 |
|
Hippocampus and Amygdala Morphology in Attention-Deficit/Hyperactivity Disorder
Kerstin J. Plessen, MD;
Ravi Bansal, PhD;
Hongtu Zhu, PhD;
Ronald Whiteman, BA;
Jose Amat, MD;
Georgette A. Quackenbush, MA;
Laura Martin, BS;
Kathleen Durkin, MS;
Clancy Blair, PhD, MPH;
Jason Royal, DMA;
Kenneth Hugdahl, PhD;
Bradley S. Peterson, MD
Arch Gen Psychiatry. 2006;63:795-807.
ABSTRACT
| |
Context Limbic structures are implicated in the genesis of attention-deficit/hyperactivity disorder (ADHD) by the presence of mood and cognitive disturbances in affected individuals and by elevated rates of mood disorders in family members of probands with ADHD.
Objective To study the morphology of the hippocampus and amygdala in children with ADHD.
Design A cross-sectional case-control study of the hippocampus and amygdala using anatomical magnetic resonance imaging.
Settings University research institute.
Patients One hundred fourteen individuals aged 6 to 18 years, 51 with combined-type ADHD and 63 healthy controls.
Main Outcome Measures Volumes and measures of surface morphology for the hippocampus and amygdala.
Results The hippocampus was larger bilaterally in the ADHD group than in the control group (t = 3.35; P<.002). Detailed surface analyses of the hippocampus further localized these differences to an enlarged head of the hippocampus in the ADHD group. Although conventional measures did not detect significant differences in amygdalar volumes, surface analyses indicated the presence of reduced size bilaterally over the area of the basolateral complex. Correlations with prefrontal measures suggested abnormal connectivity between the amygdala and prefrontal cortex in the ADHD group. Enlarged subregions of the hippocampus tended to accompany fewer symptoms.
Conclusions The enlarged hippocampus in children and adolescents with ADHD may represent a compensatory response to the presence of disturbances in the perception of time, temporal processing (eg, delay aversion), and stimulus seeking associated with ADHD. Disrupted connections between the amygdala and orbitofrontal cortex may contribute to behavioral disinhibition. Our findings suggest involvement of the limbic system in the pathophysiology of ADHD.
INTRODUCTION
The neural basis of attention-deficit/hyperactivity disorder (ADHD) is currently unknown. Affecting 3% to 7% of all children and adolescents,1-2 ADHD is defined by distractibility, hyperactivity, and impulsivity.3 Children with ADHD often also struggle with deficits in executive functioning,4 working5 and visuospatial memory,6 temporal processing,7 and difficulty tolerating delayed rewards.8 The hippocampus likely subserves these functions in attention and cognition, disturbances of which are among the defining hallmarks of ADHD.1, 5, 9-13 Involvement of the amygdala in the pathophysiology of ADHD14-15 likely contributes to the increased risk for affective disorders in children with ADHD and their family members,11, 16-18 even in family members who themselves do not have ADHD.19-21 Indeed, the association of affective and ADHD symptoms is sufficiently tight that affective symptoms previously were listed as associated features of ADHD in DSM-III-R.22
Replicated findings in anatomical magnetic resonance imaging studies of children with ADHD include reduced cerebral volumes23-25 and more localized reductions in volume of the prefrontal cortex (PFC),25-27 particularly its inferior aspect.28 Connectivity of these prefrontal regions, especially the ventral medial PFC, with the hippocampus and amygdala regulates a variety of attentional, memory, and emotional processes29-31 implicated in the pathophysiology of ADHD. Circuits connecting the amygdala and orbitofrontal cortex (OFC) support decision making32 and reward reinforcement,33 and disturbances of these circuits seem to cause behavioral disinhibition and impulsivity.34-37
We used magnetic resonance imaging to study hippocampus and amygdala morphologies in children with ADHD and age-matched healthy controls. Our a priori hypothesis was that volumes would differ across diagnostic groups.
METHODS
Subjects included 114 children and adolescents aged 7 to 18 years. We recruited children who met DSM-IV criteria3 for the combined-type ADHD. Healthy controls were recruited randomly from a telemarketing list of 10 000 names, matched by zip code to subjects with ADHD. Exclusion criteria for controls included a lifetime history of ADHD, tic disorder, or obsessive-compulsive disorder or a current DSM-IV Axis I disorder. Exclusion criteria for children with ADHD included lifetime obsessive-compulsive disorder or tics or premature birth (gestation  36 weeks). Additional exclusion criteria for both groups included epilepsy, head trauma with loss of consciousness, lifetime substance abuse, psychotic disorder, developmental delay, or IQ less than 80, as measured by the Wechsler Intelligence Scale for Children–III,38 the Wechsler Adult Intelligence Scale–III,39 or the Kaufmann Brief Intelligence Test.40 Written informed consent was obtained from all parents, and participants provided written assent.
Clinical diagnoses were established using the Schedule for Affective Disorders and Schizophrenia for School-Age Children–Present and Lifetime Version41 and a "best-estimate consensus procedure" that considered all available clinical and diagnostic information.42 The ADHD symptoms were further assessed by the Conners Parent and Teacher Rating scales43-44 and the DuPaul-Barkley ADHD rating scale45-46; anxiety symptoms were assessed with the Revised Children's Manifest Anxiety Scale47; and depressive symptoms, with the Children's Depression Inventory.48 Socioeconomic status was estimated using the Hollingshead Four-Factor Index of Social Status.49
Subjects were predominantly right-handed (90.2% of children with ADHD, 93.7% of controls).50 Statistical analyses included 51 children with ADHD and 63 controls of comparable age (mean [SD] age, children with ADHD, 12.3 [3.01] years; controls, 11.5 [3.04] years; t = 1.4; P = .16), socioeconomic status (mean [SD] Hollingshead index score, children with ADHD, 45.0 [13.0]; controls, 48.3 [9.9]; t = 1.5; P = .14), and IQ (mean [SD] full-scale IQ, children with ADHD, 108.3 [19.3]; controls, 114.6 [17.1]; t = –1.7; P = .08). The ADHD group contained fewer females (ADHD, 9%; controls, 21%;  2 P<.06). Thirty-five (69%) of the subjects with ADHD were taking medication: all of them were taking stimulants, 3 were taking  -agonists, and 2 were taking selective serotonin reuptake inhibitors. No controls were taking psychotropic medication. In the ADHD group, 14 (27%) had a lifetime diagnosis of depression, 3 of whom were currently depressed; 14 subjects (27%) had oppositional defiant disorder in their lifetimes, 5 currently; 8 (16%) met lifetime criteria for specific developmental disorder (eg, reading, mathematics, written expression, or motor coordination); and 6 (11%) had a lifetime diagnosis of specific phobia, 2 had a current diagnosis of specific phobia.
MAGNETIC RESONANCE IMAGING AND IMAGE ANALYSIS
Pulse Sequence
Head position was standardized using canthomeatal landmarks. T1-weighted, sagittal, 3-dimensional volume images were acquired using a spoiled gradient echo pulse sequence with repetition time = 24 milliseconds, echo time = 5 milliseconds, 45° flip angle, 256 x 192 matrix, 30-cm field of view, 2 excitations, section thickness = 1.2 mm, and 124 contiguous sections.
Preprocessing
Image processing was performed on Sun Ultra 10 workstations with ANALYZE 7.5 software (Biomedical Imaging Resource, Mayo Foundation, Rochester, Minn). Operators were blind to subject characteristics and hemisphere (images were randomly flipped left to right prior to analysis). Large-scale variations in image intensity were removed,51 and images were reformatted to standardize head positioning prior to region definition.52 Axial sections were oriented parallel to both the anterior and posterior commissures, and sagittal sections were oriented parallel to standard midline landmarks.52
Amygdala and Hippocampus
Methods for defining the hippocampus and the amygdala followed previously published algorithms (Figure 1).53 The rostral extent of the amygdala coincided with the most anterior section in which the anterior commissure crossed the midline. The transition between the amygdala and hippocampus was determined with a line connecting the inferior horn of the lateral ventricle with the amygdaloid sulcus or, when the sulcus was not obvious, with a straight horizontal line connecting the inferior horn of the lateral ventricle with the surface on the uncus.54 The most posterior section was the last section in which the crus of the fornix and the fimbria of the hippocampal formation could be delineated. Intraclass correlation coefficients, calculated using 2-way random effects,55 were 0.91 and 0.92 for the right and left hippocampus and 0.89 and 0.88 for the right and left amygdala, respectively.
|
|
|
|
Figure 1. Location of hippocampus and amygdala in the context of the surrounding structures in the coronal (A) and sagittal (B) views.
|
|
|
Whole Brain Volume
An isointensity contour function was used in conjunction with manual editing to isolate the cerebrum. This whole brain volume (WBV) measure included gray and white matter, ventricular cerebrospinal fluid, cisterns, fissures, and cortical sulci. Cerebrospinal fluid was included using a connected components analysis. The WBV did not differ significantly between the diagnostic groups and was therefore used as a covariate in statistical analyses to control for scaling effects.56
Cerebral Subdivisions
Prefrontal regions were delineated by subdividing the cerebrum into dorsal prefrontal, inferior occipital, midtemporal, orbitofrontal, premotor, parieto-occipital, subgenual, and sensorimotor regions, as described previously.52 Additionally, corresponding gray matter volumes were defined and calculated for the different cortical regions. Volumes of gray matter in the dorsal prefrontal cortex (DPFC) and OFC were used for further analyses. Intraclass correlation coefficients were >0.98 for WBV and all cortical subdivisions.
