Rodent EEG: Expanding the Spectrum of Analysis

Substance specific EEG patterns in mice undergoing slow anesthesia induction

The exact mechanisms and the neural circuits involved in anesthesia induced unconsciousness are still not fully understood. To elucidate them valid animal models are necessary. Since the most commonly used species in neuroscience are mice, we established a murine model for commonly used anesthetics/sedatives and evaluated the epidural electroencephalographic (EEG) patterns during slow anesthesia induction and emergence. Forty-four mice underwent surgery in which we inserted a central venous catheter and implanted nine intracranial electrodes above the prefrontal, motor, sensory, and visual cortex. After at least one week of recovery, mice were anesthetized either by inhalational sevoflurane or intravenous propofol, ketamine, or dexmedetomidine. We evaluated the loss and return of righting reflex (LORR/RORR) and recorded the electrocorticogram. For spectral analysis we focused on the prefrontal and visual cortex. In addition to analyzing the power spectral density at specific time points we evaluated the changes in the spectral power distribution longitudinally. The median time to LORR after start anesthesia ranged from 1080 [1st quartile: 960; 3rd quartile: 1080]s under sevoflurane anesthesia to 1541 [1455; 1890]s with ketamine. Around LORR sevoflurane as well as propofol induced a decrease in the theta/alpha band and an increase in the beta/gamma band. Dexmedetomidine infusion resulted in a shift towards lower frequencies with an increase in the delta range. Ketamine induced stronger activity in the higher frequencies. Our results showed substance-specific changes in EEG patterns during slow anesthesia induction. These patterns were partially identical to previous observations in humans, but also included significant differences, especially in the low frequencies. Our study emphasizes strengths and limitations of murine models in neuroscience and provides an important basis for future studies investigating complex neurophysiological mechanisms.

Sleep Fragmentation, Electroencephalographic Slowing, and Circadian Disarray in a Mouse Model for Intensive Care Unit Delirium

BACKGROUND

We aimed to further validate our previously published animal model for delirium by testing the hypothesis that in aged mice, Anesthesia, Surgery and simulated ICU conditions (ASI) induce sleep fragmentation, electroencephalographic (EEG) slowing, and circadian disarray consistent with intensive care unit (ICU) patients with delirium.

METHODS

A total of 41 mice were used. Mice were implanted with EEG electrodes and randomized to ASI or control groups. ASI mice received laparotomy, anesthesia, and simulated ICU conditions. Controls did not receive ASI. Sleep was recorded at the end of ICU conditions, and hippocampal tissue was collected on EEG recording. Arousals, EEG dynamics, and circadian gene expression were compared with t tests. Two-way repeated measures analysis of variance (RM ANOVA) was used to assess sleep according to light.

RESULTS

ASI mice experienced frequent arousals (36.6 ± 3.2 vs 26.5 ± 3.4; P = .044; 95% confidence interval [CI], 0.29-19.79; difference in mean ± SEM, 10.04 ± 4.62) and EEG slowing (frontal theta ratio, 0.223 ± 0.010 vs 0.272 ± 0.019; P = .026; 95% CI, -0.091 to -0.007; difference in mean ± SEM, -0.05 ± 0.02) relative to controls. In ASI mice with low theta ratio, EEG slowing was associated with a higher percentage of quiet wakefulness (38.2 ± 3.6 vs 13.4 ± 3.8; P = .0002; 95% CI, -35.87 to -13.84; difference in mean ± SEM, -24.86 ± 5.19). ASI mice slept longer during the dark phases of the circadian cycle (nonrapid eye movement [NREM], dark phase 1 [D1]: 138.9 ± 8.1 minutes vs 79.6 ± 9.6 minutes, P = .0003, 95% CI, -95.87 to -22.69, predicted mean difference ± SE: -59.28 ± 13.89; NREM, dark phase 2 (D2): 159.3 ± 7.3 minutes vs 112.6 ± 15.5 minutes, P = .006, 95% CI, -83.25 to -10.07, mean difference ± SE, -46.66 ± 13.89; rapid eye movement (REM), D1: 20.5 ± 2.1 minutes vs 5.8 ± 0.8 minutes, P = .001, 95% CI, -24.60 to -4.71, mean difference ± SE, -14. 65 ± 3.77; REM, D2: 21.0 ± 2.2 minutes vs 10.3 ± 1.4 minutes, P = .029, 95% CI, -20.64 to -0.76, mean difference ± SE, -10.70 ± 3.77). The expression of essential circadian genes was also lower in ASI mice (basic helix-loop-helix ARNT like [BMAL1] : -1.3 fold change; circadian locomotor output cycles protein kaput [CLOCK] : -1.2).

CONCLUSIONS

ASI mice experienced EEG and circadian changes mimicking those of delirious ICU patients. These findings support further exploration of this mouse approach to characterize the neurobiology of delirium.

