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Cole J.,King's College London | Weinberger D.R.,U.S. National Institutes of Health | Mattay V.S.,U.S. National Institutes of Health | Cheng X.,U.S. National Institutes of Health | And 11 more authors.
Genes, Brain and Behavior | Year: 2011

Neuroimaging research implicates the hippocampus in the aetiology of major depressive disorder (MDD). Imaging genetics studies have investigated the influence of the serotonin transporter-linked polymorphic region (5HTTLPR) and brain-derived neurotrophic factor (BDNF) Val66Met polymorphism on the hippocampus in healthy individuals and patients with depression (MDD). However, conflicting results have led to inconclusive evidence about the effect of 5HTTLPR or BDNF on hippocampal volume (HCV). We hypothesized that analysis methods based on three-dimensional (3D) hippocampal shape mapping could offer improved sensitivity to clarify these effects. Magnetic resonance imaging data were collected in parallel samples of 111 healthy individuals and 84 MDD patients. Manual hippocampal segmentation was conducted and the resulting data used to investigate the influence of 5HTTLPR and BDNF Val66Met genotypes on HCV and 3D shape within each sample. Hippocampal volume normalized by intracranial volume (ICV) showed no significant difference between 5HTTLPR S allele carriers and L/L homozygotes or between BDNF Met allele carriers and Val/Val homozygotes in the group of healthy individuals. Moreover, there was no significant difference in normalized HCV between 5HTTLPR diallelic and triallelic classifications or between the BDNF Val66Met genotypes in MDD patients, although there was a relationship between BDNF Val66Met and ICV. Shape analysis detected dispersed between-group differences, but these effects did not survive multiple testing correction. In this study, there was no evidence of a genetic effect for 5HTTLPR or BDNF Val66Met on hippocampal morphology in either healthy individuals or MDD patients despite the relatively large sample sizes and sensitive methodology. © 2011 The Authors. Genes, Brain and Behavior © 2011 Blackwell Publishing Ltd and International Behavioural and Neural Genetics Society.


Ahn J.W.,Guys and St Thomas NHS Foundation Trust | Dixit A.,King's College London | Dixit A.,Biomedical Research Center for Mental Health at South London | Johnston C.,King's College London | And 6 more authors.
Database | Year: 2013

Studies of copy number variation (genomic imbalance) are providing insight into both complex and Mendelian genetic disorders. Array comparative genomic hybridization (array CGH), a tool for detecting copy number variants at a resolution previously unattainable in clinical diagnostics, is increasingly used as a first-line test at clinical genetics laboratories. Many copy number variants are of unknown significance; correlation and comparison with other patients will therefore be essential for interpretation. We present a resource for clinicians and researchers to identify specific copy number variants and associated phenotypes in patients from a single catchment area, tested using array CGH at the SE Thames Regional Genetics Centre, London. User-friendly searching is available, with links to external resources, providing a powerful tool for the elucidation of gene function. We hope to promote research by facilitating interactions between researchers and patients. The BBGRE (Brain and Body Genetic Resource Exchange) resource can be accessed at the following website: http://bbgre.org © The Author(s) 2013. Published by Oxford University Press.


PubMed | Medical University of Lódz, University of Perugia, Aristotle University of Thessaloniki, Biomedical Research Center for Mental Health at South London and 3 more.
Type: Journal Article | Journal: PloS one | Year: 2014

We propose a novel approach to predicting disease progression in Alzheimers disease (AD)--multivariate ordinal regression--which inherently models the ordered nature of brain atrophy spanning normal aging (CTL) to mild cognitive impairment (MCI) to AD. Ordinal regression provides probabilistic class predictions as well as a continuous index of disease progression--the ORCHID (Ordinal Regression Characteristic Index of Dementia) score. We applied ordinal regression to 1023 baseline structural MRI scans from two studies: the US-based Alzheimers Disease Neuroimaging Initiative (ADNI) and the European based AddNeuroMed program. Here, the acquired AddNeuroMed dataset was used as a completely independent test set for the ordinal regression model trained on the ADNI cohort providing an optimal assessment of model generalizability. Distinguishing CTL-like (CTL and stable MCI) from AD-like (MCI converters and AD) resulted in balanced accuracies of 82% (cross-validation) for ADNI and 79% (independent test set) for AddNeuroMed. For prediction of conversion from MCI to AD, balanced accuracies of 70% (AUC of 0.75) and 75% (AUC of 0.81) were achieved. The ORCHID score was computed for all subjects. We showed that this measure significantly correlated with MMSE at 12 months ( = -0.64, ADNI and = -0.59, AddNeuroMed). Additionally, the ORCHID score can help fractionate subjects with unstable diagnoses (e.g. reverters and healthy controls who later progressed to MCI), moderately late converters (12-24 months) and late converters (24-36 months). A comparison with results in the literature and direct comparison with a binary classifier suggests that the performance of this framework is highly competitive.


Doyle O.M.,King's College London | Westman E.,King's College London | Westman E.,Karolinska Institutet | Marquand A.F.,King's College London | And 13 more authors.
PLoS ONE | Year: 2014

We propose a novel approach to predicting disease progression in Alzheimer's disease (AD) - multivariate ordinal regression - which inherently models the ordered nature of brain atrophy spanning normal aging (CTL) to mild cognitive impairment (MCI) to AD. Ordinal regression provides probabilistic class predictions as well as a continuous index of disease progression - the ORCHID (Ordinal Regression Characteristic Index of Dementia) score. We applied ordinal regression to 1023 baseline structural MRI scans from two studies: the US-based Alzheimer's Disease Neuroimaging Initiative (ADNI) and the European based AddNeuroMed program. Here, the acquired AddNeuroMed dataset was used as a completely independent test set for the ordinal regression model trained on the ADNI cohort providing an optimal assessment of model generalizability. Distinguishing CTL-like (CTL and stable MCI) from AD-like (MCI converters and AD) resulted in balanced accuracies of 82% (cross-validation) for ADNI and 79% (independent test set) for AddNeuroMed. For prediction of conversion from MCI to AD, balanced accuracies of 70% (AUC of 0.75) and 75% (AUC of 0.81) were achieved. The ORCHID score was computed for all subjects. We showed that this measure significantly correlated with MMSE at 12 months (ρ = -0.64, ADNI and ρ = -0.59, AddNeuroMed). Additionally, the ORCHID score can help fractionate subjects with unstable diagnoses (e.g. reverters and healthy controls who later progressed to MCI), moderately late converters (12-24 months) and late converters (24-36 months). A comparison with results in the literature and direct comparison with a binary classifier suggests that the performance of this framework is highly competitive. © 2014 Doyle et al.

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