Machine learning-based prediction of post-stroke cognitive status using electroencephalography-derived brain network attributes

ObjectivesMore than half of patients with acute ischemic stroke develop post-stroke cognitive impairment (PSCI), a significant barrier to future neurological recovery. Thus, predicting cognitive trajectories post-AIS is crucial. Our primary objective is to determine whether brain network properties from electroencephalography (EEG) can predict post-stroke cognitive function using machine learning approach.MethodsWe enrolled consecutive stroke patients who underwent both EEG during the acute stroke phase and cognitive assessments 3 months post-stroke. We preprocessed acute stroke EEG data to eliminate low-quality epochs, then performed independent component analysis and quantified network characteristics using iSyncBrain®. Cognitive function was evaluated using the Montreal cognitive assessment (MoCA). We initially categorized participants based on the lateralization of their lesions and then developed machine learning models to predict cognitive status in the left and right hemisphere lesion groups.ResultsEighty-seven patients were included, and the accuracy of lesion laterality prediction using EEG attributes was 97.0%. In the left hemispheric lesion group, the network attributes of the theta band were significantly correlated with MoCA scores, and higher global efficiency, clustering coefficient, and lower characteristic path length were associated with higher MoCA scores. Most features related to cognitive scores were selected from the frontal lobe. The predictive powers (R-squared) were 0.76 and 0.65 for the left and right stroke groups, respectively.ConclusionEstimating EEG-based network properties in the acute phase of ischemic stroke through a machine learning model has a potential to predict cognitive outcomes after ischemic stroke

Cost-effective and strongly integrated fabric-based wearable piezoelectric energy harvester

Fabric-based wearable electronics are becoming more important in the fourth industrial revolution (4IR) era due to their connectivity, wearability, comfort, and durability. Conventional fabric-based wearable electronics have been demonstrated by several researchers, but still need complex methods or additional supports to be fabricated and sewed in clothing. Herein, a cost-effective, high throughput, and strongly integrated fabric-based wearable piezoelectric energy harvester (fabric-WPEH) is demonstrated. The fabric-WPEH has a heterostructure of a ferroelectric polymer, poly(vinylidene fluoride-co-trifluoroethylene) [P(VDF-TrFE)] and two conductive fabrics via simple fabrication of tape casting and hot pressing. Our fabrication process would enable the direct application of the unit device to general garments using hot pressing as graphic patches can be attached to the garments by heat press. Simulation and experimental analysis demonstrate fully bendable, compact and concave interfaces and a high piezoelectric d33 coefficient (−32.0 pC N−1) of the P(VDF-TrFE) layer. The fabric-WPEH generates piezoelectric output signals from human motions (pressing, bending) and from quantitative force test machine pressing. Furthermore, a record high interfacial adhesion strength (22 N cm−1) between the P(VDF-TrFE) layer and fabric layers has been measured by surface and interfacial cutting analysis system (SAICAS) for the first time in the field of fabric-based wearable piezoelectric electronics. © 2020 Elsevier Ltd1

Image_2_Machine learning-based prediction of post-stroke cognitive status using electroencephalography-derived brain network attributes.PNG

ObjectivesMore than half of patients with acute ischemic stroke develop post-stroke cognitive impairment (PSCI), a significant barrier to future neurological recovery. Thus, predicting cognitive trajectories post-AIS is crucial. Our primary objective is to determine whether brain network properties from electroencephalography (EEG) can predict post-stroke cognitive function using machine learning approach.MethodsWe enrolled consecutive stroke patients who underwent both EEG during the acute stroke phase and cognitive assessments 3 months post-stroke. We preprocessed acute stroke EEG data to eliminate low-quality epochs, then performed independent component analysis and quantified network characteristics using iSyncBrain®. Cognitive function was evaluated using the Montreal cognitive assessment (MoCA). We initially categorized participants based on the lateralization of their lesions and then developed machine learning models to predict cognitive status in the left and right hemisphere lesion groups.ResultsEighty-seven patients were included, and the accuracy of lesion laterality prediction using EEG attributes was 97.0%. In the left hemispheric lesion group, the network attributes of the theta band were significantly correlated with MoCA scores, and higher global efficiency, clustering coefficient, and lower characteristic path length were associated with higher MoCA scores. Most features related to cognitive scores were selected from the frontal lobe. The predictive powers (R-squared) were 0.76 and 0.65 for the left and right stroke groups, respectively.ConclusionEstimating EEG-based network properties in the acute phase of ischemic stroke through a machine learning model has a potential to predict cognitive outcomes after ischemic stroke.</p

Image_3_Machine learning-based prediction of post-stroke cognitive status using electroencephalography-derived brain network attributes.PNG

