eNeuro最新文献

In natural, free-viewing settings, visual perception is driven by a series of saccades and fixations. Perceptual mechanisms are typically studied through averaged fixation-related potentials generated from simultaneous eye-tracking and EEG recordings. Lambda responses following fixation onsets signal the arrival of new visual input to the primary visual cortex. In our study, we investigate the use and preprocessing parameter dependence of independent component analysis (ICA) in separating the lambda response from other neural sources. In our experiment, 10 subjects (2 males and 8 females) viewed 80 art paintings in natural, free-viewing settings, during which EEG data were recorded. Our results show that unique lambda response components can be detected reliably and individual lambda waves can be extracted in a single-trial manner, without signal averaging. ICA decomposition is most sensitive to high-pass filtering producing best results with a minimum 1 Hz filtering. We also propose a method that automatically and accurately identifies the lambda component among other independent components for further lambda peak detection. These individual lambda waves can then be used to study saccade-related modulation effects without losing temporal and spatial resolution. The novelty of our method is the automatic detection of lambda components and extraction lambda waves, which is a new approach in saccade/fixation and visual perception research under naturalistic viewing conditions.

ArfGAP, with dual PH domain-containing protein 1/Centaurin-α1 (ADAP1/CentA1), is a brain-enriched and highly conserved Arf6 GTPase-activating and Ras-anchoring protein. CentA1 is involved in dendritic outgrowth and arborization, synaptogenesis, and axonal polarization by regulating the actin cytoskeleton dynamics. CentA1 upregulation and association with amyloid plaques in the human Alzheimer's disease (AD) brain suggest the role of this protein in AD progression. To understand the role of CentA1 in neurodegeneration, we crossbred CentA1 knock-out (KO) mice with the J20 mouse model of AD. We evaluated AD-associated behavioral and neuropathological hallmarks and gene expression profiles in J20 and J20 crossed with CentA1 KO (J20xKO) male mice to determine the impact of eliminating CentA1 expression on AD-related phenotypes. Spatial memory assessed by the Morris water maze test showed significant impairment in J20 mice, which was rescued in J20xKO mice. Moreover, neuropathological hallmarks of AD, such as amyloid plaque deposits and neuroinflammation, were significantly reduced in J20xKO mice. To identify potential mediators of AD phenotype rescue, we analyzed differentially expressed genes between genotypes. We found that changes in the gene profile by deletion of CentA1 from J20 (J20xKO vs J20) were anticorrelated with changes caused by APP overexpression (J20 vs wild type), consistent with rescue of J20 phenotypes by CentA1 KO. In summary, our data indicate that CentA1 is required for the progression of AD phenotypes in this model and that targeting CentA1 signaling might have therapeutic potential for AD prevention or treatment.

Ongoing efforts over the last 50 years have made data and methods more reproducible and transparent across the life sciences. This openness has led to transformative insights and vastly accelerated scientific progress (Gražulis et al., 2012; Munafó et al., 2017). For example, structural biology (Bruno and Groom, 2014) and genomics (Benson et al., 2013; Porter and Hajibabaei, 2018) have undertaken systematic collection and publication of protein sequences and structures over the past half century. These data, in turn, have led to scientific breakthroughs that were unthinkable when data collection first began (Jumper et al., 2021). We believe that neuroscience is poised to follow the same path, and that principles of open data and open science will transform our understanding of the nervous system in ways that are impossible to predict at the moment. New social structures supporting an active and open scientific community are essential (Saunders, 2022) to facilitate and expand the still limited adoption of open science practices in our field (Schottdorf et al., 2024). Unified by shared values of openness, we set out to organize a symposium for open data in neurophysiology (ODIN) to strengthen our community and facilitate transformative open neuroscience research at large. In this report, we synthesize insights from this first ODIN event. We also lay out plans for how to grow this movement, document emerging conversations, and propose a path toward a better and more transparent science of tomorrow.

