Contributors: Chekroud, Adam, Corlett, P, Krystal, J, Cannon, T, Trivedi, M, Johnson, M, Gueorguieva, R, Shehzad, Z, Zotti, R
... Abstract Background: Antidepressant treatment efficacy is low, but might be improved by matching patients to interventions. At present, clinicians have no empirically validated mechanisms to assess whether a patient with depression will respond to a specific antidepressant. We aimed to develop an algorithm to assess whether patients will achieve symptomatic remission from a 12-week course of citalopram. Methods: We used patient-reported data from patients with depression (n=4041, with 1949 completers) from level 1 of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D; ClinicalTrials.gov, number NCT00021528) to identify variables that were most predictive of treatment outcome, and used these variables to train a machine-learning model to predict clinical remission. We externally validated the model in the escitalopram treatment group (n=151) of an independent clinical trial (Combining Medications to Enhance Depression Outcomes [COMED]; ClinicalTrials.gov, number NCT00590863). Findings: We identified 25 variables that were most predictive of treatment outcome from 164 patient-reportable variables, and used these to train the model. The model was internally cross-validated, and predicted outcomes in the STAR*D cohort with accuracy significantly above chance (64·6% [SD 3·2]; p<0·0001). The model was externally validated in the escitalopram treatment group (N=151) of COMED (accuracy 59·6%, p=0.043). The model also performed significantly above chance in a combined escitalopram-buproprion treatment group in COMED (n=134; accuracy 59·7%, p=0·023), but not in a combined venlafaxine-mirtazapine group (n=140; accuracy 51·4%, p=0·53), suggesting specificity of the model to underlying mechanisms. Interpretation: Building statistical models by mining existing clinical trial data can enable prospective identification of patients who are likely to respond to a specific antidepressant.
A new template to study callosal growth shows specific growth in anterior and posterior regions of the corpus callosum in early childhood
Contributors: Collins, Louis, Fonov, Vladimir, Garon, Mathieu, Alexandrov, Lubomir, Karama, Sherif, Evans, Alan, Beauchamp, Miriam, Ansado, Jennyfer, and Brain Development Cooperative Group,
... Abstract Most of the studies conducted on the development of the corpus callosum (CC) have been limited to a relatively simple assessment of callosal area, providing an estimation of the size of the CC in two dimensions rather than its actual measurement. The goal of this study was to revisit callosal development in childhood and adolescence by using a three-dimensional (3D) magnetic resonance imaging template of the CC that considers the horizontal width of the CC and compares this with the two-dimensional (2D) callosal area. We mapped callosal growth in a large sample of youths followed longitudinally (N = 370 at T1; N = 304 at T2; and N = 246 at T3). Both techniques were based on a five-section subdivision of the CC. The results obtained with the 3D method revealed that the rate of CC growth over a 4-year period in the rostrum, the genu, the anterior body and the splenium was significantly higher in the youngest age group (< 7 years) than in older groups, indicating an intense period of development in early childhood for the anterior and posterior parts of the CC. Similar results were obtained when 2D callosal area was used for the anterior and posterior parts of the CC. However, divergent results were found in the mid-body and the caudal body of the CC. As shown by differences between 2D estimations and actual 3D measurements of callosal growth, our study highlights the importance of considering the horizontal width in measuring developmental changes in the CC.
Atypical pupillary light reflex and heart rate variability in children with autism spectrum disorder
Contributors: Yao, Gang, Daluwatte, C, Miles, JH, Christ, SE, Beversdorf, DQ, Takahashi, N
... Abstract We investigated pupillary light reflex (PLR) in 152 children with ASD, 116 typically developing (TD) children, and 36 children with non-ASD neurodevelopmental disorders (NDDs). Heart rate variability (HRV) was measured simultaneously to study potential impairments in the autonomic nervous system (ANS) associated with ASD. The results showed that the ASD group had significantly longer PLR latency, reduced relative constriction amplitude, and shorter constriction/redilation time than those of the TD group. Similar atypical PLR parameters were observed in the NDD group. A significant age effect on PLR latency was observed in children younger than 9 years in the TD group, but not in the ASD and NDD groups. Atypical HRV parameters were observed in the ASD and NDD groups. A significant negative correlation existed between the PLR constriction amplitude and average heart rate in children with an ASD, but not in children with typical development.
