3 Brain Cortical Thickness

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published online 5 March 2012Journal of Attention DisordersÁvila Acosta and Martín Gallegos Duarte

Luis Guillermo Almeida Montes, Hugo Prado Alcántara, Reyna Beatriz Martínez García, Lázaro Barajas De La Torre, DavidBrain Cortical Thickness in ADHD: Age, Sex, and Clinical Correlations

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Most structural magnetic resonance imaging (MRI) studies in participants with ADHD have been conducted in male children (Castellanos et al., 2002; Valera, Faraone, Murray, & Seidman, 2007). Some of these longitudinal studies have shown an abnormal decrease in cortical thickness (CT) in ADHD participants, which has been shown to disappear with age (Shaw et al., 2006, 2009). This nonpermanent decrease in CT suggests that ADHD represents a delay in brain development (Shaw, Eckstrand, et al., 2007; Shaw et al., 2006; Shaw, Gornick, et al., 2007). However, other studies performed in adults with ADHD have shown reduc-tions in CT in the following diverse brain regions: cingulate cortex (CC; Seidman et al., 2006), caudate nucleus (Almeida Montes, Ricardo-Garcell, Barajas De La Torre, et al., 2010), frontal and temporal cortices (Almeida Montes, Ricardo-Garcell, Prado, et al., 2010; Makris et al., 2007; Seidman et al., 2006), orbital region (Hesslinger et al., 2002), cerebel-lum (Biederman et al., 2008), and cerebellar cortex (Almeida Montes et al., 2011). In addition, the symptoms of ADHD are present during the adulthood. The prevalence of ADHD in the general adult population is about 4.4% (Kessler et al., 2006) and is about 18% among the adult psychiatric population (Almeida Montes, Hernández, & Ricardo-Garcell, 2007). However, only 10% of adults who were diagnosed with ADHD during childhood achieve

functional remission during adulthood (Biederman, Mick, & Faraone, 2000).

These structural and clinical findings might be corre-lated with abnormal brain development rather than a devel-opmental delay. The potential origin of these contradictory findings may be the result of some methodological approaches in ADHD research, which fall into two main categories: patient characteristics and data analysis techniques. First, most studies include male participants with a mixture of ADHD subtypes; longitudinal studies have reported a high percentage of participants who were lost during follow-up and a high percentage of participants who were treated with stimulants, which are known to induce CT changes (Shaw

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1Centro Estatal de Salud Mental, Querétaro, México2Instituto Tecnológico y de Estudios Superiores de Monterrey, Querétaro México3Instituto de Neutobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro México4Instituto Queretano Para la Atención de las Enfermedades Congénitas, Querétaro, México

Corresponding Author:Luis Guillermo Almeida Montes, Centro Estatal de Salud Mental, Servicios de Salud del Estado de Querétaro, Avenida 5 de Febrero 105, Los Virreyes, C.P. 76170, Querétaro, México. Email: [email protected]

Brain Cortical Thickness in ADHD: Age, Sex, and Clinical Correlations

Luis Guillermo Almeida Montes1, Hugo Prado Alcántara1, Reyna Beatriz Martínez García1, Lázaro Barajas De La Torre2, David Ávila Acosta3, and Martín Gallegos Duarte4

Abstract

Objective: Longitudinal magnetic resonance imaging (MRI) studies have shown reduced cortical thickness (CT) in individuals with ADHD, but this abnormality disappears with age, suggesting developmental delay. However, cross-sectional MRI studies have shown reduced CT, suggesting abnormal development. The aim of this study was to compare whole-brain CT in male and female children, adolescents, and adults with ADHD with whole-brain CT in matched control participants. Method: MRI scans were performed on ADHD and control participants. Results: CT data revealed differences in right hemisphere (RH) only. Reduced CT was observed predominantly in the frontoparietal region. However, increased CT was observed predominantly in the occipital lobe. The CT differences were correlated with severity of ADHD. Analysis of sex differences revealed that location, number, and magnitude of CT differences were different between males and females in each age group. Conclusion: These data support the hypothesis that anatomical abnormalities in ADHD represent abnormal development rather than developmental delay. (J. of Att. Dis. 2012; XX(X) 1-XX)

Keywords

ADHD, brain development, MRI, sex, cortical thickness



2 Journal of Attention Disorders XX(X)

et al., 2009). Second, the variety of image analysis tech-niques used in the studies can produce different results because each technique possesses its own flaws. For exam-ple, voxel-based morphometry (VBM; Ellison-Wright, Ellison-Wright, & Bullmore, 2008) can be obtained from imperfectly registered images (Bookstein, 2001) and gener-ate misleading results. Meanwhile, manual delineation of the regions of interest (ROIs; Hesslinger et al., 2002) pro-duces imprecise anatomical validity (Friston, 2007).

Due to those lines of evidence, it is not clear if ADHD represents an arrest or delay in brain development. In addi-tion, the localization and the progression of these abnor-malities throughout life and the sex-based differences in these abnormalities are not well understood.

ObjectiveThis study aimed to compare whole-brain CT in both sexes in three age groups (children, adolescents and adults) and to identify the CT differences between ADHD and control participants.

MethodParticipants and Clinical Measures

We used a sample population, for which results regarding CT were published in a previously published report (Almeida Montes, Ricardo-Garcell, Prado, et al., 2010).

All the participants agreed to participate in this study through informed consent. Participants were recruited from the general community in the urban areas of Querétaro and Mexico Cities. Three age groups with combined-type ADHD were selected (they had never been treated medically with stimulant or psychoactive drugs): 21 children (6-10 years of age), 18 adolescents (14-17 years of age), and 20 adults (25-35 years of age). Participants were matched for age, sex, height, weight, body mass index (BMI), IQ, handedness, and socioeconomic status with three age groups of control participants (22 children, 20 adolescents, and 20 adults). Individuals with the following criteria were excluded from the study: a history of neurological or psychiatric disease (as determined by two experienced clinicians and defined as causing more distress or global functional impairment than ADHD), predominant inattentive or hyperactive/impulsive subtypes of ADHD, evidence of general medical illness, IQ <85, current use of psychoactive or stimulant substances, or abnormalities on brain MRI scans.

