IMPORTANCE Currently, fewer than 40% of patients treated for major depressive disorder achieve remission with initial treatment. Identification of a biological marker that might improve these odds ...could have significant health and economic impact. OBJECTIVE To identify a candidate neuroimaging “treatment-specific biomarker” that predicts differential outcome to either medication or psychotherapy. DESIGN Brain glucose metabolism was measured with positron emission tomography prior to treatment randomization to either escitalopram oxalate or cognitive behavior therapy for 12 weeks. Patients who did not remit on completion of their phase 1 treatment were offered enrollment in phase 2 comprising an additional 12 weeks of treatment with combination escitalopram and cognitive behavior therapy. SETTING Mood and anxiety disorders research program at an academic medical center. PARTICIPANTS Men and women aged 18 to 60 years with currently untreated major depressive disorder. INTERVENTION Randomized assignment to 12 weeks of treatment with either escitalopram oxalate (10-20 mg/d) or 16 sessions of manual-based cognitive behavior therapy. MAIN OUTCOME AND MEASURE Remission, defined as a 17-item Hamilton Depression Rating Scale score of 7 or less at both weeks 10 and 12, as assessed by raters blinded to treatment. RESULTS Positive and negative predictors of remission were identified with a 2-way analysis of variance treatment (escitalopram or cognitive behavior therapy) × outcome (remission or nonresponse) interaction. Of 65 protocol completers, 38 patients with clear outcomes and usable positron emission tomography scans were included in the primary analysis: 12 remitters to cognitive behavior therapy, 11 remitters to escitalopram, 9 nonresponders to cognitive behavior therapy, and 6 nonresponders to escitalopram. Six limbic and cortical regions were identified, with the right anterior insula showing the most robust discriminant properties across groups (effect size = 1.43). Insula hypometabolism (relative to whole-brain mean) was associated with remission to cognitive behavior therapy and poor response to escitalopram, while insula hypermetabolism was associated with remission to escitalopram and poor response to cognitive behavior therapy. CONCLUSIONS AND RELEVANCE If verified with prospective testing, the insula metabolism-based treatment-specific biomarker defined in this study provides the first objective marker, to our knowledge, to guide initial treatment selection for depression. TRIAL REGISTRATION Registered at clinicaltrials.gov (NCT00367341)
The identification of phenotypic associations in high-dimensional brain connectivity data represents the next frontier in the neuroimaging connectomics era. Exploration of brain–phenotype ...relationships remains limited by statistical approaches that are computationally intensive, depend on a priori hypotheses, or require stringent correction for multiple comparisons. Here, we propose a computationally efficient, data-driven technique for connectome-wide association studies (CWAS) that provides a comprehensive voxel-wise survey of brain–behavior relationships across the connectome; the approach identifies voxels whose whole-brain connectivity patterns vary significantly with a phenotypic variable. Using resting state fMRI data, we demonstrate the utility of our analytic framework by identifying significant connectivity–phenotype relationships for full-scale IQ and assessing their overlap with existent neuroimaging findings, as synthesized by openly available automated meta-analysis (www.neurosynth.org). The results appeared to be robust to the removal of nuisance covariates (i.e., mean connectivity, global signal, and motion) and varying brain resolution (i.e., voxelwise results are highly similar to results using 800 parcellations). We show that CWAS findings can be used to guide subsequent seed-based correlation analyses. Finally, we demonstrate the applicability of the approach by examining CWAS for three additional datasets, each encompassing a distinct phenotypic variable: neurotypical development, Attention-Deficit/Hyperactivity Disorder diagnostic status, and L-DOPA pharmacological manipulation. For each phenotype, our approach to CWAS identified distinct connectome-wide association profiles, not previously attainable in a single study utilizing traditional univariate approaches. As a computationally efficient, extensible, and scalable method, our CWAS framework can accelerate the discovery of brain–behavior relationships in the connectome.
•Develop novel approach to connectome-wide association studies.•Identify voxels whose whole-brain connectivity maps are associated with a phenotype.•Discover associations with IQ in default, ventral attention, and visual networks.•Results robust to removal of global signal, mean connectivity, and motion.•Significant associations can guide seed-selection for seed correlation analysis.
