Neural Correlates of Forethought in ADHD

Abstract

Objective: The purpose of the present investigation was to delineate the neural correlates of forethought in the ADHD children relative to typically developing (TD) children. Method: In all, 21 TD and 23 ADHD adolescents underwent functional magnetic resonance imaging (fMRI) while performing a forethought task. The participants had to identify congruent and incongruent stimuli from cartoon stories representing sequences of action. Results: The findings revealed significantly greater activation in the bilateral prefrontal cortex (PFC) in TD versus ADHD children, and more activation in the cerebellar vermis in the adolescents with ADHD versus TD, during performance of the incongruent relative to congruent condition. Conclusion: The inverse pattern of activation of the PFC and the cerebellar vermis in both groups could reflect a compensatory role played by the cerebellum or suggest the malfunction of the neural network between those regions in ADHD. Further research of the neural correlates of forethought in ADHD is warranted.

Keywords

adolescent ADHD cognitive functioning neurobiology response inhibition

ADHD is a condition marked by a lack of concentration, short attention span, disorganization, difficulties with planning, and physical restlessness (DSM-IV-TR; American Psychiatric Association [APA], 2000). The condition is associated with an immature brain as well as deficits in executive control, working memory (WM), and poor behavioral inhibition (Barkley, 1997, 2005, 2010). Impairment of the “hot” affective aspects of executive functions (e.g., behavioral inhibition mediated primarily by the ventral and medial regions of the prefrontal cortex [PFC] and the anterior cingulate cortex [ACC]) and the more cognitive or “cool” aspects of executive functions (e.g., WM, planning, and cognitive flexibility mediated by the dorsolateral PFC) are often reported (Bush, Valera, & Seidman, 2005; Castellanos et al., 1996; Durston et al., 2003; Rubia, 2002; Rubia et al., 1999, 2000; Sergeant, Geurts, & Oosterlaan, 2002; Zelazo, Qu, & Muller, 2005).

Convergent data from neuroimaging, neuropsychological, genetic, and neurochemical studies consistently point to the involvement of the frontostriatal network as a likely contributor to the pathophysiology of ADHD. Anatomical studies suggest widespread reductions in volume of the PFC, with a marked effect on the right side, the basal ganglia (striatum), the ACC, the cerebellum, as well as the corpus callosum (Barkley, 2010; Bush et al., 2005; Castellanos et al., 1996, 2002; Durston et al., 2003, 2004; Filipek et al., 1997; Lyoo et al., 1996; Mostofsky, Cooper, Kates, Denckla, & Kaufmann, 2002). The most common observations in functional studies include underactivations in ADHD relative to controls in the cingulate cortex, medial PFC, and striatum (Bush et al., 1999; Durston et al., 2003; Konrad, Neufang, Hanisch, Fink, & Herpertz-Dahlmann, 2006; Rubia, 2002; Rubia et al., 1999; Rubia, Smith, Brammer, Toone, & Taylor, 2005; Schultz et al., 2004; Zang et al., 2005).

According to Fuster (2008), representations of goal-directed sequences of actions (cognits) are mediated through the PFC. WM is the ability to retain information for the prospective execution of an action (motor act, mental operation, or language). Reversible lesions by transcranial magnetic stimulation (TMS) are reported to segregate spatial and nonspatial deficits in WM to dorsal and ventral lateral cortex, respectively (Mottaghy, Gangitano, Sparing, Krause, & Pascual-Leone, 2002). The evidence for deficit in nonverbal WM associated with ADHD includes findings of impaired memory for spatial location (Mariani & Barkley, 1997), and impaired organization and reproduction of complex design such as in the Rey-Osterrieth task (Sadeh, Ariel, & Inbar, 1996; Seidman, Biederman, Faraone, Weber, & Ouellette, 1997).