Surface Analyses
Surface morphologies of the hippocampus and amygdala were compared across diagnostic groups while covarying statistically for age and sex to localize the portions of each structure that contributed most to the observed differences in global volume between groups. We computed the distance from each point on the surfaces of the hippocampus and amygdala of each subject to the corresponding point on the hippocampus and amygdala of a reference subject (R.B., L. H. Staib, PhD, D. Xu, PhD, H.Z., B.S.P., unpublished data, October-November 2005):
- A rigid-body similarity transformation was used to register the cerebrum of each subject with that of a reference subject. The parameters of this transformation (3 translations, 3 rotations, and global scaling) were estimated with the constraint that they maximized the mutual information in gray-scale values across the 2 brains.57
- These estimated parameters were used to transform the manually defined hippocampus and amygdala from each subject into this common coordinate space. Here the global-scaling parameter in the rigid registration process for the entire cerebrum, described in step 1, was applied to each hippocampus and amygdala, thereby accounting for scaling differences in these structures. These analyses therefore did not require further correction for overall brain size.
- The transformed hippocampus and amygdala of each subject were individually and rigidly coregistered to the corresponding structure of the reference brain to further refine and improve their rigid-body registrations.
- The hippocampus and the amygdala of each subject were warped to the hippocampus and the amygdala of a reference brain, respectively, using a high-dimensional, nonrigid warping algorithm based on fluid-flow dynamics.57-58 Structures were warped to be exactly the same size and shape as the reference structure, permitting precise identification of corresponding points on the surfaces of structures from the subject and reference brains.
- The warped hippocampus and amygdala were then unwarped into the refined coordinate space identified in step 3 by simply reversing the high-dimensional, nonlinear warping used to identify point correspondences in step 4 while maintaining the labels identifying corresponding points on the surfaces of the subject and the reference structures.
Detecting, localizing, and interpreting the statistically significant differences between groups in these surface analyses could conceivably depend on the choice of the reference brain. Therefore, in the steps to determine point correspondences between structures of each brain, we first selected a reference subject who was demographically as representative as possible of the children studied. The brains for all remaining subjects were coregistered to this preliminary reference. The point correspondences on the surfaces of their hippocampus and amygdala were determined, and we computed distances between the corresponding points. We then selected as the final reference the brain for which all points across the surface of the hippocampus and amygdala were closest, in terms of least squares, to the average of the computed distances. The procedures for registration, determination of point correspondences, and calculation of distances from the final reference structure were repeated for all subjects. The distances were then compared across groups.
STATISTICAL ANALYSES
A Priori Hypothesis Testing
We tested our hypothesis that volumes would differ across diagnostic groups by assessing the main effect of group and the group x region interaction in a mixed-model analysis with repeated measures over a spatial domain (amygdalar and hippocampal volumes in each hemisphere). The model included the within-subjects factors "hemisphere" with 2 levels (left and right) and "region" with 2 levels (amygdala and hippocampus). Diagnosis (ADHD and control) was a between-subjects factor. Covariates included age, sex, and WBV. Beyond these independent variables, we considered all 2- and 3-way interactions of diagnosis (ADHD), sex, hemisphere, region, and age, as well as the 2-way interactions of WBV with hemisphere or region. Other variables considered in the model were handedness, socioeconomic status, medication, IQ, lifetime diagnoses of depression, oppositional defiant disorder, or specific developmental disorder (and their 2-way interaction with region); these were treated as potential confounding variables. Statistically nonsignificant terms were eliminated via backward stepwise regression, with the constraint that the model at each step had to be hierarchically well formulated (ie, all possible lower-order terms were included in the model, regardless of statistical significance).59 To control for a trend toward a sex imbalance across diagnostic groups, the procedure was repeated for boys only (n = 42 in both groups). We considered P values <.025 statistically significant, given our testing of 2 a priori hypotheses. All P values were 2-sided. Statistical procedures were performed in SAS version 9.0 (SAS Institute Inc, Cary, NC) or Statistical Product and Service Solutions (SPSS Inc, Chicago, Ill).60
Correlations With Symptom Severity
Associations of hippocampal and amygdalar volumes with the severity of current ADHD symptoms were assessed in the ADHD group (n = 47) while controlling for WBV, age, and sex using multiple linear regression. The correlations were restricted to the ADHD group only because of the presence of insufficient symptom variance in the control group. Correlations of the amygdala and hippocampus with anxiety and depression symptoms were assessed similarly but in both diagnostic groups.
Group Comparisons of Prefrontal Volumes
Prefrontal gray matter volumes (OFC and DPFC volumes bilaterally) were compared between children with ADHD and controls using a 2-sided t test. This comparison was not part of our a priori hypothesis testing. We report the results of this comparison merely to document the presence of anatomical abnormalities in the frontal cortices of this sample that are similar to abnormalities reported previously in other samples of individuals with ADHD.
Correlations With Gray Matter Volumes of the PFC
We explored the presumed connectivity between the hippocampal, amygdalar, and PFC subregions (DPFC and OFC gray matter volumes). Correlations were controlled for WBV, age, and sex. Differences in correlation coefficients between diagnostic groups were tested using the test statistic D for comparing 2 Pearson correlations while correcting for dfi:
where Zi is the Fisher transformation of the correlation coefficients for samples of size ni, and dfi = n–p–1, for partial correlations with P = 3 covariates (WBV, age, and sex).
Surface Analyses
The signed Euclidean distances between points on the surfaces of the amygdala and hippocampus for each subject and corresponding points on the respective reference structures were compared statistically between groups using linear regression at each voxel on the surface while covarying for age and sex. P values were color coded at each voxel and displayed across the surface of the reference structures. To minimize type I errors, a threshold of P<.0001 was set. Similar maps were constructed for P values associated with partial correlations r of surface measures with symptom severity in the ADHD group, while covarying with sex and age.
Correction for Multiple Comparisons in Surface Morphologies
Testing the null hypothesis at each point on a surface generally requires many statistical comparisons. Correction of P values for these comparisons is complicated by intercorrelations among the signed distances at neighboring points. We therefore used the theory of gaussian random fields (GRFs)61 to correct P values appropriately for these multiple comparisons in the presence of intercorrelated measures across voxels. The signed distances determine a t statistic at each corresponding point, which together across the surface compose a random field f. The expected value of the Euler characteristic of the random field f was used to approximate the critical point for determining locations on the surface where the t statistics differ between groups at a prespecified significance level or statistical threshold (R.B., L. H. Staib, PhD, D. Xu, PhD, H.Z., B.S.P., unpublished data, October-November 2005).62 Because the expected Euler characteristic was evaluated for a GRF, t statistics at each location of the brain were first converted into values from a gaussian random variable.63 Thus, surface locations where the converted statistics were larger than the estimated critical point were considered statistically significant.
RESULTS
HYPOTHESIS TESTING
The test for fixed effects in a mixed model revealed a highly significant group x region interaction (F112 = 7.96; P<.006), demonstrating a regional specificity in group differences of amygdalar and hippocampal volumes.
POST HOC ANALYSES
Post hoc assessment of the origin of this regionally specific difference between groups in volume, using a test of differences in least-square means, indicated that the hippocampus was larger bilaterally in the children with ADHD than in the controls (3384.2 mm3 vs 3164.1 mm3; t112 = 3.35; P<.002). Amygdalar volumes did not differ significantly across diagnostic groups (ADHD, 2062.6 mm3 vs 2106.0 mm3; t112 = –0.64; P = .53). Other significant covariates in the model were WBV (F111 = 39.8; P<.0001), indicating the presence of significant scaling effects, and hemisphere (F113 = 6.1; P<.02), reflecting significantly larger volumes in the right hemisphere. A group x region x hemisphere interaction was not significant (at the point of elimination, F111 = 0.3; P = .62), indicating the absence of significant lateralizing effects across groups. The group x region x age interaction also was not significant (F322 = 0.2; P = .70), indicating the stability of findings across the age range of children studied. The variables sex (F109 = 0.5; P = .48) and age (F109 = 0.7; P = .42) were conservatively retained in the final model because of the biological plausibility that these variables could influence the overall findings.
