The location of the axon initial segment affects the bandwidth of spike initiation dynamics

The dynamics and the sharp onset of action potential (AP) generation have recently been the subject of intense experimental and theoretical investigations. According to the resistive coupling theory, an electrotonic interplay between the site of AP initiation in the axon and the somato-dendritic load determines the AP waveform. This phenomenon not only alters the shape of AP recorded at the soma, but also determines the dynamics of excitability across a variety of time scales. Supporting this statement, here we generalize a previous numerical study and extend it to the quantification of the input-output gain of the neuronal dynamical response. We consider three classes of multicompartmental mathematical models, ranging from ball-and-stick simplified descriptions of neuronal excitability to 3D-reconstructed biophysical models of excitatory neurons of rodent and human cortical tissue. For each model, we demonstrate that increasing the distance between the axonal site of AP initiation and the soma markedly increases the bandwidth of neuronal response properties. We finally consider the Liquid State Machine paradigm, exploring the impact of altering the site of AP initiation at the level of a neuronal population, and demonstrate that an optimal distance exists to boost the computational performance of the network in a simple classification task. Copyright

A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms.

Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in stroke research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires neuroanatomical expertise. We previously released an open-source dataset of stroke T1w MRIs and manually-segmented lesion masks (ATLAS v1.2, N = 304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N = 1271), a larger dataset of T1w MRIs and manually segmented lesion masks that includes training (n = 655), test (hidden masks, n = 300), and generalizability (hidden MRIs and masks, n = 316) datasets. Algorithm development using this larger sample should lead to more robust solutions; the hidden datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke research

Role of neuronal synchrony in the generation of evoked EEG/MEG responses

Evoked EEG/MEG responses are a primary real-time measure of perceptual and cognitive activity in the human brain, but their neuronal generator mechanisms are not yet fully understood. Arguments have been put forward in favor of either “phase-reset” of ongoing oscillations or “added-energy” models. Instead of advocating for one or the other model, here we show theoretically that the differentiation between these two generation mechanisms might not be possible if based solely on macroscopic EEG/MEG recordings. Using mathematical modeling, we show that a simultaneous phase reset of multiple oscillating neuronal (microscopic) sources contributing to EEG/MEG can produce evoked responses in agreement with both, the “added-energy” and the “phase-reset” model. We observe a smooth transition between the two models by just varying the strength of synchronization between the multiple microscopic sources. Consequently, because precise knowledge about the strength of microscopic ensemble synchronization is commonly not available in noninvasive EEG/MEG studies, they cannot, in principle, differentiate between the two mechanisms for macroscopic-evoked responses

Cholinergic Switch between Two Types of Slow Waves in Cerebral Cortex

Sleep slow waves are known to participate in memory consolidation, yet slow waves occurring under anesthesia present no positive effects on memory. Here, we shed light onto this paradox, based on a combination of extracellular recordings in vivo, in vitro, and computational models. We find two types of slow waves, based on analyzing the temporal patterns of successive slow-wave events. The first type is consistently observed in natural slow-wave sleep, while the second is shown to be ubiquitous under anesthesia. Network models of spiking neurons predict that the two slow wave types emerge due to a different gain on inhibitory versus excitatory cells and that different levels of spike-frequency adaptation in excitatory cells can account for dynamical distinctions between the two types. This prediction was tested in vitro by varying adaptation strength using an agonist of acetylcholine receptors, which demonstrated a neuromodulatory switch between the two types of slow waves. Finally, we show that the first type of slow-wave dynamics is more sensitive to external stimuli, which can explain how slow waves in sleep and anesthesia differentially affect memory consolidation, as well as provide a link between slow-wave dynamics and memory diseases

scipy-lectures/scipy-lecture-notes: Release 2017.1

Tutorial material on the scientific Python ecosystemTutorial material on the scientific Python ecosystem2017.