The Institutional Review Board of the General Hospital of Querétaro, México, approved the study protocol and the informed consent forms in accordance with general health regulations in Mexico and the 1964 Declaration of Helsinki. Written informed consent was obtained after the study was fully explained to all participants, or the parents of children

and adolescents. A certified pediatrician and pediatric neu-rologist obtained full medical histories and conducted the physical examinations on the children and adolescent par-ticipants; adult participants were examined by a certified internist and neurologist.

The Edinburgh Handedness Inventory (EHI) was used to assess handedness (Oldfield, 1971). A participant was con-sidered right-handed if their total score on the EHI was greater than +40 points. Participants who scored <−40 points were considered left-handed and those who scored between −40 and +40 points were considered ambidextrous.

The Mini-International Neuropsychiatric Structured Interview (MINI) was used to diagnose ADHD (Sheehan et al., 1998). The MINI-Kid Version 4 was used for the par-ticipants who were younger than 13 years of age, and the MINI Plus Version 5 was used for the adolescents and adults. This interview was administered to all participants by two certified child psychiatrists for children and adolescents and by two certified adult psychiatrists for adults. Each clinician was blinded to the diagnoses made by the other clinicians throughout the study. The kappa index of agreement (κ) for the diagnosis of ADHD according the MINI interview was κ = 0.97 for children, κ = 0.93 for adolescents, and κ = 0.93 for adults.

To assess the ADHD severity, the Conners’ Parent Rating Scale–Revised Version (Conners, Sitarenios, Parker, & Epstein, 1998a) and the Conners’ Teacher Rating Scale–Revised Version (Conners, Sitarenios, Parker, & Epstein, 1998b) were adminis-tered to the children and adolescents, respectively. In addition, to assess self-reports of problem behaviors, the adolescent par-ticipants completed the Conners-Wells Adolescent Self-Report Scale–Long Version (Conners et al., 1997).

To assess the ADHD severity in adults, the Friederichsen, Almeida, Serrano, Cortes Test (FASCT) Rating Scale was used. There are two versions of this scale: The participants completed the self-reported version (FASCT-SR). One of the parents or another closer family member of the participants completed the observer-reported version (FASCT-O). Detailed information regarding the properties of the FASCT Scale has been previ-ously published elsewhere (Almeida Montes et al., 2006).

The Wechsler Intelligence Scale for Children–Third edi-tion (Wechsler, 1991) was administered to individuals who were between the ages of 6 and 15 years by a child neuropsy-chologist. The Wechsler Adult Intelligence Scale–Third edi-tion (Wechsler, 1997) was administered to individuals who were 16 years of age and older by a neuropsychologist.

All the preceding clinical tests and scales were applied in their Spanish versions as the available and preferred language by all participants.

Data AcquisitionA 1-T Philips new Intera MRI scanner (release 10.3; Philips Medical Systems) was used for all scans. This MRI scanner was located at Instituto de Neurobiología,



Almeida Montes et al. 3

Universidad Nacional Autónoma de México-Campus Juriquilla, Querétaro, México.

A fast-field echo T1 three-dimensional (3D) volumetric sequence produced 190 continuous 1.0-mm thick coronal slices with no intervening gaps. Acquisition parameters were echo time 6.9 ms, repetition time 25 ms, flip angle 30°, acquisition matrix 230 × 230 mm, field of view 256 mm, and voxel size 1.0 mm3.

All MRI scans were evaluated by an experienced, certi-fied neuroradiologist who was blinded to the identity of the participants. There were no variations in the acquisition, such as the use of different scanners, scanner upgrade, or changes in pulse sequence. No equipment calibration was done during the MRI scanning phase of the study.

Image AnalysisCortical reconstruction and volumetric segmentation of all MRIs were performed using the FreeSurfer Image Analysis Suite (4.3.0 Version). This technique has been described in detail elsewhere (Almeida Montes, Ricardo-Garcell, Prado, et al., 2010; Dale, Fischl, & Sereno, 1999; Desikan et al., 2006; Fischl, Salat, et al., 2004; Fischl, van der Kouwe, et al., 2004). In short, the process is fully automa-tized and begins with a motion correction of multiple volumetric MRI images, removal of nonbrain tissue and automated Talairach space transformation, segmentation of white matter and subcortical structures, and intensity normalization and demarcation of white and gray matter, with their respective boundaries and according to intensity gradients. A group of deformable processes is added, including surface inflation and registration to a spherical atlas to bring every brain to the same space and make them comparable in the statistical analysis. The CT is calcu-lated, measuring the distance from gray matter boundary to the white matter boundary in 40,000 points distributed across the whole hemisphere. The final output is a map of CT of each hemisphere. This process has been validated with postmortem histological analysis (Rosas et al., 2002). The MRI scans were performed in the middle of the men-strual cycle of the females because the estrogens induce axonal sprouting during the follicular preovulatory period of the menstrual cycle— a fact demonstrated by VanderHorst and Holstege (1997) in animals—and this may affect the CT measurement.

Statistical AnalysisTo compare the nominal variables that represent the general characteristics of the sample, either the χ2 test or the Fisher’s exact test was used. For numerical data, normality tests (Kolmogorov–Smirnov and Shapiro–Wilk) followed by two-sided t tests were used. In the case of non-normally distrib-

uted data, the Mann–Whitney U test was used. Because the number of Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM-IV-TR) criteria (American Psychiatric Association, 2000) met by the participants was not normally distributed, the correlation between CT and the clinical data only in ADHD participants was assessed using the Spearman’s coefficient.

