eNeuro最新文献

The ability to inhibit and adapt our behavior in response to changing stimuli is a critical component of everyday life. Individuals with Parkinson's disease (PD) may struggle to inhibit behavior, particularly in the presence of dopaminergic therapy, which can result in impulsive behavior. Impulse control disorders are often operationalized in the laboratory using motor inhibition tasks. However, deficits of motor inhibition tasks are not always observed in PD, perhaps because of the nature of the motor inhibition that is engaged in typical tasks (e.g., suppression of incipient movement such as a button press). We employed a novel continuous movement stop task to investigate planned and unplanned motor inhibition during ongoing movement. EEG was recorded during task performance from individuals with PD (OFF and ON dopaminergic medication) and age-matched healthy controls (HC). Participants were of any sex. We found that the time it took for participants to stop a continuous movement was impaired (i.e., longer) in PD patients ON medication compared with both patients OFF medication and HC. This finding was accompanied by diminished midfrontal theta power following the stop signal in PD (ON and OFF) compared with HC. Additionally, an increase in midfrontal beta power was observed, which was higher in unplanned stopping compared with planned for all groups. However, this increase in beta occurred late-after the time of outright stopping. Together, these findings demonstrate that stopping ongoing movements was impaired in PD patients ON medication and theta and beta power play distinct roles in inhibition of movement.

Sleep-wake states bidirectionally interact with epilepsy and seizures, but the mechanisms are unknown. A barrier to comprehensive characterization and the study of mechanisms has been the difficulty of annotating large chronic recording datasets. To overcome this barrier, we sought to develop an automated method of classifying sleep-wake states, seizures, and the postictal state in mice ranging from controls to mice with severe epilepsy with accompanying background electroencephalographic (EEG) abnormalities. We utilized a large dataset of recordings, including electromyogram, EEG, and hippocampal local field potentials, from control and intra-amygdala kainic acid-treated mice. We found that an existing sleep-wake classifier performed poorly, even after retraining. A support vector machine, relying on typically used scoring parameters, also performed below our benchmark. We then trained and evaluated several multilayer neural network architectures and found that a bidirectional long short-term memory-based model performed best. This "Sleep-Wake and Ictal State Classifier" (SWISC) showed high agreement between ground-truth and classifier scores for all sleep and seizure states in an unseen and unlearned epileptic dataset (average agreement 96.41% ± SD 3.80%) and saline animals (97.77 ± 1.40%). Channel dropping showed that SWISC was primarily dependent on hippocampal signals yet still maintained good performance (∼90% agreement) with EEG alone, thereby expanding the classifier's applicability to other epilepsy datasets. SWISC enables the efficient combined scoring of sleep-wake and seizure states in mouse models of epilepsy and healthy controls, facilitating comprehensive and mechanistic studies of sleep-wake and biological rhythms in epilepsy.

Thirst is a strongly motivated internal state that is represented in central brain circuits that are only partially understood. Water seeking is a discrete step of the thirst behavioral sequence that is amenable to uncovering the mechanisms for motivational properties such as goal-oriented behavior, value encoding, and behavioral competition. In Drosophila, water seeking is regulated by the NPY-like neuropeptide NPF; however, the circuitry for NPF-dependent water seeking is unknown. To uncover the downstream circuitry, we identified the NPF receptor NPFR and the neurons it is expressed in as being acutely critical for thirsty water seeking in males. Refinement of the NPFR pattern uncovered a role for a single neuron, the L1-l, in promoting thirsty water seeking. The L1-l neuron increases its activity in thirsty flies and is involved in the regulation of dopaminergic neurons in long-term memory formation. Thus, NPFR and its ligand NPF, already known for its role in feeding behavior, are also important for a second ingestive behavior.

