scholarly journals Statistical analysis and machine learning in multimodal brain imaging of neuropsychiatric disorders

2021 ◽  
Author(s):  
◽  
Mengjiao Hu
Keyword(s):  
Machine Learning ◽  
Statistical Analysis ◽  
Brain Imaging ◽  
Neuropsychiatric Disorders

scholarly journals Stiffness and Strength of Stabilized Organic Soils—Part I/II: Experimental Database and Statistical Description for Machine Learning Modelling

Geosciences ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 243
Author(s):  
Hernandez-Martinez Francisco G. ◽  
Al-Tabbaa Abir ◽  
Medina-Cetina Zenon ◽  
Yousefpour Negin
Keyword(s):  
Machine Learning ◽  
Statistical Analysis ◽  
Portland Cement ◽  
Soil Stabilization ◽  
Data Availability ◽  
Data Repository ◽  
Organic Soils ◽  
Experimental Database ◽  
Soil Mixing ◽  
The Impact

This paper presents the experimental database and corresponding statistical analysis (Part I), which serves as a basis to perform the corresponding parametric analysis and machine learning modelling (Part II) of a comprehensive study on organic soil strength and stiffness, stabilized via the wet soil mixing method. The experimental database includes unconfined compression tests performed under laboratory-controlled conditions to investigate the impact of soil type, the soil’s organic content, the soil’s initial natural water content, binder type, binder quantity, grout to soil ratio, water to binder ratio, curing time, temperature, curing relative humidity and carbon dioxide content on the stabilized organic specimens’ stiffness and strength. A descriptive statistical analysis complements the description of the experimental database, along with a qualitative study on the stabilization hydration process via scanning electron microscopy images. Results confirmed findings on the use of Portland cement alone and a mix of Portland cement with ground granulated blast furnace slag as suitable binders for soil stabilization. Findings on mixes including lime and magnesium oxide cements demonstrated minimal stabilization. Specimen size affected stiffness, but not the strength for mixes of peat and Portland cement. The experimental database, along with all produced data analyses, are available at the Texas Data Repository as indicated in the Data Availability Statement below, to allow for data reproducibility and promote the use of artificial intelligence and machine learning competing modelling techniques as the ones presented in Part II of this paper.

scholarly journals Basal ganglia dysmorphism in patients with Aicardi syndrome

Neurology ◽  
2020 ◽  
pp. 10.1212/WNL.0000000000011237
Author(s):  
Silvia Masnada ◽  
Anna Pichiecchio ◽  
Manuela Formica ◽  
Filippo Arrigoni ◽  
Paola Borrelli ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Basal Ganglia ◽  
Clinical Outcome ◽  
Brain Imaging ◽  
Correct Diagnosis ◽  
Classification Systems ◽  
Permanent Disability ◽  
Long Term Outcome ◽  
Aicardi Syndrome

ObjectiveAiming to detect associations between neuroradiologic and EEG evaluations and long-term clinical outcome in order to detect possible prognostic factors, a detailed clinical and neuroimaging characterization of 67 cases of Aicardi syndrome (AIC), collected through a multicenter collaboration, was performed.MethodsOnly patients who satisfied Sutton diagnostic criteria were included. Clinical outcome was assessed using gross motor function, manual ability, and eating and drinking ability classification systems. Brain imaging studies and statistical analysis were reviewed.ResultsPatients presented early-onset epilepsy, which evolved into drug-resistant seizures. AIC has a variable clinical course, leading to permanent disability in most cases; nevertheless, some cases presented residual motor abilities. Chorioretinal lacunae were present in 86.56% of our patients. Statistical analysis revealed correlations between MRI, EEG at onset, and clinical outcome. On brain imaging, 100% of the patients displayed corpus callosum malformations, 98% cortical dysplasia and nodular heterotopias, and 96.36% intracranial cysts (with similar rates of 2b and 2d). As well as demonstrating that posterior fossa abnormalities (found in 63.63% of cases) should also be considered a common feature in AIC, our study highlighted the presence (in 76.36%) of basal ganglia dysmorphisms (never previously reported).ConclusionThe AIC neuroradiologic phenotype consists of a complex brain malformation whose presence should be considered central to the diagnosis. Basal ganglia dysmorphisms are frequently associated. Our work underlines the importance of MRI and EEG, both for correct diagnosis and as a factor for predicting long-term outcome.Classification of evidenceThis study provides Class II evidence that for patients with AIC, specific MRI abnormalities and EEG at onset are associated with clinical outcomes.

scholarly journals Front Cover: Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data (ChemCatChem 18/2019)

