Time-Frequency Characterization of Resting-State Brain Function Reveals Overlapping Components with Specific Topology and Frequency Content

Even at rest, functional magnetic resonance imaging (fMRI) data displays exquisitely complex temporal dynamics. Here, we deployed a time-frequency analysis to track the modulus of fMRI signals over time, across space (a set of 341 brain areas) and frequency (45 uniformly distributed bins in the 0.01-0.25 Hz range).
Decomposing the data into a set of temporally overlapping building blocks by Principal Component Analysis, we exposed diverse functional components with their own modulus pattern across brain locations and frequency sub-ranges.
In particular, the component explaining most data variance showed homogeneous modulus across space at low frequencies, fitting with the marked whole-brain signal fluctuations seen in the time courses subjected to analysis. Other components showed topologically well-defined modulus patterns (e.g., contrasting the default mode and visual networks), with characteristic frequency properties and subject-specific activation profiles.


Published in:
Icigp 2019: Proceedings Of The 2Nd International Conference On Image And Graphics Processing / 2019 5Th International Conference On Virtual Reality, 84-88
Presented at:
2nd International Conference on Image and Graphics Processing (ICIGP) / 5th International Conference on Virtual Reality, Singapore, SINGAPORE, Feb 23-25, 2019
Year:
Jan 01 2019
Publisher:
New York, ASSOC COMPUTING MACHINERY
ISBN:
978-1-4503-6092-0
Keywords:
Laboratories:




 Record created 2019-07-24, last modified 2019-08-30


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