Discussion post- neuroimaging
European Psychiatry 25 (2010) 345–354
Review
Brain imaging findings in children and adolescents with mental disorders: A cross-sectional review
S. Mana a,*, M.-L. Paillère Martinot b, J.-L. Martinot a
a Service hospitalier central de médecine nucléaire et neurospin, INSERM–CEA, Research Unit 1000 ‘‘Neuroimaging & psychiatry’’, University Paris Sud
and University Paris Descartes, 4, place Gl.-Leclerc, 91401 Orsay, France b Department of Adolescent Psychopathology and Medicine, maison de Solenn, hôpital Cochin, AP–HP, University Paris Descartes, 75014 Paris, France
A R T I C L E I N F O
Article history:
Received 23 February 2010
Received in revised form 8 April 2010
Accepted 22 April 2010
Keywords:
Adolescence
Children
Activation likelihood estimation
Principle component analysis
Neuroimaging
A B S T R A C T
Background: While brain imaging studies of juvenile patients has expanded in recent years to investigate
the cerebral neurophysiologic correlates of psychiatric disorders, this research field remains scarce. The
aim of the present review was to cluster the main mental disorders according to the differential brain
location of the imaging findings recently reported in children and adolescents reports. A second objective
was to describe the worldwide distribution and the main directions of the recent magnetic resonance
imaging (MRI) and positron tomography (PET) studies in these patients.
Methods: A survey of 423 MRI and PET articles published between 2005 and 2008 was performed. A
principal component analysis (PCA), then an activation likelihood estimate (ALE) meta-analysis, were
applied on brain regional information retrieved from articles in order to cluster the various disorders
with respect to the cerebral structures where alterations were reported. Furthermore, descriptive
analysis characterized the literature production.
Results: Two hundred and seventy-four articles involving children and adolescent patients were
analyzed. Both the PCA and ALE methods clustered, three groups of diagnosed psychiatric disorders,
according to the brain structural and functional locations: one group of affective disorders characterized
by abnormalities of the frontal-limbic regions; a group of mental disorders with ‘‘cognition deficits’’
mainly related to cortex abnormalities; and one psychomotor condition associated with abnormalities in
the basal ganglia. The descriptive analysis indicates a focus on attention deficit hyperactivity disorders
and autism spectrum disorders, a general steady rise in the number of annual reports, and lead of US
research.
Conclusion: This cross-sectional review of child and adolescent mental disorders based on neuroimaging
findings suggests overlaps of brain locations that allow to cluster the diagnosed disorders into three sets
with respectively marked affective, cognitive, and psychomotor phenomenology. Furthermore, the brain
imaging research effort was unequally distributed across disorders, and did not reflect their prevalence.
� 2010 Elsevier Masson SAS. All rights reserved.
1. Introduction
While the majority of mental disorders have onset during childhood or adolescence, brain imaging techniques have generally been applied in adult patients. Studies in adults suggest involve- ment of various neuroanatomical and/or functional regional deviations in mental disorders. Brain imaging reports in juvenile
Abbreviations: ALE, activation likelihood estimation; DTI, diffusion tensor
imaging; fMRI, functional magnetic resonance imaging; PCA, principle component
analysis; PET, positron emission tomography scan; sMRI, structural magnetic
resonance imaging; MRS, magnetic resonance spectroscopy; MNI, Montreal
Neurological Institute; NIMH, National Institutes of Mental Health.
* Corresponding author. Tel.: +33 1 69867757; fax: +33 1 16986 7810.
E-mail address: [email protected] (S. Mana).
0924-9338/$ – see front matter � 2010 Elsevier Masson SAS. All rights reserved. doi:10.1016/j.eurpsy.2010.04.010
patients subsequently suggested that some regional brain changes are also present in children or adolescents at onset, or even before, onset of psychiatric conditions [23,24,79,86,95,98]. The observa- tion that brain abnormalities might be present early in life has lead to an increasing development of neuroimaging studies in children and adolescents in recent years.
Consequently, several reviews and meta-analyses have aimed at summarising the main findings related to juvenile subjects [3,41,79,88,94,95,106,117]. However, these reviews have generally focused on only one disorder, thus, there is no outline of recent neuroimaging studies of child and adolescent psychiatric condi- tions, neither as regards the similarities or differences of brain imaging features across disorders, during childhood or adoles- cence, nor as regards the worldwide distribution and representa- tivity of the research effort.
S. Mana et al. / European Psychiatry 25 (2010) 345–354346
The aim of the present review was to determine, which regional brain abnormalities were reported for each mental disorder and to pool them on that basis. To this end, we analysed all neuroimaging reports related to child and adolescent psychiatric disorders published between 2005 and 2008, a period during which a large number of studies were produced (more than 400 articles). Brain imaging includes several techniques, which provide anatomical (with sMRI and DTI), functional (with fMRI and PET) and metabolic (with MRS) data. Two other techniques, magnetoencephalography and electroen- cephalography, which measure the electric variations of the brain were not included in the present analysis because their spatial resolution is lower than MRI. As the present review was focussed on describing the locations of findings, but not their pathophysiology, we pooled the observations from all studies whatever the technique used by cerebral region and by disorder. Then, by means of PCA, and meta-analysis using the ALE method, we plotted the reported location of cerebral alterations and mental disorders, in search of overlaps.
A second objective was to provide the worldwide distribution and the main directions of these recent neuroimaging reports. Indeed, among the previous reviews, none has reported the geographical distribution of investigations, so the respective contribution of European, American, Asian, or other teams is unknown. Because brain imaging mental disorders in juveniles is a relatively new field, it is worthwhile assessing the contribution of each country in order to determine if the neuroimaging findings arise from homogeneously distributed regions. A further lack of information concerns the identification of the DSM IV mental disorders investigated – are neuroimaging studies focused on a subset of disorders or are all disorders investigated equally? – and the respective contribution of the various imaging techniques. Therefore, we carried out a descriptive analysis to characterise these reports by extracting the following information from articles published between 2005 and 2008: geographical distribution, annual progression, nature of the mental disorders and imaging techniques.
2. Methods
Neuroimaging literature reports of mental disorders in children and adolescents were included if they met the following criteria:
(i) the patient sample met international criteria for a psychiatric disorder;
(ii) neuroimaging techniques were used; (iii) children or adolescents were the focus of the report.
Four hundred and twenty-three articles were identified in Medline databases between January, 2005 and December 31, 2008, using combinations of three keywords from the summary belonging to each of the following fields:
(i) neuropsychiatric disorders or psychotropic drugs (addiction, affective disorder, anorexia nervosa, anxiety, attention deficit hyperactivity disorder, autism, bipolar disorder, craving, dependence, depression, hyperkinetic disorder, mental retar- dation, mood disorder, obsessive compulsive disorder, phobia, posttraumatic stress disorder, schizophrenia, Tourette’s syn- drome/ alcohol, antidepressant, antipsychotic, anxiolytic, cannabis, cocaine, ecstasy, fluoxetine, inhalant induced disor- der, marijuana, nicotine, smoking, and tobacco);
(ii) age (adolescents, child, and children); (iii) neuroimaging techniques (MRI, magnetic resonance spectros-
copy, neuroimaging, and PET).
In addition, we reviewed the references of selected articles to identify other possible articles that might have escaped PubMed1.
Of the total 423 articles, 149 were excluded for any of the following reasons. The article was a review or a meta-analysis, the study included subjects older of 21 years (however we did not reject studies with a longitudinal design including subjects from children to young adults), or was conducted on high risk subjects affiliated with patient probands. We also excluded all case or pilot studies. After exclusion, a total of 274 articles remained (References are on the supplementary material in the online version of this article).
The following information was retained for analysis from each article: geographic location of the research team, year of publication, neuroimaging technique used, nature of the psychi- atric disorder, and regions where significant structural and/or functional and/or metabolic abnormalities were observed in patients compared to healthy subjects of same age. As we could not include all the variously reported brain regions for analysis, we considered a common terminology for cerebral areas, in order to lower the number of variables. The inspection of abstracts allowed to select a number of regions with a terminology appearing recurrently. Thus, the authors’ observations were gathered into the following brain regions: frontal lobe, cingulum, amygdalae, hippocampus (hippocampus and parahippocampus), temporal lobe, parietal lobe, occipital lobe, thalamus, striatum and cerebellum. Imaging techniques were not individualised because our aim was not to elucidate if the disorder was structural or functional but only to summarise the regional distribution of the reported abnormalities whatever their nature. Thus, we compiled, by cerebral region and by disorder, the observations from all studies when they revealed that brain images were significantly different in between-group (patients versus con- trols) comparisons. This was performed for addiction, anorexia nervosa, anxiety disorders (generalised anxiety, obsessive-com- pulsive disorder and post-traumatic stress), attention deficit hyperactivity disorders, autism spectrum disorders, mood dis- orders (major depressive and bipolar disorders), schizophrenia and Tourette’s syndrome. Only the mental retardation was excluded from this analysis, first because of its heterogeneity and second because no detail was provided on the location of alteration associated with this condition.
