Research reveals new clues to the energy use of ‘resting’ minds
The default mode network is a prominent brain network that becomes highly active during rest, when we are not interacting with our surroundings, and deactivates during active engagement. Research into this phenomenon has largely relied on classical imaging techniques such as functional magnetic resonance imaging (fMRI). The EU-funded SUGARCODING project hypothesised that during these energy-demanding resting states, the brain consolidates recent memories. “To explore this, we quantified the brain’s energy metabolism both at rest and during memory processing,” notes Valentin Riedl, project coordinator. “This is achieved using a novel brain imaging method we recently developed, which allows us to measure oxygen metabolism – our primary energy source powering the brain’s signalling and housekeeping functions.”
Default mode network when focusing on external tasks
The project team combined fMRI with quantitative positron emission tomography (PET) imaging using an advanced PET/MR scanner. This allowed them to link fMRI measurements to absolute energy consumption as recorded by PET. “Our findings showed that brain regions with higher functional connectivity – greater interaction with other regions – demonstrate increased energy consumption,” explains Riedl. Interestingly, evolutionarily newer brain regions tend to consume more energy, which we attributed to slower chemical signalling processes involving neurotransmitters like dopamine and serotonin.” These results allowed researchers to confirm that fMRI can effectively capture the significant energy costs associated with brain connectivity. This project activity is reported here.
Busy brain during wakeful rest
Researchers then investigated whether indeed the default mode network consumes more energy during wakeful rest, as suggested by earlier fMRI studies. “Our results were puzzling. A decrease in the fMRI signal from the default mode network – typically interpreted as a sign of reduced neuronal activity – was linked to higher energy consumption in most brain regions,” outlines Riedl. “This led us to conclude that variations in blood flow across the cortex can distort fMRI readings. Specifically, regions of the default mode network that appear less active in fMRI scans are actually metabolically more active owing to unexpected changes in blood flow.” For years, it has been widely accepted that in brain imaging using MRI, a high fMRI signal indicates increased neuronal activity, while a low signal suggests less activity. This assumption is primarily based on earlier research conducted in sensory areas of the brain. However, SUGARCODING research using advanced multimodal brain imaging – which includes methods for measuring absolute energy metabolism – challenged this interpretation. The project team found that fMRI signals do not function uniformly across the entire brain. “Certain regions of the brain have different vascular structures and blood flow patterns that can unpredictably alter fMRI results,” adds Riedl. These findings highlight the importance of considering these variations when interpreting brain activity from fMRI data.
Ageing or neurogenerative diseases could alter fMRI interpretation
The manuscript for the latter project activity is currently under review. It emphasises the need for caution when interpreting fMRI signals, particularly when comparing different brain regions or analysing individuals with varying brain vasculature. “For instance, changes in brain vasculature, such as those occurring with ageing or neurodegenerative diseases, can significantly affect fMRI data interpretation. This highlights the need of incorporating additional information about cortical blood flow to more accurately link changes in fMRI signals to neuronal activity,” concludes Riedl.
Keywords
SUGARCODING, fMRI, default mode network, neuronal activity, blood flow, wakeful rest, vasculature
