scholarly journals Functional neuroimaging and behavioural classification of a case of prosopagnosia with classic bilateral occipitotemporal lesions

2013 ◽  
Vol 13 (9) ◽  
pp. 995-995
Author(s):  
C. Hills ◽  
R. Pancaroglu ◽  
E. Alonso-Prieto ◽  
J. Davies-Thompson ◽  
I. Oruc ◽  
...  
Keyword(s):  
Functional Neuroimaging ◽  
Behavioural Classification

A behavioural classification of managerial jobs

Omega ◽  
1973 ◽  
Vol 1 (3) ◽  
pp. 297-303 ◽  
Author(s):  
Rosemary Stewart
Keyword(s):  
Behavioural Classification

scholarly journals Love Thy Neighbour: Automatic Animal Behavioural Classification of Acceleration Data Using the K-Nearest Neighbour Algorithm

PLoS ONE ◽  
2014 ◽  
Vol 9 (2) ◽  
pp. e88609 ◽  
Author(s):  
Owen R. Bidder ◽  
Hamish A. Campbell ◽  
Agustina Gómez-Laich ◽  
Patricia Urgé ◽  
James Walker ◽  
...  
Keyword(s):  
Nearest Neighbour ◽  
Acceleration Data ◽  
Behavioural Classification

Theoretical, Statistical, and Practical Perspectives on Pattern-based Classification Approaches to the Analysis of Functional Neuroimaging Data

2007 ◽  
Vol 19 (11) ◽  
pp. 1735-1752 ◽  
Author(s):  
Alice J. O'Toole ◽  
Fang Jiang ◽  
Hervé Abdi ◽  
Nils Pénard ◽  
Joseph P. Dunlop ◽  
...  
Keyword(s):  
Functional Neuroimaging ◽  
Brain Activity ◽  
Point Of View ◽  
Experimental Conditions ◽  
Theoretical Terms ◽  
Activation Patterns ◽  
Neural Representations ◽  
The Status ◽  
Neuroimaging Data

The goal of pattern-based classification of functional neuroimaging data is to link individual brain activation patterns to the experimental conditions experienced during the scans. These “brain-reading” analyses advance functional neuroimaging on three fronts. From a technical standpoint, pattern-based classifiers overcome fatal f laws in the status quo inferential and exploratory multivariate approaches by combining pattern-based analyses with a direct link to experimental variables. In theoretical terms, the results that emerge from pattern-based classifiers can offer insight into the nature of neural representations. This shifts the emphasis in functional neuroimaging studies away from localizing brain activity toward understanding how patterns of brain activity encode information. From a practical point of view, pattern-based classifiers are already well established and understood in many areas of cognitive science. These tools are familiar to many researchers and provide a quantitatively sound and qualitatively satisfying answer to most questions addressed in functional neuroimaging studies. Here, we examine the theoretical, statistical, and practical underpinnings of pattern-based classification approaches to functional neuroimaging analyses. Pattern-based classification analyses are well positioned to become the standard approach to analyzing functional neuroimaging data.

Surgical treatment of intractable epilepsy associated with focal cortical dysplasia

2008 ◽  
Vol 25 (3) ◽  
pp. E6 ◽  
Author(s):  
Roberto Jose Diaz ◽  
Elisabeth M. S. Sherman ◽  
Walter J. Hader
Keyword(s):  
Refractory Epilepsy ◽  
Functional Neuroimaging ◽  
Focal Cortical Dysplasia ◽  
Intractable Epilepsy ◽  
Cortical Dysplasia ◽  
Complete Resection ◽  
Epileptogenic Zone ◽  
Malformations Of Cortical Development ◽  
Cortical Dysplasias

Focal cortical dysplasias (FCDs) are congenital malformations of cortical development that are a frequent cause of refractory epilepsy in both children and adults. With advances in structural and functional neuroimaging, these lesions are increasingly being identified as a cause of intractable epilepsy in patients undergoing surgical management for intractable epilepsy. Comprehensive histological classification of FCDs with the establishment of uniform terminology and reproducible pathological features has aided in our understanding of FCDs as an epilepsy substrate. Complete resection of FCDs and the associated epileptogenic zone can result in a good surgical outcome in the majority of patients.

