Resting-state frontal electroencephalography (EEG) biomarkers for detecting the severity of chronic neuropathic pain.

Increasing evidence is present to enable pain measurement by using frontal channel EEG-based signals with spectral analysis and phase-amplitude coupling. To identify frontal channel EEG-based biomarkers for quantifying pain severity, we investigated band-power features to more complex features and employed various machine learning algorithms to assess the viability of these features. We utilized a public EEG dataset obtained from 36 patients with chronic pain during an eyes-open resting state and performed correlation analysis between clinically labelled pain scores and EEG features from Fp1 and Fp2 channels (EEG band-powers, phase-amplitude couplings (PAC), and its asymmetry features).

Feasibility and safety report on robotic assistance for cervical pedicle screw fixation: a cadaveric study.

This cadaveric study aimed to evaluate the safety and usability of a novel robotic system for posterior cervical pedicle screw fixation. Three human cadaveric specimens and C2-T3 were included. Freshly frozen human cadaver specimens were prepared and subjected to robot-assisted posterior cervical pedicle screw fixation using the robotic system.

TomoRay: Generating Synthetic Computed Tomography of the Spine From Biplanar Radiographs.

Objective: Computed tomography (CT) imaging is a cornerstone in the assessment of patients with spinal trauma and in the planning of spinal interventions. However, CT studies are associated with logistical problems, acquisition costs, and radiation exposure. In this proof-of-concept study, the feasibility of generating synthetic spinal CT images using biplanar radiographs was explored.

Whole Spine Segmentation Using Object Detection and Semantic Segmentation.

Objective: Virtual and augmented reality have enjoyed increased attention in spine surgery. Preoperative planning, pedicle screw placement, and surgical training are among the most studied use cases. Identifying osseous structures is a key aspect of navigating a 3-dimensional virtual reconstruction.

The Combined Effects of RhBMP-2 and Systemic RANKL Inhibitor in Patients With Bone Density Loss Undergoing Posterior Lumbar Interbody Fusion: A Retrospective Observational Analysis With Propensity Score Matching.

Objective: The risks of nonunion and subsidence are high in patients with bone density loss undergoing spinal fusion surgery. The internal application of recombinant human bone morphogenic protein 2 (rhBMP-2) in an interbody cage improves spinal fusion; however, related complications have been reported. Denosumab, a human monoclonal antibody targeting the receptor activator of nuclear factor kappa B ligand (RANKL), hinders osteoblast differentiation and function.

Risk of insomnia symptoms according to Work-Family Conflict by workers' characteristics.

Background: Work-Family Conflict means that the demands of work and family roles cannot be met simultaneously, so one cannot concentrate on one's work or family role. This conflict can negatively affect mental health and cause insomnia symptoms.

Methods: This study was conducted on 20,442 subjects.

Long-Term Outcomes after Multimodal Treatment for Clival Chordoma: Efficacy of the Endonasal Transclival Approach with Early Adjuvant Radiation Therapy.

This study investigates the long-term outcomes of clival chordoma patients treated with the endonasal transclival approach (ETCA) and early adjuvant radiation therapy. A retrospective review of 17 patients (2002-2013) showed a 10-year progression-free survival (PFS) rate of 67.4%, with the ETCA group showing fewer progressions and cranial neuropathies than those treated with combined approaches.

Long-term results comparison after anterior cervical discectomy with BGS-7 spacer (NOVOMAX®-C) and allograft spacer: A prospective observational study.

In an anterior cervical discectomy and fusion (ACDF), various types of graft materials including autograft, allograft, and synthetic graft have been used to achieve adequate spinal fusion. Allograft spacer is mainly used in cervical fusion, especially in the anterior approach. The synthetic bone graft material BGS-7(CaO-SiO2-P2O5-B2O3, bioactive Glass-Ceramics) can bind with surrounding bone tissue by forming a hydroxyapatite layer bone bridge, leading to faster graft osseointegration.

Risk factors for unplanned reoperation after corrective surgery for adult spinal deformity.

To determine the major risk factors for unplanned reoperations (UROs) following corrective surgery for adult spinal deformity (ASD) and their interactions, using machine learning-based prediction algorithms and game theory. Patients who underwent surgery for ASD, with a minimum of two-year follow-up, were retrospectively reviewed. In total, 210 patients were included and randomly allocated into training (70% of the sample size) and test (the remaining 30%) sets to develop the machine learning algorithm.

