Category: bioRxiv Subject Collection: Neuroscience

Cochlear implant (CI) users with a pre-lingual onset of hearing loss show poor sensitivity to interaural time differences (ITD), an important cue for sound localization and speech reception in noise. Similarly, neural ITD sensitivity in the inferior colliculus (IC) of neonatally-deafened animals is degraded compared to animals deafened as adults. Here, we show that chronic bilateral CI stimulation during development can partly reverse the effect of early-onset deafness on ITD sensitivity. The prevalence of ITD sensitive neurons was restored to the level of adult-deaf rabbits in the early-deaf rabbits that received chronic stimulation with wearable bilateral sound processors during development. In contrast, chronic CI stimulation did not improve temporal coding in early-deaf rabbits. The present study is the first report showing functional restoration of ITD sensitivity with CI stimulation in single neurons and highlights the importance of auditory experience during development.

Decision making is guided by memories of option values. However, retrieving items from memory renders them malleable. Here, we show that merely retrieving values from memory and making a choice between options is sufficient both to induce changes to stimulus-reward associations in the hippocampus and to bias future decision making. After allowing participants to make repeated choices between reward-conditioned stimuli, in the absence of any outcome, we observed that participants preferred stimuli they had previously chosen, and neglected previously non-chosen stimuli, over otherwise identical-valued options. Using functional brain imaging, we show that decisions induced changes to hippocampal representations of stimulus-outcome associations. These changes predicted future decision biases. Our results indicate that choice-induced preference changes are driven by choice-induced modification of memory representations and suggest that merely making a choice - even without experiencing any outcomes - induces associative plasticity.

The diagnosis of amyotrophic lateral sclerosis (ALS) relies on involvement of both upper (UMN) lower motor neurons (LMN). Yet, there remains no objective marker of UMN involvement, limiting early diagnosis of ALS. This study establishes whether TMS combined with EEG can be used to measure short-interval intracortical inhibition (SICI) via TMS evoked potentials (TEP) in healthy volunteers - an essential first step in developing an independent marker of UMN involvement in ALS. We hypothesised that a SICI paradigm would result in characteristic changes in the TMS-evoked EEG potentials that directly mirror the changes in MEP. TMS was delivered to the left motor cortex using single-pulse and three inhibitory stimulation paradigms. SICI was present in all three conditions. TEP peaks were reduced predominantly under the SICI 70 protocol but less so for SICI 80 and not at all for SICI 90. There was a significant negative correlation between MEPs and N45 TEP peak for SICI 70 (rho = -0.54 , p = 0.04). In other words, as MEPs becomes inhibited the N45 increases. The same trend was maintained across SICI 80 and 90 (SICI 80, rho = -0.5, p = 0.06; SICI 90, rho = -0.48, p = 0.07). Additional experiments suggest these results cannot be explained by artefact. We establish that motor cortical inhibition can be measured during a SICI 70 protocol expanding on previous work. We have carefully considered the role of artefact in TEPs and have taken a number of steps to show that artefact cannot explain these results and we suggesting the differences are cortical in origin. TMS-EEG has potential to aid early diagnosis and to further understand central and peripheral pathophysiology in MND.

Synaptic connectivity between neocortical neurons is highly structured. The network structure of synaptic connectivity includes first-order properties that can be described by pairwise statistics, such as strengths of connections between different neuron types and distance-dependent connectivity, and higher-order properties, such as an abundance of cliques of all-to-all connected neurons. The relative impact of first- and higher-order structure on emergent cortical network activity is unknown. Here, we compare network structure and emergent activity in two neocortical microcircuit models with different synaptic connectivity. Both models have a similar first-order structure, but only one model includes higher-order structure arising from morphological diversity within neuronal types. We find that such morphological diversity leads to more heterogeneous degree distributions, increases the number of cliques, and contributes to a small-world topology. The increase in higher-order network structure is accompanied by more nuanced changes in neuronal firing patterns, such as an increased dependence of pairwise correlations on the positions of neurons in cliques. Our study shows that circuit models with very similar first-order structure of synaptic connectivity can have a drastically different higher-order network structure, and suggests that the higher-order structure imposed by morphological diversity within neuronal types has an impact on emergent cortical activity.

Humans possess a remarkable ability to understand what is and is not being said by conservational partners. An important class of models hypothesize that listeners decode the intended meaning of an utterance by assuming speakers speak cooperatively, simulating the speaker's rational choice process and inverting this process for recovering the speaker's most probable meaning. We investigated whether and how rational simulations of speakers are represented in the listener's brain, when subjects participated in a referential communication game inside fMRI. In three experiments, we show that the listener's ventromedial prefrontal cortex encodes the probabilistic inference of what a cooperative speaker should say given a communicative goal and context. The listener's striatum responds to the amount of update on the intended meaning, consistent with inverting a simulated mental model. These findings suggest a neural generative mechanism subserved by the frontal-striatal circuits that underlies our ability to understand communicative and, more generally, social actions.

