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

Classification of Connective Tissues01:30

Classification of Connective Tissues

16.1K
The connective tissues have different properties and functions in the human body. They are broadly categorized into proper, supporting, or fluid connective tissues.
Connective Tissue Proper
Connective tissue proper is the most abundant class of connective tissues. As its name implies, it predominantly connects different tissues in the body. Depending on the cell types, ground substance, viscosity, and fiber types in the ECM, connective tissue proper is further categorized into loose and dense....
16.1K
Cognitive Dissonance01:38

Cognitive Dissonance

37.5K
Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
37.5K
Encoding01:19

Encoding

867
Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
Automatic processing involves the encoding of details like time, space, frequency, and the meaning of words, usually done without conscious...
867
Dietary Connections01:23

Dietary Connections

62.1K
In biological systems, most metabolic pathways are interconnected. The cellular respiration processes that convert glucose to ATP—such as glycolysis, pyruvate oxidation, and the citric acid cycle—tie into those that break down other organic compounds. As a result, various foods—from apples to cheese to guacamole—end up as ATP. In addition to carbohydrates, food also contains proteins and lipids—such as cholesterol and fats. All of these organic compounds are used...
62.1K
Force Classification01:22

Force Classification

2.4K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.4K
Classification of Neurotransmitters01:30

Classification of Neurotransmitters

5.3K
Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
5.3K

You might also read

Related Articles

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

Sort by
Same author

Robust Depth Estimation Under Sensor Degradations: A Multi-Sensor Fusion Perspective.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Task guided representation learning using compositional models for zero-shot domain adaptation.

Neural networks : the official journal of the International Neural Network Society·2023
Same author

Editorial: Novel perspectives and improvements in fMRI functional connectivity analysis methods used to investigate brain networks and cognitive mechanisms in humans.

Frontiers in neuroscience·2023
Same author

Deep Depth Completion From Extremely Sparse Data: A Survey.

IEEE transactions on pattern analysis and machine intelligence·2023
Same author

Analyzing Complex Problem Solving by Dynamic Brain Networks.

Frontiers in neuroinformatics·2021
Same author

Joint Adversarial Example and False Data Injection Attacks for State Estimation in Power Systems.

IEEE transactions on cybernetics·2021

Related Experiment Video

Updated: Feb 8, 2026

Transferring Cognitive Tasks Between Brain Imaging Modalities: Implications for Task Design and Results Interpretation in fMRI Studies
10:09

Transferring Cognitive Tasks Between Brain Imaging Modalities: Implications for Task Design and Results Interpretation in fMRI Studies

Published on: September 22, 2014

13.6K

Encoding the local connectivity patterns of fMRI for cognitive task and state classification.

Itir Onal Ertugrul1, Mete Ozay2, Fatos T Yarman Vural3

  • 1Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA. iertugru@andrew.cmu.edu.

Brain Imaging and Behavior
|June 28, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework using Fisher vectors (FV) to encode brain connectivity patterns from fMRI data. FV encoding effectively classifies cognitive tasks, outperforming other methods like VLAD and BoW.

Keywords:
Brain decodingFisher vector encodingMesh arc descriptorsfMRI

More Related Videos

fMRI Validation of fNIRS Measurements During a Naturalistic Task
10:36

fMRI Validation of fNIRS Measurements During a Naturalistic Task

Published on: June 15, 2015

21.6K
Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis
10:33

Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis

Published on: June 20, 2012

13.3K

Related Experiment Videos

Last Updated: Feb 8, 2026

Transferring Cognitive Tasks Between Brain Imaging Modalities: Implications for Task Design and Results Interpretation in fMRI Studies
10:09

Transferring Cognitive Tasks Between Brain Imaging Modalities: Implications for Task Design and Results Interpretation in fMRI Studies

Published on: September 22, 2014

13.6K
fMRI Validation of fNIRS Measurements During a Naturalistic Task
10:36

fMRI Validation of fNIRS Measurements During a Naturalistic Task

Published on: June 15, 2015

21.6K
Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis
10:33

Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis

Published on: June 20, 2012

13.3K

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Machine Learning

Background:

  • Understanding brain connectivity is crucial for deciphering cognitive functions.
  • Existing methods for encoding brain connectivity patterns have limitations.

Purpose of the Study:

  • To propose a novel framework for encoding local brain connectivity patterns using Fisher vectors (FV), Vector of Locally Aggregated Descriptors (VLAD), and Bag-of-Words (BoW).
  • To evaluate the effectiveness of these encoding methods in classifying cognitive tasks and states using fMRI data.

Main Methods:

  • Obtained mesh arc descriptors (MADs) from fMRI data by analyzing local meshes around anatomical regions.
  • Extracted a brain connectivity dictionary using Gaussian Mixture Models (GMM) and encoded MADs using FV, VLAD, and BoW.
  • Classified cognitive tasks and states using Support Vector Machines (SVM) with the encoded descriptors.

Main Results:

  • Fisher vector encoding of MADs demonstrated successful classification of cognitive tasks.
  • FV encoding outperformed VLAD and BoW representations in classification accuracy.
  • Identified significant components within the GMM and analyzed their impact on classification.

Conclusions:

  • Fisher vectors provide a powerful method for encoding brain connectivity patterns from fMRI data.
  • The proposed framework offers a robust approach for analyzing and classifying cognitive states based on brain activity.
  • A novel visualization method for brain connectivity dictionary codewords was suggested.