scholarly journals Shared and specific signatures of locomotor ataxia in mutant mice

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Ana S Machado ◽  
Hugo G Marques ◽  
Diogo F Duarte ◽  
Dana M Darmohray ◽  
Megan R Carey
Keyword(s):  
Discriminant Analysis ◽  
Motor Coordination ◽  
Locomotor Behavior ◽  
Whole Body ◽  
Gait Ataxia ◽  
Cell Degeneration ◽  
Linear Discriminant ◽  
Mouse Mutants ◽  
Compare And Contrast ◽  
The Brain

Several spontaneous mouse mutants with deficits in motor coordination and associated cerebellar neuropathology have been described. Intriguingly, both visible gait alterations and neuroanatomical abnormalities throughout the brain differ across mutants. We previously used the LocoMouse system to quantify specific deficits in locomotor coordination in mildly ataxic Purkinje cell degeneration mice (pcd; Machado et al., 2015). Here, we analyze the locomotor behavior of severely ataxic reeler mutants and compare and contrast it with that of pcd. Despite clearly visible gait differences, direct comparison of locomotor kinematics and linear discriminant analysis reveal a surprisingly similar pattern of impairments in multijoint, interlimb, and whole-body coordination in the two mutants. These findings capture both shared and specific signatures of gait ataxia and provide a quantitative foundation for mapping specific locomotor impairments onto distinct neuropathologies in mice.

scholarly journals Shared and specific signatures of locomotor ataxia in mutant mice

2020 ◽  
Author(s):  
Ana S. Machado ◽  
Hugo G. Marques ◽  
Diogo F. Duarte ◽  
Dana M. Darmohray ◽  
Megan R. Carey
Keyword(s):  
Discriminant Analysis ◽  
Motor Coordination ◽  
Locomotor Behavior ◽  
Whole Body ◽  
Gait Ataxia ◽  
Cell Degeneration ◽  
Linear Discriminant ◽  
Mouse Mutants ◽  
Compare And Contrast ◽  
The Brain

AbstractSeveral spontaneous mouse mutants with deficits in motor coordination and associated cerebellar neuropathology have been described. Intriguingly, both visible gait alterations and neuroanatomical abnormalities throughout the brain differ across mutants. We previously used the LocoMouse system to quantify specific deficits in locomotor coordination in mildly ataxic Purkinje cell degeneration mice (pcd; Machado et al., 2015). Here, we analyze the locomotor behavior of severely ataxic reeler mutants and compare and contrast it with that of pcd. Despite clearly visible gait differences, direct comparison of locomotor kinematics and linear discriminant analysis reveal a surprisingly similar pattern of impairments in multijoint, interlimb, and whole-body coordination in the two mutants. These findings capture both shared and specific signatures of gait ataxia and provide a quantitative foundation for mapping specific locomotor impairments onto distinct neuropathologies in mice.

Linear Discrimination Analysis of Monkey Behavior in an Alternative Free Choice Task

2007 ◽  
Vol 19 (4) ◽  
pp. 416-422
Author(s):  
Kazuhito Takenaka ◽  
◽  
Yasuo Nagasaka ◽  
Sayaka Hihara ◽  
Hiroyuki Nakahara ◽  
...  
Keyword(s):  
Social Context ◽  
Discriminant Analysis ◽  
Free Choice ◽  
Discriminant Score ◽  
Linear Discriminant ◽  
Linear Discrimination ◽  
Choice Tasks ◽  
The Brain ◽  
Alternative Decision ◽  
A Current

When we observe people, we can often comprehend their intention from their behaviors. The intentions expressed by individuals can be considered as existing in interpersonal space and from a current social context. In our daily activity, choosing socially correct behavior through the observation of such social context is essential. However, it is not known how we can decode intention from another’s behavior. Here, we show how we can retrieve the intention of monkeys through external observation of their behavior patterns while performing alternative free choice tasks. We found that linear discriminant analysis on a monkey’s motion parameters could provide a discriminant score that appears to reflect the internal decision making process. The score showed a clear flexion point that we defined as a moment of outward expression of intention (OEI). This suggests that an alternative decision is made just before an OEI and that intention is expressed in the environment after this OEI in behavior, which in turn suggests that discriminant analysis may be useful in indicating how the brain implements nonverbal social communication. If we could embed the function in a human-machine interfaces, it could enable intuitive, smooth communication between machines and humans.

