4.5 Article

Manifold learning for fMRI time-varying functional connectivity

Related references

Note: Only part of the references are listed.
Article Neurosciences

Individualized event structure drives individual differences in whole-brain functional connectivity

Richard F. Betzel et al.

Summary: This study investigates the origins of individualized functional connectivity by decomposing it into framewise contributions and examining the role of brain network events. They developed a statistical test to identify events in empirical recordings and showed that the patterns of cofluctuation expressed during events are individualized variants of template patterns. They also proposed a simple model based on event cofluctuations that demonstrates the suboptimal nature of group-averaged cofluctuations for explaining participant-specific connectivity.

NEUROIMAGE (2022)

Article Neurosciences

On co-activation pattern analysis and non-stationarity of resting brain activity

Teppei Matsui et al.

Summary: This study found that the results of CAP analysis were similar for both real and simulated data, suggesting that CAP analysis does not necessarily reflect the non-stationarity or mixture of states in resting brain activity. Additionally, CAPs formed spatially heterogeneous modules in both real and simulated data.

NEUROIMAGE (2022)

Article Neurosciences

Multiframe Evolving Dynamic Functional Connectivity (EVOdFNC): A Method for Constructing and Investigating Functional Brain Motifs

Robyn L. Miller et al.

Summary: This article introduces a flexible and extensible data-driven framework for identifying group-level dynamic functional network connectivity states. The framework enables a better understanding of the relationship between schizophrenia and its symptoms with brain network connectivity.

FRONTIERS IN NEUROSCIENCE (2022)

Article Multidisciplinary Sciences

Neural representational geometry underlies few-shot concept learning

Ben Sorscher et al.

Summary: This article proposes a simple and feasible neural mechanism for learning new concepts from few examples. It suggests that neural activity in higher-order sensory areas can simulate the learning of natural concepts, and discrimination can be achieved through a simple plasticity rule. Numerical simulations demonstrate the high accuracy of this mechanism and a mathematical theory is developed to predict the performance of few-shot learning.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2022)

Article Biology

Decoding brain states on the intrinsic manifold of human brain dynamics across wakefulness and sleep

Joan Rue-Queralt et al.

Summary: The study presents a method to calculate low dimensional manifolds in human brain dynamics, revealing nonlinear differences between wakefulness and various sleep stages, with a high decoding accuracy of 96%. Interestingly, the intrinsic manifolds of all participants share a common topology, indicating a consistent underlying structure in brain activity dynamics.

COMMUNICATIONS BIOLOGY (2021)

Article Physics, Multidisciplinary

Scikit-Dimension: A Python Package for Intrinsic Dimension Estimation

Jonathan Bac et al.

Summary: This technical note introduces an open-source Python package called scikit-dimension for intrinsic dimension estimation. The package provides a uniform implementation of various known ID estimators based on scikit-learn API, allowing evaluation of global and local intrinsic dimension as well as generating synthetic datasets. It is developed with tools to assess code quality, coverage, unit testing and continuous integration.

ENTROPY (2021)

Article Neurosciences

Representation learning of resting state fMRI with variational autoencoder

Jung-Hoon Kim et al.

Summary: By training a variational autoencoder with rsfMRI data, researchers have been able to untangle the underlying sources of brain cortical activity and connectivity, representing spatiotemporal characteristics and driving changes in cortical networks. The resultant latent variables can be used as a reliable feature for accurate subject identification, even with limited data available. This demonstrates the value of VAE for unsupervised representation learning in resting state fMRI activity.

NEUROIMAGE (2021)

Article Mathematical & Computational Biology

Embedding Functional Brain Networks in Low Dimensional Spaces Using Manifold Learning Techniques

Ramon Casanova et al.

Summary: The study investigates the potential of t-SNE and UMAP for representing functional brain networks, finding that both methods effectively project the networks onto 2D manifolds with differences in classification accuracy and preservation of topology. The results suggest that UMAP is an excellent tool for visualizing functional brain networks, and networks from different studies align correctly in the embedding space despite variations in data collection and protocols.

FRONTIERS IN NEUROINFORMATICS (2021)

Article Neurosciences

Dynamic Functional Connectivity Predicts Treatment Response to Electroconvulsive Therapy in Major Depressive Disorder

Hossein Dini et al.

Summary: This study compared the rs-fMRI of DEP patients with healthy participants, finding that DEP patients spent less time in a state with higher connectivity between CCN and DMN, which was correlated with HDRS changes. Post-ECT analysis showed an increased time spent in this state for DEP patients, indicating the effect of ECT on DEP patients.

FRONTIERS IN HUMAN NEUROSCIENCE (2021)

Article Neurosciences

Nonlinear manifold learning in functional magnetic resonance imaging uncovers a low-dimensional space of brain dynamics

Siyuan Gao et al.

Summary: This study demonstrates that a shared, robust, and interpretable low-dimensional space of brain dynamics can be recovered from a rich repertoire of task-based fMRI data. By relying on nonlinear approaches, the embedding maintains the proper temporal progression of tasks and reveals the dynamics of brain states and network integration. Additionally, resting-state data fully embeds onto the same task embedding, suggesting similar brain states are present in both types of data.

HUMAN BRAIN MAPPING (2021)

Article Neurosciences

Construction of embedded fMRI resting-state functional connectivity networks using manifold learning

Ioannis K. Gallos et al.

Summary: This study utilized various manifold learning algorithms to construct functional connectivity networks from resting-state fMRI data of schizophrenia patients and healthy controls. The diffusion maps with cross correlation metric outperformed other combinations in terms of classification potential.

COGNITIVE NEURODYNAMICS (2021)

Review Computer Science, Artificial Intelligence

An Introduction to Topological Data Analysis: Fundamental and Practical Aspects for Data Scientists

Frederic Chazal et al.

