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

Three-Phase Short Circuit—Unloaded Synchronous Machine01:21

Three-Phase Short Circuit—Unloaded Synchronous Machine

718
Conducting a three-phase short circuit test on an unloaded synchronous machine helps understand its impact on the system. The AC fault current's oscillogram, with the DC offset removed, reveals that the waveform amplitude decreases from an initially high value to a steady-state level for one phase of the machine.
This behavior occurs due to the magnetic flux produced by the short-circuit armature currents. Initially, these currents follow high-reluctance paths but eventually shift to...
718
Phase Diagrams02:39

Phase Diagrams

50.4K
A phase diagram combines plots of pressure versus temperature for the liquid-gas, solid-liquid, and solid-gas phase-transition equilibria of a substance. These diagrams indicate the physical states that exist under specific conditions of pressure and temperature and also provide the pressure dependence of the phase-transition temperatures (melting points, sublimation points, boiling points). Regions or areas labeled solid, liquid, and gas represent single phases, while lines or curves represent...
50.4K
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

797
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
797
Phase Transitions02:31

Phase Transitions

23.3K
Whether solid, liquid, or gas, a substance's state depends on the order and arrangement of its particles (atoms, molecules, or ions). Particles in the solid pack closely together, generally in a pattern. The particles vibrate about their fixed positions but do not move or squeeze past their neighbors. In liquids, although the particles are closely spaced, they are randomly arranged. The position of the particles are not fixed—that is, they are free to move past their neighbors to...
23.3K
Cable Subjected to Its Own Weight01:13

Cable Subjected to Its Own Weight

807
Overhead power transmission lines rely on cables to carry electricity across large distances. To ensure the stability and functionality of these lines, it is crucial to understand the shape and tension experienced by the cables under the influence of their weight.
A generalized loading function is employed to analyze a cable subjected to its own weight. This function considers the force acting along the cable's arc length rather than its projected length, providing a more accurate...
807
Inductance: Single-Phase And Three-Phase Line01:28

Inductance: Single-Phase And Three-Phase Line

643
Understanding the inductance of transmission lines is crucial for efficient design and operation in electrical power systems. This discussion delves into the inductance characteristics of single-phase two-wire and three-phase three-wire transmission lines with equal phase spacing.
Single-Phase Two-Wire Line:
A single-phase line consists of two solid cylindrical conductors, denoted as x and y. Each conductor carries phasor currents ix and iy, respectively. Given that the sum of these currents is...
643

You might also read

Related Articles

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

Sort by
Same author

Distinct Brain Systems Support Afferent and Efferent Autonomic Activity.

bioRxiv : the preprint server for biology·2026
Same author

Reconstructing physiological signals from fMRI across the adult lifespan.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same author

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

Human brain mapping·2026
Same author

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

bioRxiv : the preprint server for biology·2026
Same author

The dynamic functional connectivity peak index: Detection of interictal epileptic activity with fMRI.

Epilepsia·2026
Same author

Mapping relative proximity within an internalizing symptoms network.

Journal of anxiety disorders·2026

Related Experiment Video

Updated: Feb 11, 2026

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

Inter-subject phase synchronization for exploratory analysis of task-fMRI.

Taylor Bolt1, Jason S Nomi1, Shruti G Vij1

  • 1Department of Psychology, University of Miami, Coral Gables, FL, USA.

Neuroimage
|April 15, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel inter-subject synchronization method for analyzing functional magnetic resonance imaging (fMRI) data. This data-driven approach effectively reveals brain network activity during tasks, offering a powerful alternative to traditional hypothesis-driven analyses.

Keywords:
Brain synchronizationExploratory fMRIGeneral linear modelInstantaneous phase analysisInter-subject correlationTask fMRI

More Related Videos

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study
05:33

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

Published on: September 8, 2021

7.5K
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 11, 2026

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
How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study
05:33

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

Published on: September 8, 2021

7.5K
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
  • Cognitive Neuroscience
  • Data Analysis

Background:

  • Task-based functional magnetic resonance imaging (fMRI) analysis typically uses hypothesis-driven methods correlating blood-oxygen-level dependent (BOLD) signals with expected temporal patterns.
  • Defining a precise temporal structure for BOLD signals can be challenging in certain experimental designs.
  • An exploratory, data-driven approach is desirable for detecting task-driven BOLD activity without prior assumptions.

Purpose of the Study:

  • To demonstrate the efficacy and power of an inter-subject synchronization approach for exploratory analysis of task-based fMRI data.
  • To characterize whole-brain task-driven responses by examining group-wise similarity in the temporal dynamics of brain networks.
  • To provide a robust data-driven framework for identifying task-related neural activity.

Main Methods:

  • Utilized a combination of instantaneous phase synchronization and independent component analysis (ICA).
  • Applied the framework to fMRI data acquired during a simple motor task and a social cognitive task.
  • Analyzed whole-brain responses by assessing synchronization patterns across subjects.

Main Results:

  • The inter-subject phase synchronization approach identified numerous brain networks that exhibited dynamic synchronization with task features.
  • These synchronized networks often responded to aspects of the task not anticipated by the hypothesized temporal structure.
  • The method proved efficient and powerful in detecting task-driven BOLD activity.

Conclusions:

  • The proposed inter-subject synchronization framework offers a powerful, exploratory, data-driven method for analyzing task-based fMRI data.
  • This approach effectively reveals dynamic synchronization within brain networks related to task performance.
  • The methodology is compatible with existing tools in the fMRI community, facilitating its adoption.