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

Biological Influences on Intelligence01:30

Biological Influences on Intelligence

174
Intelligence is often thought to be linked to brain size, but the relationship is more complex than that. While brain size does correlate modestly with some abilities, like verbal skills, the connection is weaker for others, such as spatial reasoning. Other factors, like brain structure, also play crucial roles. For instance, despite Einstein's smaller-than-average brain, his parietal cortex, which is involved in spatial reasoning, was 15% wider, suggesting that neural density might matter...
174
Brain Imaging01:14

Brain Imaging

275
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
275
Multiple Intelligences Theory01:20

Multiple Intelligences Theory

8.0K
Howard Gardner's theory of Multiple Intelligence proposes that there are nine distinct types of intelligence, each reflecting different ways of interacting with the world. Introduced in 1983 and expanded in subsequent years, Gardner's framework challenges the traditional notion of a single, generalized intelligence.
8.0K
Measures of Intelligence01:29

Measures of Intelligence

7.7K
Psychologists measure intelligence by using standardized tests that produce a score known as the intelligence quotient or IQ. To understand IQ tests, it's important to recognize the key principles behind their construction: validity, reliability, and standardization.
Validity refers to how well a test measures what it claims to measure. An intelligence test should accurately assess intelligence rather than another characteristic, like anxiety. Criterion validity is one way to evaluate this;...
7.7K
Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

963
Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...
963
Triarchic Theory of Intelligence01:24

Triarchic Theory of Intelligence

8.6K
Robert Sternberg's triarchic theory of intelligence posits that intelligence is composed of three distinct but interrelated components: analytical, creative, and practical intelligence.
8.6K

You might also read

Related Articles

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

Sort by
Same author

Trait-Relevant Tasks Improve Personality Prediction From Structural-Functional Brain Network Coupling.

Human brain mapping·2026
Same author

Sustainable neuroscience through open science.

Nature human behaviour·2026
Same author

Comprehensive large-scale analyses reveal association between brain structure and cognitive ability during adolescence.

Communications biology·2026
Same author

Decoding the human brain during intelligence testing.

Communications biology·2025
Same author

Network Neuroscience of Human Multitasking: Local Connections Matter.

Human brain mapping·2025
Same author

Trait-Relevant Tasks Improve Personality Prediction from Structural-Functional Brain Network Coupling.

bioRxiv : the preprint server for biology·2025
Same journal

Ocular speech tracking persists in blindness, but its dynamics and oculo-cerebral connectivity depend on visual status.

eNeuro·2026
Same journal

Emergent multidien cycles from partial circadian synchrony.

eNeuro·2026
Same journal

Adolescent social isolation induces persistent impairments in emotional discrimination and helping behavior.

eNeuro·2026
Same journal

Increased Ih Current Is Associated with Reduced Hippocampal CA1 Excitability in a Mouse Model of Multiple Sclerosis.

eNeuro·2026
Same journal

Reduced SuM Activation Accompanies Impaired Social Novelty Recognition in Mouse Models of Neurodevelopmental Disorders.

eNeuro·2026
Same journal

Do Not Forget the Stimulus: A Missing Control in Naturalistic Studies of Neural Entrainment.

eNeuro·2026
See all related articles

Related Experiment Video

Updated: Aug 13, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.0K

Multimodal Brain Signal Complexity Predicts Human Intelligence.

Jonas A Thiele1, Aylin Richter2, Kirsten Hilger1,3

  • 1Department of Psychology I, University of Würzburg, Würzburg 97070, Germany jonas.thiele@uni-wuerzburg.de kirsten.hilger@uni-wuerzburg.de.

Eneuro
|January 19, 2023
PubMed
Summary
This summary is machine-generated.

Intelligence is linked to brain signal complexity. Higher intelligence correlates with lower local neural complexity and reduced default-mode network activity, as shown by electroencephalography (EEG) in healthy adults.

Keywords:
EEGbrain signal complexitycognitive abilityintelligencemicrostatesresting-state

More Related Videos

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

33.8K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.9K

Related Experiment Videos

Last Updated: Aug 13, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.0K
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

33.8K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.9K

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Psychology

Background:

  • Spontaneous brain activity underpins cognitive functions.
  • Previous functional magnetic resonance imaging (fMRI) studies linked intrinsic brain dynamics to intelligence but lacked temporal resolution.
  • Electroencephalography (EEG) offers millisecond-level temporal resolution crucial for studying neural fluctuations.

Purpose of the Study:

  • To investigate the relationship between the complexity of temporally resolved intrinsic brain signals and individual differences in intelligence.
  • To explore various complexity measures (multiscale entropy, Shannon entropy, Fuzzy entropy, microstates) in relation to intelligence.
  • To develop and validate a multimodal model for predicting intelligence from brain signal complexity.

Main Methods:

  • Resting-state electroencephalography (EEG) recordings from 144 healthy adults.
  • Analysis of brain signal complexity using multiscale entropy, Shannon entropy, Fuzzy entropy, and microstate characteristics.
  • Correlation analyses and multimodal modeling with 10-fold cross-validation and external replication.

Main Results:

  • Associations between brain signal complexity and intelligence were small (r ~ 0.20) and scale-dependent.
  • Higher intelligence scores correlated with lower complexity in local neural processing.
  • Reduced activity in default-mode network regions was observed in individuals with higher intelligence.

Conclusions:

  • The temporal and spatial characteristics of intrinsic brain dynamics are crucial for understanding intelligence.
  • Multimodal approaches combining various complexity measures show promise for predicting individual intelligence.
  • Future neuroscientific research on complex traits should consider temporal and spatial dependencies in brain activity.