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Results for function norm regularization

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11 papers · ranked by Valyu relevance

01
BbioRxiv12 Apr 2022
Extended Brain Sources Estimation via Unrolled Optimization Neural Network
Meng Jiao, Feng Liu
Electroencephalography (EEG)/Magnetoencephalography (MEG) source imaging aims to seek an estimation of underlying activated brain sources to explain the observed EEG/MEG recording. Due to the ill-posed nature of inverse problem, solving EEG/MEG Source Imaging (ESI) requires design of regularization or prior terms to…
65relevance
02
BbioRxiv16 Apr 2023
Tuning Minimum-Norm regularization parameters for optimal MEG connectivity estimation
Elisabetta Vallarino, Ana Sofia Hincapié, Karim Jerbi, Richard Leahy + 3 more
The accurate characterization of cortical functional connectivity from Magnetoencephalography (MEG) data remains a challenging problem due to the subjective nature of the analysis, which requires several decisions at each step of the analysis pipeline, such as the choice of a source estimation algorithm, a connectivity…
65relevance
03
BbioRxiv3 Oct 2022
Improved whole-brain multivariate hemodynamic deconvolution for multi-echo fMRI with stability selection
Eneko Uruñuela, Javier Gonzalez-Castillo, Charles Zheng, Peter Bandettini + 1 more
Blind estimation of neuronal-related activity from functional magnetic resonance imaging (fMRI) data of resting-state, naturalistic paradigms or clinical conditions can be performed with paradigm free analysis methods such as hemodynamic deconvolution. These methods usually employ a linear hemodynamic convolution model…
64relevance
04
BbioRxiv2 Jul 2022
OSCAR: Optimal subset cardinality regression using the L0-pseudonorm with applications to prognostic modelling of prostate cancer
Anni S. Halkola, Kaisa Joki, Tuomas Mirtti, Marko M. Mäkelä + 2 more
In many real-world applications, such as those based on patient electronic health records, prognostic prediction of patient survival is based on heterogeneous sets of clinical laboratory measurements. To address the trade-off between the predictive accuracy of a prognostic model and the costs related to its clinical…
64relevance
05
BbioRxiv22 Dec 2022
Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection
Alain J. Mbebi, Zoran Nikoloski
Despite extensive research efforts, reconstruction of gene regulatory networks (GRNs) from transcriptomics data remains a pressing challenge in systems biology. While non-linear approaches for reconstruction of GRNs show improved performance over simpler alternatives, we do not yet have understanding if joint modelling…
62relevance
06
BbioRxiv16 Aug 2022
Scalable variational inference for low-rank spatio-temporal receptive fields
Lea Duncker, Kiersten M. Ruda, Greg D. Field, Jonathan W. Pillow
An important problem in systems neuroscience is to characterize how a neuron integrates sensory inputs across space and time. The linear receptive field provides a mathematical characterization of this weighting function, and is commonly used to quantify neural response properties and classify cell types. However…
62relevance
07
BbioRxiv12 Mar 2023
Brain fingerprinting using EEG graph inference
Maliheh Miri, Vahid Abootalebi, Enrico Amico, Hamid Saeedi-Sourck + 2 more
Taking advantage of the human brain functional connectome as an individual’s fingerprint has attracted great research in recent years. Conventionally, Pearson correlation between regional time-courses is used as a pairwise measure for each edge weight of the connectome. Building upon recent advances in graph signal…
60relevance
08
BbioRxiv9 May 2025
Myelin Mapping in the Human Brain Using an Empirical Extension of the Ridge Regression Theorem
Griffin S. Hampton, Ryan Neff, Zezheng Song, Mustapha Bouhrara + 2 more
Myelin water fraction (MWF) mapping in the central nervous system is a topic of intense research activity. One framework for this requires parameter estimation from a decaying biexponential signal. However, this is often an ill-posed nonlinear problem resulting in unreliable parameter estimates. For linear…
60relevance
09
BbioRxiv26 May 2025
EEG-to-fMRI Prediction for Neurofeedback: Evaluating Regularized Regression and Clustering Approaches
Riccardo De Feo, Ekaterina Antipushina, Ivan Tyukin, Yury Koush
While functional Magnetic Resonance Imaging (fMRI) can provide detailed information regarding the functional activity of the whole brain, its cumbersome experimental setting and high cost prevent its application in ecological conditions. This represents a challenge in the context of neurofeedback therapy. To address…
54relevance
10
BbioRxiv13 Dec 2025
Evaluating the effects of regularization and cross-validation parameters on the performance of SVM-based decoding of EEG data
Guanghui Zhang, Xinran Wang, Steven J. Luck
Regularization has been extensively used in multivariate pattern classification (MVPA; decoding) of EEG data to mitigate the risk of overfitting. N-fold cross-validation is also used to mitigate this risk, and it is often combined with averaging across trials to improve the signal-to-noise ratio. However, the impact of…
53relevance
11
BbioRxiv22 Sep 2023
Exploring an EM-algorithm for banded regression in computational neuroscience
Søren A. Fuglsang, Kristoffer H. Madsen, Oula Puonti, Hartwig R. Siebner + 1 more
Regression is a principal tool for relating brain responses to stimuli or tasks in computational neuroscience. This often involves fitting linear models with predictors that can be divided into groups, such as distinct stimulus feature subsets in encoding models or features of different neural response channels in…
51relevance

Results for function norm regularization

Extended Brain Sources Estimation via Unrolled Optimization Neural Network

Tuning Minimum-Norm regularization parameters for optimal MEG connectivity estimation

Improved whole-brain multivariate hemodynamic deconvolution for multi-echo fMRI with stability selection

OSCAR: Optimal subset cardinality regression using the L0-pseudonorm with applications to prognostic modelling of prostate cancer

Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection

Scalable variational inference for low-rank spatio-temporal receptive fields

Brain fingerprinting using EEG graph inference

Myelin Mapping in the Human Brain Using an Empirical Extension of the Ridge Regression Theorem

EEG-to-fMRI Prediction for Neurofeedback: Evaluating Regularized Regression and Clustering Approaches

Evaluating the effects of regularization and cross-validation parameters on the performance of SVM-based decoding of EEG data

Exploring an EM-algorithm for banded regression in computational neuroscience

Extended Brain Sources Estimation via Unrolled Optimization Neural Network

Tuning Minimum-Norm regularization parameters for optimal MEG connectivity estimation

Improved whole-brain multivariate hemodynamic deconvolution for multi-echo fMRI with stability selection

OSCAR: Optimal subset cardinality regression using the L0-pseudonorm with applications to prognostic modelling of prostate cancer

Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection

Scalable variational inference for low-rank spatio-temporal receptive fields

Brain fingerprinting using EEG graph inference

Myelin Mapping in the Human Brain Using an Empirical Extension of the Ridge Regression Theorem

EEG-to-fMRI Prediction for Neurofeedback: Evaluating Regularized Regression and Clustering Approaches

Evaluating the effects of regularization and cross-validation parameters on the performance of SVM-based decoding of EEG data

Exploring an EM-algorithm for banded regression in computational neuroscience