Changes in Brain Electrical Activity after Transient Middle Cerebral Artery Occlusion in Rats

Objectives. Ischemic stroke is a leading cause of death and disability worldwide. To search for new therapeutic and pharmacotherapeutic strategies, numerous models of this disease have been proposed, the most popular being transient middle cerebral artery occlusion. Behavioral and sensorimotor testing, biochemical, and histological methods are traditionally used in conjunction with this model to assess the effectiveness of potential treatment options. Despite its wide overall popularity, electroencephalography/electrocorticography is quite rarely used in such studies. Materials and methods. In the present work, we explored the changes in brain electrical activity at days 3 and 7 after 30- and 45-min of transient middle cerebral artery occlusion in rats. Results. Cerebral ischemia altered the amplitude and spectral electrocorticogram characteristics, and led to a reorganization of inter- and intrahemispheric functional connections. Ischemia duration affected the severity as well as the nature of the observed changes. Conclusions. The dynamics of changes in brain electrical activity may indicate a spontaneous partial recovery of impaired cerebral functions at post-surgery day 7. Our results suggest that electrocorticography can be used successfully to assess the functional status of the brain following ischemic stroke in rats as well as to investigate the dynamics of functional recovery.

Emerging imaging methods to study whole-brain function in rodent models

In the past decade, the idea that single populations of neurons support cognition and behavior has gradually given way to the realization that connectivity matters and that complex behavior results from interactions between remote yet anatomically connected areas that form specialized networks. In parallel, innovation in brain imaging techniques has led to the availability of a broad set of imaging tools to characterize the functional organization of complex networks. However, each of these tools poses significant technical challenges and faces limitations, which require careful consideration of their underlying anatomical, physiological, and physical specificity. In this review, we focus on emerging methods for measuring spontaneous or evoked activity in the brain. We discuss methods that can measure large-scale brain activity (directly or indirectly) with a relatively high temporal resolution, from milliseconds to seconds. We further focus on methods designed for studying the mammalian brain in preclinical models, specifically in mice and rats. This field has seen a great deal of innovation in recent years, facilitated by concomitant innovation in gene-editing techniques and the possibility of more invasive recordings. This review aims to give an overview of currently available preclinical imaging methods and an outlook on future developments. This information is suitable for educational purposes and for assisting scientists in choosing the appropriate method for their own research question.

Inhibition of Death-associated Protein Kinase 1 protects against Epileptic Seizures in mice

Epilepsy is a chronic encephalopathy and one of the most common neurological disorders. Death-associated protein kinase 1 (DAPK1) expression has been shown to be upregulated in the brains of human epilepsy patients compared with those of normal subjects. However, little is known about the impact of DAPK1 on epileptic seizure conditions. In this study, we aim to clarify whether and how DAPK1 is regulated in epilepsy and whether targeting DAPK1 expression or activity has a protective effect against epilepsy using seizure animal models. Here, we found that cortical and hippocampal DAPK1 activity but not DAPK1 expression was increased immediately after convulsive pentylenetetrazol (PTZ) exposure in mice. However, DAPK1 overexpression was found after chronic low-dose PTZ insults during the kindling paradigm. The suppression of DAPK1 expression by genetic knockout significantly reduced PTZ-induced seizure phenotypes and the development of kindled seizures. Moreover, pharmacological inhibition of DAPK1 activity exerted rapid antiepileptic effects in both acute and chronic epilepsy mouse models. Mechanistically, PTZ stimulated the phosphorylation of NR2B through DAPK1 activation. Combined together, these results suggest that DAPK1 regulation is a novel mechanism for the control of both acute and chronic epilepsy and provide new therapeutic strategies for the treatment of human epilepsy.

An Automatic Epilepsy Detection Method Based on Improved Inductive Transfer Learning

Epilepsy is a chronic disease caused by sudden abnormal discharge of brain neurons, causing transient brain dysfunction. The seizures of epilepsy have the characteristics of being sudden and repetitive, which has seriously endangered patients' health, cognition, etc. In the current condition, EEG plays a vital role in the diagnosis, judgment, and qualitative location of epilepsy among the clinical diagnosis of various epileptic seizures and is an indispensable means of detection. The study of the EEG signals of patients with epilepsy can provide a strong basis and useful information for in-depth understanding of its pathogenesis. Although, intelligent classification technologies based on machine learning have been widely used to the classification of epilepsy EEG signals and show the effectiveness. In fact, it is difficult to ensure that there is always enough EEG data available for training the model in real life, which will affect the performance of the algorithms. In view of this, to reduce the impact of insufficient data on the detection performance of the algorithms, a novel discriminate least squares regression- (DLSR-) based inductive transfer learning method was introduced which is on the basis of DLSR and the inductive transfer learning. And, it is applied to promote the adaptability and accuracy of the epilepsy EEG signal recognition. The proposed method inherits the advantages of DLSR; it can be more suitable for classification scenarios by expanding the interval between different classes. Meanwhile, it can simultaneously use the data of the target domain and the knowledge of the source domain, which is helpful for getting better performance. The results show that the improved method has more advantages in EEG signal recognition comparing to several other representative methods.