scholarly journals Quantitative analysis of intracranial electrocorticography signals using the concept of statistical parametric mapping

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Hirotaka Motoi ◽  
Jeong-Won Jeong ◽  
Csaba Juhász ◽  
Makoto Miyakoshi ◽  
Yasuo Nakai ◽  
...  
Keyword(s):  
Regression Model ◽  
Slow Wave ◽  
Statistical Parametric Mapping ◽  
Classification Model ◽  
Seizure Outcome ◽  
Z Score ◽  
Parametric Mapping ◽  
Sensitivity Specificity ◽  
Statistical Deviation ◽  
Full Regression Model

AbstractStatistical parametric mapping (SPM) is a technique with which one can delineate brain activity statistically deviated from the normative mean, and has been commonly employed in noninvasive neuroimaging and EEG studies. Using the concept of SPM, we developed a novel technique for quantification of the statistical deviation of an intracranial electrocorticography (ECoG) measure from the nonepileptic mean. We validated this technique using data previously collected from 123 patients with drug-resistant epilepsy who underwent resective epilepsy surgery. We determined how the measurement of statistical deviation of modulation index (MI) from the non-epileptic mean (rated by z-score) improved the performance of seizure outcome classification model solely based on conventional clinical, seizure onset zone (SOZ), and neuroimaging variables. Here, MI is a summary measure quantifying the strength of in-situ coupling between high-frequency activity at >150 Hz and slow wave at 3–4 Hz. We initially generated a normative MI atlas showing the mean and standard deviation of slow-wave sleep MI of neighboring non-epileptic channels of 47 patients, whose ECoG sampling involved all four lobes. We then calculated ‘MI z-score’ at each electrode site. SOZ had a greater ‘MI z-score’ compared to non-SOZ in the remaining 76 patients. Subsequent multivariate logistic regression analysis and receiver operating characteristic analysis to the combined data of all patients revealed that the full regression model incorporating all predictor variables, including SOZ and ‘MI z-score’, best classified the seizure outcome with sensitivity/specificity of 0.86/0.76. The model excluding ‘MI z-score’ worsened its sensitivity/specificity to 0.86/0.48. Furthermore, the leave-one-out analysis successfully cross-validated the full regression model. Measurement of statistical deviation of MI from the non-epileptic mean on invasive recording is technically feasible. Our analytical technique can be used to evaluate the utility of ECoG biomarkers in epilepsy presurgical evaluation.

Discipline and Sex Differences in Angle-specific Isokinetic Analysis in Elite Skiers

2019 ◽  
Vol 40 (05) ◽  
pp. 317-330 ◽  
Author(s):  
Marine Alhammoud ◽  
Baptiste Morel ◽  
Clint Hansen ◽  
Mathew Wilson ◽  
Regis Mecca ◽  
...  
Keyword(s):  
Statistical Parametric Mapping ◽  
Knee Extension ◽  
Current Data ◽  
Screening Methods ◽  
Full Extension ◽  
Isokinetic Testing ◽  
Parametric Mapping ◽  
National Team ◽  
Alpine Skiers ◽  
The Impact

AbstractStandard outcomes of traditional isokinetic testing do not detect differences between various muscle mechanical properties. This study i) explored a novel analysis throughout the range of motion based on statistical parametric mapping and ii) examined the impact of sex and discipline on hamstrings/quadriceps torque in elite alpine skiers. Twenty-eight national team skiers (14 females, 14 males; 14 technical, 14 speed) undertook an isokinetic evaluation of the knee flexors/extensors (range 30–90°, 0° representing full extension). There was no effect of sex (p=0.864, d=0.03) and discipline (p=0.360, d=0.17) on maximal hamstrings-to-quadriceps ratio and no effect of discipline on maximal torque (p>0.156, d≤0.25). Hamstrings torque and hamstrings-to-quadriceps ratio were lower in females than males toward knee extension only (p<0.05). Quadriceps torque was greater after 72° of knee flexion in technicians than downhill skiers (p<0.05). The current data showed that statistical parametric mapping analysis identified angle-specific differences that could not be evidenced when analyzing only maximal torques and reconstructed ratios. This may enhance screening methods to identify pathologic knee function or monitor rehabilitation programs, and inform sex- and discipline-specific training in alpine skiing.

Application of statistical parametric mapping to SPET in the assessment of intractable childhood epilepsy

2004 ◽  
Vol 31 (3) ◽  
pp. 369-377 ◽  
Author(s):  
Jason M. Bruggemann ◽  
Seu S. Som ◽  
John A. Lawson ◽  
Walter Haindl ◽  
Anne M. Cunningham ◽  
...  
Keyword(s):  
Statistical Parametric Mapping ◽  
Childhood Epilepsy ◽  
Parametric Mapping

scholarly journals Population-specific brain [18F]-FDG PET templates of Chinese subjects for statistical parametric mapping

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hongkai Wang ◽  
Yang Tian ◽  
Yang Liu ◽  
Zhaofeng Chen ◽  
Haoyu Zhai ◽  
...  
Keyword(s):  
Fdg Pet ◽  
Statistical Parametric Mapping ◽  
Brain Regions ◽  
Brain Images ◽  
Parametric Mapping ◽  
Brain Morphometry ◽  
Brain Pet ◽  
Positron Emission ◽  
The Family ◽  
Function Impairment