The retrospective and prospective functions defined by Fuster (1997) and Goldman-Rakic (1995), also known as hindsight and forethought, are crucial for the understanding of WM. In Barkley’s hybrid model of executive functions, hindsight and forethought functions are dependent of WM and associated with anticipatory set and self-awareness. Deficiency in WM leaves those with ADHD influenced by more immediate events and consequences. Hindsight and forethought function in a parallel or “temporally symmetrical” way (Barkley, 2005). Information about the past is less likely to be held in mind (hindsight). As a consequence, those with ADHD are less able to conjecture about the possible futures related to events (forethought). Hindsight, defined as the ability to alter subsequent responses based on immediate preceding event, has been proposed to be impaired in ADHD (Barkley, 2005, 2011). The model predicts that those with ADHD are less prepared to meet the arrival of future events and that the timing of the preparatory behavior (anticipation set) and self-awareness may also be deficient. However, evidence supporting hindsight and forethought deficits have not yet been well studied in ADHD, as they are likely to pertain to measures of planning (Barkley, 1997, 2005), sustained attention (Chee, Logan, Schachar, Lindsay & Wachsmuth, 1989), and reaction time (RT; Zahn, Kruesi, & Rapoport, 1991). For example, findings reveal that children with ADHD often fail to use a warning stimulus to prepare for the upcoming response trial, with longer preparatory intervals making their performance worse than in control children. The findings of perseverations errors on the Wisconsin Card Sorting Task (WCST) also suggest such a problem (Barkley, 1997). Thus, research on the prospective or forethought functions in ADHD remains sparse. The main goal of the present study was to investigate the neural correlates of forethought in ADHD. The existence of a prefrontal dysfunction associated with behavioral inhibition and executive control deficits in ADHD and the link of forethought to WM and inhibition (Barkley, 2011) has led us to hypothesize that children with ADHD will show atypical patterns of prefrontal activations while performing a task related to forethought.

Method

Participants

Typically developing participants were recruited through schools in Montreal area. Participants with ADHD were recruited through a specialized clinic for ADHD at Hôpital Rivière-des-Prairies and a Montreal association for parents with children having ADHD. All participants with ADHD had received a diagnosis by a specialized medical team based on DSM-IV-TR criteria (APA, 2000). Motor, auditory, and vision deficits were considered as exclusion criteria as well as central nervous system anomalies and major health problems. Parents filled out the Risk Factor Questionnaire (RFQ; Poissant & Lecomte, 2007) to confirm the diagnosis in adolescents with ADHD and exclude signs of ADHD in adolescents with TD. Symptoms of inattention and hyperactivity were also taken into consideration with ADHD Rating Scale-IV (DuPaul et al., 1998) and attention problem with the Achenbach System of Empirically Based Assessment (CBCL 6-18; Achenbach & Rescorla, 2001). The 44 selected participants were divided into two groups (TD and ADHD) composed of 21 and 23 participants, respectively (see Table 1). Given the mean age of the participants (years: months; ADHD: M = 11:2, SD = 2:6, range = 7-17; TD: M = 12:4, SD = 2:2, range = 10-17), we refer to the participants collectively as adolescents. All adolescents were assessed in two sessions: neuropsychological and cerebral imagery while they were deprived of methylphenidate (MPH) for at least 24 hr.

Table 1.

Descriptive Statistics of Participants’ Characteristics.

Diagnostic groups	Age (year:month)	VIQ^a	PIQ	FSIQ	CBCL_Att_T	DP_HY	DP_IN	DP_Tot	CCC Children	McGill-Anomaly test (a)	Picture Arrang. (a)
ADHD (n = 23: 7 girls, 16 boys)
M	11:2	101	3	105	63	12	14	27	36	25	9
SD	2:6	13	18-31	14	6	4	6	9	11	3	3
Range	7-17	75-130		83-134	52-77	6-21	4-24	10-43	15-52	18-31	3-14
TD (n = 21: 12 girls, 9 boys)
M	12:4	103	4	109	52	2	3	5	48	23	11
SD	2:2	15	17-31	16	3	3	3	5	13	4	3
Range	10-17	72-119	0.06	74-129	50-61	0-9	0-9	0-18	24-90	17-31	6-17
Levene’s F	0.39	0.41	1.52	0.73	6.79*	6.36*	9.62**	5.43*	0.15	0.06	0.01
t(42)	−1.65	−0.52	1.52	−0.86	7.23**	8.87**	8.24**	9.96**	−3.53**	1.52	−2.03