Forming long-term memory (LTM) in many cases requires repetitive experience spread over time. In Drosophila, aversive olfactory LTM is optimal following spaced training, multiple trials of differential odor conditioning with rest intervals. Studies often compare memory after spaced to that after massed training, same number of trials without interval. Here we show flies acquire additional information after spaced training, forming an aversive memory for the shock-paired odor and a safety-memory for the explicitly unpaired odor. Safety-memory requires repetition, order and spacing of the training trials and relies on specific subsets of rewarding dopaminergic neurons. Co-existence of the aversive and safety memories can be measured as depression of odor-specific responses at different combinations of junctions in the mushroom body output network. Combining two particular outputs appears to signal relative safety. Learning a complementary safety memory thereby augments LTM performance after spaced training by making the odor preference more certain.

Microstates reflect transient brain states resulting from the activity of synchronously active brain networks that predominate in the broadband EEG time series. Despite increasing interest in understanding how the functional organization of the brain varies across individuals, or the extent to which its spatiotemporal dynamics are state dependent, comparatively little research has examined within and between-person correlates of microstate temporal parameters in healthy populations. In the present study, neuroelectric activity recorded during eyes-closed rest and during simple visual fixation was segmented into a time series of transient microstate intervals. It was found that five data-driven microstate configurations explained the preponderance of topographic variance in the EEG time series of the 374 recordings (from 187 participants) included in the study. We observed that the temporal dynamics of microstates varied within individuals to a greater degree than they differed between persons, with within-person factors explaining a large portion of the variance in mean microstate duration and occurrence rate. Nevertheless, several individual differences were found to predict the temporal dynamics of microstates. Of these, age and gender were the most reliable. These findings suggest that not only do the rich temporal dynamics of whole-brain neuronal networks vary considerably within-individuals, but that microstates appear to differentiate persons based on trait individual differences. The current findings suggest that rather than focusing exclusively on between-person differences in microstates as measures of brain function, researchers should turn their attention towards understanding the factors contributing to within-person variation.

Visual perception relies on cortical representations of visual objects that remain relatively stable with respect to the variation in object appearance typically encountered during natural vision (e.g., because of position changes). Such stability, known as transformation tolerance, is built incrementally along the ventral stream (the cortical hierarchy devoted to shape processing), but early evidence of position tolerance is already found in primary visual cortex (V1) for complex cells. To date, it remains unknown what mechanisms drive the development of this class of neurons, as well as the emergence of tolerance across the ventral stream. Leading theories suggest that tolerance is learned, in an unsupervised manner, either from the temporal continuity of natural visual experience or from the spatial statistics of natural scenes. However, neither learning principle has been empirically proven to be at work in the postnatal developing cortex. Here we show that passive exposure to temporally continuous visual inputs during early postnatal life is essential for normal development of complex cells in rat V1. This was causally demonstrated by rearing newborn rats with frame-scrambled versions of natural movies, resulting in temporally unstructured visual input, but with unaltered, natural spatial statistics. This led to a strong reduction of the fraction of complex cells, which also displayed an abnormally fast response dynamics and a reduced ability to support stable decoding of stimulus orientation over time. Conversely, our manipulation did not prevent the development of simple cells, which showed orientation tuning and multi-lobed, Gabor-like receptive fields as sharp as those found in rats reared with temporally continuous natural movies. Overall, these findings causally implicate unsupervised temporal learning in the postnatal development of transformation tolerance but not of shape tuning, in agreement with theories that place the latter under the control of unsupervised adaptation to spatial, rather than temporal, image statistics.

In this technical note, we present the challenges that prevent us from directly comparing sequences with and without MB acceleration at the single subject level. Using fMRI data collected with MB1S2 (TR 2.45s), MB2S2 (TR 1.22s) and MB4S2 (TR 0.63s), we note the CNR differences in the images acquired with different sequences which leads to global mean scaling that render the direct comparison of parameter estimates meaningless. Directly comparing t-values of participants across different acquisition sequences is meaningless because of the difference in degrees of freedom (df) introduced by the higher number of volumes acquired at higher multiband. Z-transformation of the t-statics to correct for the difference in degree of freedoms suggests that sequences without MB outperform sequences with MB acceleration. However, this may be due to an excessive penalty caused by inappropriate df estimation. Thus with the current evidence presented in this and previous studies that tested the impact of MB on task related-statistics, the field lacks empirical evidence for the effects of MB on individual subject statistics. We discuss the possible alternatives such as use of Bayesian statistics.

Pain is a common sensation which inescapably arises due to injuries, as well as, various diseases and disorders. However, for the same intensity of disturbance arising due to the forgoing causes, the threshold for pain sensation and perception varies among individuals. Here, we present a computational approach using bifurcation theory to understand how the pain sensation threshold varies and how it can be controlled, the threshold being quantified by the electrical activity of a pain-sensing neuron. To this end, we explored the bifurcations arising from a mathematical model representing the dynamics of this neuron. Our findings indicate that the bifurcation points are sensitive to specific model parameters. This demonstrates that the pain sensation threshold can change as shown in experimental studies found in literature. Further investigation using our bifurcation approach coupled with experimental studies can facilitate rigorous understanding of pain response mechanism and provide strategies to control the pain sensation threshold.