3,361 research outputs found
Construction of embedded fMRI resting state functional connectivity networks using manifold learning
We construct embedded functional connectivity networks (FCN) from benchmark
resting-state functional magnetic resonance imaging (rsfMRI) data acquired from
patients with schizophrenia and healthy controls based on linear and nonlinear
manifold learning algorithms, namely, Multidimensional Scaling (MDS), Isometric
Feature Mapping (ISOMAP) and Diffusion Maps. Furthermore, based on key global
graph-theoretical properties of the embedded FCN, we compare their
classification potential using machine learning techniques. We also assess the
performance of two metrics that are widely used for the construction of FCN
from fMRI, namely the Euclidean distance and the lagged cross-correlation
metric. We show that the FCN constructed with Diffusion Maps and the lagged
cross-correlation metric outperform the other combinations
Topological exploration of artificial neuronal network dynamics
One of the paramount challenges in neuroscience is to understand the dynamics
of individual neurons and how they give rise to network dynamics when
interconnected. Historically, researchers have resorted to graph theory,
statistics, and statistical mechanics to describe the spatiotemporal structure
of such network dynamics. Our novel approach employs tools from algebraic
topology to characterize the global properties of network structure and
dynamics.
We propose a method based on persistent homology to automatically classify
network dynamics using topological features of spaces built from various
spike-train distances. We investigate the efficacy of our method by simulating
activity in three small artificial neural networks with different sets of
parameters, giving rise to dynamics that can be classified into four regimes.
We then compute three measures of spike train similarity and use persistent
homology to extract topological features that are fundamentally different from
those used in traditional methods. Our results show that a machine learning
classifier trained on these features can accurately predict the regime of the
network it was trained on and also generalize to other networks that were not
presented during training. Moreover, we demonstrate that using features
extracted from multiple spike-train distances systematically improves the
performance of our method
Phenotyping functional brain dynamics:A deep learning prespective on psychiatry
This thesis explores the potential of deep learning (DL) techniques combined with multi-site functional magnetic resonance imaging (fMRI) to enable automated diagnosis and biomarker discovery for psychiatric disorders. This marks a shift from the convention in the field of applying standard machine learning techniques on hand-crafted features from a single cohort.To enable this, we have focused on three main strategies: utilizing minimally pre-processed data to maintain spatio-temporal dynamics, developing sample-efficient DL models, and applying emerging DL training techniques like self-supervised and transfer learning to leverage large population-based datasets.Our empirical results suggest that DL models can sometimes outperform existing machine learning methods in diagnosing Autism Spectrum Disorder (ASD) and Major Depressive Disorder (MDD) from resting-state fMRI data, despite the smaller datasets and the high data dimensionality. Nonetheless, the generalization performance of these models is currently insufficient for clinical use, raising questions about the feasibility of applying supervised DL for diagnosis or biomarker discovery due to the highly heterogeneous nature of the disorders. Our findings suggest that normative modeling on functional brain dynamics provides a promising alternative to the current paradigm
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