Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Regression Toward the Mean01:52

Regression Toward the Mean

6.9K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.9K
Convolution Properties II01:17

Convolution Properties II

583
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
583
Multiple Regression01:25

Multiple Regression

3.8K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.8K
The Resting Membrane Potential01:21

The Resting Membrane Potential

142.1K
Overview
142.1K
Convolution Properties I01:20

Convolution Properties I

571
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
571
Resting Membrane Potential01:24

Resting Membrane Potential

21.5K
The relative difference in electrical charge, or voltage, between the inside and the outside of a cell membrane, is called the membrane potential. It is generated by differences in permeability of the membrane to various ions and the concentrations of these ions across the membrane.
The Inside of a Neuron is More Negative
The membrane potential of a cell can be measured by inserting a microelectrode into a cell and comparing the charge to a reference electrode in the extracellular fluid. The...
21.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Nonlinear KCCA in fMRI activation analysis: Self-supervised optimization and robust back-reconstruction.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Comprehensive Lineage Tracing Maps the Landscape of Cell Fate Decisions in Mouse Embryogenesis.

bioRxiv : the preprint server for biology·2026
Same author

Advancing biomarker development for chronic traumatic encephalopathy: Summary and recommendations from the 2025 Leon Thal Summit.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

Age-varying DNA methylation patterns associated with blood pressure in mid-to-late adulthood.

Clinical epigenetics·2026
Same author

Spatial organization and detection of social odors in mouse primary olfactory system.

Cell·2026
Same author

Dynamic neurocognitive adaptation: childhood and adult-midlife engagement associated with later-life brain structure and cognition in older adults with and without mild cognitive impairment.

Brain imaging and behavior·2026
Same journal

Evaluation of an open-face 8-channel transmit 64-channel receive 7T head coil for neuroimaging.

Frontiers in neuroscience·2026
Same journal

Acoustic stimulation in pain management: neurobiological mechanisms and clinical applications-a narrative review.

Frontiers in neuroscience·2026
Same journal

Local brain connectome parameters across the spectrum of clinical cognitive decline.

Frontiers in neuroscience·2026
Same journal

Body mass index affects EEG microstate dynamics through blood viscosity in high-altitude environments.

Frontiers in neuroscience·2026
Same journal

Disrupted glymphatic function and its relationship with sleep and cognitive impairment in ME/CFS assessed via DTI-ALPS.

Frontiers in neuroscience·2026
Same journal

Neuromorphic-inspired multi-view global-local fusion for IR-UWB radar dynamic gesture recognition.

Frontiers in neuroscience·2026
See all related articles

Related Experiment Video

Updated: Jan 25, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.8K

Robust Motion Regression of Resting-State Data Using a Convolutional Neural Network Model.

Zhengshi Yang1, Xiaowei Zhuang1, Karthik Sreenivasan1

  • 1Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States.

Frontiers in Neuroscience
|May 7, 2019
PubMed
Summary
This summary is machine-generated.

A novel convolutional neural network (CNN) effectively generates optimal motion regressors for resting-state functional MRI (rs-fMRI), significantly reducing motion artifacts and preserving neural signals in brain imaging studies.

Keywords:
convolutional neural networkdenoisingfMRImotion artifactnuisance regression

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.0K
Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.5K

Related Experiment Videos

Last Updated: Jan 25, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.8K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.0K
Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.5K

Area of Science:

  • Neuroimaging
  • Brain Imaging Analysis
  • Functional Magnetic Resonance Imaging (fMRI)

Background:

  • Resting-state functional magnetic resonance imaging (rs-fMRI) is crucial for studying brain function in various states.
  • Head motion is a significant confounder in rs-fMRI data, impacting the interpretation of blood-oxygen-level-dependent (BOLD) signals.
  • Traditional nuisance regression methods using motion parameters may not fully capture complex motion artifacts.

Purpose of the Study:

  • To develop a robust and automated convolutional neural network (CNN) model for generating optimal motion regressors in rs-fMRI.
  • To improve the accuracy of rs-fMRI data analysis by better modeling and mitigating the effects of in-scanner head motion.
  • To compare the efficacy of CNN-derived regressors against traditional nuisance regression approaches.

Main Methods:

  • A convolutional neural network (CNN) with two temporal convolutional layers was designed to derive motion regressors.
  • The CNN model non-parametrically models the prolonged effects of head motion on rs-fMRI data.
  • The performance of CNN-derived regressors was evaluated against standard motion regressors in nuisance regression.

Main Results:

  • The CNN-derived motion regressors demonstrated superior effectiveness in reducing motion-related artifacts compared to traditional methods.
  • The proposed CNN approach provides a more comprehensive modeling of head motion's impact on rs-fMRI data.
  • This method helps preserve neural signal integrity by more accurately accounting for motion confounds.

Conclusions:

  • The developed CNN model offers an advanced and automated solution for motion artifact correction in rs-fMRI.
  • CNN-derived regressors enhance the reliability and accuracy of rs-fMRI analyses, particularly in patient populations.
  • This technique holds significant potential for improving the quality of neuroimaging research and clinical applications.