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Deep brain stimulation of the entorhinal cortex modulates CA1 theta-gamma oscillations in mouse models of preclinical Alzheimer’s disease

Luo Yinpei, Sun Yuwei, Wen Huizhong, Wang Xing, Zheng Xiaolin, Ge Hongfei, Yin Yi, Wu Xiaoying, Li Weina, Hou Wensheng

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 1

246--260

EN

Deep brain stimulation (DBS) is a neuromodulation method that modulates neuronal activity. A trend in the treatment of Alzheimer’s disease (AD) is targeting key points of neural circuits with DBS. Here, we explored the effects of DBS targeted to the entorhinal cortex (EC) on neurons in the hippocampal CA1 in a mouse model of preclinical AD. Specifically, we recorded field potential signals from CA1 in preclinical AD mice after DBS of the EC (1 h/day for 21 days of 100 lA, 90 ls, 10 Hz, biphasic square wave pulse) with in-vivo electrophysiology and evaluated corresponding changes in behavior with the open field task and Morris water maze (MWM) task. We also assessed changes in pathological markers and neurogenesis in the hippocampus with immunohistological staining. DBS of the EC increased theta and gamma power and modulated theta in the high gamma band (50-100 Hz) in preclinical AD mice. After DBS of the EC, these mice performed better in the MWM task and exhibited reduced deposition of beta-amyloid and neuronal changes including significant increases in proliferating neurons and immature neurons. This is the first study to target the EC with DBS and analyze resulting neural oscillations in the hippocampal CA1 in a model of preclinical AD. The findings support the use of DBS as a potential treatment for AD.

2

Detection of subthalamic nucleus using novel higher-order spectra features in microelectrode recordings signals

Hosny Mohamed, Zhu Minwei, Gao Wenpeng, Fu Yili

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 2

704--716

EN

Successful deep brain stimulation surgery for Parkinson’s disease (PD) patients hinges on accurate clustering of the functional regions along the electrode insertion trajectory. Microelectrode recording (MER) is employed as a substantial tool for neuro-navigation and localizing the optimal target, such as the subthalamic nucleus (STN), intraoperatively. MER signals deliver a framework to reveal the underlying characteristics of STN. The motivation behind this work is to explore the application of Higher-order statistics and spectra (HOS) for an automated delineation of the neurophysiological borders of STN using MER signals. Database collected from 21 PD patients were used. Two HOS methods (Bispectrum and cumulant) were exploited to probe non-Gaussian properties of STN region. This is followed by utilizing classifiers, namely K-nearest neighbor, decision tree, Boosting and support vector machine (SVM), to choose the superior classifier. Comparison of the performance achieved via HOS alongside the state-of-the-art techniques shows that the proposed features are better suited for identifying STN borders and achieve higher results. Average classification accuracy, sensitivity, specificity, area under the curve and Youden’s J statistics of 94.81%, 96.73%, 92.15%, 0.9444% and 0.8888, respectively, were yielded using SVM with 8 bispectrum and 241 cumulants features. The proposed model can aid the neurosurgeon in STN detection.

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A novel deep recurrent convolutional neural network for subthalamic nucleus localization using local field potential signals

Hosny Mohamed, Zhu Minwei, Su Yixian, Gao Wenpeng, Fu Yili

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 4

1561--1574

EN

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a well-established interventional treatment for improving motor symptoms of patients suffering from Parkinson’s disease (PD). While STN is originally localized using imaging modalities, additional intraoperative guidance such as microelectrode recording (MER) is crucial to refine the final electrode trajectory. Analysis of MER by an experienced neurophysiologist maintains good clinical outcomes, although the procedure requires long duration and jeopardizes the safety of the surgery. Lately, local field potentials (LFP) investigation has inspired the emergence of adaptive DBS and revealed beneficial perception of PD mechanisms. Several studies confronting LFP analysis to detect the anatomical borders of STN, have focused on handcrafted feature engineering, which does not certainly capture delicate differences in LFP. This study gauges the ability of deep learning to exhibit valuable insight into the electrophysiological neural rhythms of STN using LFP. A recurrent convolutional neural network (CNN) strategy is presented, where local features are extracted from LFP signals via CNN, followed by recurrent layers to aggregate the best features for classification. The proposed model outperformed the state-of-the-art techniques, yielding highest average accuracy of 96.79%. This is the first study on the analysis of LFP signals to localize STN using deep recurrent CNN. The developed model has the potential to extract high level biomarkers regarding STN region, which would boost the neurosurgeon’s confidence in adjusting the trajectory intraoperatively for optimal lead implantation. LFP is a robust guidance tool and could be an alternative solution to the current scenario using MER.

4

Data mining and neural network simulations can help to improve deep brain stimulation effects in parkinson’s disease

Szymański A., Kubis A., Przybyszewski A. W.