Memoirs: The Metamorphosis of Calandra Oryzae

1935 ◽  
Vol s2-77 (307) ◽  
pp. 405-495
Author(s):  
FLORENCE V. MURRAY ◽  
O. W. Tiegs
Keyword(s):  
Fat Body ◽  
Nerve Tissue ◽  
Whole Body ◽  
Gut Contents ◽  
Fat Cells ◽  
Cell Degeneration ◽  
Tracheal System ◽  
Division Cell ◽  
Food Reserves ◽  
The Brain

1. Metamorphosis of the external form of the larva commences with a voiding of the mid-gut contents; thus arises the prepupa in which the external features of the imago are more nearly revealed. In the prepupa the rostrum, legs, and wings grow out. In the pupa the whole body, and particularly the appendages, shrink and differentiate into the elegant form of the imago. 2. The larval epidermis (hypodermis) is not divisible into dormant imaginal and functional larval cells. The ‘imaginal disks’ of the appendages are functional epidermis in the larva and are distinguishable only by their stronger basiphil staining especially in the late larva; as, indeed, are other parts of the epidermis (e.g. rostrum ‘Anlage’) where rapid growth is to occur. The epidermal cells divide in the growing larva. During metamorphosis they extrude chromatic material and some cytoplasm, and undergo renewed cell-division. Cell degeneration is rare. Epidermal gland-cells disintegrate and are partly phagocytosed. 3. The mid-gut disintegrates and is regenerated from the larval fore-gut, i.e. from ectoderm(?); the mid-gut ‘replacing cells’ survive at the tips of the mid-gut caeca. The cells of foreand hind-guts behave like those of the epidermis. Mycetocytes wander from the mycetoma into the anterior mid-gut caeca. 4. Salivary glands disintegrate, are partly phagocytosed, and regenerate from cells at the openings of the larval ducts. 5. The Malpighian tubes disintegrate and regenerate from local mid-gut cells; there is no phagocytosis. 6. The heart and alary muscles pass into the imago. The nephrocytes survive from the early larva and without further division into the imago; there is occasional chromatic globule extrusion early in the metamorphosis. Leucocytes multiply in the blood; they seem to arise largely from masses of ‘pericardial tissue’ in the dorsal sinus. 7. The fat-cells divide in the growing larva, and accumulate food reserves, which are partly depleted during metamorphosis. Destruction of fat-cells does not occur. In the pupa the clumps of fat-body break into individual cells. The fat-body is phagocytic during metamorphosis. In the imago the fat-body retrogresses. 8. The oenocytes grow in size in the larva; there is no multiplication. In the pupa they undergo histolysis, partly by phagocytosis ; some survive for a time into the imago. The imaginal oenocytes arise apparently from the epidermis. 9. The tracheal system undergoes extensive elaboration during metamorphosis, particularly to meet the needs of the enlarged thorax and of the appendages. The cellular changes in the larva and during metamorphosis are similar to those of the epidermis. The chitinous intima, even of minute tracheoles, is withdrawn, at the last larval moult, through the stigmata. 10. The larval musculature (somatic and intestinal) degenerates and is reformed from myoblasts that have proliferated in the larval period. The myoblasts may be (a) parts of the larval muscle-fibre syncytium, as in all the muscles that regenerate in connexion with pre-existing larval muscles, and (b) scattered cells, in cases where they regenerate independently of larval muscles (leg muscles). Tendons are epidermal invaginations. The highly specialized condition of the imaginal musculature is mainly the outcome of changes in the region of insertion and origin of the muscles, owing to the altered form of the imago. The histogenesis of the wing-muscles is quite different from that of all other muscles. 11. The thirteen ganglia of the larval nerve-cord enlarge and become concentrated into five ganglia, not extending beyond the thorax. In the brain there occurs an elaboration of all its parts: the optic ganglia are new structures. Dormant neuroblasts occur in the larval cord in addition to nerve-cells. The former (and latter?) exhibit chromatic globule extrusion and proliferate during metamorphosis. There is no histolysis of larval nerve-tissue; specialization of the nervous system seems to be mainly on its sensory side. 12. The larval ocelli survive as pigment spots on the brain of the imago. Compound eyes develop from larval epidermis; they are of the exocone type. 13. Growth of the gonads proceeds throughout the larval stage, but is greatly accelerated in the prepupa and early pupa. The copulatory organs are developed as invaginations probably entirely from the ninth segment. 14. The hypothesis is offered that the abrupt changes that occur in the larval tissues at metamorphosis are the outcome of the hypertrophy of their cells.

