scholarly journals A multivariate neuromonitoring approach to neuroplasticity-based computerized cognitive training in recent onset psychosis

2020 ◽  
Author(s):  
Shalaila S. Haas ◽  
Linda A. Antonucci ◽  
Julian Wenzel ◽  
Anne Ruef ◽  
Bruno Biagianti ◽  
...  
Keyword(s):  
Cognitive Training ◽  
Brain Activity ◽  
Support Vector ◽  
Svm Classifier ◽  
Individual Response ◽  
Resting State Functional Connectivity ◽  
Recent Onset ◽  
Computerized Cognitive Training ◽  
Cognitive Improvement ◽  
Out Of Sample

Abstract Two decades of studies suggest that computerized cognitive training (CCT) has an effect on cognitive improvement and the restoration of brain activity. Nevertheless, individual response to CCT remains heterogenous, and the predictive potential of neuroimaging in gauging response to CCT remains unknown. We employed multivariate pattern analysis (MVPA) on whole-brain resting-state functional connectivity (rsFC) to (neuro)monitor clinical outcome defined as psychosis-likeness change after 10-hours of CCT in recent onset psychosis (ROP) patients. Additionally, we investigated if sensory processing (SP) change during CCT is associated with individual psychosis-likeness change and cognitive gains after CCT. 26 ROP patients were divided into maintainers and improvers based on their SP change during CCT. A support vector machine (SVM) classifier separating 56 healthy controls (HC) from 35 ROP patients using rsFC (balanced accuracy of 65.5%, P < 0.01) was built in an independent sample to create a naturalistic model representing the HC-ROP hyperplane. This model was out-of-sample cross-validated in the ROP patients from the CCT trial to assess associations between rsFC pattern change, cognitive gains and SP during CCT. Patients with intact SP threshold at baseline showed improved attention despite psychosis status on the SVM hyperplane at follow-up (p < 0.05). Contrarily, the attentional gains occurred in the ROP patients who showed impaired SP at baseline only if rsfMRI diagnosis status shifted to the healthy-like side of the SVM continuum. Our results reveal the utility of MVPA for elucidating treatment response neuromarkers based on rsFC-SP change and pave the road to more personalized interventions.

scholarly journals Who benefits from computerized cognitive training? Lower processing speed predicts greater cognitive improvement

2021 ◽  
Vol 5 ◽  
pp. 100149
Author(s):  
Sophie Schiff ◽  
Dakota A. Egglefield ◽  
Jeffrey N. Motter ◽  
Alice Grinberg ◽  
Sara N. Rushia ◽  
...  
Keyword(s):  
Cognitive Training ◽  
Processing Speed ◽  
Computerized Cognitive Training ◽  
Cognitive Improvement

scholarly journals Feasibility and Effectiveness of Remotely Supervised tDCS Combined With Computerized Cognitive Training for Patients With Stroke: A Randomized Sham-Controlled Study

2021 ◽  
Author(s):  
Ju-Yul Yoon ◽  
Mi-Nam Son ◽  
Yun-Ju Jo ◽  
Da-Sol Kim ◽  
Gi-Wook Kim ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Cognitive Training ◽  
Word Association ◽  
Functional Independence ◽  
Chronic Stroke ◽  
Controlled Study ◽  
Adherence Rate ◽  
Computerized Cognitive Training ◽  
Cognitive Improvement ◽  
Home Based

Abstract BackgroundCognitive impairment after stroke is an unfavorable factor for long-term functional independence. Transcranial direct current stimulation (tDCS) is a promising tool for improving cognitive function in patients with stroke. Home-based rehabilitation is increasingly required for patients with stroke, with greater benefits expected if supplemented with remotely supervised tDCS (RS-tDCS). We evaluated cognitive improvement and the feasibility of RS-tDCS in patients with chronic stroke.MethodsThirty chronic stroke patients with cognitive impairment (K-MoCA <26) received a computerized cognitive training package (ComcogTM, Neofact, Seoul, Korea), and were randomized into real RS-tDCS and sham RS-tDCS groups according to the application of tDCS. Participants were treated 5 days/week for 4 weeks. To ensure correct self-application of tDCS (Mindd Stim®, Ybrain Inc., Korea), patients and caregivers received training and were monitored. Several cognitive evaluations were performed. Rate of adherence to the appropriate RS-tDCS session was also investigated. ResultsAmong the 30 participants, 2 chose to withdraw and 2 were excluded due to noncompliance. In the within-group comparison, unlike the sham group (n= 14), the real group (n=12) showed a significant improvement in K-MoCA (intra-p=0.004 vs. 0.132), particularly in patients with moderate cognitive impairment (K-MoCA: 10–17; intra-p=0.001 vs. 0.835, K-MoCA: 18-25; intra-p=0.060 vs. 0.064). However, in K-DRS2, Stoop and K-BNT, both groups showed significant improvement, but there was no group time interaction. In the Trail Making Test, Go/no Go, and Controlled Oral Word Association Test, both groups did not show statistically significant improvement. A total of 551 of the 560 sessions conducted by 28 people were successfully performed (adherence rate: 98.4%) and no serious adverse effects were detected.ConclusionRS-tDCS is a safe and feasible rehabilitation modality for post-stroke cognitive dysfunction. It has the potential to enhance the effect of home-based CT. RS-tDCS seems to be particularly effective in patients with relatively more severe cognitive impairment who are capable of home-based training. Trial registration: Clinical Research information Service (KCT0003427). Registered 26 June 2018, https://cris.nih.go.kr/cris/en/search/search_result_st01.jsp?seq=12363