Two-sided t tests, based on the general linear model (t-GLM), were used to compare the CT between the con-trol and the ADHD participants. The following covariates were introduced into the model: age, sex, BMI, and total brain volume. To correct for the effect of multiple com-parisons, the Monte Carlo test (threshold set at p value < .05 and 10,000 iterations) was used. The output of this statistical procedure rendered a color-coded statistical map (Figure 1) that represented whole-brain CT differ-ences between the control and the ADHD participants. In regions of significant differences between ADHD and control participants, a mask of each region was generated using FreeSurfer Qdec tools. This mask was applied to each individual’s reconstructed and normalized brain. This procedure rendered the CT and the area of each region in cm2 for every participant. We performed the fol-lowing two t-GLM analyses: (a) comparison of the CT between control and ADHD participants based on age group and (b) comparison between females and males in each age group (Figure 2).

Exploratory factor analysis (FA) was performed to iden-tify the cortical regions that contained the majority of the variation in the CT differences between the control and the ADHD participants in each age group. Principal component extraction was used with the following settings: eigenvalue = 1, maximum iterations for convergence = 25, and the method of rotation = varimax and suppressing coefficients < 0.3. The Kaiser–Meyer–Olkin (KMO) test and the Bartlett’s test of sphericity were used. This procedure was used for the age group comparisons but not for the sex group comparisons because there were not enough patients for each sex (reduced sample size) to make an appropriate FA.

ResultsThe characteristics of the study population have been described in detail elsewhere (Almeida Montes et al., 2011; Almeida Montes, Ricardo-Garcell, Barajas De La Torre, et al., 2010; Almeida Montes, Ricardo-Garcell, Prado, et al., 2010), and the participant characteristics are shown in Tables 1, 2, and 3. There were no statistical differences in age, sex, BMI, brain volume, race, birth weight, or medical history between the children, adolescents, or adults with ADHD and the matched control participants. None of the ADHD participants had a history of stimulant treatment. All participants were right-handed.




Psychiatric Comorbidities

In the three age groups, the presence of other psychiatric disorders was increased in ADHD participants compared with control participants as follows: 61.9% versus 9.5% (χ2 = 12.48, df = 1, p = .0001) in children, 50% versus 5% (χ2 = 9.89, df = 1, p = .002) in adolescents, and 25% versus 5% (Fisher’s exact test, p = .091) in adults.

In the children, 9.5% of the control participants met the criteria for separation anxiety disorder. In the children with ADHD, 4.8% (n = 1), 38% (n = 8), 52.4% (n = 11), and 4.8% (n = 1) met the criteria for adjustment disorder, con-duct disorder, oppositional defiant disorder, and generalized anxiety disorder (GAD), respectively.

In the adolescent, 5% of the control participants met the criteria for adjustment disorder. Among the adolescents with ADHD, 11.5% (n = 2), 22.2% (n = 4), 33% (n = 6), 11% (n = 2), and 5.6% (n = 1) met the criteria for adjust-ment disorder, conduct disorder, oppositional defiant disor-der, GAD, and dysthymia, respectively.

In the adult, 5% of the control participants met the crite-ria for GAD. Among the adults with ADHD, 5% (n = 1), 15% (n = 3), 15% (n = 3), 5% (n = 3), and 5% (n = 1) of the sample met the criteria for premenstrual dysphoric disorder, GAD, previous major depressive disorder (MDD), specific phobia, and previous dysthymia, respectively.

Percentage of the Cortical Surface Accounted for by the Differences in CT Between the Control and the ADHD Participants

The CT differences between the control and the ADHD participants in the three age groups were observed in all lobes of the brain (frontal, parietal, temporal, occipital) and in the limbic cortex of both hemispheres. However, when the Monte Carlo Test was applied, only the CT differences in the right hemisphere (RH) reached statistical signifi-cance. Figure 1 shows the regions in which increased CT was observed in control participants compared with the

Figure 1. Statistical map of the right hemisphere in lateral (a), medial (b), and frontal (c) views, by age group, where the CT was different between control and ADHD participantsNote: CT = cortical thickness. Dark grey areas represent the p value where the CT was thicker in control participants. Red–orange areas represent the p values where the CT was thicker in ADHD participants. The statistical map is plotted against an average inflated brain. The lines represent the boundaries of the atlas of Desikan et al. (2006). The color bar represents the p value code. Note that increased CT was predominantly located in the posterior regions of the cortex in children. The decrease of CT was observed predominantly in the frontal lobe in adults.Requests for color figures may be sent to corresponding author’s email address.




ADHD participants (CT Control greater than CT ADHD, blue areas) and the regions in which increased CT was observed in the ADHD participants compared with the control partici-pants (CT ADHD greater than CT Control, red areas).

In the children age group, the sum of all of the regions in which CT Control greater than CT ADHD totaled 1,062.22 mm2, which corresponded to 1.21% of the total surface area of the RH. The sum of all of the regions in which CT ADHD greater than CT Control totaled 2,001.18 mm2, which cor-responded to 2.29% of the total area of the RH (Figure 3).

In the adolescent age group, the sum of all of the regions in which CT Control greater than CT ADHD totaled 2,151.53 mm2, which corresponded to 2.38% of the total area of the RH. The sum of all regions in which CT ADHD greater than CT Control totaled 926.02 mm2, which corre-sponded to 1.02% of the total area of the RH (Figure 3).

In the adult age group, the sum of all of the regions in which CT Control greater than CT ADHD totaled 4,252.45 mm2, which corresponded to 4.79% of the total area of the RH. The sum of all of the regions in which CT ADHD

Figure 2. Statistical map of the right hemisphere in lateral (a) and medial (b) views displayed by age (columns) and by sex (rows), where the CT was different between control and ADHD participantsNote: CT = cortical thickness. Blue areas represent the p value where the CT was thicker in control participants. light grey areas represent the p values where the CT was thicker in ADHD participants. The statistical map is plotted against an average inflated brain. The lines represent the boundaries of the atlas of Desikan et al. (2006). The color bar represents the p value code.Requests for color figures may be sent to corresponding author’s email address.




