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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy
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SuperAging functional connectomics from resting-state functional MRI.

Bram R Diamond1,2,3, Jaiashre Sridhar1, Jessica Maier4

  • 1Mesulam Center for Cognitive Neurology and Alzheimer's Disease, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.

Brain Communications
|July 9, 2024
PubMed
Summary
This summary is machine-generated.

Functional connectivity (FC) within and between brain networks does not differentiate SuperAging from typical cognitive aging. This finding suggests FC is not the primary driver of exceptional memory in older adults.

Keywords:
memoryneuroimagingneuropsychologysuccessful aging

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Area of Science:

  • Neuroscience
  • Cognitive Aging Research
  • Neuroimaging

Background:

  • Functional connectivity (FC) of neurocognitive networks is linked to cognitive decline in Alzheimer's disease and related dementias (ADRD).
  • The role of FC in typical cognitive aging and exceptional cognitive performance (SuperAging) is inconsistent, possibly due to methodological limitations.
  • SuperAging is characterized by preserved or enhanced cognitive function, particularly episodic memory, in individuals aged 80 and older.

Purpose of the Study:

  • To investigate differences in within- and between-network FC in the SuperAging phenotype compared to cognitively average older adults.
  • To examine FC within seven large-scale resting-state networks and within subcomponents of the default mode network (DMN).
  • To assess network segregation as a potential differentiator between SuperAging and average cognitive aging.

Main Methods:

  • Used resting-state functional MRI (rs-fMRI) to compare FC in 24 SuperAgers and 16 cognitively average older-aged controls (OACs).
  • Analyzed FC within and between seven large-scale networks and within six DMN subcomponents.
  • Compared network segregation indices and used independent t-tests with multiple comparison correction for group analyses.

Main Results:

  • No significant differences were found in demographic characteristics between SuperAgers and OACs.
  • Within-network FC, between-network FC, and network segregation did not significantly differentiate SuperAgers from OACs.
  • FC within or between large-scale networks, including DMN subcomponents, is not a primary factor distinguishing SuperAging from average aging.

Conclusions:

  • Functional connectivity within or between large-scale neurocognitive networks does not appear to be the primary driver of exceptional memory performance in SuperAging.
  • These findings help distinguish the neural underpinnings of successful cognitive aging from those associated with neurodegenerative diseases like ADRD.
  • Future research may explore other neural mechanisms contributing to the SuperAging phenotype.