Contributors: Sanders, Stephan, He, X, Willsey, AJ, Ercan-Sencicek, AG, Samocha, KE, Cicek, AE, Murtha, MT, Bal, VH, Bishop, SL, Dong, S
... Abstract Analysis of de novo CNVs (dnCNVs) from the full Simons Simplex Collection (SSC) (N = 2,591 families) replicates prior findings of strong association with autism spectrum disorders (ASDs) and confirms six risk loci (1q21.1, 3q29, 7q11.23, 16p11.2, 15q11.2-13, and 22q11.2). The addition of published CNV data from the Autism Genome Project (AGP) and exome sequencing data from the SSC and the Autism Sequencing Consortium (ASC) shows that genes within small de novo deletions, but not within large dnCNVs, significantly overlap the high-effect risk genes identified by sequencing. Alternatively, large dnCNVs are found likely to contain multiple modest-effect risk genes. Overall, we find strong evidence that de novo mutations are associated with ASD apart from the risk for intellectual disability. Extending the transmission and de novo association test (TADA) to include small de novo deletions reveals 71 ASD risk loci, including 6 CNV regions (noted above) and 65 risk genes (FDR ≤ 0.1).
The Number of Parvalbumin-Expressing Interneurons Is Decreased in the Medial Prefrontal Cortex in Autism.
Contributors: Veronica, Martinez Cerdeno, Hashemi, E, Ariza, J, Rogers, H, Noctor, SC, Martínez-Cerdeño, V
... Abstract UNASSIGNED: The cognitive phenotype of autism has been correlated with an altered balance of excitation to inhibition in the cerebral cortex, which could result from a change in the number, function, or morphology of GABA-expressing interneurons. The number of GABAergic interneuron subtypes has not been quantified in the autistic cerebral cortex. We classified interneurons into 3 subpopulations based on expression of the calcium-binding proteins parvalbumin, calbindin, or calretinin. We quantified the number of each interneuron subtype in postmortem neocortical tissue from 11 autistic cases and 10 control cases. Prefrontal Brodmann Areas (BA) BA46, BA47, and BA9 in autism and age-matched controls were analyzed by blinded researchers. We show that the number of parvalbumin+ interneurons in these 3 cortical areas-BA46, BA47, and BA9-is significantly reduced in autism compared with controls. The number of calbindin+ and calretinin+ interneurons did not differ in the cortical areas examined. Parvalbumin+ interneurons are fast-spiking cells that synchronize the activity of pyramidal cells through perisomatic and axo-axonic inhibition. The reduced number of parvalbumin+ interneurons could disrupt the balance of excitation/inhibition and alter gamma wave oscillations in the cerebral cortex of autistic subjects. These data will allow development of novel treatments specifically targeting parvalbumin interneurons.
Contributors: Khundrakpam, Budhachandra, Tohka, Jussi, Evans, Alan, and the Brain Development Cooperative Group,
... Abstract Several studies using magnetic resonance imaging (MRI) scans have shown developmental trajectories of cortical thickness. Cognitive milestones happen concurrently with these structural changes, and a delay in such changes has been implicated in developmental disorders such as attention-deficit/hyperactivity disorder (ADHD). Accurate estimation of individuals' brain maturity, therefore, is critical in establishing a baseline for normal brain development against which neurodevelopmental disorders can be assessed. In this study, cortical thickness derived from structural magnetic resonance imaging (MRI) scans of a large longitudinal dataset of normally growing children and adolescents (n = 308), were used to build a highly accurate predictive model for estimating chronological age (cross-validated correlation up to R = 0.84). Unlike previous studies which used kernelized approach in building prediction models, we used an elastic net penalized linear regression model capable of producing a spatially sparse, yet accurate predictive model of chronological age. Upon investigating different scales of cortical parcellation from 78 to 10,240 brain parcels, we observed that the accuracy in estimated age improved with increased spatial scale of brain parcellation, with the best estimations obtained for spatial resolutions consisting of 2560 and 10,240 brain parcels. The top predictors of brain maturity were found in highly localized sensorimotor and association areas. The results of our study demonstrate that cortical thickness can be used to estimate individuals' brain maturity with high accuracy, and the estimated ages relate to functional and behavioural measures, underscoring the relevance and scope of the study in the understanding of biological maturity.
Contributors: Ducharme, Simon, Albaugh, M, Nguyen, T, Hudziak, J, Mateos-Pérez, J, Labbe, A, Evans, A, Karama, S
... Abstract Several reports have described cortical thickness (CTh) developmental trajectories, with conflicting results. Some studies have reported inverted-U shape curves with peaks of CTh in late childhood to adolescence, while others suggested predominant monotonic decline after age 6. In this study, we reviewed CTh developmental trajectories in the NIH MRI Study of Normal Brain Development, and in a second step, evaluated the impact of post-processing quality control (QC) procedures on identified trajectories. The quality-controlled sample included 384 individual subjects with repeated scanning (1-3 per subject, total scans n=753) from 4.9 to 22.3years of age. The best-fit model (cubic, quadratic, or first-order linear) was identified at each vertex using mixed-effects models. The majority of brain regions showed linear monotonic decline of CTh. There were few areas of cubic trajectories, mostly in bilateral temporo-parietal areas and the right prefrontal cortex, in which CTh peaks were at, or prior to, age 8. When controlling for total brain volume, CTh trajectories were even more uniformly linear. The only sex difference was faster thinning of occipital areas in boys compared to girls. The best-fit model for whole brain mean thickness was a monotonic decline of 0.027mm per year. QC procedures had a significant impact on identified trajectories, with a clear shift toward more complex trajectories (i.e., quadratic or cubic) when including all scans without QC (n=954). Trajectories were almost exclusively linear when using only scans that passed the most stringent QC (n=598). The impact of QC probably relates to decreasing the inclusion of scans with CTh underestimation secondary to movement artifacts, which are more common in younger subjects. In summary, our results suggest that CTh follows a simple linear decline in most cortical areas by age 5, and all areas by age 8. This study further supports the crucial importance of implementing post-processing QC in CTh studies of development, aging, and neuropsychiatric disorders.