MULTIPLE CLASSIFICATION TECHNIQUES TOWARD A ROBUST AND RELIABLE P300 BCI SYSTEM

2020 ◽  
Vol 32 (02) ◽  
pp. 2050010
Author(s):  
Fatma EL-Zahraa M. Labib ◽  
Islam A. Fouad ◽  
Mai S. Mabrouk ◽  
Amr A. Sharawy
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Linear Discriminant ◽  
The Brain ◽  
Bci System

A brain–computer interface (BCI) can be used for people with severe physical disabilities such as ALS or amyotrophic lateral sclerosis. BCI can allow these individuals to communicate again by creating a new communication channel directly from the brain to an output device. BCI technology can allow paralyzed people to share their intent with others, and thereby demonstrate that direct communication from the brain to the external world is possible and that it might serve useful functions. BCI systems include machine learning algorithms (MLAs). Their performance depends on the feature extraction and classification techniques employed. In this paper, we propose a system to exploit the P300 signal in the brain, a positive deflection in event-related potentials. The P300 signal can be incorporated into a spelling device. There are two benefits behind this kind of research. First of all, this work presents the research status and the advantages of communication via a BCI system, especially the P300 BCI system for disordered people, and the related literature review is presented. Secondly, the paper discusses the performance of different machine learning algorithms. Two different datasets are presented: the first dataset 2004 and the second dataset 2019. A preprocessing step is introduced to the subjects in both datasets first to extract the important features before applying the proposed machine learning methods: linear discriminant analysis (LDA I and LDA II), support vector machine (SVM I, SVM II, SVM III, and SVM IV), linear regression (LREG), Bayesian linear discriminant analysis (BLDA), and twin support vector machine (TSVM). By comparing the performance of the different machine learning systems, in the first dataset it is found that BLDA and SVMIV classifiers yield the highest performance for both subjects “A” and “B”. BLDA yields 98% and 66% for 15th and 5th sequences, respectively, whereas SVMIV yields 98% and 54.4% for 15th and 5th sequences, respectively. While in the second dataset, it is obvious that BLDA classifier yields the highest performance for both subjects “1” and “2”, it achieves 90.115%. The paper summarizes the P300 BCI system for the two introduced datasets. It discusses the proposed system, compares the classification methods performances, and considers some aspects for the future work to be handled. The results show high accuracy and less computational time which makes the system more applicable for online applications.

scholarly journals Mining the Mind: Linear Discriminant Analysis of MEG Source Reconstruction Time Series Supports Dynamic Changes in Deep Brain Regions During Meditation Sessions

2021 ◽  
Author(s):  
Daniela Calvetti ◽  
Brian Johnson ◽  
Annalisa Pascarella ◽  
Francesca Pitolli ◽  
Erkki Somersalo ◽  
...  
Keyword(s):  
Time Series ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Longitudinal Studies ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Atlas ◽  
Meditation Practice ◽  
Linear Discriminant ◽  
The Brain

AbstractMeditation practices have been claimed to have a positive effect on the regulation of mood and emotions for quite some time by practitioners, and in recent times there has been a sustained effort to provide a more precise description of the influence of meditation on the human brain. Longitudinal studies have reported morphological changes in cortical thickness and volume in selected brain regions due to meditation practice, which is interpreted as an evidence its effectiveness beyond the subjective self reporting. Using magnetoencephalography (MEG) or electroencephalography to quantify the changes in brain activity during meditation practice represents a challenge, as no clear hypothesis about the spatial or temporal pattern of such changes is available to date. In this article we consider MEG data collected during meditation sessions of experienced Buddhist monks practicing focused attention (Samatha) and open monitoring (Vipassana) meditation, contrasted by resting state with eyes closed. The MEG data are first mapped to time series of brain activity averaged over brain regions corresponding to a standard Destrieux brain atlas. Next, by bootstrapping and spectral analysis, the data are mapped to matrices representing random samples of power spectral densities in $$\alpha$$ α , $$\beta$$ β , $$\gamma$$ γ , and $$\theta$$ θ frequency bands. We use linear discriminant analysis to demonstrate that the samples corresponding to different meditative or resting states contain enough fingerprints of the brain state to allow a separation between different states, and we identify the brain regions that appear to contribute to the separation. Our findings suggest that the cingulate cortex, insular cortex and some of the internal structures, most notably the accumbens, the caudate and the putamen nuclei, the thalamus and the amygdalae stand out as separating regions, which seems to correlate well with earlier findings based on longitudinal studies.

scholarly journals Characterizing Functional Connectivity Network Based on Multi-Domain Analysis for Epilepsy Classification

