Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Long-term Potentiation

Long-term Potentiation

Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.

Long-term Potentiation

Long-term Potentiation

Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Hebbian LTP
LTP can occur when...

Sleep-Wake Cycles

Sleep-Wake Cycles

Sleep is an essential physiological process vital to maintaining overall well-being. The reticular activating system (RAS), a network of neurons in the brainstem, regulates wakefulness and sleep. While it may seem passive, sleep consists of distinct cycles, each with its unique characteristics and functions. Two key sleep phases are non-rapid eye movement (NREM) and rapid eye movement (REM).
NREM Sleep
NREM sleep comprises four progressive stages that seamlessly merge:

Implicit Memories

Implicit Memories

Implicit memories, also known as non-declarative memories, are long-term memories that function outside of conscious awareness. These memories influence behavior and skills without explicit knowledge. This type of memory is evident in tasks like playing tennis, snowboarding, and texting. Implicit memory has three subsystems: procedural memory, conditioning, and priming. This type of memory is essential in various activities, from everyday tasks to specialized skills.
One key aspect of implicit...

You might also read

Related Articles

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

Sort by

Same author

Efficient Deep Learning Models for Predicting Individualized Task Activation From Resting-State Functional Connectivity.

Human brain mapping·2026

Same author

An open, fully-processed data resource for studying mood and sleep variability in the developing brain.

Aperture neuro·2026

Same author

Symptom Dimension-Specific Neurotransmitter Correlates of Psychopathology and Cognition in Early Psychosis.

bioRxiv : the preprint server for biology·2026

Same author

Patterns of brain-wide associations reflect socioeconomics.

Science (New York, N.Y.)·2026

Same author

A view-engage-predict framework for enhancing brain-behavior mapping with naturalistic movie-watching fMRI.

Communications biology·2026

Same author

Refining RDoC Using Individual-Level Task fMRI Factor Models Reveals Reproducible and Clinically Relevant Brain-Wide Motifs.

bioRxiv : the preprint server for biology·2026

Same journal

Layered social competition coordinates reproductive hierarchy formation in ants.

bioRxiv : the preprint server for biology·2026

Same journal

Combination epigenetic-targeted therapy increases the immunogenicity of poorly immunogenic sarcomas.

bioRxiv : the preprint server for biology·2026

Same journal

Loss of LanC-like proteins delays post-injury regeneration of aging skeletal muscles.

bioRxiv : the preprint server for biology·2026

Same journal

Integrative Transfer Network: Deep Transfer Learning Across Populations and Prediction Targets.

bioRxiv : the preprint server for biology·2026

Same journal

Confidence-supported label-free metabolic imaging with FPhaS phase autofluorescence microscopy.

bioRxiv : the preprint server for biology·2026

Same journal

Sequence-encoded autoinhibition couples mRNA decapping activity to phase separation.

bioRxiv : the preprint server for biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 3, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Predicting Task Activation Maps from Resting-State Functional Connectivity using Deep Learning.

Soren J Madsen¹, Young-Eun Lee¹, Lucina Q Uddin²

¹Department of Psychiatry, Stanford University, USA.

Biorxiv : the Preprint Server for Biology

|September 24, 2024

Summary

This summary is machine-generated.

This study enhances deep learning models for predicting brain activity using functional MRI data. Researchers explored new architectures and found individual variability impacts prediction accuracy, advancing neuroimaging applications.

Keywords:

deep learning functional MRI resting-state task contrast

More Related Videos

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

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

Published on: August 5, 2014

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Related Experiment Videos

Last Updated: May 3, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

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

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

Published on: August 5, 2014

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Area of Science:

Neuroimaging
Computational Neuroscience
Machine Learning

Background:

Deep learning models effectively predict brain activation patterns from resting-state functional MRI (fMRI) data.
The BrainSurfCNN model represents a state-of-the-art approach in this domain.
Understanding individual variability is crucial for improving predictive model performance.

Purpose of the Study:

To replicate and evaluate the BrainSurfCNN model for predicting task-based brain activation from resting-state fMRI data.
To explore novel deep learning architectures, BrainSERF and BrainSurfGCN, for enhanced performance and scalability.
To investigate the influence of individual variability in task performance and data quality on prediction accuracy.

Main Methods:

Replication of the BrainSurfCNN model using Human Connectome Project (HCP) resting-state and task fMRI data.
Assessment of input feature space variations on task contrast prediction.
Implementation and evaluation of two new architectures: BrainSERF (incorporating Squeeze-and-Excitation attention) and BrainSurfGCN (using graph neural networks).
Analysis of the impact of individual differences in task performance and data quality on model predictions.

Main Results:

Successful replication of the BrainSurfCNN model was achieved.
Exploration of architectural modifications provided insights into improving performance and scalability.
Quantification of the impact of individual variability on the predictive accuracy of deep learning models.

Conclusions:

The study validates existing deep learning approaches and proposes novel architectures for brain activation prediction.
Findings highlight the significant role of individual variability in model performance, suggesting personalized approaches may be necessary.
This work contributes to advancing the application of deep learning in neuroimaging for a better understanding of brain function.