Note: VIQ = verbal IQ; PIQ = performance IQ; FSIQ = full IQ, all measured with K-BIT; CBCL_ATT-t: T-score for Attention subscale of CBCL; DP_HY: DuPaul_Parent, hyperactivity subscale, raw score; DP_IN: DuPaul_Parent, inattention subscale, raw score; DP_Tot: DuPaul_Parent, total raw score; Auditory Consonant Trigrams (CCC-Children) has a possible range of raw score of 0 to 60; McGill-Anomaly test has a possible range of raw scores of 0 to 32; Picture Arrangement scaled scores subtest of the Wechsler Intelligence Scale for Children-III (WISC-III). (a) TD = 16, ADHD = 18 and t(30)

p < .05. **p < .01; all ps are two-tailed.

Control Measures

Verbal, performance, and full scale IQ scores (verbal IQ [VIQ], performance IQ [PIQ], and full scale IQ [FSIQ]) were obtained using the Kaufman Brief Intelligence Test (K-Bit; Kaufman & Kaufman, 1990). Adolescents with ADHD (n = 23; 7 girls, 16 boys) were group-matched to adolescents with TD (n = 21; 12 girls, 9 boys) so that IQ scores and chronological age did not differ between groups. We chose the Auditory Consonant Trigrams (CCC-Children) test developed by Paniak, Millar, Murphy, and Keizer (1997) to assess WM. Independent samples t tests confirmed significant differences between groups (except in the 0-s interval) showing a deficit in the ADHD group (see Table 1). To distinguish forethought from anomaly detection and sequential reasoning, we used two additional tests. We assessed pictorial comprehension with a short version of the McGill picture anomaly test (32 pictures out of the original test; Hebb & Morton, 1943). Independent samples t test showed no significant differences between diagnostic groups. In addition, we assessed perceptual organization with the Picture Arrangement (PA) subtest of the Wechsler Intelligence Scale for Children–III (WISC-III) in order to reflect the ability to reason sequentially (Spreen & Strauss, 1998). The PA’s scaled scores indicated an average/normal performance for both diagnostic groups, and independent samples t test showed no significant differences between groups.

Stimulus Creation and Experimental Procedure

We designed an original functional magnetic resonance imaging (fMRI) task to assess forethought and verify the expected hypoactivation in the related regions of interest in ADHD: lateral prefrontal, dorsal anterior cingulate and inferior prefrontal cortices, basal ganglia, thalamus, and portions of parietal cortex (from Dickstein, Bannon, Castellanos, & Milham, 2006). All stories were previously validated (out of scan) with 82 university students. With the specific constraints of fMRI in mind (strict limitation of movement), we adapted and operationalized the forethought task so that no effective actions were required from the participant in the scan (except for pushing the response key). We asked students whether from a first image (“stimulus”), the second image represented a coherent or incoherent consequence (“response”). Fifty-six stories were retained for the experiment. Half of the stories represent congruent sequence of actions (Figure 1A), and the other half represent incongruent sequence of actions (Figure 1B). For example, in the congruent (CO) situation, participant sees a woman taking a liter of milk (Slide 1) followed by the picture of the same woman pouring the milk in a bowl of cereal (Slide 2; Figure 1A). In the incongruent (INCO) situation, participant sees the same first slide (Slide 1) followed by the same woman but pouring water in a flower-pot (Slide 2; Figure 1B). Being plausible in itself (pouring water in a flower-pot), Slide 2 in INCO situation is not what one would expect from Slide 1. All stories (28 CO: 28 INCO) were presented in blocks of seven stories (4 CO blocks vs. 4 INCO blocks, with 6:1 ratios) in a randomized manner. A rest period of 16 s was accorded between blocks with a total series lasting 9 min. Participants had to indicate whether the stories (sequence of actions) made sense according to their expectation (Figure S1) by pressing either “yes” (CO) or “no” (INCO) button on a handheld response device. Because INCO situation presents an implausible outcome, we expected a higher cognitive demand for this condition.

