Cortical stroke affects activity and stability of theta/delta states in remote hippocampal regions

Abnormal Low-Frequency Corticokinematic Coherence in Stroke: An Electroencephalography and Acceleration Study

Motor control is a complex process of coordination and information interaction among neural, motor, and sensory functions. Investigating the correlation between motor-physiological information helps to understand the human motor control mechanisms and is important for the assessment of motor function status. In this manuscript, we investigated the differences in the neuromotor coupling analysis between healthy controls and stroke patients in different movements. We applied the corticokinematic coherence (CKC) function between the electroencephalogram (EEG) and acceleration (ACC) data. First, we collected the EEG and ACC data from 10 healthy controls and 10 stroke patients under the task of movement execution (ear touch and knee touch) and movement maintenance (ear touch and knee touch). After the preprocessing of raw data, we used frequency domain coherence method to analyze the full-frequency EEG and ACC data, which could be concluded that the CKC intensity in the movement execution was higher than that in the movement maintenance. However, there was no significant difference between healthy subjects and stroke patients. Secondly, the coherence results in local frequency bands showed that low-frequency bands could better reflect the difference between movement execution and maintenance. The intensity of coherence in healthy subjects was significantly higher than that in other bands, but not in stroke patients. Further comparison of coherence results in local frequency bands showed that the intensity of theta band in healthy controls was significantly higher than other rhythms, especially in the knee touch phase. Therefore, we infer that neurodynamic coupling analysis based on EEG and ACC data can show the differences between healthy subjects and stroke patients. Such researches could add to the understanding of neuro-motor control mechanisms and provide new quantitative indicators on the motor function assessment.

Comparative evaluation of rhFGF18 and rhGDF11 treatment in a transient ischemia stroke model

Background

Pharmacological treatments for ischemic stroke remain limited to thrombolysis, which is associated with increased risk of potentially fatal hemorrhage. Treatments with Recombinant Human Fibroblast Growth Factor 18 (rhFGF18) and Growth and Differentiation Factor 11 (rhGDF11) appear promising based on different preclinical models. The goal of this study was to compare the effects of rhFGF18 and rhGDF11 directly on survival, behavioral deficits, and histological fingerprint of cerebral ischemia in the Wistar rat middle cerebral artery occlusion (MCAO) model of stroke.

Methods

Ischemia-reperfusion injury was induced using a 2-hour transient MCAO. Animals were administered rhFGF18 (infusion), rhGDF11 (multi-injection), or Phosphate Buffered Saline (PBS) vehicle control and followed for 42 days. Motor-Cognitive deficits were evaluated using the Morris Water Maze at Days 0 (pre-MCAO), 7, 21, and 42. Histopathological assessments were performed on Days 21 and 42.

Results

Day 7 post-ischemia water maze performance times increased 38.3%, 2.1%, and 23.1% for PBS, rhFGF18, and rhGDF11-treated groups, respectively. Fraction of neurons with abnormal morphology (chromatolysis, pyknotic nuclei, somal degeneration) decreased in all groups toward Day 42 and was lowest for rhFGF18. AChE-positive fiber density and activity increased over time in the rhFGF18 group, remained unchanged in the rhGDF11 treatment arm, and declined in the PBS control. Metabolic increases were greatest in rhGDF11 treated animals, with both rhFGF18 and rhGDF11 achieving improvements over PBS, as evidenced by increased succinate dehydrogenase and lactate dehydrogenase activity. Finally, rhFGF18 treatment exhibited a trend for reduced mortality relative to PBS (5.6%, 95% CI [27.3%, 0.1% ] vs. 22.2%, 95% CI [47.6%, 6.4% ]).

Conclusions

rhFGF18 treatment appears promising in improving survival and promoting motor-cognitive recovery following cerebral ischemia-reperfusion injury.

The Differences of Metabolites in Different Parts of the Brain Induced by Shuxuetong Injection against Cerebral Ischemia-Reperfusion and Its Corresponding Mechanism

Ischemic stroke is often associated with a large disease burden. The existence of ischemia-reperfusion injury brings great challenges to the treatment of ischemic stroke. The purpose of this study was to explore the differences of metabolites in different parts of the brain induced by Shuxuetong injection against cerebral ischemia-reperfusion and to extend the corresponding mechanism. The rats were modeled by transient middle cerebral artery occlusion (t-MCAO) operation, and the success of modeling was determined by neurological function score and TTC staining. UPLC-Q/TOF-MS metabolomics technique and multivariate statistical analysis were used to analyze the changes and differences of metabolites in the cortex and hippocampus of cerebral ischemia-reperfusion rats. Compared with the model group, the neurological function score and cerebral infarction volume of the Shuxuetong treatment group were significantly different. There were differences and changes in the metabolic distribution of the cortex and hippocampus in each group, the distribution within the group was relatively concentrated. The separation trend between the groups was obvious, and the distribution of the Shuxuetong treatment group was similar to that of the sham operation group. We identified 13 metabolites that were differentially expressed in the cortex, including glutamine, dihydroorotic acid, and glyceric acid. We also found five differentially expressed metabolites in the hippocampus, including glutamic acid and fumaric acid. The common metabolic pathways of Shuxuetong on the cortex and hippocampus were D-glutamine and D-glutamate metabolism and nitrogen metabolism, which showed inhibition of cortical glutamine and promotion of hippocampal glutamic acid. Specific pathways of Shuxuetong enriched in the cortex included glyoxylate and dicarboxylate metabolism and pyrimidine metabolism, which showed inhibition of glyceric acid and dihydroorotic acid. Specific pathways of Shuxuetong enriched in the hippocampus include arginine biosynthesis and citrate cycle (TCA cycle), which promotes fumaric acid. Shuxuetong injection can restore and adjust the metabolic disorder of the cortex and hippocampus in cerebral ischemia-reperfusion rats. The expression of Shuxuetong in different parts of the brain is different and correlated.

