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2.
J Clin Neurosci ; 16(12): 1665-7, 2009 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-19766496

RESUMO

Septo-optic dysplasia is a rare congenital anomaly presenting with visual deficits or pituitary dysfunction. We report the first case of septo-optic dysplasia associated with encephalocele, partial callosal agenesis, polymicrogyria and subcortical nodular heterotopia.


Assuntos
Encefalocele/complicações , Displasia Septo-Óptica/complicações , Feminino , Humanos , Imageamento por Ressonância Magnética , Malformações do Desenvolvimento Cortical/complicações , Transtornos da Visão/etiologia , Adulto Jovem
3.
Neuroradiol J ; 21(1): 23-30, 2008 Feb 18.
Artigo em Inglês | MEDLINE | ID: mdl-24256746

RESUMO

The analysis of functional magnetic resonance imaging (fMRI) time-series data can provide information on task-related activities, functional/effective connectivity among regions and the influences of behavioral/physiologic states on connectivity. This paper illustrates the importance of the neurobiological constraints involved in using statistical parametric mapping (SPM) through Matlab simulation and thus helping the radiologist to interpret the results better. This paper also presents the results and inferences from neuroimaging data of the lip movement experiment using statistical parametric mapping (SPM). The results match with the sensory/motor activation atlas by Penfield and Rasmussen (1950).

4.
Neuroradiol J ; 21(1): 31-4, 2008 Feb 18.
Artigo em Inglês | MEDLINE | ID: mdl-24256747

RESUMO

This article visualizes the three-dimensional view of t-statistics for the lip movement experiment. It also visualizes the t-contrast image. These three dimensional views help the radiologist to understand the volume of activations in the brain. The 3D view of the t-statistics of the subject under study looks like a piece of ginger and hence is called the "ginger effect of t-statistics".

5.
Neuroradiol J ; 20(5): 491-3, 2007 Oct 31.
Artigo em Inglês | MEDLINE | ID: mdl-24299934

RESUMO

Statistical parametric map of an fMRI time series is used to identify the sensor, motor and cognitive tasks in the specific regions of the brain. This process of obtaining statistical parametric map includes realignment of the slices in various volume acquired during the scanning. This article presents the identification of micro level (10(-6)) error in the realignment phase.

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