Magnetic resonance imaging (MRI) has detected changes in pancreas volume and other characteristics in type 1 and type 2 diabetes. However, differences in MRI technology and approaches across ...locations currently limit the incorporation of pancreas imaging into multisite trials. The purpose of this study was to develop a standardized MRI protocol for pancreas imaging and to define the reproducibility of these measurements. Calibrated phantoms with known MRI properties were imaged at five sites with differing MRI hardware and software to develop a harmonized MRI imaging protocol. Subsequently, five healthy volunteers underwent MRI at four sites using the harmonized protocol to assess pancreas size, shape, apparent diffusion coefficient (ADC), longitudinal relaxation time (T1), magnetization transfer ratio (MTR), and pancreas and hepatic fat fraction. Following harmonization, pancreas size, surface area to volume ratio, diffusion, and longitudinal relaxation time were reproducible, with coefficients of variation less than 10%. In contrast, non-standardized image processing led to greater variation in MRI measurements. By using a standardized MRI image acquisition and processing protocol, quantitative MRI of the pancreas performed at multiple locations can be incorporated into clinical trials comparing pancreas imaging measures and metabolic state in individuals with type 1 or type 2 diabetes.
The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In ...this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.
People's differences in cognitive functions are partly heritable and are associated with important life outcomes. Previous genome-wide association (GWA) studies of cognitive functions have found ...evidence for polygenic effects yet, to date, there are few replicated genetic associations. Here we use data from the UK Biobank sample to investigate the genetic contributions to variation in tests of three cognitive functions and in educational attainment. GWA analyses were performed for verbal-numerical reasoning (N=36 035), memory (N=112 067), reaction time (N=111 483) and for the attainment of a college or a university degree (N=111 114). We report genome-wide significant single-nucleotide polymorphism (SNP)-based associations in 20 genomic regions, and significant gene-based findings in 46 regions. These include findings in the ATXN2, CYP2DG, APBA1 and CADM2 genes. We report replication of these hits in published GWA studies of cognitive function, educational attainment and childhood intelligence. There is also replication, in UK Biobank, of SNP hits reported previously in GWA studies of educational attainment and cognitive function. GCTA-GREML analyses, using common SNPs (minor allele frequency>0.01), indicated significant SNP-based heritabilities of 31% (s.e.m.=1.8%) for verbal-numerical reasoning, 5% (s.e.m.=0.6%) for memory, 11% (s.e.m.=0.6%) for reaction time and 21% (s.e.m.=0.6%) for educational attainment. Polygenic score analyses indicate that up to 5% of the variance in cognitive test scores can be predicted in an independent cohort. The genomic regions identified include several novel loci, some of which have been associated with intracranial volume, neurodegeneration, Alzheimer's disease and schizophrenia.
A recent genome-wide association study (GWAS) reported evidence for association between rs1344706 within ZNF804A (encoding zinc-finger protein 804A) and schizophrenia (P=1.61 × 10(-7)), and stronger ...evidence when the phenotype was broadened to include bipolar disorder (P=9.96 × 10(-9)). In this study we provide additional evidence for association through meta-analysis of a larger data set (schizophrenia/schizoaffective disorder N=18 945, schizophrenia plus bipolar disorder N=21 274 and controls N=38 675). We also sought to better localize the association signal using a combination of de novo polymorphism discovery in exons, pooled de novo polymorphism discovery spanning the genomic sequence of the locus and high-density linkage disequilibrium (LD) mapping. The meta-analysis provided evidence for association between rs1344706 that surpasses widely accepted benchmarks of significance by several orders of magnitude for both schizophrenia (P=2.5 × 10(-11), odds ratio (OR) 1.10, 95% confidence interval 1.07-1.14) and schizophrenia and bipolar disorder combined (P=4.1 × 10(-13), OR 1.11, 95% confidence interval 1.07-1.14). After de novo polymorphism discovery and detailed association analysis, rs1344706 remained the most strongly associated marker in the gene. The allelic association at the ZNF804A locus is now one of the most compelling in schizophrenia to date, and supports the accumulating data suggesting overlapping genetic risk between schizophrenia and bipolar disorder.
