A standardised open science framework for sharing and re-analysing neural data acquired to continuous stimuli

Proposal Summary Behavioral evidences indicate that fluorescent lighting among the indoor environmental variables (i.e., noise, ambient temperature, and air quality) plays a critical role in facilitating or hindering daily activities for the neurotypical population (people who do not have autism, dyslexia, developmental coordination disorder, bipolar disorder, or ADD/ADHD) (Rashid & Zimiring, 2008). For a neurodiverse population (e.g., ADD/.ADHD, Autistic, etc.), this becomes more complex (Amor, Oboyle, Pati, Pham, & Jou, 2014; Amor, Pati & OBoyle, 2013; Pati, Amor, & OBoyle, 2012). Specifically, autistic subjects become more distracted under fluorescent lighting, which generates agitation, hyperactivity, stress, and weaker cognitive skills, hence contributing to negative health and performance effects. For autistic subjects, functional neuroimaging suggests increased neural activity in sensory areas of the brain normally associated with stimulus driven processing, and decreased activity in areas normally...

Analysis of neural data with multiple modes and high density has recently become a trend with the advances in neuroscience research and practices. There exists a pressing need for an approach to accurately and uniquely capture the features without loss or destruction of the interactions amongst the modes (typically) of space, time, and frequency. Moreover, the approach must be able to quickly analyze the neural data of exponentially growing scales and sizes, in tens or even hundreds of channels, so that timely conclusions and decisions may be made. A salient approach to multi-way data analysis is the parallel factor analysis (PARAFAC) that manifests its effectiveness in the decomposition of the electroencephalography (EEG). However, the conventional PARAFAC is only suited for offline data analysis due to the high complexity, which computes to be $O(n^{2})$ with the increasing data size. In this study, a large-scale PARAFAC method has been developed, which is supported by general-purpose computing on the graphics processing unit (GPGPU). Comparing to the PARAFAC running on conventional CPU-based platform, the new approach dramatically excels by ${>}360$ times in run-time performance, and effectively scales by ${>}400$ times in all dimensions. Moreover, the proposed approach forms the basis of a model for the analysis of electrocochleography (ECoG) recordings obtained from epilepsy patients, which proves to be effective in the epilepsy state detection. The time evolutions of the proposed model are well correlated with the clinical observations. Moreover, the frequency signature is stable and high in the ictal phase. Furthermore, the spatial signature explicitly identifies the propagation of neural activities among various brain regions. The model supports real-time analysis of ECoG in ${>}1{,}000$ channels on an inexpensive and available cyber-infrastructure.

Analysis of neural data recorded with implantable microelectrode arrays poses a significant challenge to the neuroscience and the neural engineering communities. The numerous signal processing and analysis steps need to be performed in order to extract the affluent amount of information in these data to understand their correlation with observed behavior. This paper summarizes our most recent effort to develop a comprehensive neural signal processing and data analysis software that incorporates standard analysis tools in addition to our in-house advanced tools. The software, referred to herein as NeuroQuest®, is implemented using MATLAB. It has been extensively tested on simulated and experimental neural data and will be disseminated to the community in the short term.

Data preparation is an important and critical step in neural network modeling for complex data analysis and it has a huge impact on the success of a wide variety of complex data analysis tasks, such as data mining and knowledge discovery. Although data preparation in neural network data analysis is important, some existing literature about the neural network data preparation are scattered, and there is no systematic study about data preparation for neural network data analysis. In this study, we first propose an integrated data preparation scheme as a systematic study for neural network data analysis. In the integrated scheme, a survey of data preparation, focusing on problems with the data and corresponding processing techniques, is then provided. Meantime, some intelligent data preparation solution to some important issues and dilemmas with the integrated scheme are discussed in detail. Subsequently, a cost-benefit analysis framework for this integrated scheme is presented to analyze the effect of data preparation on complex data analysis. Finally, a typical example of complex data analysis from the financial domain is provided in order to show the application of data preparation techniques and to demonstrate the impact of data preparation on complex data analysis.

The integration of open science as a key pillar of responsible research and innovation has led it to become a hallmark of responsible research. However, ethical, social and regulatory challenges still remain about the implementation of an internationally- and multi-sector-recognised open science framework. In this Commentary, we discuss one important specific challenge that has received little ethical and sociological attention in the open science literature: the environmental impact of the digital infrastructure that enables open science. We start from the premise that a move towards an environmentally sustainable open science is a shared and valuable goal, and discuss two challenges that we foresee with relation to this. The first relates to questions about how to define what environmentally sustainable open science means and how to change current practices accordingly. The second relates to the infrastructure needed to enact environmentally sustainable open science ethical and social responsibilities through the open science ethics ecosystem. We argue that there are various ethical obstacles regarding how to responsibly balance any environmental impacts against the social value of open science, and how much one should be prioritised over the other. We call for all actors of the open science ethics ecosystem to engage in discussions about how to move towards open data and science initiatives that take into account the environmental impact of data and digital infrastructures. Furthermore, we call for ethics governance frameworks or policy-inscribed standards of practice to assist with this decision-making.

