Neural oscillations are widely studied using methods based on the Fourier transform, which models data as sums of sinusoids. This has successfully uncovered numerous links between oscillations and cognition or disease. However, neural data are nonsinusoidal, and these nonsinusoidal features are increasingly linked to a variety of behavioral and cognitive states, pathophysiology, and underlying neuronal circuit properties. Here, we present a new analysis framework-one that is complementary to existing Fourier- and Hilbert-transform based approaches-that quantifies oscillatory features in the time domain, on a cycle-by-cycle basis. We have released this cycle-by-cycle analysis suite as bycycle, a fully documented, open-source Python package with detailed tutorials and troubleshooting cases. This approach performs tests to assess whether an oscillation is present at any given moment and, if so, quantifies each oscillatory cycle by its amplitude, period, and waveform symmetry, the latter of which is missed using conventional approaches. In a series of simulated event-related studies, we show how conventional Fourier- and Hilbert-transform approaches can conflate event-related changes in oscillation burst duration as increased oscillatory amplitude and as a change in the oscillation frequency, even though those features were unchanged in simulation. Our approach avoids these errors. Further, we validate this approach in simulation and against experimental recordings of patients with Parkinson's disease, who are known to have nonsinusoidal beta (12-30 Hz) oscillations.
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This content will become publicly available on October 24, 2024
Cycle-to-Cycle Variation Suppression in ReRAM-Based AI Accelerators
- Award ID(s):
- 1953544
- NSF-PAR ID:
- 10492221
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- Proc. IEEE International Conference on Physical Assurance and Inspection of Electronics (PAINE'23)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Location:
- Huntsville, AL, USA
- Sponsoring Org:
- National Science Foundation
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Plasmodium vivax , patients exhibit rhythmic fevers every 48 h. These fever cycles correspond with the time the parasites take to traverse the intraerythrocytic cycle (IEC). In otherPlasmodium species that infect either humans or mice, the IEC is likely guided by a parasite-intrinsic clock [Rijo-Ferreiraet al. ,Science 368 , 746–753 (2020); Smithet al .,Science 368 , 754–759 (2020)], suggesting that intrinsic clock mechanisms may be a fundamental feature of malaria parasites. Moreover, becausePlasmodium cycle times are multiples of 24 h, the IECs may be coordinated with the host circadian clock(s). Such coordination could explain the synchronization of the parasite population in the host and enable alignment of IEC and circadian cycle phases. We utilized an ex vivo culture of whole blood from patients infected withP. vivax to examine the dynamics of the host circadian transcriptome and the parasite IEC transcriptome. Transcriptome dynamics revealed that the phases of the host circadian cycle and the parasite IEC are correlated across multiple patients, showing that the cycles are phase coupled. In mouse model systems, host–parasite cycle coupling appears to provide a selective advantage for the parasite. Thus, understanding how host and parasite cycles are coupled in humans could enable antimalarial therapies that disrupt this coupling.
