A Maturity Model for Operations in Neuroscience Research

Scientists are adopting new approaches to scale up their activities and goals. Progress in neurotechnologies, artificial intelligence, automation, and tools for collaboration promises new bursts of discoveries. However, compared to other disciplines and the industry, neuroscience laboratories have been slow to adopt key technologies to support collaboration, reproducibility, and automation. Drawing on progress in other fields, we define a roadmap for implementing automated research workflows for diverse research teams. We propose establishing a five-level capability maturity model for operations in neuroscience research. Achieving higher levels of operational maturity requires new technology-enabled methodologies, which we describe as ``SciOps''. The maturity model provides guidelines for evaluating and upgrading operations in multidisciplinary neuroscience teams.Comment: 10 pages, one figur

MS

thesisMicroelectrode arrays are being routinely used today as a means to better understand and interface with the nervous system, in order to develop prostheses that can restore function in systems that do not function properly. The development and effective use of robust high-­?channel-­?count microelectrode arrays for neuronal recording and stimulation depends on effective monitoring of electrode impedances and how these change over time. For multielectrode arrays, conventional electrode impedance measurements may be confounded by possible shunting of signals among electrodes. Additionally, most present methods to monitor impedances in high-­?electrode-­?count arrays are laborintensive, requiring manual testing of one individual electrode at a time. The research effort described herein included the development of a system capable of automatically measuring the impedances of each microelectrode on a 100-­?microelectrode array with a 1-­?kHz, 10-­?mV sine wave

Contributions of Subsurface Cortical Modulations to Discrimination of Executed and Imagined Grasp Forces through Stereoelectroencephalography.

Stereoelectroencephalographic (SEEG) depth electrodes have the potential to record neural activity from deep brain structures not easily reached with other intracranial recording technologies. SEEG electrodes were placed through deep cortical structures including central sulcus and insular cortex. In order to observe changes in frequency band modulation, participants performed force matching trials at three distinct force levels using two different grasp configurations: a power grasp and a lateral pinch. Signals from these deeper structures were found to contain information useful for distinguishing force from rest trials as well as different force levels in some participants. High frequency components along with alpha and beta bands recorded from electrodes located near the primary motor cortex wall of central sulcus and electrodes passing through sensory cortex were found to be the most useful for classification of force versus rest although one participant did have significant modulation in the insular cortex. This study electrophysiologically corroborates with previous imaging studies that show force-related modulation occurs inside of central sulcus and insular cortex. The results of this work suggest that depth electrodes could be useful tools for investigating the functions of deeper brain structures as well as showing that central sulcus and insular cortex may contain neural signals that could be used for control of a grasp force BMI

Experimental Setup.

<p>Participants had stereotactic depth electrodes placed for clinical epilepsy monitoring and were asked to perform force matching tasks in two different grasp configurations (power grasp and lateral pinch grasp–inset). Participants were prompted by a screen showing them the desired force level as well as their delivered level of force each represented as the color and size of a ball being squeezed on the screen. SEEG signals were split between the clinical monitoring system and the research neural recording system. Frequency band powers were calculated offline with a short-time-Fourier transform and used for discrete state classification.</p

Imagined single feature modulation.

<p>Panel A shows the mean ± standard deviation depth of modulation (change in z-score from baseline) for statistically significant MC (red bars) and IC (purple bars) channels, organized by participant (rows) and by frequency band (x-axis) for each of the two imagined force grasping configurations. Modulation was lower as a whole during imagination compared to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150359#pone.0150359.g003" target="_blank">Fig 3B</a> with the exception of the alpha band for participant A. Panel B shows single feature classification distributions for imagined force versus rest trials. Participant A is shown in blue and participant B is shown in red. The chance level is shown as the dotted line. Histograms are arranged by frequency band (rows) and by brain region (columns).</p

Rest versus Light versus Hard Classification Accuracies.

<p>Confusion matrices of light versus hard versus rest classification for each participant (columns) in the lateral pinch and power configurations (rows) using sequential feature selection and two levels of cross-validation. Features were averaged across a time window of -400 ms to +400 ms centered on force onset or 1 second after the rest cue. All participants showed the ability to differentially modulate neural activity between rest and force with high accuracy, but were less adept at differentially modulating neural activity between force levels. * denote significance above chance classification (1 in 3) using false-discovery rate adjusted p-values < 0.05 while ~ denotes a more stringent significance from 50% to see if light vs hard forces were significantly different.</p

Average classification of imagined force versus rest.

<p>Bars show the average classification performance across 10 sets of cross-validation. The dotted horizontal line shows prior probability level (50%). The SVM was able to achieve significant classification of imagined force versus rest for both participants (A = black, B = white bars) and in both grasp configurations (pinch and power).</p

Signal Modulation during Force.

<p>Panel A shows average z-score modulation over time on one motor cortex (MC) channel and one insular cortex (IC) channel in the alpha, beta and highest gamma features during hard force tasks. The solid vertical line corresponds to force onset with the dotted vertical representing the average target cue time. Shaded regions indicate confidence intervals for the signal means of each frequency band. Panel B shows the mean ± standard deviation depth of modulation (change in z-score from baseline) for statistically significant MC (red bars) and IC (purple bars) channels, organized by participant (rows) and by frequency band (x-axis) for each of the two grasping configurations. All participants showed significant modulation in the both motor and insular cortices (excluding D who did not have electrodes placed in the insula).</p

Classification density for single features.

<p>Histograms show the distribution of feature classification accuracies for force versus rest classification. These bar values represent the single feature classification accuracies across 10 repeats of the cross-validation and are organized by brain region (— = White matter tracts, IC = Insular Cortex, MC = Motor Cortex, PM = Premotor Cortex, S1 = Primary Sensory Cortex, and SM = Supplementary Motor Area) and feature (LMP = Local Motor Potential, δ/θ = 0–6 Hz, α = 6–12 Hz, β = 12-30Hz, γ = 30–50 Hz, γ+ = 70–110 Hz, γ++ = 130–170 Hz). Colors correspond to each participant (blue = A, red = B, green = C, and purple = D) with pinch and power grasp configurations combined. Dotted line (52.67%) shows the high end of the 95% confidence interval for chance classification. All participants had motor cortical features that could be used for discrimination of force versus rest in contrast to other brain regions which were more participant specific.</p