25,859 research outputs found
Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches
In the past two decades, functional Magnetic Resonance Imaging has been used
to relate neuronal network activity to cognitive processing and behaviour.
Recently this approach has been augmented by algorithms that allow us to infer
causal links between component populations of neuronal networks. Multiple
inference procedures have been proposed to approach this research question but
so far, each method has limitations when it comes to establishing whole-brain
connectivity patterns. In this work, we discuss eight ways to infer causality
in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality,
Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and
Transfer Entropy. We finish with formulating some recommendations for the
future directions in this area
Blending generative models with deep learning for multidimensional phenotypic prediction from brain connectivity data
Network science as a discipline has provided us with foundational machinery to study complex relational entities such as social networks, genomics, econometrics etc. The human brain is a complex network that has recently garnered immense interest within the data science community. Connectomics or the study of the underlying connectivity patterns in the brain has become an important field of study for the characterization of various neurological disorders such as Autism, Schizophrenia etc. Such connectomic studies have provided several fundamental insights into its intrinsic organisation and implications on our behavior and health.
This thesis proposes a collection of mathematical models that are capable of fusing information from functional and structural connectivity with phenotypic information. Here, functional connectivity is measured by resting state functional MRI (rs-fMRI), while anatomical connectivity is captured using Diffusion Tensor Imaging (DTI). The phenotypic information of interest could refer to continuous measures of behavior or cognition, or may capture levels of impairment in the case of neuropsychiatric disorders.
We first develop a joint network optimization framework to predict clinical severity from rs-fMRI connectivity matrices. This model couples two key terms into a unified optimization framework: a generative matrix factorization and a discriminative linear regression model. We demonstrate that the proposed joint inference strategy is successful in generalizing to prediction of impairments in Autism Spectrum Disorder (ASD) when compared with several machine learning, graph theoretic and statistical baselines. At the same time, the model is capable of extracting functional brain biomarkers that are informative of individual measures of clinical severity. We then present two modeling extensions to non-parametric and neural network regression models that are coupled with the same generative framework.
Building on these general principles, we extend our framework to incorporate multimodal information from Diffusion Tensor Imaging (DTI) and dynamic functional connectivity. At a high level, our generative matrix factorization now estimates a time-varying functional decomposition. At the same time, it is guided by anatomical connectivity priors in a graph-based regularization setup. This connectivity model is coupled with a deep network that predicts multidimensional clinical characterizations and models the temporal dynamics of the functional scan. This framework allows us to simultaneously explain multiple impairments, isolate stable multi-modal connectivity signatures, and study the evolution of various brain states at rest.
Lastly, we shift our focus to end-to-end geometric frameworks. These are designed to characterize the complementarity between functional and structural connectivity data spaces, while using clinical information as a secondary guide. As an alternative to the previous generative framework for functional connectivity, our representation learning scheme of choice is a matrix autoencoder that is crafted to reflect the underlying data geometry. This is coupled with a manifold alignment model that maps from function to structure and a deep network that maps to phenotypic information. We demonstrate that the model reliably recovers structural connectivity patterns across individuals, while robustly extracting predictive yet interpretable brain biomarkers. Finally, we also present a preliminary analytical and experimental exposition on the theoretical aspects of the matrix autoencoder representation
From Correlation to Causation: Estimation of Effective Connectivity from Continuous Brain Signals based on Zero-Lag Covariance
Knowing brain connectivity is of great importance both in basic research and
for clinical applications. We are proposing a method to infer directed
connectivity from zero-lag covariances of neuronal activity recorded at
multiple sites. This allows us to identify causal relations that are reflected
in neuronal population activity. To derive our strategy, we assume a generic
linear model of interacting continuous variables, the components of which
represent the activity of local neuronal populations. The suggested method for
inferring connectivity from recorded signals exploits the fact that the
covariance matrix derived from the observed activity contains information about
the existence, the direction and the sign of connections. Assuming a sparsely
coupled network, we disambiguate the underlying causal structure via
-minimization. In general, this method is suited to infer effective
connectivity from resting state data of various types. We show that our method
is applicable over a broad range of structural parameters regarding network
size and connection probability of the network. We also explored parameters
affecting its activity dynamics, like the eigenvalue spectrum. Also, based on
the simulation of suitable Ornstein-Uhlenbeck processes to model BOLD dynamics,
we show that with our method it is possible to estimate directed connectivity
from zero-lag covariances derived from such signals. In this study, we consider
measurement noise and unobserved nodes as additional confounding factors.
