Ventresca, M. | bioRxiv Science

bioRxiv2024

Elevated body mass index in youth is associated with dysregulated surrogate markers of neural inhibition & excitation, and internetwork functional dysconnectivity

The developing child and adolescent brain is thought to have an increased vulnerability to the negative impact of obesity and excessive consumption of hyperpalatable and energy-rich foods. In this study, we investigated the neurophysiological effects of overweight and obesity in 30 participants spanning childhood and adolescence (8-19 years), using a naturalistic viewing paradigm with Inscapes, in a pseudo-resting-state protocol scan with magnetoencephalography (MEG). Subjects were median split on body mass indices (BMI), categorised into two groups comprising: lower <25 kg/m2 (n=15) and higher [≥]25 kg/m2 (n=15). We assessed spontaneous, regional neural function indexed by oscillatory activity, and functional connectivity within and between intrinsic resting brain networks, including the default mode network, dorsal and ventral attention, somatomotor, visual, language, central executive and salience networks. Elevated BMI was associated with significant reductions in activity of the posterior dominant rhythm, and gamma hyperactivity across widespread cortical areas, suggesting intrinsic neuronal hyperexcitability and disinhibition in children and adolescents. Additionally, we observed low-frequency theta hypoconnectivity between resting state networks including the salience, visual, and default mode networks, and overall reduced global efficiency in brain network structure, suggesting reduced effectiveness in neural communication. These findings underscore the neural impact of body composition on the developing brain, suggesting deleterious alterations in excitation and inhibition from surrogate neural markers associated with neurochemistry and brain networks linked with cognitive and behavioural functioning. These alterations may contribute to the persistent behavioural rigidity and difficulties in adopting healthier eating behaviours into adulthood.

neuroscience↗

bioRxiv2024

Predicting brain age across the adult lifespan with spontaneous oscillations and functional coupling in resting brain networks captured with magnetoencephalography

The functional repertoire of the human brain changes dramatically throughout the developmental trajectories of early life and even all the way throughout the adult lifespan into older age. Capturing this arc is important to understand healthy brain ageing, and conversely, how injury and diseased states can lead to accelerated brain ageing. Regression modelling using lifespan imaging data can reliably predict an individuals brain age based on expected arcs of ageing. One feature of brain function that is important in this respect, and understudied to date, is neural oscillations - the rhythmic fluctuations of brain activity that index neural cell assemblies and their functioning, as well as coordinating information flow around networks. Here, we analysed resting-state magnetoencephalography (MEG) recordings from 367 healthy participants aged 18 to 83, using two distinct statistical approaches to link neural oscillations & functional coupling with that of healthy ageing. Spectral power and leakage-corrected amplitude envelope correlations were calculated for each canonical frequency band from delta through gamma ranges. Spatially and spectrally consistent associations between healthy ageing and neurophysiological features were found across the applied methods, showing differential effects on neural oscillations, with decreasing amplitude of low frequencies throughout the adult lifespan, and increasing high frequency amplitude. Functional connectivity within and between resting-state brain networks mediated by alpha coupling generally decreased throughout adulthood and increased in the beta band. Predictive modelling of brain age via regression showed an age dependent prediction bias resulting in overestimating the age of younger people (<40 years old) and underestimating the age of older individuals. These findings evidence strong age-related neurophysiological changes in oscillatory activity and functional networks of the brain as measured by resting-state MEG and that cortical oscillations are moderately reliable markers for predictive modelling. For researchers in the field of predictive brain age modelling with neurophysiological data, we recommend attention is paid to predictive biases for younger and older age ranges and consider using specific models for different age brackets. Nevertheless, these results suggest brain age prediction from MEG data can be used to model arcs of ageing throughout the adult lifespan and predict accelerated ageing in pathological brain states.

neuroscience↗