Bipolar disorder and schizophrenia are two often severe disorders with high heritabilities. Recent studies have demonstrated a large overlap of genetic risk loci between these disorders but ...diagnostic and molecular distinctions still remain. Here, we perform a combined genome-wide association study (GWAS) of 19 779 bipolar disorder (BP) and schizophrenia (SCZ) cases versus 19 423 controls, in addition to a direct comparison GWAS of 7129 SCZ cases versus 9252 BP cases. In our case-control analysis, we identify five previously identified regions reaching genome-wide significance (CACNA1C, IFI44L, MHC, TRANK1 and MAD1L1) and a novel locus near PIK3C2A. We create a polygenic risk score that is significantly different between BP and SCZ and show a significant correlation between a BP polygenic risk score and the clinical dimension of mania in SCZ patients. Our results indicate that first, combining diseases with similar genetic risk profiles improves power to detect shared risk loci and second, that future direct comparisons of BP and SCZ are likely to identify loci with significant differential effects. Identifying these loci should aid in the fundamental understanding of how these diseases differ biologically. These findings also indicate that combining clinical symptom dimensions and polygenic signatures could provide additional information that may someday be used clinically.
We report results from the Bipolar Exome (BipEx) collaboration analysis of whole-exome sequencing of 13,933 patients with bipolar disorder (BD) matched with 14,422 controls. We find an excess of ...ultra-rare protein-truncating variants (PTVs) in patients with BD among genes under strong evolutionary constraint in both major BD subtypes. We find enrichment of ultra-rare PTVs within genes implicated from a recent schizophrenia exome meta-analysis (SCHEMA; 24,248 cases and 97,322 controls) and among binding targets of CHD8. Genes implicated from genome-wide association studies (GWASs) of BD, however, are not significantly enriched for ultra-rare PTVs. Combining gene-level results with SCHEMA, AKAP11 emerges as a definitive risk gene (odds ratio (OR) = 7.06, P = 2.83 × 10
). At the protein level, AKAP-11 interacts with GSK3B, the hypothesized target of lithium, a primary treatment for BD. Our results lend support to BD's polygenicity, demonstrating a role for rare coding variation as a significant risk factor in BD etiology.
The kinase domains of receptor tyrosine kinases (RTKs) are highly conserved, yet they are able to discriminate among potential substrates to selectively activate downstream signaling pathways. In ...this study, we tested the importance of catalytic domain specificity by creating two series of chimeric RTKs. In one set, the kinase domain of insulin-like growth factor I receptor (IGF1R) was replaced by the kinase domains from insulin receptor (IR), macrophage stimulating protein 1 receptor/Ron (Ron) or Src. In the other set of chimeras, the kinase domain of epidermal growth factor receptor (EGFR) was similarly replaced by the kinase domains of IR, Ron, or Src. We expressed the wild-type and chimeric forms of the receptors in mammalian cells. For some signaling events, such as recognition of IRS1, the identity of the tyrosine kinase catalytic domain did not appear to be crucial. In contrast, recognition of some sites, such as the C-terminal autophosphorylation sites on EGFR, did depend on the identity of the kinase domain. Our data also showed that ligand dependence was lost when the native kinase domains were replaced by Src, suggesting that the identity of the kinase domains could be important for proper receptor regulation. Overall, the results are consistent with the idea that the fidelity of RTK signaling depends on co-localization and targeting with substrates, as well as on the intrinsic specificity of the kinase domain.
•EGFR and IGF1R are functional receptors when their kinase domains are replaced with heterologous tyrosine kinase domains.•Recognition of IRS-1 by IGF1R did not depend on the identity of the kinase domain.•Recognition of EGFR C-terminal sites depended on the identity of the kinase domain.•Introduction of the Src kinase domain disrupted normal ligand-dependent regulation.
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