eNeuro最新文献

Aberrant dopamine transmission is a hallmark of several psychiatric disorders. Dopamine neurons in the ventral tegmental area (VTA) display distinct activity states that are regulated by discrete afferent inputs. For example, burst firing requires excitatory input from the mesopontine tegmentum, while dopamine neuron population activity, defined as the number of spontaneously active dopamine neurons, is thought to be dependent on inhibitory drive from the ventral pallidum (VP). Rodent models used to study psychiatric disorders, such as psychosis, consistently exhibit elevated dopamine neuron population activity, due to decreased tonic inhibition from the VP. However, it remains unclear whether the VP can modulate all dopamine neurons or if only a specific subset of VTA dopamine neurons receive innervation from the VP to be recruited as required. This knowledge is critical for understanding dopamine regulation in normal and pathological conditions. Here, we used in vivo electrophysiology in male and female rats to record VTA dopamine neurons inhibited by electrical stimulation of the VP. Specifically, VP stimulation inhibited ∼22% of spontaneously active dopamine neurons; however, activation of the ventral hippocampus, a modulator of VTA population activity, increased the proportion to ∼48%. This increase suggests that VP selectively modulates a subset of dopamine neurons that can be recruited by afferent activation. Anterograde monosynaptic tracing revealed that approximately half of the VTA dopamine neurons receive input from the VP. Taken together, we demonstrate that a subset of VTA dopamine neurons receives monosynaptic input from the VP, providing valuable information regarding the regulation of VTA neuron activity.

Although clinical and experimental evidence highlight the role of the thalamus in voluntary movement production, the involvement of the thalamus in complex motor tasks such as speech production remains to be elucidated. The present study examined neural activity within the bilateral thalamus in 13 participants (seven females) with essential tremor undergoing awake deep brain stimulation implantation surgery, using three speech tasks of varied complexity [vowel vocalization, a diadochokinetic task (DDK), and sentence repetition]. Low-frequency neural activity (delta/theta band) activity was significantly increased during sentence and DDK compared with vowel vocalization in the bilateral motor thalamus and, to a lesser extent, increased for sentence repetition compared with DDK. Moreover, there was prominent prespeech beta band activity, with a greater decrease in the power of beta activity for sentence compared with DDK and vowel vocalization. The greater low-frequency activity in more complex speech tasks may reflect the allocation of additional cognitive resources to monitor the execution of speech motor plans through cortico-thalamo-cortical pathways in a temporally precise manner. The greater decrease in the power of beta activity prior to the onset of sentence repetition may imply greater involvement of the bilateral thalamus in the planning of complex speech tasks. These findings provide new insights into the role of the bilateral thalamus in speech production and may have clinical implications for neurological disorders that affect speech production.

Epileptic seizures involve the brain transitioning from a resting state to an abnormal state of synchronized bursting, akin to a bifurcation in dynamical systems where a parameter shift triggers a qualitative change in behavior. A comprehensive model was previously developed that used dynamical equations capable of simulating 16 "dynamotypes" of seizures that span the full range of theoretical first-order dynamics. The current work is a tool to understand and implement this model with the goal of generating a wide range of synthetic seizures. We present a dynamical atlas of all 16 possible onset-offset bifurcation combinations, each characterized by distinct features in simulated EEG-like recordings. We include a tutorial and graphical user interphase that generates diverse simulated seizures. In addition, we include methods to add realistic noise and filtering effects to enhance their resemblance to human EEG data. This toolbox has two purposes: it is a practical, educational demonstration of the dynamical principles underlying seizure bifurcations, and it provides the algorithms necessary to produce large numbers of realistic, diverse seizure patterns that have similar noise and filtering characteristics as human EEG. This generative model can aid in training seizure detection algorithms, understanding brain dynamical behavior for clinicians, and exploring the impact of noise on EEG recordings and detection algorithms.

Most statistical inferences in neuroscience and psychology are based on frequentist statistics, which rely on sampling distributions: the long-run outcomes of multiple experiments, given a certain model. Yet, sampling distributions are poorly understood and rarely explicitly considered when making inferences. In this tutorial and commentary, I demonstrate how to use simulations to illustrate sampling distributions to answer simple practical questions: for instance, if we could run thousands of experiments, what would the outcome look like? What do these simulations tell us about the results from a single experiment? Such simulations can be run a priori, given expected results, or a posteriori, using existing datasets. Both approaches can help make explicit the data generating process and the sources of variability; they also reveal the large uncertainty in our experimental estimation and lead to the sobering realization that, in most situations, we should not make a big deal out of results from a single experiment. Simulations can also help demonstrate how the selection of effect sizes conditional on some arbitrary cutoff (p ≤ 0.05) leads to a literature filled with false positives, a powerful illustration of the damage done in part by researchers' over-confidence in their statistical tools. The tutorial focuses on graphical descriptions and covers examples using correlation analyses, proportion data, and response latency data. All the figures and numerical values in this article can be reproduced using code available at https://github.com/GRousselet/sampdist.

