Voxel-based morphometry

Summary

Voxel-based morphometry is a computational approach to neuroanatomy that measures differences in local concentrations of brain tissue, through a voxel-wise comparison of multiple brain images. In traditional morphometry, volume of the whole brain or its subparts is measured by drawing regions of interest (ROIs) on images from brain scanning and calculating the volume enclosed. However, this is time consuming and can only provide measures of rather large areas. Smaller differences in volume may be overlooked. The value of VBM is that it allows for comprehensive measurement of differences, not just in specific structures, but throughout the entire brain. VBM every brain to a template, which gets rid of most of the large differences in brain anatomy among people. Then the brain images are smoothed so that each voxel represents the average of itself and its neighbors. Finally, the image volume is compared across brains at every voxel. However, VBM can be sensitive to various artifacts

Official source

https://en.wikipedia.org/wiki/Voxel-based_morphometry

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Differentiating Parkinson's disease motor subtypes using automated volume-based morphometry incorporating white matter and deep gray nuclear lesion load

Bénédicte Marie Maréchal

Background Periventricular leukoaraiosis may be an important pathological change in postural instability gait disorder (PIGD), a motor subtype of Parkinson's disease (PD). Clinical diagnosis of PIGD may be challenging for the general neurologist. Purpose To evaluate 1) the utility of a fully automated volume-based morphometry (Vol-BM) in characterizing imaging diagnostic markers in PD and PIGD, including, 2) novel deep gray nuclear lesion load (GMab), and 3) discriminatory performance of a Vol-BM model construct in classifying the PIGD subtype. Study Type Prospective. Subjects In all, 23 PIGD, 21 PD, and 20 age-matched healthy controls (HC) underwent MRI brain scans and clinical assessments. Field Strength/Sequence 3.0T, sagittal 3D-magnetization-prepared rapid gradient echo (MPRAGE), and fluid-attenuated inversion recovery imaging (FLAIR) sequences. Assessment Clinical assessment was conducted by a movement disorder neurologist. The MR brain images were then segmented using an automated multimodal Vol-BM algorithm (MorphoBox) and reviewed by two authors independently. Statistical Testing Brain segmentation and clinical parameter differences and dependence were assessed using analysis of variance (ANOVA) and regression analysis, respectively. Logistic regression was performed to differentiate PIGD from PD, and discriminative reliability was evaluated using receiver operating characteristic (ROC) analysis. Results Significantly higher white matter lesion load (WMab) (P < 0.01), caudate GMab (P < 0.05), and lateral and third ventricular (P < 0.05) volumetry were found in PIGD, compared with PD and HC. WMab, caudate and putamen GMab, and caudate, lateral, and third ventricular volumetry showed significant coefficients (P < 0.005) in linear regressions with balance and gait assessments in both patient groups. A model incorporating WMab, caudate GMab, and caudate GM discriminated PIGD from PD and HC with a sensitivity = 0.83 and specificity = 0.76 (AUC = 0.84). Data Conclusion Fast, unbiased quantification of microstructural brain changes in PD and PIGD is feasible using automated Vol-BM. Composite lesion load in the white matter and caudate, and caudate volumetry discriminated PIGD from PD and HC, and showed potential in classification of these disorders using supervised machine learning. Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019.

WILEY2019

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Introduction: Several imaging modalities are under investigation to unravel the pathophysiological mystery of delayed performance deficits in patients after mild traumatic brain injury (mTBI). Although both imaging and neuropsychological studies have been conducted, only few data on longitudinal correlations of diffusion tensor imaging (DTI), susceptibility weighted imaging (SWI) and extensive neuropsychological testing exist. Methods: MRI with T1- and T2-weighted, SWI and DTI sequences at baseline and 12 months of 30 mTBI patients were compared with 20 healthy controls. Multiparametric assessment included neuropsychological testing of cognitive performance and post-concussion syndrome (PCS) at baseline, 3 and 12 months post-injury. Data analysis encompassed assessment of cerebral microbleeds (Mb) in SWI, tract-based spatial statistics (TBSS) and voxel-based morphometry (VBM) of DTI (VBM-DTI). Imaging markers were correlated with neuropsychological testing to evaluate sensitivity to cognitive performance and post-concussive symptoms. Results: Patients with Mb in SWI in the acute phase showed worse performance in several cognitive tests at baseline and in the follow-ups during the chronic phase and higher symptom severity in the post concussion symptom scale (PCSS) at twelve months post-injury. In the acute phase there was no statistical difference in structural integrity as measured with DTI between mTBI patients and healthy controls. At twelve months post-injury, loss of structural integrity in mTBI patients was found in nearly all DTI indices compared to healthy controls. Conclusions: Presence of Mb detected by SWI was associated with worse cognitive outcome and persistent PCS in mTBI patients, while DTI did not prove to predict neuropsychological outcome in the acute phase. (C) 2018 Polish Neurological Society. Published by Elsevier Sp. z o.o. All rights reserved.

ELSEVIER URBAN & PARTNER SP Z O O2018

Background and Purpose- Poststroke fatigue affects a large proportion of stroke survivors and is associated with a poor quality of life. In a recent trial, modafinil was shown to be an effective agent in reducing poststroke fatigue; however, not all patients reported a significant decrease in fatigue with therapy. We sought to investigate clinical and radiological predictors of fatigue reduction with modafinil therapy in a stroke survivor cohort. Methods- Twenty-six participants with severe fatigue (multidimensional fatigue inventory-20 >= 60) underwent magnetic resonance imaging at baseline and during the last week of a 6-week treatment period of 200 mg modafinil taken daily. Resting-state functional magnetic resonance imaging and high-resolution structural imaging data were obtained, and functional connectivity and regional brain volumes within the fronto-striato-thalamic network were obtained. Linear regression analysis was used to identify predictors of modafinil-induced fatigue reduction. Results- Multiple regression analysis showed that baseline multidimensional fatigue inventory-20 score (beta=0.576, P=0.006) and functional connectivity between the dorsolateral prefrontal cortex and the caudate nucleus (beta=-0.424, P=0.008) were significant predictors of modafinil-associated decreases in poststroke fatigue (adjusted r(2)=0.52, area under the receiver operator characteristic curve=0.939). Conclusions- Fronto-striato-thalamic functional connectivity predicted modafinil response for poststroke fatigue. Fatigue in other neurological disease has been attributed to altered function of the fronto-striato-thalamic network and may indicate that poststroke fatigue has a similar mechanism to other neurological injury related fatigue. Self-reported fatigue in patients with normal fronto-striato-thalamic functional connectivity may have a different mechanism and require alternate therapeutic approache

LIPPINCOTT WILLIAMS & WILKINS2019

Differentiating Parkinson's disease motor subtypes using automated volume-based morphometry incorporating white matter and deep gray nuclear lesion load

Bénédicte Marie Maréchal

WILEY2019