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Related Concept Videos

Atomic Force Microscopy01:08

Atomic Force Microscopy

Atomic force microscopy (AFM) is a type of scanning probe microscopy that can analyze topographic details of various specimens like ceramics, glass, polymers, and biological samples. AFM offers over 1000 times more resolution than the optical imaging system. Images generated from AFM are three-dimensional surface profiles, offering an advantage over the flat, two-dimensional images from other imaging techniques.
The AFM Probe
The probe is regarded as the heart of any AFM setup and comprises the...

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Dynamic Fingerprinting of the Human Functional Connectome.

Amin Ghaffari1, Yufei Zhao1, Xu Chen2

  • 1Department of Bioengineering, University of California, Riverside, Riverside, California, USA.

Brain Connectivity
|February 19, 2026
PubMed
Summary
This summary is machine-generated.

Dynamic brain connectivity patterns offer a unique individual fingerprint, outperforming static methods for brain identification. Specific brain states within this dynamic connectivity may predict cognitive abilities, serving as potential biomarkers for neurological disorders.

Keywords:
brain fingerprintsbrain networkscognitive performance predictiondynamic functional connectivityparticipant identificationresting-state functional MRI

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Area of Science:

  • Neuroscience
  • Brain Imaging
  • Network Science

Background:

  • Resting-state functional connectivity (FC) exhibits personalized patterns, acting as a unique brain fingerprint.
  • The brain is a dynamic system with varying FC maps across metastable states, unlike static approaches.

Purpose of the Study:

  • To investigate dynamic FC for improved individual brain identification.
  • To explore the utility of state-specific FC in predicting cognitive scores.

Main Methods:

  • Derived state-specific functional connectivity (FC) maps using sliding window correlation and clustering.
  • Evaluated the performance of dynamic FC fingerprints against static methods for individual identification and cognitive prediction.

Main Results:

  • Dynamic brain fingerprints demonstrated superior identification accuracy compared to static fingerprints.
  • Certain brain states showed higher accuracy in predicting cognitive scores, suggesting their informativeness.

Conclusions:

  • Dynamic FC information captures subject-specific connectivity features missed by static FC alone.
  • Specific brain states identified through dynamic analysis show potential as biomarkers for cognitive abilities and brain disorders.