PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Biol Psychiatry. Author manuscript; available in PMC 2009 September 16.
Published in final edited form as:
PMCID: PMC2745053
NIHMSID: NIHMS111213

Cingulate - Precuneus Interactions: A New Locus of Dysfunction in Adult Attention-Deficit/Hyperactivity Disorder

Abstract

Background

Pathophysiological models of Attention-Deficit/Hyperactivity Disorder (ADHD) have focused on frontal-striatal circuitry with alternative hypotheses relatively unexplored. Based on evidence that negative interactions between frontal foci involved in cognitive control and the non-goal directed ‘default-mode’ network prevent attentional lapses, we hypothesized abnormalities in functional connectivity of these circuits in ADHD.

Methods

Resting state BOLD fMRI scans were obtained at 3.0 Tesla in 20 adults with ADHD and 20 age- and sex-matched healthy volunteers.

Results

Examination of healthy controls verified presence of an antiphasic or negative relationship between activity in dorsal anterior cingulate cortex (centered at x=8, y=7, z=38) and in default-mode network components. Group analyses revealed ADHD-related compromises in this relationship, with decreases in the functional connectivity between the anterior cingulate and precuneus/posterior cingulate cortex regions (p<.0004, corrected). Secondary analyses revealed an extensive pattern of ADHD-related decreases in connectivity between precuneus and other default-mode network components, including ventromedial prefrontal cortex (p<3×10−11, corrected) and portions of posterior cingulate (p<.02, corrected).

Conclusions

Together with prior unbiased anatomic evidence of posterior volumetric abnormalities, our findings suggest that the long-range connections linking dorsal anterior cingulate to posterior cingulate and precuneus should be considered as a candidate locus of dysfunction in ADHD.

Pathophysiological models of Attention-Deficit/Hyperactivity Disorder (ADHD) have focused on prefrontal-striatal and mesolimbic circuits (1), based on findings of executive and motivational dysfunction (2, 3). However, as pointed out in a recent quantitative meta-analysis of functional imaging studies of ADHD (4), the focus on executive dysfunction and the frontal lobes has left alternative hypotheses unexplored. Additionally, standard task-based functional imaging studies depend on the precise specification of underlying neuropsychological deficits. Although the hypothesis that ADHD results from a primary inhibitory deficit was proposed a decade ago (5), the accumulated evidence has been equivocal at best (1).

A complementary approach is to examine the neural substrates of ADHD-relevant behaviors, such as attentional lapses (6, 7), and assess whether the underlying circuits are implicated in ADHD through analysis of the temporal correlations among distributed brain regions. This method of functional connectivity (8), first applied to mapping the motor cortical system (9), provides remarkably detailed spatial maps of putatively functionally related regions (10).

Momentary lapses in attention have been associated with failure to suppress activity in the ‘default-mode’ network (11). The ‘default-mode’ network, first identified by Raichle et al. (12), is a large and robustly replicable network (13, 14, 15) that comprises medial ventral prefrontal cortex (MVPFC) and posterior cingulate cortex (PCC) and precuneus in the midline and which has been linked to non-goal-directed processes (16). By definition, default-mode network activity can be detected during resting state scans, and abnormalities in this network have been reported in ADHD (17, 18), Alzheimer’s disease (19, 20), autism (21), schizophrenia (22), and depression (23).

The starting point for the present study was the finding that decreased cue-related activation in three prefrontal regions predicted slower response times and decreased default-mode network suppression in healthy adult volunteers (11), suggesting potential control loci for default-mode regulation. We hypothesized that these fronto-default mode interactions may represent loci of dysfunction in ADHD. Given reports that fronto-default-mode interactions are intrinsically represented in spontaneous activity at rest (24, 14), we tested for the presence of ADHD-related differences in functional connectivity between each of the three previously identified frontal foci (dorsal anterior cingulate cortex, dACC; right inferior frontal gyrus, rIFG; right medial frontal gyrus, rMFG) and the default-mode network.

