The 2021 Epilepsy Research Benchmarks—Respecting Core Principles, Reflecting Evolving Community Priorities

Epiliepsy currents/Epilepsy currents, Journal Year: 2021, Volume and Issue: 21(5), P. 389 - 393

Published: June 30, 2021

Keywords Curing the Epilepsies, basic science, translational Epilepsy Research Benchmarks, Benchmark Stewards

Language: Английский

---

Oligodendrocyte calcium signaling sculpts myelin sheath morphology

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: April 11, 2023

SUMMARY Myelin is essential for rapid nerve signaling and increasingly found to play important roles in learning diverse diseases of the CNS. Morphological parameters myelin such as sheath length thickness are regulated by neuronal activity can precisely tune conduction velocity, but mechanisms controlling morphology poorly understood. Local calcium has been observed nascent sheaths be modulated activity. However, role formation remodeling unknown. Here, we used genetic tools attenuate oligodendrocyte during active myelination developing mouse Surprisingly, that attenuation did not grossly affect number myelinated axons or thickness. Instead, caused striking defects resulting shorter, dysmorphic sheaths. Mechanistically, reduced actin filaments oligodendrocytes, an intact cytoskeleton was necessary sufficient achieve accurate morphology. Together, our work reveals a novel cellular mechanism required CNS provides mechanistic insight into how oligodendrocytes may respond sculpt throughout nervous system.

Language: Английский

---

Super-Selective Reconstruction of Causal and Direct Connectivity With Application to in vitro iPSC Neuronal Networks

Frontiers in Neuroscience, Journal Year: 2021, Volume and Issue: 15

Published: July 16, 2021

Despite advancements in the development of cell-based in-vitro neuronal network models, lack appropriate computational tools limits their analyses. Methods aimed at deciphering effective connections between neurons from extracellular spike recordings would increase utility vitro local neural circuits, especially for studies human and disease based on induced pluripotent stem cells (hiPSC). Current techniques allow statistical inference functional couplings but are fundamentally unable to correctly identify indirect apparent neurons, generating redundant maps with limited ability model causal dynamics network. In this paper, we describe a novel mathematically rigorous, model-free method map effective—direct causal—connectivity networks multi-electrode array data. The algorithm uses combination deterministic indicators which, first, enables identification all existing links then reconstructs directed connection diagram via super-selective rule enabling highly accurate classification direct, indirect, links. Our can be generally applied characterization any networks. Here, show that, given its accuracy, it offer important insights into hiPSC-derived cultures.

Language: Английский

---

Habit learning supported by efficiently controlled network dynamics in naive macaque monkeys

arXiv (Cornell University), Journal Year: 2020, Volume and Issue: unknown

Published: Jan. 1, 2020

Primates display a marked ability to learn habits in uncertain and dynamic environments. The associated perceptions actions of such engage distributed neural circuits. Yet, precisely how circuits support the computations necessary for habit learning remain far from understood. Here we construct formal theory network energetics account changes brain state produce sequential behavior. We exercise context multi-unit recordings spanning caudate nucleus, prefrontal cortex, frontal eyefields female macaque monkeys engaged 60-180 sessions free scan task that induces motor habits. relies on determination effective connectivity between recording channels, stipulation is taken be trial-specific firing rate across those channels. then predicts much energy will required transition one into another, given constraint activity can spread solely through connections. Consistent with theory's predictions, observed smaller requirements transitions more similar complex trial saccade patterns, characterized by less entropic selection patterns. Using virtual lesioning approach, demonstrate resilience relationships minimum control behavior significant disruptions inferred connectivity. Our theoretically principled approach study paves way future efforts examining arises changing patterns circuitry.

Language: Английский

---

The Influence of Autoregressive Relation Strength and Search Strategy on Directionality Recovery in GIMME

Published: Aug. 28, 2020

Unified structural equation modeling (uSEM) implemented in the Group Iterative Multiple Model Estimation (GIMME) framework has recently been widely used for characterizing within-person network dynamics of behavioral and functional neuroimaging variables. Previous studies have established that GIMME accurately recovers presence relations between However, recovery relation directionality is less consistent, which concerning given importance estimates many research questions. There evidence strong autoregressive may aid indirect a novel version allowing multiple solutions could improve when such are weak, but it remains unclear how these strategies perform under range study conditions. Using comprehensive simulations varied strength among other factors, this evaluated two search strategies: 1) estimating by default null model (GIMME-AR), 2) generating solution paths (GIMME-MS). Both recovered best – were roughly equivalent performance (e.g., β = .60). When they weak (β <= .10), GIMME-MS displayed an advantage, although overall was modest. Analyses empirical data characteristically (resting state fMRI) versus (daily diary) mirrored simulation results confirmed can disagree on weak. Findings important implications psychological applications uSEM/GIMME suggest specific scenarios researchers might or not be confident results.

Language: Английский

---