BOYS ONLY
The group x region interaction remained significant (F82 = 4.37; P<.04), with a larger hippocampus in boys with ADHD compared with controls (3398.8 mm3 vs 3222.6 mm3; t82 = 2.23; P<.03).
SURFACE ANALYSES
Statistical maps revealed that global differences in hippocampal volume between groups arose mainly from enlargement of the anterior hippocampus in children with ADHD (Figure 2A), particularly over the anatomical subfields cornu ammonis (CA) and dentate gyrus (DG) (Figure 3). In posterior portions of the hippocampus, in contrast, smaller indented regions bilaterally suggested the presence of reduced volumes locally in underlying tissue in the ADHD group.
|
|
|
|
Figure 2. Group differences in surface measures of the hippocampus and amygdala. A, Views of the right (upper row) and left (lower row) hippocampus, anterior (A) and posterior (P) orientation. Arrows point to the main protrusion, most visible laterally in the right and dorsoanteriorly in the left head region of the hippocampus (HH), overlaying the dentate gyrus (DG) and the cornu ammonis (CA) subfields as well as the smaller, posteriorly located indentations and protrusions in the tail (HT). B, Views of the right (upper row) and left (lower row) amygdala, A and P orientation. Arrows point to the indentation located mainly over the ventroposterior aspect of the structure, corresponding to the basal nucleus (BN) in the right hemisphere and the lateral nucleus (LN) in the left hemisphere, thus corresponding to the basolateral complex bilaterally. The color bar depicts the statistical significance of group differences for t tests, ranging from P<.0001 in red (protrusions, or local volume enlargements, in the attention-deficit/hyperactivity group) to P<.0001 in purple (indentations, or local volume reductions, in the attention-deficit/hyperactivity group). The 2 outermost columns on the right side of the figure show the gaussian random field (GRF)–corrected dorsal and ventral views. TS indicates terminal segment of the HT; HB, hippocampal body; DH, digitationes hippocampi; ES, endorhinal sulcus; CoN, cortical nucleus; GA, gyrus ambiens; EA, entorhinal area; and CS, collateral sulcus.
|
|
|
|
|
|
|
Figure 3. Subregions of the hippocampus and amygdala. A, Subregions of the hippocampus showing the head of the hippocampus (HH), the digitationes hippocampi (DH), the hippocampal body (HB), the hippocampal tail (HT), the terminal segment of the HT (TS), the dentate gyrus (DG), and the fields of the cornu ammonis (CA1-CA4). Adapted with permission from Springer Verlag, Heidelberg, Germany.64 B, Subregions of the amygdala in the sagittal view (C), with the corresponding coronal views from anterior to posterior (D-F), showing the basal nucleus (BN), the lateral nucleus (LN), the medial nucleus (MN), the cortical nucleus (CoN), the central nucleus (CeN), the collateral sulcus (CS), the endorhinal sulcus (ES), the gyrus ambiens (GA), the entorhinal area (EA), the hippocampus (H), and the temporal horn of the lateral ventricle (THLV). The black arrowhead at the top of D, E, and F pointing downward through the amygdala indicates where the sagittal section depicted in C crosses the coronal plane.65-68
|
|
|
Several portions of the surface of the amygdala suggested the presence of locally reduced volumes in the ADHD group that were not evident in the more conventional measures of overall volume of this structure, with several clusters of voxels reaching P values <.0001 (Figure 2B). Differences in size were located primarily over the basal nucleus of the right amygdala and lateral nucleus of the left.
Gaussian random field–based corrections for multiple comparisons produced clusters of significant voxels that were similar in location to, but smaller in size than, clusters identified in uncorrected comparisons at a threshold of P<.0001 (Figures 2, 4, and 5).
|
|
|
|
Figure 4. Symptom correlations with hippocampus surface morphology in children with attention-deficit/hyperactivity disorder. Correlation of surface measures, in signed Euclidean distances, with current hyperactivity scores (A), inattention scores (B), and total Conners Parent Rating Scale43 scores (C) in the attention-deficit/hyperactivity disorder group, controlling for sex and age. The color bar depicts the P value for the partial Pearson correlation r, ranging from P<.0001 in red (highly significant positive correlations) to P<.0001 in purple (highly significant inverse correlations). The 2 outermost columns on the right side of the figure show the gaussian random field (GRF)–corrected dorsal and ventral views. A indicates anterior; P, posterior.
|
|
|
|
|
|
|
Figure 5. Symptom correlations with amygdala surface morphology in children with attention-deficit/hyperactivity disorder. A, Correlation of surface measures, in signed Euclidean distances, with current hyperactivity scores. B, Correlation of surface measures, in signed Euclidean distances, with inattention scores, controlling for sex and age, with the right amygdala (upper row) and left amygdala (lower row). The color bar depicts the P value for the partial Pearson correlation r, ranging from P<.0001 in red (highly significant positive correlation) to P<.0001 in purple (highly significant inverse correlations). The 2 outermost columns on the right side of the figure show the gaussian random field (GRF)–corrected dorsal and ventral views. A indicates anterior; P, posterior.
|
|
|
CORRELATIONS WITH SYMPTOM SEVERITY
In children with ADHD only, while controlling for WBV, sex, and age, a statistical trend was detected for an inverse correlation of hippocampal volume with ratings of the severity of ADHD symptoms in the right (r = –0.29; P = .06) and left (r = –0.27; P<.07) hemispheres (Figure 6). Surface analyses also suggested that symptom severity correlated inversely with the local features of hippocampus morphology, particularly in portions that were enlarged relative to controls (Figure 4).
|
|
|
|
Figure 6. Scatterplots demonstrating correlations with symptom severity. A, Right hippocampal volumes (adjusted for age, sex, and whole brain volume) correlate inversely with Conners Parent Rating Scale43 scores (n = 47) (r = 0.3; P<.06). B, Right amygdalar volumes (adjusted for age, sex, and whole brain volume) correlate positively with current hyperactivity scores (n = 47) (r = 0.3; P<.06).
|
|
|
In children with ADHD only, volumes of the left (r = 0.3; P<.07) and right (r = 0.3; P<.06) amygdala showed strong trends toward positive correlations with hyperactivity scores (Figure 6B). Supporting the validity of these trends detected for overall volumes, analyses of symptom severity with surface features exhibited large clusters of positive correlations for hyperactivity scores bilaterally (Figure 5A). Inattention scores, in contrast, correlated inversely with surface morphology mainly in the left amygdala (Figure 5B). Symptoms of anxiety and depression did not correlate significantly with amygdalar or hippocampal volumes.
GROUP COMPARISONS OF PREFRONTAL VOLUMES
The ADHD group had significantly smaller volumes of the left OFC gray matter (ADHD, 10.535 cm3 vs controls, 11.979 cm3; t = 2.24; P<.03) and a trend toward lower mean volumes of right OFC gray matter (ADHD, 10.973 cm3 vs controls, 12.033 cm3; t = 1.68; P<.09) (Table 1). Groups did not differ in volumes of DPFC gray matter.
|
|
|
|
Table 1. Comparison of Brain Morphometric Measures*
|
|
|
CORRELATIONS WITH VOLUMES OF PFC GRAY MATTER
Interregional correlation analyses revealed positive correlations in the control group (n = 56) for the right and left amygdala with OFC gray matter (right, r = 0.66; P<.001; left, r = 0.48; P<.001) (Table 2). None of these correlations were significant in the ADHD group (n = 47). The test statistic D for comparing 2 Pearson correlation coefficients confirmed significant group differences for these correlations for the left (P<.02) and right (P<.001) amygdala.
|
|
|
|
Table 2. Interregional Correlations*
|
|
|
POTENTIAL CONFOUNDS
In separate assessments of the statistical model used for hypothesis testing, none of the possible confounds reached statistical significance: lifetime diagnosis of depression (F111 = 0.3; P = .61), oppositional defiant disorder (F111 = 0.2; P = .67), specific developmental disorder (F111 = 1.96; P = .17), full-scale IQ (F99 = 0.3; P = .62), handedness (F109 = 1.5; P = .22), socioeconomic status (F101 = 1.0; P = .31), medication status (F110 = 1.7; P = .20), and stimulant medication (F96 = 2.2; P = .14). In addition, verbal (right hippocampus, r = –0.10; P = .32; left hippocampus, r = –0.10; P = .35; right amygdala, r = 0.17; P = .11; left amygdala, r = 0.15; P = .16), performance (right hippocampus, r = –0.10; P = .35; left hippocampus, r = –0.19; P = .07; right amygdala, r = 0.15; P = .15; left amygdala, r = 0.07; P = .47), and full-scale IQs (right hippocampus, r = –0.10; P = .33; left hippocampus, r = –0.14; P = .16; right amygdala, r = 0.17; P = .09; left amygdala, r = 0.12; P = .22) did not correlate significantly with regional volumes of either the hippocampus or amygdala while controlling for WBV, sex, and age, further suggesting that IQ measures did not unduly influence findings of the primary analyses.
