Biology subjects

Soto, J. L.

Publications and source records attributed to Soto, J. L..

6 recordsLinked to original sources

bioRxiv2023

Local field potentials in human motor and non-motor brain areas encode the direction of upcoming movements: An intracerebral EEG classification study

Limb movement direction can be inferred from motor cortex activity. In humans, such decoding has been predominantly demonstrated using the spectral power of electrophysiological signals recorded in sensorimotor areas during movement execution. Yet, it remains unclear to what extent intended hand movement direction can be predicted from brain signals recorded during movement planning. Furthermore, whether other oscillatory features beyond power are also involved in direction encoding is not fully understood. Here, we set out to probe the directional-tuning of oscillatory phase, amplitude and Phase-Amplitude Coupling (PAC) during motor planning and execution, using a machine learning framework on multi-site local field potentials (LFPs) in humans. To this end, we recorded intracranial EEG data from implanted epilepsy patients as they performed a four-direction delayed center-out motor task. We found that LFP power significantly predicted hand-movement direction at execution but also during planning. While successful classification during planning primarily involved low-frequency power in a fronto-parietal circuit, decoding during execution was largely mediated by higher frequency activity in motor and premotor areas. Interestingly, LFP phase at very low frequencies (<1.5 Hz) led to significant decoding in premotor brain regions during execution. The machine learning framework also showed PAC to be uniformly modulated across directions through the task. Cross-temporal generalization analyses revealed that several stable brain patterns in prefrontal and premotor brain regions encode directions across both planning and execution. Finally, multivariate classification led to an increase in overall decoding accuracy (>80%) during both planning and execution. The novel insights revealed here extend our understanding of the role of neural oscillations in encoding motor plans.

neuroscience↗

bioRxiv2022

Extended family with germline pathogenic variant in polymerase delta provides strong evidence for recessive effect of proofreading inactivation

Mutational processes in germline and in somatic cells are vastly different, and it remains unclear how the same genetic background affects somatic and transmissible mutations. Here, we estimate the impact of an inherited pathogenic variant in the exonuclease domain of polymerase delta (Pol{delta}) on somatic and germline mutational processes and cancer development. In germline cells and in non-cancer somatic cells, the POLD1 L474P variant increases the mutation burden only slightly, contributing [~]11.8% and [~]14.7% of mutations respectively, although it strongly distorts the mutational spectra. By contrast, tumors developed by carriers of inherited pathogenic variants in POLD1 harbor a DNA rearrangement that results in a homozygous state of the pathogenic variant, leading to an extremely high mutation rate. Thus, mutations in both alleles of POLD1 gene are required for strong increase in mutation rate suggesting recessiveness of Pold{delta} proofreading. These results show a similar role of Pol{delta} in germline and somatic replication, and, together with previous findings, illustrate the important differences between Pol{delta} and Pol{varepsilon} in the disruption of their replication fidelity.

genetics↗

bioRxiv2020

Tensorpac : an open-source Python toolbox for tensor-based Phase-Amplitude Couplingmeasurement in electrophysiological brain signals

Despite being the focus of a thriving field of research, the biological mechanisms that underlie information integration in the brain are not yet fully understood. A theory that has gained a lot of traction in recent years suggests that multi-scale integration is regulated by a hierarchy of mutually interacting neural oscillations. In particular, there is accumulating evidence that phase-amplitude coupling (PAC), a specific form of cross-frequency interaction, plays a key role in numerous cognitive processes. Current research in the field is not only hampered by the absence of a gold standard for PAC analysis, but also by the computational costs of running exhaustive computations on large and high-dimensional electrophysiological brain signals. In addition, various signal properties and analyses parameters can lead to spurious PAC. Here, we present Tensorpac, an open-source Python toolbox dedicated to PAC analysis of neurophysiological data. The advantages of Tensorpac include (1) higher computational efficiency thanks to software design that combines tensor computations and parallel computing, (2) the implementation of all most widely used PAC methods in one package, (3) the statistical analysis of PAC measures, and (4) extended PAC visualization capabilities. Tensorpac is distributed under a BSD-3-Clause license and can be launched on any operating system (Linux, OSX and Windows). It can be installed directly via pip or downloaded from Github (https://github.com/EtienneCmb/tensorpac). By making Tensorpac available, we aim to enhance the reproducibility and quality of PAC research, and provide open tools that will accelerate future method development in neuroscience.

bioinformatics↗

bioRxiv2018

Estimation of task-based modulations in functional connectivity with MEG: a comparison of methods