scholarly journals Altered Static and Dynamic Interhemispheric Resting-State Functional Connectivity in Patients With Thyroid-Associated Ophthalmopathy

2021 ◽  
Vol 15 ◽  
Author(s):  
Wen Chen ◽  
Hao Hu ◽  
Qian Wu ◽  
Lu Chen ◽  
Jiang Zhou ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Rating Scale ◽  
Area Under The Curve ◽  
Support Vector ◽  
Svm Classifier ◽  
Resting State Functional Connectivity ◽  
Ophthalmic Manifestations ◽  
Thyroid Associated Ophthalmopathy ◽  
Parietal Lobule

Purpose: Thyroid-associated ophthalmopathy (TAO) is a debilitating and sight-threatening autoimmune disease that severely impairs patients’ quality of life. Besides the most common ophthalmic manifestations, the emotional and psychiatric disturbances are also usually observed in clinical settings. This study was to investigate the interhemispheric functional connectivity alterations in TAO patients using resting-state functional magnetic resonance imaging (rs-fMRI).Methods: Twenty-eight TAO patients and 22 healthy controls (HCs) underwent rs-fMRI scans. Static and dynamic voxel-mirrored homotopic connectivity (VMHC) values were calculated and compared between the two groups. A linear support vector machine (SVM) classifier was used to examine the performance of static and dynamic VMHC differences in distinguishing TAOs from HCs.Results: Compared with HCs, TAOs showed decreased static VMHC in lingual gyrus (LG)/calcarine (CAL), middle occipital gyrus, postcentral gyrus, superior parietal lobule, inferior parietal lobule, and precuneus. Meanwhile, TAOs demonstrated increased dynamic VMHC in orbitofrontal cortex (OFC). In TAOs, static VMHC in LG/CAL was positively correlated with visual acuity (r = 0.412, P = 0.036), whilst dynamic VMHC in OFC was positively correlated with Hamilton Anxiety Rating Scale (HARS) score (r = 0.397, P = 0.044) and Hamilton Depression Rating Scale (HDRS) score (r = 0.401, P = 0.042). The SVM model showed good performance in distinguishing TAOs from HCs (area under the curve, 0.971; average accuracy, 94%).Conclusion: TAO patients had altered static and dynamic VMHC in the occipital, parietal, and orbitofrontal areas, which could serve as neuroimaging prediction markers of TAO.

scholarly journals Feasibility and effectiveness of remotely supervised tDCS combined with computerized cognitive training for patients with stroke: A randomized sham-controlled study

2021 ◽  
Author(s):  
Ju-Yul Yoon ◽  
Mi-Nam Son ◽  
Yun-Ju Jo ◽  
Da-Sol Kim ◽  
Gi-Wook Kim ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Cognitive Training ◽  
Word Association ◽  
Functional Independence ◽  
Chronic Stroke ◽  
Controlled Study ◽  
Adherence Rate ◽  
Computerized Cognitive Training ◽  
Cognitive Improvement ◽  
Home Based

