scholarly journals Geodesic Uncertainty in Diffusion MRI

2021 ◽  
Vol 3 ◽  
Author(s):  
Rick Sengers ◽  
Luc Florack ◽  
Andrea Fuster
Keyword(s):  
A Priori ◽  
Analytical Prediction ◽  
Huge Amount ◽  
Geodesic Deviation ◽  
Small Perturbations ◽  
First Order ◽  
Weighted Magnetic Resonance Imaging ◽  
Operational Issues ◽  
The Brain

We study theoretical and operational issues of geodesic tractography, a geometric methodology for retrieving biologically plausible neural fibers in the brain from diffusion weighted magnetic resonance imaging. The premise is that true positives are geodesics in a suitably constructed metric space, but unlike traditional first order methods these are not a priori constrained to connect nongeneric points on subdimensional manifolds, such as the characteristics in traditional streamline methods. By virtue of the Hopf-Rinow theorem geodesic tractography furnishes a huge amount of redundancy, ensuring the a priori existence of at least one tentative fiber between any two points and permitting additional tractometric and data-extrinsic constraints for (fuzzy or crisp) classification of true and false positives. In our feasibility study we consider a hybrid paradigm that unifies existing ideas on tractography, combining deterministic and probabilistic elements in a way naturally supported by metric geometry. Particular attention is paid to an analytical prediction of geodesic deviation on numerically computed geodesics, a ‘tidal’ effect induced by small perturbations resulting from data noise. Taking these effects into account clarifies the inherent uncertainty of geodesics, while simultaneosuly offering a dimensionality reduction of the tractography problem.

Comparison of Nonlinear Spatial Correlation Models by the Influence of the Data Augmentation to the Classification Risk

2002 ◽  
Vol 7 (1) ◽  
pp. 31-42
Author(s):  
J. Šaltytė ◽  
K. Dučinskas
Keyword(s):  
Spatial Correlation ◽  
Random Fields ◽  
Data Augmentation ◽  
Gaussian Random Fields ◽  
Classification Rule ◽  
Numerical Comparison ◽  
First Order ◽  
Bayesian Risk ◽  
Correlation Models

The Bayesian classification rule used for the classification of the observations of the (second-order) stationary Gaussian random fields with different means and common factorised covariance matrices is investigated. The influence of the observed data augmentation to the Bayesian risk is examined for three different nonlinear widely applicable spatial correlation models. The explicit expression of the Bayesian risk for the classification of augmented data is derived. Numerical comparison of these models by the variability of Bayesian risk in case of the first-order neighbourhood scheme is performed.

scholarly journals An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network

2021 ◽  
Author(s):  
Muhammad Irfan Sharif ◽  
Jian Ping Li ◽  
Javeria Amin ◽  
Abida Sharif
Keyword(s):  
Brain Tumor ◽  
Tumor Detection ◽  
Entropy Method ◽  
Feature Extraction And Selection ◽  
Tumor Region ◽  
Selection For ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain ◽  
Complicated Task

AbstractBrain tumor is a group of anomalous cells. The brain is enclosed in a more rigid skull. The abnormal cell grows and initiates a tumor. Detection of tumor is a complicated task due to irregular tumor shape. The proposed technique contains four phases, which are lesion enhancement, feature extraction and selection for classification, localization, and segmentation. The magnetic resonance imaging (MRI) images are noisy due to certain factors, such as image acquisition, and fluctuation in magnetic field coil. Therefore, a homomorphic wavelet filer is used for noise reduction. Later, extracted features from inceptionv3 pre-trained model and informative features are selected using a non-dominated sorted genetic algorithm (NSGA). The optimized features are forwarded for classification after which tumor slices are passed to YOLOv2-inceptionv3 model designed for the localization of tumor region such that features are extracted from depth-concatenation (mixed-4) layer of inceptionv3 model and supplied to YOLOv2. The localized images are passed toMcCulloch'sKapur entropy method to segment actual tumor region. Finally, the proposed technique is validated on three benchmark databases BRATS 2018, BRATS 2019, and BRATS 2020 for tumor detection. The proposed method achieved greater than 0.90 prediction scores in localization, segmentation and classification of brain lesions. Moreover, classification and segmentation outcomes are superior as compared to existing methods.

scholarly journals fNIRS Signal Classification Based on Deep Learning in Rock-Paper-Scissors Imagery Task

2021 ◽  
Vol 11 (11) ◽  
pp. 4922
Author(s):  
Tengfei Ma ◽  
Wentian Chen ◽  
Xin Li ◽  
Yuting Xia ◽  
Xinhua Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Total Duration ◽  
Signal Classification ◽  
Imagery Task ◽  
Neural Activities ◽  
Computer Interfaces ◽  
Acquisition Device ◽  
The Time Domain ◽  
The Brain

To explore whether the brain contains pattern differences in the rock–paper–scissors (RPS) imagery task, this paper attempts to classify this task using fNIRS and deep learning. In this study, we designed an RPS task with a total duration of 25 min and 40 s, and recruited 22 volunteers for the experiment. We used the fNIRS acquisition device (FOIRE-3000) to record the cerebral neural activities of these participants in the RPS task. The time series classification (TSC) algorithm was introduced into the time-domain fNIRS signal classification. Experiments show that CNN-based TSC methods can achieve 97% accuracy in RPS classification. CNN-based TSC method is suitable for the classification of fNIRS signals in RPS motor imagery tasks, and may find new application directions for the development of brain–computer interfaces (BCI).

