Cuprizone (CPZ) is a widely used toxin that induces demyelinating diseases in animal models, producing multiple sclerosis (MS)-like pathology in rodents. CPZ is one of the few toxins that triggers demyelination and subsequent remyelination following the cessation of its application. This study examines the functional consequences of CPZ-induced demyelination and the subsequent recovery of neural communication within the anterior cingulate cortex (ACC), with a particular focus on inter-hemispheric connectivity via the corpus callosum. By employing wide-field, high-speed, voltage-sensitive dye imaging, we were able to provide real-time mapping of neural activity in the ACC of CPZ-fed mice. Although we could not record physiological signals from the corpus callosum, the results demonstrated a notable impairment in inter-hemispheric connections within the ACC via the corpus callosum, with the most pronounced loss observed in a specific coronal slice among a series of slices examined. Notably, the latency of ne...

The social environment has long been recognized to play an important role in substance use, which is often modeled in rodents using operant conditioning. However, most operant chambers only accommodate one rodent at a time. We present PeerPub - a unique social operant chamber. PeerPub employs touch sensors to track the licking behavior on drinking spouts. When the number of licks meets a set reinforcement schedule, it dispenses a drop of solution with a fixed volume as a reward at the tip of the spout. A radio-frequency identification (RFID) chip implanted on each rat's skull identifies it throughout the experiment. The system is managed by a Raspberry Pi computer. We evaluated PeerPub using Sprague Dawley rats in daily one-hour sessions, where supersac (a glucose and saccharin solution) was provided under a fixed ratio 5 schedule. We discovered that male rats consumed more supersac in dual rat conditions compared to single rat conditions. These findings illustrate PeerPub's effectiveness in modeling the i...

Epilepsy, a neurological disorder characterized by recurrent unprovoked seizures, significantly impacts patient quality of life. Current classification methods focus primarily on clinical observations and electroencephalography (EEG) analysis, often overlooking the underlying dynamics driving seizures. This study uses surface EEG data to identify seizure transitions using a dynamical systems–based framework—the taxonomy of seizure dynamotypes—previously examined only in invasive data. We applied principal component and independent component analysis to surface EEG recordings from 1,177 seizures in 158 patients with focal epilepsy, decomposing the signals into independent components (ICs). The ICs were visually labeled for clear seizure transitions and bifurcation morphologies, which were then examined using Bayesian multilevel modeling in the context of clinical factors. Our analysis reveals that certain onset bifurcations (SNIC and SupH) are more prevalent during wakefulness compared to their reduced rat...

Psychiatric disorders, including anxiety and depression, are highly comorbid in people with epilepsy. However, the mechanisms mediating the shared pathophysiology are currently unknown. There is considerable evidence implicating the basolateral amygdala (BLA) in the network communication of anxiety and fear, a process demonstrated to involve parvalbumin-positive (PV) interneurons. The loss of PV interneurons has been well described in the hippocampus of chronically epileptic mice and in postmortem human tissue of patients with temporal lobe epilepsy (TLE). We hypothesize that a loss of PV interneurons in the BLA may contribute to comorbid mood disorders in epilepsy. To test this hypothesis, we employed a ventral intrahippocampal kainic acid (vIHKA) model of temporal lobe epilepsy in mice, which exhibits profound behavioral deficits associated with chronic epilepsy. We demonstrate a loss of PV interneurons and dysfunction of remaining PV interneurons in the BLA of chronically epileptic mice. Further, we dem...

Five neuroscience program faculty share the traits that they think can help graduate students excel in their training. Here’s what to keep in mind.

Our decisions are influenced by the value of the outcome we expect to obtain. For example, a person who prefers pizza over pasta will naturally choose pizza if both are on a menu.

In the article “Transcranial Direct Current Stimulation Over the Posterior Parietal Cortex Increases Nontarget Retrieval During Visual Working Memory,” by Shengfeng Ye, Menglin Wu, Congyun Yao, Gui Xue, and Ying Cai, which published …

In the article, “The Paraventricular Thalamic Nucleus and Its Projections in Regulating Reward and Context Associations,” by Dillon S. McDevitt, Quinn W. Wade, Greer E. McKendrick, Jacob Nelsen, Mariya …

Epilepsy, a neurological disorder characterized by recurrent unprovoked seizures, significantly impacts patient quality of life. Current classification methods focus primarily on clinical observations and electroencephalography (EEG) analysis, often overlooking the underlying dynamics driving seizures. This study uses surface EEG data to identify seizure transitions using a dynamical systems–based framework—the taxonomy of seizure dynamotypes—previously examined only in invasive data. We applied principal component and independent component (IC) analysis to surface EEG recordings from 1,177 seizures in 158 patients with focal epilepsy, decomposing the signals into ICs. The ICs were visually labeled for clear seizure transitions and bifurcation morphologies (BifMs), which were then examined using Bayesian multilevel modeling in the context of clinical factors. Our analysis reveals that certain onset bifurcations (saddle node on invariant circle and supercritical Hopf) are more prevalent during wakefulness c...

A comprehensive analysis of everyday sound perception can be achieved using electroencephalography (EEG) with the concurrent acquisition of information about the environment. While extensive research has been dedicated to speech perception, the complexities of auditory perception within everyday environments, specifically the types of information and the key features to extract, remain less explored. Our study aims to systematically investigate the relevance of different feature categories: discrete sound-identity markers, general cognitive state information, and acoustic representations, including discrete sound onset, the envelope, and mel-spectrogram. Using continuous data analysis, we contrast different features in terms of their predictive power for unseen data and thus their distinct contributions to explaining neural data. For this, we analyze data from a complex audio-visual motor task using a naturalistic soundscape. The results demonstrated that the feature sets that explain the most neural varia...