210 research outputs found
NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics
Machine learning provides a valuable tool for analyzing high-dimensional
functional neuroimaging data, and is proving effective in predicting various
neurological conditions, psychiatric disorders, and cognitive patterns. In
functional magnetic resonance imaging (MRI) research, interactions between
brain regions are commonly modeled using graph-based representations. The
potency of graph machine learning methods has been established across myriad
domains, marking a transformative step in data interpretation and predictive
modeling. Yet, despite their promise, the transposition of these techniques to
the neuroimaging domain has been challenging due to the expansive number of
potential preprocessing pipelines and the large parameter search space for
graph-based dataset construction. In this paper, we introduce NeuroGraph, a
collection of graph-based neuroimaging datasets, and demonstrated its utility
for predicting multiple categories of behavioral and cognitive traits. We delve
deeply into the dataset generation search space by crafting 35 datasets that
encompass static and dynamic brain connectivity, running in excess of 15
baseline methods for benchmarking. Additionally, we provide generic frameworks
for learning on both static and dynamic graphs. Our extensive experiments lead
to several key observations. Notably, using correlation vectors as node
features, incorporating larger number of regions of interest, and employing
sparser graphs lead to improved performance. To foster further advancements in
graph-based data driven neuroimaging analysis, we offer a comprehensive
open-source Python package that includes the benchmark datasets, baseline
implementations, model training, and standard evaluation.Comment: NeurIPS2
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Network Designs Via Signaling Dynamics On Geometric Dynamic Graphs
Artificial neural networks are treated as black boxes. Generally,only the states of a subset of the network are considered to determine its efficacy, while the relationship between a neural network’s topology and its function remains under-theorized. For my analysis, I use a new class of event-driven recurrent neural networks—a geometric dynamic network modeled on canonical neurobiological signaling principles that allows to directly encode input data into its evolving dynamics—to forward a new type of machine learning approach. I accomplish this by first, mapping causal neuronal signal flows in the C. elegans connectome to show how the dynamic evolution of signal flows results in a unique internal representation of particular input data. Second, I propose two distinct approaches to determine the upper-bound for the amount of network dynamics needed for capturing the signaling evolution of the system. Using the upper-bound values, I construct a mathematical object representing the causal neuronal signaling dynamics, and delineate the interaction of sub-sub structures at various scales/heights of sub-graphs. Finally, based on recent theoretical propositions regarding optimal signaling in a geometric dynamic network, I show that neurons modify their axonal morphology so that the propagation time of an action potential, and the membrane’s refractory period become balanced. Thus, this work not only lays the foundation to construct and analyze a new class of artificial neural networks whose overall behavior and underlying dynamics are transparently coupled, it also provides fertile grounds for future work on biologically inspired artificial intelligence
The topology of structural brain connectivity in diseases and spatio-temporal connectomics
The brain is a complex system, composed of multiple neural units interconnected at different spatial and temporal scales. Diffusion MRI allows probing in vivo the anatomical connectivity between different cortical areas through white matter tracts. In parallel, functional MRI records neural-related signals of brain activity. Particularly, during rest (in absence of specific external task) reproducible dynamical patterns of functional synchronization have been shown across different brain areas. This rich information can be conveniently represented in the form of a graph, a mathematical object where nodes correspond to cortical regions and are connected by edges representing anatomical connections. On the top of this structural network, or brain connectome, individual nodes are associated to functional signals representing neural activity over observation periods. Network science has fundamentally contributed to the characterization of the human connectome. The brain is a small-world network, able to combine segregation and integration aspects. These properties allow functional specialization on the one side, and efficient communication between distant brain areas on the other side, supporting complex cognitive and executive functions. Graph theoretical methods quantify brain topological properties, and allow their comparison between different populations and conditions. In fact, brain connectivity patterns and interdependences between anatomical substrate and functional synchronization have been proved to be impaired in a variety of brain disorders, and to change across human development and aging. Despite these important advancements in the understanding of the brain structure and functioning, many questions are currently unanswered. It is not clear for instance how structural connectivity features are related to individual cognitive capabilities and deficits, and if they have the concrete potential to distinguish pathological subgroups for early diagnosis of brain diseases. Most importantly, it is not yet understood how the connectome topology relates to specific brain functions, and how the transmission of information happens on the top of the structural connectivity infrastructure in order to generate observed functional dynamics. This thesis was motivated by these interdisciplinary inputs, and is the result of a strong interaction between biological and clinical questions on the one hand, and methodological development needs on the other hand. First, we have contributed to the characterization of the human connectome in health and pathologies by adapting and developing network measures for the description of the brain architecture at different scales. Particularly, we have focused on the topological characterization of subnetworks role within the overall brain network. Importantly, we have shown that the topological alteration of distinct brain subsystems may be a biomarker for different brain disorders. Second, we have proposed an original network model for the joint representation of brain structural and functional connectivity properties. This flexible spatio-temporal framework allows the investigation of functional dynamics at multiple temporal scales. Importantly, the investigation of spatio-temporal graphs in healthy subjects have allowed to disclose temporal relationships between local brain activations in resting state recordings, and has highlighted functional communication principles across the brain structural network
