Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Journal of neural engineering
A roadmap to navigate the future of neural engineering
A group of leaders in neural engineering collaborated to develop a roadmap to navigate the future of neural engineering. We covered a range of themes, including brain machine interfaces, neural modeling, artificial intelligence and machine learning, neural interfaces, neural imaging, augmented rehabilitation, and neuromaterials. For each topic we reviewed the current status, identified current and future challenges, and speculated on the emerging and necessary advances in science and technology to meet these challenges. Neural engineering will continue to yield the approaches and insights that advance the diagnosis and treatment of nervous system disorders, as well as provide new understanding of neural function.
28 July 2026
Read appraisal →Neurology
Hypnotic Cognitive Therapy for Chronic Pain in Spinal Cord Injury: A Randomized Controlled Trial
BACKGROUND AND OBJECTIVES: Chronic pain is highly prevalent, disabling, and difficult to treat in people with spinal cord injury (SCI). Our objective was to determine whether hypnotic cognitive therapy (HYP-CT) significantly reduces average pain intensity. METHODS: This single-blind, randomized, parallel-group trial investigated the efficacy of HYP-CT in treating moderate-to-severe chronic pain in community-residing adults with SCI. Participants were recruited nationwide through SCI organizations and randomized 1:1 to HYP-CT vs usual care (UC). We used computerized permuted block randomization with variable block sizes and stratified by sex and worst pain type. HYP-CT was delivered by a psychologist over the telephone or through Zoom and consisted of 6 weekly 60-minute sessions plus instructions to practice self-hypnosis daily independently by listening to session recordings. Controls were encouraged to continue current treatments or seek additional pain treatments. The primary outcome was average pain intensity assessed 4 times per week on a 0-10 numerical rating scale and averaged. The primary end point was at 6 weeks after randomization, with a follow-up assessment at 12 weeks. All outcomes were assessed through structured telephone interviews performed by blinded assessors. RESULTS: A total of 127 participants were randomized to HYP-CT (n = 64) vs UC (n = 63). Forty-eight percent were treated over the telephone and 52% through Zoom. The group was 42% female, had a mean age of 51.3 years (15.4 years after SCI), and had a mean baseline pain intensity of 5.93. The worst pain was neuropathic in 57% of cases. The primary outcome, average pain intensity, decreased more in the HYP-CT group compared with UC controls at 6 (-0.55, 95% CI -1.04 to -0.06) and 12 (-0.79, 95% CI -1.28 to -0.29) weeks. Depression declined significantly more in the HYP-CT vs UC groups at 6 and 12 weeks. Planned exploratory analyses suggested that the effect of HYP-CT on pain intensity was similar when delivered over the telephone vs through Zoom, and that pain intensity decreased more in those with all neuropathic pain vs those with mixed pain at 12 weeks. DISCUSSION: The study suggests that HYP-CT is an effective adjunctive treatment for SCI-related pain. Replication studies are needed. TRIAL REGISTRATION INFORMATION: The study was registered on ClinicalTrials.gov (NCT03857672) on February 28, 2019 and study enrollment commenced on April 24, 2019. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that, in patients with SCI, HYP-CT improves average pain intensity at 6 weeks compared with UC.
3 May 2026
Read appraisal →Biomedizinische Technik. Biomedical engineering
Evaluation of an adaptive system for breathing control in closed-loop diaphragm pacing
OBJECTIVES: Existing diaphragm pacing (DP) system use an open-loop control method with a fixed stimulation mode to control breathing. It requires doctors to manually adjust stimulation parameters to meet the patient's ventilation needs. METHODS: A neural network adaptive controller was tested to control breathing in DP system. For the diaphragm motion model, the respiratory airflow of healthy adults and rabbits was collected and compared with the simulation calculation results to verify the accuracy of the model. The performance of the adaptive controller was evaluated comparatively with that of the PID controller. Adaptive controller consists of a neural network and adaptively adjusts stimulation parameters to produce the desired respiratory volume waveform. Superiority of the output performance of the adaptive controller was further studied by setting various diaphragm model parameters. We further verified the feasibility of the adaptive controller through animal testing. RESULTS: The adaptive controller is better than the PID controller in maintaining the stability of the desired breath volume. Application of the adaptive controller can reduce the root mean square (RMS) error between the desired breath volume and the actual value to less than 6 %. CONCLUSIONS: This study demonstrates the potential application of adaptive controllers in closed-loop DP systems.
27 Apr 2026
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