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Frontal infarcts can produce cognitive impairments that affect an individual's ability to function in everyday life. However, the precise types of deficits, and their underlying mechanisms, are not well-understood. Here we used a prefrontal photothrombotic stroke model in C57BL/6J mice to characterise specific cognitive changes that occur in the 6 weeks post-stroke. Behavioural experiments were paired with in vivo electrophysiology to assess whether changes in oscillatory communication between the prefrontal cortex (PFC) and the hippocampus (HPC) mirrored any observed behavioural changes. We found that mice in the stroke group exhibited a delayed onset impairment in tasks of spatial working memory (object location recognition and Y-maze) and that this correlated with reduced PFC-HPC theta band coherence (5-12 Hz) during the task. In the open field, mice in the stroke group exhibited hyperactivity as compared to controls, and stroke animals also exhibited significantly higher beta band activity (13-30 Hz) in the PFC and the HPC. Taken together our results suggest that infarcts in the PFC result in PFC-HPC oscillatory communication changes in the theta and beta bands, correlating with altered performance in spatial memory and open field tasks respectively. Of particular interest, early open field changes in PFC beta band power post-stroke correlated to later-stage spatial memory impairments, highlighting this as a potential biomarker for detecting when spatial memory impairments are likely to occur.
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http://dx.doi.org/10.1007/s12017-019-08557-3 | DOI Listing |
Vestn Oftalmol
September 2025
Krasnov Research Institute of Eye Diseases, Moscow, Russia.
Primary open-angle glaucoma (POAG) is characterized by chronic progressive damage to the retinal ganglion cell layer (GCL) and their axons, leading to gradual visual function loss. Currently, the gold standards for structural and functional assessment of the retina in glaucoma are static automated perimetry (SAP) and optical coherence tomography (OCT). However, in clinical practice, data from SAP and OCT may be insufficient to reliably determine the stage of glaucomatous optic neuropathy, monitor its progression, or differentiate it from other causes of visual dysfunction.
View Article and Find Full Text PDFVestn Oftalmol
September 2025
OOO Diagnosticheskij tsentr Zreniye, Saint Petersburg, Russia.
Objective: This study evaluated the effect of sequential therapy with different dosages of Mexidol on the stabilization of glaucomatous optic neuropathy (GON) in patients with primary open-angle glaucoma (POAG).
Material And Methods: The study included 80 patients (160 eyes) with stage II and III POAG, randomized into three groups comparable by age, gender, and distribution of glaucoma stage. All patients received sequential therapy with Mexidol (14 days parenterally followed by 90 days orally).
Ophthalmic Physiol Opt
September 2025
School of Optometry, Indiana University Bloomington, Bloomington, Indiana, USA.
Purpose: Recent work has shown potential benefits for perimetry with dense spacing. To investigate the impact of normal inhomogeneity of perimetric sensitivity on perimetry with dense spacing, suprathreshold perimetry was used near the optic disc where shadows of blood vessels affect sensitivity in healthy eyes.
Methods: Three groups of participants were tested: 58 healthy older controls, 29 healthy younger controls and 18 patients with glaucoma.
J Am Coll Surg
September 2025
Department of Surgery, Division of Cardiothoracic Surgery, University of Colorado, Aurora, CO.
Background: Gender disparities exist in cardiothoracic surgery (CT), though qualitative investigations are lacking. We aimed to explore the impact of workplace culture on belonging, burnout, and career exit for women in CT.
Study Design: We conducted virtual semi-structured interviews with women cardiothoracic surgeons in practice for ≥5 years across the United States from 9/2024 to 12/2024.
J Integr Neurosci
August 2025
School of Computer Science, Guangdong Polytechnic Normal University, 510665 Guangzhou, Guangdong, China.
Background: Emotion recognition from electroencephalography (EEG) can play a pivotal role in the advancement of brain-computer interfaces (BCIs). Recent developments in deep learning, particularly convolutional neural networks (CNNs) and hybrid models, have significantly enhanced interest in this field. However, standard convolutional layers often conflate characteristics across various brain rhythms, complicating the identification of distinctive features vital for emotion recognition.
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