Graph Neural Network for spatiotemporal data: methods and applications
In the era of big data, there has been a surge in the availability of data
containing rich spatial and temporal information, offering valuable insights
into dynamic systems and processes for applications such as weather
forecasting, natural disaster management, intelligent transport systems, and
precision agriculture. Graph neural networks (GNNs) have emerged as a powerful
tool for modeling and understanding data with dependencies to each other such
as spatial and temporal dependencies. There is a large amount of existing work
that focuses on addressing the complex spatial and temporal dependencies in
spatiotemporal data using GNNs. However, the strong interdisciplinary nature of
spatiotemporal data has created numerous GNNs variants specifically designed
for distinct application domains. Although the techniques are generally
applicable across various domains, cross-referencing these methods remains
essential yet challenging due to the absence of a comprehensive literature
review on GNNs for spatiotemporal data. This article aims to provide a
systematic and comprehensive overview of the technologies and applications of
GNNs in the spatiotemporal domain. First, the ways of constructing graphs from
spatiotemporal data are summarized to help domain experts understand how to
generate graphs from various types of spatiotemporal data. Then, a systematic
categorization and summary of existing spatiotemporal GNNs are presented to
enable domain experts to identify suitable techniques and to support model
developers in advancing their research. Moreover, a comprehensive overview of
significant applications in the spatiotemporal domain is offered to introduce a
broader range of applications to model developers and domain experts, assisting
them in exploring potential research topics and enhancing the impact of their
work. Finally, open challenges and future directions are discussed
Dynamical complexity of large-scale neurocognitive networks in healthy and pathological brain states
Tracing Network Evolution Using the PARAFAC2 Model
Characterizing time-evolving networks is a challenging task, but it is
crucial for understanding the dynamic behavior of complex systems such as the
brain. For instance, how spatial networks of functional connectivity in the
brain evolve during a task is not well-understood. A traditional approach in
neuroimaging data analysis is to make simplifications through the assumption of
static spatial networks. In this paper, without assuming static networks in
time and/or space, we arrange the temporal data as a higher-order tensor and
use a tensor factorization model called PARAFAC2 to capture underlying patterns
(spatial networks) in time-evolving data and their evolution. Numerical
experiments on simulated data demonstrate that PARAFAC2 can successfully reveal
the underlying networks and their dynamics. We also show the promising
performance of the model in terms of tracing the evolution of task-related
functional connectivity in the brain through the analysis of functional
magnetic resonance imaging data.Comment: 5 pages, 5 figures, conferenc
Stability of spontaneous, correlated activity in mouse auditory cortex
Neural systems can be modeled as networks of functionally connected neural
elements. The resulting network can be analyzed using mathematical tools from
network science and graph theory to quantify the system's topological
organization and to better understand its function. While the network-based
approach is common in the analysis of large-scale neural systems probed by
non-invasive neuroimaging, few studies have used network science to study the
organization of networks reconstructed at the cellular level, and thus many
very basic and fundamental questions remain unanswered. Here, we used
two-photon calcium imaging to record spontaneous activity from the same set of
cells in mouse auditory cortex over the course of several weeks. We reconstruct
functional networks in which cells are linked to one another by edges weighted
according to the correlation of their fluorescence traces. We show that the
networks exhibit modular structure across multiple topological scales and that
these multi-scale modules unfold as part of a hierarchy. We also show that, on
average, network architecture becomes increasingly dissimilar over time, with
similarity decaying monotonically with the distance (in time) between sessions.
Finally, we show that a small fraction of cells maintain strongly-correlated
activity over multiple days, forming a stable temporal core surrounded by a
fluctuating and variable periphery. Our work provides a careful methodological
blueprint for future studies of spontaneous activity measured by two-photon
calcium imaging using cutting-edge computational methods and machine learning
algorithms informed by explicit graphical models from network science. The
methods are easily extended to additional datasets, opening the possibility of
studying cellular level network organization of neural systems and how that
organization is modulated by stimuli or altered in models of disease.Comment: 15 pages, 3 figure
A transformer model for learning spatiotemporal contextual representation in fMRI data
AbstractRepresentation learning is a core component in data-driven modeling of various complex phenomena. Learning a contextually informative representation can especially benefit the analysis of fMRI data because of the complexities and dynamic dependencies present in such datasets. In this work, we propose a framework based on transformer models to learn an embedding of the fMRI data by taking the spatiotemporal contextual information in the data into account. This approach takes the multivariate BOLD time series of the regions of the brain as well as their functional connectivity network simultaneously as the input to create a set of meaningful features that can in turn be used in various downstream tasks such as classification, feature extraction, and statistical analysis. The proposed spatiotemporal framework uses the attention mechanism as well as the graph convolution neural network to jointly inject the contextual information regarding the dynamics in time series data and their connectivity into the representation. We demonstrate the benefits of this framework by applying it to two resting-state fMRI datasets, and provide further discussion on various aspects and advantages of it over a number of other commonly adopted architectures
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