Clinical Effect of Transverse Process Hook with K-Means Clustering-Based Stratification of Computed Tomography Hounsfield Unit at Upper Instrumented Vertebra Level in Adult Spinal Deformity Patients.

Objective: This study aimed to investigate the efficacy of transverse process (TP) hook system at the upper instrumented vertebra (UIV) for preventing screw pullout in adult spinal deformity surgery using the pedicle Hounsfield unit (HU) stratification based on K-means clustering.

Methods: We retrospectively reviewed 74 patients who underwent deformity correction surgery between 2011 and 2020 and were followed up for >12 months. Pre- and post-operative data were used to determine the incidence of screw pullout, UIV TP hook implementation, vertebral body HU, pedicle HU, and patient outcomes.

Clozapine-Induced Chemogenetic Neuromodulation Rescues Post-Stroke Deficits After Chronic Capsular Infarct.

Long-term disabilities induced by stroke impose a heavy burden on patients, families, caregivers, and public health systems. Extensive studies have demonstrated the therapeutic value of neuromodulation in enhancing post-stroke recovery. Among them, chemogenetic neuromodulation activated by clozapine-N-oxide (CNO) has been proposed as the potential tool of neuromodulation.

Pilot study of a single-channel EEG seizure detection algorithm using machine learning.

Objective: Seizures are one of the most common emergencies in the neonatal intensive care unit (NICU). They are identified through visual inspection of electroencephalography (EEG) reports and treated by neurophysiologic experts. To support clinical seizure detection, several feature-based automatic neonatal seizure detection algorithms have been proposed.

Optimizing clozapine for chemogenetic neuromodulation of somatosensory cortex.

Clozapine (CLZ) has been proposed as an agonist for Designer Receptors Exclusively Activated by Designer Drugs (DREADDs), to replace Clozapine-N-oxide (CNO); however, there are no reliable guidelines for the use of CLZ for chemogenetic neuromodulation. We titrated the optimal dose of CLZ required to evoke changes in neural activity whilst avoiding off-target effects. We also performed [F]Fluoro-deoxy-glucose micro positron emission tomography (FDG-microPET) scans to determine the global effect of CLZ-induced hM3D(Gq) DREADD activation in the rat brain.

Mild Cognitive Impairment Diagnosis Using Extreme Learning Machine Combined With Multivoxel Pattern Analysis on Multi-Biomarker Resting-State FMRI.

This paper proposed a classification framework that integrates hybrid multivoxel pattern analyses (MVPA) and extreme learning machine (ELM) for automated Mild Cognitive Impairment (MCI) diagnosis applied on concatenations of multi-biomarker resting-state fMRI. Given three-dimensional (3D) regional coherences and functional connectivity patterns measured during resting state, we performed 3D univariate t-tests to obtain initial univariate features which show the significant changes. To enhance discriminative patterns, we employed multivariate feature reductions using recursive feature elimination in combination with univariate t-test.

3D-Deep Learning Based Automatic Diagnosis of Alzheimer's Disease with Joint MMSE Prediction Using Resting-State fMRI.

We performed this research to 1) evaluate a novel deep learning method for the diagnosis of Alzheimer's disease (AD) and 2) jointly predict the Mini Mental State Examination (MMSE) scores of South Korean patients with AD. Using resting-state functional Magnetic Resonance Imaging (rs-fMRI) scans of 331 participants, we obtained functional 3-dimensional (3-D) independent component spatial maps for use as features in classification and regression tasks. A 3-D convolutional neural network (CNN) architecture was developed for the classification task.

Evaluation of Functional Decline in Alzheimer's Dementia Using 3D Deep Learning and Group ICA for rs-fMRI Measurements.

: To perform automatic assessment of dementia severity using a deep learning framework applied to resting-state functional magnetic resonance imaging (rs-fMRI) data. : We divided 133 Alzheimer's disease (AD) patients with clinical dementia rating (CDR) scores from 0.5 to 3 into two groups based on dementia severity; the groups with very mild/mild (CDR: 0.

Hybrid multivariate pattern analysis combined with extreme learning machine for Alzheimer's dementia diagnosis using multi-measure rs-fMRI spatial patterns.

Background: Early diagnosis of Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) is essential for timely treatment. Machine learning and multivariate pattern analysis (MVPA) for the diagnosis of brain disorders are explicitly attracting attention in the neuroimaging community. In this paper, we propose a voxel-wise discriminative framework applied to multi-measure resting-state fMRI (rs-fMRI) that integrates hybrid MVPA and extreme learning machine (ELM) for the automated discrimination of AD and MCI from the cognitive normal (CN) state.