David B. Keator

Brain metabolic and clinical effects of rivastigmine in Alzheimer's disease

In-vivo metabolic measures with positron emission tomography using (18)F-fluorodeoxyglucose (FDG-PET) have demonstrated hypometabolism in temporal, frontal, and hippocampal areas during the early stages of Alzheimers disease (AD). Progression of the dementia in AD involves compromised cholinergic functioning. Cholinesterase inhibitors have demonstrated efficacy in improving cognition and behaviour in AD. In this study, we demonstrate the usefulness of FDG-PET in measuring the progression of untreated AD and its modification by treatment with rivastigmine (Exelon, Novartis Pharmaceuticals, East Hanover, New Jersey, USA), a centrally selective cholinesterase inhibitor of the carbamate type. Patients with mild to moderate probable AD (Mini-Mental Status Exam scores of 10-26, inclusive) were enrolled in a double-blind, placebo controlled comparison of three fixed daily doses of rivastigmine (3, 6, or 9 mg/d) or placebo for 26 wk. FDG-PET scans were obtained on 27 patients at baseline and following 26 wk of treatment using the Snodgrass Picture Naming activation task. A total of 71.4% of the patients treated with placebo deteriorated clinically compared to only 25.0% of the patients treated with rivastigmine (chi2 = 4.8; p & 0.03). Rivastigmine-responders (i.e. those who clinically improved or remained clinically stable as measured by the Clinicianaposs Interview-Based Impression of Change-plus) showed a marked increase in brain metabolism (p <0.01) involving, but not limited to, structures comprising the memory-related cortices and the prefrontal system. These metabolic changes were not observed in the placebo-treated patients or the rivastigmine non-responders. Of note is that responders increased hippocampal metabolism by 32.5% (p < 0.03) compared to a non-significant decrease in the non-responders (6.4%) and placebo-treated patients (4.1%). These results are consistent with the literature suggesting that FDG-PET can sensitively measure the progression of AD and its improvement with cholinesterase inhibitors. Rivastigmine prevented the expected deterioration in clinical status and dramatically increased brain metabolic activity in a majority of patients.

Increased dopamine activity associated with stuttering

POSITRON emission tomography using 6-FDOPA as a marker of presynaptic dopaminergic activity was used to investigate the role of the dopamine system in stuttering. Three patients with moderate to severe developmental stuttering were compared with six normal controls. Stuttering subjects showed significantly higher 6-FDOPA uptake than normal controls in medial pre-frontal cortex, deep orbital cortex, insular cortex, extended amygdala, auditory cortex and caudate tail. Elevated 6-FDOPA uptake in ventral limbic cortical and subcortical regions is compatible with the hypothesis that stuttering is associated with an overactive pre-synaptic dopamine system in brain regions that modulate verbalization.

Data sharing in neuroimaging research

Significant resources around the world have been invested in neuroimaging studies of brain function and disease. Easier access to this large body of work should have profound impact on research in cognitive neuroscience and psychiatry, leading to advances in the diagnosis and treatment of psychiatric and neurological disease. A trend toward increased sharing of neuroimaging data has emerged in recent years. Nevertheless, a number of barriers continue to impede momentum. Many researchers and institutions remain uncertain about how to share data or lack the tools and expertise to participate in data sharing. The use of electronic data capture (EDC) methods for neuroimaging greatly simplifies the task of data collection and has the potential to help standardize many aspects of data sharing. We review here the motivations for sharing neuroimaging data, the current data sharing landscape, and the sociological or technical barriers that still need to be addressed. The INCF Task Force on Neuroimaging Datasharing, in conjunction with several collaborative groups around the world, has started work on several tools to ease and eventually automate the practice of data sharing. It is hoped that such tools will allow researchers to easily share raw, processed, and derived neuroimaging data, with appropriate metadata and provenance records, and will improve the reproducibility of neuroimaging studies. By providing seamless integration of data sharing and analysis tools within a commodity research environment, the Task Force seeks to identify and minimize barriers to data sharing in the field of neuroimaging.

Function biomedical informatics research network recommendations for prospective multicenter functional MRI studies.

This report provides practical recommendations for the design and execution of multicenter functional MRI (MC‐fMRI) studies based on the collective experience of the Function Biomedical Informatics Research Network (FBIRN). The study was inspired by many requests from the fMRI community to FBIRN group members for advice on how to conduct MC‐fMRI studies. The introduction briefly discusses the advantages and complexities of MC‐fMRI studies. Prerequisites for MC‐fMRI studies are addressed before delving into the practical aspects of carefully and efficiently setting up a MC‐fMRI study. Practical multisite aspects include: (i) establishing and verifying scan parameters including scanner types and magnetic fields, (ii) establishing and monitoring of a scanner quality program, (iii) developing task paradigms and scan session documentation, (iv) establishing clinical and scanner training to ensure consistency over time, (v) developing means for uploading, storing, and monitoring of imaging and other data, (vi) the use of a traveling fMRI expert, and (vii) collectively analyzing imaging data and disseminating results. We conclude that when MC‐fMRI studies are organized well with careful attention to unification of hardware, software and procedural aspects, the process can be a highly effective means for accessing a desired participant demographics while accelerating scientific discovery. J. Magn. Reson. Imaging 2012;36:39–54.