ObjectivesMore than half of patients with acute ischemic stroke develop post-stroke cognitive impairment (PSCI), a significant barrier to future neurological recovery. Thus, predicting cognitive trajectories post-AIS is crucial. Our primary objective is to determine whether brain network properties from electroencephalography (EEG) can predict post-stroke cognitive function using machine learning approach.MethodsWe enrolled consecutive stroke patients who underwent both EEG during the acute stroke phase and cognitive assessments 3 months post-stroke. We preprocessed acute stroke EEG data to eliminate low-quality epochs, then performed independent component analysis and quantified network characteristics using iSyncBrain®. Cognitive function was evaluated using the Montreal cognitive assessment (MoCA). We initially categorized participants based on the lateralization of their lesions and then developed machine learning models to predict cognitive status in the left and right hemisphere lesion groups.ResultsEighty-seven patients were included, and the accuracy of lesion laterality prediction using EEG attributes was 97.0%. In the left hemispheric lesion group, the network attributes of the theta band were significantly correlated with MoCA scores, and higher global efficiency, clustering coefficient, and lower characteristic path length were associated with higher MoCA scores. Most features related to cognitive scores were selected from the frontal lobe. The predictive powers (R-squared) were 0.76 and 0.65 for the left and right stroke groups, respectively.ConclusionEstimating EEG-based network properties in the acute phase of ischemic stroke through a machine learning model has a potential to predict cognitive outcomes after ischemic stroke.</p

Table_1_Machine learning-based prediction of post-stroke cognitive status using electroencephalography-derived brain network attributes.docx

ObjectivesMore than half of patients with acute ischemic stroke develop post-stroke cognitive impairment (PSCI), a significant barrier to future neurological recovery. Thus, predicting cognitive trajectories post-AIS is crucial. Our primary objective is to determine whether brain network properties from electroencephalography (EEG) can predict post-stroke cognitive function using machine learning approach.MethodsWe enrolled consecutive stroke patients who underwent both EEG during the acute stroke phase and cognitive assessments 3 months post-stroke. We preprocessed acute stroke EEG data to eliminate low-quality epochs, then performed independent component analysis and quantified network characteristics using iSyncBrain®. Cognitive function was evaluated using the Montreal cognitive assessment (MoCA). We initially categorized participants based on the lateralization of their lesions and then developed machine learning models to predict cognitive status in the left and right hemisphere lesion groups.ResultsEighty-seven patients were included, and the accuracy of lesion laterality prediction using EEG attributes was 97.0%. In the left hemispheric lesion group, the network attributes of the theta band were significantly correlated with MoCA scores, and higher global efficiency, clustering coefficient, and lower characteristic path length were associated with higher MoCA scores. Most features related to cognitive scores were selected from the frontal lobe. The predictive powers (R-squared) were 0.76 and 0.65 for the left and right stroke groups, respectively.ConclusionEstimating EEG-based network properties in the acute phase of ischemic stroke through a machine learning model has a potential to predict cognitive outcomes after ischemic stroke.</p

Image_1_Machine learning-based prediction of post-stroke cognitive status using electroencephalography-derived brain network attributes.PNG

ObjectivesMore than half of patients with acute ischemic stroke develop post-stroke cognitive impairment (PSCI), a significant barrier to future neurological recovery. Thus, predicting cognitive trajectories post-AIS is crucial. Our primary objective is to determine whether brain network properties from electroencephalography (EEG) can predict post-stroke cognitive function using machine learning approach.MethodsWe enrolled consecutive stroke patients who underwent both EEG during the acute stroke phase and cognitive assessments 3 months post-stroke. We preprocessed acute stroke EEG data to eliminate low-quality epochs, then performed independent component analysis and quantified network characteristics using iSyncBrain®. Cognitive function was evaluated using the Montreal cognitive assessment (MoCA). We initially categorized participants based on the lateralization of their lesions and then developed machine learning models to predict cognitive status in the left and right hemisphere lesion groups.ResultsEighty-seven patients were included, and the accuracy of lesion laterality prediction using EEG attributes was 97.0%. In the left hemispheric lesion group, the network attributes of the theta band were significantly correlated with MoCA scores, and higher global efficiency, clustering coefficient, and lower characteristic path length were associated with higher MoCA scores. Most features related to cognitive scores were selected from the frontal lobe. The predictive powers (R-squared) were 0.76 and 0.65 for the left and right stroke groups, respectively.ConclusionEstimating EEG-based network properties in the acute phase of ischemic stroke through a machine learning model has a potential to predict cognitive outcomes after ischemic stroke.</p

A Universal Perovskite Nanocrystal Ink for High-Performance Optoelectronic Devices

Semiconducting lead halide perovskite nanocrystals (PNCs) are regarded as promising candidates for next-generation optoelectronic devices due to their solution processability and outstanding optoelectronic properties. While the field of light-emitting diodes (LEDs) and photovoltaics (PVs), two prime examples of optoelectronic devices, has recently seen a multitude of efforts toward high-performance PNC-based devices, realizing both devices with high efficiencies and stabilities through a single PNC processing strategy has remained a challenge. In this work, diphenylpropylammonium (DPAI) surface ligands, found through a judicious ab-initio-based ligand search, are shown to provide a solution to this problem. The universal PNC ink with DPAI ligands presented here, prepared through a solution-phase ligand-exchange process, simultaneously allows single-step processed LED and PV devices with peak electroluminescence external quantum efficiency of 17.00% and power conversion efficiency of 14.92% (stabilized output 14.00%), respectively. It is revealed that a careful design of the aromatic rings such as in DPAI is the decisive factor in bestowing such high performances, ease of solution processing, and improved phase stability up to 120 days. This work illustrates the power of ligand design in producing PNC ink formulations for high-throughput production of optoelectronic devices; it also paves a path for &quot;dual-mode&quot; devices with both PV and LED functionalities