A central mechanism of exposure-based cognitive behavioral therapy for anxiety and trauma-related disorders is fear extinction. However, the mechanisms underlying fear extinction are deficient in some individuals, leading to treatment resistance. Recent animal studies demonstrate that upon omission of the aversive, unconditioned stimulus (US) during fear extinction, dopamine (DA) neurons in the ventral tegmental area (VTA) produce a prediction error (PE)-like signal. However, whether this VTA-DA neuronal PE-like signal is altered in animals exhibiting deficient fear extinction has not been studied. Here, we used a mouse model of impaired fear extinction [129S1/SvImJ (S1) inbred mouse strain] to monitor and manipulate VTA-DA neurons during extinction. Male DAT-Cre mice backcrossed onto an S1 background (S1-DAT-Cre) exhibited impaired extinction but normal VTA-DA neuron number, as compared with BL6-DAT-Cre mice. In vivo fiber photometry showed that impaired extinction in male S1-DAT-Cre mice was associated with abnormally sustained US omission-related VTA-DA neuronal calcium activity during extinction training and retrieval. Neither in vivo optogenetic photoexcitation of VTA-DA neuronal cell bodies nor their axons in the infralimbic cortex was sufficient to rescue deficient extinction in male S1-DAT-Cre mice, at least within the optogenetic and behavioral parameters used. These data suggest that alterations in the activity of VTA-DA neurons during extinction learning and retrieval may be associated with deficient fear extinction in male S1 mice and could potentially contribute to extinction impairments in patient populations.

Imagine yourself in a car race waiting for the traffic light to go green. Impulsivity could push you to accelerate when the light is still red. In contrast, temporally guided anticipation could lead you to accelerate at the time the light goes green. Whether these two types of early responses rely on the same or different neural processes is an open question. This question was investigated using an oculomotor task where the delay between a warning and an imperative visual stimuli was predictable. The spatial uncertainty of the "go" signal was also varied. On average, 10% of experimental trials were associated with a response before the "go" signal ("early saccade"). After the offset of the warning stimulus, the latency distribution of early saccades was bimodal, with a first mode peaking after 200 ms (1st mode saccades) and a second one starting to build-up after 375 ms (2nd mode saccades). With increasing delay duration: the number of 1st mode responses decreased whereas the number of 2nd mode responses remained approximately constant; the latency and variance of 2nd mode saccades increased; the maximum velocity of 2nd mode responses decreased. In general, the amplitude of 2nd mode responses was larger. These results show that there are probably two independent processes taking place before an expected event: an unintentional release of inhibition evoking an impulsive 1st mode saccade and an anticipatory process leading to a 2nd mode saccade.

The progressive ratio (PR) schedule is a popular test of motivation. Despite its popularity, the PR task hinges on a low-dimensional behavioral readout-breakpoint or the maximum work requirement subjects are willing to complete before abandoning the task. Here, we show that with a simple modification, the PR task can be transformed into an optimization problem reminiscent of the patch-leaving foraging scenario, which has been analyzed extensively by behavioral ecologists, psychologists, and neuroscientists. In the PR with reset (PRR) task, male and female rats performed the PR task on one lever but could press a second lever to reset the current ratio requirement back to its lowest value at the cost of enduring a reset delay, during which both levers were retracted. Rats used the reset lever adaptively on the PRR task, and their ratio reset decisions were sensitive to the cost of the reset delay. We derived an approach for computing the optimal bout length-the number of rewards to earn before pressing the reset lever that produces the greatest long-term rate of reward-and found that rats flexibly changed their behavior to approximate the optimal strategy. However, rats showed a systematic bias for bout lengths that exceeded the optimal length, an effect reminiscent of "overharvesting" in patch-leaving tasks. The PRR task thus represents a novel means of testing how rats adapt to the cost-benefit structure of the environment in a way that connects deeply to the broader literature on associative learning and optimal foraging theory.

Social animals compete for rewards to survive, yet the neural circuits underlying reward-based social competition remain unclear. The medial prefrontal cortex (mPFC) plays a key role in reward processing and social dominance, but whether its subregions contribute differently to competitions for reward remains unknown. Using c-Fos mapping in male CD1 mice, we examined reward-induced neural activation in mPFC subregions and key interconnected subcortical areas across social and nonsocial reward contexts. Noncompetitive social contexts produced global c-Fos activation relative to competitive contexts. Cross-regional correlation analyses revealed that receiving rewards in isolation involved widespread network coordination, while social contexts exhibited distinct, sparse correlation patterns. Surprisingly, social rank effects on neural activity were most pronounced during isolated reward experiences rather than during competition, with dominant mice showing increased anterior cingulate, basolateral amygdala, and hippocampal activation when alone. Different dominance ranks (reward-based, territorial, and agonistic) correlated with distinct neural activity patterns across contexts. Overall, our results show that social context fundamentally reorganizes prefrontal-subcortical networks during reward processing in a social rank-dependent manner. These results provide new insights into how social rank shapes the neural basis of reward processing across different social contexts.