Figure 1.

Illustrations of a congruent story (A) and an incongruent story (B).

All scans were performed at the Institut Universitaire de Gériatrie de Montreal (IUGM). Participants were ready for the scan session after practice in a mock scanner. The maximum possible score was 56 (28 for each situation). Adolescents were tested in the scan that lasted between 40 and 50 min. Informed consent was obtained from parents and adolescents. Participants received CAN $50 as compensation. The research received ethical approval from both Université du Quebec à Montreal and IUGM.

Behavioral Data Analysis

First, we classified the participant’s answers in five categories: (a) hits (good answer in authorized delay), (b) miss (error in authorized delay), (c) omission (no answer during the authorized delay), and (d) RT (mean in ms) and reaction time-per-hit (mean RT/hit score). For hits, miss, and omission measurements, we used nonparametric statistical tests, the Kruskal–Wallis (χ²) tests to assess diagnostic group comparison (ADHD vs. TD) and interaction effect (Diagnostic × Task) as well as two-tailed signed rank tests (S) for task comparison (CO vs. INCO). We performed independent sample t tests on the two measures of time (RT and RT/hit) and repeated measures ANOVAs to assess task and interaction effects. We took ANCOVA measures for hit, RT, and RT/hit to look for a potential effect of WM on performance. Other potential covariates such as chronological age, intellectual abilities (VIQ, PIQ, FSIQ), perceptual organization (PA), and pictorial comprehension (McGill picture anomaly test) were dropped from analysis because of their absence of effect in determining diagnostic group differences (see earlier discussion). Because of unequal distribution of gender between ADHD and TD groups, this factor was included in ANCOVAs.

FMRI Data Analysis

A fMRI blood oxygenation level dependent (BOLD) procedure (Kwong et al., 1992; Ogawa et al., 1992) was used during performance of the task. BOLD signals were recorded using a single-shot, gradient-recalled echo-planar imaging sequence (repetition time [TR] = 2,000 ms, echo time [TE] = 30 ms, flip angle = 90 degrees, matrix 64 × 64 voxels) on a MRI Siemens TRIO system at 3.0 Tesla at IUGM. The images consisted of 32 contiguous axial slices, with a 3 mm × 3 mm in-plane resolution. The slice thickness was 3 mm. During the run, 270 volumes were continuously acquired over a total duration of 540 s. A high-resolution T1-weighted scan (1 mm³ voxel size) was acquired for each subject for anatomical coregistration.

The participants were children and adolescents, and were thus potentially capable of relatively strong head movements. We therefore decided to use images provided by the scanner with prospective acquisition correction, allowing better control of movements. fMRI data were then processed using statistical parametric mapping (SPM5; Wellcome Department of Cognitive Neurology, London, UK), according to methods outlined by Friston and colleagues (1995). Functional images were realigned to correct for artifacts due to minor head movements, parameters of coregistration of mean images with their corresponding high-resolution T1 images were estimated, and then functional images were spatially normalized into the standardized Montreal Neurological Institute (MNI) brain template (voxel size: 3 × 3 × 3 mm) and smoothed with a 12 × 12 × 12 mm³ full width at half maximum Gaussian kernel. Although the MNI template is that of an adult brain, there is fMRI evidence that the spatial normalization to an adult derived template is feasible in children older than age 7 (Kang, Burgund, Lugar, Petersen, & Schlagger, 2003).