Studying locomotor activity in animal models is crucial for understanding physiological, behavioral, and pathological processes. This study aimed to develop an artificial intelligence-based tracking system called Goblotrop, designed to localize rodents within their laboratory environment. The Goblotrop system uses two infrared cameras to record videos of rodents in their home cages. A neural network analyzes these videos to determine the rodent's position at each time point. By tracking changes in position over time, the system provides detailed insights into rodent behavior, including speed, mobility, and climbing activity. To evaluate the system's reliability, we utilized a starvation-induced hyperactivity model, employed as a female mouse model for anorexia nervosa. This model is characterized by pronounced hyperactivity, typically assessed using electronically monitored running wheels. Both the Goblotrop system and running wheel measurements demonstrated that starvation increases food-anticipatory activity (up to 4 h before food availability) while reducing nocturnal activity. The results from the Goblotrop system and running wheel measurements exhibited remarkable consistency. Thus, the Goblotrop system proves to be a valuable tool for studying locomotor activity and circadian rhythms in different cage areas in animal models. This tool provides potential for various scientific fields, including neuroscience, pharmacology, toxicology, and behavioral research.

Recent investigations have revealed that selective attention to lateralized speech increases ipsilateral tonic electromyographic activity in the vestigial human auriculomotor system. However, it has yet to be determined whether this modulation depends upon predictive cues that are inherent in continuous speech or whether it is a general concomitant of selective attention to sounds in the auditory periphery. The present study addressed this question by replacing speech with randomized, unpredictable sequences of brief tonal stimuli in a dichotic listening task that necessitated a sustained anticipatory focus of attention. Participants (8 female, 23 male) were presented with sequences of brief tone bursts in one ear and frequency-modulated "chirps" in the other ear and were instructed to focus on sounds in one ear and report attenuated deviant stimuli in that ear. Posterior auricular muscle (PAM) activity was recorded behind both ears, and non-rectified stimulus-locked responses were assessed to ensure the reliability of PAM activity. Recordings of non-stimulus-locked rectified activity indicated that ipsilateral tonic PAM amplitudes were elevated when same-side sounds were attended, and follow-up analyses demonstrated that these modulations were independent of sound-evoked PAM reflexes. These findings provide evidence that this ipsilateral tonic increase in PAM activity is generally present in scenarios of lateralized selective listening and not reliant on predictive linguistic cues that may facilitate tracking of the attended stream. Due to its accessibility and capability of decoding the spatial focus of attention, this PAM modulation could support the development of intelligent hearing devices that maximize sensitivity toward a user's listening goals.

Puerto Rico's cultural identity, shaped by Taíno heritage, Spanish colonization, and US governance, emphasizes family, music, food, and collective participation. Drawing on these traditions, we developed NeuroBoricuas, a grassroots neuroscience education movement that reimagines outreach through cultural metaphors of parrandas and peer teaching. What began as a reflection on conventional Brain Awareness Week evolved into student-led demonstrations, the creation of the first neuroscience laboratory in a Puerto Rican high school, and a network of university chapters and high school clubs across the Island. These groups lead workshops, classroom visits, and community events, positioning students as both learners and teachers of neuroscience. A partnership with Backyard Brains provided affordable, hands-on tools that made neuroscience tangible for K-12 and university students, while events such as Explora tu Cerebro en la SanSe integrated science into Puerto Rico's most iconic cultural festival. To extend beyond outreach, the Bravo Lab Immersive Summer Program (BLISP) was launched in 2022, immersing undergraduates in advanced approaches including optogenetics, fiber photometry, and behavioral assays. BLISP emphasizes mentoring and peer-to-peer training, building confidence and scientific identity while creating pathways to graduate training. Recent collaborations, including NeuroBridges with the University of California, Irvine, have further expanded opportunities for Puerto Rican students through international partnerships. Together, NeuroBoricuas and BLISP demonstrate a scalable model of science education rooted in culture, identity, and community. Like a parranda, this movement grows as new voices join in, showing that neuroscience thrives when it is shared, celebrated, and carried forward collectively.

