The state of pyramidal cell populations in CA1 and CA4 hippocampal fields was studied in resuscitated and intact rats with different learning ability. Morphometry showed that postresuscitation damage to neurons was more pronounced in good learners compared to poor learners. Interferometry revealed higher protein content in neurons in poor learners compared to successfully trained rats. It was hypothesized that different neuronal resistance to ischemia in rats characterized by different learning ability is determined by some peculiarities in protein metabolism preexisting in intact animals and manifesting in the postresuscitation period.

Download full-text PDF

Source
http://dx.doi.org/10.1023/a:1013650824247DOI Listing

Publication Analysis

Top Keywords

learning ability
12
rats learning
8
learners compared
8
poor learners
8
postresuscitation changes
4
changes neuronal
4
neuronal hippocampal
4
hippocampal populations
4
rats
4
populations rats
4

Similar Publications

Sex Differences in the Striatal Contributions to Longitudinal Fine Motor Development in Autistic Children.

Biol Psychiatry

January 2025

MIND Institute and Department of Psychiatry and Behavioral Sciences, UC Davis School of Medicine, University of California Davis, Sacramento, CA, USA.

Background: Fine motor challenges are prevalent in autistic populations. However, little is known about their neurobiological underpinnings or how their related neural mechanisms are influenced by sex. The dorsal striatum, comprised of the caudate nucleus and putamen, is associated with motor learning and control and may hold critical information.

View Article and Find Full Text PDF

Multi-channel spatio-temporal graph attention contrastive network for brain disease diagnosis.

Neuroimage

January 2025

College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China. Electronic address:

Dynamic brain networks (DBNs) can capture the intricate connections and temporal evolution among brain regions, becoming increasingly crucial in the diagnosis of neurological disorders. However, most existing researches tend to focus on isolated brain network sequence segmented by sliding windows, and they are difficult to effectively uncover the higher-order spatio-temporal topological pattern in DBNs. Meantime, it remains a challenge to utilize the structure connectivity prior in the DBNs analysis.

View Article and Find Full Text PDF

EEG microstate analysis and machine learning classification in patients with obsessive-compulsive disorder.

J Psychiatr Res

January 2025

Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China. Electronic address:

Background: Microstate characterization of electroencephalogram (EEG) is a data-driven approach to explore the functional changes and interrelationships of multiple brain networks on a millisecond scale. This study aimed to explore the pathological changes of whole-brain functional networks in patients with obsessive-compulsive disorders (OCD) through microstate analysis and further to explore its potential value as an auxiliary diagnostic index.

Methods: Forty-eight OCD patients (33 with more than moderate anxiety symptoms, 15 with mild anxiety symptoms) and 52 healthy controls (HCs) were recruited.

View Article and Find Full Text PDF

Quantum mixed-state self-attention network.

Neural Netw

January 2025

Mechanical, Electrical and Information Engineering College, Putian University, Putian, 351100, China.

Attention mechanisms have revolutionized natural language processing. Combining them with quantum computing aims to further advance this technology. This paper introduces a novel Quantum Mixed-State Self-Attention Network (QMSAN) for natural language processing tasks.

View Article and Find Full Text PDF

Dynamic planning in hierarchical active inference.

Neural Netw

January 2025

Institute of Cognitive Sciences and Technologies, National Research Council, Padova, Italy. Electronic address:

By dynamic planning, we refer to the ability of the human brain to infer and impose motor trajectories related to cognitive decisions. A recent paradigm, active inference, brings fundamental insights into the adaptation of biological organisms, constantly striving to minimize prediction errors to restrict themselves to life-compatible states. Over the past years, many studies have shown how human and animal behaviors could be explained in terms of active inference - either as discrete decision-making or continuous motor control - inspiring innovative solutions in robotics and artificial intelligence.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!