Intrinsic functional connectivity reflects spontaneous neuronal fluctuations that are functionally coupled and organized in networks (the so-called resting-state networks, RSNs) in the absence of active tasks. Functional magnetic resonance imaging (fMRI) studies suggest that the coherent spontaneous activity accounts for variability in event-related BOLD responses, and it predicts inter-individual differences in brain responses during task performance. Recent evidence shows that spontaneous activity patterns are related to inter-individual differences in cognition, personality traits, and behavioral performance. In the present research, we investigated how intrinsic functional connectivity predicts the inter-individual difference in task-evoked connectivity in the alpha and beta bands, the neurophysiological correlates of RSNs.We analyzed MEG data from the Human Connectome Project collected from 51 subjects (age range 22-35 yo) during visual fixation and during a motor task (i.e., finger tapping or toe squeezing). We computed the leakage-corrected band-limited power (BLP) correlation across 164 node regions - parcelled into 10 networks - in alpha (6.3-16.5 Hz), low beta (12.5-29 Hz), and high beta band (22.5-39 Hz). Then, we used a univariate model to explore if the variance of functional connectivity can be explained in terms of the band, subject, and task. Finally, we used a leaveone-out approach to predict task-evoked connectivity patterns from the connectivity at rest. We evaluated how accurate is the model in predicting the single subject’s connectivity pattern using the identification rate (ID rate). Our results show that 1) the frequency band and the subject account for most of the explained variance (41.13% and 22.32%, respectively), while the task explains only 6.02% of the variance. 2) ID rate values are higher (above 58%) in the Motor Network (MN) and the Visual Network (VIS) in alpha and low beta bands for both tasks (i.e., finger tapping and toe squeezing). In all the other RSNs and frequency bands, the ID rate was lower than 50%. Our findings show that inter-individual variability modulates functional connectivity. Moreover, we demonstrate that from intrinsic connectivity patterns, it is possible to draw inferences about the connectivity patterns during a motor task, at the level of the single subject, in specific RSNs (MN and VIS) and frequency bands. This evidence supports the existence of a MEG connectivity fingerprint already present at rest. This fingerprint is unique for each individual and can accurately predict the connections reorganization during a motor task.
SPECTRAL CONNECTIVITY FINGERPRINT IN THE RESTING HUMAN BRAIN: A MEG STUDY / Maddaluno, O.; Guidotti, R.; Vettoruzzo, A.; Marzetti, L.; Cisotto, G.; Betti, V.. - (2022). (Intervento presentato al convegno Neuroscience 2022 tenutosi a San Diego, Stati Uniti).
SPECTRAL CONNECTIVITY FINGERPRINT IN THE RESTING HUMAN BRAIN: A MEG STUDY
Maddaluno O.;Betti V.
2022
Abstract
Intrinsic functional connectivity reflects spontaneous neuronal fluctuations that are functionally coupled and organized in networks (the so-called resting-state networks, RSNs) in the absence of active tasks. Functional magnetic resonance imaging (fMRI) studies suggest that the coherent spontaneous activity accounts for variability in event-related BOLD responses, and it predicts inter-individual differences in brain responses during task performance. Recent evidence shows that spontaneous activity patterns are related to inter-individual differences in cognition, personality traits, and behavioral performance. In the present research, we investigated how intrinsic functional connectivity predicts the inter-individual difference in task-evoked connectivity in the alpha and beta bands, the neurophysiological correlates of RSNs.We analyzed MEG data from the Human Connectome Project collected from 51 subjects (age range 22-35 yo) during visual fixation and during a motor task (i.e., finger tapping or toe squeezing). We computed the leakage-corrected band-limited power (BLP) correlation across 164 node regions - parcelled into 10 networks - in alpha (6.3-16.5 Hz), low beta (12.5-29 Hz), and high beta band (22.5-39 Hz). Then, we used a univariate model to explore if the variance of functional connectivity can be explained in terms of the band, subject, and task. Finally, we used a leaveone-out approach to predict task-evoked connectivity patterns from the connectivity at rest. We evaluated how accurate is the model in predicting the single subject’s connectivity pattern using the identification rate (ID rate). Our results show that 1) the frequency band and the subject account for most of the explained variance (41.13% and 22.32%, respectively), while the task explains only 6.02% of the variance. 2) ID rate values are higher (above 58%) in the Motor Network (MN) and the Visual Network (VIS) in alpha and low beta bands for both tasks (i.e., finger tapping and toe squeezing). In all the other RSNs and frequency bands, the ID rate was lower than 50%. Our findings show that inter-individual variability modulates functional connectivity. Moreover, we demonstrate that from intrinsic connectivity patterns, it is possible to draw inferences about the connectivity patterns during a motor task, at the level of the single subject, in specific RSNs (MN and VIS) and frequency bands. This evidence supports the existence of a MEG connectivity fingerprint already present at rest. This fingerprint is unique for each individual and can accurately predict the connections reorganization during a motor task.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
