Neurons as will and representation

Scalp block improves electrophysiological stability and patient cooperation during deep brain stimulation surgery

Deep Brain Stimulation (DBS) is a critical intervention for various neurological disorders. While effective, the traditional local infiltration anesthesia used in DBS surgeries often hinders electrophysiological recording quality and patient cooperativeness. The research aims to evaluate the impact of local infiltration versus scalp block anesthetic methods on electrophysiological signal quality and patient cooperativeness during DBS surgeries. This study involved patients who participated in an intraoperative task during the bilateral subthalamic nucleus DBS surgery for Parkinson's Disease between Jan 2020 and Dec 2022. Patients were either administered the traditional local infiltration anesthesia or the modified scalp block anesthesia. Intraoperative electrophysiological recording data and anesthetic data was collected. Spike sorting was performed to evaluate the recording stability. Patient cooperativeness and intraoperative experience was assessed and compared. The patients under scalp block anesthesia exhibited shorter pre-acquisition time, longer stable recording time, higher number of tasks per site, higher number of neurons recorded per task (all ps < 0.05). In behavior, patients under scalp block anesthesia showed higher accuracy in tasks (p < 0.05), while the response time was comparable. The overall satisfaction of anesthesia was also higher in scalp block, as revealed by the visual analogue scale, Likert scale and mean arterial pressure (all ps < 0.05). The modified scalp block anesthetic method offers considerable advantages over traditional local infiltration anesthesia in DBS surgeries. It helps to improve both patient comfort and cooperation during the surgery, and thereby enhancing the overall quality of neurological data and efficacy of DBS procedures.

Slow ramping emerges from spontaneous fluctuations in spiking neural networks

1. We reveal a mechanism for slow-ramping signals before spontaneous voluntary movements. 2. Slow synapses stabilize spontaneous fluctuations in spiking neural network. 3. We validate model predictions in human frontal cortical single-neuron recordings. 4. The model recreates the readiness potential in an EEG proxy signal. 5. Neurons that ramp together had correlated activity before ramping onset. The capacity to initiate actions endogenously is critical for goal-directed behavior. Spontaneous voluntary actions are typically preceded by slow-ramping activity in medial frontal cortex that begins around two seconds before movement, which may reflect spontaneous fluctuations that influence action timing. However, the mechanisms by which these slow ramping signals emerge from single-neuron and network dynamics remain poorly understood. Here, we developed a spiking neural-network model that produces spontaneous slow ramping activity in single neurons and population activity with onsets ∼2 seconds before threshold crossings. A key prediction of our model is that neurons that ramp together have correlated firing patterns before ramping onset. We confirmed this model-derived hypothesis in a dataset of human single neuron recordings from medial frontal cortex. Our results suggest that slow ramping signals reflect bounded spontaneous fluctuations that emerge from quasi-winner-take-all dynamics in clustered networks that are temporally stabilized by slow-acting synapses.

Nanomaterials for brain metastasis

Tumor metastasis is a significant contributor to the mortality of cancer patients. Specifically, current conventional treatments are unable to achieve complete remission of brain metastasis. This is due to the unique pathological environment of brain metastasis, which differs significantly from peripheral metastasis. Brain metastasis is characterized by high tumor mutation rates and a complex microenvironment with immunosuppression. Additionally, the presence of blood-brain barrier (BBB)/blood tumor barrier (BTB) restricts drug leakage into the brain. Therefore, it is crucial to take account of the specific characteristics of brain metastasis when developing new therapeutic strategies. Nanomaterials offer promising opportunities for targeted therapies in treating brain metastasis. They can be tailored and customized based on specific pathological features and incorporate various treatment approaches, which makes them advantageous in advancing therapeutic strategies for brain metastasis. This review provides an overview of current clinical treatment options for patients with brain metastasis. It also explores the roles and changes that different cells within the complex microenvironment play during tumor spread. Furthermore, it highlights the use of nanomaterials in current brain treatment approaches.