Table 1. Characteristics of the Child Participants

ADHD Control

n = 21 n = 22

M (± SD) or n (%) M (± SD) or n (%) Statistical value df p value

Age (years) 7.61 (± 1.28) 7.63 (± 1.39) M-W U = 230, z = −0.02 — .980Sex Males 10 (47.6%) Males 10 (45.5%) χ2 = 0.200 1 .800 Females 11 (52.4%) Females 12 (54.5%) Right-handed (EHI > 40) 21 (100%) 22 (100%) — — —BMI 17.23 (± 2.44) 17.24 (± 2.96) M-W U = 229, z = −0.04 — .960Brain volume (mL) 1,340.80 (± 151.55) 1,323.50 (± 132.35) t = −0.136 41 .893School problemsa 17 (81.0%) 1 (4.8%) χ2 = 0.248 1 .000Inattentionb 8.38 (± 0.92) 1.09 (± 1.26) M-W U = 0.000, z = −5.738 — .000Hyperactivityb 4.66 (± 1.23) 1.22 (± 1.26) M-W U = 16.5, z = −5.270 — .000Impulsivityb 2.86 (± 2.83) 0.54 (± 0.73) M-W U = 18.0, z = −5.396 — .000WISC-R total score 107.23 (± 3.03) 115.61 (± 3.47) t = 1.87 41 .077

Note: M-W U = Mann–Whitney U test; EHI = Edinburgh Handedness Inventory; BMI = body mass index; WISC-R = Wechsler Intelligence Scale for Children–Revised; DSM-IV-TR = Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.).aFailed academic years, school expulsion, physical or verbal aggression toward peers and teachers.bDSM-IV-TR criteria for ADHD.

Table 2. Characteristics of the Adolescent Participants

ADHD Control

n = 18 n = 20


Age (years) 15.44 (± 1.24) 14.75 (± 1.06) M-W U = 120.50, z = −1.88 — .082Sex Males 10 (55.6%) Males 10 (50%) χ2 = 0.117 1 .757 Females 8 (44.4%) Females 10 (50%) Right-handed (EHI

greater than 40)18 (100%) 20 (100%) — — —

BMI 22.42 (± 3.05) 21.27 (± 3.25) M-W U = 146.00, z = −0.994 — .326Brain volume (mL) 1,521 (± 226) 1,530 (± 150) t = 0.138 36 .891School problemsa 13 (72.2%) 1 (5%) Fisher’s exact test, p = .000 — —Inattentionb 6.72 (± 0.25) 1.05 (± 0.99) M-W U = 0.000, z = −5.360 — .000Hyperactivityb 3.94 (± 1.73) 0.70 (± 0.80) M-W U = 24.00, z = −4.650 — .000Impulsivityb 2.33 (± 0.90) 0.45 (± 0.68) M-W U = 27.50, z = −4.640 — .000WISC-R total score 100.55 (± 1.91) 104.15 (± 2.52) t = 1.09 36 .429

Note: M-W U = Mann–Whitney U test; EHI = Edinburgh Handedness Inventory; BMI = body mass index; WISC-R = Wechsler Intelligence Scale for Children–Revised; DSM-IV-TR = Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.).aFailed academic years, school expulsion, physical or verbal aggression toward peers and teachers.bDSM-IV-TR criteria for ADHD.

greater than CT Control totaled 174.27 mm2, which corre-sponded to 0.19% of the total area of the RH (Figure 3).

Degree of CT Differences Between the Control and the ADHD ParticipantsIn the children age group, the mean CT of all of the regions in which CT Control greater than CT ADHD was 3.17 (±

0.48) mm for control participants and 2.86 (± 0.49) mm for ADHD participants.

The mean CT of all of the regions in which CT Control greater than CT ADHD for control participants and ADHD participants was 2.57 (± 0.63) mm and 2.28 (± 0.60) mm, respectively, in the adolescent age group and 2.49 (± 0.60) mm and 2.21 (± 0.56) mm, respectively, in the adult age group.




The mean CT of all of the regions in which CT ADHD greater than CT Control for control and ADHD participants was 2.94 (± 0.83) mm and 2.58 (± 0.76) mm, respectively, in the children age group; 2.53 (± 0.63) mm and 2.85 (± 0.64) mm, respectively, in the adolescent age group; and 2.23 (± 0.54) mm and 2.48 (± 0.53) mm, respectively, in the adult age group.

Localization of CT Differences Between the Control and the ADHD Participants

In the children age group, the FA showed that the regions in which CT Control greater than CT ADHD were predomi-nantly located in the superior frontal gyrus, paracentral lobule, rostral middle frontal area, and precuneus (44% of

Table 3. Characteristics of the Adult Participants

ADHD Control

n = 20 n = 20


Age (years) 28.95 (± 4.01) 27.57 (± 2.6) t = −1.25 38 .217Sex Males 10 (50%) Males 10 (50%) χ2 = 0.000 1 1.0 Females 10 (50%) Females 10 (50%) Right-handed (EHI

greater than 40)20 (100%) 20 (100%) — — —

BMI 25.64 (± 2.44) 24.42 (± 2.35) t = −1.305 38 .200Brain volume (mL) 1,512 (± 132) 1,499 (± 133) t = −0.316 38 .754School problemsa 8 (40%) 1 (5%) Fisher’s exact test, p = .000 — —Inattentionb 7.65 (± 1.18) 0.40 (± 0.68) M-W U = 0.000, z = −5.360 — .000Hyperactivityb 4.35 (± 1.72) 0.85 (± 1.38) M-W U = 34.50, z = −4.570 — .000Impulsivityb 2.03 (± 1.30) 0.45 (± 0.75) M-W U = 41.00, z = −4.520 — .000WAIS total score 103.85 (± 1.86) 104.05 (± 1.86) t = −0.790 38 .430FASCT-SR 34.25 (± 1.45) 11.10 (± 1.31) t = 11.80 38 .000FASCT-O 29.25 (± 1.87) 11.15 (± 3.03) t = 6.54 38 .000

Note: M-W U = Mann–Whitney U test; EHI = Edinburgh Handedness Inventory; BMI = body mass index; WAIS-III = Wechsler Adult Intelligence Scale–Third Edition; FASCT-SR = The Friederichsen, Almeida, Serrano, Cortes Test–Self-Reported Version; FASCT-O = The Friederichsen, Almeida, Serrano, Cortes Test–Observer-Reported Version; DSM-IV-TR = Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.).aFailed academic years, school expulsion, physical or verbal aggression toward peers and teachers.bDSM-IV-TR criteria for ADHD.

