New paradigms in femtosecond lasers for non-linear imaging of the brain and other tissues (Conference Presentation)

2020 ◽  
Author(s):  
Marco Arrigoni ◽  
Darryl McCoy
Keyword(s):  
Femtosecond Lasers ◽  
Non Linear ◽  
The Brain

scholarly journals FMRI Functional Connectivity Evaluation in Alzheimer’s Stages: Linear and Non-Linear Approaches

2021 ◽  
Author(s):  
Hessam Ahmadi ◽  
Emad Fatemizadeh ◽  
Ali Motie Nasrabadi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Permutation Test ◽  
Pearson Correlation ◽  
Functional Integration ◽  
Clustering Coefficient ◽  
Kernel Trick ◽  
Non Linear ◽  
The Brain

Abstract Neuroimaging data analysis reveals the underlying interactions in the brain. It is essential, yet controversial, to choose a proper tool to manifest brain functional connectivity. In this regard, researchers have not reached a definitive conclusion between the linear and non-linear approaches, as both have pros and cons. In this study, to evaluate this concern, the functional Magnetic Resonance Imaging (fMRI) data of different stages of Alzheimer’s disease are investigated. In the linear approach, the Pearson Correlation Coefficient (PCC) is employed as a common technique to generate brain functional graphs. On the other hand, for non-linear approaches, two methods including Distance Correlation (DC) and the kernel trick are utilized. By the use of the three mentioned routines and graph theory, functional brain networks of all stages of Alzheimer’s disease (AD) are constructed and then sparsed. Afterwards, graph global measures are calculated over the networks and a non-parametric permutation test is conducted. Results reveal that the non-linear approaches have more potential to discriminate groups in all stages of AD. Moreover, the kernel trick method is more powerful in comparison to the DC technique. Nevertheless, AD degenerates the brain functional graphs more at the beginning stages of the disease. At the first phase, both functional integration and segregation of the brain degrades, and as AD progressed brain functional segregation further declines. The most distinguishable feature in all stages is the clustering coefficient that reflects brain functional segregation.

scholarly journals THE CONCEPT «BRAIN-PSYCHE-(MIND/CONSCIOUSNESS...)» AND THE QUESTION OF HYPERTHEORY OF THE CONCEPT

2021 ◽  
pp. 405-412
Author(s):  
Irina V. Yershova-Babenko ◽  
Keyword(s):  
Human Life ◽  
Natural Phenomenon ◽  
Research Attention ◽  
Scientific Methods ◽  
Linear Behavior ◽  
Non Linear ◽  
Scientific Mind ◽  
The Brain ◽  
Non Equilibrium

In the context of research on problematization of human, the aspect of the human essence in a human is emphasized and the need to develop a hyper-level theory of the concept of «brain-psyche-(mind/consciousness...)» as a natural phenomenon in human life is proposed. We consider this phenomenon and, therefore, the concept, as non-linear macro-/hyperintegrity (holism). The basis for such an idea — the idea of hypertheory of the concept of «brain-psyche-(mind/consciousness...)» — is that the brain and the psyche, being components of the concept , are considered and investigated by specialists, including us, as an environment/system of synergistic order in whose behavior self-organization, chaos and dissipation play a controlling role. The brain and the psyche, included in the concept, are non-linear by definition. The fact that the issues of integrity are currently in the focus of research attention is due to transdisciplinarity as a new, deeper level of integration, which implies not only convergent penetration of scientific methods and disciplines but also the creation of such cognitive situations in which the scientific mind is forced to make the transition to practical life in search of integrity. In addition, it becomes relevant to search for theoretical-methodological research tools adequate to phenomena of this class for their description as nonlinear integralities of a given level of complexity. It is understood that the human brain and mind, in the unity of their activities throughout human life, express some indivisible unity. One of such tools is the conceptual model (philosophical category) «the Whole in the Whole», which includes integrity, nonlinearity and complexity, making it possible to consider the presented macro-/hyperintegrity in an integral unity. The article analyzes one of the aspects of the concept of «brain-psyche-(mind/consciousness...)» related to chaotization, which is inevitable in its non-linear behavior and which shows that both brain and psyche are continuously changing entities: from structure and system to the manifestation of the quality of environment and dynamic chaos. Their non-equilibrium and extreme non-equilibrium are prerequisites for their survival.

