Background: Among several potential neuroanatomical targets pursued for deep brain stimulation (DBS) for treating those with treatment-resistant depression (TRD), the superolateral-branch of the medial forebrain bundle (MFB) is emerging as a privileged location. We investigated the antidepressant-like phenotypic and chemical changes associated with reward-processing dopaminergic systems in rat brains after MFB-DBS.
Methods: Male Wistar rats were divided into three groups: sham-operated, DBS-Off, and DBS-On. For DBS, a concentric bipolar electrode was stereotactically implanted into the right MFB. Exploratory activity and depression-like behavior were evaluated using the open-field and forced-swimming test (FST), respectively. MFB-DBS effects on the dopaminergic system were evaluated using immunoblotting for tyrosine hydroxylase (TH), dopamine transporter (DAT), and dopamine receptors (D1-D5), and high-performance liquid chromatography for quantifying dopamine, 3,4-dihydroxyphenylacetic acid (DOPAC), and homovanillic acid (HVA) in brain homogenates of prefrontal cortex (PFC), hippocampus, amygdala, and nucleus accumbens (NAc).
Results: Animals receiving MFB-DBS showed a significant increase in swimming time without alterations in locomotor activity, relative to the DBS-Off (p<0.039) and sham-operated groups (p<0.014), indicating an antidepressant-like response. MFB-DBS led to a striking increase in protein levels of dopamine D2 receptors and DAT in the PFC and hippocampus, respectively. However, we did not observe appreciable differences in the expression of other dopamine receptors, TH, or in the concentrations of dopamine, DOPAC, and HVA in PFC, hippocampus, amygdala, and NAc.
Limitations: This study was not performed on an animal model of TRD.
Conclusion: MFB-DBS rescues the depression-like phenotypes and selectively activates expression of dopamine receptors in brain regions distant from the target area of stimulation.
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http://dx.doi.org/10.1016/j.jad.2017.03.074 | DOI Listing |
Sensors (Basel)
December 2024
Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
This systematic review examines EEG-based imagined speech classification, emphasizing directional words essential for development in the brain-computer interface (BCI). This study employed a structured methodology to analyze approaches using public datasets, ensuring systematic evaluation and validation of results. This review highlights the feature extraction techniques that are pivotal to classification performance.
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December 2024
Faculty of Engineering in Bilbao, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain.
In recent years, the application of AI has expanded rapidly across various fields. However, it has faced challenges in establishing a foothold in medicine, particularly in invasive medical procedures. Medical algorithms and devices must meet strict regulatory standards before they can be approved for use on humans.
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December 2024
College of Computer and Information Sciences (CCIS), King Saud University, Riyadh 11543, Saudi Arabia.
One of the most promising applications for electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is motor rehabilitation through motor imagery (MI) tasks. However, current MI training requires physical attendance, while remote MI training can be applied anywhere, facilitating flexible rehabilitation. Providing remote MI training raises challenges to ensuring an accurate recognition of MI tasks by healthcare providers, in addition to managing computation and communication costs.
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December 2024
School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
Deep unfolding networks (DUNs) have attracted growing attention in compressed sensing (CS) due to their good interpretability and high performance. However, many DUNs often improve the reconstruction effect at the price of a large number of parameters and have the problem of feature information loss during iteration. This paper proposes a novel adaptive memory-augmented unfolding network for compressed sensing (AMAUN-CS).
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ActiGraph LLC, Pensacola, FL 32502, USA.
Wearable accelerometers are widely used as an ecologically valid and scalable solution for long-term at-home sleep monitoring in both clinical research and care. In this study, we applied a deep learning domain adversarial convolutional neural network (DACNN) model to this task and demonstrated that this new model outperformed existing sleep algorithms in classifying sleep-wake and estimating sleep outcomes based on wrist-worn accelerometry. This model generalized well to another dataset based on different wearable devices and activity counts, achieving an accuracy of 80.
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