METHODS

Participants

Twenty adult participants with ADHD were recruited from the NYU School of Medicine Adult ADHD Program (directed by L.A.). Twenty age-matched comparisons were recruited through local media advertisements. All prospective participants were screened with the Symptom Checklist-90-Revised (SCL-90 R), to exclude a broad range of psychiatric psychopathology (25). Exclusion criteria for both groups included: 1) lifetime history of psychotic, bipolar or substance use disorders, 2) current history of mood, psychotic, anxiety, or substance use disorders, 3) lifetime history of treatment with psychotropics other than stimulants (for ADHD group only), or 4) history of neurological or chronic medical illness. All participants were evaluated with the Adult ADHD Clinical Diagnostic Scale (ACDS) Version1.2 (26), a semi-structured interview that probes for the presence, severity and impairment associated with ADHD symptoms in childhood and adulthood, and which has been validated with informant-only reports (27). All participants with ADHD met lifetime criteria for Combined Type ADHD; they were also administered the Structured Clinical Interview for DSM-IV (SCID) to rule out other axis I comorbid diagnoses. Best estimate diagnoses for probands were established by LA. A psychiatrist (AK, MG) administered a semi-structured clinical interview to healthy comparison subjects to rule out all axis I psychiatric disorders, including presence of ADHD or learning disorders in childhood or adulthood. Educational attainment (level attained, e.g., high school graduate = 12; college graduate = 16) and strong right-handedness were obtained from self-report. Demographic characteristics are shown in Table 1. All participants reported graduating from high school and attending at least one year of college. Nine patients were currently being treated with stimulants which were discontinued for at least one day prior to scanning. All participants provided signed informed consent as approved by the IRBs of NYU and the NYU School of Medicine and were compensated.

Table 1
Demographics

Scanning

Each participant underwent one resting state scan consisting of 197 contiguous whole-brain functional volumes using echo planar imaging on a Siemens 3.0 Tesla Allegra (TR=2000 ms; TE=25 ms; flip angle=90, 39 slices, matrix=64x64; FOV=192mm; acquisition voxel size=3×3×3mm; 6.5 min). Participants were verbally instructed to relax and remain still with eyes open while the word “Relax” was centrally displayed. A high-resolution T1-weighted magnetization prepared gradient echo sequence was also obtained.

Preprocessing

Initial image preprocessing, including motion correction, despiking, and slice time correction, was done using AFNI (www.afni.nimh.nih.gov/afni). Subsequent preprocessing, including spatial filtering (FWHM=6mm), temporal bandpass filtering, and spatial normalization, was performed using FSL (www.fmrib.ox.ac.uk/fsl).

Study Specific Template Generation

In order to optimize automated spatial normalization and avoid possible group differences related to registration errors (28), we created a study specific template based upon the approach employed in optimized voxel based morphometry protocols (28). To create the template, all participants’ brain images were: 1) spatially normalized to MNI space using a 12 parameter affine transformation in FLIRT (www.fmrib.ox.ac.uk/fsl), 2) spatially filtered (FWHM = 8mm), and 3) averaged. Using this template, we recalculated the spatial transformation for each participant using a 12-parameter affine implemented in FLIRT. Repeating our analyses using the standard adult 152 brain MNI template rather than the study specific template yielded nearly identical results.

Functional Connectivity Analyses

In order to obtain timeseries for each seed in each participant, we 1) transform the subject’s timeseries into MNI space using a 12 DOF linear affine implemented in FLIRT (voxel size = 1 × 1 × 1 mm), and(2) calculate the mean timeseries for each of the three mask-spheres centered around the three spherical regions-of-interest (ROIs; 123 voxels, radius=3.5mm) centered at the coordinates reported by Weissman et al. (2006) converted to MNI space (dACC: x=8, y=7, z=38; rIFG: x=34, y=45, z=23; rMFG: x=49, y=19, z=0). For each ROI, individual participant analyses were carried out using the GLM implemented in FEAT, using a seed-based regression approach employed in our prior work (10), with the timeseries for the ROI as well as for the nuisance covariates (timeseries regressors for global signal intensity, white matter, cerebrospinal fluid, and six motion parameters) as predictors. (See Figure 1 for schematic outline.) We produced individual subject-level maps of all positively- and negatively-predicted voxels for each regressor, correcting for multiple comparisons at the cluster level using Gaussian random field theory (min Z>2.3; cluster significance: p<0.05, corrected). Group-level analyses (within and between) were conducted using a mixed-effects model (FLAME) implemented in FSL, which produced thresholded z-score maps of activity associated with each ROI. Based on the assumption that cortical connectivity should be reciprocal (29), we used the timeseries corresponding to the region which differed significantly between groups in our primary analyses, and derived the corresponding positively and negatively predicted networks and their significant group differences.