In order to cluster the disorders according to the locations of brain regional alteration, we performed a PCA using the R software package [78]. This statistical clustering method allows plotting of the association between items (i.e. mental disorders) and variables (i.e. cerebral regions), with the aid of a procedure extracting the main factors (graph axes F1 and F2) that reduce the dimensionality of multivariate data while preserving most of the variance therein. The output from PCA analysis consisted of score plots, which provided an indication of the differentiation of the mental disorders in terms of similarities in abnormalities locations, and a correlation plot giving an indication of correlations between variables with respect to their proximity. The PCA was performed on normalised data. Indeed, as the number of data was not the same for all disorders, to avoid a bias related to this difference, all data were reduced to unit one before PCA analysis. Thus, for each disorder, all numbers were divided by the maximum value, ranging data from 0 to 1.
Hence, in order to assess the consistency of the ACP results, we used the ALE meta-analytic technique developed by Turkeltaub et al. [103] in the subset of articles expressing the brain findings in Talairach’s or MNI stereotaxic coordinates. This method combines the coordinates of foci maxima from multiple studies into an ALE map for the brain, revealing between-study consistencies that may not be immediately evident by simple visual comparison of individual reports. We individually screened all the articles for the
S. Mana et al. / European Psychiatry 25 (2010) 345–354 347
presence of Talairach or MNI coordinates. Only foci in the source articles reported as significant at p < 0.05 corrected or p < 0.01 uncorrected were included. The studies were tabulated into the groups corresponding to the ACP results. Specifically, we analyzed the coordinates of 966 foci elicited by 82 different studies.
Detailed description of the ALE method and the statistical approach employed can be found in Turkeltaub et al. [103] and in Laird et al. [56]. Before analysis, the coordinates from studies that used the MNI templates were transformed into Talairach coordi- nate space [99]. Afterwards, all the coordinates were imported into a Java-based version of ALE software (http://www.brainmap.org/ ale) and analyzed with a fully automated procedure. A statistical threshold of p < 0.05 was used for the generated activation likelihood estimation map, with a minimum cluster size of 100 mm3.
The Talairach space was divided into 2 � 2 � 2 mm3 voxels and a whole-brain ALE map created by modeling the foci as localization probability distributions centered at the given coordinates; the probability each voxel was located within a particular focus was calculated using a 3D Gaussian function of 12 mm full-width half- maximum, computing the ALE value as the union of these probabilities, and then assessing statistical significance using the threshold determined by a permutation test of randomly generated sets of foci.
3. Results
3.1. Distribution of cerebral structures according to mental disorders
in children and adolescents
The percentages of observations reported in each cerebral region with respect to each disorder are reported in Table 1. Results of the PCA and ALE analyses performed to cluster the mental disorders in terms of similarities in locations are reported respectively in Fig. 1, and in Table 2 and Fig. 2.
3.1.1. Location of brain abnormalities according to mental disorders.
Major depressive disorder was mainly associated with reports of structural and functional abnormalities in the frontal lobe (26.7% of observations), the anterior cingulate cortex (26.7%) and in the striatum (20%). Similar locations were reported in the bipolar disorder investigations. Studies on bipolar disorder also revealed abnormalities in the ventral prefrontal cortex, cingulate cortex, amygdalae and hippocampus, respectively in 23.8%, 19% and 14.3% of observations.
In children and adolescents with anxiety disorders, the regions with the greatest numbers of observations were the frontal cortex (29.7%) and the amygdalae (18.9%). The separate analysis of obsessive-compulsive disorder (six articles) revealed abnormali- ties involving the frontal (27.7%) and parietal (22.2%) cortices, the striatum (16.6%) and the thalamus (16.6%).
Data from studies on Tourette’s syndrome revealed that alterations mainly involved the striatum (33.3%), thalamus (27.8%), and frontal cortex (22.2%). These regions were also suggested by neuroimaging studies on attention deficit hyperac- tivity disorders, with observations localised in the frontal cortex (26.3%), striatum (20.4%), parietal (16.8%), and cingulate cortices (12.4%).
Finally, autism spectrum disorders, schizophrenia, anorexia nervosa, and addiction mainly involved cortex regions. Indeed, most reports pointed out that autism was associated with abnormalities in the frontal (22.8%), temporal (18.8%), and parietal (14.9%) cortices, as schizophrenia with in addition abnormalities in hippocampus.
3.1.2. Principal component analysis.
Fig. 1A illustrates the dispersion of variables (i.e., regions) on the two first factorial axes (F1 and F2). As the data are mean- centred and scaled to unit variance, the angles between variables and factors (axes) represent correlation coefficients.
3.1.2.1. Factors. The first two principal factors (F1 and F2) jointly accounted for 68.31% of the total variance. The F1 axis (or x-axis) strongly correlated with posterior cortex variables (occipital, parietal, and temporal regions) on one side and lesser with amygdalae and striatum on the other side. Thus, the F1 axis might separate cortical and subcortical regions. The F2 axis (or y-axis) correlated, on the one hand, with fronto-limbic structures (ventral frontal and cingulate cortex, amygdala and hippocampus) and, on the other hand, with the thalamus and striatum and might separate the fronto-limbic regions and the thalamo-striatal structures. The dispersion of variables (i.e., regions) and items (i.e., mental disorders) according to these two factors allowed plotting three groups of cerebral regions and three groups of disorders.
3.1.2.2. Variables. The following regions were included as vari- ables: amygdalae, cerebellum, cingulate areas, frontal lobe, hippocampus/parahippocampus, occipital lobe, parietal lobe, stria- tum, temporal lobe, and thalamus. The correlation plots indicated that the two axes (F1 and F2) allowed for the identification of three clusters of regions positively correlated within a group. This means that in most cases, if the number of observations in one region for a disorder was important, the number of observations in other correlated regions was also important for this disorder. A first group composed of neocortical regions was localised to the left of the x-axis (occipital, parietal, and temporal cortices), a second group composed mainly of limbic regions (amygdalae, cingulate cortex, and hippocampus) was localised at top right of the y-axis, and a third group, including the striatum and the thalamus, was localised at the bottom right of y-axis.
3.1.2.3. Items. The following disorders were included as items: addiction, anorexia nervosa, anxiety disorders, attention deficit hyperactivity disorders, autism spectrum disorders, bipolar disor- der, major depressive disorder, schizophrenia, and Tourette’s syndrome. The two main factors or axes allowed pooling the disorders into three groups as shown on the score plots (Fig. 1B). The position of a disorder in a given direction within the score plots can be related to the variables regions that lie in the same direction on the correlation plots. The top right of the plot gathered affective disorders (anxiety, major depressive, and bipolar disorders) that all include emotional dysregulation. This first group of disorders appeared associated with the subcortical and fronto-limbic regions. The second group of disorders gathered addiction, anorexia nervosa, attention deficit hyperactivity disorders, autism spectrum disorders, and schizophrenia. All these mental disorders involve some degree of deviation in cognitive functions. This group clustered on the x-axis, thus appearing related to (posterior) cortical regions. The third group included only Tourette’s syndrome, isolated on the y-axis, which appeared to be related to the striatum and thalamus regions. Its distance from the y- intercept denoted its singularity.
3.1.3. ALE analysis.
Three analyses were performed according to the groups of disorders revealed by the ACP. The coordinates and ALE value for each of the local maxima, as well as the volume of the corresponding cluster, are reported in Table 2.
Table 1 Number of (2005–2008) articles according to each disorder, and location of research teams, brain imaging techniques used, number of abnormalities reported in each brain
region.