Local field potentials identify features of cortico-hippocampal communication impacted by stroke and environmental enrichment therapy

Objective. Cognitive and memory impairments are common sequelae after stroke, yet how middle cerebral artery (MCA) stroke chronically affects the neural activity of the hippocampus, a brain region critical for memory but remote from the stroke epicenter, is poorly understood. Environmental enrichment (EE) improves cognition following stroke; however, the electrophysiology that underlies this behavioral intervention is still elusive.Approach.We recorded extracellular local field potentials simultaneously from sensorimotor cortex and hippocampus in rats during urethane anesthesia following MCA occlusion and subsequent EE treatment.Main results.We found that MCA stroke significantly impacted the electrophysiology in the hippocampus, in particular it disrupted characteristics of sharp-wave associated ripples (SPW-Rs) altered brain state, and disrupted phase amplitude coupling (PAC) within the hippocampus and between the cortex and hippocampus. Importantly, we show that EE mitigates stroke-induced changes to SPW-R characteristics but does not restore hippocampal brain state or PAC.Significance.These results begin to uncover the complex interaction between cognitive deficit following stroke and EE treatment, providing a testbed to assess different strategies for therapeutics following stroke.

Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals

The emergence of an aging society is inevitable due to the continued increases in life expectancy and decreases in birth rate. These social changes require new smart healthcare services for use in daily life, and COVID-19 has also led to a contactless trend necessitating more non-face-to-face health services. Due to the improvements that have been achieved in healthcare technologies, an increasing number of studies have attempted to predict and analyze certain diseases in advance. Research on stroke diseases is actively underway, particularly with the aging population. Stroke, which is fatal to the elderly, is a disease that requires continuous medical observation and monitoring, as its recurrence rate and mortality rate are very high. Most studies examining stroke disease to date have used MRI or CT images for simple classification. This clinical approach (imaging) is expensive and time-consuming while requiring bulky equipment. Recently, there has been increasing interest in using non-invasive measurable EEGs to compensate for these shortcomings. However, the prediction algorithms and processing procedures are both time-consuming because the raw data needs to be separated before the specific attributes can be obtained. Therefore, in this paper, we propose a new methodology that allows for the immediate application of deep learning models on raw EEG data without using the frequency properties of EEG. This proposed deep learning-based stroke disease prediction model was developed and trained with data collected from real-time EEG sensors. We implemented and compared different deep-learning models (LSTM, Bidirectional LSTM, CNN-LSTM, and CNN-Bidirectional LSTM) that are specialized in time series data classification and prediction. The experimental results confirmed that the raw EEG data, when wielded by the CNN-bidirectional LSTM model, can predict stroke with 94.0% accuracy with low FPR (6.0%) and FNR (5.7%), thus showing high confidence in our system. These experimental results demonstrate the feasibility of non-invasive methods that can easily measure brain waves alone to predict and monitor stroke diseases in real time during daily life. These findings are expected to lead to significant improvements for early stroke detection with reduced cost and discomfort compared to other measuring techniques.

Experimental cortical stroke induces aberrant increase of sharp-wave-associated ripples in the hippocampus and disrupts cortico-hippocampal communication

The functional consequences of ischemic stroke in the remote brain regions are not well characterized. The current study sought to determine changes in hippocampal oscillatory activity that may underlie the cognitive impairment observed following distal middle cerebral artery occlusion (dMCAO) without causing hippocampal structural damage. Local field potentials were recorded from the dorsal hippocampus and cortex in urethane-anesthetized rats with multichannel silicon probes during dMCAO and reperfusion, or mild ischemia induced by bilateral common carotid artery occlusion (CCAO). Bilateral change of brain state was evidenced by reduced theta/delta amplitude ratio and shortened high theta duration following acute dMCAO but not CCAO. An aberrant increase in the occurrence of sharp-wave-associated ripples (150-250 Hz), crucial for memory consolidation, was only detected after dMCAO reperfusion, coinciding with an increased occurrence of high-frequency discharges (250-450 Hz). dMCAO also significantly affected the modulation of gamma amplitude in the cortex coupled to hippocampal theta phase, although both hippocampal theta and gamma power were temporarily decreased during dMCAO. Our results suggest that MCAO may disrupt the balance between excitatory and inhibitory circuits in the hippocampus and alter the function of cortico-hippocampal network, providing a novel insight in how cortical stroke affects function in remote brain regions.