A voxel-wise assessment of growth differences in infants developing autism spectrum disorder.

Autism Spectrum Disorder (ASD) is a phenotypically and etiologically heterogeneous developmental disorder typically diagnosed around 4 years of age. The development of biomarkers to help in earlier, presymptomatic diagnosis could facilitate earlier identification and therefore earlier intervention and may lead to better outcomes, as well as providing information to help better understand the underlying mechanisms of ASD. In this study, magnetic resonance imaging (MRI) scans of infants at high familial risk, from the Infant Brain Imaging Study (IBIS), at 6, 12 and 24 months of age were included in a morphological analysis, fitting a mixed-effects model to Tensor Based Morphometry (TBM) results to obtain voxel-wise growth trajectories. Subjects were grouped by familial risk and clinical diagnosis at 2 years of age. Several regions, including the posterior cingulate gyrus, the cingulum, the fusiform gyrus, and the precentral gyrus, showed a significant effect for the interaction of group and age associated with ASD, either as an increased or a decreased growth rate of the cerebrum. In general, our results showed increased growth rate within white matter with decreased growth rate found mostly in grey matter. Overall, the regions showing increased growth rate were larger and more numerous than those with decreased growth rate. These results detail, at the voxel level, differences in brain growth trajectories in ASD during the first years of life, previously reported in terms of overall brain volume and surface area.

Language delay aggregates in toddler siblings of children with autism spectrum disorder.

BACKGROUND: Language delay is extremely common in children with autism spectrum disorder (ASD), yet it is unclear whether measurable variation in early language is associated with genetic liability for ASD. Assessment of language development in unaffected siblings of children with ASD can inform whether decreased early language ability aggregates with inherited risk for ASD and serves as an ASD endophenotype. METHODS: We implemented two approaches: (1) a meta-analysis of studies comparing language delay, a categorical indicator of language function, and language scores, a continuous metric, in unaffected toddlers at high and low familial risk for ASD, and (2) a parallel analysis of 350 unaffected 24-month-olds in the Infant Brain Imaging Study (IBIS), a prospective study of infants at high and low familial risk for ASD. An advantage of the former was its detection of group differences from pooled data across unique samples; an advantage of the latter was its sensitivity in quantifying early manifestations of language delay while accounting for covariates within a single large sample. RESULTS: Meta-analysis showed that high-risk siblings without ASD (HR-noASD) were three to four times more likely to exhibit language delay versus low-risk siblings without ASD (LR-noASD) and had lower mean receptive and expressive language scores. Analyses of IBIS data corroborated that language delay, specifically receptive language delay, was more frequent in the HR-noASD (n = 235) versus LR-noASD group (n = 115). IBIS language scores were continuously and unimodally distributed, with a pathological shift towards decreased language function in HR-noASD siblings. The elevated inherited risk for ASD was associated with lower receptive and expressive language scores when controlling for sociodemographic factors. For receptive but not expressive language, the effect of risk group remained significant even when controlling for nonverbal cognition. CONCLUSIONS: Greater frequency of language delay and a lower distribution of language scores in high-risk, unaffected toddler-aged siblings support decreased early language ability as an endophenotype for ASD, with a more pronounced effect for receptive versus expressive language. Further characterization of language development is warranted to refine genetic investigations of ASD and to elucidate factors influencing the progression of core autistic traits and related symptoms.

Analysis of cortical morphometric variability using labeled cortical distance maps.