A National Human Neuroimaging Collaboratory Enabled by the Biomedical Informatics Research Network (BIRN)

The aggregation of imaging, clinical, and behavioral data from multiple independent institutions and researchers presents both a great opportunity for biomedical research as well as a formidable challenge. Many research groups have well-established data collection and analysis procedures, as well as data and metadata format requirements that are particular to that group. Moreover, the types of data and metadata collected are quite diverse, including image, physiological, and behavioral data, as well as descriptions of experimental design, and preprocessing and analysis methods. Each of these types of data utilizes a variety of software tools for collection, storage, and processing. Furthermore sites are reluctant to release control over the distribution and access to the data and the tools. To address these needs, the biomedical informatics research network (BIRN) has developed a federated and distributed infrastructure for the storage, retrieval, analysis, and documentation of biomedical imaging data. The infrastructure consists of distributed data collections hosted on dedicated storage and computational resources located at each participating site, a federated data management system and data integration environment, an extensible markup language (XML) schema for data exchange, and analysis pipelines, designed to leverage both the distributed data management environment and the available grid computing resources.

D1 receptor alleles predict PET metabolic correlates of clinical response to clozapine

A goal of pharmacogenetics is to clarify associations between allelic variation and risk factors in psychiatric illness. We report changes in regional brain metabolism based on dopamine alleles. Treatment-resistant schizophrenic subjects were positron emission tomography scanned with 18F-fluorodeoxyglucose after 5 weeks each of placebo and clozapine treatment. Significant regional brain metabolic effects were found for the D1 receptor genotypes (P<0.05), adjusted for multiple comparisons. Metabolic decreases for the 2,2 genotype but not the 1,2 genotype were observed in all major sectors of the brain, with the exception of the ventral parts of the caudate and putamen. Frontal, temporal, parietal, and occipital neocortices showed decreased metabolism as did the cingulate juxta-allocortex and the parahippocampal allocortex. Decreases were also observed in the thalamus, amygdala, and cerebellum bilaterally. No significant metabolic differences by genotype were observed for D3, 5HT2A, and 5HT2C polymorphisms. In terms of clinical response, the DRD1 2,2 genotype significantly improved with clozapine treatment, demonstrating a 30% decrease in the Brief Psychiatric Rating Scale positive symptoms in contrast to a 7% worsening for the 1,2 genotype (P<0.05). In this preliminary study, brain metabolic and clinical response to clozapine are related to the D1 receptor genotype.

Frontal lobe metabolic decreases with sleep deprivation not totally reversed by recovery sleep

We studied the effects of total sleep deprivation and recovery sleep in normal subjects using position emission tomography with 18F-deoxyglycose. Sleep deprivation resulted in a significant decrease in relative metabolism of the frontal cortex, thalamus, and striatum. Recovery sleep was found to have only a partial restorative effect on frontal lobe function with minimal reversal of subcortical deficits. Sleep may be especially important for maintenance of frontal lobe activity.

Genome-wide strategies for discovering genetic influences on cognition and cognitive disorders: Methodological considerations

Introduction. Genes play a well-documented role in determining normal cognitive function. This paper focuses on reviewing strategies for the identification of common genetic variation in genes that modulate normal and abnormal cognition with a genome-wide association scan (GWAS). GWASs make it possible to survey the entire genome to discover important but unanticipated genetic influences. Methods. The use of a quantitative phenotype in combination with a GWAS provides many advantages over a case-control design, both in power and in physiological understanding of the underlying cognitive processes. We review the major features of this approach, and show how, using a General Linear Model method, the contribution of each Single Nucleotide Polymorphism (SNP) to the phenotype is determined, and adjustments then made for multiple tests. An example of the strategy is presented, in which fMRI measures of cortical inefficiency while performing a working memory task are used as the quantitative phenotype. We estimate power under different effect sizes (10–30%) and variations in allelic frequency for a Quantitative Trait (QT) (10–20%), and compare them to a case-control design with an Odds Ratio (OR) of 1.5, showing how a QT approach is superior to a traditional case-control. In the presented example, this method identifies putative susceptibility genes for schizophrenia which affect prefrontal efficiency and have functions related to cell migration, forebrain development and stress response. Conclusion. The use of QT as phenotypes provide increased statistical power over categorical association approaches and when combined with a GWAS creates a strategy for identification of unanticipated genes that modulate cognitive processes and cognitive disorders.

A phase1 study of stereotactic gene delivery of AAV2-NGF for Alzheimer's disease

Nerve growth factor (NGF) is an endogenous neurotrophic‐factor protein with the potential to restore function and to protect degenerating cholinergic neurons in Alzheimers disease (AD), but safe and effective delivery has proved unsuccessful.

Gene discovery through imaging genetics: identification of two novel genes associated with schizophrenia

We have discovered two genes, RSRC1 and ARHGAP18, associated with schizophrenia and in an independent study provided additional support for this association. We have both discovered and verified the association of two genes, RSRC1 and ARHGAP18, with schizophrenia. We combined a genome-wide screening strategy with neuroimaging measures as the quantitative phenotype and identified the single nucleotide polymorphisms (SNPs) related to these genes as consistently associated with the phenotypic variation. To control for the risk of false positives, the empirical P-value for association significance was calculated using permutation testing. The quantitative phenotype was Blood-Oxygen-Level Dependent (BOLD) Contrast activation in the left dorsal lateral prefrontal cortex measured during a working memory task. The differential distribution of SNPs associated with these two genes in cases and controls was then corroborated in a larger, independent sample of patients with schizophrenia (n=82) and healthy controls (n=91), thus suggesting a putative etiological function for both genes in schizophrenia. Up until now these genes have not been linked to any neuropsychiatric illness, although both genes have a function in prenatal brain development. We introduce the use of functional magnetic resonance imaging activation as a quantitative phenotype in conjunction with genome-wide association as a gene discovery tool.