5 research outputs found
HVOX: Scalable Interferometric Synthesis and Analysis of Spherical Sky Maps
Analysis and synthesis are key steps of the radio-interferometric imaging
process, serving as a bridge between visibility and sky domains. They can be
expressed as partial Fourier transforms involving a large number of non-uniform
frequencies and spherically-constrained spatial coordinates. Due to the data
non-uniformity, these partial Fourier transforms are computationally expensive
and represent a serious bottleneck in the image reconstruction process. The
W-gridding algorithm achieves log-linear complexity for both steps by applying
a series of 2D non-uniform FFTs (NUFFT) to the data sliced along the so-called
frequency coordinate. A major drawback of this method however is its
restriction to direction-cosine meshes, which are fundamentally ill-suited for
large field of views. This paper introduces the HVOX gridder, a novel algorithm
for analysis/synthesis based on a 3D-NUFFT. Unlike W-gridding, the latter is
compatible with arbitrary spherical meshes such as the popular HEALPix scheme
for spherical data processing. The 3D-NUFFT allows one to optimally select the
size of the inner FFTs, in particular the number of W-planes. This results in a
better performing and auto-tuned algorithm, with controlled accuracy guarantees
backed by strong results from approximation theory. To cope with the
challenging scale of next-generation radio telescopes, we propose moreover a
chunked evaluation strategy: by partitioning the visibility and sky domains,
the 3D-NUFFT is decomposed into sub-problems which execute in parallel, while
simultaneously cutting memory requirements. Our benchmarking results
demonstrate the scalability of HVOX for both SKA and LOFAR, considering
state-of-the-art challenging imaging setups. HVOX is moreover computationally
competitive with W-gridder, despite the absence of domain-specific
optimizations in our implementation
Decoding brain states on the intrinsic manifold of human brain dynamics across wakefulness and sleep
Current state-of-the-art functional magnetic resonance imaging (fMRI) offers remarkable
imaging quality and resolution, yet, the intrinsic dimensionality of brain dynamics in different
states (wakefulness, light and deep sleep) remains unknown. Here we present a method to
reveal the low dimensional intrinsic manifold underlying human brain dynamics, which is
invariant of the high dimensional spatio-temporal representation of the neuroimaging technology. By applying this intrinsic manifold framework to fMRI data acquired in wakefulness
and sleep, we reveal the nonlinear differences between wakefulness and three different sleep
stages, and successfully decode these different brain states with a mean accuracy across
participants of 96%. Remarkably, a further group analysis shows that the intrinsic manifolds
of all participants share a common topology. Overall, our results reveal the intrinsic manifold
underlying the spatiotemporal dynamics of brain activity and demonstrate how this manifold
enables the decoding of different brain states such as wakefulness and various sleep stages.J.R.-Q. is funded by the Fundació Catalunya—La Pedrera Masters of Excellence Fellowship.
M.L.K. and S.A. are supported by the ERC Consolidator Grant CAREGIVING (no. 615539)
and Center for Music in the Brain, funded by the Danish National Research Foundation
(DNRF117). G.D. is supported by a Spanish national research project (ref. PID2019-
105772GB-I00 /AEI/10.13039/501100011033 MCIU AEI) funded by the Spanish Ministry
of Science, Innovation and Universities (MCIU), State Research Agency (AEI)
The connectome spectrum as a canonical basis for a sparse representation of fast brain activity
The functional organization of neural processes is constrained by the brain's intrinsic structural connectivity, i.e., the connectome. Here, we explore how structural connectivity can improve the representation of brain activity signals and their dynamics. Using a multi-modal imaging dataset (electroencephalography, structural MRI, and diffusion MRI), we represent electrical brain activity at the cortical surface as a time-varying composition of harmonic modes of structural connectivity. These harmonic modes are known as connectome harmonics. Here we describe brain activity signal as a time-varying combination of connectome harmonics. We term this description as the connectome spectrum of the signal. We found that: first, the brain activity signal is represented more compactly by the connectome spectrum than by the traditional area-based representation; second, the connectome spectrum characterizes fast brain dynamics in terms of signal broadcasting profile, revealing different temporal regimes of integration and segregation that are consistent across participants. And last, the connectome spectrum characterizes fast brain dynamics with fewer degrees of freedom than area-based signal representations. Specifically, we show that a smaller number of dimensions capture the differences between low-level and high-level visual processing in the connectome spectrum. Also, we demonstrate that connectome harmonics capture more sensitively the topological properties of brain activity. In summary, this work provides statistical, functional, and topological evidence indicating that the description of brain activity in terms of structural connectivity fosters a more comprehensive understanding of large-scale dynamic neural functioning
The coupling between the spatial and temporal scales of neural processes revealed by a joint time-vertex connectome spectral analysis
Brain oscillations are produced by the coordinated activity of large groups of neurons and different rhythms are thought to reflect different modes of information processing. These modes, in turn, are known to occur at different spatial scales. Nevertheless, how these rhythms support different spatial modes of information processing at the brain scale is not yet fully understood. Here we use ''Joint Time-Vertex Spectral Analysis'' to characterize the joint spectral content of brain activity both in time (temporal frequencies) and in space over the connectivity graph (spatial connectome harmonics). This method allows us to characterize the relationship between spatially localized or distributed neural processes on one side and their respective temporal frequency bands in source-reconstructed M/EEG signals. We explore this approach on two different datasets, an auditory steady-state response (ASSR) and a visual grating task. Our results suggest that different information processing mechanisms are carried out at different frequency bands: while spatially distributed activity (which may also be interpreted as integration) specifically occurs at low temporal frequencies (alpha and theta) and low graph spatial frequencies, localized electrical activity (i.e., segregation) is observed at high temporal frequencies (high and low gamma) over restricted high spatial graph frequencies. Crucially, the estimated contribution of the distributed and localized neural activity predicts performance in a behavioral task, demonstrating the neurophysiological relevance of the joint time-vertex spectral representation