The statistical analysis of the data was performed to determine the dynamic cerebral changes associated with forethought and consisted of two stages. First, based on approximately 42-s boxcar functions representing the pictures viewing periods and general linear approach, all the normalized and smoothed single-subject brain volume images were computed for the two conditions (CO and INCO), and each subject’s data were convolved with the canonical hemodynamic response function. Additional regressors representing estimated head movements (translation and rotation with 6 degrees of freedom) were added as covariates of no interest into these models to account for artifacts due to head movements during scanning. These models were used to create subtraction contrasts between conditions of interest (INCO − CO) for each participant. Because the literature on forethought in ADHD has been diffuse, an exploratory whole brain analysis was conducted. These individual contrasts were then entered into a one-sample t test to perform a random-effects group analysis to investigate the cerebral activations patterns associated with forethought in each group. To reduce Type II error, we set up the threshold level for statistical significance at a p =.001 uncorrected for multiple comparisons. We also examined any potential cerebral activation differences between ADHD and control groups using a two-sample t test at the same threshold. For all the statistical analyses (first- and second-level analysis), only contiguous voxels ≥10 were considered as significant.

Results

WM and Forethought

ANCOVA measures conducted with diagnostic group (ADHD and TD) as a between-subject factor and WM as a covariate failed to reach significance when hit, RT, and TR/hit were considered as the dependant variables for neither tasks (CO and INCO). ANCOVA measures with gender as a covariate also revealed no significant effects for neither task’s conditions nor any dependant variables.

Performance of each diagnostic group on behavioral assessments measured with hits, miss, and omission is reported in Table 2. Visual examination of the table reveals that the mean performances of the ADHD group are consistently below those of the TD group. Kruskal–Wallis tests conducted to compare diagnostic groups indicated significant effects. Adolescents with ADHD were less accurate on the hit criteria for both task’s conditions, and they made more omissions for the incoherent condition compared with adolescents with TD. However, no significant main effects for the task’s conditions—CO versus INCO (hit: S = 30, p = .52; miss: S = −7.5, p = .82; omission: S = −26.5, p = .27)—nor interaction (Task × Diagnostic group) were found (hit: χ² = 0.05, p = .82; miss: χ² = 0.83, p = .36; omission: χ² = 1.06, p = .3) were found.

Table 2.

Hits, Miss, and Omission for Participants With ADHD and TD: Diagnostic Group Effect.

Diagnostic Group	CO-hit	INCO-hit	CO-miss	INCO–miss	CO-omission	INCO-omission
ADHD (n = 23)
M	25.7	25.2	1.3	1.3	1.0	1.5
SD	2.5	2.8	1.5	1.9	1.8	1.9
Range	19-28	19-28	0-5	0-7	0-8	0-6
TD (n = 21)
M	27.1	27.0	0.5	.7	.4	.4
SD	.9	1.1	.7	.9	.6	07
Range	25-28	25-28	0-3	0-3	0-2	0-2
Kruskal–Wallis (χ²)	5.00*	4.47*	3.50	0.55	1.88	5.22*

Note: CO-hit = good answer in authorized delay for coherent condition; INCO-hit = good answer in authorized delay for incoherent condition; CO-miss = error in authorized delay for coherent condition; INCO-miss = error in authorized delay for incoherent condition; CO-omission = no answer during the authorized delay for coherent condition; INCO-omission = no answer during the authorized delay for incoherent condition.

p < .05. **p < .01; all ps are two-tailed.

Performance on the two measures of time is reported in Table 3. Independent sample t tests conducted on mean RT and RT/hit revealed significant diagnostic group main effect. In all conditions, adolescents with ADHD were slower to respond than adolescents with TD. Marginal significant main effects for task’s conditions for both dependant variables—RT: F(1, 42) = 3.56, p = .07; RT/hit: F(1, 42) = 2.9, p = .09—indicated lower scores for the CO condition meaning longer times for the INCO condition. No interaction (Task × Diagnostic group) was found for either dependant variable—RT: F(1, 42) = 1.99, p = .16; RT/hit: F(1, 42) = 0.44, p = .51.

Table 3.

Reaction Time (RT) and Reaction Time per Hits (RT/Hit) for Adolescents with ADHD and TD: Diagnostic Group Effect.