With the accelerated development of neural recording technology over the past few decades, research in integrative neuroscience has become increasingly reliant on data analysis methods that are scalable to high-dimensional recordings and computationally tractable. Latent process models have shown promising results in estimating the dynamics of cognitive processes using individual models for each neuron's receptive field. However, scaling these models to work on high-dimensional neural recordings remains challenging. Not only is it impractical to build receptive field models for individual neurons of a large neural population, but most neural data analyses based on individual receptive field models discard the local history of neural activity, which has been shown to be critical in the accurate inference of the underlying cognitive processes. Here, we propose a novel, scalable latent process model that can directly estimate cognitive process dynamics without requiring precise receptive field models of individual neurons or brain nodes. We call this the direct discriminative decoder (DDD) model. The DDD model consists of (1) a discriminative process that characterizes the conditional distribution of the signal to be estimated, or state, as a function of both the current neural activity and its local history, and (2) a state transition model that characterizes the evolution of the state over a longer time period. While this modeling framework inherits advantages of existing latent process modeling methods, its computational cost is tractable. More important, the solution can incorporate any information from the history of neural activity at any timescale in computing the estimate of the state process. There are many choices in building the discriminative process, including deep neural networks or gaussian processes, which adds to the flexibility of the framework. We argue that these attributes of the proposed methodology, along with its applicability to different modalities of neural data, make it a powerful tool for high-dimensional neural data analysis. We also introduce an extension of these methods, called the discriminative-generative decoder (DGD). The DGD includes both discriminative and generative processes in characterizing observed data. As a result, we can combine physiological correlates like behavior with neural data to better estimate underlying cognitive processes. We illustrate the methods, including steps for inference and model identification, and demonstrate applications to multiple data analysis problems with high-dimensional neural recordings. The modeling results demonstrate the computational and modeling advantages of the DDD and DGD methods.

The complexity of neural data changes as the brain processes information during events. Universal lossless compression algorithms, which are broadly applicable and grounded in information theory, identify and exploit redundancies in data in order to compress it to essentially-optimal sizes regardless of underlying statistics. These algorithms may be used to conveniently and efficiently estimate a given signal's Shannon entropy rate, a biologically relevant measure of the complexity of a signal. It is therefore natural to explore their effectiveness in the analysis of spiking neural data. This work focuses on using compression to analyze recordings (96-channel Utah arrays) taken from motor cortex of animals performing reaching tasks for three days before and three days after administering electrolytic lesions (Subject U: 4 lesions, H: 3). In particular, we use the inverse compression ratio (ICR), which compares the sizes of compressed and uncompressed data to estimate the amount of statistically unique information. We calculate ICR with temporally-independent lossless compression (gzip) and temporally-dependent lossy compression (H.264, MPEG-2). Compression-based ICR was compared to single-neuron measures used to understand spiking data, such as average firing rates and Fano factor. Compression is also compared to common dimensionality reduction techniques, principal component analysis (PCA) and factor analysis (FA). Statistical tests on aggregate data comparing each metric before and after lesioning reveal that ICR is able to significantly (Mann-Whitney U test, p < 0.01) detect lesions with higher accuracy than single-neuron metrics, but not dimensionality reduction (ICR methods: 85.7%, single-neuron methods: 78.6%, dimensionality reduction: 100%). Additionally, statistical results on the same data show that ICR metrics remain more stable than single-neuron methods after lesion. The bitrate parameter of lossy compression algorithms is swept to better understand the effect of information rates and "optimal" compression on lesion detection performance. Our conclusions are confirmed by the same analyses performed on several different simulated neural datasets. These results suggest that compression algorithms may be a useful tool to detect and better understand perturbations to the underlying structure of neural data. Information-theoretic analyses may complement techniques like dimensionality reduction and firing rate tuning as a convenient and useful tool to characterize neural data.

Open Science in Suicide Research Is Open for Business.

FIND — A unified framework for neural data analysis

Computational analysis of behavioural and neural data is nowadays an essential part of neuroethology, allowing an ever deeper understanding of how natural behaviour and neural activity are interrelated at the molecular, cellular and network level. The range of computational techniques applied in neuroethological research is currently so broad as to preclude an exhaustive survey in a succinct chapter. Here, we focus on a specific approach termed Bayesian statistical modelling that has proven to be a powerful method for relating neural activity to natural behavioural performance. As we illustrate in a specific example, this approach naturally dovetails with classic neural coding concepts such as population vector codes. It is also flexible enough to be applicable to a broad range of neuroethological questions.

Neural decoding is a constantly adapting field of applied machine learning, using many different machine learning algorithms to analyze neural data. One such analysis of neural data is the interpretation and analysis of data originating the motoro cortex and the prediction of stimuli given to an organism using the motor response. In looking at a specific dataset, containing data from the motor cortex of a macaque monkey, where its neural response to directional stimuli was measured. Two different neural decoding algorithms were previously used to analyze this dataset, yet the highest accuracy they yielded was below 90%. There is a need for machine-learning based neural decoding algorithms to decode movement-relating neural data with higher accuracy. Support vector regression (SVR), a linear-regression based model of the machine learning algorithm support vector machines, was chosen for analysis. In this study, we aim to evaluate the predictive accuracy of SVR using a dataset obtained from a macaque monkey. The model predicted the directional stimuli with an accuracy of 82.47% percent.