Visualization of Hidden Structure and Shape in Ct Image via Non-Linear Perspective Foreground and Back Ground Projection

2021 ◽  
Author(s):  
parthee pan ◽  
Raja Paul Perinbam ◽  
Krishna Murthy ◽  
Shanker Rajendiran Nagalingam ◽  
krishna kumari s ◽  
...  
Keyword(s):  
Occipital Lobe ◽  
Brain Regions ◽  
Medical Image Segmentation ◽  
Linear Perspective ◽  
Brain Images ◽  
Non Linear ◽  
Back Ground ◽  
Percent Accuracy ◽  
The Brain ◽  
Ct And Mri

Abstract The neurologist analyse the brain images to diagnose the disease via structure and shape of the part in the scanned Medical images such as CT, MRI, and PET.The Medical image segmentation perform less in the regions where no or little contrast,artefacts over the different boundary regions. The manual process of segmentation show poor boundary differentiation dueto discernibility in shape and location, intra and inter observer reliability. In this paper, we propose a dyadic Cat optimization (DCO) algorithm to segment the regions in the brain from CT and MRI image via Non- linear perspective Foreground and Back Ground projection. The DCO algorithm remove the artefacts in the boundary regions and provide the exact structure and shape of the brain regions. The DCO algorithm show the region boundary such as plerygomaxillary fissure, occipital lobe, vaginal process zygomatic arch, maxilla and piriform aperture with high visibility in the regions of inadequately visible boundary and distinguish the deformable shape. The DCO algorithm show the increased SSIM and 90 percent accuracy.

Visualization of Pterygomaxillary Fissure Structure and Shape in CT Image via Non-Linear Perspective Projection

2021 ◽  
Vol 11 (6) ◽  
pp. 1580-1589
Author(s):  
R. Partheepan ◽  
J. Raja Paul Perinbam ◽  
M. Krishnamurthy ◽  
N. R. Shanker
Keyword(s):  
Similarity Index ◽  
Occipital Lobe ◽  
Structural Similarity ◽  
Brain Regions ◽  
Medical Image Segmentation ◽  
Linear Perspective ◽  
Brain Images ◽  
Non Linear ◽  
Back Ground ◽  
The Brain

The neurologist analyses the brain images to diagnose disease via structure and shape of the part in scanned Medical images such as CT, MRI, and PET. The Medical image segmentation performs less in the regions where no or little contrast, artifacts over the different boundary regions. The manual process of segmentation shows poor boundary differentiation due to discernibility in shape and location, intra and inter observer reliability. In this paper, we propose dyadic CAT optimization (DCO) algorithm to segment the regions in the brain from CT and MRI image via Non-linear perspective Foreground and Back Ground projection. The DCO algorithm removes the artifacts in the boundary regions and provide the exact structure and shape of the brain regions. The DCO algorithm shows the region boundary for pterygomaxillary fissure, occipital lobe, vaginal process zygomatic arch, maxilla and piriform aperture in brain image with high visibility in the regions of inadequately visible boundary and distinguishes the deformable shape. The DCO algorithm applies on 50 images and eight images with complex bone and muscle mass structure for performance evaluation. The DCO algorithm shows the increased Structural similarity index (SSIM) with 90% accuracy.

Non-Linear Mechanisms in the Brain

1998 ◽  
Vol 53 (7-8) ◽  
pp. 677-685 ◽  
Author(s):  
Gottfried Mayer-Kress
Keyword(s):  
Large Scale ◽  
Brain Activity ◽  
External Noise ◽  
Neuronal System ◽  
Neuron Models ◽  
General Role ◽  
Non Linear ◽  
Spatio Temporal ◽  
The Brain

Abstract Non-linear dynamical models of brain activity can describe the spontaneous emergence of large-scale coherent structures both in a temporal and spatial domain. We discuss a number of discrete time dynamical neuron models that illustrate some of the mechanisms involved. Of special interest is the phenomenon of spatio-temporal stochastic resonance in which co­herent structures emerge as a result of the interaction of the neuronal system with external noise at a given level punitive data. We then discuss the general role of stochastic noise in brain dynamics and how similar concepts can be studied in the context of networks of con­nected brains on the Internet.