COMMENT
Children and adolescents with ADHD had larger hippocampal volumes than did healthy controls, primarily deriving from larger volumes of the head of the hippocampus. Larger volumes tended to accompany less severe ADHD symptoms. Although overall volumes of the amygdala did not differ between subjects with ADHD and controls, surface analyses showed that several amygdalar subregions were smaller in children with ADHD than in controls, and these same regions generally correlated significantly and positively with the severity of ADHD symptoms. Finally, interregional correlations suggested that connectivity between the amygdala and the OFC was disrupted in the ADHD group. Medication, comorbid illnesses of affective and anxiety disorders, symptoms of depression and anxiety, and group differences in IQ did not account for our findings.
HIPPOCAMPUS
Surface analyses revealed enlarged anterior-most portions of the hippocampus in the ADHD group, particularly in its dorsal and lateral aspects, corresponding respectively to the DG and CA1-CA2 subregions.54, 64 Significant inverse correlations with hyperactivity scores were localized laterally over the CA1 and CA2 subfields, and inverse correlations with inattentive symptoms were located medially, primarily over the CA3 and DG subfields. Although we cannot infer causation from these cross-sectional, correlational findings,69-70 the most likely explanation for the association of more prominent enlargement with fewer ADHD symptoms, particularly in the presence of overall enlargement (ie, progressively fewer symptoms that accompany an increasingly more prominent morphological abnormality relative to controls), would seem to be that the hippocampal enlargement represents a compensatory plastic hypertrophic response to the presence of ADHD symptoms. This interpretation is consistent with abundant preclinical evidence for the presence of synaptic remodeling71-72 and neurogenesis73 within the hippocampus, which supports improved learning and memory functions in response to experiential demands.74-75
An enlarged anterior hippocampus could represent a localized compensatory response of neural processes to the presence of functional disturbances in these same neural systems within the anterior hippocampus, as is thought to occur in the presence of impaired neural processing.76-77 Alternatively, given evidence herein and elsewhere24, 28, 78-79 for the presence of impaired structure and function of the PFC in children with ADHD, the enlarged anterior portions of the hippocampus may represent a neural compensation for disturbances in prefrontal portions of a PFC-hippocampal network. The absence of a significant contribution of age to the correlations of hippocampus morphology with the severity of symptoms could evidence an initiation of a plastic response early in the course of disease, a possibility consistent with the shorter time frames (days to weeks) in which plasticity typically manifests.80
The anterior hippocampus encodes the spatial and temporal relationships between sensory experiences,81-84 which the posterior hippocampus then consolidates for storage in long-term memory.85-86 Working within a distributed network that includes the PFC, the encoding of temporal relationships within the hippocampus helps to define and encode the serial ordering of events,82, 87-90 the cognitive function probably most consistently disturbed in children with ADHD.7, 91-96 In humans, the anterior hippocampus plays a prominent role in indexing novelty, detecting change, and exploring new environments,86, 97-99 and thus, the stimulus-seeking behaviors of children with ADHD100 may engage these anterior hippocampal functions. Given that stimulus-enriched environments101-102 and physical activity103-105 potently enhance DG neurogenesis, the anterior hippocampal hypertrophy that we detected conceivably could also be a neuronal consequence of exaggerated stimulus-seeking behaviors in the children with ADHD. Moreover, stimulus seeking and attention to nontemporal stimuli are hypothesized to serve as strategies that reduce the length of experienced time while children with ADHD are experiencing the delayed delivery of an anticipated reward,94 an experience to which they have an intense aversion.8 Thus, both stimulus seeking and its presumed morphological consequence, plastic hypertrophy in the anterior hippocampus, could help to allay disturbances in the perception of time and difficulties with delay aversion in children with ADHD.
The hippocampus is also the pacemaker for theta wave activity in the central nervous system,106 and individuals with ADHD have unusually high relative theta activity (4-8 Hz) in their electroencephalograms.107-109 Thus, an enlarged hippocampus could account for excess theta activity in this group, particularly given that theta activity underlies working memory processes and the retrieval and consolidation of long-term memories110-112 and has been documented in animals during exploratory behaviors in unfamiliar surroundings.113-115
AMYGDALA
Although overall volume of the amygdala did not differ between subjects with ADHD and controls, surface analyses indicated the presence of significant reductions in volume overlying the lateral and basal nuclei, which together with the accessory basal nucleus have been designated the basolateral complex,116 a portion of the amygdala that is particularly densely connected with the PFC.33, 117-118 Hyperactivity scores showed a trend toward positive correlation with overall volume of the amygdala and should as a statistical trend be interpreted cautiously. Nevertheless, surface analyses also detected positive correlations of hyperactivity symptoms with amygdala morphology at considerably greater levels of statistical significance, particularly in the region overlying the basolateral complex bilaterally, where volumes were reduced locally in the ADHD group. Inattention scores correlated inversely with surface measures most prominently over the basal and lateral nuclei of the left amygdala. Volume reductions and correlations with measures of symptom severity were localized primarily over the basolateral complex, the portion of the amygdala most consistently implicated in the attribution of affective valence to sensory stimuli,119-121 and the nuclei most likely to subserve fear conditioning.122-125 We postulate that morphological disturbances in the basolateral complex may interfere with both the attribution of valence to sensory stimuli and the development of normal fear responses in children with ADHD, which may in turn disrupt emotional learning and the affective drive to sustain attention to otherwise mundane sensory stimuli.
INTERREGIONAL CONNECTIVITY
Interregional correlations suggested the presence of disturbed connectivity between the amygdala and OFC in the children with ADHD. The significant positive correlation of amygdalar volumes bilaterally with volumes of OFC gray matter in healthy controls was inverted significantly in the ADHD group. Connections between these regions are rich,126-127 and they support decision making by supplying information about positive and negative outcomes during choice behaviors.36-37 Neurons in the amygdala are thought to signal the value of specific reinforcers, information that is used subsequently by OFC neurons firing in expectation of the behavioral outcome to guide and reinforce behavior.128 Interaction of the OFC and amygdala is therefore needed to learn reinforcements and to suppress unwanted behaviors,129 as well as to evaluate the emotional and reinforcing salience of sensory stimuli.32, 130-132 The poor performance of children with ADHD on delay-aversion tasks,8 their preferences for smaller immediate rewards,133 and their more frequent risk-taking behaviors134 all suggest that they are impaired in decision-making capabilities.135 More generally, learning and behavioral control depend on the integrity of limbic-prefrontal connections, and we suspect that disturbances in these connections contribute to the impulsive behaviors that are a defining hallmark of ADHD.13, 136
The basolateral complex of the amygdala, in concert with the hippocampus and medial PFC, plays a central role in the consolidation of learning and memory functions, a role mediated through adrenergic, dopaminergic, and cholinergic neurotransmitter systems.137-138 Disruption of connectivity in amygdala-PFC pathways in children with ADHD is therefore consistent with some of the cognitive deficits associated with the disorder and with the cognition-enhancing effects of stimulant medications, which potentiate noradrenergic and dopaminergic transmission.139
RELATION TO PREVIOUS STUDIES
Two previous studies have reported normal hippocampal volumes in children and adolescents with ADHD. In both studies, 1 comprising 5726 and the other 15 boys with ADHD,27 larger hippocampal volumes were detected in the ADHD group, though not at the level of statistical significance. The statistical significance of our hippocampal findings may be attributable to a large sample size and to the use of images with higher resolution and improved signal-to-noise characteristics. Moreover, neither of the prior studies conducted detailed surface analyses of the hippocampus, which in our analyses revealed larger anterior and smaller posterior regions, effects that tend to offset one another when comparing overall volumes across diagnostic groups. These opposing effects within the same structure may explain why morphological abnormalities were difficult to detect previously.
LIMITATIONS
The ultrastructural determinants of group differences in morphology of the hippocampus and amygdala are unknown, as is the extent to which disturbances in surface morphology relate to abnormalities in the underlying nuclei within these structures. Addressing these limitations will require detailed post-mortem studies. Additionally, the multiple statistical tests performed in our analyses increased the likelihood of type I error, which we minimized in our surface-based analyses through use of conservative statistical thresholds and GRF-based corrections for multiple comparisons.62, 140 Voxels that did not survive GRF correction of course should be interpreted with caution. Furthermore, correlations of surface morphology with clinical symptoms were exploratory and hypothesis generating and therefore also should be interpreted cautiously, as well as confirmed in future studies. Finally, we cannot entirely discount the possibility that medications or comorbid affective and anxiety disorders contributed to our findings, although we did not detect any evidence for these effects.