Classification of EEG Mental Patterns by Using Two Scalp Electrodes and Mahalanobis Distance-Based Classifiers

2002 ◽  
Vol 41 (04) ◽  
pp. 337-341 ◽  
Author(s):  
F. Cincotti ◽  
D. Mattia ◽  
C. Babiloni ◽  
F. Carducci ◽  
L. Bianchi ◽  
...  
Keyword(s):  
Mahalanobis Distance ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Correct Classification ◽  
Interface Area ◽  
System A ◽  
The Brain ◽  
Full Covariance Matrix ◽  
Recording Electrodes

Summary Objectives: In this paper, we explored the use of quadratic classifiers based on Mahalanobis distance to detect mental EEG patterns from a reduced set of scalp recording electrodes. Methods: Electrodes are placed in scalp centro-parietal zones (C3, P3, C4 and P4 positions of the international 10-20 system). A Mahalanobis distance classifier based on the use of full covariance matrix was used. Results: The quadratic classifier was able to detect EEG activity related to imagination of movement with an affordable accuracy (97% correct classification, on average) by using only C3 and C4 electrodes. Conclusions: Such a result is interesting for the use of Mahalanobis-based classifiers in the brain computer interface area.

scholarly journals Regularity of Weakly Well-Posed Characteristic Boundary Value Problems

2010 ◽  
Vol 2010 ◽  
pp. 1-39 ◽  
Author(s):  
Alessandro Morando ◽  
Paolo Secchi
Keyword(s):  
Energy Estimate ◽  
Boundary Value ◽  
A Priori ◽  
Regularity Of Solutions ◽  
First Order ◽  
Characteristic Boundary ◽  
Partial Differential System ◽  
Constant Multiplicity ◽  
Well Posed ◽  
Smooth Data

We study the boundary value problem for a linear first-order partial differential system with characteristic boundary of constant multiplicity. We assume the problem to be “weakly” well posed, in the sense that a uniqueL2-solution exists, for sufficiently smooth data, and obeys an a priori energy estimate with a finite loss of tangential/conormal regularity. This is the case of problems that do not satisfy the uniform Kreiss-Lopatinskiĭ condition in the hyperbolic region of the frequency domain. Provided that the data are sufficiently smooth, we obtain the regularity of solutions, in the natural framework of weighted conormal Sobolev spaces.

scholarly journals Application of MRI for the Diagnosis of Neoplasms

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Ewa Bejer-Oleńska ◽  
Michael Thoene ◽  
Andrzej Włodarczyk ◽  
Joanna Wojtkiewicz
Keyword(s):  
Benign Neoplasms ◽  
Young Males ◽  
Classification Of Diseases ◽  
Icd 10 ◽  
Mri Scans ◽  
International Statistical Classification ◽  
The Central Nervous System ◽  
Spss Software ◽  
The Brain

Aim. The aim of the study was to determine the most commonly diagnosed neoplasms in the MRI scanned patient population and indicate correlations based on the descriptive variables. Methods. The SPSS software was used to determine the incidence of neoplasms within the specific diagnoses based on the descriptive variables of the studied population. Over a five year period, 791 patients and 839 MRI scans were identified in neoplasm category (C00-D48 according to the International Statistical Classification of Diseases and Related Health Problems ICD-10). Results. More women (56%) than men (44%) represented C00-D48. Three categories of neoplasms were recorded. Furthermore, benign neoplasms were the most numerous, diagnosed mainly in patients in the fifth decade of life, and included benign neoplasms of the brain and other parts of the central nervous system. Conclusions. Males ≤ 30 years of age with neoplasms had three times higher MRI scans rate than females of the same age group; even though females had much higher scans rate in every other category. The young males are more often selected for these scans if a neoplasm is suspected. Finally, the number of MRI-diagnosed neoplasms showed a linear annual increase.