Abstract Background Cognitive impairment after stroke is an unfavorable factor for long-term functional independence. Transcranial direct current stimulation (tDCS) is a promising tool for improving cognitive function in patients with stroke. Home-based rehabilitation is increasingly required for patients with stroke, with greater benefits expected if supplemented with remotely supervised tDCS (RS-tDCS). We evaluated cognitive improvement and the feasibility of RS-tDCS in patients with chronic stroke. Methods Thirty chronic stroke patients with cognitive impairment (K-MoCA <26) received a computerized cognitive training package (ComcogTM, Neofact, Seoul, Korea), and were randomized into real RS-tDCS and sham RS-tDCS groups according to the application of tDCS. Participants were treated 5 days/week for 4 weeks. To ensure correct self-application of tDCS (Mindd Stim®, Ybrain Inc., Korea), patients and caregivers received training and were monitored. Several cognitive evaluations were performed. Rate of adherence to the appropriate RS-tDCS session was also investigated. Results Among the 30 participants, 2 chose to withdraw and 2 were excluded due to noncompliance. In the within-group comparison, unlike the sham group (n= 14), the real group (n=12) showed a significant improvement in K-MoCA (intra-p=0.004 vs. 0.132), particularly in patients with moderate cognitive impairment (K-MoCA: 10–17; intra-p=0.001 vs. 0.835, K-MoCA: 18-25; intra-p=0.060 vs. 0.064). However, in K-DRS2, Stoop and K-BNT, both groups showed significant improvement, but there was no group time interaction. In the Trail Making Test, Go/no Go, and Controlled Oral Word Association Test, both groups did not show statistically significant improvement. A total of 551 of the 560 sessions conducted by 28 people were successfully performed (adherence rate: 98.4%) and no serious adverse effects were detected. Conclusion RS-tDCS is a safe and feasible rehabilitation modality for post-stroke cognitive dysfunction. It has the potential to enhance the effect of home-based CT. RS-tDCS seems to be particularly effective in patients with relatively more severe cognitive impairment who are capable of home-based training. Trial registration: Clinical Research information Service (KCT0003427). Registered 26 June 2018, https://cris.nih.go.kr/cris/en/search/search_result_st01.jsp?seq=12363

Computerized cognitive training in pediatric sickle cell disease: A randomized controlled pilot study.

2020 ◽  
Vol 8 (4) ◽  
pp. 390-401 ◽  
Author(s):  
Taryn M. Allen ◽  
Lindsay M. Anderson ◽  
Samuel M. Brotkin ◽  
Jennifer A. Rothman ◽  
Melanie J. Bonner
Keyword(s):  
Pilot Study ◽  
Sickle Cell Disease ◽  
Cognitive Training ◽  
Sickle Cell ◽  
Cell Disease ◽  
Computerized Cognitive Training ◽  
Randomized Controlled ◽  
Pediatric Sickle Cell Disease

Binary Spectrum Feature for Improved Classifier Performance

2020 ◽  
Author(s):  
Nalika Ulapane ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda
Keyword(s):  
Machine Learning ◽  
Classification Performance ◽  
Feature Reduction ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Monitoring Task ◽  
Classifier Performance ◽  
Spectrum Feature

<div>Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

Identification of Chronic Hypersensitivity Pneumonitis Biomarkers with Machine Learning and Differential Co-expression Analysis

2020 ◽  
Vol 20 ◽  
Author(s):  
Hongwei Zhang ◽  
Steven Wang ◽  
Tao Huang
Keyword(s):  
Feature Selection ◽  
Expression Analysis ◽  
Hypersensitivity Pneumonitis ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Great Promise ◽  
Support Vector ◽  
Svm Classifier ◽  
Clinical Tool ◽  
Chronic Hypersensitivity Pneumonitis

Aims: We would like to identify the biomarkers for chronic hypersensitivity pneumonitis (CHP) and facilitate the precise gene therapy of CHP. Background: Chronic hypersensitivity pneumonitis (CHP) is an interstitial lung disease caused by hypersensitive reactions to inhaled antigens. Clinically, the tasks of differentiating between CHP and other interstitial lungs diseases, especially idiopathic pulmonary fibrosis (IPF), were challenging. Objective: In this study, we analyzed the public available gene expression profile of 82 CHP patients, 103 IPF patients, and 103 control samples to identify the CHP biomarkers. Method: The CHP biomarkers were selected with advanced feature selection methods: Monte Carlo Feature Selection (MCFS) and Incremental Feature Selection (IFS). A Support Vector Machine (SVM) classifier was built. Then, we analyzed these CHP biomarkers through functional enrichment analysis and differential co-expression analysis. Result: There were 674 identified CHP biomarkers. The co-expression network of these biomarkers in CHP included more negative regulations and the network structure of CHP was quite different from the network of IPF and control. Conclusion: The SVM classifier may serve as an important clinical tool to address the challenging task of differentiating between CHP and IPF. Many of the biomarker genes on the differential co-expression network showed great promise in revealing the underlying mechanisms of CHP.

