198 research outputs found
Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model
Recently brain networks have been widely adopted to study brain dynamics,
brain development and brain diseases. Graph representation learning techniques
on brain functional networks can facilitate the discovery of novel biomarkers
for clinical phenotypes and neurodegenerative diseases. However, current graph
learning techniques have several issues on brain network mining. Firstly, most
current graph learning models are designed for unsigned graph, which hinders
the analysis of many signed network data (e.g., brain functional networks).
Meanwhile, the insufficiency of brain network data limits the model performance
on clinical phenotypes predictions. Moreover, few of current graph learning
model is interpretable, which may not be capable to provide biological insights
for model outcomes. Here, we propose an interpretable hierarchical signed graph
representation learning model to extract graph-level representations from brain
functional networks, which can be used for different prediction tasks. In order
to further improve the model performance, we also propose a new strategy to
augment functional brain network data for contrastive learning. We evaluate
this framework on different classification and regression tasks using the data
from HCP and OASIS. Our results from extensive experiments demonstrate the
superiority of the proposed model compared to several state-of-the-art
techniques. Additionally, we use graph saliency maps, derived from these
prediction tasks, to demonstrate detection and interpretation of phenotypic
biomarkers
Graph Signal Processing: Overview, Challenges and Applications
Research in Graph Signal Processing (GSP) aims to develop tools for
processing data defined on irregular graph domains. In this paper we first
provide an overview of core ideas in GSP and their connection to conventional
digital signal processing. We then summarize recent developments in developing
basic GSP tools, including methods for sampling, filtering or graph learning.
Next, we review progress in several application areas using GSP, including
processing and analysis of sensor network data, biological data, and
applications to image processing and machine learning. We finish by providing a
brief historical perspective to highlight how concepts recently developed in
GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE
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Application of Deep Learning to Brain Connectivity Classification in Large MRI Datasets
The use of machine learning for whole-brain classification of magnetic resonance imaging (MRI) data is of clear interest, both for understanding phenotypic differences in brain structure and function and for diagnostic applications. Developments of deep learning models in the past decade have revolutionized photographic image and speech recognition, bringing promise to do the same to other fields of science. However, there are many practical and theoretical challenges in the translation of such methods to the unique context of MRIs of the brain. This thesis presents a theoretical underpinning for whole-brain classification of extremely large datasets of multi-site MRIs, including machine learning model architecture, dataset curation methods, machine learning visualization methods, encoding of MRI data, and feature extraction. To replicate large sample sizes typically applied to deep learning models, a dataset of over 50,000 functional and structural MRIs was amassed from nine different databases, and the undertaken analyses were conducted on three covariates commonly found across these collections: sex, resting state/task, and autism spectrum disorder. I find that deep learning is not only a method that has promise for clinical application in the future, but also a powerful statistical tool for analyzing complex, nonlinear relationships in brain data where conventional statistics may fail. However, results are also dependent on factors such as dataset imbalances, confounding factors such as motion and head size, selected methods of encoding MRI data, variability of machine learning models and selected methods of visualizing the machine learning results. In this thesis, I present the following methodological innovations: (1) a method of balancing datasets as a means of regressing out measurable confounding factors; (2) a means of removing spatial biases from deep learning visualization methods; (3) methods of encoding functional and structural datasets as connectivity matrices; (4) the use of ensemble models and convolutional neural network architectures to improve classification accuracy and consistency; (5) adaptation of deep learning visualization methods to study brain connections utilized in the classification process. Additionally, I discuss interpretations, limitations, and future directions of this research.Gates Cambridge Scholarshi
Motion-Invariant Variational Auto-Encoding of Brain Structural Connectomes
Mapping of human brain structural connectomes via diffusion MRI offers a
unique opportunity to understand brain structural connectivity and relate it to
various human traits, such as cognition. However, motion artifacts from head
