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Total 1309 results found since Jan 2013.

Machine learning models of plasma proteomic data predict mood in chronic stroke and tie it to aberrant peripheral immune responses
Brain Behav Immun. 2023 Aug 7:S0889-1591(23)00225-8. doi: 10.1016/j.bbi.2023.08.002. Online ahead of print.ABSTRACTPost-stroke depression is common, long-lasting and associated with severe morbidity and death, but mechanisms are not well-understood. We used a broad proteomics panel and developed a machine learning algorithm to determine whether plasma protein data can predict mood in people with chronic stroke, and to identify proteins and pathways associated with mood. We used Olink to measure 1,196 plasma proteins in 85 participants aged 25 and older who were between 5 months and 9 years after ischemic stroke. Mood was a...
Source: Brain, Behavior, and Immunity - August 9, 2023 Category: Neurology Authors: Neda H Bidoki Kristy A Zera Huda Nassar Lauren L Drag Michael Mlynash Elizabeth Osborn Muhith Musabbir Da Eun K Kim Maria Paula Mendez Maarten G Lansberg Nima Aghaeepour Marion S Buckwalter Source Type: research

Interpretable machine learning for predicting 28-day all-cause in-hospital mortality for hypertensive ischemic or hemorrhagic stroke patients in the ICU: a multi-center retrospective cohort study with internal and external cross-validation
ConclusionsThe XGBoost model accurately predicted 28-day all-cause in-hospital mortality among hypertensive ischemic or hemorrhagic stroke patients admitted to the ICU. The SHAP method can provide explicit explanations of personalized risk prediction, which can aid physicians in understanding the model.
Source: Frontiers in Neurology - August 8, 2023 Category: Neurology Source Type: research

Machine learning in the prediction of post-stroke cognitive impairment: a systematic review and meta-analysis
ConclusionML is a potential tool for predicting PSCI and may be used to develop simple clinical scoring scales for subsequent clinical use.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=383476.
Source: Frontiers in Neurology - August 3, 2023 Category: Neurology Source Type: research

A randomized controlled trial of Changes in resting-state functional connectivity associated with short-term motor learning of chopstick use with the non-dominant hand
CONCLUSION]: Offline enhancement of RSFC in these networks was shown to contribute to early chopstick-use motor learning with the left hand. These results serve as a basis for future studies on compensatory networks in individuals with stroke.PMID:37506851 | DOI:10.1016/j.bbr.2023.114599
Source: Brain Research - July 28, 2023 Category: Neurology Authors: Sayori Takeda Reiko Miyamoto Source Type: research

Corrigendum: Ischemic and haemorrhagic stroke risk estimation using a machine-learning-based retinal image analysis
Source: Frontiers in Neurology - July 27, 2023 Category: Neurology Source Type: research

Identification of a miRNA –mRNA regulatory network for post-stroke depression: a machine-learning approach
ConclusionThe study highlighted gene signatures for PSD with three genes (SPATA2, ZNF208, and YTHDC1) and four upstream miRNAs (miR-6883-5p, miR-6873-3p, miR-4776-3p, and miR-6738-3p). These biomarkers could further our understanding of the pathogenesis of PSD.
Source: Frontiers in Neurology - July 17, 2023 Category: Neurology Source Type: research

Neurofunctional correlates of a neurorehabilitation system based on eye movements in chronic stroke impairment levels: A pilot study
ConclusionThese promising results have a potential application as a new game-based neurorehabilitation approach to enhance the motor activity of stroke patients.
Source: Brain and Behavior - July 12, 2023 Category: Neurology Authors: B árbara R. García‐Ramos, Rebeca Villarroel, José L. González‐Mora, Consuelo Revert, Cristián Modroño Tags: ORIGINAL ARTICLE Source Type: research

Connectomic insight into unique stroke patient recovery after rTMS treatment
This study demonstrates for the first time the feasibility of using individualized connectivity analyses in differentiating unique phenotypes in rTMS treatment responses and recovery. This personalized connectomic approach may be utilized in the future to better understand patient recovery trajectories with neuromodulatory treatment.
Source: Frontiers in Neurology - July 6, 2023 Category: Neurology Source Type: research

Moderate-intensity cardiovascular exercise performed before motor practice attenuates offline implicit motor learning in stroke survivors but not age-matched neurotypical adults
Exp Brain Res. 2023 Jul 3. doi: 10.1007/s00221-023-06659-w. Online ahead of print.ABSTRACTThe acute impact of cardiovascular exercise on implicit motor learning of stroke survivors is still unknown. We investigated the effects of cardiovascular exercise on implicit motor learning of mild-moderately impaired chronic stroke survivors and neurotypical adults. We addressed whether exercise priming effects are time-dependent (e.g., exercise before or after practice) in the encoding (acquisition) and recall (retention) phases. Forty-five stroke survivors and 45 age-matched neurotypical adults were randomized into three sub-group...
Source: Brain Research - July 3, 2023 Category: Neurology Authors: Giordano Marcio Gatinho Bonuzzi Flavio Henrique Bastos Nicolas Schweighofer Eric Wade Carolee Joyce Winstein Camila Torriani-Pasin Source Type: research