Publications by authors named "G G Gangadharan"

Social memory, a fundamental component of social behavior, is essential for the recognition and recall of familiar and novel animals/humans which is disrupted in several neuropsychiatric disorders. Although hippocampal circuitry is crucial for social memory, the role of extra-hippocampal regions in this behavior remains elusive. Here, we identified the physiological link between medial septal dependent cholinergic theta oscillations in the hippocampus and social memory behavior.

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Article Synopsis
  • The study highlights how deep learning (DL) models are improving Alzheimer's disease diagnosis by using counterfactual inference to enhance explainability and clinical adoption.
  • A new method is proposed that combines U-Net and GAN models to create counterfactual diagnostic maps, which help in understanding how changes in brain features affect diagnosis.
  • Results show this new approach significantly outperforms existing methods, achieving 95% accuracy, and provides detailed visual explanations, making diagnostics more reliable and enhancing clinicians' understanding of disease progression.
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The extensive usage of nonbiodegradable plastic materials for food packaging is a major environmental concern. To address this, researchers focus on developing biocompatible and biodegradable food packaging from natural biopolymers, such as polysaccharides, proteins, and polyesters. These biopolymer-based packaging materials extend the shelf life of food due to their inherent antimicrobial and antioxidant properties.

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Social memory, the ability to recognize and remember individuals within a social group, is crucial for social interactions and relationships. Deficits in social memory have been linked to several neuropsychiatric and neurodegenerative disorders. The hippocampus, especially the circuit that links dorsal CA2 and ventral CA1 neurons, is considered a neural substrate for social memory formation.

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The study presents an innovative diagnostic framework that synergises Convolutional Neural Networks (CNNs) with a Multi-feature Kernel Supervised within-class-similar Discriminative Dictionary Learning (MKSCDDL). This integrative methodology is designed to facilitate the precise classification of individuals into categories of Alzheimer's Disease, Mild Cognitive Impairment (MCI), and Cognitively Normal (CN) statuses while also discerning the nuanced phases within the MCI spectrum. Our approach is distinguished by its robustness and interpretability, offering clinicians an exceptionally transparent tool for diagnosis and therapeutic strategy formulation.

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