movement during image acquisition can impact the connectome reconstructions,
rendering the subsequent inference results unreliable. We aim to develop a
generative model to learn low-dimensional representations of structural
connectomes that are invariant to motion artifacts, so that we can link brain
networks and human traits more accurately, and generate motion-adjusted
connectomes. We applied the proposed model to data from the Adolescent Brain
Cognitive Development (ABCD) study and the Human Connectome Project (HCP) to
investigate how our motion-invariant connectomes facilitate understanding of
the brain network and its relationship with cognition. Empirical results
demonstrate that the proposed motion-invariant variational auto-encoder
(inv-VAE) outperforms its competitors on various aspects. In particular,
motion-adjusted structural connectomes are more strongly associated with a wide
array of cognition-related traits than other approaches without motion
adjustment
Decoding Task-Based fMRI Data Using Graph Neural Networks, Considering Individual Differences
Functional magnetic resonance imaging (fMRI) is a non-invasive technology that provides high spatial resolution in determining the human brain\u27s responses and measures regional brain activity through metabolic changes in blood oxygen consumption associated with neural activity. Task fMRI provides an opportunity to analyze the working mechanisms of the human brain during specific task performance. Over the past several years, a variety of computational methods have been proposed to decode task fMRI data that can identify brain regions associated with different task stimulations. Despite the advances made by these methods, several limitations exist due to graph representations and graph embeddings transferred from task fMRI signals. In the present study, we proposed an end-to-end graph convolutional network by combining the convolutional neural network with graph representation, with three convolutional layers to classify task fMRI data from the Human Connectome Project (302 participants, 22–35 years of age). One goal of this dissertation was to improve classification performance. We applied four of the most widely used node embedding algorithms—NetMF, RandNE, Node2Vec, and Walklets—to automatically extract the structural properties of the nodes in the brain functional graph, then evaluated the performance of the classification model. The empirical results indicated that the proposed GCN framework accurately identified the brain\u27s state in task fMRI data and achieved comparable macro F1 scores of 0.978 and 0.976 with the NetMF and RandNE embedding methods, respectively. Another goal of the dissertation was to assess the effects of individual differences (i.e., gender and fluid intelligence) on classification performance. We tested the proposed GCN framework on sub-datasets divided according to gender and fluid intelligence. Experimental results indicated significant differences in the classification predictions of gender, but not high/low fluid intelligence fMRI data. Our experiments yielded promising results and demonstrated the superior ability of our GCN in modeling task fMRI data
Predicting the Outcome of Cognitive Training in Parkinson’s Disease using Magnetic Resonance Imaging
Motivation: Cognitive impairment is an important symptom of Parkinson’s Disease (PD),
usually having a substantial negative impact on the quality of life of patients, families,
and caregivers. Cognitive Training (CT) have been proven effective in halting the process
of cognitive decline in PD. However, the efficacy of CT is unpredictable from subject to
subject.
Objective: Investigate the possibility of predicting the outcome of CT in PD patients
with Mild Cognitive Impairment using structural and functional Magnetic Resonance
Imaging (MRI) data.
Methods: Before CT, a sample of 42 PD patients underwent structural and functional
MRI. Graph measures were then extracted from their structural and functional con nectomes and used as features for random forest (RFo) and decision tree (DT) machine
learning (ML) regression algorithms with and without prior latent component analysis
(LCA). CT response was evaluated by assessing the outcomes of the Tower of London
task pre- and post-treatment. Finally, the 4 ML models were used to predict CT response
and their performances were assessed. Post hoc analyses were conducted to investigate
whether these algorithms could predict age using connectomic measures on a sample of
80 PD patients.
Results: The performances of the aforementioned algorithms did not differ signifi cantly from the baseline performance predicting the subject-specific CT outcome. The
performance of the RFo without LCA differed significantly from the baseline performance
in the age prediction task for the sample of 80 patients.
Conclusion: Notwithstanding the lack of statistical significance in predicting our
xicognitive outcomes, the relative success of the age prediction task points towards the
potential of this approach. We hypothesise that bigger sample sizes are needed in order
to predict the outcome of CT using ML
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