CONCLUSIONS
Our findings of hippocampal enlargement in children with ADHD and the association of progressively fewer symptoms with an increasing degree of this morphological abnormality suggest that hippocampal enlargement may represent neural responses within the hippocampus that compensate for problems in temporal processing and delay aversion. Disturbances in connectivity between the amygdala and OFC may contribute to problems of self-regulatory control and goal-directed behaviors. This study provides further evidence that the pathophysiology of ADHD involves limbic structures and limbic-prefrontal circuits.
AUTHOR INFORMATION
Correspondence: Bradley S. Peterson, MD, Columbia University and the New York State Psychiatric Institute, 1051 Riverside Dr, Unit 74, New York, NY 10032 (petersob{at}childpsych.columbia.edu).
Submitted for Publication: June 14, 2005; final revision received December 29, 2005; accepted January 6, 2006.
Funding/Support: This work was supported in part by National Institute of Mental Health grants MHK02-74677, MH59139, and MH068318; grants from the National Alliance for Research in Schizophrenia and Affective Disorders; the Thomas D. Klingenstein & Nancy D. Perlman Family Fund; the Suzanne Crosby Murphy Endowment at Columbia University; and the Center for Child and Adolescent Mental Health, University of Bergen, Bergen, Norway.
Acknowledgment: We thank Xuejun Hao, PhD, and Ning Dong, MASc, for their technical assistance.
Author Affiliations: Columbia College of Physicians and Surgeons and the New York State Psychiatric Institute, New York (Drs Plessen, Bansal, Zhu, Amat, Royal, and Peterson; Mr Whiteman; and Mss Quackenbush, Martin, and Durkin); Center for Child and Adolescent Mental Health (Dr Plessen) and Department of Biological and Medical Psychology (Dr Hugdahl), University of Bergen, and Division of Psychiatry, Haukeland University Hospital (Drs Plessen and Hugdahl), Bergen, Norway; and Human Development and Family Studies, Pennsylvania State University, State College (Dr Blair).
REFERENCES
| |
1. Swanson JM, Sergeant JA, Taylor E, Sonuga-Barke EJ, Jensen PS, Cantwell DP. Attention-deficit hyperactivity disorder and hyperkinetic disorder. Lancet. 1998;351:429-433.
FULL TEXT
|
ISI
| PUBMED
2. Goldman LS, Genel M, Bezman RJ, Slanetz PJ. Diagnosis and treatment of attention-deficit/hyperactivity disorder in children and adolescents: Council on Scientific Affairs, American Medical Association. JAMA. 1998;279:1100-1107.
FREE FULL TEXT
3. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. Washington, DC: American Psychiatric Association; 1994.4. Sergeant JA, Geurts H, Oosterlaan J. How specific is a deficit of executive functioning for attention-deficit/hyperactivity disorder? Behav Brain Res. 2002;130:3-28.
FULL TEXT
|
ISI
| PUBMED
5. Castellanos FX, Tannock R. Neuroscience of attention-deficit/hyperactivity disorder: the search for endophenotypes. Nat Rev Neurosci. 2002;3:617-628.
ISI
| PUBMED
6. Bedard AC, Martinussen R, Ickowicz A, Tannock R. Methylphenidate improves visual-spatial memory in children with attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry. 2004;43:260-268.
FULL TEXT
|
ISI
| PUBMED
7. Barkley RA. Behavioral inhibition, sustained attention, and executive functions: constructing a unifying theory of ADHD. Psychol Bull. 1997;121:65-94.
FULL TEXT
|
ISI
| PUBMED
8. Sonuga-Barke EJ, Taylor E, Sembi S, Smith J. Hyperactivity and delay aversion, I: the effect of delay on choice. J Child Psychol Psychiatry. 1992;33:387-398.
ISI
| PUBMED
9. Sonuga-Barke EJ. The dual pathway model of AD/HD: an elaboration of neuro-developmental characteristics. Neurosci Biobehav Rev. 2003;27:593-604.
FULL TEXT
|
ISI
| PUBMED
10. Biederman J, Spencer T. Attention-deficit/hyperactivity disorder (ADHD) as a noradrenergic disorder. Biol Psychiatry. 1999;46:1234-1242.
FULL TEXT
|
ISI
| PUBMED
11. Pliszka SR. Patterns of psychiatric comorbidity with attention-deficit/hyperactivity disorder. Child Adolesc Psychiatr Clin N Am. 2000;9:525-540, vii.
ISI
| PUBMED
12. Cantwell DP, Baker L. Association between attention deficit-hyperactivity disorder and learning disorders. J Learn Disabil. 1991;24:88-95.
FREE FULL TEXT
13. Barkley RA. Major life activity and health outcomes associated with attention-deficit/hyperactivity disorder. J Clin Psychiatry. 2002;63(suppl 12):10-15.
14. King JA, Tenney J, Rossi V, Colamussi L, Burdick S. Neural substrates underlying impulsivity. Ann N Y Acad Sci. 2003;1008:160-169.
FULL TEXT
|
ISI
| PUBMED
15. Levy F. Synaptic gating and ADHD: a biological theory of comorbidity of ADHD and anxiety. Neuropsychopharmacology. 2004;29:1589-1596.
FULL TEXT
|
ISI
| PUBMED
16. Faraone SV, Biederman J, Mick E, Doyle AE, Wilens T, Spencer T, Frazier E, Mullen K. A family study of psychiatric comorbidity in girls and boys with attention-deficit/hyperactivity disorder. Biol Psychiatry. 2001;50:586-592.
FULL TEXT
|
ISI
| PUBMED
17. Peterson BS, Pine DS, Cohen P, Brook JS. Prospective, longitudinal study of tic, obsessive-compulsive, and attention-deficit/hyperactivity disorders in an epidemiological sample. J Am Acad Child Adolesc Psychiatry. 2001;40:685-695.
FULL TEXT
|
ISI
| PUBMED
18. Teicher MH, Andersen SL, Polcari A, Anderson CM, Navalta CP. Developmental neurobiology of childhood stress and trauma. Psychiatr Clin North Am. 2002;25:397-426, vii-viii.
FULL TEXT
|
ISI
| PUBMED
19. Biederman J, Newcorn J, Sprich S. Comorbidity of attention deficit hyperactivity disorder with conduct, depressive, anxiety, and other disorders. Am J Psychiatry. 1991;148:564-577.
FREE FULL TEXT
20. Biederman J, Faraone SV, Keenan K, Benjamin J, Krifcher B, Moore C, Sprich-Buckminster S, Ugaglia K, Jellinek MS, Steingard R, Spencer T, Norman D, Kolodny R, Kraus I, Perrin J, Keller MB, Tsuang MT. Further evidence for family-genetic risk factors in attention deficit hyperactivity disorder: patterns of comorbidity in probands and relatives psychiatrically and pediatrically referred samples. Arch Gen Psychiatry. 1992;49:728-738.
FREE FULL TEXT
21. Braaten EB, Beiderman J, Monuteaux MC, Mick E, Calhoun E, Cattan G, Faraone SV. Revisiting the association between attention-deficit/hyperactivity disorder and anxiety disorders: a familial risk analysis. Biol Psychiatry. 2003;53:93-99.
FULL TEXT
|
ISI
| PUBMED
22. American Psychiatric Association. Diagnostical and Statistical Manual of Mental Disorders, Revised Third Edition. Washington, DC: American Psychiatric Association; 1987.23. Rapoport JL, Castellanos FX, Gogate N, Janson K, Kohler S, Nelson P. Imaging normal and abnormal brain development: new perspectives for child psychiatry. Aust N Z J Psychiatry. 2001;35:272-281.
FULL TEXT
|
ISI
| PUBMED
24. Castellanos FX, Lee PP, Sharp W, Jeffries NO, Greenstein DK, Clasen LS, Blumenthal JD, James RS, Ebens CL, Walter JM, Zijdenbos A, Evans AC, Giedd JN, Rapoport JL. Developmental trajectories of brain volume abnormalities in children and adolescents with attention-deficit/hyperactivity disorder. JAMA. 2002;288:1740-1748.
FREE FULL TEXT
25. Castellanos FX, Giedd JN, Berquin PC, Walter JM, Sharp W, Tran T, Vaituzis AC, Blumenthal JD, Nelson J, Bastain TM, Zijdenbos A, Evans AC, Rapoport JL. Quantitative brain magnetic resonance imaging in girls with attention-deficit/hyperactivity disorder. Arch Gen Psychiatry. 2001;58:289-295.
