Leppanen, J. | Search bioRxiv

bioRxiv2023

Predicting Anorexia Nervosa and Autistic Characteristics in Individuals with Anorexia Nervosa from Resting State Cyclic Connectivity.

BackgroundResting state functional Magnetic Resonance Imaging (rsfMRI) differences have been reported in individuals with anorexia nervosa (AN). However, methodological issues limit inferences, the dynamic cyclic nature of the resting state signal has not been explored, neither has the association with known elevated autistic characteristics. Methods92 participants, 65 Individuals with AN and 27 controls underwent rsfMRI. Cyclic analysis was conducted to obtain pairwise relationships between regions. Classification of group and regression to predict autistic characteristics within the AN group was conducted, with model weights being explored to ascertain the most predictive pairwise relationships. ResultsPairwise relationships in the temporal, dorsal and ventral attention networks were most predictive of group. The anterior intraparietal sulcus, salience, dorsal attention, auditory dorsal posterior cingulate cortex, and ventral attention networks were most predictive of autistic characteristics. DiscussionSeveral distinct pairwise relationships predicting group and autistic characteristics were found, however, the global disruption of the temporal ordering of the cyclic wave, and variation in temporal ordering across resting state scan are also a neurophenotype in individuals with AN and the relationship to autistic characteristics. Characteristics associated with AN and autism are also predicted by distinct neural regions.

neuroscience↗

bioRxiv2020

Sailing in rough waters: examining volatility of fMRI noise

BackgroundThe assumption that functional magnetic resonance imaging (fMRI) noise has constant volatility has recently been challenged by studies examining heteroscedasticity arising from head motion and physiological noise. The present study builds on this work using latest methods from the field of financial mathematics to model fMRI noise volatility. MethodsMulti-echo nd human fMRI scans were used and realised volatility was estimated. The Hurst parameter H [isin] (0, 1), which governs the roughness/irregularity of realised volatility time series, was estimated. Calibration of H was performed pathwise, using well-established neural network calibration tools. ResultsIn all experiments the volatility calibrated to values within the rough case, H < 0.5, and on average fMRI noise was very rough with 0.03 < H < 0.05. Some edge effects were also observed, whereby H was larger near the edges of the phantoms. DiscussionThe findings suggest that fMRI volatility is not only non-constant, but also substantially more irregular than a standard Brownian motion. Thus, further research is needed to examine the impact such pronounced oscillations in the volatility of fMRI noise have on data analyses.

neuroscience↗