A Priori Typology-Based Prediction of Benthic Macroinvertebrate Fauna for Ecological Classification of Rivers

2008 ◽  
Vol 42 (5) ◽  
pp. 894-906 ◽  
Author(s):  
Jukka Aroviita ◽  
Esa Koskenniemi ◽  
Juho Kotanen ◽  
Heikki Hämäläinen
Keyword(s):  
A Priori ◽  
Benthic Macroinvertebrate ◽  
Ecological Classification ◽  
Macroinvertebrate Fauna

Coding of Temporally Varying Signals in Networks of Spiking Neurons with Global Delayed Feedback

2005 ◽  
Vol 17 (10) ◽  
pp. 2139-2175 ◽  
Author(s):  
Naoki Masuda ◽  
Brent Doiron ◽  
André Longtin ◽  
Kazuyuki Aihara
Keyword(s):  
Information Processing ◽  
Stimulus Frequency ◽  
Spiking Neurons ◽  
Feedforward Networks ◽  
Dynamical Characteristics ◽  
Neural Activities ◽  
Inherent Frequency ◽  
Types Of Information ◽  
The Brain

Oscillatory and synchronized neural activities are commonly found in the brain, and evidence suggests that many of them are caused by global feedback. Their mechanisms and roles in information processing have been discussed often using purely feedforward networks or recurrent networks with constant inputs. On the other hand, real recurrent neural networks are abundant and continually receive information-rich inputs from the outside environment or other parts of the brain. We examine how feedforward networks of spiking neurons with delayed global feedback process information about temporally changing inputs. We show that the network behavior is more synchronous as well as more correlated with and phase-locked to the stimulus when the stimulus frequency is resonant with the inherent frequency of the neuron or that of the network oscillation generated by the feedback architecture. The two eigenmodes have distinct dynamical characteristics, which are supported by numerical simulations and by analytical arguments based on frequency response and bifurcation theory. This distinction is similar to the class I versus class II classification of single neurons according to the bifurcation from quiescence to periodic firing, and the two modes depend differently on system parameters. These two mechanisms may be associated with different types of information processing.

Clinical and neuroimaging features of chronic cerebral ischemia in polycythemia vera

2021 ◽  
pp. 58-62
Author(s):  
G. V. Zyrina ◽  
T. A. Slyusa
Keyword(s):  
Cerebral Ischemia ◽  
Polycythemia Vera ◽  
Mini Mental State Examination ◽  
Comparison Group ◽  
Cognitive Disorders ◽  
Chronic Cerebral Ischemia ◽  
State Examination ◽  
The Brain

The purpose of the study. To study clinical and neuroimaging features of chronic cerebral ischemia (CCI) in polycythemia vera (PV).Materials and methods. 66 patients with PV were examined – the main group (43 men, 23 women; mean age 62.0 ± 3.4 years), of which 64 (97.0%) patients were diagnosed with CCI. The comparison group consisted of 85 patients with CCI (34 men, 51 women; mean age 67.7 ± 4.6 years), who developed against the background of cerebral vascular atherosclerosis and arterial hypertension. To identify cognitive disorders, we used Mini Mental State Examination (MMSE). Insomnia was studied in accordance with the criteria of the International Classification of Sleep ICDS‑22005. The quality of sleep was determined using a questionnaire from the Federal Somnological Center. Neuroimaging (MRI of the brain) was performed on Siemens Symphony 1.5 T and GE Signa 1.5 T tomographs.Results. Subjective symptoms CCI are characterized by a greater representation of asthenic and insomniac disorders. Transient ischemic attacks in patients with PV are significantly more common than in the comparison group, their frequency depends on the duration of PV. The revealed changes in MRI of the brain in the majority of PV patients with CCI are characteristic of multiinfarction vascular encephalopathy; in the comparison group, changes that characteristic for subcortical arteriosclerotic encephalopathy were more often recorded.

The relevance of oral language skills to early literacy: A multivariate analysis

1999 ◽  
Vol 20 (2) ◽  
pp. 167-190 ◽  
Author(s):  
DEBORAH L. SPEECE ◽  
FROMA P. ROTH ◽  
DAVID H. COOPER ◽  
SUSAN DE LA PAZ
Keyword(s):  
Early Literacy ◽  
Oral Language ◽  
Language Skills ◽  
A Priori ◽  
Kindergarten Children ◽  
Language And Literacy ◽  
Direct Role ◽  
Oral Language Skills ◽  
Subtype Differences

This study examined relationships between oral language and literacy in a two-year, multivariate design. Through empirical cluster analysis of a sample of 88 kindergarten children, four oral language subtypes were identified based on measures of semantics, syntax, metalinguistics, and oral narration. Validation efforts included (a) concurrent and predictive analyses of subtype differences on reading, spelling, and listening comprehension measures based on a priori hypotheses and (b) a comparison of the teacher classification of the children with the empirical classification. The subtypes represented high average, low average, high narrative, and low overall patterns of oral language skill. The high average subtype received the most consistent evidence for validation. The pattern of validation results indicates that the relationship between oral language and literacy is not uniform and suggests a modification of the assumption that oral language skills have a direct role in reading acquisition.

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