FREE FULL TEXT
26. Castellanos FX, Giedd JN, Marsh WL, Hamburger SD, Vaituzis AC, Dickstein DP, Sarfatti SE, Vauss YC, Snell JW, Lange N, Kaysen D, Krain AL, Ritchie GF, Rajapakse JC, Rapoport JL. Quantitative brain magnetic resonance imaging in attention-deficit hyperactivity disorder. Arch Gen Psychiatry. 1996;53:607-616.
FREE FULL TEXT
27. Filipek PA, Semrud-Clikeman M, Steingard RJ, Renshaw PF, Kennedy DN, Biederman J. Volumetric MRI analysis comparing subjects having attention-deficit hyperactivity disorder with normal controls. Neurology. 1997;48:589-601.
FREE FULL TEXT
28. Sowell ER, Thompson PM, Welcome SE, Henkenius AL, Toga AW, Peterson BS. Cortical abnormalities in children and adolescents with attention-deficit hyperactivity disorder. Lancet. 2003;362:1699-1707.
FULL TEXT
|
ISI
| PUBMED
29. Davidson RJ, Jackson DC, Kalin NH. Emotion, plasticity, context, and regulation: perspectives from affective neuroscience. Psychol Bull. 2000;126:890-909.
FULL TEXT
|
ISI
| PUBMED
30. Davidson RJ, Putnam KM, Larson CL. Dysfunction in the neural circuitry of emotion regulation: a possible prelude to violence. Science. 2000;289:591-594.
FREE FULL TEXT
31. Posner MI, Rothbart MK. Attention, self-regulation and consciousness. Philos Trans R Soc Lond B Biol Sci. 1998;353:1915-1927.
FREE FULL TEXT
32. Bechara A. The role of emotion in decision-making: evidence from neurological patients with orbitofrontal damage. Brain Cogn. 2004;55:30-40.
FULL TEXT
|
ISI
| PUBMED
33. Baxter MG, Murray EA. The amygdala and reward. Nat Rev Neurosci. 2002;3:563-573.
FULL TEXT
|
ISI
| PUBMED
34. Schoenbaum G, Setlow B, Ramus SJ. A systems approach to orbitofrontal cortex function: recordings in rat orbitofrontal cortex reveal interactions with different learning systems. Behav Brain Res. 2003;146:19-29.
FULL TEXT
|
ISI
| PUBMED
35. Winstanley CA, Theobald DE, Cardinal RN, Robbins TW. Contrasting roles of basolateral amygdala and orbitofrontal cortex in impulsive choice. J Neurosci. 2004;24:4718-4722.
FREE FULL TEXT
36. Bechara A, Damasio H, Damasio AR. Emotion, decision making and the orbitofrontal cortex. Cereb Cortex. 2000;10:295-307.
FREE FULL TEXT
37. Bechara A, Damasio H, Damasio AR. Role of the amygdala in decision-making. Ann N Y Acad Sci. 2003;985:356-369.
ISI
| PUBMED
38. Wechsler D. WISC-III Manual. Canadian Supplement. Toronto, Ontario: Psychological Corp; 1996.39. Wechsler D. Wechsler Adult Intelligence Scale-III. New York, NY: Psychological Corp; 1991.40. Kaufman AS, Kaufman NL. Kaufman Brief Intelligence Test Manual. Circle Pines, Minn: American Guidance Service; 1990.41. Kaufman J, Birmaher B, Brent D, Rao U, Flynn C, Moreci P, Williamson D, Ryan N. Schedule for Affective Disorders and Schizophrenia for School-Age Children—Present and Lifetime Version (K-SADS-PL): initial reliability and validity data. J Am Acad Child Adolesc Psychiatry. 1997;36:980-988.
FULL TEXT
|
ISI
| PUBMED
42. Leckman JF, Sholomskas D, Thompson WD, Belanger A, Weissman MM. Best estimate of lifetime psychiatric diagnosis: a methodological study. Arch Gen Psychiatry. 1982;39:879-883.
FREE FULL TEXT
43. Conners CK, Sitarenios G, Parker JD, Epstein JN. The revised Conners' Parent Rating Scale (CPRS-R): factor structure, reliability, and criterion validity. J Abnorm Child Psychol. 1998;26:257-268.
FULL TEXT
|
ISI
| PUBMED
44. Conners CK, Sitarenios G, Parker JD, Epstein JN. Revision and restandardization of the Conners Teacher Rating Scale (CTRS-R): factor structure, reliability, and criterion validity. J Abnorm Child Psychol. 1998;26:279-291.
FULL TEXT
|
ISI
| PUBMED
45. Swanson JM, Kraemer HC, Hinshaw SP, Arnold LE, Conners CK, Abikoff HB, Clevenger W, Davies M, Elliott GR, Greenhill LL, Hechtman L, Hoza B, Jensen PS, March JS, Newcorn JH, Owens EB, Pelham WE, Schiller E, Severe JB, Simpson S, Vitiello B, Wells K, Wigal T, Wu M. Clinical relevance of the primary findings of the MTA: success rates based on severity of ADHD and ODD symptoms at the end of treatment. J Am Acad Child Adolesc Psychiatry. 2001;40:168-179.
FULL TEXT
|
ISI
| PUBMED
46. DuPaul GJ. Parent and teacher ratings of ADHD symptoms: psychometric properties in a community-based sample. J Clin Child Psychol. 1991;20:245-253.
FULL TEXT
|
ISI
47. Reynolds CR, Richmond BO. Revised Children's Manifest Anxiety Scale (RCMAS). 3rd ed. Washington, DC: Western Psychological Services; 1985.48. Kovac M. Children's Depression Inventory. North Tonawanda, NY: Multi Health Systems; 1992.49. Hollingshead A. Four-Factor Index of Social Status. New Haven, Conn: Yale University Press; 1975.50. Oldfield RC. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia. 1971;9:97-113.
FULL TEXT
|
ISI
| PUBMED
51. Sled JG, Zijdenbos AP, Evans AC. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging. 1998;17:87-97.
FULL TEXT
|
ISI
| PUBMED
52. Peterson BS, Staib L, Scahill L, Zhang H, Anderson C, Leckman JF, Cohen DJ, Gore JC, Albert J, Webster R. Regional brain and ventricular volumes in Tourette syndrome. Arch Gen Psychiatry. 2001;58:427-440.
FREE FULL TEXT
53. Kates WR, Abrams MT, Kaufmann WE, Breiter SN, Reiss AL. Reliability and validity of MRI measurement of the amygdala and hippocampus in children with fragile X syndrome. Psychiatry Res. 1997;75:31-48.
ISI
| PUBMED
54. Watson C, Andermann F, Gloor P, Jones-Gotman M, Peters T, Evans A, Olivier A, Melanson D, Leroux G. Anatomic basis of amygdaloid and hippocampal volume measurement by magnetic resonance imaging. Neurology. 1992;42:1743-1750.
FREE FULL TEXT
55. Shrout P, Fleiss J. Intraclass correlations: uses in assessing rater reliability. Psychol Bull. 1979;86:420-428.
FULL TEXT
|
ISI
| PUBMED
56. Arndt S, Cohen G, Alliger RJ, Swayze VW II, Andreasen NC. Problems with ratio and proportion measures of imaged cerebral structures. Psychiatry Res. 1991;40:79-89.
FULL TEXT
|
ISI
| PUBMED
57. Bansal R, Staib LH, Whiteman R, Wang YM, Peterson BS. ROC-based assessments of 3D cortical surface-matching algorithms. Neuroimage. 2005;24:150-162.
FULL TEXT
|
ISI
| PUBMED
58. Christensen GE, Joshi S, Miller M. Volumetric transformation of brain anatomy. IEEE Trans Med Imaging. 1997;16:864-877.
FULL TEXT
|
ISI
| PUBMED
59. Morrell CH, Pearson JD, Brant LJ. Linear transformations of linear mixed-effects models. Am Stat. 1997;51:338-343.
FULL TEXT
60. SPSS Base 10.0 for Windows User's Guide. Chicago, Ill: SPSS Inc; 1999.61. Adler RJ. The Geometry of Random Fields. New York, NY: J Wiley; 1981.62. Taylor E, Adler RJ. Euler characteristics for Gaussian fields on manifolds. Ann Prob. 2003;31:533-563.
FULL TEXT
63. Worsley KJ, Evans AC, Marrett S, Neelin P. A three-dimensional statistical analysis for CBF activation studies in human brain. J Cereb Blood Flow Metab. 1992;12:900-918.
ISI
| PUBMED
64. Duvernoy H. The Human Hippocampus. New York, NY: Springer Verlag; 2005.65. Duvernoy H. The Human Brain Surface, Three-Dimensional Sectional Anatomy with MRI, and Blood Supply. New York, NY: Springer; 1999.66. Hanaway J, Roberts MP, Woolsey TA, Gado MH, Roberts MPJ. The Brain Atlas. Weinheim, Germany: Wiley-VCH Verlag GmbH; 2000.67. Aggleton JP. The Amygdala, a Functional Analysis. New York, NY: Oxford University Press; 2000.68. McDonald AJ. Is there an amygdala and how far does it extend? an anatomical perspective. Ann N Y Acad Sci. 2003;985:1-21.
