Our paper ‘Node-based Gaussian Graphical Model for Identifying Discriminative Brain Regions from Connectivity Graphs’ has won the best paper award at MLMI 2015 (held in conjunction with MICCAI in Munich, Germany).
Our paper entitled “Regional brain hypometabolism is unrelated to regional amyloid plaque burden” is now available online at Brain.
Summary:
In its original form, the amyloid cascade hypothesis of Alzheimer’s disease holds that fibrillar deposits of amyloid are an early, driving force in pathological events leading ultimately to neuronal death. Early clinicopathological investigations highlighted a number of inconsistencies leading to an updated hypothesis in which amyloid plaques give way to amyloid oligomers as the driving force in pathogenesis. Rather than focusing on the inconsistencies, amyloid imaging studies have tended to highlight the overlap between regions that show early amyloid plaque signal on positron emission tomography and that also happen to be affected early in Alzheimer’s disease. Recent imaging studies investigating the regional dependency between metabolism and amyloid plaque deposition have arrived at conflicting results, with some showing regional associations and other not. We extracted multimodal neuroimaging data from the Alzheimer’s disease neuroimaging database for 227 healthy controls and 434 subjects with mild cognitive impairment. We analysed regional patterns of amyloid deposition, regional glucose metabolism and regional atrophy using florbetapir (18F) positron emission tomography, 18F-fluordeoxyglucose positron emission tomography and T1-weighted magnetic resonance imaging, respectively. Specifically, we derived grey matter density and standardized uptake value ratios for both positron emission tomography tracers in 404 functionally defined regions of interest. We examined the relation between regional glucose metabolism and amyloid plaques using linear models. For each region of interest, correcting for regional grey matter density, age, education and disease status, we tested the association of regional glucose metabolism with (i) cortex-wide florbetapir uptake; (ii) regional (i.e. in the same region of interest) florbetapir uptake; and (iii) regional florbetapir uptake while correcting in addition for cortex-wide florbetapir uptake. P-values for each setting were Bonferroni corrected for 404 tests. Regions showing significant hypometabolism with increasing cortex-wide amyloid burden were classic Alzheimer’s disease-related regions: the medial and lateral parietal cortices. The associations between regional amyloid burden and regional metabolism were more heterogeneous: there were significant hypometabolic effects in posterior cingulate, precuneus, and parietal regions but also significant positive associations in bilateral hippocampus and entorhinal cortex. However, after correcting for global amyloid burden, few of the negative associations remained and the number of positive associations increased. Given the wide-spread distribution of amyloid plaques, if the canonical cascade hypothesis were true, we would expect wide-spread, cortical hypometabolism. Instead, cortical hypometabolism appears to be linked to global amyloid burden. Thus we conclude that regional fibrillar amyloid deposition has little to no association with regional hypometabolism.
Our paper on predicting sleep stages from resting-state fMRI has been accepted for publication in NeuroImage! The title of the manuscript is “Validation of non-REM sleep stage decoding from resting state fMRI using linear support vector machines”. A Poster related to this were can be accessed here.
Abstract: TBD
Our microarray probe re-annotation pipeline has been accepted for publication in PLoS ONE. The paper has the title “Re-Annotator: Annotation Pipeline for Microarray Probe Sequences”. A preprint of the manuscript can be accessed on bioRxiv!
Abstract:
Microarray technologies are established approaches for high throughput gene expression, methylation and genotyping analysis. An accurate mapping of the array probes is essential to generate reliable biological findings. However, manufacturers of the microarray platforms typically provide incomplete and outdated annotation tables, which often rely on older genome and transcriptome versions that differ substantially from up-to-date sequence databases. Here, we present the Re-Annotator, a re-annotation pipeline for microarray probe sequences. It is primarily designed for gene expression microarrays but can also be adapted to other types of microarrays. The Re-Annotator uses a custom-built mRNA reference database to identify the positions of gene expression array probe sequences. We applied Re-Annotator to the Illumina Human-HT12 v4 microarray platform and found that about one quarter (25%) of the probes differed from the manufacturer’s annotation. In further computational experiments on experimental gene expression data, we compared Re-Annotator to another probe re-annotation tool, ReMOAT, and found that Re-Annotator provided an improved re-annotation of microarray probes. A thorough re-annotation of probe information is crucial to any microarray analysis. The Re-Annotator pipeline is freely available at http://sourceforge.net/projects/reannotator along with re-annotated files for Illumina microarrays HumanHT-12 v3/v4 and MouseRef-8 v2.