AbstractStatistical Parametric Mapping (SPM) is a computational approach for analysing functional brain images like Positron Emission Tomography (PET). When performing SPM analysis for different patient populations, brain PET template images representing population-specific brain morphometry and metabolism features are helpful. However, most currently available brain PET templates were constructed using the Caucasian data. To enrich the family of publicly available brain PET templates, we created Chinese-specific template images based on 116 [18F]-fluorodeoxyglucose ([18F]-FDG) PET images of normal participants. These images were warped into a common averaged space, in which the mean and standard deviation templates were both computed. We also developed the SPM analysis programmes to facilitate easy use of the templates. Our templates were validated through the SPM analysis of Alzheimer’s and Parkinson’s patient images. The resultant SPM t-maps accurately depicted the disease-related brain regions with abnormal [18F]-FDG uptake, proving the templates’ effectiveness in brain function impairment analysis.

scholarly journals Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification

Cancers ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 277
Author(s):  
Zuzanna Anna Magnuska ◽  
Benjamin Theek ◽  
Milita Darguzyte ◽  
Moritz Palmowski ◽  
Elmar Stickeler ◽  
...  
Keyword(s):  
Breast Lesion ◽  
Characteristic Curve ◽  
Cancer Diagnostics ◽  
Lesion Detection ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
P Value ◽  
Future Design ◽  
Sensitivity Specificity ◽  
Derivatives Of

Automation of medical data analysis is an important topic in modern cancer diagnostics, aiming at robust and reproducible workflows. Therefore, we used a dataset of breast US images (252 malignant and 253 benign cases) to realize and compare different strategies for CAD support in lesion detection and classification. Eight different datasets (including pre-processed and spatially augmented images) were prepared, and machine learning algorithms (i.e., Viola–Jones; YOLOv3) were trained for lesion detection. The radiomics signature (RS) was derived from detection boxes and compared with RS derived from manually obtained segments. Finally, the classification model was established and evaluated concerning accuracy, sensitivity, specificity, and area under the Receiver Operating Characteristic curve. After training on a dataset including logarithmic derivatives of US images, we found that YOLOv3 obtains better results in breast lesion detection (IoU: 0.544 ± 0.081; LE: 0.171 ± 0.009) than the Viola–Jones framework (IoU: 0.399 ± 0.054; LE: 0.096 ± 0.016). Interestingly, our findings show that the classification model trained with RS derived from detection boxes and the model based on the RS derived from a gold standard manual segmentation are comparable (p-value = 0.071). Thus, deriving radiomics signatures from the detection box is a promising technique for building a breast lesion classification model, and may reduce the need for the lesion segmentation step in the future design of CAD systems.

scholarly journals Identification of distinct characteristics of antibiofilm peptides and prospection of diverse sources for efficacious sequences

2021 ◽  
Author(s):  
Bipasa Bose ◽  
Taylor Downey ◽  
Anand K. Ramasubramanian ◽  
David C. Anastasiu
Keyword(s):  
Regression Model ◽  
Sequence Similarity ◽  
Drug Repurposing ◽  
Classification Model ◽  
Antibiofilm Activity ◽  
High Sequence Similarity ◽  
Small Molecule Drugs ◽  
Microbial Infections ◽  
Positive Dataset ◽  
Therapeutic Properties

A majority of microbial infections are associated with biofilms. Targeting biofilms is considered an effective strategy to limit microbial virulence while minimizing the development of antibiotic resistance. Towards this need, antibiofilm peptides are an attractive arsenal since they are bestowed with properties orthogonal to small molecule drugs. In this work, we developed machine learning models to identify the distinguishing characteristics of known antibiofilm peptides, and to mine peptide databases from diverse habitats to classify new peptides with potential antibiofilm activities. Additionally, we used the reported minimum inhibitory/eradication concentration (MBIC/MBEC) of the antibiofilm peptides to create a regression model on top of the classification model to predict the effectiveness of new antibiofilm peptides. We used a positive dataset containing 242 antibiofilm peptides, and a negative dataset which, unlike previous datasets, contains peptides that are likely to promote biofilm formation. Our model achieved a classification accuracy greater than 98% and harmonic mean of precision-recall (F1) and Matthews correlation coefficient (MCC) scores greater than 0.90; the regression model achieved an MCC score greater than 0.81. We utilized our classification-regression pipeline to evaluate 135,015 peptides from diverse sources and identified antibiofilm peptide candidates that are efficacious against preformed biofilms at micromolar concentrations. Structural analysis of the top 37 hits revealed a larger distribution of helices and coils than sheets. Sequence alignment of these hits with known antibiofilm peptides revealed that, while some of the hits showed relatively high sequence similarity with known peptides, some others did not indicate the presence of antibiofilm activity in novel sources or sequences. Further, some of the hits had previously recognized therapeutic properties or host defense traits suggestive of drug repurposing applications. Taken together, this work demonstrates a new in silicio approach to predicting antibiofilm efficacy, and identifies promising new candidates for biofilm eradication.

scholarly journals Statistical parametric mapping (SPM)

Scholarpedia ◽  
2008 ◽  
Vol 3 (4) ◽  
pp. 6232 ◽  
Author(s):  
Guillaume Flandin ◽  
Karl Friston
Keyword(s):  
Statistical Parametric Mapping ◽  
Parametric Mapping
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