Figure 3. Percentage of the total surface of the right hemisphere in which the CT was significantly different between the control and the ADHD participants in the three age groupsNote: CT = cortical thickness.Requests for color figures may be sent to corresponding author’s email address.




the variance, KMO = 0.613, p = .0001). The regions in which CT ADHD greater than CT Control were predomi-nantly located on the medial surface of the right occipital lobe, lingual gyrus, pericalcarine area, and cuneus (48% of the variance, KMO = 0.661, p = .0001; Figure 1).

In the adolescent age group, the FA showed that the regions in which CT Control greater than CT ADHD were predominantly located on the lateral surface of the right frontal lobe, including the superior frontal gyrus, rostral middle frontal area, precentral gyrus, and orbital areas, and in other areas that were located in the superior parietal and postcentral regions (81% of the variance, KMO = 0.616, p = .0001).

The regions in which CT ADHD greater than CT Control were predominantly located on the lateral surface of the right temporal and occipital lobes (KMO = 0.616, p = .0001; Figure 1).

In the adult age group, the FA showed that the regions in which CT Control greater than CT ADHD were pre-dominantly located on the lateral surface of the right fron-toparietal regions (87% of the variance, KMO = 0.636, p = .000). The regions in which CT ADHD greater than CT Control were predominantly located on the superior pari-etal surface (68% of the variance, KMO = 0.530, p =.009; Figure 1).

Correlations Between the CT and the DSM-IV-TR CriteriaIn each age group, all of the regions in which CT Control greater than CT ADHD in the frontal, parietal, temporal, and occipital lobes as well as in the CC showed a signifi-cant, negative correlation (range: r

S = −0.331 and −0.516)

with ADHD DSM-IV-TR criteria. Similarly, all of the regions in the four lobes and the CC in which CT ADHD greater than CT Control showed a positive correlation with the number of ADHD DSM-IV-TR criteria in all age groups (range: r

S = 0.340 and 0.426). The correlation coefficients

for these data for one region per lobe and the CC are pre-sented (Tables 4, 5, and 6). Only larger areas from each lobe and the CC are listed.

Analysis Based on SexIn Figure 2, we show the CT differences, based on sex, in the regions in which CT Control greater than CT ADHD. The correlation coefficients obtained by comparing the CT to the number of ADHD DSM-IV-TR criteria ranged from r

S = −3.325 to −0.516. In the regions in which CT ADHD

greater than CT Control, the correlation coefficients obtained by comparing the number of ADHD DSM-IV-TR criteria to the CT in the three age groups ranged from r

S =

3.352 to 0.416.

Discussion

Data on the anatomical changes that occur in ADHD indi-viduals of different age groups are not restricted to recent studies, but surprisingly, the research regarding the progression of ADHD from childhood to adulthood is limited and has been hindered by contradictory findings. Some studies report that the anatomical encephalic aberrations in ADHD participants disappear during adolescence, which suggests that the pathological process leading to ADHD is a neuro-developmental delay (Castellanos et al., 2002; Shaw, Eckstrand, et al., 2007; Shaw et al., 2006). However, other studies have found anatomical differences in adults with ADHD (Almeida Montes et al., 2011; Almeida Montes, Ricardo-Garcell, Barajas De La Torre, et al., 2010; Almeida Montes, Ricardo-Garcell, Prado, et al., 2010; Biederman et al., 2008; Hesslinger et al., 2002; Makris et al., 2007; Seidman et al., 2006), which suggests that the pathological process leading to ADHD represents abnormal neurodevel-opment. For this reason, we propose that there is no defini-tive conclusion regarding the underlying anatomical abnormalities of ADHD, and further research (including studies in females, adults, and unmedicated participants) is required to address this question.

The findings of the present study are consistent with previous studies that reported decreased CT in the right frontal lobe in ADHD individuals (Makris et al., 2007; Seidman et al., 2006). Nonetheless, our findings are incon-sistent with other studies (Shaw, Eckstrand, et al., 2007; Shaw et al., 2006) that report that the differences in the CT between control and ADHD children disappear during ado-lescence. These conflicting findings may be due to incon-sistent methodologies.

For example, one of the studies that reported a disappear-ance of the CT difference over time (Shaw et al., 2006) used MRI scans in 40% of the participants to estimate cortical development trajectories. Hence, the results were based on predicted CT values rather than actual values. In addition, 36% to 41% of the individuals were medicated with stimu-lants at the time of the MRI scan (Shaw et al., 2006). In two different studies that were published by Shaw, Gornick, et al. (2007; Shaw et al., 2009), 66% of the participants had received prior stimulant treatment, and 62% to 81% of the participants were undergoing treatment at the beginning of the study or during the follow-up period. The Shaw research group also demonstrated that stimulant treatment in ADHD adolescents results in CT values that are similar to those of control individuals (Shaw et al., 2009). We excluded patients with previous or current stimulant use from our study, which is a methodological difference that could explain the differ-ences between previous results and our findings.