For the assessment of functional interactions between distinct brain regions there is a great variety of mathematical techniques, with well-known properties, relative merits and shortcomings; however, the methods that deal specifically with task-based fluctuations in interareal coupling are scarce, and their relative performance is unclear. In the present article, we compare two approaches used in the estimation of correlation changes between the envelope amplitudes of narrowband brain activity obtained from magnetoencephalography (MEG) recordings. One approach is an implementation of semipartial canonical correlation analysis (SP-CCA), which is formally equivalent to the psychophysiological interactions technique successfully applied to functional magnetic resonance data. The other approach, which has been used in recent electrophysiology studies, consists of simply computing linear correlation coefficients of signals from two experimental conditions and taking their differences. We compared the two approaches with simulations and with multi-subject MEG signals acquired during a visuomotor coordination study. The analyses with simulated activity showed that computing differences in correlation coefficients (DCC) provided better discrimination between true coupling changes and spurious effects; on the other hand, SP-CCA resulted in significant effects around the reference location which were not found with DCC, and which may be due to field spread. Based on our findings, we recommend the use of DCC for the detection of task-based changes in connectivity, as it provided better performance than SP-CCA.

neuroscience↗

bioRxiv2018

Molecular features of premenopausal breast cancers in Latin American women: pilot results from the PRECAMA study

BackgroundIn Latin America (LA), there is a high incidence rate of breast cancer (BC) in premenopausal women, and the genomic features of these BC remain unknown. Here, we aim to characterize the molecular features of BC in young LA women within the framework of the PRECAMA study, a multicenter population-based case-control study on breast cancer in premenopausal women.\n\nMethodsPathological tumor tissues were collected from incident cases from four LA countries. Immunohistochemistry (IHC) was performed centrally for ER, PR, HER2, Ki67, EGFR, CK5/6 and p53 protein markers. Targeted deep sequencing was done on genomic DNA extracted from formalin-fixed paraffin-embedded (FFPE) tumour tissues and their paired blood samples to screen for somatic mutations in eight genes frequently mutated in BC. A subset of samples was analyzed by exome sequencing to identify somatic mutational signatures.\n\nResultsThe majority of cases were positive for ER or PR (168/233; 72%) and there were 21% triple negative (TN) cases, mainly of basal type. Most tumors were positive for Ki67 (189/233; 81%). In 126 sequenced cases, TP53 and PIK3CA were the most frequently mutated genes (32.5% and 21.4%, respectively), followed by AKT1 (9.5%). TP53 mutations were more frequent in HER2-enriched and TN IHC subtypes, while PIK3CA/AKT1 mutations were more frequent in ER positive tumors, as expected. Interestingly, a higher proportion of G:C>T:A mutations was observed in TP53 gene in PRECAMA cases compared to TCGA and METABRIC breast cancer series (27% vs 14%). Exome-wide mutational patterns in 10 TN cases revealed alterations in signaling transduction pathways and major contributions of mutational signatures caused by altered DNA repair pathways.\n\nConclusionsThis pilot results on PRECAMA tumors gives a preview of the molecular features of premenopausal BC in LA. Although, the overall mutation burden was as expected from data in other populations, mutational patterns observed in TP53 and exome-wide suggested possible differences in mutagenic processes giving rise to these tumors compared to other populations. Further omics analyses of a larger number of cases in the near future will allow investigating relationships between these molecular features and risk factors.

cancer biology↗

bioRxiv2018

Estimation of narrowband amplitude and phase from electrophysiology signals for phase-amplitude coupling studies: a comparison of methods

Many functional connectivity studies based on electrophysiological measurements, such as electro- and magnetoencephalography (EEG/MEG), start their investigations by extracting a narrowband representation of brain activity time series, and then computing their envelope amplitudes and instantaneous phases, which serve as inputs to subsequent data processing. The two most popular approaches for obtaining these narrowband amplitudes and phases are: bandpass filtering followed by Hilbert transform (we call this the Hilbert approach); and convolution with wavelet kernels (the wavelet approach). In this work, we investigate how these two approaches perform in detecting the phenomenon of phase-amplitude coupling (PAC), whereby the amplitude of a high-frequency signal is driven by the phase of a low-frequency signal. The comparison of both approaches is carried out by means of simulated brain activity, from which we run receiver operating characteristic (ROC) analyses, and of experimental MEG data from a visuomotor coordination study. The ROC analyses show that both approaches have comparable accuracy, except in the presence of interfering signals with frequencies near the high-frequency band. As for the visuomotor data, the most noticeable impact of the choice of approach was observed when evaluating task-based changes in PAC between the delta (2-5 Hz) and the high-gamma (60-90 Hz) frequency bands, as we were able to identify widespread brain areas with statistically significant effects only with the Hilbert approach. These results provide preliminary evidence of the advantages of the Hilbert approach over the wavelet approach, at least in the context of PAC estimates.

neuroscience↗