Furthermore, we investigate the amount of data required for a reliable
estimate. Additionally, we apply the proposed method on a fMRI dataset. The
resulting network exhibits a tendency for close-by areas being connected as
well as inter-hemispheric connections between corresponding areas. Also, we
found that a large fraction of identified connections were inhibitory.Comment: 18 pages, 10 figure
Learning and comparing functional connectomes across subjects
Functional connectomes capture brain interactions via synchronized
fluctuations in the functional magnetic resonance imaging signal. If measured
during rest, they map the intrinsic functional architecture of the brain. With
task-driven experiments they represent integration mechanisms between
specialized brain areas. Analyzing their variability across subjects and
conditions can reveal markers of brain pathologies and mechanisms underlying
cognition. Methods of estimating functional connectomes from the imaging signal
have undergone rapid developments and the literature is full of diverse
strategies for comparing them. This review aims to clarify links across
functional-connectivity methods as well as to expose different steps to perform
a group study of functional connectomes
A statistical model for brain networks inferred from large-scale electrophysiological signals
Network science has been extensively developed to characterize structural
properties of complex systems, including brain networks inferred from
neuroimaging data. As a result of the inference process, networks estimated
from experimentally obtained biological data, represent one instance of a
larger number of realizations with similar intrinsic topology. A modeling
approach is therefore needed to support statistical inference on the bottom-up
local connectivity mechanisms influencing the formation of the estimated brain
networks. We adopted a statistical model based on exponential random graphs
(ERGM) to reproduce brain networks, or connectomes, estimated by spectral
coherence between high-density electroencephalographic (EEG) signals. We
validated this approach in a dataset of 108 healthy subjects during eyes-open
(EO) and eyes-closed (EC) resting-state conditions. Results showed that the
tendency to form triangles and stars, reflecting clustering and node
centrality, better explained the global properties of the EEG connectomes as
compared to other combinations of graph metrics. Synthetic networks generated
by this model configuration replicated the characteristic differences found in
brain networks, with EO eliciting significantly higher segregation in the alpha
frequency band (8-13 Hz) as compared to EC. Furthermore, the fitted ERGM
parameter values provided complementary information showing that clustering
connections are significantly more represented from EC to EO in the alpha
range, but also in the beta band (14-29 Hz), which is known to play a crucial
role in cortical processing of visual input and externally oriented attention.
These findings support the current view of the brain functional segregation and
integration in terms of modules and hubs, and provide a statistical approach to
extract new information on the (re)organizational mechanisms in healthy and
diseased brains.Comment: Due to the limitation "The abstract field cannot be longer than 1,920
characters", the abstract appearing here is slightly shorter than that in the
PDF fil
Neural Circuit Inference from Function to Structure
Advances in technology are opening new windows on the structural connectivity and functional dynamics of brain circuits. Quantitative frameworks are needed that integrate these data from anatomy and physiology. Here, we present a modeling approach that creates such a link. The goal is to infer the structure of a neural circuit from sparse neural recordings, using partial knowledge of its anatomy as a regularizing constraint. We recorded visual responses from the output neurons of the retina, the ganglion cells. We then generated a systematic sequence of circuit models that represents retinal neurons and connections and fitted them to the experimental data. The optimal models faithfully recapitulated the ganglion cell outputs. More importantly, they made predictions about dynamics and connectivity among unobserved neurons internal to the circuit, and these were subsequently confirmed by experiment. This circuit inference framework promises to facilitate the integration and understanding of big data in neuroscience
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