ChemCatChem ◽  
2019 ◽  
Vol 11 (18) ◽  
pp. 4443-4443
Author(s):  
Keisuke Suzuki ◽  
Takashi Toyao ◽  
Zen Maeno ◽  
Satoru Takakusagi ◽  
Ken‐ichi Shimizu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Statistical Analysis ◽  
Heterogeneous Catalysts ◽  
Published Data ◽  
Front Cover

scholarly journals Evaluation of Biomarkers in Critical Care and Perioperative Medicine

2020 ◽  
Vol 134 (1) ◽  
pp. 15-25
Author(s):  
Sabri Soussi ◽  
Gary S. Collins ◽  
Peter Jüni ◽  
Alexandre Mebazaa ◽  
Etienne Gayat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Critical Care ◽  
Statistical Analysis ◽  
Research Methods ◽  
Statistical Methods ◽  
Perioperative Medicine ◽  
Scientific Rigor ◽  
Starting Point ◽  
Novel Biomarkers

SUMMARY Interest in developing and using novel biomarkers in critical care and perioperative medicine is increasing. Biomarkers studies are often presented with flaws in the statistical analysis that preclude them from providing a scientifically valid and clinically relevant message for clinicians. To improve scientific rigor, the proper application and reporting of traditional and emerging statistical methods (e.g., machine learning) of biomarker studies is required. This Readers’ Toolbox article aims to be a starting point to nonexpert readers and investigators to understand traditional and emerging research methods to assess biomarkers in critical care and perioperative medicine.

Techniques and Methods That Help to Make Big Data the Simplest Recipe for Success

2019 ◽  
pp. 161-194
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Statistical Analysis ◽  
Data Analytics ◽  
Big Data Analysis ◽  
Customer Segmentation ◽  
Learning Context ◽  
Feature Vectors

Data analytics has grown in a machine learning context. Whatever the reason data is used or exploited, customer segmentation or marketing targeting, it must be processed first and represented on feature vectors. Many algorithms, such as clustering, regression, classification, and others, need to be represented and clarified in order to facilitate processing and statistical analysis. If we have seen, through the previous chapters, the importance of big data analysis (the Why?), as with every major innovation, the biggest confusion lies in the exact scope (What?) and its implementation (How?). In this chapter, we will take a look at the different algorithms and techniques analytics that we can use in order to exploit the large amounts of data.

scholarly journals A Statistical Analysis of Risk Factors and Biological Behavior in Canine Mammary Tumors: A Multicenter Study

Animals ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1687
Author(s):  
Giovanni P. Burrai ◽  
Andrea Gabrieli ◽  
Valentina Moccia ◽  
Valentina Zappulli ◽  
Ilaria Porcellato ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Statistical Analysis ◽  
Multicenter Study ◽  
Malignant Tumors ◽  
Mammary Tumors ◽  
Supervised Machine Learning ◽  
Biological Behavior ◽  
Clinical Staging ◽  
Canine Mammary Tumors

Canine mammary tumors (CMTs) represent a serious issue in worldwide veterinary practice and several risk factors are variably implicated in the biology of CMTs. The present study examines the relationship between risk factors and histological diagnosis of a large CMT dataset from three academic institutions by classical statistical analysis and supervised machine learning methods. Epidemiological, clinical, and histopathological data of 1866 CMTs were included. Dogs with malignant tumors were significantly older than dogs with benign tumors (9.6 versus 8.7 years, p < 0.001). Malignant tumors were significantly larger than benign counterparts (2.69 versus 1.7 cm, p < 0.001). Interestingly, 18% of malignant tumors were smaller than 1 cm in diameter, providing compelling evidence that the size of the tumor should be reconsidered during the assessment of the TNM-WHO clinical staging. The application of the logistic regression and the machine learning model identified the age and the tumor’s size as the best predictors with an overall diagnostic accuracy of 0.63, suggesting that these risk factors are sufficient but not exhaustive indicators of the malignancy of CMTs. This multicenter study increases the general knowledge of the main epidemiologica-clinical risk factors involved in the onset of CMTs and paves the way for further investigations of these factors in association with CMTs and in the application of machine learning technology.

scholarly journals KEEP YOUR BRAIN YOUNG WITH MUSIC: INSIGHTS FROM MACHINE LEARNING AND BRAIN IMAGING

2018 ◽  
Vol 2 (suppl_1) ◽  
pp. 849-849
Author(s):  
J Kernbach ◽  
L Rogenmoser ◽  
G Schlaug ◽  
C Gaser
Keyword(s):  
Machine Learning ◽  
Brain Imaging
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