Diagnostic group	Age (year:month)	CO-RT	INCO-RT	CO-RT/H	INCO-RT\H
ADHD (n = 23)
M	11:2	1,420.7	1,433.3	57.5	59.1
SD	2:6	485.8	580.0	26.7	28.9
Range	7-17	868.6-2,619.8	859.1-2,698.8	31.0-133.4	32.6-128.5
TD (n = 21)
M	12:4	1,056.8	1,144.3	39.0	42.7
SD	2:2	256.0	301.5	9.7	12.1
Range	10-17	754.0-1,603.0	734.8-1,720.2	27.9-61.7	27.2-68.8
Levene’s F	.39	3.79	4.95*	3.96	7.68**
T	−1.65	3.06**	2.04*	2.99**	2.41*

Note: Adjusted ts for unequal variances are on same level of probability than reported ts. CO-RT = mean reaction time (mse.) for coherent condition; INCO-RT = mean reaction time (mse.) for incoherent condition; CO-TperH = Mean reaction time (mse.)/hit for coherent condition; INCO-TperH = mean reaction time (mse.)/hit for incoherent condition.

p < .05. **p < .01; all ps are two-tailed.

Localization of Brain Activation

The one-sample t test in the group of adolescents with TD revealed significant activations during performance of the INCO relative to CO condition in the left middle orbito-frontal cortex, the right superior and inferior frontal gyri (IFG), right frontal inferior operculum, as well as the left supplementary motor area (SMA; Table 4, Figure S2). The opposite contrast (CO – INCO) did not reveal any significant results. In comparison, the one-sample t test in the group of adolescents with ADHD showed significant activations during performance of the INCO versus CO condition only in the right IFG and the right portion of the basal ganglia (globus pallidus) as well as relative deactivations in the superior frontal cortex for the opposite contrast (Table 4, Figure S3). The direct comparisons, with the two-sample t test, between the diagnostic groups during INCO versus CO condition, revealed significantly greater activations in the bilateral PFC in the TD adolescents and more activations in the cerebellar vermis in the adolescents with ADHD (see Table 4 and Figure 2).

Table 4.

Regions of Significant Activations and Deactivations During Performance of the Forethought Task by Adolescents With Typical Development and With ADHD: Individual Groups and Group Differences.

			MNI
	L/R	Brain region	x	y	z	z score	Voxels	p value
TD only
Activations (INCO − CO)	L	Middle orbito-Frontal gyrus	−30	42	−15	3.48	127	.000
	R	Inferior orbito-Frontal gyrus	36	27	−18	3.29	52	.000
	R	Superior frontal gyrus	27	33	45	3.17	231	.001
	L	Supplementary motor area	−6	6	48	2.97	140	.001
	R	Frontal inf operculum	36	6	33	2.97	99	.001
Deactivations (CO − INCO)	No activated voxels
ADHD only
Activations (INCO − CO)	R	Inferior frontal gyrus	54	18	18	3.07	47	.001
	R	Pallidum	27	−15	−6	2.99	24	.001
Deactivation CO – INCO	R	Superior frontal gyrus	30	−9	69	3.03	18	.001
TD > ADHD
	R	Superior frontal gyrus	24	−6	69	3.47	83	.000
	L	Superior frontal gyrus	−18	51	0	3.12	53	.001
	R	SupraMarginal gyrus	63	−24	27	2.97	23	.001
ADHD > TD
		Cerebellar vermis	0	−27	−15	3.43	105	.000

Note: L = left; R = right; cluster threshold = 10 voxels; TD = typically developing adolescents; ADHD = adolescents with attention deficit and hyperactivity disorders; CO = coherent sequence of actions, INCO = incoherent sequence of actions; MNI = Montreal Neurological Institute.

Figure 2.

Difference map of results for the TD and the ADHD for the forethought task.

