Journal
EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS
Volume 232, Issue 1, Pages 123-133Publisher
SPRINGER HEIDELBERG
DOI: 10.1140/epjs/s11734-022-00696-2
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Aging affects brain structure and cognitive capabilities, but current magnetic resonance imaging (MRI) methods are unable to accurately reflect cognitive decline. However, MRI signals may contain information about brain activity beyond commonly used signals. A study using a zero-spin echo (ZSE) weighted MRI sequence found that signals evoked by heartbeats (HES) have properties similar to electrophysiology. Comparing different age groups, the complexity of HES was found to decrease with age, and stability and chaoticity were particularly sensitive to age. Complexity measures were related to cognitive performance, indicating the potential use of HES for understanding brain dynamics.
Getting older affects both the structure of the brain and some cognitive capabilities. Until now, magnetic resonance imaging (MRI) approaches have been unable to give a coherent reflection of the cognitive declines. It shows the limitation of the contrast mechanisms used in most MRI investigations, which are indirect measures of brain activities depending on multiple physiological and cognitive variables. However, MRI signals may contain information of brain activity beyond these commonly used signals caused by the neurovascular response. Here, we apply a zero-spin echo (ZSE) weighted MRI sequence, which can detect heartbeat-evoked signals (HES). Remarkably, these MRI signals have properties only known from electrophysiology. We investigated the complexity of the HES arising from this sequence in two age groups; young (18-29 years) and old (over 65 years). While comparing young and old participants, we show that the complexity of the HES decreases with age, where the stability and chaoticity of these HES are particularly sensitive to age. However, we also found individual differences which were independent of age. Complexity measures were related to scores from different cognitive batteries and showed that higher complexity may be related to better cognitive performance. These findings underpin the affinity of the HES to electrophysiological signals. The profound sensitivity of these changes in complexity shows the potential of HES for understanding brain dynamics that need to be tested in more extensive and diverse populations with clinical relevance for all neurovascular diseases.
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