A fuzzy gaussian rank aggregation ensemble feature selection method for microarray data

2021 ◽  
Vol 24 (4) ◽  
pp. 289-301
Author(s):  
B. Venkatesh ◽  
J. Anuradha
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Classification Accuracy ◽  
Performance Metrics ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Binary Particle Swarm Optimization ◽  
Selection Methods

In Microarray Data, it is complicated to achieve more classification accuracy due to the presence of high dimensions, irrelevant and noisy data. And also It had more gene expression data and fewer samples. To increase the classification accuracy and the processing speed of the model, an optimal number of features need to extract, this can be achieved by applying the feature selection method. In this paper, we propose a hybrid ensemble feature selection method. The proposed method has two phases, filter and wrapper phase in filter phase ensemble technique is used for aggregating the feature ranks of the Relief, minimum redundancy Maximum Relevance (mRMR), and Feature Correlation (FC) filter feature selection methods. This paper uses the Fuzzy Gaussian membership function ordering for aggregating the ranks. In wrapper phase, Improved Binary Particle Swarm Optimization (IBPSO) is used for selecting the optimal features, and the RBF Kernel-based Support Vector Machine (SVM) classifier is used as an evaluator. The performance of the proposed model are compared with state of art feature selection methods using five benchmark datasets. For evaluation various performance metrics such as Accuracy, Recall, Precision, and F1-Score are used. Furthermore, the experimental results show that the performance of the proposed method outperforms the other feature selection methods.

scholarly journals The Primacy of High B-Value 3T-DWI Radiomics in the Prediction of Clinically Significant Prostate Cancer

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 739
Author(s):  
Alessandro Bevilacqua ◽  
Margherita Mottola ◽  
Fabio Ferroni ◽  
Alice Rossi ◽  
Giampaolo Gavelli ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Quantitative Imaging ◽  
B Value ◽  
Support Vector ◽  
Svm Classifier ◽  
Primary Role ◽  
High B Value ◽  
Wilcoxon Rank Sum Test ◽  
Apparent Diffusion ◽  
Clinically Significant

Predicting clinically significant prostate cancer (csPCa) is crucial in PCa management. 3T-magnetic resonance (MR) systems may have a novel role in quantitative imaging and early csPCa prediction, accordingly. In this study, we develop a radiomic model for predicting csPCa based solely on native b2000 diffusion weighted imaging (DWIb2000) and debate the effectiveness of apparent diffusion coefficient (ADC) in the same task. In total, 105 patients were retrospectively enrolled between January–November 2020, with confirmed csPCa or ncsPCa based on biopsy. DWIb2000 and ADC images acquired with a 3T-MRI were analyzed by computing 84 local first-order radiomic features (RFs). Two predictive models were built based on DWIb2000 and ADC, separately. Relevant RFs were selected through LASSO, a support vector machine (SVM) classifier was trained using repeated 3-fold cross validation (CV) and validated on a holdout set. The SVM models rely on a single couple of uncorrelated RFs (ρ < 0.15) selected through Wilcoxon rank-sum test (p ≤ 0.05) with Holm–Bonferroni correction. On the holdout set, while the ADC model yielded AUC = 0.76 (95% CI, 0.63–0.96), the DWIb2000 model reached AUC = 0.84 (95% CI, 0.63–0.90), with specificity = 75%, sensitivity = 90%, and informedness = 0.65. This study establishes the primary role of 3T-DWIb2000 in PCa quantitative analyses, whilst ADC can remain the leading sequence for detection.

scholarly journals Analog Circuit Fault Diagnosis Based on Support Vector Machine Classifier and Fuzzy Feature Selection

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1496
Author(s):  
Hao Liang ◽  
Yiman Zhu ◽  
Dongyang Zhang ◽  
Le Chang ◽  
Yuming Lu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Mutual Information ◽  
Analog Circuit ◽  
Fault Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Fault Parameters ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis

In analog circuit, the component parameters have tolerances and the fault component parameters present a wide distribution, which brings obstacle to classification diagnosis. To tackle this problem, this article proposes a soft fault diagnosis method combining the improved barnacles mating optimizer(BMO) algorithm with the support vector machine (SVM) classifier, which can achieve the minimum redundancy and maximum relevance for feature dimension reduction with fuzzy mutual information. To be concrete, first, the improved barnacles mating optimizer algorithm is used to optimize the parameters for learning and classification. We adopt six test functions that are on three data sets from the University of California, Irvine (UCI) machine learning repository to test the performance of SVM classifier with five different optimization algorithms. The results show that the SVM classifier combined with the improved barnacles mating optimizer algorithm is characterized with high accuracy in classification. Second, fuzzy mutual information, enhanced minimum redundancy, and maximum relevance principle are applied to reduce the dimension of the feature vector. Finally, a circuit experiment is carried out to verify that the proposed method can achieve fault classification effectively when the fault parameters are both fixed and distributed. The accuracy of the proposed fault diagnosis method is 92.9% when the fault parameters are distributed, which is 1.8% higher than other classifiers on average. When the fault parameters are fixed, the accuracy rate is 99.07%, which is 0.7% higher than other classifiers on average.

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