Memory for faces and names has increasingly become a focus of cognitive assessment and research in Alzheimer's disease (AD). This paper reviews evidence from cognitive and clinical neuroscience regarding the question of whether AD is associated with a specific deficit in face recognition, face-name association, and retrieval of semantic information and names. Cognitive approaches conceptualizing face recognition and face-name association have revealed that, compared to other types of visual stimuli, faces are "special" because of their complexity and high intraclass similarity, and because their association with proper names is arbitrary and unique. Neuroimaging has revealed that due to this particular status, face perception requires a complex interplay of highly specialized secondary visual areas located in the occipitotemporal cortex with a widely distributed system of cortical areas subserving further task-dependent processing. Our review of clinical research suggests that AD-related deficits in face recognition are primarily due to mnestic rather than perceptual deficits. Memory for previously studied or famous faces is closely related to mediotemporal and temporocortical brain regions subserving episodic and semantic memory in general, suggesting that AD-related impairments in this domain are due to neural degeneration in these areas. Despite limited specificity due to the apparent absence of a "genuine" domain-specific deficit of face memory in AD, testing memory for faces and names is useful in clinical contexts, as it provides highly sensitive indices of episodic and semantic memory performance. Therefore, clinical assessment of face memory can usefully contribute to early detection of memory deficits in prodromal and initial stages of AD, and represents a basis for further attempts at rehabilitation. Further advantages, such as ecological validity, high task comprehensibility and, in the case of novel face learning, independence from premorbid intelligence level, render measures of face recognition valuable for clinical assessment in early AD.
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http://dx.doi.org/10.1016/s0010-9452(08)70689-0 | DOI Listing |
BMC 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 PDFComput Biol Med
January 2025
Department of Computer Science, Jamia Hamdard University, Near Batra Hospital, New Delhi, 110062, India. Electronic address:
Schizophrenia detection involves identifying the schizophrenia by analyzing specific patterns in Electroencephalogram (EEG) signals, which reflect brain activity associated with symptoms, like hallucinations and cognitive impairments. Existing models face challenges due to the complex and variable nature of EEG data, which may struggle to accurately capture critical temporal dependencies and relevant features. Traditional approaches often lack adaptability, limiting their ability to differentiate schizophrenia patterns from other brain activities.
View Article and Find Full Text PDFCell Prolif
January 2025
Key Laboratory of Organ Regeneration and Reconstruction, State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.
Cells face two challenges after transplantation: recognition and killing by lymphocytes, and cell apoptosis induced by the transplantation environment. Our hypoimmune cells aim to address these two challenges through editing of immunomodulatory proteins and overexpression of anti-apoptotic proteins.
View Article and Find Full Text PDFActa Neurochir (Wien)
January 2025
Department of Neurosurgery, University Medicine Greifswald, Greifswald, Germany.
Purpose: Currently available grading and classification systems for hemifacial spasm either rely on subjective assessments or are excessively intricate. Here, we make use of facial recognition and facial tracking technologies towards accurately grouping patients according to severity and characteristics of the spasms.
Methods: A retrospective review of our prospectively maintained preoperative videos database for hemifacial spasm was done.
PLoS One
January 2025
College of Artificial Intelligence Technology, Changchun Institute of Technology, Changchun, China.
The application of face recognition technology in Library Access Control System (LACS) has an important impact on improving the security and management efficiency of the library. However, the traditional face recognition methods have some limitations in the face of complex environmental conditions such as illumination and posture change. To solve this problem, an improved method combining the Aggregating Spatial Embeddings for Face Recognition (ASEF) algorithm and Principal Component Analysis (PCA) is proposed.
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