Our results revealed CT differences in the RH only. Several studies (Valera et al., 2007) have shown that right




Table 4. Correlation Between CT and the Number of DSM-IV-TR ADHD Criteria of the Largest Cortical Regions Where a Difference Between Control and ADHD Participants Were Observed in the Children Age Group

DSM-IV-TR ADHD criteria

Brain lobeCortex region or

gyrus Inattention Hyperactivity Impulsivity

CT Control greater than CT ADHD

Frontal lobe Pars opercularis rS 19

= −0.437 rS 19

= −0.456 rS 19

= −0.342

p = .003 p = .002 p = .025 Temporal lobe Superior temporal r

S 19

= −0.035 rS 19

= −0.120 rS 19

= −0.013 p = .826 p = .442 p = .933 Parietal lobe Postcentral r

S 19

= −0.379 rS 19

= −0.436 rS 19

= −0.400 p = .012 p = .003 p = .008 Occipital lobe NA NA NA NA Limbic system Posterior cingulate r

S 19

= −0.310 rS 19

= −0.234 rS 19

= −0.310 p = .043 p = .131 p = .043CT ADHD greater than

CT ControlFrontal lobe Pars triangularis r

S 19

= 0.388 rS 19

= 0.452 rS 19

= 0.424

p = .010 p = .002 p = .005 Temporal lobe Inferior temporal r

S 19

= 0.439 rS 19

= 0.452 rS 19

= 0.485 p = .003 p = .002 p = .001 Parietal lobe Precuneus r

S 19

= 0.325 rS 19

= 0.328 rS 19

= 0.387 p = .033 p = .032 p = .010 Occipital lobe Lingual r

S 19

= 0.661 rS 19

= 0.554 rS 19

= 0.634 p = .0001 p = .0001 p = .0001

Limbic system NA NA NA NA

Note: CT = cortical thickness; DSM-IV-TR = Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.); rS = Spearman correlation coefficient

and degree of freedom; NA = no comparative area.

Table 5. Correlation Between CT and the Number of DSM-IV-TR ADHD Criteria of the Largest Cortical Regions Where a Difference Between Control and ADHD Participants Were Observed in the Adolescent Age Group


Brain lobe Cortex region or gyrus Inattention Hyperactivity Impulsivity

CT Control greater than ADHD

Frontal lobe Precentral gyrus rS 16

= −0.613 rS 16

= −0.468 rS 16

= −0.551

p = .000 p = .003 p = .000 Parietal lobe Superior parietal cortex r

S 16

= −0.529 rS 16

= −0.598 rS 16

= −0.499 p = .001 p = .000 p = .001 Temporal lobe Inferior temporal gyrus r

S 16

= −0.370 rS 16

= −0.384 rS 16

= −0.383 p = .040 p = .017 p = .017 Occipital lobe Lingual gyrus r

S 16

= −0.424 rS 16

= −0.427 rS 16

= −0.337 p = .008 p = .007 p =.039 Limbic region Posterior cingulate r

S 16

= −0.537 rS 16

= −0.376 rS 16

= −0.458 p = .001 p = .020 p = .004CT ADHD greater

than ControlFrontal lobe Precentral gyrus r

S 16

= 0.376 rS 16

= 0.325 rS 16

= 0.357

p = .041 p = .047 p = .024 Parietal lobe Inferior parietal cortex r

S 16

= 0.468 rS 16

= 0.386 rS 16

= 0.426 p = .003 p = .017 p = .008 Temporal lobe Superior temporal gyrus r

S 16

= 0.461 rS 16

= 0.341 rS 16

= 0.378 p = .004 p = .036 p = .019 Occipital lobe Lateral occipital cortex r

S 16

= 0.337 rS 16

= 0.398 rS 16

= 0.436 p = .038 p = .013 p = .006 Limbic region Anterior cingulate r

S 16

= 0.410 rS 16

= 0.224 rS 16

= 0.257

p = .011 p = .176 p = .120

Note: CT = cortical thickness; DSM-IV-TR = Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.); rS = Spearman correlation coefficient

and degree of freedom.




Table 6. Correlation Between CT and the Number of DSM-IV-TR ADHD Criteria of the Largest Cortical Regions Where a Difference Between Control and ADHD Participants Were Observed in the Adult Age Group


Brain lobe Cortex region or gyrus Inattention Hyperactivity Impulsivity FASCT-SR FASCT-O

CT Control greater than ADHD

Frontal lobe Rostral middle frontal gyrus

rS 18

= −0.458p = .002

rS 18

= −0.562p = .000

rS 18

= −0.513p = .001

rS 18

= −0.522p = .001

rS 18

= −0.461p = .003

Parietal lobe Superior parietal region rS 18

= −0.530 rS 18

= −0.508 rS 18

= −0.532 rS 18

= −0.527 rS 18

= −0.543 p = .000 p = .001 p = .000 p = .000 p = .000 Temporal lobe Middle temporal gyrus r

S 18

= −0.376 rS 18

= −0.344 rS 18

= −0.405 rS 18

= −0.325 rS 18

= −0.381 p = .017 p = .035 p = .010 p = .041 p = .015 Occipital lobe Lingual gyrus r

S18 = −0.422 r

S 18

= −0.362 rS 18

= −0.457 rS 18

= −0.342 rS 18

= −0.357 p = .007 p = .022 p = .003 p = .031 p = .024 Limbic region Posterior cingulate gyrus r

S 18

= −0.287 rS 18

= −0.197 rS 18

= −0.329 rS 18

= −0.254 rS 18

= −2.68 p = .073 p = .224 p = .038 p = .113 p = .095CT ADHD greater

than ControlFrontal lobe NA NA NA NA NA NA

Parietal lobe Superior parietal gyrus rS 18

= 0.371 rS 18

= 0.342 rS 18

= 0.385 rS 18

= 0.320 rS 18

= 0.313 p = .019 p = .031 p = .014 p = .044 p = .050 Temporal lobe Superior temporal gyrus r

S 18

= 0.471 rS 18

= 0.439 rS 18

=0.342 rS 18

= 0.355 rS 18

= 0.325 p = .002 p = .005 p = .035 p = .025 p = .041 Occipital lobe Pericalcarine cortex r