Morphometric (i.e., shape and size) differences in the anatomy of cortical structures are associated with neurodevelopmental and neuropsychiatric disorders. Such differences can be quantized and detected by a powerful tool called Labeled Cortical Distance Map (LCDM). The LCDM method provides distances of labeled gray matter (GM) voxels from the GM/white matter (WM) surface for specific cortical structures (or tissues). Here we describe a method to analyze morphometric variability in the particular tissue using LCDM distances. To extract more of the information provided by LCDM distances, we perform pooling and censoring of LCDM distances. In particular, we employ Brown-Forsythe (BF) test of homogeneity of variance (HOV) on the LCDM distances. HOV analysis of pooled distances provides an overall analysis of morphometric variability of the LCDMs due to the disease in question, while the HOV analysis of censored distances suggests the location(s) of significant variation in these differences (i.e., at which distance from the GM/WM surface the morphometric variability starts to be significant). We also check for the influence of assumption violations on the HOV analysis of LCDM distances. In particular, we demonstrate that BF HOV test is robust to assumption violations such as the non-normality and within sample dependence of the residuals from the median for pooled and censored distances and are robust to data aggregation which occurs in analysis of censored distances. We recommend HOV analysis as a complementary tool to the analysis of distribution/location differences. We also apply the methodology on simulated normal and exponential data sets and assess the performance of the methods when more of the underlying assumptions are satisfied. We illustrate the methodology on a real data example, namely, LCDM distances of GM voxels in ventral medial prefrontal cortices (VMPFCs) to see the effects of depression or being of high risk to depression on the morphometry of VMPFCs. The methodology used here is also valid for morphometric analysis of other cortical structures.

STATISTICAL ANALYSIS OF CORTICAL MORPHOMETRICS USING POOLED DISTANCES BASED ON LABELED CORTICAL DISTANCE MAPS.

Neuropsychiatric disorders have been demonstrated to manifest shape differences in cortical structures. Labeled Cortical Distance Mapping (LCDM) is a powerful tool in quantifying such morphometric differences and characterizes the morphometry of the laminar cortical mantle of cortical structures. Specifically, LCDM data are distances of labeled gray matter (GM) voxels with respect to the gray/white matter cortical surface. Volumes and descriptive measures (such as means and variances for each subject) based on LCDM distances provide descriptive summary information on some of the shape characteristics. However, additional morphometrics are contained in the data and their analysis may provide additional clues to underlying differences in cortical characteristics. To use more of this information, we pool (merge) LCDM distances from subjects in the same group. These pooled distances can help detect morphometric differences between groups, but do not provide information about the locations of such differences in the tissue in question. In this article, we check for the influence of the assumption violations on the analysis of pooled LCDM distances. We demonstrate that the classical parametric tests are robust to the non-normality and within sample dependence of LCDM distances and nonparametric tests are robust to within sample dependence of LCDM distances. We specify the types of alternatives for which the tests are more sensitive. We also show that the pooled LCDM distances provide powerful results for group differences in distribution of LCDM distances. As an illustrative example, we use GM in the ventral medial prefrontal cortex (VMPFC) in subjects with major depressive disorder (MDD), subjects at high risk (HR) of MDD, and healthy subjects. Significant morphometric differences were found in VMPFC due to MDD or being at HR. In particular, the analysis indicated that distances in left and right VMPFCs tend to decrease due to MDD or being at HR, possibly as a result of thinning. The methodology can also be applied to other cortical structures.

Segmentation of arteries in MPRAGE images of the ventral medial prefrontal cortex.

A method for removing arteries that appear bright with intensities similar to white matter in Magnetized Prepared Rapid Gradient Echo images of the ventral medial prefrontal cortex is described. The Fast Marching method is used to generate a curve within the artery. Then, the largest connected component is selected to segment the artery which is used to mask the image. The surface reconstructed from the masked image yielded cortical thickness maps similar to those generated by manually pruning the arteries from surfaces reconstructed from the original image. The method may be useful in masking vasculature in other cortical regions.

Validating cortical surface analysis of medial prefrontal cortex.

This paper describes cortical analysis of 19 high resolution MRI subvolumes of medial prefrontal cortex (MPFC), a region that has been implicated in major depressive disorder. An automated Bayesian segmentation is used to delineate the MRI subvolumes into cerebrospinal fluid (CSF), gray matter (GM), white matter (WM), and partial volumes of either CSF/GM or GM/WM. The intensity value at which there is equal probability of GM and GM/WM partial volume is used to reconstruct MPFC cortical surfaces based on a 3-D isocontouring algorithm. The segmented data and the generated surfaces are validated by comparison with hand segmented data and semiautomated contours, respectively. The L(1) distances between Bayesian and hand segmented data are 0.05-0.10 (n = 5). Fifty percent of the voxels of the reconstructed surface lie within 0.12-0.28 mm (n = 14) from the semiautomated contours. Cortical thickness metrics are generated in the form of frequency of occurrence histograms for GM and WM labelled voxels as a function of their position from the cortical surface. An algorithm to compute the surface area of the GM/WM interface of the MPFC subvolume is described. These methods represent a novel approach to morphometric chacterization of regional cortex features which may be important in the study of psychiatric disorders such as major depression.

