Reduced working memory is a hallmark of Alzheimer’s disease (AD). Analyzing local field potentials (LFPs) across neural networks provides crucial insights into the mechanisms underlying these cognitive deficits. Standard LFP studies often rely on spontaneous exploration, while we designed a novel Y-maze paradigm with aversive voltage feedback. This paradigm enable precise time-locked temporal subdivision of trials (first choice vs. second choice, correct vs. incorrect decisionst) to compare memory-related electrophysiology between AD and normal mice. Our analysis reveals four key findings: (1) LFP exhibit task-specific lateralization, suggesting the right hemisphere is prominently engaged during this specific spatial navigation context; (2) correct behavioral outcomes correlate with pre-decision neural states, highlighting distinct network encoding mechanisms compared to error processing; (3) causal connectivity suggests a disrupted functional balance between the cingulate gyrus and hippocampus in AD mice, it may reflect early network compensation; (4) AD mice display widespread oscillatory and directional connectivity disruptions during memory process, which strongly correlate with spatial memory failures. These findings characterize AD memory impairment as a dynamic network disconnectivity syndrome and highlight cingulate gyrus-hippocampal interactions as potential biomarkers to improve memory dysfunction in AD.
Progress in translating spinal cord injury (SCI) therapies from bench to bedside remains bottlenecked by a critical paradox in preclinical phenotyping: the frequent divergence between behavioral recovery metrics and objective neurophysiological outcomes. For decades, the Basso, Beattie, and Bresnahan (BBB) open-field locomotor scale has served as the standard subjective metric for assessing hindlimb function in rodent models. However, while behavioral scales often capture substantial endogenous recovery and central pattern generator (CPG) autonomy, they frequently fail to correlate with objective neuro-electrophysiological metrics, such as motor and somatosensory evoked potentials (MEPs/SSEPs), particularly during the acute and sub-acute phases of injury. This perspective evaluates the neuroanatomical and pathophysiological mechanisms underlying this discrepancy. We dissect how the spatial distribution of contusive forces, the demyelination of anatomically spared fibers, and transient spinal shock alter early electrophysiological signaling without permanently arresting long-term behavioral plasticity. Furthermore, we highlight key neuroanatomical differences—specifically the dorsal preservation of the corticospinal tract in rodents versus the lateral architecture in humans—that complicate cross-species translation. Ultimately, we argue that relying solely on locomotor scales overestimates functional tract integrity, and we advocate for a paradigm shift toward integrating synchronized behavioral and electrophysiological profiling to improve the predictive validity of preclinical SCI trials.
Background The most prevalent type of radiologist error is failing to detect abnormalities on images, the so-called “perceptual error.” The prevalence of this type of false-negative (FN) error remains essentially unchanged since it was first described in 1949. Reducing this error rate would be highly beneficial for patients who often depend on radiological observations for a timely diagnosis. Purpose To identify a potential neurocognitive mechanism contributing to radiologists’ perceptual errors, in order to inform intervention strategies to reduce such errors in practice. These experiments evaluated the relationship between brain network activation states and radiologists’ perceptual errors on two distinct visual tasks utilizing functional MRI (fMRI) and functional Near Infrared Spectroscopy (fNIRS). Materials and Methods A prospective, pilot study was carried out on a small number of radiologist participants utilizing two distinct types of visual tasks while monitoring activity in two targeted brain networks, the default mode network (DMN) and the “executive” frontoparietal network (FPN), to test the hypothesis that simultaneous co-activation of these networks produces an error-prone neurocognitive state (EPS). A third experiment using fNIRS alone was an observational study of subjects’ neurocognitive states during their usual practice to determine the baseline prevalence of the EPS in the clinical setting. Results An approximately threefold increased risk of FN perceptual errors (misses) on the visual tasks was observed in the presence of simultaneous co-activation of elements of the Default Mode Network (DMN) and Frontoparietal Network (FPN), which supports the “error-prone state” (EPS) hypothesis. EPS prevalence declined with participant age. Conclusion Our results suggest that dynamic interactions between the Default Mode Network (DMN) and Frontoparietal Network (FPN) create a unique error-prone state (EPS), associated with an approximately threefold increased risk of perceptual error in radiologists. These results suggest potential intervention strategies for perceptual error, the largest class of radiologist errors in practice.
Objective: Recursive Informational Curvature (RIC) was recently introduced as an information-geometric framework for describing the balance between recursive structure and entropy change in dynamical systems. Here, we present the first empirical implementation and benchmark evaluation of its scalar curvature term, K in high-density macaque electrocorticography (ECoG). Methods: We analyzed 2 open-access datasets: a 128-channel eyes-open versus eyes-closed benchmark derived from the George anesthesia-and-sleep recording package, and a 64-channel food-tracking dataset with synchronized motion capture. Following standardized preprocessing and symbolic discretization, we extracted Shannon entropy, recursive gain, and empirical curvature for each channel and epoch. We then evaluated these features using single-feature, multichannel, and combined-feature models, together with ablation and sensitivity analyses across symbolic bin counts, epoch lengths, and classifier families. Results: In the primary eyes-open versus eyes-closed benchmark, multichannel curvature features supported near-perfect state discrimination. In the food-tracking task, the strongest performance was obtained from combined curvature and amplitude features. Across analyses, entropy alone was weak, whereas recursive gain and curvature showed closely matched performance profiles, indicating that in the present implementation much of the discriminative structure captured by curvature is concentrated in the recursive term. Conclusion: These findings establish the empirical RIC curvature term as an interpretable and state-sensitive descriptor of neural dynamics in ECoG, provide a reproducible benchmark for future refinement of the framework, and clarify both the promise and the current limits of curvature-based analysis in neural states.
Working memory (WM) is a capacity-limited control system that maintains and updates task-relevant representations for goal-directed cognition. Because WM declines with aging and is disrupted across neuropsychiatric and neurodegenerative disorders, neuromodulation has emerged as both a causal probe of circuit function and a potential therapeutic adjunct. This mechanistic narrative review evaluates evidence across transcranial direct current stimulation, transcranial magnetic stimulation/theta-burst stimulation, transcranial alternating current stimulation, transcranial random noise stimulation, and deep brain stimulation. Rather than asking whether neuromodulation uniformly enhances WM, we examine when different modalities alter distinct WM subprocesses. Across modalities, effects are generally modest and heterogeneous, but the literature increasingly suggests that stimulation acts less by adding “capacity” than by biasing gain, timing, and plasticity-related control variables that shape WM trajectories. More precise dosing, targeting, and computation-aligned outcomes are needed to establish reproducible and mechanistically interpretable effects.
