Pynapple, a toolbox for data analysis in neuroscience.

Datasets collected in neuroscientific studies are of ever-growing complexity, often combining high-dimensional time series data from multiple data acquisition modalities. Handling and manipulating these various data streams in an adequate programming environment is crucial to ensure reliable analysis, and to facilitate sharing of reproducible analysis pipelines. Here, we present Pynapple, the PYthon Neural Analysis Package, a lightweight python package designed to process a broad range of time-resolved data in systems neuroscience. The core feature of this package is a small number of versatile objects that support the manipulation of any data streams and task parameters. The package includes a set of methods to read common data formats and allows users to easily write their own. The resulting code is easy to read and write, avoids low-level data processing and other error-prone steps, and is open source. Libraries for higher-level analyses are developed within the Pynapple framework but are contained within a collaborative repository of specialized and continuously updated analysis routines. This provides flexibility while ensuring long-term stability of the core package. In conclusion, Pynapple provides a common framework for data analysis in neuroscience.

Age-dependent increased sag amplitude in human pyramidal neurons dampens baseline cortical activity.

Aging involves various neurobiological changes, although their effect on brain function in humans remains poorly understood. The growing availability of human neuronal and circuit data provides opportunities for uncovering age-dependent changes of brain networks and for constraining models to predict consequences on brain activity. Here we found increased sag voltage amplitude in human middle temporal gyrus layer 5 pyramidal neurons from older subjects and captured this effect in biophysical models of younger and older pyramidal neurons. We used these models to simulate detailed layer 5 microcircuits and found lower baseline firing in older pyramidal neuron microcircuits, with minimal effect on response. We then validated the predicted reduced baseline firing using extracellular multielectrode recordings from human brain slices of different ages. Our results thus report changes in human pyramidal neuron input integration properties and provide fundamental insights into the neuronal mechanisms of altered cortical excitability and resting-state activity in human aging.

Optical phased array neural probes for beam-steering in brain tissue.

Implantable silicon neural probes with integrated nanophotonic waveguides can deliver patterned dynamic illumination into brain tissue at depth. Here, we introduce neural probes with integrated optical phased arrays and demonstrate optical beam steering in vitro. Beam formation in brain tissue is simulated and characterized. The probes are used for optogenetic stimulation and calcium imaging.

CellExplorer: A framework for visualizing and characterizing single neurons.

The large diversity of neuron types provides the means by which cortical circuits perform complex operations. Neuron can be described by biophysical and molecular characteristics, afferent inputs, and neuron targets. To quantify, visualize, and standardize those features, we developed the open-source, MATLAB-based framework CellExplorer. It consists of three components: a processing module, a flexible data structure, and a powerful graphical interface. The processing module calculates standardized physiological metrics, performs neuron-type classification, finds putative monosynaptic connections, and saves them to a standardized, yet flexible, machine-readable format. The graphical interface makes it possible to explore the computed features at the speed of a mouse click. The framework allows users to process, curate, and relate their data to a growing public collection of neurons. CellExplorer can link genetically identified cell types to physiological properties of neurons collected across laboratories and potentially lead to interlaboratory standards of single-cell metrics.

Cluster tendency assessment in neuronal spike data.

Sorting spikes from extracellular recording into clusters associated with distinct single units (putative neurons) is a fundamental step in analyzing neuronal populations. Such spike sorting is intrinsically unsupervised, as the number of neurons are not known a priori. Therefor, any spike sorting is an unsupervised learning problem that requires either of the two approaches: specification of a fixed value k for the number of clusters to seek, or generation of candidate partitions for several possible values of c, followed by selection of a best candidate based on various post-clustering validation criteria. In this paper, we investigate the first approach and evaluate the utility of several methods for providing lower dimensional visualization of the cluster structure and on subsequent spike clustering. We also introduce a visualization technique called improved visual assessment of cluster tendency (iVAT) to estimate possible cluster structures in data without the need for dimensionality reduction. Experimental results are conducted on two datasets with ground truth labels. In data with a relatively small number of clusters, iVAT is beneficial in estimating the number of clusters to inform the initialization of clustering algorithms. With larger numbers of clusters, iVAT gives a useful estimate of the coarse cluster structure but sometimes fails to indicate the presumptive number of clusters. We show that noise associated with recording extracellular neuronal potentials can disrupt computational clustering schemes, highlighting the benefit of probabilistic clustering models. Our results show that t-Distributed Stochastic Neighbor Embedding (t-SNE) provides representations of the data that yield more accurate visualization of potential cluster structure to inform the clustering stage. Moreover, The clusters obtained using t-SNE features were more reliable than the clusters obtained using the other methods, which indicates that t-SNE can potentially be used for both visualization and to extract features to be used by any clustering algorithm.