Loss-of-function (LOF) Frazzled/DCC mutants disrupt synaptogenesis in the Giant Fiber (GF) System of Drosophila We observed weaker physiology in LOF male and female specimens, characterized by longer latencies and reduced response frequencies between the GFs and the motor neurons. These physiological phenotypes are linked to a loss of gap junctions in the GFs, specifically the loss of the shaking-B(neural+16) isoform of innexin in the presynaptic terminal. We present evidence of Frazzled's role in gap junction regulation by utilizing the UAS-GAL4 system in Drosophila to rescue mutant phenotypes. Expression of various UAS-Frazzled constructs in a Frazzled LOF background was used to dissect the role of different parts of the Frazzled receptor in the assembly of electrical synapses. Expressing Frazzled's intracellular domain in Frazzled LOF mutants rescued axon pathfinding and synaptogenesis. This is supported by the complementary result that Frazzled fails to rescue synaptic function when the transcriptional activation domain is disrupted, as shown by the deletion of the highly conserved intracellular P3 domain or by a construct with a point mutation in the highly conserved P3 domain known to be required for transcriptional activation. A computational model clarifies the role of gap junctions and the function of the GF System. The present work shows how various domains of a guidance molecule regulate synaptogenesis through the regulation of synaptic components.

Humans rapidly update the control of an ongoing movement following changes in contextual parameters. This involves adjusting the controller to exploit redundancy in the movement goal, such as when reaching for a narrow or wide target, and adapting to dynamic changes such as velocity-dependent force fields (FFs). Although flexible control and motor adaptation are computationally distinct, the fact that both unfold within the same movement suggests that they interact functionally to support task-specific adjustments. To test this hypothesis, we conducted a series of experiments combining changes in the target structure and a force field presented separately or in combination. Seventy-six human participants (both sexes) took part in this study, with each experiment involving different participants. They were asked to reach for a target that could change from a narrow square to a wide rectangle between or during trials. Step loads were used to assess whether participants exploited target redundancy. In a separate experiment, we added a force field in addition to target changes and step loads. Our results revealed a reduced ability to exploit target redundancy when sudden target changes occurred concurrently with FF adaptation. Furthermore, the magnitude of adaptation was reduced when step loads were added to the FF. Crucially, this interference emerged specifically when all perturbations impacted motor execution simultaneously. These results indicate that flexible control and motor adaptation interact in a nontrivial manner, suggesting interdependence between these behavioral mechanisms, and a clear identification of the timescale at which they are engaged-namely, during movement.

This study aims to examine the changes in AQP4 polarity and pericyte vascularity during temporal lobe epilepsy (TLE) progression, with the goal of identifying potential drug targets or strategies to delay the onset and progression of TLE. Chronic TLE was induced in male rats using pilocarpine. AQP4 polarity and pericyte vascular coverage were assessed by immunofluorescence. The effects of modulating AQP4 polarity on PTZ-induced TLE model using male mice were studied. Molecular mechanisms of AQP4 polarity were explored using transwell coculture and transcriptomics, validated at the protein level. ELISA was used to measure PDGF-BB levels in serum and cerebrospinal fluid. Following pilocarpine-induced chronic TLE model establishment, AQP4 polarity and pericyte vascular coverage rapidly increased but later declined, reaching the lowest levels in epileptic animals. Trifluoperazine prevented AQP4 redistribution, reduced seizure duration, and alleviated brain edema in PTZ-induced TLE mouse model. Transcriptomic analysis revealed that pericyte coculture did not alter the expression of dystrophin-associated protein components in astrocytes. Pericyte LAMA1 and LAMA2 levels were significantly higher than endothelial cells, and the levels of pericyte LAMA1 and LAMA2 were significantly increased after coculture with astrocytes. Expression of LAMA1 and LAMA2 around pericytes initially increased and then decreased during chronic TLE progression. PDGF-BB levels decreased over time, reaching the lowest levels during epilepsy. Disrupted AQP4 polarity is closely associated with TLE development. Pericyte vascular coverage appears to influence AQP4 polarity, and key molecules such as laminins and PDGF-BB may help maintain AQP4 polarity, potentially contributing to the attenuation of TLE progression and epileptogenesis.

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.