Attachment theory offers an important clinical framework for understanding and treating negative effects of early life adversity. Attachment styles emerge during critical periods of development in response to caregivers' ability to consistently meet their offspring's needs. Attachment styles are classified as secure or insecure (anxious, avoidant, or disorganized), with rates of insecure attachment rising in high-risk populations and correlating with a plethora of negative health outcomes throughout life. Despite its importance, little is known about the neural basis of attachment. Work in rats has demonstrated that limited bedding and nesting (LB) impairs maternal care and produces abnormal maternal attachment linked to increased pup corticosterone. However, the effects of LB on attachment-like behavior have not been investigated in mice where additional genetic and molecular tools are available. Furthermore, no group has utilized home-cage monitoring to link abnormal maternal care with deficits in attachment-like behavior. Using home-cage monitoring, we confirmed a robust increase in maternal fragmentation among LB dams. Abnormal maternal care was correlated with elevated corticosterone levels on postnatal day 7 (P7) and a stunted growth trajectory that persisted later in life. LB did not alter maternal buffering at P8 or maternal preference at P18, indicating that certain attachment-like behaviors remain unaffected despite exposure to high levels of erratic maternal care. However, LB male and female pups vocalized less in response to maternal separation at P8, did not readily approach their dam at P13, and exhibited higher anxiety-like behavior at P18, suggesting that LB induces avoidant-like attachment deficits in mice.

Listening effort reflects the cognitive and motivational resources allocated to speech comprehension, particularly under challenging conditions. Visual cues are known to enhance speech perception, potentially by reducing the cognitive demands of the task. However, the neurophysiological mechanisms underlying this facilitation, especially in terms of effort-related changes, remain unclear. In this study, we combined pupillometry and electroencephalography (EEG) to investigate how visual speech cues modulate cognitive effort during speech recognition. Twenty-two participants (seven females) performed a speech-in-noise task under three modalities: (1) auditory-only, (2) audiovisual, and (3) visual-only. Task difficulty was manipulated via signal-to-noise ratio (SNR) in the first two modalities. Firstly, we found an inverted U-shape relationship between pupil dilation and frontal midline theta with SNR for audiovisual and auditory-only speech, consistent with prior models of effort allocation. Secondly, we observed the SNR at which the neurophysiological measures peaked was at a lower SNR for audiovisual speech. Surprisingly, we found pupil dilation to be larger overall in audiovisual speech, while frontal midline theta did not show differences in either modality. These findings highlight the complexity of interpreting physiological markers of effort and suggest that visual cues may alter the temporal dynamics or resource allocation strategies during speech processing. Our results support the extension of auditory-based models of listening effort to audiovisual contexts and underscore the value of integrating multimodal neurophysiological measures to better understand the cognitive and neural mechanisms of effortful listening.

Identifying neural signatures of slow-wave sleep (SWS) is important for a number of reasons including diagnosing potential sleep disorders and examining its role in memory consolidation ( Diekelmann and Born, 2010; Klinzing et al., 2019; Brodt et al., 2023). Studies of sleep in the common marmoset (Callithrix jacchus) have revealed similarities to humans and other nonhuman primates, including distinct sleep stages ( Crofts et al., 2001) and diurnal sleep patterns ( Hoffmann et al., 2012). Advances in applying wireless technology for recording neural activity during natural, unrestrained behaviors ( Walker et al., 2021) position the marmoset as an excellent model for studying sleep-related neural activity associated with learning. Here, we identify putative SWS epochs based on the spatially correlated activity of local field potentials (LFPs) recorded from a multielectrode planar array implanted in the sensorimotor cortex of two marmosets (one female and one male). The average correlation of the LFP signal measured between electrodes decreased gradually with the distance between pairs. We modeled this spatial structure as an exponential decay function, where the spatial decay constant varied significantly over time, reaching its lowest values during epochs where LFP power dynamics were consistent with SWS. These periods of widespread high correlations across the sensorimotor cortex closely matched SWS identification commonly used in rodent models based on the changes in power in the gamma (30-60 Hz) and delta/slow oscillation (0.1-4 Hz) frequency bands. These findings demonstrate that putative SWS epochs can be reliably identified using spatially correlated LFP activity across the sensorimotor cortex.