Multisegment Kinematics of the Spinal Column: Soft Tissue Artifacts Assessment.

A major challenge in the assessment of intersegmental spinal column angles during trunk motion is the inherent error in recording the movement of bony anatomical landmarks caused by soft tissue artifacts (STAs). This study aims to perform an uncertainty analysis and estimate the typical errors induced by STA into the intersegmental angles of a multisegment spinal column model during trunk bending in different directions by modeling the relative displacement between skin-mounted markers and actual bony landmarks during trunk bending. First, we modeled the maximum displacement of markers relative to the bony landmarks with a multivariate Gaussian distribution. In order to estimate the distribution parameters, we measured these relative displacements on five subjects at maximum trunk bending posture. Then, in order to model the error depending on trunk bending angle, we assumed that the error grows linearly as a function of the bending angle. Second, we applied our error model to the trunk motion measurement of 11 subjects to estimate the corrected trajectories of the bony landmarks and investigate the errors induced into the intersegmental angles of a multisegment spinal column model. For this purpose, the trunk was modeled as a seven-segment rigid-body system described using 23 reflective markers placed on various bony landmarks of the spinal column. Eleven seated subjects performed trunk bending in five directions and the three-dimensional (3D) intersegmental angles during trunk bending were calculated before and after error correction. While STA minimally affected the intersegmental angles in the sagittal plane (<16%), it considerably corrupted the intersegmental angles in the coronal (error ranged from 59% to 551%) and transverse (up to 161%) planes. Therefore, we recommend using the proposed error suppression technique for STA-induced error compensation as a tool to achieve more accurate spinal column kinematics measurements. Particularly, for intersegmental rotations in the coronal and transverse planes that have small range and are highly sensitive to measurement errors, the proposed technique makes the measurement more appropriate for use in clinical decision-making processes.

Sensitivity of intersegmental angles of the spinal column to errors due to marker misplacement.

The ranges of angular motion measured using multisegmented spinal column models are typically small, meaning that minor experimental errors can potentially affect the reliability of these measures. This study aimed to investigate the sensitivity of the 3D intersegmental angles, measured using a multisegmented spinal column model, to errors due to marker misplacement. Eleven healthy subjects performed trunk bending in five directions. Six cameras recorded the trajectory of 22 markers, representing seven spinal column segments. Misplacement error for each marker was modeled as a Gaussian function with a standard deviation of 6 mm, and constrained to a maximum value of 12 mm in each coordinate across the skin. The sensitivity of 3D intersegmental angles to these marker misplacement errors, added to the measured data, was evaluated. The errors in sagittal plane motions resulting from marker misplacement were small (RMS error less than 3.2 deg and relative error in the angular range less than 15%) during the five trunk bending direction. The errors in the frontal and transverse plane motions, induced by marker misplacement, however, were large (RMS error up to 10.2 deg and relative error in the range up to 58%), especially during trunk bending in anterior, anterior-left, and anterior-right directions, and were often comparable in size to the intersubject variability for those motions. The induced errors in the frontal and transverse plane motions tended to be the greatest at the intersegmental levels in the lower lumbar region. These observations questioned reliability of angle measures in the frontal and transverse planes particularly in the lower lumbar region during trunk bending in anterior direction, and thus did not recommend interpreting these measures for clinical evaluation and decision-making.