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This book explores how we can measure consciousness. It clarifies what consciousness is, how it can be generated from a physical system, and how it can be measured. It also shows how conscious states can be expressed mathematically and how precise predictions can be made using data from neurophysiological studies.

The advent of high-yield electrophysiology using Neuropixels probes is now enabling researchers to simultaneously record hundreds of neurons with remarkably high signal to noise. However, these probes have not been well-suited to use in freely moving mice. It is critical to study neural activity in unrestricted animals for many reasons, such as leveraging ethological approaches to study neural circuits. We designed and implemented a novel device that allows Neuropixels probes to be customized for chronically implanted experiments in freely moving mice. We demonstrate the ease and utility of this approach in recording hundreds of neurons during an ethological behavior across weeks of experiments. We provide the technical drawings and procedures for other researchers to do the same. Importantly, our approach enables researchers to explant and reuse these valuable probes, a transformative step which has not been established for recordings with any type of chronically-implanted probe.

Advances in large-scale single-unit human neurophysiology, single-cell RNA sequencing, spatial transcriptomics and long-term ex vivo tissue culture of surgically resected human brain tissue have provided an unprecedented opportunity to study human neuroscience. In this Perspective, we describe the development of these paradigms, including Neuropixels and recent brain-cell atlas efforts, and discuss how their convergence will further investigations into the cellular underpinnings of network-level activity in the human brain. Specifically, we introduce a workflow in which functionally mapped samples of human brain tissue resected during awake brain surgery can be cultured ex vivo for multi-modal cellular and functional profiling. We then explore how advances in human neuroscience will affect clinical practice, and conclude by discussing societal and ethical implications to consider. Potential findings from the field of human neuroscience will be vast, ranging from insights into human neurodiversity and evolution to providing cell-type-specific access to study and manipulate diseased circuits in pathology. This Perspective aims to provide a unifying framework for the field of human neuroscience as we welcome an exciting era for understanding the functional cytoarchitecture of the human brain. This Perspective considers the implications of advances in human physiology, single-cell and spatial transcriptomics and long-term culture of resected human brain tissue for the study of network-level activity in human neuroscience.

Detecting meaningful structure in neural activity and connectivity data is challenging in the presence of hidden nonlinearities, where traditional eigenvalue-based methods may be misleading. We introduce a novel approach to matrix analysis, called clique topology, that extracts features of the data invariant under nonlinear monotone transformations. These features can be used to detect both random and geometric structure, and depend only on the relative ordering of matrix entries. We then analyzed the activity of pyramidal neurons in rat hippocampus, recorded while the animal was exploring a 2D environment, and confirmed that our method is able to detect geometric organization using only the intrinsic pattern of neural correlations. Remarkably, we found similar results during nonspatial behaviors such as wheel running and rapid eye movement (REM) sleep. This suggests that the geometric structure of correlations is shaped by the underlying hippocampal circuits and is not merely a consequence of position coding. We propose that clique topology is a powerful new tool for matrix analysis in biological settings, where the relationship of observed quantities to more meaningful variables is often nonlinear and unknown.

Genetically encoded calcium indicators (GECIs) allow measurement of activity in large populations of neurons and in small neuronal compartments, over times of milliseconds to months. Although GFP-based GECIs are widely used for in vivo neurophysiology, GECIs with red-shifted excitation and emission spectra have advantages for in vivo imaging because of reduced scattering and absorption in tissue, and a consequent reduction in phototoxicity. However, current red GECIs are inferior to the state-of-the-art GFP-based GCaMP6 indicators for detecting and quantifying neural activity. Here we present improved red GECIs based on mRuby (jRCaMP1a, b) and mApple (jRGECO1a), with sensitivity comparable to GCaMP6. We characterized the performance of the new red GECIs in cultured neurons and in mouse, Drosophila, zebrafish and C. elegans in vivo. Red GECIs facilitate deep-tissue imaging, dual-color imaging together with GFP-based reporters, and the use of optogenetics in combination with calcium imaging. Neurons encode information with brief electrical pulses called spikes. Monitoring spikes in large populations of neurons is a powerful method for studying how networks of neurons process information and produce behavior. This activity can be detected using fluorescent protein indicators, or “probes”, which light up when neurons are active. The best existing probes produce green fluorescence. However, red fluorescent probes would allow us to see deeper into the brain, and could also be used with green probes to image the activity and interactions of different neuron types simultaneously. However, existing red fluorescent probes are not as good at detecting neural activity as green probes. By optimizing two existing red fluorescent proteins, Dana et al. have now produced two new red fluorescent probes, each with different advantages. The new protein indicators detect neural activity with high sensitivity and allow researchers to image previously unseen brain activity. Tests showed that the probes work in cultured neurons and allow imaging of the activity of neurons in mice, flies, fish and worms. History has shown that enhancing the techniques used to study biological processes can lead to fundamentally new insights. In the future, Dana et al. would therefore like to make even more sensitive protein indicators that will allow larger networks of neurons deeper in the brain to be imaged.

