SpikeDeeptector: A deep-learning based method for detection of neural spiking activity

Objective. In electrophysiology, microelectrodes are the primary source for recording neural data (single unit activity). These microelectrodes can be implanted individually or in the form of arrays containing dozens to hundreds of channels. Recordings of some channels contain neural activity, which are often contaminated with noise. Another fraction of channels does not record any neural data, but only noise. By noise, we mean physiological activities unrelated to spiking, including technical artifacts and neural activities of neurons that are too far away from the electrode to be usefully processed. For further analysis, an automatic identification and continuous tracking of channels containing neural data is of great significance for many applications, e.g. automated selection of neural channels during online and offline spike sorting. Automated spike detection and sorting is also critical for online decoding in brain–computer interface (BCI) applications, in which only simple threshold crossing events are often considered for feature extraction. To our knowledge, there is no method that can universally and automatically identify channels containing neural data. In this study, we aim to identify and track channels containing neural data from implanted electrodes, automatically and more importantly universally. By universally, we mean across different recording technologies, different subjects and different brain areas. Approach. We propose a novel algorithm based on a new way of feature vector extraction and a deep learning method, which we call SpikeDeeptector. SpikeDeeptector considers a batch of waveforms to construct a single feature vector and enables contextual learning. The feature vectors are then fed to a deep learning method, which learns contextualized, temporal and spatial patterns, and classifies them as channels containing neural spike data or only noise. Main results. We trained the model of SpikeDeeptector on data recorded from a single tetraplegic patient with two Utah arrays implanted in different areas of the brain. The trained model was then evaluated on data collected from six epileptic patients implanted with depth electrodes, unseen data from the tetraplegic patient and data from another tetraplegic patient implanted with two Utah arrays. The cumulative evaluation accuracy was 97.20% on 1.56 million hand labeled test inputs. Significance. The results demonstrate that SpikeDeeptector generalizes not only to the new data, but also to different brain areas, subjects, and electrode types not used for training. Clinical trial registration number. The clinical trial registration number for patients implanted with the Utah array is NCT 01849822. For the epilepsy patients, approval from the local ethics committee at the Ruhr-University Bochum, Germany, was obtained prior to implantation

SpikeDeeptector: A deep-learning based method for detection of neural spiking activity

Objective. In electrophysiology, microelectrodes are the primary source for recording neural data (single unit activity). These microelectrodes can be implanted individually or in the form of arrays containing dozens to hundreds of channels. Recordings of some channels contain neural activity, which are often contaminated with noise. Another fraction of channels does not record any neural data, but only noise. By noise, we mean physiological activities unrelated to spiking, including technical artifacts and neural activities of neurons that are too far away from the electrode to be usefully processed. For further analysis, an automatic identification and continuous tracking of channels containing neural data is of great significance for many applications, e.g. automated selection of neural channels during online and offline spike sorting. Automated spike detection and sorting is also critical for online decoding in brain–computer interface (BCI) applications, in which only simple threshold crossing events are often considered for feature extraction. To our knowledge, there is no method that can universally and automatically identify channels containing neural data. In this study, we aim to identify and track channels containing neural data from implanted electrodes, automatically and more importantly universally. By universally, we mean across different recording technologies, different subjects and different brain areas. Approach. We propose a novel algorithm based on a new way of feature vector extraction and a deep learning method, which we call SpikeDeeptector. SpikeDeeptector considers a batch of waveforms to construct a single feature vector and enables contextual learning. The feature vectors are then fed to a deep learning method, which learns contextualized, temporal and spatial patterns, and classifies them as channels containing neural spike data or only noise. Main results. We trained the model of SpikeDeeptector on data recorded from a single tetraplegic patient with two Utah arrays implanted in different areas of the brain. The trained model was then evaluated on data collected from six epileptic patients implanted with depth electrodes, unseen data from the tetraplegic patient and data from another tetraplegic patient implanted with two Utah arrays. The cumulative evaluation accuracy was 97.20% on 1.56 million hand labeled test inputs. Significance. The results demonstrate that SpikeDeeptector generalizes not only to the new data, but also to different brain areas, subjects, and electrode types not used for training. Clinical trial registration number. The clinical trial registration number for patients implanted with the Utah array is NCT 01849822. For the epilepsy patients, approval from the local ethics committee at the Ruhr-University Bochum, Germany, was obtained prior to implantation

SpikeDeep-classifier: a deep-learning based fully automatic offline spike sorting algorithm