ALCOHOLIC INDEX USING NON-LINEAR FEATURES EXTRACTED FROM DIFFERENT FREQUENCY BANDS

2017 ◽  
Vol 17 (07) ◽  
pp. 1740009
Author(s):  
G. MURALIDHAR BAIRY ◽  
U. C. NIRANJAN ◽  
SHU LIH OH ◽  
JOEL E. W. KOH ◽  
VIDYA K. SUDARSHAN ◽  
...  
Keyword(s):  
Fuzzy Entropy ◽  
Multiscale Entropy ◽  
Permutation Entropy ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Linear Features ◽  
Entropy Formula ◽  
Non Linear ◽  
The Brain

Alcoholism is a complex condition that mainly disturbs the neuronal networks in Central Nervous System (CNS). This disorder not only disturbs the brain, but also affects the behavior, emotions, and cognitive judgements. Electroencephalography (EEG) is a valuable tool to examine the neuropsychiatric disorders like alcoholism. The EEG is a well-established modality to diagnose the electrical activity produced by the populations of neurons in cerebral cortex. However, EEG signals are non-linear in nature; hence very challenging to interpret the valuable information from them using linear methods. Thus, using non-linear methods to analyze EEG signals can be beneficial in order to predict the brain signals condition. This paper presents a computer-aided diagnostic method for the detection of alcoholic EEG signals from normal by employing the non-linear techniques. First, the EEG signals are subjected to six levels of Wavelet Packet Decomposition (WPD) to obtain seven wavebands (delta ([Formula: see text]), theta ([Formula: see text]), lower alpha (la), upper alpha (ua), lower beta (lb), upper beta (ub), lower gamma (lg)). From each wavebands (activity bands), 19 non-linear features such as Recurrence Quantification Analysis (RQA) ([Formula: see text]), Approximate Entropy ([Formula: see text]), Energy ([Formula: see text]), Fractal Dimension (FD) ([Formula: see text]), Permutation Entropy ([Formula: see text]), Detrended Fluctuation Analysis ([Formula: see text]), Hurst Exponent ([Formula: see text]), Largest Lyapunov Exponent ([Formula: see text]), Sample Entropy ([Formula: see text]), Shannon’s Entropy ([Formula: see text]), Renyi’s entropy ([Formula: see text]), Tsalli’s entropy ([Formula: see text]), Fuzzy entropy ([Formula: see text]), Wavelet entropy ([Formula: see text]), Kolmogorov–Sinai entropy ([Formula: see text]), Modified Multiscale Entropy ([Formula: see text]), Hjorth’s parameters (activity ([Formula: see text]), mobility ([Formula: see text]), and complexity ([Formula: see text])) are extracted. The extracted features are then ranked using Bhattacharyya, Entropy, Fuzzy entropy-based Max-Relevancy and Min-Redundancy (mRMR), Receiver Operating Characteristic (ROC), [Formula: see text]-test, and Wilcoxon. These ranked features are given to train Support Vector Machine (SVM) classifier. The SVM classifier with radial basis function (RBF) achieved 95.41% accuracy, 93.33% sensitivity and 97.50% specificity using four non-linear features ranked by Wilcoxon method. In addition, an integrated index called Alcoholic Index (ALCOHOLI) is developed using highly ranked two features for identification of normal and alcoholic EEG signals using a single number. This system is rapid, efficient, and inexpensive and can be employed as an EEG analysis assisting system by clinicians in the detection of alcoholism. In addition, the proposed system can be used in rehabilitation centers to evaluate person with alcoholism over time and observe the outcome of treatment provided for reducing or reversing the impact of the condition on the brain.

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