Non-invasive neuroimaging marker shows great potential for Alzheimer’s risk detection.

Individuals with amnestic mild cognitive impairment (aMCI) are at twice the risk of others in their age group of progressing to Alzheimer’s disease. Currently there is no conclusive test that exists to predict who will develop Alzheimer’s.  Now, research from The University of Texas, University of Illinois, UT Southwestern and John Hopkins University is attempting to identify a potential biomarker that could offer a more complete picture of who is most at risk.

The new study has identified a specific variation in brain waves of individuals with aMCI. The team state that the findings depict a pattern of delayed neural activity that is directly related to the severity of impairment in cognitive performance on a word finding task and may indicate an early dysfunction of progression to Alzheimer’s disease.

Previous studies show that impaired episodic memory, the ability to retain new memories such as recent conversations, events, or upcoming appointments, is a hallmark symptom of Alzheimer’s disease. While mild cognitive impairment (MCI) is the recognized clinical state between healthy aging and Alzheimer’s disease, aMCI is a specific type characterized by deficits in episodic memory.

In the current study the potential diagnostic approach utilizes electroencephalogram (EEG) technology, a more affordable and non-invasive alternative to other available methods such as MRI or a spinal tap, to measure neural responses while participants access semantic memory or long-term memory representative of general knowledge and concepts.  The researchers explain that this is a promising start at looking at a group of MCI patients. They go on to add that the long-term goal is whether this can be applied to individual patients one day.

The data findings show that individuals with aMCI performed less accurately and more slowly on the semantic memory task than healthy controls. The results also showed that the EEG illustrated delayed brain activity during the task. When researchers took into account performance on an episodic memory evaluation, they found that the worse the episodic memory performance, the greater the delayed activity that appeared in the EEG.

For the current study 16 individuals with aMCI and 17 age matched healthy controls were monitored via EEG and presented with pairs of words that either described features of an object or were randomly paired. For example, ‘humps’ and ‘desert’ would evoke the memory of the word ‘camel’, however, ‘humps’ and ‘monitor’ would be considered a random pair. Participants were then asked to indicate by button press whether the pair conjured any particular object memory or not.

The majority of earlier EEG research in aMCI has focused on looking at the mind ‘at rest’, however, the current study observes the brain while it is engaged in the object memory retrieval process. The team hypothesize that this might be more sensitive and more specific in pointing out certain cognitive deficits, in this case semantic memory, than other non-EEG methods available, because EEG reflects direct neural activity.  The researchers state that in the future this protocol could potentially provide complementary information for diagnosis of pre-dementia stages including MCI and identify neural changes that can occur in cases of Alzheimer’s disease.

The lab will continue to validate this prospective diagnostic tool that has the potential to help identify or predict those who may progress to Alzheimer’s disease. The team plans to recruit more participants and to follow them longitudinally in combination with other objective measures to examine the potentiality of applying this EEG tool as an early disease marker.

Source:   Center for BrainHealth at The University of Texas at Dallas


Photograph by George Mattel/Photo Researchers.
Photograph by George Mattel/Photo Researchers.


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