Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for Parkinson's disease (PD). In spite of proven therapeutic success, the mechanism underlying the benefits of DBS has not been resolved. A multiple-channel single-unit recording technique was used in the present study to investigate basal ganglia (BG) neural responses during behaviorally effective DBS of the STN in a rat model of PD. Rats underwent unilateral dopamine (DA) depletion by injection of 6-hydroxyDA (6-OHDA) into one side of the medial forebrain bundle and subsequently developed a partial akinesia, which was assessed during the treadmill locomotion task. High frequency stimulation (HFS) of the STN restored normal treadmill locomotion behavior. Simultaneous recording of single unit activity in the striatum (STR), globus pallidus (GP), substantia nigra pars reticulata (SNr), and STN revealed a variety of neural responses during behaviorally effective HFS of the STN. Predominant inhibitory responses appeared in the STN stimulation site. Nearly equal numbers of excitatory and inhibitory responses were found in the GP and SNr, whereas more rebound excitatory responses were found in the STR. Mean firing rate did not change significantly in the STR, GP, and SNr, but significantly decreased in both sides of STN during DBS. A decrease in firing rate in the contralateral side of STN provides neural substrate for the clinical observation that unilateral DBS produces bilateral benefits in patients with PD. In addition to the firing rate changes, a decrease in burst firing was observed in the GP and STN. The present study indicates that DBS induces complex modulations of the BG circuit and further suggests that BG network reorganization, rather than a simple excitation or inhibition, may underlie the therapeutic effects of DBS in patients with PD.
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Brief Bioinform
November 2024
Department of Automation, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China.
Studying the changes in cellular transcriptional profiles induced by small molecules can significantly advance our understanding of cellular state alterations and response mechanisms under chemical perturbations, which plays a crucial role in drug discovery and screening processes. Considering that experimental measurements need substantial time and cost, we developed a deep learning-based method called Molecule-induced Transcriptional Change Predictor (MiTCP) to predict changes in transcriptional profiles (CTPs) of 978 landmark genes induced by molecules. MiTCP utilizes graph neural network-based approaches to simultaneously model molecular structure representation and gene co-expression relationships, and integrates them for CTP prediction.
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Department of Psychology, City College, City University of New York, New York, NY 10031.
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Tanzania Field Epidemiology and Laboratory Training Program, Tanzania Ministry of Health, Dodoma, Tanzania.
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View Article and Find Full Text PDFACS Nano
January 2025
Department of Biochemistry and Molecular Biology, School of Life Sciences, Central South University, Changsha 410078, Hunan, China.
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View Article and Find Full Text PDFFront Syst Neurosci
January 2025
International research center for Cognitive Applied Neuroscience (IrcCAN), Università Cattolica del Sacro Cuore, Milan, Italy.
This study examines the impact of positive and negative feedback on recall of past decisions, focusing on behavioral performance and electrophysiological (EEG) responses. Participants completed a decision-making task involving 10 real-life scenarios, each followed by immediate positive or negative feedback. In a recall phase, participants' accuracy (ACC), errors (ERRs), and response times (RTs) were recorded alongside EEG data to analyze brain activity patterns related to recall.
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