I am looking for a PhD student to work on Imaging Genetics in Alzheimer’s disease and frontotemporal dementia (FTD). More information is available here:
USING IMAGING GENETICS TO BETTER UNDERSTAND ALZHEIMER’S AND FRONTOTEMPORAL DEMENTIA
Our paper with the title “Regional Brain Hypometabolism Is Unrelated to Regional Amyloid Plaque Burden” was accepted in Brain!
Abstract: TBA
Our paper with the title “Node-based Gaussian Graphical Model for Identifying Discriminative Brain Regions from Connectivity Graphs” was accepted for oral presentation at this year’s MLMI held in Munich (in conjunction with MICCAI2015).
Abstract: Despite that the bulk of our knowledge on brain function is established around brain regions, current methods for comparing connectivity graphs largely take an edge-based approach with the aim of identifying discriminative connections. In this paper, we explore a node-based Gaussian Graphical Model (NBGGM) that facilitates identification of brain regions attributing to connectivity differences seen between a pair of graphs. To enable group analysis, we propose an extension of NBGGM via incorporation of stability selection. We evaluate NBGGM on two functional magnetic resonance imaging (fMRI) datasets pertaining to within and between-group studies. We show that NBGGM more consistently selects the same brain regions over random data splits than using node-based graph measures. Importantly, the regions found by NBGGM correspond well to those known to be involved for the investigated conditions.
Our paper with the title “Correlated gene expression supports synchronous activity in brain networks” appeared in this week’s issue of Science.
Press releases: Stanford (English) and University Geneva (French and German)
Abstract:
During rest, brain activity is synchronized between different regions widely distributed throughout the brain, forming functional networks. However, the molecular mechanisms supporting functional connectivity remain undefined. We show that functional brain networks defined with resting-state functional magnetic resonance imaging can be recapitulated by using measures of correlated gene expression in a post mortem brain tissue data set. The set of 136 genes we identify is significantly enriched for ion channels. Polymorphisms in this set of genes significantly affect resting-state functional connectivity in a large sample of healthy adolescents. Expression levels of these genes are also significantly associated with axonal connectivity in the mouse. The results provide convergent, multimodal evidence that resting-state functional networks correlate with the orchestrated activity of dozens of genes linked to ion channel activity and synaptic function.
We did Science!
Our paper with the title “Genetic Differences in the Immediate Transcriptome Response to Stress Predict Risk-Related Brain Function and Psychiatric Disorders” was published in Neuron.
Summary:
Depression risk is exacerbated by genetic factors and stress exposure; however, the biological mechanisms through which these factors interact to confer depression risk are poorly understood. One putative biological mechanism implicates variability in the ability of cortisol, released in response to stress, to trigger a cascade of adaptive genomic and non-genomic processes through glucocorticoid receptor (GR) activation. Here, we demonstrate that common genetic variants in long-range enhancer elements modulate the immediate transcriptional response to GR activation in human blood cells. These functional genetic variants increase risk for depression and co-heritable psychiatric disorders. Moreover, these risk variants are associated with inappropriate amygdala reactivity, a transdiagnostic psychiatric endophenotype and an important stress hormone response trigger. Network modeling and animal experiments suggest that these genetic differences in GR-induced transcriptional activation may mediate the risk for depression and other psychiatric disorders by altering a network of functionally related stress-sensitive genes in blood and brain.
Video abstract:
Our paper with the title “TBA” was accepted for publication in Science.
Abstract:
TBA