Biology subjects

de Oliveira, E. F.

Publications and source records attributed to de Oliveira, E. F..

5 recordsLinked to original sources

bioRxiv2024

Identifying patterns differing between high-dimensional datasets with generalized contrastive PCA

High-dimensional data have become ubiquitous in the biological sciences, and it is often desirable to compare two datasets collected under different experimental conditions to extract low-dimensional patterns enriched in one condition. However, traditional dimensionality reduction techniques cannot accomplish this because they operate on only one dataset. Contrastive principal component analysis (cPCA) has been proposed to address this problem, but it has seen little adoption because it requires tuning a hyperparameter resulting in multiple solutions, with no way of knowing which is correct. Moreover, cPCA uses foreground and background conditions that are treated differently, making it ill-suited to compare two experimental conditions symmetrically. Here we describe the development of generalized contrastive PCA (gcPCA), a flexible hyperparameter-free approach that solves these problems. We first provide analyses explaining why cPCA requires a hyperparameter and how gcPCA avoids this requirement. We then describe an open-source gcPCA toolbox containing Python and MATLAB implementations of several variants of gcPCA tailored for different scenarios. Finally, we demonstrate the utility of gcPCA in analyzing diverse high-dimensional biological data, revealing unsupervised detection of hippocampal replay in neurophysiological recordings and heterogeneity of type II diabetes in single-cell RNA sequencing data. As a fast, robust, and easy-to-use comparison method, gcPCA provides a valuable resource facilitating the analysis of diverse high-dimensional datasets to gain new insights into complex biological phenomena.

neuroscience↗

bioRxiv2022

Off-manifold coding in visual cortex revealed by sleep

Low-dimensional neural manifolds are controversial in part because it is unclear how to reconcile them with high-dimensional representations observed in areas such as primary visual cortex (V1). We addressed this by recording neuronal activity in V1 during slow-wave sleep, enabling us to identify internally-generated low-dimensional manifold structure and evaluate its role during visual processing. We found that movements and visual stimuli were both encoded in the "on-manifold" subspace preserved during sleep. However, only stimuli were encoded in the "off-manifold" subspace, which contains activity patterns that are less likely than chance to occur spontaneously during sleep. This off-manifold activity comprises sparse firing in neurons with the strongest low-dimensional modulation by movement, which paradoxically prevents movement-evoked activity from interfering with stimulus representations. These results reveal an unexpected link between low-dimensional dynamics and sparse coding, which together create a protected off-manifold coding space keeping high-dimensional representations separable from movement-evoked activity.

neuroscience↗

bioRxiv2022

An open-source, ready-to-use and validated ripple detector plugin for the Open Ephys GUI

Sharp wave-ripples (SWRs, 100-250 Hz) are oscillatory events extracellularly recorded in the CA1 subfield of the hippocampus during sleep and quiet wakefulness. SWRs are thought to be involved in the dialogue between the hippocampus and cortical regions to promote memory consolidation during sleep and memory-guided decision making. Many studies employed closed-loop strategies to either detect and abolish SWRs within the hippocampus or manipulate other relevant areas upon ripple detection. However, the code and schematics necessary to replicate the detection system are not always available, which hinders the reproducibility of experiments among different research groups. Furthermore, information about performance is not usually reported. Here, we present the development and validation of an open-source, real-time ripple detection plugin integrated into the Open Ephys GUI. It contains a built-in movement detector based on accelerometer or electromyogram data that prevents false ripple events (due to chewing, grooming, or moving, for instance) from triggering the stimulation/manipulation device. To determine the accuracy of the detection algorithm, we first carried out simulations in Matlab with synthetic and real ripple recordings. Using a specific combination of detection parameters (amplitude threshold of 5 standard deviations above the mean, time threshold of 10 ms, and RMS block size of 7 samples), we obtained a 97% true positive rate and 2.48 false positives per minute on the real data. Next, an Open Ephys plugin based on the same detection algorithm was developed, and a closed-loop system was set up to evaluate the round trip (ripple onset-to-stimulation) latency over synthetic data. The lowest latency obtained was 34.5 {+/-} 0.5 ms. Besides contributing to increased reproducibility, we anticipate that the developed ripple detector plugin will be helpful for many closed-loop applications in the field of systems neuroscience.

neuroscience↗

bioRxiv2020

Time-Encoding Migrates from Prefrontal Cortex to Dorsal Striatum During Learning of a Self-Timed Response Duration Task

Although time is a fundamental dimension of life, we do not know how the brain encodes the temporal information. Several brain areas underlie the temporal information, such as the hippocampus, prefrontal cortex, and striatum, but evidence of how they cooperate to process temporal information is scarce. Notably, the analysis of neural activity during learning are rare, mainly because timing tasks usually take a long time to train. Here we investigated how the time encoding evolves when animals learn to time a 1.5 s interval. We designed a novel training protocol where rats go from naive- to proficient-level timing performance within a single session, allowing us to investigate neuronal activity from very early learning stages. We used pharmacological experiments and machine-learning algorithms to evaluate the level of time encoding in the medial prefrontal cortex and the dorsal striatum. Our results show a double dissociation between the roles of the medial prefrontal cortex and the dorsal striatum during temporal learning, where the former commits to early learning stages while the latter become more engaged as animals become more proficient in the task.

neuroscience↗

bioRxiv2020

Network asynchrony underlying increased broadband gamma power

Synchronous activity of cortical inhibitory interneurons expressing parvalbumin (PV) underlies the expression of cortical gamma rhythms. Paradoxically, deficient PV inhibition is associated with increased broadband gamma power. Increased baseline broadband gamma is also a prominent characteristic in schizophrenia, and a hallmark of network alterations induced by N-methyl-D-aspartate receptor (NMDAR) antagonists such as ketamine. It has been questioned if enhanced broadband gamma power is a true rhythm, and if rhythmic PV inhibition is involved or not. It has been suggested that asynchronous and increased firing activities underlie broadband power increases spanning the gamma band. Using mice lacking NMDAR activity specifically in PV neurons to model deficient PV inhibition, we here show that local LFP (local field potential) oscillations and neuronal activity with decreased synchronicity generate increases in prefrontal broadband gamma power. Specifically, reduced spike time precision of both local PV interneurons and wide-spiking (WS) excitatory neurons contribute to increased firing rates, and spectral leakage of spiking activity (spike "contamination") affecting the broadband gamma band. Desynchronization was evident at multiple time scales, with reduced spike-LFP entrainment, reduced cross-frequency coupling, and fragmentation of brain states. While local application of S(+)-ketamine in wildtype mice triggered network desynchronization and increases in broadband gamma power, our investigations suggest that disparate mechanisms underlie increased power of broadband gamma caused by genetic alteration of PV interneurons, and ketamine-induced power increases in broadband gamma. Our studies, thus, confirm that broadband gamma increases can arise from asynchronous activities, and demonstrate that long-term deficiency of PV inhibition can be a contributor.

neuroscience↗