Is attention-deficit/hyperactivity disorder an energy deficiency syndrome?

Attention-deficit/hyperactivity disorder (ADHD) is a highly heritable yet clinically heterogeneous syndrome associated with hypocatecholamine function in subcortical and prefrontal cortical regions and clinical response to medications that enhance catecholamine function. The goal of this article is to present a hypothesis about the etiology of ADHD by synthesizing these findings with recent experiments indicating that activity-dependent neuronal energy consumption is regulated by cortical astrocytes. The scientific literature was searched from 1966 to the present using MEDLINE and relevant key words. Inattention and impulsivity may be related to hypofunctionality of catecholamine projection pathways to prefrontal cortical areas, resulting in decreased neuronal energy availability. This may be mediated by astrocyte catecholamine receptors that normally regulate energy availability during neuronal activation. At least some forms of ADHD may be viewed as cortical, energy-deficit syndromes secondary to catecholamine-mediated hypofunctionality of astrocyte glucose and glycogen metabolism, which provides activity-dependent energy to cortical neurons. Several tests of this hypothesis are proposed.

Obsessive-compulsive scale of the child behavior checklist: specificity, sensitivity, and predictive power.

OBJECTIVE: To create an obsessive-compulsive disorder subscale (OCS) of the Child Behavior Checklist (CBCL) and to determine its internal consistency, sensitivity, specificity, and positive and negative predictive power to identify obsessive-compulsive disorder (OCD) in children and adolescents. METHODS: Three samples of equal size (n = 73) of children and adolescents, matched for age, gender, and race, were selected for these analyses: 1) a clinically ascertained OCD group, 2) a psychiatrically treated group whose records revealed no evidence of OCD, and 3) a general population control group. An OCS was created by applying factor analysis to 11 CBCL items. Examinations of internal consistency, sensitivity, specificity, and positive and negative predictive value were undertaken. RESULTS: Of 11 items hypothesized to predict OCD, 8 items were retained after factor analyses (smallest factor loading: 0.49) and used to calculate OCS scores. The retained items displayed excellent internal consistency (Cronbach's alpha coefficient = 0.84). OCD participants had significantly higher OCS scores than either psychiatrically treated or general population control groups. With the use of the 2 cutoff scores closest to the true rate of OCD in the overall sample, sensitivity was 75.3% to 84.9%, specificity was 82.2% to 92.5%, positive predictive value was 70.5% to 83.3%, and negative predictive value was 88.2% to 91.6%. CONCLUSION: The performance of the proposed CBCL OCS compares favorably with that of the only previously studied screening instrument for OCD, the Leyton Obsessional Inventory-Child Version. Unlike the Leyton Obsessional Inventory-Child Version, the CBCL is already in widespread use as a screen for most other forms of psychopathology. As the performance of the CBCL OCS will need to be replicated in other sample populations, data with various cutoff levels are provided to enable investigators and clinicians to tailor its use to specific study populations.

Genetic analysis of brain imaging abnormalities.

A number of child psychiatric disorders have recently been associated with specific structural brain abnormalities. This article discusses the advantages of neuroimaging for genetic studies, such as clarifying etiologic heterogeneity and establishing pathophysiology. The advantages of twin or family designs for neuroimaging investigations are also discussed.

Family, genetic, and imaging studies of early-onset depression.

As with other psychiatric disorders, most studies dealing with the familiality and genetics of depression have been limited to adults; however, several studies suggest that there is continuity between childhood- and adolescent-onset depression and adult depression. More direct estimates of the heritability of depressive symptoms or episodes in children and adolescents are compatible with the genetic contributions being greater than 50%. A number of functional and structural imaging studies have implicated particular circuitry as being involved in the generation of emotion and depression. Imaging studies of twins have suggested that regional brain volume and characteristics of brain shape are heritable. This suggests that a potentially important new avenue of research will be the correlation of the genetics of brain structure and/or function with the genetics of depression. Preliminary studies of adolescent and young adult twins suggest a significant correspondence between the genetic contributions to some regional brain volumes and early-onset depression.