Geographic distribution Anxiety Depression Bipolar Tourette ADHD Schizophrenia Autism Addiction Anorexia MR Total
Europe 3 0 0 2 21 12 13 0 5 8 64
United States 16 15 28 5 29 15 36 18 0 6 168
Others 1 0 0 3 17 0 12 2 0 7 42
Total 20 15 28 10 67 27 61 20 5 21 274
Annual distribution Anxiety Depression Bipolar Tourette ADHD Schizophrenia Autism Addiction Anorexia MR Totala
2005 4 2 8 1 13 4 8 3 2 1 46
2006 5 4 3 4 17 5 20 2 1 7 68
2007 3 4 7 2 20 9 17 6 1 6 75
2008 8 5 10 3 17 9 16 8 1 7 84
Neuroimaging techniques Anxiety Depression Bipolar Tourette ADHD Schizophrenia Autism Addiction Anorexia MR Totalb
DTI 0 0 3 1 2 4 2 2 0 1 15
fMRI 12 5 6 2 40 3 15 12 2 2 99
sMRI 6 8 16 5 18 20 38 6 2 15 134
TEP 0 0 0 1 3 0 2 0 0 1 7
MRS 1 2 3 1 3 0 4 0 1 0 15
Brain alteration locations Anxiety Depression Bipolar Tourette ADHD Schizophrenia Autism Addiction Anorexia Total
Occipital 0 0 1 0 5 2 8 4 1 21
Temporal 1 0 3 0 9 4 19 5 3 44
Parietal 4 0 3 0 23 5 15 5 2 57
Frontal 11 4 10 4 36 11 23 12 2 113
Cingular 4 4 8 1 17 4 4 4 1 47
Amygdalae 7 3 6 1 1 1 8 2 0 29
Hippocampus 2 1 6 1 2 5 7 4 0 28
Striatum 3 3 4 6 28 0 8 2 0 54
Thalamus 3 0 5 3 2 3 2 0 18
Cerebellum 2 0 1 0 13 1 6 4 0 27
a One article is missing in the ‘‘annual distribution’’ because referenced in PubMed1 on December 2008 but published on 2009. b Among the 274 studies, four were single photon emission computed tomography (SPECT) studies (two on mental retardation, one on anxiety disorders and one on
attention deficit hyperactivity disorders).
Fig. 1. A. Correlation plots. The graph represents the correlations between the cerebral regions and the two principal factors (F1 and F2). B. Score plots. The graph represents the dispersion of mental disorders, according to the two principal factors (F1 and F2). The proximity between disorders represents their similarities, according to the location
of cerebral alterations reported in articles. ADHD: attention deficit hyperactivity disorders; Amy: amygdalae; Cing: cingular cortex; Fron: frontal cortex; Hip: hippocampus;
Occ: occipital cortex; Par: parietal cortex; Stria: stiatum; Temp: temporal cortex; Thal: thalamus.
S. Mana et al. / European Psychiatry 25 (2010) 345–354348
3.1.3.1. Affective disorders. The ALE meta-analysis was carried out on 16 articles (10 on anxiety disorders, four on bipolar disorder and two on major depressive disorder) (Fig. 2A). It revealed 12 clusters of significant likelihood for activation (p < 0.05) found in right amygdala, bilateral cingulate cortex (Brodmann areas 24 and 32), bilateral prefrontal association cortex (Brodmann areas 47, 10, and 11), left hypothalamus and left caudate body.
3.1.3.2. Mental disorders with cognition deviations. Sixty-four stud- ies were included in the meta-analysis (attention deficit hyperac- tivity disorders, 25; autism spectrum disorders, 17; addiction, 12; schizophrenia, eight; and anorexia nervosa, two) (Fig. 2B). ALE results based on cognition disorders revealed 23 clusters of significant likelihood for foci. We found significant likelihoods of foci notably in cortical regions, including areas in bilateral frontal (Brodmann areas 47, 10, 9, 8, 6, and 4), parietal (Brodmann areas 40
Table 2 Results of ALE analysis.
Brain region Brodmann area Talairach coordinatea Volume (mm3) Maximum ALE value
x y z
Cognitive disorders
Frontal lobe
Left frontal precentral gyrus 4 �24 �26 64 760 0.003897039 Left middle frontal gyrus 10 �32 56 6 520 0.0041358084 Left inferior frontal gyrus 47 �34 22 �4 208 0.003096192 Right medial frontal gyrus 8 2 26 38 584 0.0034987493
Right superior frontal gyrus 10 36 52 14 504 0.0033852656
Right medial frontal gyrus 9 2 46 20 248 0.0033964433
Right frontal sub-gyral 6 18 �2 56 232 0.0032061862 Right inferior frontal gyrus 47 40 20 �6 160 0.0027463438
Parietal lobe
Left inferior parietal 40 �38 �46 50 632 0.0035875316 Left parietal lobe precuneus 7 �16 �78 36 224 0.003106816 Left inferior parietal lobule 40 �50 �28 24 200 0.0027753697 Left parietal lobe precuneus 7 �4 �64 34 136 0.002805182 Right parietal lobe precuneus 7 26 �68 34 192 0.0028971154 Right parietal postcentral gyrus 7 6 �52 68 104 0.0029332046
Temporal lobe
Left superior temporal gyrus 41 �38 �40 0 872 0.0035883 Left superior temporal gyrus 22 �62 �26 2 144 0.0027808354 Right superior temporal gyrus 13 56 �44 16 104 0.002798074
Occipital lobe
Left occipital lingual gyrus 18 �14 �74 �4 424 0.0037331947
Other regions
Left caudate body �10 14 14 472 0.0038857735 Left putamen �24 4 4 272 0.003502091 Left substania nigra �14 �18 �4 168 0.0030601877 Right sub-lobar insula 13 36 �14 14 184 0.0029053392 Right sub-lobar insula 13 30 26 16 160 0.0028951943
Affective disorders
Right amygdala 28 �2 �18 1728 0.002551533 Right amygdala 18 �4 �10 0.0020100167 Right cingulate gyrus 24 2 0 42 384 0.0017522577
Right inferior frontal gyrus 47 34 30 0 1648 0.0025296172
Right inferior frontal gyrus 10 38 46 0 528 0.0018875101
Right medial frontal gyrus 10 12 40 10 424 0.0022108718
Right anterior lobe culmen 14 �28 �12 144 0.0015658871 Left anterior cingulate 32 �8 40 10 216 0.0017129772 Left middle frontal gyrus 11 �30 40 �4 376 0.0015381095 Left hypothalamus �10 �6 �6 664 0.0019934988 Left caudate body �18 �14 28 616 0.0025947029 Left middle occipital gyrus 18 �22 �96 2 160 0.0015739395
Tourette disorder
Left putamen �22 6 0 216 0.0013042025
ALE: activation likelihood estimation. a Talairach coordinate for the maximum ALE value.
S. Mana et al. / European Psychiatry 25 (2010) 345–354 349
and 7) and temporal lobes (Brodmann areas 41, 22 and 13) and left occipital lingual gyrus (Brodmann area 18). Significant clusters were also found in regions of basal ganglia.
3.1.3.3. Tourette’s syndrome. We found only two articles with Talairach coordinates usable for the ALE analysis. They draw one cluster of significant likelihood for focus (p < 0.05) localized in left putamen.
3.2. Characteristics of neuroimaging studies of psychiatric disorders in
children and adolescents
Information was gathered on the worldwide distribution and the main directions of these recent neuroimaging studies (Table 1).
3.2.1. Geographic distribution
Most studies published between 2005 and 2008 were conducted in the United States laboratories (61.3% of the
274 reported articles). US research teams were mainly located in the states of California, Maryland, Michigan, and New-York. Around 23.4% of the studies were from Europe, with United Kingdom in first position, Germany in second, and Spain in third position. The remaining 15.3% involved research primarily from China and Canada (for details, see Fig. 3 and the additional table found in the online version of this article).
3.2.2. Neuroimaging techniques
Among the various MR imaging approaches, sMRI was in first and represented 49.6% of studies. This method was mostly used to investigate mental retardation (78.9% of studies), schizophrenia (74.1%), autism spectrum disorders (62.3%), and bipolar disorder (57.1%). fMRI was the second most common method, with 36.6% of the studies. It was mainly used to investigate anxiety disorders (63.2% of studies), attention deficit hyperactivity disorders (60.6%), and addiction (60%).
Fig. 2. A. Location of cerebral alterations in the ‘‘affective disorders’’ group. Brain regions showing a significant activation for meta-analysis are highlighted in red on
coronal (a and b) and axial (c and d) slices of individual brain in Talairach space.