Figure 1
Data analytical path for preprocessing, extraction of region of interest and nuisance covariate timeseries, individual subject multiple regression analyses, and mixed effects analyses of group results.

Exploratory correlations between inattention and hyperactivity/impulsivity symptom severity reports were performed with the regression parameter estimates for the significant between group difference at a two-sided alpha of 0.05.

RESULTS

Although both rIFG and rMFG ROIs were significantly negatively related to precuneus and posterior cingulate cortex (PCC) in both groups (see Supplementary Figure 1), these relationships did not differentiate the groups significantly. By contrast, functional connectivity analyses of the dACC ROI demonstrated significantly less negatively correlated activity (p<.0004, corrected; 3989 cubic voxels 1.5mm per side =13,436mm3) in precuneus/PCC in subjects with ADHD (see Figure 2 and Table 2).

Figure 2
Functional connectivity analyses were carried out using a spherical ROI located in ACC (diameter = 7mm, number of voxels = 123; x=8, y=9, z=35), based upon the findings of Weissman et al. (2006) (depicted in panel A). Voxels positively (red) and negatively ...
Table 2
ADHD−Related Decreases in Functional Connectivity with ACC (x=8, y=9, z=35)

As shown in Figure 3, the precuneus/PCC mask obtained from the dACC ROI-based group difference robustly predicted positively and negatively correlated activity in healthy volunteers. The positively correlated regions coincided with the default-mode network in controls, but the ADHD group lacked the anterior component of that network. As the right panel of Figure 3 shows, the groups differed significantly in medial prefrontal cortex and superior frontal gyrus (p<3×10−11, corrected) and also in PCC/precuneus (p<.02, corrected; see Table 3). The ADHD group also showed significantly less negatively correlated functional activity in ACC (BA 24, 31, 32) and medial frontal gyrus (BA 6) (p<0.00002, corrected) and in superior temporal gyrus (BA 22, 41; p<0.04, corrected), including claustrum and precentral gyrus (BA 43, 44). Figure 4 shows the distribution of regression parameter estimates for the negatively correlated relationship between dACC ROI and PCC/precuneus in the two groups (means ±SD −0.23 ± 0.12 and −0.01 ± 0.15 for healthy control and ADHD groups, respectively; t(38) = 5.10, p=9.6 × 10−6; d= 1.61, 95% CI 0.79 – 2.43). The regression parameter estimates were significantly negatively correlated with ADHD symptoms across the entire sample (Pearson correlation coefficients ranged between −0.61 and −0.54 (n=38, p<.001) for the childhood or adulthood symptoms of hyperactivity/impulsivity or inattention. However, despite our prediction, we did not detect significant relationships between the parameter estimates and severity of inattentive symptoms in the ADHD group (or the control group), presumably reflecting the highly restricted ranges of symptom counts within each group.

Figure 3
Secondary functional connectivity analyses were carried out using the posterior cingulate/precuneus cluster identified in our primary analyses as the seed region (see panel A). For each group (ADHD, controls), voxels positively (red) and negatively (blue) ...
Figure 4
Decreased ACC-Precuneus Connectivity in ADHD. The scatter plot depicts the mean parameter estimates for dACC connectivity (seed region: x=8, y=7, z=38) in the precuneus/PCC region found to exhibit ADHD-related decrease in anti-relationship with anterior ...

DISCUSSION

By analyzing the intrinsic functional connectivity of putative frontal control loci in unbiased whole brain comparisons, we confirmed that the negative relationship posited by Weissman et al. (11) between control regions and the default-mode network is intrinsically represented in brain. Consistent with our hypothesis that ADHD is associated with abnormalities in fronto-default-mode interactions implicated in preventing attentional lapses, we found ADHD-related decreases in functional connectivity between a dorsal ACC seed and posterior components of the default-mode network (i.e., precuneus and PCC). Iteratively using the precuneus/PCC region as a ‘seed’ for an additional regression analysis revealed ADHD-related decreases in connectivity among components of the default-mode network, notably between precuneus/PCC and ventromedial prefrontal cortex. We did not find a specific relationship between functional connectivity measures and inattention symptoms within the ADHD group, perhaps because inattentive and hyperactive symptoms, both of which were significantly negatively correlated with the regression parameter estimates, were highly correlated in our combined-type ADHD sample (r=0.68). Of note, these novel results fit with the three unbiased voxel-based morphometric findings of diminished volume (30, 31) or decreased cortical thickness (32) in precuneus and PCC in ADHD.

