Comparison of whole-brain task-modulated functional connectivity methods for fMRI task connectomics DOI Creative Commons
Ruslan Masharipov, Irina Knyazeva, Alexander Korotkov

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 22, 2024

Higher brain functions require flexible integration of information across widely distributed regions depending on the task context. Resting-state functional magnetic resonance imaging (fMRI) has provided substantial insight into large-scale intrinsic network organisation, yet principles rapid context-dependent reconfiguration that organisation are much less understood. A major challenge for connectome mapping is absence a gold standard deriving whole-brain task-modulated connectivity matrices. Here, we perform biophysically realistic simulations to control ground-truth over wide range experimental settings. We reveal best-performing methods different types designs and their fundamental limitations. Importantly, demonstrate (100 ms) modulations oscillatory neuronal synchronisation can be recovered from sluggish haemodynamic fluctuations even at typically low fMRI temporal resolution (2 s). Finally, provide practical recommendations design statistical analysis foster mapping.

Language: Английский

Semantic language decoding across participants and stimulus modalities DOI

Jerry Tang,

Alexander G. Huth

Current Biology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

0
Comparison of whole-brain task-modulated functional connectivity methods for fMRI task connectomics DOI Creative Commons
Ruslan Masharipov, Irina Knyazeva, Alexander Korotkov

et al.

Communications Biology, Journal Year: 2024, Volume and Issue: 7(1)

Published: Oct. 26, 2024

Language: Английский

Citations

3
Innovating beyond electrophysiology through multimodal neural interfaces DOI
Mehrdad Ramezani,

Yundong Ren,

Ertugrul Cubukcu

et al.

Nature Reviews Electrical Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 16, 2024

Language: Английский

Citations

1
Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey) DOI Creative Commons
Subba Reddy Oota, Manish Gupta, Raju S. Bapi

et al.

arXiv (Cornell University), Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 1, 2023

How does the brain represent different modes of information? Can we design a system that automatically understands what user is thinking? Such questions can be answered by studying recordings like functional magnetic resonance imaging (fMRI). As first step, neuroscience community has contributed several large cognitive datasets related to passive reading/listening/viewing concept words, narratives, pictures and movies. Encoding decoding models using these have also been proposed in past two decades. These serve as additional tools for basic research science neuroscience. aim at generating fMRI representations given stimulus automatically. They practical applications evaluating diagnosing neurological conditions thus help therapies damage. Decoding solve inverse problem reconstructing stimuli fMRI. are useful designing brain-machine or brain-computer interfaces. Inspired effectiveness deep learning natural language processing, computer vision, speech, recently neural encoding proposed. In this survey, will discuss popular language, vision speech stimuli, present summary datasets. Further, review based architectures note their benefits limitations. Finally, conclude with brief discussion about future trends. Given amount published work `computational neuroscience' community, believe survey nicely organizes plethora presents it coherent story.

Language: Английский

Citations

2
Comparison of whole-brain task-modulated functional connectivity methods for fMRI task connectomics DOI Creative Commons
Ruslan Masharipov, Irina Knyazeva, Alexander Korotkov

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 22, 2024

Higher brain functions require flexible integration of information across widely distributed regions depending on the task context. Resting-state functional magnetic resonance imaging (fMRI) has provided substantial insight into large-scale intrinsic network organisation, yet principles rapid context-dependent reconfiguration that organisation are much less understood. A major challenge for connectome mapping is absence a gold standard deriving whole-brain task-modulated connectivity matrices. Here, we perform biophysically realistic simulations to control ground-truth over wide range experimental settings. We reveal best-performing methods different types designs and their fundamental limitations. Importantly, demonstrate (100 ms) modulations oscillatory neuronal synchronisation can be recovered from sluggish haemodynamic fluctuations even at typically low fMRI temporal resolution (2 s). Finally, provide practical recommendations design statistical analysis foster mapping.

Language: Английский

Citations

0