Contributors: Pevsner, Jonathan, Freed, Donald
... Abstract DOI is being issued as a reserved DOI.
Paternal sperm DNA methylation associated with early signs of autism risk in an autism-enriched cohort
Contributors: Feinberg, Andrew P., Fallin, M. Daniele, Feinberg, J, Bakulski, K, Jaffe, A, Tryggvadottir, R, Brown, S, Goldman, L, Croen, L, Hertz-Picciotto, I
... Abstract Epigenetic mechanisms such as altered DNA methylation have been suggested to play a role in autism, beginning with the classical association of Prader-Willi syndrome, an imprinting disorder, with autistic features. Here we tested for the relationship of paternal sperm DNA methylation with autism risk in offspring, examining an enriched-risk cohort of fathers of autistic children. We examined genome-wide DNA methylation (DNAm) in paternal semen biosamples obtained from an autism spectrum disorder (ASD) enriched-risk pregnancy cohort, the Early Autism Risk Longitudinal Investigation (EARLI) cohort, to estimate associations between sperm DNAm and prospective ASD development, using a 12-month ASD symptoms assessment, the Autism Observation Scale for Infants (AOSI). We analysed methylation data from 44 sperm samples run on the CHARM 3.0 array, which contains over 4 million probes (over 7 million CpG sites), including 30 samples also run on the Illumina Infinium HumanMethylation450 (450K) BeadChip platform (∼485 000 CpG sites). We also examined associated regions in an independent sample of post-mortem human brain ASD and control samples for which Illumina 450K DNA methylation data were available. Using region-based statistical approaches, we identified 193 differentially methylated regions (DMRs) in paternal sperm with a family-wise empirical P-value [family-wise error rate (FWER)] <0.05 associated with performance on the Autism Observational Scale for Infants (AOSI) at 12 months of age in offspring. The DMRs clustered near genes involved in developmental processes, including many genes in the SNORD family, within the Prader-Willi syndrome gene cluster. These results were consistent among the 75 probes on the Illumina 450K array that cover AOSI-associated DMRs from CHARM. Further, 18 of 75 (24%) 450K array probes showed consistent differences in the cerebellums of autistic individuals compared with controls. These data suggest that epigenetic differences in paternal sperm may contribute to autism risk in offspring, and provide evidence that directionally consistent, potentially related epigenetic mechanisms may be operating in the cerebellum of individuals with autism.
Rare-Variant Extensions of the Transmission Disequilibrium Test: Application to Autism Exome Sequence Data
Contributors: Leal, Suzanne, He, Z, O'Roak, B, Smith, J, Wang, G, Hooker, S, Santos-Cortez, R, Li, B, Kan, M, Krumm, N
... Abstract Many population-based rare-variant (RV) association tests, which aggregate variants across a region, have been developed to analyze sequence data. A drawback of analyzing population-based data is that it is difficult to adequately control for population substructure and admixture, and spurious associations can occur. For RVs, this problem can be substantial, because the spectrum of rare variation can differ greatly between populations. A solution is to analyze parent-child trio data, by using the transmission disequilibrium test (TDT), which is robust to population substructure and admixture. We extended the TDT to test for RV associations using four commonly used methods. We demonstrate that for all RV-TDT methods, using proper analysis strategies, type I error is well-controlled even when there are high levels of population substructure or admixture. For trio data, unlike for population-based data, RV allele-counting association methods will lead to inflated type I errors. However type I errors can be properly controlled by obtaining p values empirically through haplotype permutation. The power of the RV-TDT methods was evaluated and compared to the analysis of case-control data with a number of genetic and disease models. The RV-TDT was also used to analyze exome data from 199 Simons Simplex Collection autism trios and an association was observed with variants in ABCA7. Given the problem of adequately controlling for population substructure and admixture in RV association studies and the growing number of sequence-based trio studies, the RV-TDT is extremely beneficial to elucidate the involvement of RVs in the etiology of complex traits.