They include the left globus pallidus (a), the right amygdalae (a and b), the left
cingulate (b and c), the left hypothalamus (d), the bilateral middle and right inferior
frontal gyri (c and d). B. Location of cerebral alterations in the group of disorders
with cognition deviations. Sagittal section (a) showing loci in the left inferior
parietal lobe (BA 40) and in the left superior temporal gyrus (BA 41). The axial slices
(b, c, and d) show loci in the inferior frontal gyri bilaterally (BA 47), the substantia
nigra, and the lingual gyrus (BA 18) for Z = �5 (b), in the bilateral middle frontal gyri (BA 10), the left caudate nucleus, the right insula and the right superior temporal
gyrus (BA 13) for Z = 13 (c), in the right medial frontal gyrus (BA 6) and the
precuneus bilaterally (BA 7) for Z = 37 (d).
S. Mana et al. / European Psychiatry 25 (2010) 345–354350
DTI and SRM were less frequently used. DTI was used primarily in the US (13/15 studies) as an investigative tool for mental disorders in children and adolescents, with the aim to study schizophrenia, bipolar disorder, attention deficit hyperactivity disorders, autism spectrum disorders, addiction, and Tourette’s syndrome.
Only seven studies in children and adolescents were performed with PET, mainly in Europe, to investigate attention deficit hyperactivity disorders and autism spectrum disorders.
3.2.3. 2005 to 2008 annual progression and distribution of studies
with respect to the psychiatric disorders
The number of neuroimaging studies in children and adolescent psychiatry rose from 46 in 2005 to 84 in 2008. This two-fold
progression involved all mental disorders, with exception of bipolar disorder, for which the number of articles was constant throughout this 4 years period.
There were two predominant research fields: 46.8% of neuroimaging studies focused on attention deficit hyperactivity disorders and on autism spectrum disorders. Attention deficit hyperactivity disorders research was shared between the US (43.3%), Europe (31.3%), and other countries (25.4%), notably China. The main imaging techniques were fMRI (60.6% of studies) and sMRI (27.3% of studies). Autism spectrum disorders was primarily investigated with sMRI (62.3% of studies) in US laboratories (59% of studies).
Bipolar disorder represented 10.2% of neuroimaging studies (all reports came from the US), with 57.7% for sMRI and 21.4% for fMRI. Schizophrenia represented 9.9% of studies, with 55.6% from the US and the remainder from Europe. Almost all reports were sMRI (74.1%). The DTI technique was also frequently used to investigate the structural abnormalities related to these two disorders (14.8% of schizophrenia studies and 10.7% of bipolar disorder studies).
Mental retardation was a condition less-frequently investigated (7.7% of studies), more studied in Europe (38.1%) than in other parts of the world, and mainly with sMRI (78.9%). In contrast, addiction (7.3% of studies) and anxiety disorders (7.3% of studies) disorders were primarily investigated in the US (90% and 80% of studies, respectively). Anxiety disorders reports included subjects with generalized anxiety, obsessive-compulsive disorder and post- traumatic stress disorder. Regarding addiction, despite inclusion of all drugs of abuse in our keyword Medline search, the few brain imaging studies we found in adolescents concerned alcohol (12 articles), cannabis (eight articles) and tobacco (four articles) dependences. Major depressive disorder was a less investigated condition (5.5% of studies) and all reports were from the USA.
Finally, scarce studies focused on the Tourette’s syndrome (3.6% of studies) and anorexia nervosa (1.8% of studies); for this last disorder, only five articles were found from European research teams.
4. Discussion
This review examined brain abnormalities reported in neuro- imaging studies of children and adolescents. The main finding was that brain abnormalities locations distinguished three groups of psychiatric diagnoses, including affective disorders associated with frontal – limbic changes, mental disorders involving cognition deviations associated with cortex changes, and a psychomotor disorder associated with basal ganglia abnormalities.
4.1. Mental disorders and location of brain abnormalities
Three groups of disorders were clustered using the PCA method according to location of the observed regional alterations. A group of emotion disorders, including anxiety and mood disorders, involved structural and functional abnormalities in the frontal cortex and the limbic system (amygdalae, cingulate cortex, and hippocampus). A group of ‘‘cognitive’’ deficits (autism spectrum disorders, schizophrenia, attention deficit hyperactivity disorders, and addiction) mainly involved cortex abnormalities. A motor disorder, Tourette’s syndrome, was associated with alterations in the basal ganglia (the thalamus and striatum).
These results were supported by the ALE meta-analysis since this method, combining the stereotactic coordinates of the reported maxima from multiple studies into an ALE map, revealed that consistencies in cerebral regions between-studies were the same than those of the PCA procedure for the three groups of mental disorders. Thus, the ALE analysis carried out on articles
Fig. 3. Geographic distribution of research teams using neuroimaging techniques to investigate mental disorders in children and adolescents. Dots show states or countries that have published one or more neuroimaging articles on mental disorders in children or adolescent during the 2005–2008 period. The diameters of the dots on the maps are
proportional of the number of articles. For further details on the origin and production of articles, see the additional table on the online version of this article.
S. Mana et al. / European Psychiatry 25 (2010) 345–354 351
related to emotion disorders revealed clusters of significant likelihood for activation in amygdala, cingulate cortex (Brodmann areas 24 and 32), prefrontal cortex (Brodmann areas 47, 10 and 11), hypothalamus, and caudate; those on Tourette’s syndrome isolated one cluster in striatum; and those involving psychiatric disorders with ‘‘cognitive deficits’’, clusters mainly located in frontal, parietal, temporal, and occipital cortices.
Present results support the models of early distinct brain regional systems in mental disorders. Thus, anxiety disorders and major depressive disorder characterised by emotional dysregula- tion are considered to be associated with dysfunction of the frontal-limbic system, and comorbidity between both disorders is often observed [45,96]. Consistently, the results presented herein compile all reports over 4 years and cluster both disorders with abnormalities of the frontal cortex and limbic regions. Moreover, the present report supports the idea that most abnormalities found in adults are already present in juvenile subjects with anxiety disorders [39,49,54,67–70,114], major depressive disorder [9,14, 15,32,50,51,59,64,83,87], and bipolar disorder [2,8,18,19,28,29, 46,72,75,82]. In spite of the implied similarities, compilation of these results suggests that each of these disorders had its own pattern of cerebral implications. Thus, at variance with anxiety disorders, major depressive disorder also involved cingulum and striatum abnormalities as revealed, for instance, by Forbes et al. [27] in an fMRI study. Also, within anxiety disorders, the separate estimation of structures involved in obsessive compulsive disorder revealed an implication of the fronto-striatal network (for instance, [114]), supporting this disorder would be distinct from others anxiety disorders with an involvement of cerebral dysfunctions more similar to those in Tourette’s syndrome (see below).
The present results are also in agreement with the involvement of the frontal-striatal-thalamo-cortical loop in Tourette’s syn- drome because this disorder is linked on the score plots to striatum and thalamus abnormalities. Indeed, there is mounting evidence that Tourette’s syndrome is an inherited developmental alteration of synaptic neurotransmission within the cortical-striatal-tha- lamo-cortical loops that would contribute to the relative inability to control tic behaviours by releasing motor production from regulatory control of the basal ganglia [4,6,31,57,58,60,63].
Attention deficit hyperactivity disorders is also a disorder with a motor component, and evidence suggests that it is associated with a disruption of the frontal-striatal-thalamo-cortical loop [13,90], as recent structural analyses revealed abnormalities in basal ganglia nuclei [91,102,104,109,111,115]. However, on the score plots of the PCA graph, attention deficit hyperactivity disorders appears to be linked with cortical abnormalities. This finding might be explained by the fact that attention deficit
hyperactivity disorders is also related to parietal cortex abnormal- ities, as pointed out in recent literature [17,66,92,109]. Such cortical dysfunction might be related to the attention deficits that characterise this disorder and might also contribute in motor aspects of attention deficit hyperactivity disorders impulsivity. Consistently, functional neuroimaging studies during various attention tasks have revealed a dysfunction of the fronto-parietal system [10,16,52,53,93,97,100,105,107].
