) relative to the anterior commissure (positive = anterior),
and superior–inferior position (z) relative to the commissural line (positive = superior).
Designation of Brodmann areas for cortical areas are also based on this atlas. CON:
Control; EXP: Experimental; L: Left; R: Right.
12Beauregard and L´ evesque
Fig. 1. Statistical activation maps at Time 1 and Time 2 produced by the Interference minus Neutral contrast.
Images are sagittal sections for the data averaged across subjects. At Time 1, significant loci of activation were
detected in the left superior parietal lobule for both the CON (A) and EXP (C) groups. At Time 2, activations
were also noted in this cortical region for the CON (B) and EXP (F) groups. In addition, for the EXP group only,
significant loci of activation were measured in the right ACcd (D), as well as the left caudate nucleus and left
substantia nigra (E).
ACcd (BA 32) and left caudate nucleus was significantly greater at Time 2 than Time 1
For the Go trials at Time 1, the average accuracy scores (percentage of correct re-
sponses) were not statistically different between CON (92.4%, SD=20) and EXP (88%,
SD=15) subjects (Table V). At Time 2, the average accuracy score of the CON subjects
(84%, SD=15) was comparable to that of Time 1. For the EXP group, this score was
significantly greater (p < 0.05) at Time 2 (95%, SD=4.9) than Time 1 (Table V).
For the No-Go trials at Time 1, the average accuracy scores of the CON (74%,
SD=11.3) and EXP (78%, SD=13.8) groups were comparable (Table V). At Time 2, the
average accuracy score of the CON subjects (84%, SD=11.5) was not different than that
of Time 1. For the EXP group, this score was significantly higher (p < 0.0005) at Time 2
(94%, SD 4.8) than Time 1 (Table V).
Functional Magnetic Resonance Imaging Investigation of the Effects13
Table IV. EXP Group: Time 1 v. Time 2 (Interference Minus Neutral Contrast)
L Caudate nucleus
Note. Stereotaxic coordinates are derived from the human atlas of Talairach and Tournoux
(1988) and refer to medial–lateral position (x) relative to medline (positive = right),
anterior–posterior position (y) relative to the anterior commissure (positive = anterior),
and superior–inferior position (z) relative to the commissural line (positive = superior).
EXP: Experimental; L: Left; R: Right.
Functional MRI Data
At both Time 1 and Time 2, the No-Go minus Go contrast did not produce any signif-
icant locus of activation. The two-sample t-test performed to compare BOLD responses at
Time 1 and Time 2 did not reveal anything significant.
The No-Go minus Go contrast did not produce any significant locus of activation at
Time 1. At Time 2, significant loci of activation were noted in the right ACcd (BA 24/32),
right ventrolateral prefrontal cortex (BA 47), left thalamus, left caudate nucleus, and left
substantia nigra (Table VI, Fig. 2). A two-sample t-test confirmed that BOLD signal in
these brain regions was significantly greater at Time 2 relative to Time 1.
the CPRS-R reveals that the neurofeedback protocol used here led to a significant decrease
Go/No-Go task (Percentage of Correct
Time 1Time 2
CON EXPCON EXP
Note. CON: Control; EXP: Experimental.
∗p < 0.05;∗∗p < 0.0005.
14Beauregard and L´ evesque
Table VI. Go/No-Go No-Go Minus Go Contrast at Time 2
Group RegionBrodmann area
L Substantia nigra
L Caudate nucleus
Note. Stereotaxic coordinates are derived from the human atlas of Talairach and Tournoux (1988) and
refer to medial–lateral position (x) relative to medline (positive = right), anterior–posterior position
(y)relativetotheanteriorcommissure(positive = anterior),andsuperior–inferiorposition(z)relative
to the commissural line (positive = superior). Designation of Brodmann areas for cortical areas are
also based on this atlas. CON: Control; EXP: Experimental; L: Left; R: Right.
of inattention and hyperactivity, which are primary symptoms of AD/HD. Indeed, the EXP
group showed marked improvement in attention and behavioral inhibition following NFT.
This improvement was associated with a better performance on the Counting Stroop task
(for both Neutral and Interference trials) and the Go/No-Go task (for both Go and No-Go
Fig. 2. Statistical activation maps measured at Time 2 in the EXP group (No-Go minus Go contrast). Significant
loci of activation were measured in the right ACcd (A), left thalamus and left substantia nigra (B), left caudate
nucleus (C), and right ventrolateral prefrontal cortex (D).
Functional Magnetic Resonance Imaging Investigation of the Effects15
trials).With respect to the CON group, no such change was noted at Time 2 relative to
Time 1. These neuropsychological and behavioral findings accord with those of previous
studies showing that NFT can significantly improve attention and response inhibition in
AD/HD children (Fuchs et al., 2003; Kropotov et al., 2005; Linden et al., 1996; Lubar &
Shouse, 1976; Lubar & Lubar, 1984; Lubar et al., 1995; Monastra et al., 2002; Rossiter
& LaVaque, 1995; Rossiter, 2004; Shouse & Lubar, 1979; Tansey, 1984, 1985; Thompson
& Thompson, 1998). Our neuropsychological and behavioral findings provide further
empirical support to the view that neurofeedback can be considered an effective treatment
for children with AD/HD.
At Time 1 and Time 2, for EXP and CON groups the performance on the Counting
Stroop task (Interference minus Neutral contrast) was associated with significant loci of
activation in the left superior parietal lobule (BA 7). Functional neuroimaging studies have
associated activity in superior parietal lobule—a component of the posterior attentional
shifts of visual spatial attention (Corbetta, Miezin, Shulman, & Petersen, 1993; Vanden-
berghe, Gitelman, Parrish, & Mesulam, 2001). The left superior parietal lobule activations
seen here for both groups of subjects may be related to such attentional processes.
