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Deconvolution

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

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Related Experiment Video

Updated: May 13, 2025

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Bayesian deconvolution for computational cognitive modeling of fMRI data.

Jonathon R Howlett¹, Katia M Harlé¹, Alan N Simmons¹

¹VA San Diego Healthcare System, San Diego, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.

|April 13, 2025

Summary

This summary is machine-generated.

This study introduces a new Bayesian deconvolution method to analyze fMRI data, revealing striatal activity reflects prediction error during reward anticipation. This technique offers more reliable parameter estimation than traditional fMRI analysis.

Keywords:

Bayesian inference Computational psychiatry Monetary incentive delay Reinforcement learning Reward processing Striatum

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Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

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Last Updated: May 13, 2025

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

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Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

Area of Science:

Cognitive Neuroscience
Neuroimaging Analysis
Computational Psychiatry

Background:

Current functional magnetic resonance imaging (fMRI) analysis tools struggle to directly estimate latent parameters from computational cognitive models using blood-oxygen-level-dependent (BOLD) signals.
Inferring cognitive processes from observable neural data is a key objective in cognitive neuroscience.

Purpose of the Study:

To present a novel Bayesian deconvolution technique for hierarchical generative cognitive modeling of fMRI time-series data.
To validate the technique by applying it to the Monetary Incentive Delay (MID) task and identifying processes in incentive anticipation.

Main Methods:

Developed and applied a Bayesian deconvolution technique for fMRI time-series analysis.
Utilized a hierarchical generative cognitive modeling approach.
Validated the method on data from 54 individuals undergoing fMRI scans during a clinical trial for anxiety and depression, using the MID task.

Main Results:

Striatal reward region activity during anticipation of monetary gain or loss reflects incentive prediction error, not raw incentive value.
Individual parameters from a generative Bayesian learning model were estimated more reliably than traditional fMRI analysis indices.
Key reliable parameters included a persistent prior and a beta scaling term between prediction error and BOLD signal.

Conclusions:

The novel Bayesian deconvolution method enables direct estimation of latent cognitive parameters from fMRI BOLD signals.
This approach provides more reliable parameter estimation compared to traditional fMRI analyses.
The technique has broad potential for studying diverse neural processes and individual differences in health and disease.