Summary: With the rapid growth of available data, topological data analysis (TDA) has emerged as a field providing new mathematical theories and computational tools for inferring relevant features of complex data. TDA can be used independently or in combination with other data analysis and statistical learning techniques.

FRONTIERS IN ARTIFICIAL INTELLIGENCE (2021)

Article Multidisciplinary Sciences

Nearest neighbours reveal fast and slow components of motor learning

Sepp Kollmorgen et al.

NATURE (2020)

Article Biochemical Research Methods

The intrinsic dimension of protein sequence evolution

Elena Facco et al.

PLOS COMPUTATIONAL BIOLOGY (2019)

Article Multidisciplinary Sciences

The art of using t-SNE for single-cell transcriptomics

Dmitry Kobak et al.

NATURE COMMUNICATIONS (2019)

Article Genetics & Heredity

UMAP reveals cryptic population structure and phenotype heterogeneity in large genomic cohorts

Alex Diaz-Papkovich et al.

PLOS GENETICS (2019)

Article Neurosciences

Using Low-Dimensional Manifolds to Map Relationships Between Dynamic Brain Networks

Mohsen Bahrami et al.

FRONTIERS IN HUMAN NEUROSCIENCE (2019)

Article Neurosciences

The importance of the whole: Topological data analysis for the network neuroscientist

Ann E. Sizemore et al.

NETWORK NEUROSCIENCE (2019)

Review Neurosciences

Task-based dynamic functional connectivity: Recent findings and open questions

Javier Gonzalez-Castillo et al.

NEUROIMAGE (2018)

Review Neurosciences

Discovering dynamic brain networks from big data in rest and task

Diego Vidaurre et al.

NEUROIMAGE (2018)

Review Neurosciences

Co-activation patterns in resting-state fMRI signals

Xiao Liu et al.

NEUROIMAGE (2018)

Article Neurosciences

Resting-state functional connectivity predicts neuroticism and extraversion in novel individuals

Wei-Ting Hsu et al.

SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE (2018)

Article Multidisciplinary Sciences

Towards a new approach to reveal dynamical organization of the brain using topological data analysis

Manish Saggar et al.

NATURE COMMUNICATIONS (2018)

Article Biochemistry & Molecular Biology

Molecular Architecture of the Mouse Nervous System

Amit Zeisel et al.

Proceedings Paper Optics

Tensor-based vs. matrix-based rank reduction in dynamic brain connectivity

Fatemeh Mokhtaria et al.

MEDICAL IMAGING 2018: IMAGE PROCESSING (2018)

Article Neurosciences

Instantaneous brain dynamics mapped to a continuous state space

Jacob C. W. Billings et al.

NEUROIMAGE (2017)

Article Multidisciplinary Sciences

Estimating the intrinsic dimension of datasets by a minimal neighborhood information

Elena Facco et al.

SCIENTIFIC REPORTS (2017)

Article Neurosciences

Multimodal population brain imaging in the UK Biobank prospective epidemiological study

Karla L. Miller et al.

NATURE NEUROSCIENCE (2016)

Article Multidisciplinary Sciences

Situating the default-mode network along a principal gradient of macroscale cortical organization

Daniel S. Margulies et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2016)

Review Engineering, Multidisciplinary

Intrinsic Dimension Estimation: Relevant Techniques and a Benchmark Framework

P. Campadelli et al.

MATHEMATICAL PROBLEMS IN ENGINEERING (2015)

Article Multidisciplinary Sciences

Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns

Javier Gonzalez-Castillo et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2015)

Article Neurosciences

Tracking Whole-Brain Connectivity Dynamics in the Resting State

Elena A. Allen et al.

CEREBRAL CORTEX (2014)

Article Neurosciences

Intrinsic and Task-Evoked Network Architectures of the Human Brain

Michael W. Cole et al.

NEURON (2014)

Article Biology

Reconfigurable task-dependent functional coupling modes cluster around a core functional architecture

Fenna M. Krienen et al.

PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES (2014)

Article Multidisciplinary Sciences

Resting-brain functional connectivity predicted by analytic measures of network communication

Joaquin Goni et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2014)

Article Neurosciences

Effects of image contrast on functional MRI image registration

Javier Gonzalez-Castillo et al.

NEUROIMAGE (2013)

Article Multidisciplinary Sciences

Time-varying functional network information extracted from brief instances of spontaneous brain activity

Xiao Liu et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2013)

Article Neurosciences

FATCAT: (An Efficient) Functional And Tractographic Connectivity Analysis Toolbox

Paul A. Taylor et al.

BRAIN CONNECTIVITY (2013)

Article Neurosciences

A whole brain fMRI atlas generated via spatially constrained spectral clustering

R. Cameron Craddock et al.

HUMAN BRAIN MAPPING (2012)

Article Neurosciences

Periodic changes in fMRI connectivity

Daniel A. Handwerker et al.

NEUROIMAGE (2012)

Article Biochemical Research Methods

Large-scale automated synthesis of human functional neuroimaging data

Tal Yarkoni et al.

NATURE METHODS (2011)

Article Multidisciplinary Sciences

Adaptive reconfiguration of fractal small-world human brain functional networks

Danielle S. Bassettt et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2006)

Article Multidisciplinary Sciences

Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps

RR Coifman et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2005)

Article Computer Science, Artificial Intelligence

Laplacian eigenmaps for dimensionality reduction and data representation

M Belkin et al.

NEURAL COMPUTATION (2003)

Article Multidisciplinary Sciences

A global geometric framework for nonlinear dimensionality reduction

JB Tenenbaum et al.

SCIENCE (2000)