Neuroscience

Normative mapping is a framework used to map population-level features of health-related variables. It is widely used in neuroscience research, but the literature lacks established protocols in modalities that do not support healthy control measurements, such as intracranial electroencephalograms (icEEG). An icEEG normative map would allow researchers to learn about population-level brain activity and enable the comparison of individual data against these norms to identify abnormalities. Currently, no standardised guide exists for transforming clinical data into a normative, regional icEEG map. Papers often cite different software and numerous articles to summarise the lengthy method, making it laborious for other researchers to understand or apply the process. Our protocol seeks to fill this gap by providing a dataflow guide and key decision points that summarise existing methods. This protocol was heavily used in published works from our own lab (twelve peer-reviewed journal publications). Briefly, we take as input the icEEG recordings and neuroimaging data from people with epilepsy who are undergoing evaluation for resective surgery. As final outputs, we obtain a normative icEEG map, comprising signal properties localised to brain regions. Optionally, we can also process new subjects through the same pipeline and obtain their z-scores (or centiles) in each brain region for abnormality detection and localisation. To date, a single, cohesive dataflow pipeline for generating normative icEEG maps, along with abnormality mapping, has not been created. We envisage that this dataflow guide will not only increase understanding and application of normative mapping methods but will also improve the consistency and quality of studies in the field.

The electroencephalogram (EEG) is a powerful tool for analyzing neural activity in various neurological disorders, both in animals and in humans. This technology has enabled researchers to record the brain’s abrupt changes in electrical activity with high resolution, thus facilitating efforts to understand the brain’s response to internal and external stimuli. The EEG signal acquired from implanted electrodes can be used to precisely study the spiking patterns that occur during abnormal neural discharges. These patterns can be analyzed in conjunction with behavioral observations and serve as an important means for accurate assessment and quantification of behavioral and electrographic seizures. Numerous algorithms have been developed for the automated quantification of EEG data; however, many of these algorithms were developed with outdated programming languages and require robust computational hardware to run effectively. Additionally, some of these programs require substantial computation time, reducing the relative benefits of automation. Thus, we sought to develop an automated EEG algorithm that was programmed using a familiar programming language (MATLAB), and that could run efficiently without extensive computational demands. This algorithm was developed to quantify interictal spikes and seizures in mice that were subjected to traumatic brain injury. Although the algorithm was designed to be fully automated, it can be operated manually, and all the parameters for EEG activity detection can be easily modified for broad data analysis. Additionally, the algorithm is capable of processing months of lengthy EEG datasets in the order of minutes to hours, reducing both analysis time and errors introduced through manual-based processing.

Densitometric analysis is often used to quantify Na_V1.1 protein on immunoblots, although the sensitivity and dilution linearity of the method are usually poor. Here we present a protocol for quantification of Na_V1.1 in mouse brain tissues using a Meso Scale Discovery-Electrochemiluminescence (MSD-ECL) method. MSD-ECL is based on ELISA (enzyme-linked immunosorbent assay) and uses electrochemiluminescence to produce measurable signals. Two different antibodies are used in this assay to capture and detect Na_V1.1 respectively in brain tissue lysate. The specificity of the antibodies is confirmed by Scn1a gene knock-out tissue. The calibration curve standards used in this assay were generated with mouse liver lysate spiked with mouse brain lysate, instead of using a recombinant protein. We showed that this method was qualified and used for quantification of Na_V1.1 in mouse brain tissues with specificity, accuracy and precision.

Perineuronal nets (PNNs) are extracellular matrix assemblies of highly negatively charged proteoglycans that wrap around fast-spiking parvalbumin (PV) expressing interneurons in the cerebral cortex. PNNs play important roles in neuronal plasticity and modulate biophysical properties of the enclosed interneurons. Various central nervous system diseases including schizophrenia, Alzheimer disease and epilepsy present with qualitative alteration in PNNs, however prior studies failed to quantitatively assess such changes at single PNN level and correlate them with functional changes in disease. We describe a method to quantify the structural integrity of PNNs using high magnification image analysis of Wisteria Floribunda Agglutinin (WFA)-labeled PNNs in combination with cell-type-specific marker such as PV and NeuN. A polyline intensity profile of WFA along the entire perimeter of cell shows alternate segments with and without WFA labeling, indicating the intact chondroitin sulfate proteoglycan (CSPG) and holes of PNN respectively. This line intensity profile defines CSPG peaks, where intact PNN is present, and CSPG valleys (holes) where the PNN is missing. The average number of peaks reflect the integrity of the lattice assembly of PNN. The average size of PNN holes can be readily computed using image analysis software. Furthermore, degradation of PNNs using a bacterial-derived enzyme, Chondroitinase ABC (ChABC), allows to experimentally manipulate PNNs in situ brain slices during which biophysical properties can be assessed by patch-clamp recordings. We describe optimized experimental parameters to degrade PNNs in brain slices before as well as during recordings to study the possible change in function in real time. Our protocols provide effective and appropriate methods to modulate and quantify the PNN’s experimental manipulations.