Single-subject Single-session Temporally-Independent Functional Modes of Brain Activity

NeuroImage

Alberto Llera¹  Christian F. Beckmann²  Daniel E.P. Gomez³  David G. Norris³  José P.R. F. Marques³ 

[1] Corresponding author.;Department of Cognitive Neuroscience, Radboud University Medical Centre, 6525 EN, Nijmegen, the Netherlands;Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Donders Centre for Cognitive Neuroimaging, Trigon 204, P.O. Box 9101, 6500 HB, Nijmegen, the Netherlands;

关键词: multiband;    sms;    mesh;    fmri;    Ultra-fast;    tfm;   

DOI  :  

来源: DOAJ

【 摘 要 】

Temporally independent functional modes (TFMs) are functional brain networks identified based on their temporal independence. The rationale behind identifying TFMs is that different functional networks may share a common anatomical infrastructure yet display distinct temporal dynamics. Extracting TFMs usually require a larger number of samples than acquired in standard fMRI experiments, and thus have therefore previously only been performed at the group level. Here, using an ultra-fast fMRI sequence, MESH-EPI, with a volume repetition time of 158 ​ms, we conducted an exploratory study with n ​= ​6 subjects and computed TFMs at the single subject level on both task and resting-state datasets. We identified 6 common temporal modes of activity in our participants, including a temporal default mode showing patterns of anti-correlation between the default mode and the task-positive networks, a lateralised motor mode and a visual mode integrating the visual cortex and the visual streams. In alignment with other findings reported recently, we also showed that independent time-series are largely free from confound contamination. In particular for ultra-fast fMRI, TFMs can separate the cardiac signal from other fluctuations. Using a non-linear dimensionality reduction technique, UMAP, we obtained preliminary evidence that combinations of spatial networks as described by the TFM model are highly individual. Our results show that it is feasible to measure reproducible TFMs at the single-subject level, opening new possibilities for investigating functional networks and their integration. Finally, we provide a python toolbox for generating TFMs and comment on possible applications of the technique and avenues for further investigation.

【 授权许可】

Unknown