2020 ◽  
Vol 9 (3) ◽  
pp. 2379-2386
Keyword(s):  
Discriminant Analysis ◽  
Detection System ◽  
Performance Measure ◽  
Machine Learning Algorithms ◽  
Bayesian Optimization ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
The Brain

Epileptic is a neural disease exemplified through untypical concurrent signal discharge from the neurons present in the brain region. This abnormal brain functionality could be captured through electroencephalography (EEG) system. Generally the observed EEG signals are examined by the experienced neurologist, which may be time consuming when observing hours of EEG signal. Therefore, this proposed work provides a fully automatic epileptic seizure detection system by means of the multi-domain features along with various machine learning algorithms. Initially, the obtained EEG signals are processed to clear noise and artefacts. Subsequently, the pre-processed signals are segregated as 5 seconds epochs and for each epoch various features are extracted from frequency domain, time domain. Additionally entropy, correlation and graph theory approaches has been used for analysis the connectivity of the brain network. Subsequently, distinguishable features are chosen carefully in this regard from the immense feature set by virtue of multi-objective evolutionary method and convincingly, classification has been performed using support vector machine(SVM).A Bayesian optimization (BaO) algorithm was utilized to optimize the SVM's hyper-plane parameters. In addition, Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA),Random Forest Ensemble (RFE) and k-Nearest Neighbor Ensemble (k- NNE) was also used for comparing the proposed results. These obtained results validates by considering the performance of this work is competing along with state-of the-arts approaches. The proposed work is implemented on a CHB-MIT database .The obtained performance measure of the classifiers are 99.09%, 81.49%,80.90%,76.85% and 84.14 % in SVM , LDA, QDA, k- NNE and RFE respectively. Finally SVM with Bayesian Optimization (BaO) algorithm outperforms than other classifiers with accuracy, AUC, sensitivity and specificity, as 99.09%, 99.67%, 98.06% and 98.12%, respectively.

scholarly journals Predicting Aphasia from Strokes

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Joseph Jia ◽  
Joanna Gilberti
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Brain Lesion ◽  
Brain Cells ◽  
Lesion Volume ◽  
Classification Methods ◽  
Linear Discriminant ◽  
Lesion Location ◽  
Predictive Rate ◽  
The Brain

Strokes can occur when someone’s blood vessels get blocked and the nutrients and oxygen being transported will not reach the brain. When a stroke happens, the brain cells don’t get the nutrients they need and start to die [3]. This could cause different side effects after stroke. In this study, we try to predict the possibility of one type of after-stroke side effect, aphasia, using Machine Learning (ML) techniques. Using the data of a study about brain lesion damage after a stroke and what effects the patients were experiencing afterward, we trained a model to predict whether a person may have aphasia based on where their lesion was, how big the lesion was, how long ago their stroke was, and some other factors. We evaluated several classification methods and found that using linear discriminant analysis was the most accurately predicting when we used age, sex, lesion location, lesion volume, and many more. By linear discriminant analysis, we were able to have a 91% overall predictive rate of patients having aphasia or not after experiencing a stroke.

scholarly journals A quantitative framework for whole-body coordination reveals specific deficits in freely walking ataxic mice

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Ana S Machado ◽  
Dana M Darmohray ◽  
João Fayad ◽  
Hugo G Marques ◽  
Megan R Carey
Keyword(s):  
Motor Control ◽  
Body Size ◽  
Purkinje Cell ◽  
Walking Speed ◽  
The Body ◽  
Whole Body ◽  
Gait Ataxia ◽  
Cell Degeneration ◽  
Forward Motion ◽  
Circuit Defects

The coordination of movement across the body is a fundamental, yet poorly understood aspect of motor control. Mutant mice with cerebellar circuit defects exhibit characteristic impairments in locomotor coordination; however, the fundamental features of this gait ataxia have not been effectively isolated. Here we describe a novel system (LocoMouse) for analyzing limb, head, and tail kinematics of freely walking mice. Analysis of visibly ataxic Purkinje cell degeneration (pcd) mice reveals that while differences in the forward motion of individual paws are fully accounted for by changes in walking speed and body size, more complex 3D trajectories and, especially, inter-limb and whole-body coordination are specifically impaired. Moreover, the coordination deficits in pcd are consistent with a failure to predict and compensate for the consequences of movement across the body. These results isolate specific impairments in whole-body coordination in mice and provide a quantitative framework for understanding cerebellar contributions to coordinated locomotion.

Sign in / Sign up

Export Citation Format

Share Document