While not the primary focus of the present study, our finding of ADHD-related decreases in connectivity between anterior and posterior default mode components may suggest a novel locus of dysfunction for working memory deficits commonly observed in ADHD (33). Better performance on a working memory task has been reported to be positively related to the strength of functional connectivity between anterior and posterior default-mode components (VMPFC and PCC) (34). In this light, our findings of ADHD-related decreases in functional connectivity among default-mode components and abnormal connectivity with control regions suggest a potential neural basis for a linkage between working memory deficits and attentional fluctuations that will be addressed in future studies. Similarly, future studies combining pharmacological manipulations and electrophysiological methods or magnetoencephalography will be required to move beyond functional connectivity to effective connectivity, the influence exerted by one neural system on another (8).

Our results do not confirm a prior report of greater functional connectivity in adolescents with ADHD between dACC and widespread regions including thalamus, cerebellum, insula and pons (17). There are several possible explanations for our divergent findings. First, ACC is functionally and structurally complex (35, 10); we ‘seeded’ an ACC subregion empirically linked to default-mode interactions, while the seed ROI used by Tian et al. comprised the entire dorsal ACC. Second, Tian et al. did not differentiate the anti-phase relationships (“anti-correlations”) (24, 14) that accounted for our findings. It is possible that undiagnosed learning disorders in the ADHD participants may have contributed to our finding, although the reasonably high level of educational attainment in all participants tends to diminish this concern. Finally, the samples differed with respect to age group (child, adult). In future studies we plan to examine age effects and directly assess cognitive measures such as attentional lapses (6, 7), working memory (33), response variability (36), and medication response.

In summary, we found strong evidence of disconnection between an anterior cingulate control region implicated in ADHD (32, 37) and posterior components of the default-mode network, i.e., precuneus and PCC. In the context of increasing awareness of the complex role of precuneus and PCC in “high-level integration between posterior association processes and anterior executive functions” (38, p. 578), our findings suggest that structural and functional circuits linking the dACC to precuneus and PCC may represent ‘small-world network’ long-range connections (39) that should be considered as a candidate locus of dysfunction in ADHD.

Supplementary Material

SupplementaryFig1

Supplementary Figure 1:

Based on the findings of Weissman et al. (2006), functional connectivity analyses were carried out for each of the following three frontal-regions posited to be involved in top-down control: anterior cingulate cortex (ACC; BA24; x=8, y=9, z=35), right medial frontal gyrus (rMFG; BA10; x=34, y=45, z=19), and right inferior frontal gyrus (rIFG; BA47; x=49, y=18, z=−1). ACC was the only seed region to show significant group differences with decreased negative connectivity in ADHD compared to controls.

Acknowledgements

This work was supported in part by grants provided to FXC by the Stavros S. Niarchos Foundation, NIMH (5R21MH066393 and 5T32MH067763), the Leon Lowenstein Foundation, NARSAD and gifts from Linda and Richard Schaps, Jill and Bob Smith and the Taubman Foundation.

Footnotes

Disclosure of Biomedical Financial Interests and Potential Conflicts of Interest:

Drs. Castellanos, Kelly, Uddin, Ghaffari, Kirsch, Di Martino, Biswal, Milham and Messrs. Margulies, Shaw, and Shehzad reported no biomedical financial interests or potential conflicts of interest.

Dr. Sonuga-Barke has potential conflicts of interest in relation to UCB Pharmaceuticals (consultancy, advisory board and speaker honoraria), Janssen Cilag (consultancy, research grant, speaker honoraria), Shire Pharmaceuticals (advisory board) and Medice (speaker honoraria).

Dr. Rotrosen reports consulting income from Axonyx, Inc., and United BioSource Corp.; research support from Alkermes, Inc., NIDA, and NIMH; equity interests in Alcon, American Oriental Bioengineering, Amgen, Atrion, Avon Products, Biogen Idec, Biosite, Colgate-Palmolive, Cooper Companies, Cyberoptics, Dentsply International, Dov Pharmaceutical, Dow Chemical, Elan, Genentech, Gillette, ICOS, Invitrogen, Ivax, Kinetic Concepts, Lyondell Chemical, Mikron Infrared, Mylan Labs, Polymedica, Procter and Gamble, PDL Biopharma Inc, Respironics, St. Jude Medical, and Tyco; and no potential conflicts of interest.

