2015
DOI: 10.3174/ajnr.a4505
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Principal Component Analysis of Diffusion Tensor Images to Determine White Matter Injury Patterns Underlying Postconcussive Headache

Abstract: BACKGROUND AND PURPOSE:Principal component analysis, a data-reduction algorithm, generates a set of principal components that are independent, linear combinations of the original dataset. Our study sought to use principal component analysis of fractional anisotropy maps to identify white matter injury patterns that correlate with posttraumatic headache after mild traumatic brain injury.

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Cited by 24 publications
(22 citation statements)
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“…Several other studies have identified relationships between headaches and structural and functional brain alterations in the frontal cortex. Results of a recent diffusion tensor study that applied a principal component analysis found that decreased fractional anisotropy in the genu and the midsplenium of the corpus callosum increased the risk for developing PTH, and in a previously published study we have found a relationship between headache frequency and dorsolateral prefrontal activation during pain processing in patients with migraine without history of concussion . These studies provide some evidence of a potential relationship between frontal cortex structure and function and headache burden in migraine patients as well as in patients with PPTH.…”
Section: Discussionsupporting
confidence: 67%
“…Several other studies have identified relationships between headaches and structural and functional brain alterations in the frontal cortex. Results of a recent diffusion tensor study that applied a principal component analysis found that decreased fractional anisotropy in the genu and the midsplenium of the corpus callosum increased the risk for developing PTH, and in a previously published study we have found a relationship between headache frequency and dorsolateral prefrontal activation during pain processing in patients with migraine without history of concussion . These studies provide some evidence of a potential relationship between frontal cortex structure and function and headache burden in migraine patients as well as in patients with PPTH.…”
Section: Discussionsupporting
confidence: 67%
“…NL-PCA detects statistical patterns, incorporating multiple variables independent of their scale and decomposing them into a smaller set, representing multidimensional clusters of variables (principal components (PCs)) that covary. 32, 33 We then used non-linear regression approaches to benchmark different MRI assessments against each other for predicting neurological impairment at discharge. We hypothesized that MRI measures of acute cervical SCI would group together as coherent multivariate PC ensemble, and that distinct PCs (PC1, PC2 etc.)…”
Section: Introductionmentioning
confidence: 99%
“…1 Migraine is the phenotype observed in the majority of individuals with PTH. [2][3][4][5][6][7][8] Investigations into mechanisms of PTH have used brain imaging, [9][10][11] measurement of pain modulatory systems and cranial sensitization with thermal and pressure stimulation, 12 and animal models. [13][14][15] At present, it is unknown if PTH is a unique headache type with separate pathophysiology and clinical features from migraine and there have been few studies investigating this possibility.…”
mentioning
confidence: 99%