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By monitoring brain neural signals, neural recorders allow for the study of neurological mechanisms underlying specific behavioural and cognitive states. However, the large brain volumes of non-human primates and their extensive range of uncontrolled movements and inherent wildness make it difficult to carry out covert and long-term recording and analysis of deep-brain neural signals. Here we report the development and performance of a stealthy neural recorder for the study of naturalistic behaviours in non-human primates. The neural recorder includes a fully implantable wireless and battery-free module for the recording of local field potentials and accelerometry data in real time, a flexible 32-electrode neural probe with a resorbable insertion shuttle, and a repeater coil-based wireless-power-transfer system operating at the body scale. We used the device to record neurobehavioural data for over 1 month in a freely moving monkey and leveraged the recorded data to train an artificial intelligence model for the classification of the animals' eating behaviours.
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http://dx.doi.org/10.1038/s41551-024-01280-w | DOI Listing |
Adv Healthc Mater
September 2025
Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, M5T 0S8, Canada.
Accurate brain signal recording and precise electrode placement are critical for the success of neuromodulation therapies such as deep brain stimulation (DBS). Addressing these challenges requires deep brain electrodes that provide high-quality, stable recordings while remaining compatible with high-resolution medical imaging modalities like magnetic resonance imaging (MRI). Moreover, such electrodes shall be cost-effective, easy to manufacture, and patient-compatible.
View Article and Find Full Text PDFJ Clin Exp Neuropsychol
September 2025
Program in Physical Therapy and Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA.
Introduction: An important frontier for neuropsychology involves developing additional technologies that could complement current behavioral approaches. Concurrent electroencephalographic (EEG) markers are especially promising for informing the neural processes underlying cognitive performance during neuropsychological assessments. The EEG aperiodic exponent shows sensitivity to both age and task-related effects, with prior studies relating smaller exponents to poorer performance in older adults, and larger exponents to greater task engagement in general.
View Article and Find Full Text PDFMov Disord Clin Pract
September 2025
Neurology Unit, Neuromotor and Rehabilitation Department, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy.
Background: GBA1 variants are the major genetic risk factor for Parkinson's Disease (PD) and account for 5-30% of PD cases depending on the population and age at onset of the disease.
Objectives: The aim of this study was to assess whether Artificial Intelligence (AI) could predict GBA1-mutated genotype in PD (GBA1-PD). Particularly, the main objective was to identify a Machine Learning (ML) model capable of accurately providing a pre-test estimate of GBA1-mutated status, relying on the clinical and demographic variables with the highest predictive value.
PLoS Biol
September 2025
Center for Neural Science, Department of Biology and Department of Psychology, New York University, New York, New York, United States of America.
Investigating social and independent behavior structure in early life is critical for understanding development and brain maturation in social mammals. However, this investigation necessitates monitoring animals over weeks to months often with subsecond time resolution creating challenges for both lab studies focused on brief observation periods and field studies in which animal tracking can be imprecise. Here we used machine vision and two-week long continuous behavior recordings of families of gerbils, a highly social rodent, in large, undisturbed home environments to quantify the behavioral development of individual pups.
View Article and Find Full Text PDFPsychol Rev
September 2025
Neural Computation Group, Max-Planck Institute for Human Cognitive and Brain Sciences.
It has been suggested that episodic memory relies on the well-studied machinery of spatial memory. This influential notion faces hurdles that become evident with dynamically changing spatial scenes and an immobile agent. Here I propose a model of episodic memory that can accommodate such episodes via temporal indexing.
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