Aim of the research was to investigate whether a temporal structure could be detected in the behavioural response to an aversive stimulation. A fear-related memory task was used in rats, placed in a modified version of the Novel Object Recognition task known as Emotional Object Recognition task, i.e. a behavioural assay that orbits around the declarative memory for an aversive experience. To this purpose, twelve male Wistar rats, divided in two groups (Control and Aversive memory), observed after 4 h (OR4h) and after 24 h (OR24h) from the delivery of an aversive stimulation, associated to a specific object, were used. Data were evaluated both in terms of conventional quantitative approaches and by means of T-pattern analysis, namely a multivariate technique able to unveil the temporal structure of behaviour and the relationships amongst the behavioural items in time. Results evidenced several changes between groups and over time as well. Mean occurrences and mean durations showed significant differences between OR4h and OR24h sessions and between Control and Aversive memory groups for behavioural items of Exploration, Object-related aversion and Immobility. T-pattern analysis revealed important changes of behavioural variability, complexity and repetitiveness, (i.e., the three main qualitative features of T-patterns) in the Aversive memory group. These outcomes highlight a simpler and linear behavioural profile, focused only on specific sequences of particularly repetitive events. Overall, the present study demonstrates a) the presence of a temporal organization of fear-related behavioural events and b) the influence of learning on the modifications observed over time.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.physbeh.2021.113481 | DOI Listing |
BMC Med
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, Hunan, 410011, China.
Background: The heterogeneity of cognitive impairments in schizophrenia has been widely observed. However, reliable cognitive boundaries to differentiate the subgroups remain elusive. The key challenge for cognitive subtyping is applying an integrated and standardized cognitive assessment and understanding the subgroup-specific neurobiological mechanisms.
View Article and Find Full Text PDFBMC Bioinformatics
January 2025
Centro de Salud Retiro, Hospital Universitario Gregorio Marañon, C/Lope de Rueda, 43, 28009, Madrid, Spain.
Background: Natural language processing (NLP) enables the extraction of information embedded within unstructured texts, such as clinical case reports and trial eligibility criteria. By identifying relevant medical concepts, NLP facilitates the generation of structured and actionable data, supporting complex tasks like cohort identification and the analysis of clinical records. To accomplish those tasks, we introduce a deep learning-based and lexicon-based named entity recognition (NER) tool for texts in Spanish.
View Article and Find Full Text PDFNPJ Syst Biol Appl
January 2025
Center for Interdisciplinary Digital Sciences (CIDS), Department Information Services and High-Performance Computing (ZIH), Dresden University of Technology, 01062, Dresden, Germany.
Predicting the biological behavior and time to recurrence (TTR) of high-grade diffuse gliomas (HGG) after maximum safe neurosurgical resection and combined radiation and chemotherapy plays a pivotal role in planning clinical follow-up, selecting potentially necessary second-line treatment and improving the quality of life for patients diagnosed with a malignant brain tumor. The current standard-of-care (SoC) for HGG includes follow-up neuroradiological imaging to detect recurrence as early as possible and relies on several clinical, neuropathological, and radiological prognostic factors, which have limited accuracy in predicting TTR. In this study, using an in-silico analysis, we aim to improve predictive power for TTR by considering the role of (i) prognostically relevant information available through diagnostics used in the current SoC, (ii) advanced image-based information not currently part of the standard diagnostic workup, such as tumor-normal tissue interface (edge) features and quantitative data specific to biopsy positions within the tumor, and (iii) information on tumor-associated macrophages.
View Article and Find Full Text PDFNeurobiol Dis
January 2025
Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, , Heinrich Heine University, 40225 Düsseldorf, Germany. Electronic address:
Corticobasal syndrome (CBS) is characterized not only by parkinsonism but also by higher-order cortical dysfunctions, such as apraxia. However, the electrophysiological mechanisms underlying these symptoms remain poorly understood. To explore the pathophysiology of CBS, we recorded magnetoencephalographic (MEG) data from 17 CBS patients and 20 age-matched controls during an observe-to-imitate task.
View Article and Find Full Text PDFNeural Netw
January 2025
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi'an, 710054, China; Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, 710054, China.
The presence of substantial similarities and redundant information within video data limits the performance of video object recognition models. To address this issue, a Global-Local Storage Enhanced video object recognition model (GSE) is proposed in this paper. Firstly, the model incorporates a two-stage dynamic multi-frame aggregation module to aggregate shallow frame features.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!