Finally, the present review supports autism spectrum disorders and schizophrenia in juvenile subjects are associated with marked cortical dysfunctions in parietal and temporal cortices, consistently with recent studies in autism spectrum disorders [7,11,22,30,33,36,37,48,61,65,76,80,85,89,110,113] and in schizo- phrenia [5,12,25,38,40,44,55,62,71,74,81,108,112]. It might be objected that some authors did report temporal abnormalities [5,38,40,112], a less consistently finding in the early-onset schizophrenia literature, and the present results might be influenced by the integration of young adults in two of the studies [38,112]. However, temporal abnormalities were also found in children [5,40], and even in the longitudinal study of Greenstein et al. [38], they were reported during early adolescence, before adulthood. The preeminence of posterior cortex abnormalities in adolescent schizophrenia patients is compatible with the model by Gogtay et al. [34,35] and Thompson et al. [101] findings of impaired cortex maturation processes in adolescents with schizophrenia involving a dynamic wave of white matter growth and a back-to- front wave of cortex gray matter loss, both maturation processes that have been revealed to be altered in adolescents with schizophrenia, by these authors.
4.2. Characteristics of neuroimaging studies
The present review also pointed out that structural and functional MRI studies in child and adolescent psychiatry in the US outnumber their European counterparts. This may be linked to the recommendations of the NIMH to prevent mental disorders. NIMH has elaborated a strategic plan that notably supports research that improves the understanding of the development, structure, and function of neural circuits, with a focus on those most relevant to mental disorders. This plan emphasizes the detection of biomarkers, biological indicators of disease processes, including brain abnormalities allowing early detection of disorders and, in turn, early intervention ([43], http://www.nimh.nih.gov/ about/strategic-planning-reports/index.shtml). It includes a fo- cused research effort to define the developmental trajectories of mental disorders through the investigation of links between brain development and behavioural development, to understand how
S. Mana et al. / European Psychiatry 25 (2010) 345–354352
brain regions critical for mental disorders are associated with typical and atypical behavioural functioning.
Aside from the lead of US research, the results reveal a steady rise in the number of annual studies showing interest in brain imaging psychiatric disorders in children and adolescents. Such an increase might be explained one hand by the need to reduce biases related to disease chronicity, long term treatments, and addictions, that are often inherent to studies conducted in adults, and on the other hand by the need to identify early brain abnormalities that might predate the onset of disorders. Thus, the identification of these abnormalities might be an important element of the quest for vulnerability factors and of biomarkers.
Regarding diagnoses, autism spectrum disorders, and attention deficit hyperactivity disorders were the most investigated. It is likely that interest has largely focused on these mental disorders in recent years because of their severity and frequency [26,116]. Still, brain imaging research in children and adolescents does not reflects the prevalence of psychiatric conditions at that age, since anxiety disorders and affective disorders, addictions, and eating disorders, which are more frequent, are notably under-investigat- ed, based on this criteria [20,116].
5. Limitations
The present review has some limitations. First, the small number of studies in disorders such as anorexia nervosa, major depressive disorder, and Tourette’s syndrome. Second, some studies in the present analysis included young adults within their participants, leading to the possible inclusion of cerebral structures where abnormalities are not related to childhood. However, there are less than 10 such studies, so that most results correspond to structures identified in children or adolescents. Third, the analysis was conducted through 2005 to 2008. Thus, previous studies were not included and consequently, cerebral regions strongly investigated before 2005 have escaped the present report. For instance, the first imaging studies on autism spectrum disorders focused on the cerebellum [21,42,47,73,77]. Fourth, the literature review was performed in the Medline database; despite it includes most scientific journals, it is not fully exhaustive. We searched for extra references however, when inspecting the bibliography of the articles. Fifthly, the present review plotted fMRI, sMRI, PET and SRM studies, thus no conclusion can be drawn as regards pathophysiology mechan- isms. Sixthly, the ALE analysis was carried out only on the reports (82 over 274) expressing results normalized as 3D-stereotactic coordinates. Indeed, multiple studies in the literature did report their results only in anatomically determined regions-of-interest. However, despite exclusion of a large part of studies, our ALE results are broadly consistent with those of our qualitative ACP that included both studies with and without stereotactic coordinates. A final limit regards the difficulty to name the second group of disorders identified in the present study. Indeed, the term ‘‘cognition deviations’’ is ambiguous because cognitive impairments are found also in Tourette’s syndrome and in affective disorders. However, we found no designation that might fit all the four disorders (schizophrenia, autism spectrum disorders, attention deficit hyperactivity disorders and addiction) without referring to features pertaining to the other groups. Although debatable, our point was based on the theoretical distinction linking cognitive biases to ‘‘hot’’, or ‘‘cold’’ cognition. In the group 2, the cognitive deviations involve disorders with marked alteration on emotion-independent, or ‘‘cold’, cognitive tasks requiring non-emotional information processing, while in group 1, emotion-dependent, or ‘hot’ cognition alteration is more readily related or secondary to changes in emotional information processing [1,84].
6. Conclusion
This review of the 2005–2008 literature on brain imaging in children and adolescents with mental disorders clustered dis- orders according to the location of cerebral abnormalities. It supports the hypothesis that the clustered diagnoses include behaviour deviations that might share dimensions linked with common regional brain systems. The findings also point out the scarcity of studies on major depressive disorder, a common group of childhood disorders that tends to worsen if not treated early, and on anorexia nervosa.
Acknowledgements
The authors thank Pr. Tomas Paus for thoughtful comments.
Appendix A. Supplementary data
Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.eurpsy.2010.04.010.
References
[1] Abelson RP. Computer simulation of ‘‘hot cognitions’’. In: Tomkins S, Mesick S, editors. Computer simulation of personality. New York: Wiley; 1963.
[2] Adler CM, Adams J, DelBello MP, Holland SK, Schmithorst V, Levine A, et al. Evidence of white matter pathology in bipolar disorder adolescents experiencing their first episode of mania: A diffusion tensor imaging study. Am J Psychiatry 2006;163(2):322–4.
[3] Amaral DG, Schumann CM, Nordahl CW. Neuroanatomy of autism. Trends Neurosci 2008;31:137–45.
[4] Amat JA, Bronen RA, Saluja S, Sato N, Zhu H, Gorman DA, et al. Increased number of subcortical hyperintensities on MRI in children and adolescents with tourette’s syndrome, obsessive-compulsive disorder, and attention deficit hyperactivity disorder. Am J Psychiatry 2006;163(6):1106–8.
[5] Ashtari M, Cottone J, Ardekani BA, Cervellione K, Szeszko PR, Wu J, et al. Disruption of white matter integrity in the inferior longitudinal fasciculus in adolescents with schizophrenia as revealed by fiber tractography. Arch Gen Psychiatry 2007;64(11):1270–80.
[6] Baym CL, Corbett BA, Wright SB, Bunge SA. Neural correlates of tic severity and cognitive control in children with tourette syndrome. Brain 2008;131: 165–79.
[7] Bigler ED, Mortensen S, Neeley ES, Ozonoff S, Krasny L, Johnson M, et al. Superior temporal gyrus, language function, and autism. Dev Neuropsychol 2007;31(2):217–38.
[8] Blumberg HP, Fredericks C, Wang F, Kalmar JH, Spencer L, Papademetris X, et al. Preliminary evidence for persistent abnormalities in amygdala volumes in adolescents and young adults with bipolar disorder. Bipolar Disord 2005;7(6):570–6.
[9] Boes AD, McCormick LM, Coryell WH, Nopoulos P. Rostral anterior cingulate cortex volume correlates with depressed mood in normal healthy children. Biol Psychiatry 2008;63(4):391–7.
[10] Booth JR, Burman DD, Meyer JR, Lei Z, Trommer BL, Davenport ND, et al. Larger deficits in brain networks for response inhibition than for visual selective attention in attention deficit hyperactivity disorder (ADHD). J Child Psychol Psychiatry 2005;46(1):94–111.
[11] Brieber S, Neufang S, Bruning N, Kamp-Becker I, Remschmidt H, Herpertz- Dahlmann B, et al. Structural brain abnormalities in adolescents with autism spectrum disorder and patients with attention deficit/hyperactivity disorder. J Child Psychol Psychiatry 2007;48(12):1251–8.
[12] Burke L, Androutsos C, Jogia J, Byrne P, Frangou S. The maudsley early onset schizophrenia study: The effect of age of onset and illness duration on fronto- parietal gray matter. Eur Psychiatry 2008;23(4):233–6.
[13] Bush G, Valera EM, Seidman LJ. Functional neuroimaging of attention-deficit/ hyperactivity disorder: a review and suggested future directions. Biol Psy- chiatry 2005;57:1273–84.
[14] Caetano SC, Fonseca M, Olvera RL, Nicoletti M, Hatch JP, Stanley JA, et al. Proton spectroscopy study of the left dorsolateral prefrontal cortex in pedi- atric depressed patients. Neurosci Lett 2005;384(3):321–6.
