Open-Source Tools for Processing and Analysis of In Vitro Extracellular Neuronal Signals

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

The meaning behind neural single unit activity has constantly been a challenge, so it will persist in the foreseeable future. As one of the most sourced strategies, detecting neural activity in high-resolution neural sensor recordings and then attributing them to their corresponding source neurons correctly, namely the process of spike sorting, has been prevailing so far. Support from ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information and abundance in clustering procedures turned spike sorting into an indispensable tool in electrophysiological analysis. This review attempts to illustrate that in all stages of spike sorting algorithms, the past 5 years innovations' brought about concepts, results, and questions worth sharing with even the non-expert user community. By thoroughly inspecting latest innovations in the field of neural sensors, recording procedures, and various spike sorting strategies, a skeletonization of relevant knowledge lays here, with an initiative to get one step closer to the original objective: deciphering and building in the sense of neural transcript.

Multi-Electrode Array Analysis Identifies Complex Dopamine Responses and Glucose Sensing Properties of Substantia Nigra Neurons in Mouse Brain Slices

Dopaminergic (DA) midbrain neurons within the substantia nigra (SN) display an autonomous pacemaker activity that is crucial for dopamine release and voluntary movement control. Their progressive degeneration is a hallmark of Parkinson's disease. Their metabolically demanding activity-mode affects Ca²⁺ homeostasis, elevates metabolic stress, and renders SN DA neurons particularly vulnerable to degenerative stressors. Accordingly, their activity is regulated by complex mechanisms, notably by dopamine itself, via inhibitory D2-autoreceptors and the neuroprotective neuronal Ca²⁺ sensor NCS-1. Analyzing regulation of SN DA neuron activity-pattern is complicated by their high vulnerability. We studied this activity and its control by dopamine, NCS-1, and glucose with extracellular multi-electrode array (MEA) recordings from midbrain slices of juvenile and adult mice. Our tailored MEA- and spike sorting-protocols allowed high throughput and long recording times. According to individual dopamine-responses, we identified two distinct SN cell-types, in similar frequency: dopamine-inhibited and dopamine-excited neurons. Dopamine-excited neurons were either silent in the absence of dopamine, or they displayed pacemaker-activities, similar to that of dopamine-inhibited neurons. Inhibition of pacemaker-activity by dopamine is typical for SN DA neurons, and it can undergo prominent desensitization. We show for adult mice, that the number of SN DA neurons with desensitized dopamine-inhibition was increased (~60–100%) by a knockout of NCS-1, or by prevention of NCS-1 binding to D2-autoreceptors, while time-course and degrees of desensitization were not altered. The number of neurons with desensitized D2-responses was also higher (~65%) at high glucose-levels (25 mM), compared to lower glucose (2.5 mM), while again desensitization-kinetics were unaltered. However, spontaneous firing-rates were significantly higher at high glucose-levels (~20%). Moreover, transient glucose-deprivation (1 mM) induced a fast and fully-reversible pacemaker frequency reduction. To directly address and quantify glucose-sensing properties of SN DA neurons, we continuously monitored their electrical activity, while altering extracellular glucose concentrations stepwise from 0.5 mM up to 25 mM. SN DA neurons were excited by glucose, with EC₅₀ values ranging from 0.35 to 2.3 mM. In conclusion, we identified a novel, common subtype of dopamine-excited SN neurons, and a complex, joint regulation of dopamine-inhibited neurons by dopamine and glucose, within the range of physiological brain glucose-levels.

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer's disease, Parkinson's disease and schizophrenia

Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders-focusing on Alzheimer's disease, Parkinson's disease and schizophrenia-from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.