ISI
| PUBMED
69. Peterson BS. Conceptual, methodological, and statistical challenges in brain imaging studies of developmentally based psychopathologies. Dev Psychopathol. 2003;15:811-832.
FULL TEXT
|
ISI
| PUBMED
70. Kraemer HC, Yesavage JA, Taylor JL, Kupfer D. How can we learn about developmental processes from cross-sectional studies, or can we? Am J Psychiatry. 2000;157:163-171.
FREE FULL TEXT
71. Bliss TV, Lomo T. Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. J Physiol. 1973;232:331-356.
FREE FULL TEXT
72. Hebb D. The Organization of Behavior: A Neuropsychological Theory. New York, NY: Wiley; 1949.73. Eriksson PS, Perfilieva E, Bjork-Eriksson T, Alborn AM, Nordborg C, Peterson DA, Gage FH. Neurogenesis in the adult human hippocampus. Nat Med. 1998;4:1313-1317.
FULL TEXT
|
ISI
| PUBMED
74. Gould E, Tanapat P, Hastings NB, Shors TJ. Neurogenesis in adulthood: a possible role in learning. Trends Cogn Sci. 1999;3:186-192.
FULL TEXT
|
ISI
| PUBMED
75. Gould E, Beylin A, Tanapat P, Reeves A, Shors TJ. Learning enhances adult neurogenesis in the hippocampal formation. Nat Neurosci. 1999;2:260-265.
FULL TEXT
|
ISI
| PUBMED
76. Johnston MV. Brain plasticity in paediatric neurology. Eur J Paediatr Neurol. 2003;7:105-113.
FULL TEXT
|
ISI
| PUBMED
77. Gage FH, Bjorklund A, Stenevi U. Local regulation of compensatory noradrenergic hyperactivity in the partially denervated hippocampus. Nature. 1983;303:819-821.
FULL TEXT
| PUBMED
78. Rubia K, Smith AB, Brammer MJ, Toone B, Taylor E. Abnormal brain activation during inhibition and error detection in medication-naive adolescents with ADHD. Am J Psychiatry. 2005;162:1067-1075.
FREE FULL TEXT
79. Durston S, Hulshoff Pol HE, Schnack HG, Buitelaar JK, Steenhuis MP, Minderaa RB, Kahn RS, van Engeland H. Magnetic resonance imaging of boys with attention-deficit/hyperactivity disorder and their unaffected siblings. J Am Acad Child Adolesc Psychiatry. 2004;43:332-340.
FULL TEXT
|
ISI
| PUBMED
80. Chen R, Cohen LG, Hallett M. Nervous system reorganization following injury. Neuroscience. 2002;111:761-773.
FULL TEXT
|
ISI
| PUBMED
81. Bast T, Feldon J. Hippocampal modulation of sensorimotor processes. Prog Neurobiol. 2003;70:319-345.
FULL TEXT
|
ISI
| PUBMED
82. Agster KL, Fortin NJ, Eichenbaum H. The hippocampus and disambiguation of overlapping sequences. J Neurosci. 2002;22:5760-5768.
FREE FULL TEXT
83. Huerta PT, Sun LD, Wilson MA, Tonegawa S. Formation of temporal memory requires NMDA receptors within CA1 pyramidal neurons. Neuron. 2000;25:473-480.
FULL TEXT
|
ISI
| PUBMED
84. Shors TJ. Memory traces of trace memories: neurogenesis, synaptogenesis and awareness. Trends Neurosci. 2004;27:250-256.
FULL TEXT
|
ISI
| PUBMED
85. Kandel ER. The molecular biology of memory storage: a dialog between genes and synapses. Biosci Rep. 2001;21:565-611.
FULL TEXT
|
ISI
| PUBMED
86. Strange B, Dolan R. Functional segregation within the human hippocampus. Mol Psychiatry. 1999;4:508-511.
FULL TEXT
|
ISI
| PUBMED
87. Beiser DG, Houk JC. Model of cortical-basal ganglionic processing: encoding the serial order of sensory events. J Neurophysiol. 1998;79:3168-3188.
FREE FULL TEXT
88. Eichenbaum H. A cortical-hippocampal system for declarative memory. Nat Rev Neurosci. 2000;1:41-50.
FULL TEXT
|
ISI
| PUBMED
89. Fortin NJ, Agster KL, Eichenbaum HB. Critical role of the hippocampus in memory for sequences of events. Nat Neurosci. 2002;5:458-462.
ISI
| PUBMED
90. Shapiro ML, Eichenbaum H. Hippocampus as a memory map: synaptic plasticity and memory encoding by hippocampal neurons. Hippocampus. 1999;9:365-384.
FULL TEXT
|
ISI
| PUBMED
91. Barkley RA, Koplowitz S, Anderson T, McMurray MB. Sense of time in children with ADHD: effects of duration, distraction, and stimulant medication. J Int Neuropsychol Soc. 1997;3:359-369.
PUBMED
92. Kerns KA, McInerney RJ, Wilde NJ. Time reproduction, working memory, and behavioral inhibition in children with ADHD. Child Neuropsychol. 2001;7:21-31.
PUBMED
93. Smith A, Taylor E, Rogers JW, Newman S, Rubia K. Evidence for a pure time perception deficit in children with ADHD. J Child Psychol Psychiatry. 2002;43:529-542.
FULL TEXT
|
ISI
| PUBMED
94. Shaw G, Brown G. Arousal, time estimation, and time use in attention-disordered children. Dev Neuropsychol. 1999;16:227-242.
FULL TEXT
|
ISI
95. Toplak ME, Rucklidge JJ, Hetherington R, John SC, Tannock R. Time perception deficits in attention-deficit/hyperactivity disorder and comorbid reading difficulties in child and adolescent samples. J Child Psychol Psychiatry. 2003;44:888-903.
FULL TEXT
|
ISI
| PUBMED
96. Sonuga-Barke EJ, De Houwer J, De Ruiter K, Ajzenstzen M, Holland S. AD/HD and the capture of attention by briefly exposed delay-related cues: evidence from a conditioning paradigm. J Child Psychol Psychiatry. 2004;45:274-283.
FULL TEXT
|
ISI
| PUBMED
97. Strange BA, Fletcher PC, Henson RN, Friston KJ, Dolan RJ. Segregating the functions of human hippocampus. Proc Natl Acad Sci U S A. 1999;96:4034-4039.
FREE FULL TEXT
98. Tulving E, Markowitsch HJ, Craik FE, Habib R, Houle S. Novelty and familiarity activations in PET studies of memory encoding and retrieval. Cereb Cortex. 1996;6:71-79.
FREE FULL TEXT
99. Dolan RJ, Fletcher PC. Dissociating prefrontal and hippocampal function in episodic memory encoding. Nature. 1997;388:582-585.
FULL TEXT
| PUBMED
100. Antrop I, Roeyers H, Van Oost P, Buysse A. Stimulation seeking and hyperactivity in children with ADHD: attention deficit hyperactivity disorder. J Child Psychol Psychiatry. 2000;41:225-231.
FULL TEXT
|
ISI
| PUBMED
101. Kempermann G, Kuhn HG, Gage FH. More hippocampal neurons in adult mice living in an enriched environment. Nature. 1997;386:493-495.
FULL TEXT
| PUBMED
102. Brown J, Cooper-Kuhn CM, Kempermann G, Van Praag H, Winkler J, Gage FH, Kuhn HG. Enriched environment and physical activity stimulate hippocampal but not olfactory bulb neurogenesis. Eur J Neurosci. 2003;17:2042-2046.
FULL TEXT
|
ISI
| PUBMED
103. van Praag H, Shubert T, Zhao C, Gage FH. Exercise enhances learning and hippocampal neurogenesis in aged mice. J Neurosci. 2005;25:8680-8685.
FREE FULL TEXT
104. van Praag H, Kempermann G, Gage FH. Running increases cell proliferation and neurogenesis in the adult mouse dentate gyrus. Nat Neurosci. 1999;2:266-270.
FULL TEXT
|
ISI
| PUBMED
105. Holmes MM, Galea LA, Mistlberger RE, Kempermann G. Adult hippocampal neurogenesis and voluntary running activity: circadian and dose-dependent effects. J Neurosci Res. 2004;76:216-222.