[15] Caetano SC, Fonseca M, Hatch JP, Olvera RL, Nicoletti M, Hunter K, et al. Medial temporal lobe abnormalities in pediatric unipolar depression. Neu- rosci Lett 2007;427(3):142–7.
[16] Cao Q, Zang Y, Zhu C, Cao X, Sun L, Zhou X, et al. Alerting deficits in children with attention deficit/hyperactivity disorder: Event-related fMRI evidence. Brain Res 2008;1219:159–68.
[17] Carmona S, Vilarroya O, Bielsa A, Tremols V, Soliva JC, Rovira M, et al. Global and regional gray matter reductions in ADHD: A voxel-based morphometric study. Neurosci Lett 2005;389(2):88–93.
S. Mana et al. / European Psychiatry 25 (2010) 345–354 353
[18] Chang K, Karchemskiy A, Barnea-Goraly N, Garrett A, Simeonova DI, Reiss A. Reduced amygdalar gray matter volume in familial pediatric bipolar disorder. J Am Acad Child Adolesc Psychiatry 2005;44(6):565–73.
[19] Chiu S, Widjaja F, Bates ME, Voelbel GT, Pandina G, Marble J, et al. Anterior cingulate volume in pediatric bipolar disorder and autism. J Affect Disord 2008;105(1–3):93–9.
[20] Costello EJ, Mustillo S, Erkanli A, Keeler G, Angold A. Prevalence and devel- opment of psychiatric disorders in childhood and adolescence. Arch Gen Psychiatry 2003;60(8):837–44.
[21] Courchesne E, Saitoh O, Townsend JP, Yeung-Courchesne R, Press GA, Lincoln AJ, et al. Cerebellar hypoplasia and hyperplasia in infantile autism. Lancet 1994;343:63–4.
[22] Dapretto M, Davies MS, Pfeifer JH, Scott AA, Sigman M, Bookheimer SY, et al. Understanding emotions in others: Mirror neuron dysfunction in children with autism spectrum disorders. Nat Neurosci 2006;9(1): 28–30.
[23] De Bellis MD, Keshavan M, Clark DB, Casey BJ, Giedd J, Boring AM, et al. AE Bennett Research Award. Developmental traumatology part II: Brain devel- opment. Biol Psychiatry 1999;45:1271–84.
[24] De Bellis MD, Keshavan M, Shifflett H, Iyengar S, Beers SR, Hall J, et al. Brain structures in pediatric maltreatment-related PTSD: A sociodemographically matched study. Biol Psychiatry 2002;52:1066–78.
[25] Douaud G, Smith S, Jenkinson M, Behrens T, Johansen-Berg H, Vickers J, et al. Anatomically related grey and white matter abnormalities in adolescent- onset schizophrenia. Brain 2007;130(Pt 9):2375–86.
[26] Faraone SV, Sergeant J, Gillberg C, Biederman J. The worldwide prevalence of ADHD: Is it an american condition? World Psychiatry 2003;2: 104–13.
[27] Forbes EE, Christopher May J, Siegle GJ, Ladouceur CD, Ryan ND, Carter CS, et al. Reward-related decision-making in pediatric major depressive disorder: An fMRI study. J Child Psychol Psychiatry 2006;47(10):1031–40.
[28] Frazier JA, Chiu S, Breeze JL, Makris N, Lange N, Kennedy DN, et al. Structural brain magnetic resonance imaging of limbic and thalamic volumes in pedi- atric bipolar disorder. Am J Psychiatry 2005;162(7):1256–65.
[29] Frazier JA, Breeze JL, Papadimitriou G, Kennedy DN, Hodge SM, Moore CM, et al. White matter abnormalities in children with and at risk for bipolar disorder. Bipolar Disord 2007;9(8):799–809.
[30] Freitag CM, Konrad C, Haberlen M, Kleser C, von Gontard A, Reith W, et al. Perception of biological motion in autism spectrum disorders. Neuropsycho- logia 2008;46(5):1480–94.
[31] Frey KA, Albin RL. Neuroimaging of tourette syndrome. J Child Neurol 2006;21(8):672–7.
[32] Gabbay V, Hess DA, Liu S, Babb JS, Klein RG, Gonen O. Lateralized caudate metabolic abnormalities in adolescent major depressive disorder: A proton MR spectroscopy study. Am J Psychiatry 2007;164(12):1881–9.
[33] Gendry Meresse I, Zilbovicius M, Boddaert N, Robel L, Philippe A, Sfaello I, et al. Autism severity and temporal lobe functional abnormalities. Ann Neurol 2005;58(3):466–9.
[34] Gogtay N, Giedd JN, Lusk L, Hayashi KM, Greenstein D, Vaituzis AC, et al. Dynamic mapping of human cortical development during childhood through early adulthood. PNAS 2004;101:8174–9.
[35] Gogtay N, Lu A, Leow AD, Klunder AD, Lee AD, Chavez A, et al. Three- dimensional brain growth abnormalities in childhood-onset schizophre- nia visualized byusing tensor-based morphometry. PNAS 2008;105: 15979–84.
[36] Gomot M, Bernard FA, Davis MH, Belmonte MK, Ashwin C, Bullmore ET, et al. Change detection in children with autism: An auditory event-related fMRI study. Neuroimage 2006;29(2):475–84.
[37] Gomot M, Belmonte MK, Bullmore ET, Bernard FA, Baron-Cohen S. Brain hyper-reactivity to auditory novel targets in children with high-functioning autism. Brain 2008;131(Pt 9):2479–88.
[38] Greenstein D, Lerch J, Shaw P, Clasen L, Giedd J, Gochman P, et al. Childhood onset schizophrenia: Cortical brain abnormalities as young adults. J Child Psychol Psychiatry 2006;47(10):1003–12.
[39] Guyer AE, Lau JY, McClure-Tone EB, Parrish J, Shiffrin ND, Reynolds RC, et al. Amygdala and ventrolateral prefrontal cortex function during anticipated peer evaluation in pediatric social anxiety. Arch Gen Psychiatry 2008;65(11): 1303–12.
[40] Haenschel C, Bittner RA, Haertling F, Rotarska-Jagiela A, Maurer K, Singer W, et al. Contribution of impaired early-stage visual processing to working memory dysfunction in adolescents with schizophrenia: A study with event-related potentials and functional magnetic resonance imaging. Arch Gen Psychiatry 2007;64(11):1229–40.
[41] Hajek T, Carrey N, Alda M. Neuroanatomical abnormalities as risk factors for bipolar disorder. Bipolar Disord 2005;7:393–403.
[42] Hardan AY, Minshew NJ, Harenski K, Keshavan MS. Posterior fossa magnetic resonance imaging in autism. J Am Acad Child Adolesc Psychiatry 2001;40: 666–72.
[43] Insel TR. Translating scientific opportunity into public health impact. A strate- gic plan for research on mental illness. Arch Gen Psychiatry 2009;66(2):128– 33.
[44] Janssen J, Reig S, Parellada M, Moreno D, Graell M, Fraguas D, et al. Regional gray matter volume deficits in adolescents with first-episode psychosis. J Am Acad Child Adolesc Psychiatry 2008;47(11):1311–20.
[45] Jaracz J. The anatomy of depression in light of evidence from neuroimaging studies. Psychiatr Pol 2008;42(6):875–88.
[46] Kafantaris V, Kingsley P, Ardekani B, Saito E, Lencz T, Lim K, et al. Lower orbital frontal white matter integrity in adolescents with bipolar I disorder. J Am Acad Child Adolesc Psychiatry 2009;48(1):79–86.
[47] Kaufmann WE, Cooper KL, Mostofsky SH, Capone GT, Kates WR, Newschaffer CJ, et al. Specificity of cerebellar vermian abnormalities in autism: a quanti- tative magnetic resonance imaging study. J Child Neurol 2003;18:463–70.
[48] Keehn B, Brenner L, Palmer E, Lincoln AJ, Muller RA. Functional brain organization for visual search in ASD. J Int Neuropsychol Soc 2008;14(6): 990–1003.
[49] Killgore WD, Yurgelun-Todd DA. Social anxiety predicts amygdala activation in adolescents viewing fearful faces. Neuroreport 2005;16(15):1671–5.
[50] Killgore WD, Yurgelun-Todd DA. Ventromedial prefrontal activity correlates with depressed mood in adolescent children. Neuroreport 2006;17(2):167– 71.