Sensory attenuation of auditory evoked potentials (AEPs), particularly N1 and P2 components, has been widely demonstrated in response to simple, repetitive stimuli sequences of isolated synthetic sounds. It remains unclear, however, whether these effects generalize to complex soundscapes where temporal and acoustic features vary more broadly and dynamically. In this study, we investigated whether the inter-onset interval (IOI), the time between successive sound events, modulates AEP amplitudes in a complex auditory scene. We derived acoustic onsets from a naturalistic soundscape and applied temporal response function (TRF) analysis to electroencephalography data recorded from normal hearing human listeners (N = 22, 16 females, 6 males). Our results showed that shorter IOIs are associated with attenuated N1 and P2 amplitudes, replicating classical adaptation effects in a naturalistic soundscape. These effects remained stable when controlling for other acoustic features such as intensity and envelope sharpness and across different TRF model specifications. Integrating IOI information into predictive modeling revealed that neural dynamics were captured more effectively than simpler onset models when training data were matched. These findings highlight the brain's sensitivity to temporal structure even in highly variable auditory environments, and show that classical lab findings generalize to naturalistic soundscapes. Our results underscore the need to include temporal features alongside acoustic ones in models of real-world auditory processing.

Previous studies emphasize the importance of prestimulus neural oscillations in shaping endogenous brain states that substantially impact perceptual outcomes. However, what features in such oscillations drive perception remains unknown. Furthermore, research has shown that non-oscillatory activity is also important for cognitive processing. However, their interaction prior to perceiving a multisensory stimulus remains unexplored. In this human EEG study (n = 18, 10 males and 8 females), we investigated the role of prestimulus periodic power and aperiodic activity in modulating perception of the widely studied McGurk illusion on a trial-by-trial basis. Using logistic mixed-effects models, we reveal that the illusion perception is associated with reduced prestimulus alpha (8-12 Hz) and beta (15-30 Hz) power over frontal and occipital regions; increased theta (4-7 Hz) power in parietal, central, and occipital regions; and increased gamma (31-45 Hz) power across the scalp. Furthermore, lower aperiodic offset and exponent values in central, parietal, and occipital regions also predicted illusory responses. Our logistic mixed interaction models revealed that the aperiodic exponent and periodic power jointly influence the perception of upcoming McGurk stimuli. Specifically, a decrease in occipital theta and global beta power and an increase in occipital and parietal gamma power were associated with a steeper slope. We conclude that the predominant source of variations in the prestimulus state is the aperiodic activity and that fluctuations in both periodic and aperiodic activity account for inter-trial variability in the perception of the McGurk illusion.

The association between brain structural connectivity (BSC) and different subtypes of stroke has not been reported. The current study determined whether some BSC patterns may contribute to the risk of stroke. A two-sample, bidirectional, multivariate Mendelian randomization (MR) analysis was performed. Genome-wide association summary statistics for BSC were obtained from the GWAS Catalog at the European Bioinformatics Institute, while stroke outcome data were obtained from the FinnGen study for intracerebral hemorrhage (ICH) and from the MEGASTROKE Consortium for ischemic stroke (IS) and its subtypes. A colocalization analysis was performed to determine whether the association between BSC and stroke was driven by loci within genomic regions. Reverse MR was performed to evaluate potential stroke-induced changes in BSC. Among the significant findings, left hemisphere (LH) somatomotor network-to-LH salience/ventral attention network white matter (WM) structural connectivity (SC) [OR = 1.30; p = 5.96 × 10^-4; p value after Bonferroni's correction [p.bfr] = 0.0125] and right hemisphere (RH) dorsal attention network (DAN)-to-thalamus WM-SC (OR = 1.23; p = 1.60 × 10^-3; p.bfr = 0.0125) were shown to have a positive association with the risk of IS. RH DAN-to-amygdala WM-SC (OR = 0.78; p = 1.26 × 10^-3; p.bfr = 0.0125) showed a negative relationship with the risk of IS. A high LH somatomotor network-to-RH visual network WM-SC (OR = 1.62; p = 9.10 × 10^-3; p.bfr = 0.025) was associated with an increased risk of large atherosclerotic stroke. In conclusion, the results of the current study provided some evidence from the perspective of genetics that different BSCs may have close associations with ICH, IS, and stroke subtypes. These findings may facilitate the screening and the risk stratification for stroke patients.