No activation of the ACcd was detected at Time 1 for both groups of subjects. This
is consistent with the results of an fMRI study recently carried by Bush et al. (1999). In
this study, adults with AD/HD did not activate the ACcd while they performed a Counting
ACcd (BA32),leftcaudate, and leftsubstantia nigra.For the CONgroup, noactivation was
detected in these cerebral structures. As for the ACcd, a large corpus of functional brain
imaging data reveals that this brain region exerts a pivotal role in the cognitive processes
involved in the Stroop task (Bush et al., 1998; Bush et al., 2000), being critically implicated
in selective attention, the selection of an appropriate response, and the suppression of
inappropriate behavioral responses (Carter et al., 1998; Corbetta, Miezin, Dobmeyer,
Shulman, & Petersen, 1991; Pardo, Pardo, Janer, & Raichle, 1990; Paus, Petrides, Evans,
& Meyer, 1993; Peterson et al., 1999). Given this, we posit that the better performance of
the EXP subjects at Time 2 v. Time 1 was related to the normalization, following NFT, of
neural activity in the ACcd, a central component of the anterior attentional system.
locus of activation. This findings is in line with the results of previous fMRI studies
having demonstrated an underactivation of the striatum (Booth et al., 2005; Durston et al.,
2003; Rubia et al., 1999; Teicher et al., 2000; Vaidya et al., 1998) and diverse prefrontal
areas (Booth et al., 2005; Rubia et al., 1999; Tamm et al., 2004; Vaidya et al., 1998) in
children and adolescent with AD/HD during Go/No-Go tasks. For the EXP group at Time
2, however, significant loci of activation were detected in the right ACcd (BA 24/32),
right ventrolateral prefrontal cortex (BA 47), left thalamus, left caudate nucleus, and left
substantia nigra. These findings agree with the results of functional neuroimaging studies
showing that the ACcd (Liddle et al., 2001; Menon et al., 2001), the ventrolateral prefrontal
cortex (Garavan et al., 1999; Liddle et al., 2001; Menon et al., 2001), and the caudate
nucleus (Booth et al., 2005; Menon et al., 2001) are implicated in the various cognitive
processes underlying behavioral inhibition. Thus, the ACcd would be involved in decision
implicated in response inhibition (Liddle et al., 2001). Regarding the caudate nucleus, there
16 Beauregard and L´ evesque
is some evidence that this brain region is involved in the motor inhibition of inappropriate
from the frontal cortex and sends input back via the globus pallidus and then thalamus
(Goldman-Rakic, 1987). This fronto-striatal network modulates neural computations in the
supplementary motor area, which plays a pivotal role in motor planning, initiation, and
timing (Deiber, Honda, Ibanez, Sadato, & Hallett, 1999). The involvement of the thalamus
in this network may explain the thalamic activation noted here during the No-Go trials.
group at Time 2, for both the Counting Stroop task and the Go/No-Go task, suggest that
the normalizing effect of NFT was mediated, at least partially, by dopamine. This biogenic
amine exerts a pivotal neuromodulatory effect in the brain (Seamans & Yang, 2004).
striatal circuits is related to AD/HD. First, AD/HD symptoms can be successfully treated
with methylphenidate, a potent blocker of the reuptake of dopamine which augments the
availability of this neuromodulator/neurotransmitter into the extraneuronal space (Dresel
et al., 2000). Second, molecular genetic evidence suggests an association between AD/HD
and polymorphism of the dopamine transporter gene, as well as the dopamine D4 and D5
receptor genes (for a review, see Bobb, Castellanos, Addington, & Rapoport, 2005). Third,
structural MRI studies of individuals with AD/HD have reported volumetric reductions
in the frontal lobes and striatum (Aylward et al., 1996; Castellanos et al., 1994, 1996;
Durston et al., 2004; Filipek et al., 1997; Mataro et al., 1997; Mostofsky et al., 2002).
Fourth, SPECT and PET studies carried out in AD/HD children, adolescents or adults have
found decreased metabolism in diverse frontal and striatal regions (Amen & Carmichael,
1997; Kim et al., 2002; Lou et al., 1989; Sieg et al., 1995; Zametkin et al., 1990). Lastly,
dopamine modulation offrontalactivity duringtheperformance oftheStrooptaskhas been
previously shown (Dolan et al., 1995).
The nigrostriatal dopaminergic system is involved in motor control whereas attention
processes are regulated in part by mesocortical dopaminergic neurons (for a review, see
Nieoullon & Coquerel, 2003). There is also some evidence indicating that dopamine under-
lies the integrative properties of the fronto-striatal circuits and supports synaptic plasticity
processes such as long-term potentiation (Calabresi, Pisani, Mercuri, & Bernardi, 1996).
On this basis, we postulate that the neurofeedback protocol used here led to the neuromod-
ulation by dopamine of neural activity in fronto-striatal circuits. Furthermore, given the
association between AD/HD and polymorphism of the D4 and D5 receptor genes, we also
hypothesize that this neuroplastic phenomenon implicated long-term potentiation as well
as the D4 and D5 receptors.
This work was supported by grants from the Fondation Lucie et Andr´ e Chagnon and
the International Society for Neuronal Regulation (ISNR). We would like to acknowledge
Dr. Isabelle Fortier and her team (Hˆ opital Ste-Justine) for the recruitment and cognitive
evaluation of the participants, as well as Dr. Phillipe Robaey, the child psychiatrist who
diagnosed the participants (Hˆ opital Ste-Justine). We also thank M´ elanie Veilleux and the
staff of the D´ epartement de radiologie, CHUM, Hˆ opital Notre-Dame, for their proficient
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