565 research outputs found
GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network
We propose a novel convolutional neural network for lesion detection from
weak labels. Only a single, global label per image - the lesion count - is
needed for training. We train a regression network with a fully convolutional
architecture combined with a global pooling layer to aggregate the 3D output
into a scalar indicating the lesion count. When testing on unseen images, we
first run the network to estimate the number of lesions. Then we remove the
global pooling layer to compute localization maps of the size of the input
image. We evaluate the proposed network on the detection of enlarged
perivascular spaces in the basal ganglia in MRI. Our method achieves a
sensitivity of 62% with on average 1.5 false positives per image. Compared with
four other approaches based on intensity thresholding, saliency and class maps,
our method has a 20% higher sensitivity.Comment: Article published in MICCAI 2017. We corrected a few errors from the
first version: padding, loss, typos and update of the DOI numbe
Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks
Evaluating similarity between graphs is of major importance in several
computer vision and pattern recognition problems, where graph representations
are often used to model objects or interactions between elements. The choice of
a distance or similarity metric is, however, not trivial and can be highly
dependent on the application at hand. In this work, we propose a novel metric
learning method to evaluate distance between graphs that leverages the power of
convolutional neural networks, while exploiting concepts from spectral graph
theory to allow these operations on irregular graphs. We demonstrate the
potential of our method in the field of connectomics, where neuronal pathways
or functional connections between brain regions are commonly modelled as
graphs. In this problem, the definition of an appropriate graph similarity
function is critical to unveil patterns of disruptions associated with certain
brain disorders. Experimental results on the ABIDE dataset show that our method
can learn a graph similarity metric tailored for a clinical application,
improving the performance of a simple k-nn classifier by 11.9% compared to a
traditional distance metric.Comment: International Conference on Medical Image Computing and
Computer-Assisted Interventions (MICCAI) 201
Extracting the Groupwise Core Structural Connectivity Network: Bridging Statistical and Graph-Theoretical Approaches
Finding the common structural brain connectivity network for a given
population is an open problem, crucial for current neuro-science. Recent
evidence suggests there's a tightly connected network shared between humans.
Obtaining this network will, among many advantages , allow us to focus
cognitive and clinical analyses on common connections, thus increasing their
statistical power. In turn, knowledge about the common network will facilitate
novel analyses to understand the structure-function relationship in the brain.
In this work, we present a new algorithm for computing the core structural
connectivity network of a subject sample combining graph theory and statistics.
Our algorithm works in accordance with novel evidence on brain topology. We
analyze the problem theoretically and prove its complexity. Using 309 subjects,
we show its advantages when used as a feature selection for connectivity
analysis on populations, outperforming the current approaches
Spectral Graph Convolutions for Population-based Disease Prediction
Exploiting the wealth of imaging and non-imaging information for disease
prediction tasks requires models capable of representing, at the same time,
individual features as well as data associations between subjects from
potentially large populations. Graphs provide a natural framework for such
tasks, yet previous graph-based approaches focus on pairwise similarities
without modelling the subjects' individual characteristics and features. On the
other hand, relying solely on subject-specific imaging feature vectors fails to
model the interaction and similarity between subjects, which can reduce
performance. In this paper, we introduce the novel concept of Graph
Convolutional Networks (GCN) for brain analysis in populations, combining
imaging and non-imaging data. We represent populations as a sparse graph where
its vertices are associated with image-based feature vectors and the edges
encode phenotypic information. This structure was used to train a GCN model on
partially labelled graphs, aiming to infer the classes of unlabelled nodes from
the node features and pairwise associations between subjects. We demonstrate
the potential of the method on the challenging ADNI and ABIDE databases, as a
proof of concept of the benefit from integrating contextual information in
classification tasks. This has a clear impact on the quality of the
predictions, leading to 69.5% accuracy for ABIDE (outperforming the current
state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion,
significantly outperforming standard linear classifiers where only individual
features are considered.Comment: International Conference on Medical Image Computing and
Computer-Assisted Interventions (MICCAI) 201
Simultaneous Matrix Diagonalization for Structural Brain Networks Classification
This paper considers the problem of brain disease classification based on
connectome data. A connectome is a network representation of a human brain. The
typical connectome classification problem is very challenging because of the
small sample size and high dimensionality of the data. We propose to use
simultaneous approximate diagonalization of adjacency matrices in order to