Discussion

Forethought

Overall, our results are compatible with previous reports of an impairment of the cognitive or “cool” aspects of executive functions such as WM, planning, and cognitive flexibility in ADHD (Barkley, 1997; Geurts, van der Oord, & Crone, 2006; Martinussen, Hayden, Hogg-Johnson, & Tannock, 2005; Sergeant et al., 2002; Sergeant, Geurts, Huijbregts, Scheres, & Oosterlaan, 2003). As expected, the results revealed that adolescents with ADHD were less accurate and slower than adolescents with TD at forethought. The trend for the CO condition to be judged faster than the INCO condition is consistent with the idea of an additional cognitive load for the incoherent situation where adolescents had to judge an unexpected sequence of events. Moreover, the absence of interaction (Task × Diagnostic group) indicates that forethought performance was affected in a similar manner regardless of the stimuli congruency. Overall, the preceding results confirm difficulty for adolescents with ADHD to accomplish a task demanding cool executive functioning such as forethought.

Contrary to our expectations, the effect of WM in forethought performance was limited as WM was not found to covary in the model with the diagnostic group as a factor, implying that forethought was the main and sole factor in explaining between-group differences in performance and cerebral activations. Nevertheless, the fact that adolescents with ADHD showed a deficit in WM confirms previous studies on executive function theory of ADHD (see meta-analysis of Willcutt, Doyle, Nigg, Faraone, & Pennignton, 2005). In summary, the main effect of diagnostic group, showing a disadvantage of adolescents with ADHD relative to control adolescents, can be considered as an evidence of a forethoughts deficit in ADHD, and fMRI data should be interpreted accordingly.

Forethought’s Neuronal Correlates

The current results are consistent with studies of executive functions and inhibition (Barkley, 2011), which found the involvement of the frontostriatal network in the pathophysiology of ADHD (Aron, Robbins, & Poldrack, 2004; Bush et al., 1999; Durston et al., 2003; Konrad et al., 2006; Schultz et al., 2004; Zang et al., 2005). Several of the predicted regions of interest have shown to be differentially implicated in the forethought task in ADHD relative to TD, including lateral prefrontal and inferior prefrontal cortices, as well as basal ganglia (Dickstein et al., 2006). Because we had expected a strong association between forethought and WM, we hypothesized activations in the medial and lateral posterior parietal cortex and portions of the premotor cortex in healthy adolescents (see meta-analysis of Owen, McMillan, Laird, & Bullmore, 2005). Instead, adolescents with TD showed significant activations in the left middle orbito-frontal cortex, the right superior and IFG, the right frontal inferior operculum, and the left SMA, a region also known to (a) be involved in procedural learning and connected reciprocally with basal ganglia and the cerebellum (Ackermann, Daum, Schugens, & Grodd, 1996) and (b) overlap a neural network that includes Brodmann area (BA) 44 (Gerardin et al., 2000) and ventral premotor cortex implied in action understanding (Rizzolatti, Fogassi, & Gallese, 2002). In fact, the association between forethought and WM was weak and this could explain why no specific activations were found in medial and lateral posterior parietal cortex in TD adolescents. The opposite contrast (CO vs. INCO) did not reveal any significantly activated regions in adolescents with TD. In contrast, adolescents with ADHD showed significant activations only in the right IFG and the right portion of the basal ganglia (globus pallidus) in INCO versus CO contrast, as well as relative deactivation in the right superior frontal cortex during the opposite contrast. The right IFG, which has been typically implicated in the Go-No go or stop-signal tasks known to require inhibition of a prepotent response (Aron, Fletcher, Bullmore, Sahakian, & Robbins, 2003; Aron et al., 2004) was activated in both diagnostic groups. This is consistent with the idea that forethought is linked with inhibition (Barkley, 2011) and that adolescents with ADHD can use this network to help them solve the forethought task. The overall number and extent of activated regions in the adolescents with TD were more significant than in the adolescents with ADHD. Brain activation during the processing of INCO versus CO conditions for TD was mainly located in the right superior frontal gyrus, whereas for ADHD it was mainly in the IFG.