Computer Science

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2015

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Vol. 16 (2)

199--215

EN

Parkinson’s Disease (PD) is primary related to substantia nigra degeneration and, thus, dopamine insufficiency. L-DOPA as a precursor of dopamine is the standard medication in PD. However, disease progression causes L-DOPA therapy efficiency decay (on-off symptom fluctuation), and neurologists often decide to classify patients for DBS (Deep Brain Stimulation) surgery. DBS treatment is based on stimulating the specific subthalamic structure: subthalamic nucleus (STN) in our case. As STN consists of parts with different physiological functions, finding the appropriate placement of the DBS electrode contacts is challenging. In order to predict the neurological effects related to different electrodecontact stimulations, we have tracked connections between the stimulated part of STN and the cortex with the help of diffusion tensor imaging (DTI). By changing a contacts number and amplitude of stimulus (proportional in size to stimulated area), we have determined connections to cortical areas and related neurological effects. We have applied data mining methods to predict which contact (and at what amplitude) should be stimulated in order to improve a particular symptom. We have compared different data mining methods: Wekas Random Forest classifier and Rough Set Exploration System (RSES). We have demonstrated that the Weka classifier was more accurate when predicting the effects of stimulations on general neurological improvements, while RSES was more accurate when using specific neurological symptoms. We have simulated other effects of stimulation related to the interruption of pathological oscillation in the basal ganglia found in PD. Our model represents possible STN neural population with inhibitory and excitatory connections that have pathologically synchronized oscillations. High-frequency electrical stimulation has interrupted synchronization. something that is also observed in PD patients.

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Verification of the functionality of device for monitoring human tremor

Szumilas M., Lewenstein K., Ślubowska E.

Biocybernetics and Biomedical Engineering

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2015

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Vol. 35, no. 4

240--246

EN

Tremor accompanying the Parkinson's disease is perceived as one of its most disturbing symptoms. Among available treatments there is a deep brain stimulation, which effectively reduces unwanted oscillations of patient's muscles. Nevertheless, setting parameters of the stimulation is a highly empirical process and the final outcome depends primarily on the experience of involved medical personnel. We present a device which is meant to provide a clinician with feedback based on the measurable parameters of tremor, monitored in many points of the body simultaneously. Functionality of the device was verified at a basic level. During the verification, the vibrations were recorded: (1) in a relaxed arm, (2) during voluntary contraction of muscles and (3) after being damped by tissues (in this case the vibrations were introduced from an external generator). Moreover, a method of selecting optimal place for mounting vibration probes is presented.

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Subthalamic nucleus deep brain stimulation in Parkinson's disease

Mandat T., Koziara H., Nauman P., Tykocki T., Bonicki W.

Biocybernetics and Biomedical Engineering

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2011

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Vol. 31, no. 3

47-55

EN

A group of 37 patients diagnosed with Parkinson's disease (PD) were treated with subthalamic deep brain stimulation (STN DBS). The mean age at implantation was 59 - 11 years and PD has been present from 6 to 17 years (mean 9). The STN was identified by direct and indirect methods: macro stimulation and microrecording in all cases. At a three month follow-up, the authors observed a mean reduction of 49% in UPDRS II score and a mean reduction of 65% in UPDRS III score. Mean reduction of 1-dopa consumption was 62%. The authors concluded that STN DBS safely reduces disabling symptoms of PD.

7

Deep brain stimulation in generalized dystonia

Mandat T., Koziara H., Nauman P., Bonicki W.

Biocybernetics and Biomedical Engineering

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2011

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Vol. 31, no. 3

57-63

EN

Eleven patients with diagnosed generalized dystonia (GD) were treated with deep brain stimulation (DBS). The clinical status of the patients was evaluated and recorded pre- and post-operatively. The target globus pallidus or subthalamic nucleus was identified with direct and indirect methods and confirmed electrophysiologically in the operating room. All eleven patients reported subjective improvement following the surgery what was confirmed using scales tailored for the group. The improvement lasted from 10 months to 40 months. DBS can be effectively and safely utilized to alleviate symptoms of generalized dystonia in selected patients.

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Deep brain recordings

Neagu B., Chen R.

Biocybernetics and Biomedical Engineering

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2011

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Vol. 31, no. 3

19-33

EN

Depth recordings from human subcortical structures have improved our knowledge of human brain circuitries and provided better understanding of the effects and mechanism of action of deep brain stimulation. Two types of signals can be recorded: single unit spikes and local field potentials (LFP). The basal ganglia (BG) are particularly well suited for deep brain recordings and here we review how the oscillatory activities recorded in these structures helped improve our understanding of the sensorimotor brain functions in particular, along with cognitive and emotional-motivational. The oscillations may be classified by frequency into bands at < 8, 8-30 and> 60 Hz. The best characterized band is the 8-30 Hz and existing evidence suggests that it is antikinetic and inversely related to motor processing. On the other hand, accumulating evidence suggests that the > 60 Hz band may be related to normal function.