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Related Concept Videos

Storage01:23

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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 each...
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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 playing an...
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Implicit Memories01:24

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Related Experiment Video

Updated: Jun 2, 2026

Transcranial Direct Current Stimulation (tDCS) for Memory Enhancement
10:37

Transcranial Direct Current Stimulation (tDCS) for Memory Enhancement

Published on: September 18, 2021

Dynamic neural networks supporting memory retrieval.

Peggy L St Jacques1, Philip A Kragel, David C Rubin

  • 1Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, USA. peggyls@wjh.harvard.edu

Neuroimage
|May 10, 2011
PubMed
Summary
This summary is machine-generated.

Neural networks interact to support autobiographical memory (AM) retrieval. Connectivity changes between networks, driven by the medial prefrontal cortex (PFC), are modulated by recollection and memory accessibility.

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Area of Science:

  • Neuroscience
  • Cognitive Psychology
  • Neuroimaging

Background:

  • Autobiographical memory (AM) retrieval involves complex cognitive processes.
  • Multiple large-scale neural networks are recruited during AM retrieval.
  • The interaction and modulation of these networks remain unclear.

Purpose of the Study:

  • To investigate the interaction between neural networks supporting AM retrieval.
  • To understand how memory retrieval properties modulate network connectivity.

Main Methods:

  • Functional magnetic resonance imaging (fMRI) was employed.
  • Independent component analysis (ICA) identified distinct neural networks.
  • Dynamic causal modeling (DCM) analyzed network interactions.

Main Results:

  • Four networks were identified: medial prefrontal cortex (PFC), medial temporal lobe (MTL), frontoparietal, and cingulooperculum.
  • The medial PFC network was found to drive activation within the system.
  • Recollection and memory accessibility differentially modulated connectivity between networks.

Conclusions:

  • Neural network integration is crucial for autobiographical memory retrieval.
  • Network connectivity is dynamically modulated by memory properties like recollection and accessibility.
  • Specific network interactions support different aspects of memory retrieval, such as re-experience and retrieval fluency.