Animals depend on navigation to find food, water, mate(s), shelter, etc. Different species use diverse strategies that utilise forms of motion- and location-related information derived from the environment to navigate to their goals and back. We start by describing behavioural studies undertaken to unearth different strategies used in navigation. Then we move on to outline what we know about the brain area most associated with spatial navigation, namely the hippocampal formation. While doing so, we first briefly explain the anatomical connections in the area and then proceed to describe the neural correlates that are considered to play a role in navigation. We conclude by looking at how the strategies might interact and complement each other in certain contexts.
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http://dx.doi.org/10.1007/s41745-017-0053-1 | DOI Listing |
NPJ Digit Med
January 2025
Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Adaptive deep brain stimulation (DBS) provides individualized therapy for people with Parkinson's disease (PWP) by adjusting the stimulation in real-time using neural signals that reflect their motor state. Current algorithms, however, utilize condensed and manually selected neural features which may result in a less robust and biased therapy. In this study, we propose Neural-to-Gait Neural network (N2GNet), a novel deep learning-based regression model capable of tracking real-time gait performance from subthalamic nucleus local field potentials (STN LFPs).
View Article and Find Full Text PDFBMC Geriatr
January 2025
Department of Rehabilitation Medicine (Rehabilitation Center), Qilu Hospital of Shandong University, No. 107, Wenhuaxi Road, Jinan , Shandong, 250012, China.
Background: Mild cognitive impairment (MCI) is a high-risk factor for dementia and dysphagia; therefore, early intervention is vital. The effectiveness of intermittent theta burst stimulation (iTBS) targeting the right dorsal lateral prefrontal cortex (rDLPFC) remains unclear.
Methods: Thirty-six participants with MCI were randomly allocated to receive real (n = 18) or sham (n = 18) iTBS.
J Affect Disord
January 2025
Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China; Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's Diseases (Yantai Yuhuangding Hospital), Yantai, Shandong 264000, PR China; Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China. Electronic address:
Purpose: To elucidate the structural-functional connectivity (SC-FC) coupling in white matter (WM) tracts in patients with major depressive disorder (MDD).
Methods: A total of 178 individuals diagnosed with MDD and 173 healthy controls (HCs) were recruited for this study. The Euclidean distance was calculated to assess SC-FC coupling.
Sci Total Environ
January 2025
Key Laboratory of Coastal Biology and Biological Resource Utilization, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China; University of Chinese Academy of Sciences, Beijing 101408, China. Electronic address:
The biogeochemical processes of organic matter exhibit notable variability and unpredictability in marginal seas. In this study, the abiologically and biologically driving effects on particulate organic matter (POM) and dissolved organic matter (DOM) were investigated in the Yellow Sea and Bohai Sea of China, by introducing the cutting-edge network inference tool of deep learning. The concentration of particulate organic carbon (POC) was determined to characterize the status of POM, and the fractions and fluorescent properties of DOM were identified through 3D excitation-emission-matrix spectra (3D-EEM) combined parallel factor analysis (PARAFAC).
View Article and Find Full Text PDFComput Biol Med
January 2025
LMA Laboratory, University of Bejaia, Bejaia 06000, Algeria. Electronic address:
Social networks are increasingly taking over daily life, creating a volume of unsecured data and making it very difficult to capture safe data, especially in times of crisis. This study aims to use a Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM)-based hybrid model for health monitoring and health crisis forecasting. It consists of efficiently retrieving safe content from multiple social media sources.
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