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

56
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...
56
Functional Brain Systems: Limbic System01:15

Functional Brain Systems: Limbic System

2.1K
The limbic system, often called the "emotional brain," is a complex set of structures located deep within the brain. The intricate network of the limbic system supports a wide range of psychological functions, from emotional regulation to memory formation and sensory processing. This functional brain region encompasses specific parts of the diencephalon and the cerebrum, integrating the higher mental functions of the cerebral cortex with the primitive emotional responses of the deep...
2.1K
Storage01:23

Storage

57
A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
57
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

611
Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
611
Measures of Intelligence01:29

Measures of Intelligence

5.9K
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;...
5.9K
Role of Cerebellum and Prefrontal Cortex in Memory01:14

Role of Cerebellum and Prefrontal Cortex in Memory

300
The cerebellum, while traditionally associated with motor control, also plays a crucial role in memory, particularly in procedural memory, which involves learning motor tasks that become automatic through repetition. For example, studies have shown that when the cerebellum is damaged, individuals or animals lose the ability to learn conditioned motor responses, such as the conditioned eye-blink response in classical conditioning experiments with rabbits. This study demonstrates the...
300

You might also read

Related Articles

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

Sort by
Same author

Feature overlap in transdiagnostic connectome-based models of sustained attention and autism symptoms.

medRxiv : the preprint server for health sciences·2026
Same author

Biological validity, test-retest reliability, and behavioral relevance of the single-subject brain volumetric similarity network.

NeuroImage·2026
Same author

Connectome-Based Predictive Models of General and Specific Executive Functions.

Human brain mapping·2025
Same author

High performers demonstrate greater neural synchrony than low performers across behavioral domains.

Imaging neuroscience (Cambridge, Mass.)·2025
Same author

Connectome-based Predictive Models of General and Specific Executive Functions.

bioRxiv : the preprint server for biology·2024
Same author

Images with harder-to-reconstruct visual representations leave stronger memory traces.

Nature human behaviour·2024
Same journal

The Relevance of a Philosophical Toolkit to Advance Neuroscience.

The European journal of neuroscience·2026
Same journal

The Brain Response to Reflectional Symmetry Is Not Uniquely Preattentive.

The European journal of neuroscience·2026
Same journal

The Design of Music Rhythm-Based Optical-Magnetic Stimulator and Its Study on LTP/LTD in the CA1 Region of the Hippocampus.

The European journal of neuroscience·2026
Same journal

The Inspiring Journeys of Women in Science.

The European journal of neuroscience·2026
Same journal

Gaining Insight Into the Nonfocality of Beta Oscillation Suppression Along the Sensorimotor Cortex Using Corticomuscular Coherence.

The European journal of neuroscience·2026
Same journal

Human Steering Control Under Unpredictable Disturbances.

The European journal of neuroscience·2026
See all related articles

Related Experiment Video

Updated: May 22, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

967

Modularity Measures of Functional Brain Networks Predict Individual Differences in Long-Term Memory.

Michael B Zhou1, Marvin M Chun2,3,4, Qi Lin2,5,6

  • 1Yale College, Yale University, New Haven, Connecticut, USA.

The European Journal of Neuroscience
|March 17, 2025
PubMed
Summary
This summary is machine-generated.

Brain network organization explains individual differences in long-term memory (LTM). Modularity measures of brain activity during memory encoding predict LTM capacity, highlighting the role of network hubs.

Keywords:
functional connectivityindividual differenceslong‐term memorymodularitypredictive modeling

More Related Videos

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.1K
Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

7.2K

Related Experiment Videos

Last Updated: May 22, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

967
A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.1K
Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

7.2K

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Network Science

Background:

  • Long-term memory (LTM) capacity varies significantly across individuals, impacting daily functioning.
  • Understanding the neural basis of individual differences in LTM is a key challenge in cognitive neuroscience.
  • Brain functional organization, particularly its modularity, is increasingly recognized as a potential factor influencing cognitive abilities.

Purpose of the Study:

  • To investigate how the brain's functional organization, specifically network modularity, predicts individual differences in long-term memory (LTM) performance.
  • To examine the predictive power of modularity measures (diversity and locality) of brain activity during encoding for general recognition and recollection memory.
  • To identify key brain networks and regions involved in supporting individual LTM differences using connectome-predictive modeling (CPM).

Main Methods:

  • Utilized graph theory to calculate modularity measures (diversity and locality) from brain activity during memory encoding.
  • Employed connectome-predictive modeling (CPM) to predict individual differences in LTM performance from brain functional connectivity and modularity features.
  • Analyzed functional neuroimaging data focusing on two key LTM forms: general recognition and recollection memory.

Main Results:

  • Modularity measures of diversity and locality significantly predicted individual differences in both general recognition and recollection memory.
  • While predictive, modularity-based models showed weaker performance compared to CPM models using only connectivity features.
  • The default mode network emerged as the most consistently selected brain network across predictive models, indicating its importance in LTM.

Conclusions:

  • Brain network modularity, characterized by diversity and locality, plays a significant role in explaining individual differences in long-term memory.
  • Successful LTM relies on critical connector hubs that facilitate coordination within and between brain networks during memory encoding.
  • A graph-based approach, integrating modularity and connectivity, offers valuable insights into the neural underpinnings of individual variations in LTM.