In systems neuroscience, it is becoming increasingly common to record the activity of hundreds of neurons simultaneously via electrode arrays. The ability to accurately measure the causal interactions among multiple neurons in the brain is crucial to understanding how neurons work in concert to generate specific brain functions. The development of new statistical methods for assessing causal influence between spike trains is still an active field of neuroscience research. Here, we suggest a copula-based Granger causality measure for the analysis of neural spike train data. This method is built upon our recent work on copula Granger causality for the analysis of continuous-valued time series by extending it to point-process neural spike train data. The proposed method is therefore able to reveal nonlinear and high-order causality in the spike trains while retaining all the computational advantages such as model-free, efficient estimation, and variability assessment of Granger causality. The performance of our algorithm can be further boosted with time-reversed data. Our method performed well on extensive simulations, and was then demonstrated on neural activity simultaneously recorded from primary visual cortex of a monkey performing a contour detection task.

The open science framework has garnered increased visibility and has been partially implemented in recent years. Open science underscores the importance of transparency and reproducibility to conduct rigorous science. Recently, several journals published by the American Psychological Association have begun adopting the open science framework. At the same time, the field of psychology has been reckoning with the current sociopolitical climate regarding anti-Blackness and White supremacy. As psychology begins to adopt the open science framework into its journals, the authors underscore the importance of embracing and aligning open science with frameworks and theories that have the potential to move the field toward antiracism and away from the embedded White supremacy value systems and ideals. The present article provides an overview of the open science framework; an examination of White supremacy ideology in research and publishing; guidance on how to move away from these pernicious values; and a proposal on alternate value systems to center equity, diversity, and inclusion with the aim of establishing an antiracist open science framework. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Aim: to improve the efficiency of predicting a clinically narrow pelvis (СNP) using neural network data analysis and to evaluate its prognostic characteristics.Materials and Мethods. The study was designed as a retrospective non-randomized clinical trial. An analysis of 184 born neonates was carried out: group 1 included 135 female patients whose delivery occurred through the natural birth canal, group 2 – 49 patients whose delivery was complicated by СNP development and ended up with emergency caesarean section. Examination of patients was carried out on the eve of childbirth (1–2 days) and included anamnesis, general and special obstetric examination, including pelvimetry, a clinical assessment of cephalopelvic disproportion was carried out during childbirth. The condition of newborns was assessed using the Apgar scale, height and body weight were measured. Neural network analysis was performed using the built-in Neural Networks module of SPSS Statistics Version 25.0 (IBM, USA).Results. Despite hypothetically important role of anatomically narrowed pelvis in development of cephalopelvic disproportion, no significant inter-group differences were found. Significant parameters (abdominal circumference, uterine fundus height and woman’s weight, fetal head circumference, as well as data on the presence or absence of oligohydramnios and fetal macrosomia) were determined, which were included in the test database to create the basis for training the multilayer perceptron. Out of 135 patients of group 1, the prognosis was negative in 131 (97.0 %), positive in 4 (3.0 %); out of 49 patients in group 2, negative in 0 (0.0 %), positive in 49 (100.0 %). The forecast accuracy of the developed model was 98 % (sensitivity – 100 %, specificity –97 %). The information content of neural network data analysis in СNP predicting is presented in ROC analysis: area under the curve (AUC) = 0.99 (95 % confidence interval = 0.97–1.00). Neonatal anthropometric parameters were significantly higher in group 2 vs. group 1, and the Apgar score at 1 minute was correspondingly lower.Conclusion. The use of neural network analysis of clinical data obtained on the eve of childbirth allows to predict СNP development at sufficient degree of accuracy (98.0 %), which, in the future, after being introduced into clinical practice, will optimize a choice of delivery method in patients at risk (anatomically narrow pelvis, large fetus), reduce emergency caesarean sections and improve birth outcomes.

Preparing data is an important and critical step in neural network data analysis and it has an immense impact on the success of a wide variety of complex data analysis, such as data mining and knowledge discovery (Hu, 2003). The main reason is that the quality of the input data into neural network models may strongly influence the results of the data analysis (Sattler and Schallehn, 2001). As Lou (1993) stated, the effect on the neural network’s performance can be significant if important input data are missing or distorted. In general, properly prepared data are easy to handle, which makes the data analysis task simple. On the other hand, improperly prepared data may make data analysis difficult, if not impossible. Furthermore, data from different sources and growing amounts of data produced by modern data acquisition techniques have made data preparation a time-consuming task. It has been claimed that 50–70 percent of the time and effort in data analysis projects is required for data preparation (Sattler and Schallehn, 2001; Pyle, 1999). Therefore, data preparation involves enhancing the data in an attempt to improve the performance of data analysis.