Bayesian construction of geometrically based cortical thickness metrics.

This paper describes the construction of cortical metrics quantifying the probabilistic occurrence of gray matter, white matter, and cerebrospinal fluid compartments in their correlation to the geometry of the neocortex as measured in 0.5-1.0 mm magnetic resonance imagery. These cortical profiles represent the density of the tissue types as a function of distance to the cortical surface. These metrics are consistent when generated across multiple brains indicating a fundamental property of the neocortex. Methods are proposed for incorporating such metrics into automated Bayes segmentation.

Refractory depression in children and adolescents.

Refractory or treatments resistant depression in child and adolescent populations is a difficult construct to operationalize currently. To date, only one of the small number of completed double-blind placebo-controlled treatment investigations have not demonstrated a significant effect of antidepressants in comparison to placebo. However, it has been established that child and adolescent MDD is a serious disorder that appears to have clinical continuity with adult affective disorders and is generally of long duration with high rates of recurrence and eventual progression to mania, substance abuse, or other serious psychopathology. In addition, families of children with affective disorders evidence substantial genetic loading with high rates of affective disorders contributing both genetic vulnerability and potential environmental risk as well. There have been no empirically identified treatments that alter the long-term course of the illness. Thus treatment resistance is a significant issue for this population. This review will focus on controlled treatment trials and will examine the potential relevance of psychosocial impairment, genetic-familial risk, and neuromorphometric brain differences to treatment resistance in children and adolescents with major depression.

Treatment-resistant depression in children and adolescents.

Treatment resistance in depressed children and adolescents cannot yet be defined fully because of the paucity of controlled studies that demonstrate efficacy. Therefore, this article addresses several of the age-specific, developmental considerations that may impinge upon treatment response. Areas covered include familial-genetic, psychosocial, and neuroimaging studies in addition to a review of controlled studies to date.

Preliminary study of magnetic resonance imaging characteristics in 8- to 16-year-olds with mania.

OBJECTIVE: To examine magnetic resonance imaging (MRI) characteristics in children and adolescents with mania according to DSM-III-R criteria. METHOD: A convenience sample of consecutively referred 8- to 16-year-old manic (n = 10) and normal (n = 5) subjects were assessed using the Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present Episode Version, the Children's Global Assessment Scale, and the Family History-Research Diagnostic Criteria. MRI scans were obtained from unsedated subjects using a 1.5 T MR scanner to acquire T1-weighted coronal and sagittal images and T2-weighted axial images. Images were assessed by blind clinical interpretation, ratings of T2-weighted deep white matter hyperintensities and petalia, and computer-assisted volumetric analysis of ventricular and cerebral volumes. RESULTS: Eight of 10 manic subjects and all 5 controls completed the scans. Scans of 4 manic subjects and 1 control subject showed ventricular or white matter abnormalities by clinical interpretation. Significant findings were positive correlations between increasing age and both right and left ventricular volumes. Two of the 8 manic subjects and no controls had confluent subcortical hyperintensities. CONCLUSIONS: MRI brain scanning was feasible in 8- to 16-year-olds. Preliminary findings from clinical interpretations and structured ratings suggest structural differences between young manic and normal subjects. Investigations of larger samples are needed to better characterize the differences.

MRI abnormalities in adolescent bipolar affective disorder.

There is increasing evidence for structural differences in the brains of patients with affective disorders. Recent magnetic resonance imaging (MRI) studies have reported focal signal hyperintensities in the deep white matter of bipolar patients. These previous reports had focused on adult patients with prior episodes of illness. In this case report, the authors discuss a young adolescent patient during her first episode of mania and the finding of subcortical focal signal hyperintensities on brain MRI. The etiology, pathophysiology, and clinical correlates of these lesions will be reviewed.