FULL TEXT
|
ISI
| PUBMED
106. Buzsaki G. Theta oscillations in the hippocampus. Neuron. 2002;33:325-340.
FULL TEXT
|
ISI
| PUBMED
107. Willis WG, Weiler MD. Neural substrates of childhood attention-deficit/hyperactivity disorder: electroencephalographic and magnetic resonance imaging evidence. Dev Neuropsychol. 2005;27:135-182.
FULL TEXT
|
ISI
| PUBMED
108. Barry RJ, Clarke AR, Johnstone SJ. A review of electrophysiology in attention-deficit/hyperactivity disorder, I: qualitative and quantitative electroencephalography. Clin Neurophysiol. 2003;114:171-183.
FULL TEXT
|
ISI
| PUBMED
109. Bresnahan SM, Anderson JW, Barry RJ. Age-related changes in quantitative EEG in attention-deficit/hyperactivity disorder. Biol Psychiatry. 1999;46:1690-1697.
FULL TEXT
|
ISI
| PUBMED
110. Bastiaansen M, Hagoort P. Event-induced theta responses as a window on the dynamics of memory. Cortex. 2003;39:967-992.
ISI
| PUBMED
111. Kirk IJ, Mackay JC. The role of theta-range oscillations in synchronising and integrating activity in distributed mnemonic networks. Cortex. 2003;39:993-1008.
ISI
| PUBMED
112. Seidenbecher T, Laxmi TR, Stork O, Pape HC. Amygdalar and hippocampal theta rhythm synchronization during fear memory retrieval. Science. 2003;301:846-850.
FREE FULL TEXT
113. Kahana MJ, Seelig D, Madsen JR. Theta returns. Curr Opin Neurobiol. 2001;11:739-744.
FULL TEXT
|
ISI
| PUBMED
114. Green JD, Arduini AA. Hippocampal electrical activity in arousal. J Neurophysiol. 1954;17:533-557.
FREE FULL TEXT
115. Green EJ, McNaughton BL, Barnes CA. Exploration-dependent modulation of evoked responses in fascia dentata: dissociation of motor, EEG, and sensory factors and evidence for a synaptic efficacy change. J Neurosci. 1990;10:1455-1471.
ABSTRACT
116. Sah P, Faber ES, Lopez De Armentia M, Power J. The amygdaloid complex: anatomy and physiology. Physiol Rev. 2003;83:803-834.
FREE FULL TEXT
117. Vazdarjanova A, McGaugh JL. Basolateral amygdala is involved in modulating consolidation of memory for classical fear conditioning. J Neurosci. 1999;19:6615-6622.
FREE FULL TEXT
118. Setlow B, Gallagher M, Holland PC. The basolateral complex of the amygdala is necessary for acquisition but not expression of CS motivational value in appetitive Pavlovian second-order conditioning. Eur J Neurosci. 2002;15:1841-1853.
FULL TEXT
|
ISI
| PUBMED
119. LeDoux JE. Emotion circuits in the brain. Annu Rev Neurosci. 2000;23:155-184.
FULL TEXT
|
ISI
| PUBMED
120. Holland PC, Gallagher M. Amygdala circuitry in attentional and representational processes. Trends Cogn Sci. 1999;3:65-73.
FULL TEXT
|
ISI
| PUBMED
121. Cardinal RN, Parkinson JA, Hall J, Everitt BJ. Emotion and motivation: the role of the amygdala, ventral striatum, and prefrontal cortex. Neurosci Biobehav Rev. 2002;26:321-352.
FULL TEXT
|
ISI
| PUBMED
122. Maren S, Aharonov G, Fanselow MS. Retrograde abolition of conditional fear after excitotoxic lesions in the basolateral amygdala of rats: absence of a temporal gradient. Behav Neurosci. 1996;110:718-726.
FULL TEXT
|
ISI
| PUBMED
123. LeDoux JE, Cicchetti P, Xagoraris A, Romanski LM. The lateral amygdaloid nucleus: sensory interface of the amygdala in fear conditioning. J Neurosci. 1990;10:1062-1069.
ABSTRACT
124. Campeau S, Davis M. Involvement of subcortical and cortical afferents to the lateral nucleus of the amygdala in fear conditioning measured with fear-potentiated startle in rats trained concurrently with auditory and visual conditioned stimuli. J Neurosci. 1995;15:2312-2327.
ABSTRACT
125. Rosenkranz JA, Grace AA. Dopamine-mediated modulation of odour-evoked amygdala potentials during pavlovian conditioning. Nature. 2002;417:282-287.
FULL TEXT
| PUBMED
126. Ray JP, Price JL. The organization of projections from the mediodorsal nucleus of the thalamus to orbital and medial prefrontal cortex in macaque monkeys. J Comp Neurol. 1993;337:1-31.
FULL TEXT
|
ISI
| PUBMED
127. Krettek JE, Price JL. The cortical projections of the mediodorsal nucleus and adjacent thalamic nuclei in the rat. J Comp Neurol. 1977;171:157-191.
FULL TEXT
|
ISI
| PUBMED
128. Pickens CL, Saddoris MP, Setlow B, Gallagher M, Holland PC, Schoenbaum G. Different roles for orbitofrontal cortex and basolateral amygdala in a reinforcer devaluation task. J Neurosci. 2003;23:11078-11084.
FREE FULL TEXT
129. Elliott R, Dolan RJ, Frith CD. Dissociable functions in the medial and lateral orbitofrontal cortex: evidence from human neuroimaging studies. Cereb Cortex. 2000;10:308-317.
FREE FULL TEXT
130. Davidson RJ. Toward a biology of personality and emotion. Ann N Y Acad Sci. 2001;935:191-207.
ISI
| PUBMED
131. Schoenbaum G, Chiba AA, Gallagher M. Changes in functional connectivity in orbitofrontal cortex and basolateral amygdala during learning and reversal training. J Neurosci. 2000;20:5179-5189.
FREE FULL TEXT
132. Schoenbaum G, Setlow B, Saddoris MP, Gallagher M. Encoding predicted outcome and acquired value in orbitofrontal cortex during cue sampling depends upon input from basolateral amygdala. Neuron. 2003;39:855-867.
FULL TEXT
|
ISI
| PUBMED
133. Oosterlaan J, Sergeant JA. Effects of reward and response cost on response inhibition in AD/HD, disruptive, anxious, and normal children. J Abnorm Child Psychol. 1998;26:161-174.
FULL TEXT
|
ISI
| PUBMED
134. Farmer JE, Peterson L. Injury risk factors in children with attention deficit hyperactivity disorder. Health Psychol. 1995;14:325-332.
FULL TEXT
|
ISI
| PUBMED
135. Ernst M, Grant SJ, London ED, Contoreggi CS, Kimes AS, Spurgeon L. Decision making in adolescents with behavior disorders and adults with substance abuse. Am J Psychiatry. 2003;160:33-40.
FREE FULL TEXT
136. Rowland AS, Lesesne CA, Abramowitz AJ. The epidemiology of attention-deficit/hyperactivity disorder (ADHD): a public health view. Ment Retard Dev Disabil Res Rev. 2002;8:162-170.
FULL TEXT
|
ISI
| PUBMED
137. Lalumiere RT, Nguyen LT, McGaugh JL. Post-training intrabasolateral amygdala infusions of dopamine modulate consolidation of inhibitory avoidance memory: involvement of noradrenergic and cholinergic systems. Eur J Neurosci. 2004;20:2804-2810.
FULL TEXT
|
ISI
| PUBMED
138. McGaugh JL. The amygdala modulates the consolidation of memories of emotionally arousing experiences. Annu Rev Neurosci. 2004;27:1-28.
FULL TEXT
|
ISI
| PUBMED
139. Robbins TW. Chemical neuromodulation of frontal-executive functions in humans and other animals. Exp Brain Res. 2000;133:130-138.
FULL TEXT
|
ISI
| PUBMED
140. Friston KJ. Models of brain function in neuroimaging. Annu Rev Psychol. 2005;56:57-87.
FULL TEXT
|
ISI
| PUBMED
 CiteULike  Connotea  Del.icio.us  Digg  Reddit  Technorati  Twitter
What's this?
THIS ARTICLE HAS BEEN CITED BY OTHER ARTICLES
Emotional deficits in adult ADHD patients: an ERP study
Herrmann et al.
Soc Cogn Affect Neurosci 2009;0:nsp033v1-nsp033.
ABSTRACT
| FULL TEXT
Morphologic Features of the Amygdala and Hippocampus in Children and Adults With Tourette Syndrome
Peterson et al.
Arch Gen Psychiatry 2007;64:1281-1291.
ABSTRACT
| FULL TEXT
Depressed Dopamine Activity in Caudate and Preliminary Evidence of Limbic Involvement in Adults With Attention-Deficit/Hyperactivity Disorder
Volkow et al.
Arch Gen Psychiatry 2007;64:932-940.
ABSTRACT
| FULL TEXT
|