The development of neural circuits involves wiring of neurons locally following their generation and migration, as well as establishing long-distance connections between brain regions. Studying these developmental processes in the human nervous system remains difficult because of limited access to tissue that can be maintained as functional over time in vitro. We have previously developed a method to convert human pluripotent stem cells into brain region–specific organoids that can be fused and integrated to form assembloids and study neuronal migration. In contrast to approaches that mix cell lineages in 2D cultures or engineer microchips, assembloids leverage self-organization to enable complex cell–cell interactions, circuit formation and maturation in long-term cultures. In this protocol, we describe approaches to model long-range neuronal connectivity in human brain assembloids. We present how to generate 3D spheroids resembling specific domains of the nervous system and then how to integrate them physically to allow axonal projections and synaptic assembly. In addition, we describe a series of assays including viral labeling and retrograde tracing, 3D live imaging of axon projection and optogenetics combined with calcium imaging and electrophysiological recordings to probe and manipulate the circuits in assembloids. The assays take 3–4 months to complete and require expertise in stem cell culture, imaging and electrophysiology. We anticipate that these approaches will be useful in deciphering human-specific aspects of neural circuit assembly and in modeling neurodevelopmental disorders with patient-derived cells.A protocol is described for generating human brain assembloids and performing viral labeling and retrograde tracing, 3D live imaging of axon projection and optogenetics with calcium imaging and electrophysiological recordings to model neural circuits.

ObjectiveDuring the Covid-19 pandemic, government restrictions limited health care to urgent needs. Neurophysiology centers had to suddenly reschedule their activities, with a lack of specific recommendations about electroencephalography (EEG) execution. During the pandemic phase 1, we launched an online survey to understand the flaws and strengths of the EEG management in Italy at the time of Covid-19 pandemic.MethodsA 45-item online survey (published from April 16 to 30, 2020), endorsed by the Italian Society of Clinical Neurophysiology (SINC), the Italian League Against epilepsy (LICE), and the Italian Association of Neurophysiology technologists (AITN), collected EEG management data (EEG’s number and type, indications, personnel and patients safety, devices’ sanification) during the Covid-19 pandemic.ResultsWe received responses from 206 centers. The number of EEGs performed was reduced by 76 ± 20%, and several types of specific EEG (video-EEG, ambulatory-EEG, LTM, polysomnography) were reduced at a minimum. Half of the centers performed inpatient EEGs only for urgencies. Repetitive seizures, encephalitis, and non-convulsive status epilepticus were the most common indications. Covid-19-positive patients received less EEG than negative ones (p < 0.0001). EEG requests came mainly not only from neurologists (n = 176) but also from general practitioners (n = 40), emergentists (n = 79), intensivists (n = 72), and other specialists (n = 53). Those centers which continued performing outpatient EEG examinations were instructed to perform the EEG after a Covid-19-related symptom screening for patients and using personal protective equipment (PPE) through all the procedure. Inpatient EEGs were performed using FFP2/FFP3 masks by neurophysiology technologists in only 50% of cases. Patients executed hyperventilation only for real clinical needs, but often (56%) with a mask.ConclusionsItalian neurophysiology centers strongly adhered to government restrictions of lockdown. Some issues emerged, ranging from the evaluation of a proper indication for EEG, technical procedures of EEG recording, and protection of neurophysiology technicians.

Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) allow their users to send commands to a computer using their brain-activity alone (typically measured by ElectroEncephaloGraphy-EEG), which is processed while they perform specific mental tasks. While very promising, MI-BCIs remain barely used outside laboratories because of the difficulty encountered by users to control them. Indeed, although some users obtain good control performances after training, a substantial proportion remains unable to reliably control an MI-BCI. This huge variability in user-performance led the community to look for predictors of MI-BCI control ability. However, these predictors were only explored for motor-imagery based BCIs, and mostly for a single training session per subject. In this study, 18 participants were instructed to learn to control an EEG-based MI-BCI by performing 3 MI-tasks, 2 of which were non-motor tasks, across 6 training sessions, on 6 different days. Relationships between the participants' BCI control performances and their personality, cognitive profile and neurophysiological markers were explored. While no relevant relationships with neurophysiological markers were found, strong correlations between MI-BCI performances and mental-rotation scores (reflecting spatial abilities) were revealed. Also, a predictive model of MI-BCI performance based on psychometric questionnaire scores was proposed. A leave-one-subject-out cross validation process revealed the stability and reliability of this model: it enabled to predict participants' performance with a mean error of less than 3 points. This study determined how users' profiles impact their MI-BCI control ability and thus clears the way for designing novel MI-BCI training protocols, adapted to the profile of each user.

Large language models (LLMs), like ChatGPT, Google’s Bard, and Anthropic’s Claude, showcase remarkable natural language processing capabilities. Evaluating their proficiency in specialized domains such as neurophysiology is crucial in understanding their utility in research, education, and clinical applications. This study aims to assess and compare the effectiveness of Large Language Models (LLMs) in answering neurophysiology questions in both English and Persian (Farsi) covering a range of topics and cognitive levels. Twenty questions covering four topics (general, sensory system, motor system, and integrative) and two cognitive levels (lower-order and higher-order) were posed to the LLMs. Physiologists scored the essay-style answers on a scale of 0–5 points. Statistical analysis compared the scores across different levels such as model, language, topic, and cognitive levels. Performing qualitative analysis identified reasoning gaps. In general, the models demonstrated good performance (mean score = 3.87/5), with no significant difference between language or cognitive levels. The performance was the strongest in the motor system (mean = 4.41) while the weakest was observed in integrative topics (mean = 3.35). Detailed qualitative analysis uncovered deficiencies in reasoning, discerning priorities, and knowledge integrating. This study offers valuable insights into LLMs’ capabilities and limitations in the field of neurophysiology. The models demonstrate proficiency in general questions but face challenges in advanced reasoning and knowledge integration. Targeted training could address gaps in knowledge and causal reasoning. As LLMs evolve, rigorous domain-specific assessments will be crucial for evaluating advancements in their performance.