Alexia without agraphia

Quint, Douglas J.; Gilmore, Jennifer L.

doi:10.1007/bf00596338

Cited by 22 publications

(12 citation statements)

References 18 publications

(18 reference statements)

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“…This principle derives originally from clinical experience where a restricted locus of damage to the nervous system could usually be inferred from a specific pattern of deficits demonstrated by a subject [Gardner, 1975]. Occasionally, the locus of the lesion cannot accurately be directly determined by the pattern of deficits, as in the clinical ''disconnection syndromes'' (e.g., alexia without agraphia [Duffield et al, 1994;Quint and Gilmore, 1992] and pure word deafness [Takahashi et al, 1992] ), because the lesion interrupts connections between macroscopic loci required to perform some psychomotor task. This demonstrates the complementary principle of connectionism that posits that the brain regions involved in a given psychomotor function may be widely distributed, and thus the brain activity required to perform a given task may be the functional integration of activity in multiple macroscopic loci or distinct brain systems (this is a different sense of the term ''connectionism'' from that used to describe neural network models).…”

Section: Independent Component Analysismentioning

confidence: 99%

Analysis of fMRI data by blind separation into independent spatial components

et al. 1998

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Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Here we describe a new method for analyzing fMRI data based on the independent component analysis (ICA) algorithm of Bell and Sejnowski ([1995]: Neural Comput 7:1129-1159). We decomposed eight fMRI data sets from 4 normal subjects performing Stroop color-naming, the Brown and Peterson work/number task, and control tasks into spatially independent components. Each component consisted of voxel values at fixed three-dimensional locations (a component "map"), and a unique associated time course of activation. Given data from 144 time points collected during a 6-min trial, ICA extracted an equal number of spatially independent components. In all eight trials, ICA derived one and only one component with a time course closely matching the time course of 40-sec alternations between experimental and control tasks. The regions of maximum activity in these consistently task-related components generally overlapped active regions detected by standard correlational analysis, but included frontal regions not detected by correlation. Time courses of other ICA components were transiently task-related, quasiperiodic, or slowly varying. By utilizing higher-order statistics to enforce successively stricter criteria for spatial independence between component maps, both the ICA algorithm and a related fourth-order decomposition technique (Comon [1994]: Signal Processing 36:11-20) were superior to principal component analysis (PCA) in determining the spatial and temporal extent of task-related activation. For each subject, the time courses and active regions of the task-related ICA components were consistent across trials and were robust to the addition of simulated noise. Simulated movement artifact and simulated task-related activations added to actual fMRI data were clearly separated by the algorithm. ICA can be used to distinguish between nontask-related signal components, movements, and other artifacts, as well as consistently or transiently task-related fMRI activations, based on only weak assumptions about their spatial distributions and without a priori assumptions about their time courses. ICA appears to be a highly promising method for the analysis of fMRI data from normal and clinical populations, especially for uncovering unpredictable transient patterns of brain activity associated with performance of psychomotor tasks.

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Section: Independent Component Analysismentioning

confidence: 99%

Analysis of fMRI data by blind separation into independent spatial components

et al. 1998

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“…Other causes include transtentorial herniations, left occipital neoplasms, migraine, and brain abscess. [2] Our case initially had transient episodes, which later became fixed. Reported causes of transient alexia without agraphia include toxoplasma encephalitis with ring lesion in the left posterior white matter, herpes simplex encephalitis, variety of neurological and non-neurological causes.…”

Section: Reversible Disconnection Syndrome: An Unusual Presentation Omentioning

confidence: 95%

“…[1] In PKAN, mutations of PANK2 occur on chromosome 20p13 coding for pantothenate kinase 2. [2] PANK2 mutations are believed to cause oxidative stress due to mitochondrial coenzyme A (CoA) deficiency. As a substrate in CoA biosynthesis, cysteine may accumulate in cells and undergo rapid auto-oxidation in the presence of free iron, generating free radicals.…”

Section: Rajesh Verma Tushar Premraj Rautmentioning

confidence: 99%

Reversible disconnection syndrome: An unusual presentation of vitamin B12 deficiency

2013

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“…It is often combined with signs of right homonymous hemianopia depending on the site of involvement. Since the fi rst description, the crucial anatomic region which induces alexia without agraphia has been reported to be the paraventricular white matter of the left occipital lobe, which is capable of preventing bilateral visual stimuli perceived by the occipital lobes from being transferred to the left hemispheric language area via interhemispheric and intrahemispheric connections [2][3][4] . However, the interruption of this pathway has not been demonstrated in vivo.…”

Section: Discussionmentioning

confidence: 99%