Dr. Adler receives grant/research support from Abbot Laboratories, Cortex Pharmaceuticals, Bristol-Myers Squibb, Merck & Co, Novartis Pharmaceuticals, Pfizer, Shire, Eli Lilly, Ortho McNeil/Johnson and Johnson, New River Pharmaceuticals, Cephalon, Neurosearch; he is a member of speakers bureaus for Eli Lilly and Shire; serves on the advisory boards and as a consultant for Abbot Laboratories, Cortex Pharmaceuticals, Novartis Pharmaceuticals, Pfizer, Shire, Eli Lilly, Ortho McNeil//Johnson and Johnson, New River Pharmaceuticals, Cephalon, Merck, and Neurosearch.

References

1. Castellanos FX, Sonuga-Barke EJS, Milham MP, Tannock R. Characterizing cognition in ADHD: beyond executive dysfunction. Trends Cogn Sci. 2006;10:117–123. [PubMed]
2. Willcutt EG, Doyle AE, Nigg JT, Faraone SV, Pennington BF. Validity of the executive function theory of attention-deficit/hyperactivity disorder: a meta-analytic review. Biol Psychiatry. 2005;57:1336–1346. [PubMed]
3. Sonuga-Barke EJS. Causal models of ADHD: from common simple deficits to multiple developmental pathways. Biol Psychiatry. 2005;57:1231–1238. [PubMed]
4. Dickstein SG, Bannon K, Castellanos FX, Milham MP. The neural correlates of attention deficit hyperactivity disorder: an ALE meta-analysis. J Child Psychol Psychiatry. 2006;47:1051–1062. [PubMed]
5. Barkley RA. Behavioral inhibition, sustained attention, and executive functions: constructing a unifying theory of ADHD. Psychol Bull. 1997;121:65–94. [PubMed]
6. Castellanos FX, Sonuga-Barke EJS, Scheres A, Di Martino A, Hyde C, Walters JR. Varieties of attention-deficit/hyperactivity disorder-related intra-individual variability. Biol Psychiatry. 2005;57:1416–1423. [PMC free article] [PubMed]
7. Sonuga-Barke EJ, Castellanos FX. Spontaneous attentional fluctuations in impaired states and pathological conditions: A neurobiological hypothesis. Neurosci Biobehav Rev. 2007 In Press; Available online 12 March 2007. [PubMed]
8. Friston KJ. Functional and effective connectivity in neuroimaging: a synthesis. Hum Brain Mapp. 1994;2:56–78.
9. Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med. 1995;34:537–541. [PubMed]
10. Margulies DS, Kelly AMC, Uddin LQ, Biswal BB, Castellanos FX, Milham MP. Mapping the functional connectivity of anterior cingulate cortex. Neuroimage. 2007 In Press; Available online 24 May 2007. [PubMed]
11. Weissman DH, Roberts KC, Visscher KM, Woldorff MG. The neural bases of momentary lapses in attention. Nat Neurosci. 2006;9:971–978. [PubMed]
12. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci U S A. 2001;98:676–682. [PubMed]
13. Greicius MD, Menon V. Default-mode activity during a passive sensory task: uncoupled from deactivation but impacting activation. J Cogn Neurosci. 2004;16:1484–1492. [PubMed]
14. Fransson P. Spontaneous low-frequency BOLD signal fluctuations: an fMRI investigation of the resting-state default mode of brain function hypothesis. Hum Brain Mapp. 2005;26:15–29. [PubMed]
15. Damoiseaux JS, Rombouts SA, Barkhof F, Scheltens P, Stam CJ, Smith SM, et al. Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci U S A. 2006 ePub 8/31/2006. [PubMed]
16. Raichle ME, Snyder AZ. A default mode of brain function: a brief history of an evolving idea. Neuroimage. 2007 In Press; Available online 3 March 2007. [PubMed]
17. Tian L, Jiang T, Wang Y, Zang Y, He Y, Liang M, et al. Altered resting-state functional connectivity patterns of anterior cingulate cortex in adolescents with attention deficit hyperactivity disorder. Neurosci Lett. 2006;400:39–43. [PubMed]
18. Zang YF, Yong H, Chao-Zhe Z, Qing-Jiu C, Man-Qiu S, Meng L, et al. Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Dev. 2007;29:83–91. [PubMed]
19. Greicius MD, Srivastava G, Reiss AL, Menon V. Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI. Proc Natl Acad Sci U S A. 2004;101:4637–4642. [PubMed]