S 18

= 0.372 rS 18

= 0.369 rS 18

= 0.318 rS 18

= 0.368 rS 18

= 0.358 p = .018 p = .019 p = .045 p = .020 p = .023 Limbic region Isthmus cingulate r

S 18

= 0.148 rS 18

= 0.125 rS 18

= 0.092 rS 18

= 0.154 rS 18

= 0.130

p = .361 p = .441 p = .572 p = .342 p = .320

Note: CT = cortical thickness; DSM-IV-TR = Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.); FASCT-SR = The Friederichsen, Almeida, Serrano, Cortes Test–Self-Reported Version; FASCT-O = The Friederichsen, Almeida, Serrano, Cortes Test–Observer-Reported Version; r

S =

Spearman correlation coefficient and degree of freedom; NA = no area.

encephalic structures, particularly those of the right frontal lobe, are smaller in ADHD participants. These data are con-sistent with other reports that state that the RH plays a cen-tral role in attention and several ADHD-related cognitive functions (Heilman, Bowers, Valenstein, & Watson, 1986; Rhawn, 1996). However, there are also studies that report that left hemisphere encephalic regions are smaller in ADHD individuals (Biederman et al., 2008; Hesslinger et al., 2002; Makris et al., 2007; Shaw, Eckstrand, et al., 2007). These contradictory findings suggest the need for further research on this issue.

Among the child and adolescent participants in the pres-ent study, 52.4% and 33%, respectively, met the DSM-IV-TR criteria for conduct disorder. Some studies have used functional MRI (fMRI) to compare brain activation in chil-dren and adolescents with ADHD and those with conduct disorder and have found a decrease in prefrontal cortex activation in the ADHD individuals. However, individuals with conduct disorder showed a reduction in the activation of the insula, hippocampus, and anterior cingulate gyrus (Rubia et al., 2008).

Kruesi, Casanova, Mannheim, and Johnson-Bilder (2004) found that the volume of the gray matter in the right tempo-ral lobe was reduced in participants with conduct disorder

compared with control participants, but no statistical differ-ences in the volume of gray matter in the frontal lobe were observed. Lorberboym, Watemberg, Nissenkorn, Nir, and Lerman-Sagie (2004) used single-photon computed emission tomography in conjunction with a performance test and reported that children and adolescents with comorbid ADHD, conduct disorder, and oppositional defiant disorder showed decreased blood perfusion of the temporal lobe. However, individuals with noncomorbid ADHD showed decreased blood perfusion of the frontal lobe.

Another comorbidity study (Bussing, Grudnik, Mason, Wasiak, & Leonard, 2002) did not show any volumetric dif-ferences between children and adolescents with ADHD compared with those with conduct disorder. Huebner et al. (2008) used VBM to compare children and adolescents with comorbid conduct disorder and ADHD with control partici-pants. The ADHD individuals showed reduced gray matter volume in the left orbital region and bilaterally in the tem-poral lobe. Differences were also observed in the left amyg-dala and hippocampus. In addition, Sterzer, Stadler, Poustka, and Kleinschmidt (2007) found a reduction in the volume of the insular cortex in adolescents with conduct disorder. Taken together, these findings support the idea that the neurobiological correlates of conduct disorder are




different from those of ADHD. Consequently, we conclude that the comorbidity between ADHD and conduct disorder in our sample did not affect our results.

GAD was diagnosed in 4.8%, 11%, and 15% of the chil-dren, adolescents, and adults with ADHD, respectively. Various studies have shown that the structural abnormalities of GAD are predominantly located in the orbitofrontal cor-tex (Mholman et al., 2009), amygdala (De Bellis et al., 2000), and superior temporal gyrus (De Bellis et al., 2002). However, we found CT abnormalities predominantly in the frontal and parietal cortices. These data suggest that the encephalic regions involved in GAD are different from those involved in ADHD.

Increased and Decreased CT: A Compensatory Mechanism or a Disturbance of Neural Pathway Development?

Our results revealed regions of increased CT in ADHD participants of all age groups. These differences were pre-dominantly located in the posterior regions of the brain in children but seemed to disappear with age. The percentage of the total RH cortical surface area occupied by these regions was higher in the children age group (2.29%) com-pared with the adolescent (1.02%) and adult (0.19%) age groups. To the best of our knowledge, this is the first study to report increased CT in children, adolescents, and adults with ADHD.

We hypothesized that this predominantly posterior increase in CT is due to structural and physiologic compen-satory mechanisms. These mechanisms tend to reduce the impact of ADHD in the patient’s daily life. Unfortunately, this compensatory mechanism typically fails and persists into adulthood, which explains why only 10% of ADHD individuals achieve functional remission during adulthood (Biederman et al., 2000). The existence of compensatory processes has been demonstrated in neurodevelopmental disorders, such as corpus callosum agenesis (Hannay, Dennis, Kramer, Blaser, & Fletcher, 2009), dyscalculia (Kaufmann et al., 2009), and the hyperplasia of the frontal cortex observed in Gilles de la Tourette disorder (Marsh, Maia, & Peterson, 2009). Compensatory mechanisms have also been identified in the visual cortex using models of monocular deprivation (Cho, Khibnik, Philpot, & Bear, 2009).

Alternatively, the lack of regions of increased CT observed in the adolescent and adult ADHD participants in the present study may be explained by the fact that competi-tive elimination of synapses peaks during adolescence and continues into adulthood (Tau & Peterson, 2010). The increase in CT in the posterior brain regions early in life suggests a degree of compensation for the neurodevelopmental defi-cits of the frontoparietal regions in ADHD participants.

However, if ADHD is not treated, these compensatory mechanisms are not sufficient to reverse the symptoms. Due to the competitive elimination of synapses, the effects of compensatory mechanisms largely disappear, which leads to the persistence of the clinical symptoms of ADHD in the later stages of life. This hypothesis should be con-firmed or refuted by additional studies.

