3 research outputs found
Early Prediction of Alzheimer's Disease Dementia Based on Baseline Hippocampal MRI and 1-Year Follow-Up Cognitive Measures Using Deep Recurrent Neural Networks
Multi-modal biological, imaging, and neuropsychological markers have
demonstrated promising performance for distinguishing Alzheimer's disease (AD)
patients from cognitively normal elders. However, it remains difficult to early
predict when and which mild cognitive impairment (MCI) individuals will convert
to AD dementia. Informed by pattern classification studies which have
demonstrated that pattern classifiers built on longitudinal data could achieve
better classification performance than those built on cross-sectional data, we
develop a deep learning model based on recurrent neural networks (RNNs) to
learn informative representation and temporal dynamics of longitudinal
cognitive measures of individual subjects and combine them with baseline
hippocampal MRI for building a prognostic model of AD dementia progression.
Experimental results on a large cohort of MCI subjects have demonstrated that
the deep learning model could learn informative measures from longitudinal data
for characterizing the progression of MCI subjects to AD dementia, and the
prognostic model could early predict AD progression with high accuracy.Comment: Accepted by ISBI 201
A Multi-Task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients Using Dynamic Functional Connectivity
We present a novel deep learning framework that uses dynamic functional
connectivity to simultaneously localize the language and motor areas of the
eloquent cortex in brain tumor patients. Our method leverages convolutional
layers to extract graph-based features from the dynamic connectivity matrices
and a long-short term memory (LSTM) attention network to weight the relevant
time points during classification. The final stage of our model employs
multi-task learning to identify different eloquent subsystems. Our unique
training strategy finds a shared representation between the cognitive networks
of interest, which enables us to handle missing patient data. We evaluate our
method on resting-state fMRI data from 56 brain tumor patients while using task
fMRI activations as surrogate ground-truth labels for training and testing. Our
model achieves higher localization accuracies than conventional deep learning
approaches and can identify bilateral language areas even when trained on
left-hemisphere lateralized cases. Hence, our method may ultimately be useful
for preoperative mapping in tumor patients.Comment: Presented at MLCN 2020 workshop, as a part of MICCAI 202
Spatio-Temporal Graph Convolution for Functional MRI Analysis
The BOLD signal of resting-state fMRI (rs-fMRI) records the functional brain
connectivity in a rich dynamic spatio-temporal setting. However, existing
methods applied to rs-fMRI often fail to consider both spatial and temporal
characteristics of the data. They either neglect the functional dependency
between different brain regions in a network or discard the information in the
temporal dynamics of brain activity. To overcome those shortcomings, we propose
to formulate functional connectivity networks within the context of
spatio-temporal graphs. We then train a spatio-temporal graph convolutional
network (ST-GCN) on short sub-sequences of the BOLD time series to model the
non-stationary nature of functional connectivity. We simultaneously learn the
graph edge importance within ST-GCN to enable interpretation of functional
connectivities contributing to the prediction model. In analyzing the rs-fMRI
of the Human Connectome Project (HCP, N=1,091) and the National Consortium on
Alcohol and Neurodevelopment in Adolescence (NCANDA, N=773), ST-GCN is
significantly more accurate than common approaches in predicting gender and age
based on BOLD signals. The matrix recording edge importance localizes brain
regions and functional connections with significant aging and sex effects,
which are verified by the neuroscience literature