SIMS: A deep-learning label transfer tool for single-cell RNA sequencing analysis.

Cell atlases serve as vital references for automating cell labeling in new samples, yet existing classification algorithms struggle with accuracy. Here we introduce SIMS (scalable, interpretable machine learning for single cell), a low-code data-efficient pipeline for single-cell RNA classification. We benchmark SIMS against datasets from different tissues and species. We demonstrate SIMS's efficacy in classifying cells in the brain, achieving high accuracy even with small training sets (<3,500 cells) and across different samples. SIMS accurately predicts neuronal subtypes in the developing brain, shedding light on genetic changes during neuronal differentiation and postmitotic fate refinement. Finally, we apply SIMS to single-cell RNA datasets of cortical organoids to predict cell identities and uncover genetic variations between cell lines. SIMS identifies cell-line differences and misannotated cell lineages in human cortical organoids derived from different pluripotent stem cell lines. Altogether, we show that SIMS is a versatile and robust tool for cell-type classification from single-cell datasets.

Unraveling Neuronal Identities Using SIMS: A Deep Learning Label Transfer Tool for Single-Cell RNA Sequencing Analysis.

Large single-cell RNA datasets have contributed to unprecedented biological insight. Often, these take the form of cell atlases and serve as a reference for automating cell labeling of newly sequenced samples. Yet, classification algorithms have lacked the capacity to accurately annotate cells, particularly in complex datasets. Here we present SIMS (Scalable, Interpretable Machine Learning for Single-Cell), an end-to-end data-efficient machine learning pipeline for discrete classification of single-cell data that can be applied to new datasets with minimal coding. We benchmarked SIMS against common single-cell label transfer tools and demonstrated that it performs as well or better than state of the art algorithms. We then use SIMS to classify cells in one of the most complex tissues: the brain. We show that SIMS classifies cells of the adult cerebral cortex and hippocampus at a remarkably high accuracy. This accuracy is maintained in trans-sample label transfers of the adult human cerebral cortex. We then apply SIMS to classify cells in the developing brain and demonstrate a high level of accuracy at predicting neuronal subtypes, even in periods of fate refinement, shedding light on genetic changes affecting specific cell types across development. Finally, we apply SIMS to single cell datasets of cortical organoids to predict cell identities and unveil genetic variations between cell lines. SIMS identifies cell-line differences and misannotated cell lineages in human cortical organoids derived from different pluripotent stem cell lines. When cell types are obscured by stress signals, label transfer from primary tissue improves the accuracy of cortical organoid annotations, serving as a reliable ground truth. Altogether, we show that SIMS is a versatile and robust tool for cell-type classification from single-cell datasets.

Nested and robust modeling techniques for fNIRS data with demographics and experiment related factors in n-back task.

Functional near-infrared spectroscopy (fNIRS) signals are used to measure relative changes in oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) concentrations. Brain response studies constitute multilevel or nested datasets formed by different parts of the brain of individuals and multidimensional datasets. The changes in brain activities under specific stimuli are investigated with the help of statistical analysis. However, these studies ignore the dependence structure between the repeated measures of the same subject, which may cause inaccurate or incomplete findings. In this study, we adopt an advanced statistical method into HbO data controlling for variability within repeated measures of each subject while testing and measuring the degrees of the statistical significance between-subject factors and explanatory variables. The changes in HbO are investigated through a linear mixed model, taking experimental and demographic variables into account with open access neuroscience data. The channels nested within subjects are considered random to capture the differences among individuals. Our findings reveal that n-back conditions and mean response times of the subjects have statistically significant associations with mean HbO.

Fusión de datos multimodales de neurociencias generados en estudios EEG-fMRI, un análisis temático / Fusion of Multimodal Neuroscience Data Generated in EEG-fMRI Studies, a Thematic Analysis

La actividad cerebral tiene múltiples atributos, entre ellos los eléctricos, metabólicos, hemodinámicos y hormonales. Los métodos modernos para estudiar las funciones cerebrales como el PET (Tomografía por Emisión de Positrones), fMRI (Imagen de Resonancia Magnética Funcional) y MEG (Magnetoencefalograma) son ampliamente utilizados por los científicos. Sin embargo, el EEG es una herramienta utilizada para la investigación y diagnóstico debido a su bajo costo, simplicidad de uso, movilidad y la posibilidad de monitoreo a largo tiempo de adquisición. Para detectar e interpretar las características relevantes de estas señales, se describe cada proceso por su escala temporal (EEG) y espacial (fMRI). La presente investigación se enfoca en realizar una revisión bibliográfica sobre la integración de datos multimodales EEG-fMRI que propicie valorar su importancia para el desarrollo de algoritmos de fusión y su uso en el contexto cubano. Para ello se analizaron documentos con altos índices de citas en la literatura, donde se destacan autores precursores de los temas en análisis. Los estudios multimodales EEG-fMRI generan múltiples datos temporales y espaciales con alto valor para la medicina basada en evidencia. La integración de los mismos provee un valor agregado en la búsqueda de nuevos métodos diagnósticos, aplicando minería de datos, Deep learning y algoritmos de fusión. En este trabajo se pone de relieve la existencia de baja resolución temporal de fMRI y por otro lado la baja resolución espacial de EEG, por lo que la integración de ambos estudios aumentaría la calidad de su información(AU)

Brain activity has multiple attributes, including electrical, metabolic, hemodynamic, and hormonal. Modern methods for studying brain functions such as PET (Positron Emission Tomography), fMRI (Functional Magnetic Resonance Imaging), and MEG (Magnetoencephalogram) are widely used by scientists. However, the EEG is a tool used for research and diagnosis due to its low cost, simplicity of use, mobility and the possibility of long-term monitoring of acquisition. To detect and interpret the relevant characteristics of these signals, each process is described by its temporal (EEG) and spatial (fMRI) scale. The present research focuses on conducting a bibliographic review on the integration of multimodal EEG-fMRI data that favors assessing its importance for the development of fusion algorithms and their use in the Cuban context. For this, documents with high rates of citations in the literature were analyzed, where precursor authors of the topics under analysis stand out. Multimodal EEG-fMRI studies generate multiple temporal and spatial data with high value for evidence-based medicine. Their integration provides added value in the search for new diagnostic methods, applying data mining, Deep learning and fusion algorithms. This work highlights the existence of low temporal resolution of fMRI and, on the other hand, the low spatial resolution of EEG, so the integration of both studies would increase the quality of their information(AU)

Judges' perceptions of expert reports: The effect of neuroscience evidence.

This article explores the impact of neuroscience evidence on how expert reports are perceived and their effects on the decisions made by trial judges. Experimental psychology has demonstrated a number of cognitive effects arising from exposure to neuroimaging data which may bias judgments and lead to (mis)interpretations that can affect decisions. We conducted a study on a sample of 62 Swiss and French judges in order to determine whether their perceptions of the credibility, quality and scientific basis of a psychiatric evaluation of a criminal defendant vary according to whether or not the evaluation includes neuroscientific data. Quantitative analyses were conducted in order to evaluate significant differences between the two conditions (one-way analyses of variance) and moderation and conditional analyses to examine whether the participants' sex and length of professional experience moderated the effect of the conditions. Terminological and thematic analyses were carried out on open questions. Quantitative and qualitative results suggest that the presence of neuroscience data in an expert report affects judges' perceptions of the quality, credibility, and scientificity (reliability, objectivity, scientific basis) of the report, and the persuasiveness of the evidence it provided. Moreover, this phenomenon was stronger in more experienced judges than in less experienced judges.