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In this paper, our work relies on a key assumption that variations of functional MR images are sparse over time in the wavelet domain.
The key assumption of our work is that the variations of functional MR images are sparse over time in the wavelet domain.
Based on a key assumption that the variations of functional MR images are sparse over time in the wavelet domain, our method models an fMRI sequence as a linear dynamic sparse model.
This method models an fMRI sequence as a linear dynamic sparse model which is based on a key assumption that variations of functional MR images are sparse over time in the wavelet domain.
Based on the assumption that the variations of functional MR images are sparse, an fMRI sequence is modelled as a linear equation with an identity transition matrix: begin{aligned} x_t = x_{t-1} + q_t, end{aligned} (1 where random variablex t denotes the DWT coefficients of a functional MR image at time t.
High-temperature induced great variations of functional traits in tall fescue accessions.
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Studies that assess the co-variation between two variables: for example, co-variation of functional or structural properties of the brain and a behavioural variable, such as reported stress.
Interestingly, even a slight structural variation of functional group at specific position(s) resulted in diverse micro/nano-scaled self-assembled morphologies such as needles, fibrils, dendrites, plates and A-shaped structures which were characterized using optical microscopy, confocal microscopy, SEM and AFM.
The MEP problem is the variation of functional extremum in mathematics [27].
Hence, lots of efforts are made in bench-marking studies with variation of functional and basis set in combination with solvent models and empirical dispersion correction.
We were particularly interested in the brain areas that showed variation of functional connectivity with the ventral medial prefrontal cortex during the MI task.
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