Cerebellum and ADHD

A growing literature demonstrates abnormalities affecting the cerebellum including cerebellar vermis in structural and fMRI in ADHD children. In a review from Emond, Joyal, and Poissant (2008), 6 out of 12 studies indicated a diminution of volume of the right cerebellum in ADHD, 3 at the vermis level (Berquin et al., 1998; Castellanos et al., 2001; Mostofsky, Reiss, Lockhart, & Denckla, 1998). Dickstein and colleagues (2006) showed a significantly elevated activation in the posterior cerebellum on the right in patients with ADHD that is congruent with the present findings. Their meta-analysis included executive functions related to forethought such as Go-No Go, Conflict tasks, Stroop, and N-Back. Zang et al. (2005) found that the activation volume (AV) of the PFC and of the ACC during the interference condition of the Stroop in children with ADHD off MPH was smaller than in controls. These findings are coherent with our current results of relative hypofrontality. However, unlike our results, AV of the basal ganglia and cerebellum was also smaller in the interference condition compared with controls. In our study, we found an unbalance between the high activation of the basal ganglia and cerebellum and the low activation of the PFC for the forethought condition in ADHD. A compensatory network including basal ganglia and cerebellum may have intervened in forethought processing in adolescents with ADHD off MPH.

In closing, this study confirms the role of the PFC in cognitive control and in the ability to orchestrate thought and action (Miller & Cohen, 2001) and confirms its dysfunction in ADHD. However, to fully establish the primacy of frontal dysfunction in ADHD, future studies will need to provide a more comprehensive examination of executive function, using tasks known to produce consistent patterns of activity in other regions considered putative sources of dysfunction, for example, the cerebellum, ventral striatum, and parietal cortices (Dickstein et al., 2006). Because the forethought task produced patterns of activity in the cerebellum (cerebellar vermis) in adolescents with ADHD, our paradigm seems promising in the exploration of the issue of the primacy of the prefrontal region. As such, recent research has revealed significant relationships between the vermian regions of the cerebellum and cognitive functions typically associated with prefrontal lobe function. These relationships appear to be supported by anatomical connections between the two distant brain regions (Paul et al., 2009). In our study, the inverse pattern of activation of the PFC and the cerebellar vermis in the TD and the ADHD groups could be an index of the compensatory role played by the cerebellar vermis in ADHD, but it could also be an indication of the malfunction of the neural network between the two brain regions in ADHD.

Future research in the field of ADHD promises exciting findings that link the genomic, structural, and functional changes in the brain to constitute a brain model of dysfunctions in ADHD. Functional imaging studies made great progress in helping to uncover the neural substrate of ADHD. Advances in understanding the underlying neurobiology of ADHD will hopefully help to identify more specific and targeted pharmacotherapies and will help child neurologists to better manage their patients.

Footnotes

Acknowledgements

The authors would like to thank the Fond de recherche en santé du Québec. We also like to thank Bertrand Fournier, Ph.D. for statistical advices.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the Fonds de recherche en Santé du Québec (FRSQ) and Conseil de recherche en Sciences Humaines du Canada (CRSH).

Author Biographies

Hélène Poissant holds an appointment as a Full professor at Université du Québec à Montréal (Canada), where she directs a research group on ADHD using brain imagery techniques. She graduated from University of Montreal and completed her Ph.D. in Cognitive Psychology. As a recipient of a Fulbright Fellowship, she was a teaching assistant at Columbia University and City University of New York (CUNY). Also recipient of an Erasmus Mundus, she was a visiting professor at Universitat Automa de Barcelona.

Adrianna Mendrek is currently an Associate Research Professor at the Department of Psychiatry, University of Montreal and Centre de recherche Fernand-Seguin, where she has established a research program concerned with sex/gender differences in grave psychiatric disorders, such as schizophrenia. She has received her M.A. (Psychology) and Ph.D. (Neuroscience) from the University of British Columbia (UBC) and postdoctoral training at the University of California, Los Angeles (UCLA).

Noureddine Senhadji is currently a brain imagery analyst at Institut Universitaire de Gériatrie de Montreal. He completed his docotoral studies at University of Oran (Algeria) in mathematics and his postdoctoral training at the Centre de Recherche de l’Institut de Geriatrie de Montreal (CRIUGM).

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