Alternatively, we hypothesize as a speculation that deserves more research that ADHD may result from disor-dered neural pathway formation, axonal and dendritic arbo-rization, and synaptogenesis.

Once neuronal migration is almost completed by the ges-tational age (GA) of 26 to 29 weeks, neurons extend their axons and dendrites to reach their appropriate neuronal tar-gets. In the early phases of neurodevelopment, the axons and dendrites consist of neurites (Tau & Peterson, 2010), the growing tip of which is referred to as the growth cone. In turn, the leading edge of the growth cone consists of filopo-dia. Neurite growth occurs when the filopodia adhere to a surface (the extracellular matrix) and use this substrate to grow and pull the advancing growth cone forward. To deter-mine the correct pathway, the participation of special bind-ing molecules, including integrin in the filopodia, is necessary to promote axonal elongation. Growth only occurs if the extracellular matrix contains the appropriate permissive gly-coprotein (e.g., laminin). In addition, the axons express cell adhesion molecules (CAM) on their surface, which bind one axon to another and facilitate the orderly growth of the axons (Bear, Connors, & Paradiso, 2001). Dysfunction of these glycoproteins may cause abnormal wiring of the brain, which may result in an excess of connections in some regions and a deficit in others. This mechanism may explain the CT differences observed between ADHD and control participants in the present study.

Growth cones express surface receptors that interact with extracellular chemicals to determine the direction and degree of growth. Guidance chemicals can be attractive or repulsive depending on the axonal receptors. One example of these guidance chemicals is netrin, which is produced by cells of the spinal cord. Axonal guidance is also determined by chemorepellent molecules, such as slit, which interact with the receptors of the growing axons to direct them away from improper targets. (Bear et al., 2001). The expression of each of these proteins and receptors is influenced by genetic factors (Sthal, 2008). We hypothesized that a muta-tion in the genes related to these guidance molecules could result in incorrect wiring of the brains of ADHD individu-als, which may explain the CT differences observed in the present study. For example, excess chemoattractants, defi-cient chemorepellents, and/or dysfunction of their respec-tive receptors may result in an excessive accumulation of axons and connections in some regions of the brain and deficits in other regions. These imbalances might lead to the observed thickening or thinning of the cortex. Because




brain circuits are formed in a stepwise manner, with inter-mediate targets, inhibition of the growth cone may result in excess synapses in the intermediate target in the primary posterior cortex. This process would then potentially result in a deficiency of axons traveling toward frontal areas and lead to an increased CT in the occipital, parietal, or tempo-ral regions, and a decreased CT in the frontal areas, which was observed in the present study. According to studies of normal CT development, two of the first areas to reach their peak thickness are the occipital and parietal cortices, which occurs at approximately 7 years of age, and is followed by the frontal cortex at 9 years of age (Shaw et al., 2008). This finding suggests that synaptic formations and cerebral cir-cuit development progresses in a posterior-to-anterior pat-tern that is followed by an anterior-to-central pattern.

In female adolescents, we observed an increased CT in the posterior regions, which could reflect the effect of estro-gen given that this hormone was shown to induce axonal sprouting and increase synapse number (VanderHorst & Holstege, 1997). Later in adulthood, the excess synapses disappear and a predominance of CT reduction is observed in both sexes, but is particularly significant in males. Abnormally large reductions in the CT in adulthood may result from a malfunction of the mechanism of synaptic pruning and competitive elimination. The effect of estrogen on synaptic formation may explain the differences in the reduction of the CT observed between the sexes.

LimitationWith a cross-sectional design used in this study, it is not pos-sible to control the effect of the time and experiences over the cortical structure. A prospective design will be more appro-priate to observe the changes in CT across the life span.

However, 1.0 Tesla MRI equipment is a limitation of the present study because it is not possible to detect smaller dif-ferences in CT due to its resolution.

ConclusionThe results show differences in the CT between control and ADHD participants in the three age groups and between sexes. This suggests that ADHD is a deviation rather than a delay in the neurodevelopment.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the authorship and/or the publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research and/or authorship of this article: The authors received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors for the research and/or authorship of this article.

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Bios

Luis Guillermo Almeida Montes, MD, MSc, PhD, is the chief of the research department at Centro Estatal de Salud Mental in Querétaro, Mexico. He has published many international papers concerning psychiatric themes, particularly ADHD and major depressive disorder.

Hugo Prado Alcántara, MD, is a medical doctor graduated from Universidad del Valle del México in Querétaro, México. Since 2007, he is part of the research team at the Centro Estatal de Salud Mental in Querétaro, Mexico. He has published three original research international papers as the result of his contributions in the recent research projects regarding ADHD.

Reyna Beatriz Martínez García, MD, is a medical doctor gradu-ated from Universidad del Valle de México in Querétaro, México. Recently, she finished her medical social service at Centro Estatal de Salud Mental in Querétaro, Mexico, and published three origi-nal research international papers during that period. She is cur-rently studying a MSc degree at Instituto Politécnico Nacional in México D. F., México.

Lázaro Barajas De la Torre, MSc, PhD, holds a master degree in physics and a master degree in management of information systems. He is a full-time professor at the Department of Basic Science of Instituto Tecnológico y de Estudios Superiores de Monterrey, Campus Querétaro. He made substantial contributions in data and mathe-matics analysis in the recent research projects regarding ADHD.

David Ávila Acosta, PsyD, is a clinical psychologist and techni-cal neuroradiologist. He has more than 15 years of experience in the acquisition and interpretation of magnetic resonance imaging images.

Martín Gallegos Duarte, MD, ophthalmologist, is part of the clinical team at Instituto Queretano Para la Atención de las Enfermedades Congénitas, Private Assistance Institution in Querétaro, México. He is a subspecialist in strabismus and dedi-cates part of his time to elaborate research projects about it. He participated in data acquisition and analysis to learn image meth-ods and incorporate them to his own future works.



3 Brain Cortical Thickness

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