Assessing the effects of sampling frequency on behavioural classification of accelerometer data

2019 ◽  
Vol 512 ◽  
pp. 22-30 ◽  
Author(s):  
J.L. Hounslow ◽  
L.R. Brewster ◽  
K.O. Lear ◽  
T.L. Guttridge ◽  
R. Daly ◽  
...  
Keyword(s):  
Sampling Frequency ◽  
Accelerometer Data ◽  
Behavioural Classification

Note on the spectral classification of carbon stars in the photographic infrared region

1966 ◽  
Vol 24 ◽  
pp. 21-23
Author(s):  
Y. Fujita
Keyword(s):  
Infrared Region ◽  
Spectral Classification ◽  
Carbon Stars

We have investigated the spectrograms (dispersion: 8Å/mm) in the photographic infrared region fromλ7500 toλ9000 of some carbon stars obtained by the coudé spectrograph of the 74-inch reflector attached to the Okayama Astrophysical Observatory. The names of the stars investigated are listed in Table 1.

Diagnostic Value of Electron Microscopy in Soft Tissue Tumors

1970 ◽  
Vol 28 ◽  
pp. 220-221
Author(s):  
Gerald Fine ◽  
Azorides R. Morales
Keyword(s):  
Cardiac Myxoma ◽  
Osteogenic Sarcoma ◽  
Diagnostic Value ◽  
Soft Tissue Tumors ◽  
Amelanotic Melanoma ◽  
Growth Patterns ◽  
Light Microscopic Level ◽  
Mesenchymal Tumors ◽  
Good Agreement

For years the separation of carcinoma and sarcoma and the subclassification of sarcomas has been based on the appearance of the tumor cells and their microscopic growth pattern and information derived from certain histochemical and special stains. Although this method of study has produced good agreement among pathologists in the separation of carcinoma from sarcoma, it has given less uniform results in the subclassification of sarcomas. There remain examples of neoplasms of different histogenesis, the classification of which is questionable because of similar cytologic and growth patterns at the light microscopic level; i.e. amelanotic melanoma versus carcinoma and occasionally sarcoma, sarcomas with an epithelial pattern of growth simulating carcinoma, histologically similar mesenchymal tumors of different histogenesis (histiocytoma versus rhabdomyosarcoma, lytic osteogenic sarcoma versus rhabdomyosarcoma), and myxomatous mesenchymal tumors of diverse histogenesis (myxoid rhabdo and liposarcomas, cardiac myxoma, myxoid neurofibroma, etc.)

Ultrastructure of mesotheliomas

1984 ◽  
Vol 42 ◽  
pp. 98-101
Author(s):  
Irving Dardick
Keyword(s):  
Electron Microscopy ◽  
Latent Period ◽  
Spindle Cell ◽  
Spindle Cell Sarcoma ◽  
Metastatic Adenocarcinoma ◽  
Cell Sarcoma ◽  
Cytological Features ◽  
Industrial Use ◽  
Degree Of Differentiation

With the extensive industrial use of asbestos in this century and the long latent period (20-50 years) between exposure and tumor presentation, the incidence of malignant mesothelioma is now increasing. Thus, surgical pathologists are more frequently faced with the dilemma of differentiating mesothelioma from metastatic adenocarcinoma and spindle-cell sarcoma involving serosal surfaces. Electron microscopy is amodality useful in clarifying this problem.In utilizing ultrastructural features in the diagnosis of mesothelioma, it is essential to appreciate that the classification of this tumor reflects a variety of morphologic forms of differing biologic behavior (Table 1). Furthermore, with the variable histology and degree of differentiation in mesotheliomas it might be expected that the ultrastructure of such tumors also reflects a range of cytological features. Such is the case.

Interpreting HVEM of muscle-cell impulse networks

1992 ◽  
Vol 50 (1) ◽  
pp. 126-127
Author(s):  
Paul DeCosta ◽  
Kyugon Cho ◽  
Stephen Shemlon ◽  
Heesung Jun ◽  
Stanley M. Dunn
Keyword(s):  
Structural Analysis ◽  
Muscle Cell ◽  
Electron Micrographs ◽  
Relative Size ◽  
Automatic Classification ◽  
Nerve Fibers ◽  
Analysis Algorithm ◽  
Structural Networks

Introduction: The analysis and interpretation of electron micrographs of cells and tissues, often requires the accurate extraction of structural networks, which either provide immediate 2D or 3D information, or from which the desired information can be inferred. The images of these structures contain lines and/or curves whose orientation, lengths, and intersections characterize the overall network.Some examples exist of studies that have been done in the analysis of networks of natural structures. In, Sebok and Roemer determine the complexity of nerve structures in an EM formed slide. Here the number of nodes that exist in the image describes how dense nerve fibers are in a particular region of the skin. Hildith proposes a network structural analysis algorithm for the automatic classification of chromosome spreads (type, relative size and orientation).

Sign in / Sign up

Export Citation Format

Share Document