Objective. Advancements in electrode design have resulted in micro-electrode arrays with hundreds of channels for single cell recordings. In the resulting electrophysiological recordings, each implanted electrode can record spike activity (SA) of one or more neurons along with background activity (BA). The aim of this study is to isolate SA of each neural source. This process is called spike sorting or spike classification. Advanced spike sorting algorithms are time consuming because of the human intervention at various stages of the pipeline. Current approaches lack generalization because the values of hyperparameters are not fixed, even for multiple recording sessions of the same subject. In this study, a fully automatic spike sorting algorithm called "SpikeDeep-Classifier" is proposed. The values of hyperparameters remain fixed for all the evaluation data. Approach. The proposed approach is based on our previous study (SpikeDeeptector) and a novel background activity rejector (BAR), which are both supervised learning algorithms and an unsupervised learning algorithm (K-means). SpikeDeeptector and BAR are used to extract meaningful channels and remove BA from the extracted meaningful channels, respectively. The process of clustering becomes straight-forward once the BA is completely removed from the data. Then, K-means with a predefined maximum number of clusters is applied on the remaining data originating from neural sources only. Lastly, a similarity-based criterion and a threshold are used to keep distinct clusters and merge similar looking clusters. The proposed approach is called cluster accept or merge (CAOM) and it has only two hyperparameters (maximum number of clusters and similarity threshold) which are kept fixed for all the evaluation data after tuning. Main Results. We compared the results of our algorithm with ground-truth labels. The algorithm is evaluated on data of human patients and publicly available labeled non-human primates (NHPs) datasets. The average accuracy of BAR on datasets of human patients is 92.3% which is further reduced to 88.03% after (K-means + CAOM). In addition, the average accuracy of BAR on a publicly available labeled dataset of NHPs is 95.40% which reduces to 86.95% after (K-mean + CAOM). Lastly, we compared the performance of the SpikeDeep-Classifier with two human experts, where SpikeDeep-Classifier has produced comparable results. Significance. The results demonstrate that "SpikeDeep-Classifier" possesses the ability to generalize well on a versatile dataset and henceforth provides a generalized well on a versatile dataset and henceforth provides a generalized and fully automated solution to offline spike sorting

An Investigation into Neuropsychological Profiles in Anorexia Nervosa and Associated Clinical and Demographic Variables

Objective: Treatment outcomes for anorexia nervosa (AN) remain unsatisfactory. Substantial research has investigated the neuropsychological effects of AN, often with mixed results. One explanation for the inconsistencies is that there exist several distinct neuropsychological profiles within AN. Profiles have been reported, though not associated with clinical or demographic variables, limiting their utility. Suboptimal statistical techniques may undermine these findings. Method: An existing dataset of healthy controls (HCs) and AN patients (n = 423) was subjected to secondary analysis using latent profile analysis and a neural network to investigate latent profiles and the existence of non-linear neuropsychological structure. Profiles were compared with respect to demographic and clinical variables. Results: The latent profile analysis revealed five AN neuropsychological profiles. Patients in a globally neuropsychologically impaired profile were older than those in a high-average with high verbal profile and weighed less than those in an average performance profile. A non-linear neural network failed to outperform a linear neural network on a diagnosis classification task. Discussion: The five-profile solution extended the neuropsychological groups previously found in the literature. This study is the first to successfully associate latent neuropsychological profile to clinically meaningful variables, though the profile in which differences were observed was tiny (7% of patients). None of the discovered profiles differed in terms of anxiety, undermining support for the noradrenergic hypothesis of AN. The failure of the non-linear neural network to outperform the linear network indicates that AN neuropsychological ability does not contain significant non-linearity, indicating that conventional statistical techniques can model them

Neurocomputational models of corticostriatal interactions in action selection

Schema theory is a framework based on the idea that behaviour in many areas depends on abstractions over instances called schemas, which work in a cooperative or sequential fashion, but also compete with each other for activation. Cooper & Shallice (2000) provide an implementation of schema-theory with their model that simulates how routine actions works in healthy and neurologically-impaired populations. While schema theory is helpful in representing functional interactions in the action-perception cycle, it has no commitment to a specific neural implementation. Redgrave et al.’s (2001) model of the basal ganglia is, in principle, compatible with a device that regulates the competition among schemas, carrying out action selection. This thesis is mainly concerned with improving the neurobiological plausibility of the schema theoretic account of action selection without sacrificing its theoretical underpinning. We therefore start by combining an implementation of schema-theory with a reparametrised version of the original basal ganglia model, building the model from the ground up. The model simulates two widely used neuropsychological tasks, the Wisconsin Card Sorting Test (WCST), and the Brixton Task (BRX). In order to validate the model, we then present a study with 25 younger and 25 over-60 individuals performing the WCST and BRX, and we simulate their performance using the schema-theoretic basal ganglia model. Experimental results indicate a dissociation between loss of representation (present in older adults) and perseveration of response (absent in older adults) in the WCST, and the model fits adequately simulate these findings while grounding the interpretation of parameters to the neurobiology of aging. We subsequently present a further study with 50 participants, 14 of whom have an ADHD diagnosis, performing the WCST under an untimed and a timed condition, and we then use our model to fit response time. Results indicate that impulsivity traits, but not inattention ones, predict a slower tail of responses in the untimed task and an increased number of missed responses and variability across subtasks. Using the model, we show that these results can be produced by variation of a combination of two parameters representing basal ganglia activity and top-down excitation. We conclude with recommendations on how to improve and extend the model

Computer aided analysis of skin lesions

Effective screening to detect the skin cancer accurately in the early stage is essential for reducing the mortality of skin cancer. Surface features, such as texture and pigmentation area from the surface, epi-illumination images of the skin lesions have been well correlated to detect skin cancer. An increase in the lesion\u27s subsurface blood volume has been correlated to early diagnosis of malignant melanoma. A method for estimating the optimal features is obtained. The optimal features help in accurately classify the skin lesion in various grades. To make the process faster these optimal features are clustered. The optimal clusters are obtained by genetic algorithm. The optimal cluster centers act as input to the SVM classifier and the kernel parameters are obtained. Finally, parameters of the kernel function are optimized by genetic algorithm, which help in classifying the skin lesions into various grades leading to early diagnosis of skin cancer