compute their eigenstructures in more stable way. The obtained approximate
eigenvalues are further used as features for classification. The proposed
approach is demonstrated to be efficient for detection of Alzheimer's disease,
outperforming simple baselines and competing with state-of-the-art approaches
to brain disease classification
Evaluating 35 Methods to Generate Structural Connectomes Using Pairwise Classification
There is no consensus on how to construct structural brain networks from
diffusion MRI. How variations in pre-processing steps affect network
reliability and its ability to distinguish subjects remains opaque. In this
work, we address this issue by comparing 35 structural connectome-building
pipelines. We vary diffusion reconstruction models, tractography algorithms and
parcellations. Next, we classify structural connectome pairs as either
belonging to the same individual or not. Connectome weights and eight
topological derivative measures form our feature set. For experiments, we use
three test-retest datasets from the Consortium for Reliability and
Reproducibility (CoRR) comprised of a total of 105 individuals. We also compare
pairwise classification results to a commonly used parametric test-retest
measure, Intraclass Correlation Coefficient (ICC).Comment: Accepted for MICCAI 2017, 8 pages, 3 figure
GYG1 causing progressive limb girdle myopathy with onset during teenage years (polyglucosan body myopathy 2)
An 84-year-old lady with slowly progressive limb and axial muscle weakness with onset in her teens was referred for genetic investigations. Targeted next generation sequencing (NGS) revealed a homozygous mutation GYG1 in exon5:c.487delG:p.D163fs, confirming the diagnosis of Polyglucosan Body Myopathy 2 (PGBM2). Retrospective review of muscle pathology revealed a florid vacuolar myopathy with histochemical and ultrastructural features consistent with a polyglucosan storage myopathy. No cardiac symptoms were reported. Our case is consistent with the core phenotype of GYG1-related PGBM2 apart from an early onset of weakness without cardiac symptoms. The presence of α-amylase resistant PAS-positive material in skeletal muscle biopsy of patients with slowly progressive limb girdle muscle weakness should prompt the search for GYG1 mutations. This case highlights the combined role of muscle pathology and NGS in the molecular resolution of undiagnosed patients with undiagnosed neuromuscular conditions
Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction
This paper introduces a novel methodology to integrate human brain
connectomics and parcellation for brain tumor segmentation and survival
prediction. For segmentation, we utilize an existing brain parcellation atlas
in the MNI152 1mm space and map this parcellation to each individual subject
data. We use deep neural network architectures together with hard negative
mining to achieve the final voxel level classification. For survival
prediction, we present a new method for combining features from connectomics
data, brain parcellation information, and the brain tumor mask. We leverage the
average connectome information from the Human Connectome Project and map each
subject brain volume onto this common connectome space. From this, we compute
tractographic features that describe potential neural disruptions due to the
brain tumor. These features are then used to predict the overall survival of
the subjects. The main novelty in the proposed methods is the use of normalized
brain parcellation data and tractography data from the human connectome project
for analyzing MR images for segmentation and survival prediction. Experimental
results are reported on the BraTS2018 dataset.Comment: 14 pages, 5 figures, 4 tables, accepted by BrainLes 2018 MICCAI
worksho
Genetic architecture of sporadic frontotemporal dementia and overlap with Alzheimer's and Parkinson's diseases
BACKGROUND: Clinical, pathological and genetic overlap between sporadic frontotemporal dementia (FTD), Alzheimer's disease (AD) and Parkinson's disease (PD) has been suggested; however, the relationship between these disorders is still not well understood. Here we evaluated genetic overlap between FTD, AD and PD to assess shared pathobiology and identify novel genetic variants associated with increased risk for FTD.
METHODS: Summary statistics were obtained from the International FTD Genomics Consortium, International PD Genetics Consortium and International Genomics of AD Project (n>75 000 cases and controls). We used conjunction false discovery rate (FDR) to evaluate genetic pleiotropy and conditional FDR to identify novel FTD-associated SNPs. Relevant variants were further evaluated for expression quantitative loci.
RESULTS: We observed SNPs within the HLA, MAPT and APOE regions jointly contributing to increased risk for FTD and AD or PD. By conditioning on polymorphisms associated with PD and AD, we found 11 loci associated with increased risk for FTD. Meta-analysis across two independent FTD cohorts revealed a genome-wide signal within the APOE region (rs6857, 3′-UTR=PVRL2, p=2.21×10–12), and a suggestive signal for rs1358071 within the MAPT region (intronic=CRHR1, p=4.91×10−7) with the effect allele tagging the H1 haplotype. Pleiotropic SNPs at the HLA and MAPT loci associated with expression changes in cis-genes supporting involvement of intracellular vesicular trafficking, immune response and endo/lysosomal processes.
CONCLUSIONS: Our findings demonstrate genetic pleiotropy in these neurodegenerative diseases and indicate that sporadic FTD is a polygenic disorder where multiple pleiotropic loci with small effects contribute to increased disease risk
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