[51] Killgore WD, Gruber SA, Yurgelun-Todd DA. Depressed mood and lateralized prefrontal activity during a stroop task in adolescent children. Neurosci Lett 2007;416(1):43–8.
[52] Kobel M, Bechtel N, Weber P, Specht K, Klarhofer M, Scheffler K, et al. Effects of methylphenidate on working memory functioning in children with attention deficit/hyperactivity disorder. Eur J Paediatr Neurol 2009;13(6):516–23.
[53] Konrad K, Neufang S, Hanisch C, Fink GR, Herpertz-Dahlmann B. Dysfunc- tional attentional networks in children with attention deficit/hyperactivity disorder: Evidence from an event-related functional magnetic resonance imaging study. Biol Psychiatry 2006;59(7):643–51.
[54] Krain AL, Gotimer K, Hefton S, Ernst M, Castellanos FX, Pine DS, et al. A functional magnetic resonance imaging investigation of uncertainty in ado- lescents with anxiety disorders. Biol Psychiatry 2008;63(6):563–8.
[55] Kyriakopoulos M, Vyas NS, Barker GJ, Chitnis XA, Frangou S. A diffusion tensor imaging study of white matter in early-onset schizophrenia. Biol Psychiatry 2008;63(5):519–23.
[56] Laird AR, Fox PM, Price CJ, Glahn DC, Uecker AM, Lancaster JL, et al. ALE meta- analysis: Controlling the false discovery rate and performing statistical contrasts. Hum Brain Map 2005;25:155–64.
[57] Lee JS, Yoo SS, Cho SY, Ock SM, Lim MK, Panych LP. Abnormal thalamic volume in treatment-naive boys with tourette syndrome. Acta Psychiatr Scand 2006;113(1):64–7.
[58] Ludolph AG, Juengling FD, Libal G, Ludolph AC, Fegert JM, Kassubek J. Grey- matter abnormalities in boys with tourette syndrome: Magnetic resonance imaging study using optimised voxel-based morphometry. Br J Psychiatry 2006;188:484–5.
[59] MacMaster FP, Mirza Y, Szeszko PR, Kmiecik LE, Easter PC, Taormina SP, et al. Amygdala and hippocampal volumes in familial early onset major depressive disorder. Biol Psychiatry 2008;63(4):385–90.
[60] Makki MI, Behen M, Bhatt A, Wilson B, Chugani HT. Microstructural abnor- malities of striatum and thalamus in children with tourette syndrome. Mov Disord 2008;23(16):2349–56.
[61] Manjaly ZM, Bruning N, Neufang S, Stephan KE, Brieber S, Marshall JC, et al. Neurophysiological correlates of relatively enhanced local visual search in autistic adolescents. Neuroimage 2007;35(1):283–91.
[62] Marquardt RK, Levitt JG, Blanton RE, Caplan R, Asarnow R, Siddarth P, et al. Abnormal development of the anterior cingulate in childhood-onset schizo- phrenia: A preliminary quantitative MRI study. Psychiatry Res 2005;138(3): 221–33.
[63] Marsh R, Zhu H, Wang Z, Skudlarski P, Peterson BS. A developmental fMRI study of self-regulatory control in tourette’s syndrome. Am J Psychiatry 2007;164(6):955–66.
[64] Matsuo K, Rosenberg DR, Easter PC, MacMaster FP, Chen HH, Nicoletti M, et al. Striatal volume abnormalities in treatment-naive patients diagnosed with pediatric major depressive disorder. J Child Adolesc Psychopharmacol 2008;18(2):121–31.
[65] McAlonan GM, Cheung V, Cheung C, Suckling J, Lam GY, Tai KS, et al. Mapping the brain in autism. A voxel-based MRI study of volumetric differences and intercorrelations in autism. Brain 2005;128(Pt 2):268–76.
[66] McAlonan GM, Cheung V, Cheung C, Chua SE, Murphy DG, Suckling J, et al. Mapping brain structure in attention deficit-hyperactivity disorder: A voxel- based MRI study of regional grey and white matter volume. Psychiatry Res 2007;154(2):171–80.
[67] McClure EB, Monk CS, Nelson EE, Parrish JM, Adler A, Blair RJ, et al. Abnormal attention modulation of fear circuit function in pediatric generalized anxiety disorder. Arch Gen Psychiatry 2007;64(1):97–106.
[68] Milham MP, Nugent AC, Drevets WC, Dickstein DP, Leibenluft E, Ernst M, et al. Selective reduction in amygdala volume in pediatric anxiety disorders: A voxel-based morphometry investigation. Biol Psychiatry 2005;57(9): 961–6.
[69] Monk CS, Nelson EE, McClure EB, Mogg K, Bradley BP, Leibenluft E, et al. Ventrolateral prefrontal cortex activation and attentional bias in response to angry faces in adolescents with generalized anxiety disorder. Am J Psychiatry 2006;163(6):1091–7.
[70] Monk CS, Telzer EH, Mogg K, Bradley BP, Mai X, Louro HM, et al. Amygdala and ventrolateral prefrontal cortex activation to masked angry faces in children and adolescents with generalized anxiety disorder. Arch Gen Psychiatry 2008;65(5):568–76.
[71] Moreno D, Burdalo M, Reig S, Parellada M, Zabala A, Desco M, et al. Structural neuroimaging in adolescents with a first psychotic episode. J Am Acad Child Adolesc Psychiatry 2005;44(11):1151–7.
S. Mana et al. / European Psychiatry 25 (2010) 345–354354
[72] Najt P, Nicoletti M, Chen HH, Hatch JP, Caetano SC, Sassi RB, et al. Anatomical measurements of the orbitofrontal cortex in child and adolescent patients with bipolar disorder. Neurosci Lett 2007;413(3):183–6.
[73] Okugawa G, Sedvall GC, Agartz I. Smaller cerebellar vermis but not hemi- sphere volumes in patients with chronic schizophrenia. Am J Psychiatry 2003;160:1614–7.
[74] Pauly K, Seiferth NY, Kellermann T, Backes V, Vloet TD, Shah NJ, et al. Cerebral dysfunctions of emotion-cognition interactions in adolescent-onset schizo- phrenia. J Am Acad Child Adolesc Psychiatry 2008;47(11):1299–310.
[75] Pavuluri MN, O’Connor MM, Harral E, Sweeney JA. Affective neural circuitry during facial emotion processing in pediatric bipolar disorder. Biol Psychiatry 2007;62(2):158–67.
[76] Pierce K, Redcay E. Fusiform function in children with an autism spectrum disorder is a matter of ‘‘who’’. Biol Psychiatry 2008;64(7):552–60.
[77] Piven J, Saliba K, Bailey J, Arndt S. An MRI study of autism: the cerebellum revisited. Neurology 1997;49:546–51.
[78] R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2009. ISBN 3-900051-07-0, URL http://www.R-project.org.
[79] Redcay E, Courchesne E. When is the brain enlarged in autism? A meta- analysis of all brain size reports. Biol Psychiatry 2005;58:1–9.
[80] Redcay E, Courchesne E. Deviant functional magnetic resonance imaging patterns of brain activity to speech in 2-3-year-old children with autism spectrum disorder. Biol Psychiatry 2008;64(7):589–98.
[81] Reig S, Moreno C, Moreno D, Burdalo M, Janssen J, Parellada M, et al. Progression of brain volume changes in adolescent-onset psychosis. Schi- zophr Bull 2009;35(1):233–43.
[82] Rich BA, Fromm SJ, Berghorst LH, Dickstein DP, Brotman MA, Pine DS, et al. Neural connectivity in children with bipolar disorder: Impairment in the face emotion processing circuit. J Child Psychol Psychiatry 2008;49(1):88–96.
[83] Roberson-Nay R, McClure EB, Monk CS, Nelson EE, Guyer AE, Fromm SJ, et al. Increased amygdala activity during successful memory encoding in adoles- cent major depressive disorder: An FMRI study. Biol Psychiatry 2006;60(9): 966–73.
[84] Roiser JP, Cannon DM, Gandhi SK, Taylor TJ, Erickson K, Wood S, et al. Hot and cold cognition in unmedicated depressed subjects with bipolar disorder. Bipolar Disord 2009;11(2):178–89.
[85] Rojas DC, Camou SL, Reite ML, Rogers SJ. Planum temporale volume in children and adolescents with autism. J Autism Dev Disord 2005;35(4):479–86.
[86] Rosenberg DR, Keshavan MS, O’Hearn KM, Dick E, Bagwell WW, Seymour AB, et al. Frontostriatal measurement in treatment-naive children with obses- sive-compulsive disorder. Arch Gen Psychiatry 1997;54:824–30.