20. Buckner RL, Snyder AZ, Shannon BJ, LaRossa G, Sachs R, Fotenos AF, et al. Molecular, structural, and functional characterization of Alzheimer's disease: evidence for a relationship between default activity, amyloid, and memory. J Neurosci. 2005;25:7709–7717. [PubMed]
21. Kennedy DP, Redcay E, Courchesne E. Failing to deactivate: resting functional abnormalities in autism. Proc Natl Acad Sci U S A. 2006;103:8275–8280. [PubMed]
22. Liang M, Zhou Y, Jiang T, Liu Z, Tian L, Liu H, et al. Widespread functional disconnectivity in schizophrenia with resting-state functional magnetic resonance imaging. NeuroReport. 2006;17:209–213. [PubMed]
23. Greicius MD, Flores BH, Menon V, Glover GH, Solvason HB, Kenna H, et al. Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol Psychiatry. 2007 Epub; Available online 8 Jan 2007. [PMC free article] [PubMed]
24. Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van E, Raichle ME. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A. 2005;102:9673–9678. [PubMed]
25. Derogatis L. Manual for the Symptom Checklist 90 Revised (SCL-90-R) Baltimore: 1992. Available from author.
26. Adler LA, Cohen J. Diagnosis and evaluation of adult ADHD. Psychiatr Clin North Am. 2004;27:187–201. [PubMed]
27. Kessler RC, Adler L, Barkley R, Biederman J, Conners CK, Demler O, et al. The prevalence and correlates of adult ADHD in the United States: results from the National Comorbidity Survey Replication. Am J Psychiatry. 2006;163:716–723. [PMC free article] [PubMed]
28. Good CD, Johnsrude IS, Ashburner J, Henson RN, Friston KJ, Frackowiak RS. A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage. 2001;14:21–36. [PubMed]
29. Sporns O, Tononi G, Edelman GM. Connectivity and complexity: the relationship between neuroanatomy and brain dynamics. Neural Netw. 2000;13:909–922. [PubMed]
30. Overmeyer S, Bullmore ET, Suckling J, Simmons A, Williams SCR, Santosh PJ, et al. Distributed grey and white matter deficits in hyperkinetic disorder: MRI evidence for anatomical abnormality in an attentional network. Psychological Medicine. 2001;31:1425–1435. [PubMed]
31. Carmona S, Vilarroya O, Bielsa A, Tremols V, Soliva JC, Rovira M, et al. Global and regional gray matter reductions in ADHD: a voxel-based morphometric study. Neurosci Lett. 2005;389:88–93. [PubMed]
32. Makris N, Biederman J, Valera EM, Bush G, Kaiser J, Kennedy DN, et al. Cortical thinning of the attention and executive function networks in adults with attention-deficit/hyperactivity disorder. Cereb Cortex. 2007;17:1364–1375. [PubMed]
33. Martinussen R, Hayden J, Hogg-Johnson S, Tannock R. A meta-analysis of working memory impairments in children with attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry. 2005;44:377–384. [PubMed]
34. Hampson M, Driesen NR, Skudlarski P, Gore JC, Constable RT. Brain connectivity related to working memory performance. J Neurosci. 2006;26:13338–13343. [PMC free article] [PubMed]
35. Bush G, Luu P, Posner MI. Cognitive and emotional influences in anterior cingulate cortex. Trends Cogn Sci. 2000;4:215–222. [PubMed]
36. Simmonds DJ, Fotedar SG, Suskauer SJ, Pekar JJ, Denckla MB, Mostofsky SH. Functional brain correlates of response time variability in children. Neuropsychologia. 2007 ePub 1/21/2007. [PubMed]
37. Shaw P, Lerch J, Greenstein D, Sharp W, Clasen L, Evans A, et al. Longitudinal mapping of cortical thickness and clinical outcome in children and adolescents with attention-deficit/hyperactivity disorder. Arch Gen Psychiatry. 2006;63:540–549. [PubMed]
38. Cavanna AE, Trimble MR. The precuneus: a review of its functional anatomy and behavioural correlates. Brain. 2006;129:564–583. [PubMed]
39. Bassett DS, Bullmore E. Small-world brain networks. Neuroscientist. 2006;12:512–523. [PubMed]