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Enhancing individual glomerular filtration rate assessment: can we trust the equation? Development and validation of machine learning models to assess the trustworthiness of estimated GFR compared to measured GFR.
BMC Nephrol
January 2025
Department of Nephrology-Dialysis-Transplantation, University of Liège, CHU Sart Tilman, Liège, Belgium.
Background: Creatinine-based estimated glomerular filtration rate (eGFR) equations are widely used in clinical practice but exhibit inherent limitations. On the other side, measuring GFR is time consuming and not available in routine clinical practice. We developed and validated machine learning models to assess the trustworthiness (i.
View Article and Find Full Text PDFFew-shot transfer learning for individualized braking intent detection on neuromorphic hardware.
J Neural Eng
January 2025
Mechanical and Aerospace, Missouri University of Science and Technology, 400 W 13th St., Rolla, Missouri, 65409, UNITED STATES.
This work explores use of a few-shot transfer learning method to train and implement a convolutional spiking neural network (CSNN) on a BrainChip Akida AKD1000 neuromorphic system-on-chip for developing individual-level, instead of traditionally used group-level, models using electroencephalographic data. The efficacy of the method is studied on an advanced driver assist system related task of predicting braking intention. \emph{Approach}: Data are collected from participants operating an NVIDIA JetBot on a testbed simulating urban streets for three different scenarios.
View Article and Find Full Text PDFPatterns of Change in Functional Connectivity and Motor Performance Are Different in Youth Recently Recovered from Concussion.
Neurotrauma Rep
January 2025
Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Adolescents who have sustained a concussion or mild traumatic brain injury (mTBI) are prone to repeat injuries which may be related to subtle motor deficits persisting after clinical recovery. Cross-sectional research has found that these deficits are associated with altered functional connectivity among somatomotor, dorsal attention, and default mode networks. However, our understanding of how these brain-behavior relationships change over time after clinical recovery is limited.
View Article and Find Full Text PDFCognitive Outcomes After Transcranial Magnetic Stimulation for the Treatment of Late-Life Depression: Résultats cognitifs après la stimulation magnétique transcrânienne pour le traitement de la dépression chez les personnes âgées.
Can J Psychiatry
January 2025
Temerty Centre for Therapeutic Brain Intervention and Campbell Family Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.
Background: Late-life depression (LLD) is often accompanied by cognitive impairment, which may persist despite antidepressant treatment. Repetitive transcranial magnetic stimulation (rTMS) is an efficacious treatment for depression, with potential benefits on cognitive functioning. However, research on cognitive effects is inconclusive, relatively sparse in LLD, and predominantly focused on group-level cognitive changes.
View Article and Find Full Text PDFMachine learning meta-analysis identifies individual characteristics moderating cognitive intervention efficacy for anxiety and depression symptoms.
NPJ Digit Med
January 2025
School of Psychological Sciences, University of Haifa, Haifa, Israel.
Cognitive training is a promising intervention for psychological distress; however, its effectiveness has yielded inconsistent outcomes across studies. This research is a pre-registered individual-level meta-analysis to identify factors contributing to cognitive training efficacy for anxiety and depression symptoms. Machine learning methods, alongside traditional statistical approaches, were employed to analyze 22 datasets with 1544 participants who underwent working memory training, attention bias modification, interpretation bias modification, or inhibitory control training.
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