How far neuroscience is from understanding brains

The cellular biology of brains is relatively well-understood, but neuroscientists have not yet generated a theory explaining how brains work. Explanations of how neurons collectively operate to produce what brains can do are tentative and incomplete. Without prior assumptions about the brain mechanisms, I attempt here to identify major obstacles to progress in neuroscientific understanding of brains and central nervous systems. Most of the obstacles to our understanding are conceptual. Neuroscience lacks concepts and models rooted in experimental results explaining how neurons interact at all scales. The cerebral cortex is thought to control awake activities, which contrasts with recent experimental results. There is ambiguity distinguishing task-related brain activities from spontaneous activities and organized intrinsic activities. Brains are regarded as driven by external and internal stimuli in contrast to their considerable autonomy. Experimental results are explained by sensory inputs, behavior, and psychological concepts. Time and space are regarded as mutually independent variables for spiking, post-synaptic events, and other measured variables, in contrast to experimental results. Dynamical systems theory and models describing evolution of variables with time as the independent variable are insufficient to account for central nervous system activities. Spatial dynamics may be a practical solution. The general hypothesis that measurements of changes in fundamental brain variables, action potentials, transmitter releases, post-synaptic transmembrane currents, etc., propagating in central nervous systems reveal how they work, carries no additional assumptions. Combinations of current techniques could reveal many aspects of spatial dynamics of spiking, post-synaptic processing, and plasticity in insects and rodents to start with. But problems defining baseline and reference conditions hinder interpretations of the results. Furthermore, the facts that pooling and averaging of data destroy their underlying dynamics imply that single-trial designs and statistics are necessary.

Memory: Synaptic or Cellular, That Is the Question

According to the commonly accepted opinion, memory engrams are formed and stored at the level of neural networks due to a change in the strength of synaptic connections between neurons. This hypothesis of synaptic plasticity (HSP), formulated by Donald Hebb in the 1940s, continues to dominate the directions of experimental studies and the interpretations of experimental results in the field. The universal acceptance of the HSP has transformed it from a hypothesis into an incontrovertible theory. In this article, I show that the entire body of experimental and clinical data obtained in studies of long-term memory in mammals and humans is inconsistent with the HSP. Instead, these data suggest that long-term memory is formed and stored at the intracellular level where it is reliably protected from ongoing synaptic activity, including pathological epileptic activity. It seems that the generally accepted HSP became a serious obstacle to understanding the mechanisms of memory and that progress in this field requires rethinking this doctrine and shifting experimental efforts toward exploring the intracellular mechanisms.

Consciousness: a neurosurgical perspective

Neurosurgeons are in a unique position to shed light on the neural basis for consciousness, not only by their clinical care of patients with compromised states of consciousness, but also by employing neurostimulation and neuronal recordings through intracranial electrodes in awake surgical patients, as well as during stages of sleep and anethesia. In this review, we discuss several aspects of consciousness, i.e., perception, memory, and willed actions, studied by electrical stimulation and single neuron recordings in the human brain. We demonstrate how specific neuronal activity underlie the emergence of concepts, memories, and intentions in human consciousness. We discuss the representation of specific conscious content by temporal lobe neurons and present the discovery of "concept cells" and the encoding and retrieval of memories by neurons in the medial temporal lobe. We review prefrontal and parietal neuronal activation that precedes conscious intentions to act. Taken together with other studies in the field, these findings suggest that specific conscious experience may arise from stochastic fluctuations of neuronal activity, reaching a dynamic threshold. Advances in brain recording and stimulation technology coupled with the rapid rise in artificial intelligence are likely to increase the amount and analysis capabilities of data obtained from the human brain, thereby improving the decoding of conscious and preconscious states and open new horizons for modulation of human cognitive functions such as memory and volition.

Neurons in human pre-supplementary motor area encode key computations for value-based choice

Adaptive behaviour in real-world environments requires that choices integrate several variables, including the novelty of the options under consideration, their expected value and uncertainty in value estimation. Here, to probe how integration over decision variables occurs during decision-making, we recorded neurons from the human pre-supplementary motor area (preSMA), ventromedial prefrontal cortex and dorsal anterior cingulate. Unlike the other areas, preSMA neurons not only represented separate pre-decision variables for each choice option but also encoded an integrated utility signal for each choice option and, subsequently, the decision itself. Post-decision encoding of variables for the chosen option was more widely distributed and especially prominent in the ventromedial prefrontal cortex. Our findings position the human preSMA as central to the implementation of value-based decisions.