[87] Rosso IM, Cintron CM, Steingard RJ, Renshaw PF, Young AD, Yurgelun-Todd DA. Amygdala and hippocampus volumes in pediatric major depression. Biol Psychiatry 2005;57(1):21–6.
[88] Rotge JY, Guehl D, Dilharreguy B, Tignol J, Bioulac B, Allard M, et al. Meta- analysis of brain volume changes in obsessive-compulsive disorder. Biol Psychiatry 2009;65:75–83.
[89] Salmond CH, Vargha-Khadem F, Gadian DG, de Haan M, Baldeweg T. Het- erogeneity in the patterns of neural abnormality in autistic spectrum dis- orders: Evidence from ERP and MRI. Cortex 2007;43(6):686–99.
[90] Schneider M, Retz W, Coogan A, Thome J, Rösler M. Anatomical and functional brain imaging in adult attention-deficit/hyperactivity disorder (ADHD) – A neurological view. Eur Arch Psych Clin Neurosci 2006;256:32–41.
[91] Semrud-Clikeman M, Pliszka SR, Lancaster J, Liotti M. Volumetric MRI differ- ences in treatment-naive vs chronically treated children with ADHD. Neu- rology 2006;67(6):1023–7.
[92] Shaw P, Lerch J, Greenstein D, Sharp W, Clasen L, Evans A, et al. Longitudinal mapping of cortical thickness and clinical outcome in children and adoles- cents with attention-deficit/hyperactivity disorder. Arch Gen Psychiatry 2006;63(5):540–9.
[93] Smith AB, Taylor E, Brammer M, Toone B, Rubia K. Task-specific hypoactiva- tion in prefrontal and temporoparietal brain regions during motor inhibi- tion and task switching in medication-naive children and adolescents with attention deficit hyperactivity disorder. Am J Psychiatry 2006;163(6): 1044–51.
[94] Stanfield AC, McIntosh AM, Spencer MD, Philip R, Gaur S, Lawrie SM. Towards a neuroanatomy of autism: A systematic review and meta-analysis of structural magnetic resonance imaging studies. Eur Psychiatry 2008;23: 289–99.
[95] Steen RG, Courtney M, McClure R, Hamer RM, Lieberman JA. Brain volume in first-episode schizophrenia. Systematic review and meta-analysis of mag- netic resonance imaging studies. Br J Psychiatry 2006;188:510–8.
[96] Stein MB, Stein DJ. Social anxiety disorder. Lancet 2008;371(9618):1115–25. [97] Stevens MC, Pearlson GD, Kiehl KA. An FMRI auditory oddball study of
combined-subtype attention deficit hyperactivity disorder. Am J Psychiatry 2007;164(11):1737–49.
[98] Szeszko PR, MacMillian S, McMeniman M, Chen S, Baribault K, Lim KO, et al. Brain structural abnormalities in psychotropic drug-naive pediatric patients with obsessive-compulsive disorder. Am J Psychiatry 2004;161:1049–56.
[99] Talairach J, Tournoux P. Co-planar stereotaxic atlas of the human brain. Germany: Thieme, Stuttgart; 1998.
[100] Tamm L, Menon V, Reiss AL. Parietal attentional system aberrations during target detection in adolescents with attention deficit hyperactivity disorder: Event-related fMRI evidence. Am J Psychiatry 2006;163(6):1033–43.
[101] Thompson PM, Vidal C, Giedd JN, Gochman P, Blumenthal J, Nicolson R, et al. Mapping adolescent brain change reveals dynamic wave of accelerated gray matter loss in very early-onset schizophrenia. PNAS 2001;98:11650–5.
[102] Tremols V, Bielsa A, Soliva JC, Raheb C, Carmona S, Tomas J, et al. Differential abnormalities of the head and body of the caudate nucleus in attention deficit-hyperactivity disorder. Psychiatry Res 2008;163(3):270–8.
[103] Turkeltaub PE, Eden GF, Jones KM, Zeffiro TA. Meta-analysis of the functional neuroanatomy of single-word reading: Method and validation. Neuroimage 2002;16:765–80.
[104] Uhlikova P, Paclt I, Vaneckova M, Morcinek T, Seidel Z, Krasensky J, et al. Asymmetry of basal ganglia in children with attention deficit hyperactivity disorder. Neuro Endocrinol Lett 2007;28(5):604–9.
[105] Vaidya CJ, Bunge SA, Dudukovic NM, Zalecki CA, Elliott GR, Gabrieli JD. Altered neural substrates of cognitive control in childhood ADHD: Evidence from functional magnetic resonance imaging. Am J Psychiatry 2005;162(9): 1605–13.
[106] Valera EM, Faraone SV, Murray KE, Seidman LJ. Meta-analysis of structural imaging findings in attention-deficit/hyperactivity disorder. Biol Psychiatry 2007;61:1361–9.
[107] Vance A, Silk TJ, Casey M, Rinehart NJ, Bradshaw JL, Bellgrove MA, et al. Right parietal dysfunction in children with attention deficit hyperactivity disorder, combined type: A functional MRI study. Mol Psychiatry 2007;12(9). 826,32,793.
[108] Vidal CN, Rapoport JL, Hayashi KM, Geaga JA, Sui Y, McLemore LE, et al. Dynamically spreading frontal and cingulate deficits mapped in adolescents with schizophrenia. Arch Gen Psychiatry 2006;63(1):25–34.
[109] Wang J, Jiang T, Cao Q, Wang Y. Characterizing anatomic differences in boys with attention-deficit/hyperactivity disorder with the use of deformation- based morphometry. AJNR Am J Neuroradiol 2007;28(3):543–7.
[110] Wang AT, Lee SS, Sigman M, Dapretto M. Reading affect in the face and voice: Neural correlates of interpreting communicative intent in children and adolescents with autism spectrum disorders. Arch Gen Psychiatry 2007;64(6):698–708.
[111] Wellington TM, Semrud-Clikeman M, Gregory AL, Murphy JM, Lancaster JL. Magnetic resonance imaging volumetric analysis of the putamen in children with ADHD: Combined type versus control. J Atten Disord 2006;10(2):171– 80.
[112] Whalley HC, Simonotto E, Moorhead W, McIntosh A, Marshall I, Ebmeier KP, et al. Functional imaging as a predictor of schizophrenia. Biol Psychiatry 2006;60(5):4.
[113] Williams JH, Waiter GD, Gilchrist A, Perrett DI, Murray AD, Whiten A. Neural mechanisms of imitation and ‘mirror neuron’ functioning in autistic spec- trum disorder. Neuropsychologia 2006;44(4):610–21.
[114] Woolley J, Heyman I, Brammer M, Frampton I, McGuire PK, Rubia K. Brain activation in paediatric obsessive compulsive disorder during tasks of inhib- itory control. Br J Psychiatry 2008;192(1):25–31.
[115] Yang P, Wang PN, Chuang KH, Jong YJ, Chao TC, Wu MT. Absence of gender effect on children with attention-deficit/hyperactivity disorder as assessed by opti- mized voxel-based morphometry. Psychiatry Res 2008;164(3):245–53.
[116] Yeargin-Allsopp M, Rice C, Karapurkar T, Doernberg N, Boyle C, Murphy C. Prevalence of Autism in a U.S. Metropolitan Area. J Am Med Ass 2003;289(1):49–55.
[117] Zalsman G, Oquendo MA, Greenhill L, Goldberg PH, Kamali M, Martin A, et al. Neurobiology of depression in children and adolescents. Child Adolesc Psychiatr Clin N Am 2006;15:843–68.
- Brain imaging findings in children and adolescents with mental disorders: A cross-sectional review
- Introduction
- Methods
- Results
- Distribution of cerebral structures according to mental disorders in children and adolescents
- Location of brain abnormalities according to mental disorders.
- Principal component analysis.
- Factors
- Variables
- Items
- ALE analysis.
- Affective disorders
- Mental disorders with cognition deviations
- Tourette's syndrome
- Characteristics of neuroimaging studies of psychiatric disorders in children and adolescents
- Geographic distribution
- Neuroimaging techniques
- 2005 to 2008 annual progression and distribution of studies with respect to the psychiatric disorders
- Discussion
- Mental disorders and location of brain abnormalities
- Characteristics of neuroimaging studies
- Limitations
- Conclusion
- Acknowledgements
- Supplementary data
- References