ROSE: A Neurocomputational Architecture for Syntax

A comprehensive model of natural language processing in the brain must accommodate four components: representations, operations, structures and encoding. It further requires a principled account of how these different components mechanistically, and causally, relate to each another. While previous models have isolated regions of interest for structure-building and lexical access, and have utilized specific neural recording measures to expose possible signatures of syntax, many gaps remain with respect to bridging distinct scales of analysis that map onto these four components. By expanding existing accounts of how neural oscillations can index various linguistic processes, this article proposes a neurocomputational architecture for syntax, termed the ROSE model (Representation, Operation, Structure, Encoding). Under ROSE, the basic data structures of syntax are atomic features, types of mental representations (R), and are coded at the single-unit and ensemble level. Elementary computations (O) that transform these units into manipulable objects accessible to subsequent structure-building levels are coded via high frequency broadband γ activity. Low frequency synchronization and cross-frequency coupling code for recursive categorial inferences (S). Distinct forms of low frequency coupling and phase-amplitude coupling (δ-θ coupling via pSTS-IFG; θ-γ coupling via IFG to conceptual hubs in lateral and ventral temporal cortex) then encode these structures onto distinct workspaces (E). Causally connecting R to O is spike-phase/LFP coupling; connecting O to S is phase-amplitude coupling; connecting S to E is a system of frontotemporal traveling oscillations; connecting E back to lower levels is low-frequency phase resetting of spike-LFP coupling. This compositional neural code has important implications for algorithmic accounts, since it makes concrete predictions for the appropriate level of study for psycholinguistic parsing models. ROSE is reliant on neurophysiologically plausible mechanisms, is supported at all four levels by a range of recent empirical research, and provides an anatomically precise and falsifiable grounding for the basic property of natural language syntax: hierarchical, recursive structure-building.

Entropy and Cross-Level Orderliness in Light of the Interconnection between the Neural System and Consciousness

Despite recent advances, the origin and utility of consciousness remains under debate. Using an evolutionary perspective on the origin of consciousness, this review elaborates on the promising theoretical background suggested in the temporospatial theory of consciousness, which outlines world-brain alignment as a critical predisposition for controlling behavior and adaptation. Such a system can be evolutionarily effective only if it can provide instant cohesion between the subsystems, which is possible only if it performs an intrinsic activity modified in light of the incoming stimulation. One can assume that the world-brain interaction results in a particular interference pattern predetermined by connectome complexity. This is what organisms experience as their exclusive subjective state, allowing the anticipation of regularities in the environment. Thus, an anticipative system can emerge only in a regular environment, which guides natural selection by reinforcing corresponding reactions and decreasing the system entropy. Subsequent evolution requires complicated, layered structures and can be traced from simple organisms to human consciousness and society. This allows us to consider the mode of entropy as a subject of natural evolution rather than an individual entity.

Bimanual motor impairments in older adults: an updated systematic review and meta-analysis

This updated systematic review and meta-analysis further examined potential effects of aging on bimanual movements. Forty-seven qualified studies that compared bimanual motor performances between elderly and younger adults were included in this meta-analysis. Moderator variable analyses additionally determined whether altered bimanual motor performances in older adults were different based on the task types (i.e., symmetry vs. asymmetry vs. complex) or outcome measures (i.e., accuracy vs. variability vs. movement time). The random effects model meta-analysis on 80 comparisons from 47 included studies revealed significant negative overall effects indicating more bimanual movement impairments in the elderly adults than younger adults. Moderator variable analyses found that older adults showed more deficits in asymmetrical bimanual movement tasks than symmetrical and complex tasks, and the bimanual movement impairments in the elderly adults included less accurate, more variable, and greater movement execution time than younger adults. These findings suggest that rehabilitation programs for improving motor actions in older adults are necessary to focus on functional recovery of interlimb motor control including advanced motor performances as well coordination.