A semiautomated measure of whole-brain atrophy in multiple sclerosis

Bermel, Robert; Sharma, Jitendra; Tjoa, Christopher W.; Puli, Srinivas R.; Bakshi, Rohit

doi:10.1016/s0022-510x(02)00425-2

Cited by 114 publications

(123 citation statements)

References 35 publications

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“…In precedent works [27][28][29]31,32], we demonstrated the involvement of the thalamus in the pathophysiological mechanisms of RR-MS as evidenced in other works [33][34][35]. In this study, we confirm with a DTI analyses, the involvement of the thalamus in RR-MS, but we found that the afferent and efferent pathways were involved when we compared both RR-MS groups with controls.…”

Section: Discussionsupporting

confidence: 84%

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Superior Cerebellar Peduncle Atrophy Predicts Cognitive Impairment in Relapsing Remitting Multiple Sclerosis Patients with Cerebellar Symptoms: A DTI Study

Nicoletti¹,

Valentino²,

Chiriaco³

et al. 2017

J Mult Scler

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IntroductionThe presence of cognitive dysfunctions in Multiple Sclerosis (MS) has been well described as one of the most common co-morbidities [1][2][3], with a prevalence ranging from 45% to 65% [4]. The most affected cognitive domains in MS are memory, visuospatial perception, executive functions, attention and information processing speed [5].Although the cerebellum has been traditionally associated with motor control, recently a growing body of clinical and experimental evidences has suggested that it may be also involved in non-motor functions [6][7][8]. In fact, it has been shown that cerebellar abnormalities, e.g. lesions, are associated with cognitive impairment as measured by neuropsysiological tests [9]. Thus, evidences suggest that the cerebellum has an important role in monitoring sensory information and providing adaptation of both motor and non-motor functions to perform contextually relevant behaviours [10,11].The cognitive role of the cerebellum is mainly due to its strong connections with several higher-level cortical regions, such as with the controlateral cerebral hemispheres in both feed forward and feedback directions. The feed forward loop (Figure 1 in blue) connects cortical areas via middle cerebellar peduncle (MCP) with deep cerebellar nuclei, including the dentate nucleus (DN) (afferent pathway). The feedback loop (Figure 1 in orange) connects the deep cerebellar nuclei with motor cortex, via the superior cerebellar peduncle (SCP), the red nucleus and the thalamus (Th) (efferent pathway) [12,13].Disconnections of the fibers to the thalamus, negatively involve the limbic circuitry avoiding the mediation and regulation of many cognitive functions [14,15]. Although many studies have shown a strong association between thalamic atrophy and cognitive function [16][17][18], no one focused on its microstructural changes, as well as on abnormalities of MCP, SCP and DN, and on their relationships to cognitive functions in MS.Diffusion tensor imaging (DTI) is one of the most sensitive methods for detecting subtle alterations of white matter [19] (WM) by evaluating the directional movement of water molecules in the brain. Analysis of DTI images could indeed distinguish between regions where fibers present a strong alignment from those with a lower coherence. Diffusion properties are summarized by several indices, in particular by fractional anisotropy (FA) that refers to the degree to which diffusion is direction-dependent. FA is used to quantify the changes in WM microstructure, which might be related to development or progression of a specific disease [20].The aim of this study was to investigate a group of patients affected by relapsing-remitting multiple sclerosis with (RR-MSc) and without (RR-MSnc) cerebellar signs, in order to determine the alteration of FA in the Th, MCP, SCP and DN and their correlation to cognitive functions, as assessed by neuropsysiological tests. In particular, we examined two hypotheses:

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Section: Discussionsupporting

confidence: 84%

“…About SDMT, in our precedent works [27][28][29]31,32], we demonstrated that: a) RR-MS patients with cerebellar signs performed worse in the areas of attention (SDMT) and verbal fluency (COWAT) than individually matched MS patients without cerebellar damage [27].…”

Section: Discussionmentioning

confidence: 67%

Superior Cerebellar Peduncle Atrophy Predicts Cognitive Impairment in Relapsing Remitting Multiple Sclerosis Patients with Cerebellar Symptoms: A DTI Study

Nicoletti¹,

Valentino²,

Chiriaco³

et al. 2017

J Mult Scler

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“…Skull stripping is an important preprocessing step in neuroimaging analyses because brain images must typically be skull stripped before other processing algorithms such as registration, tissue classification or bias field correction can be applied (Woods et al, 1998(Woods et al, , 1999Van Horn et al, 1998;Shattuck et al, 2001;Strother et al, 2004). In practice, skull stripping is widely used in neuroimaging analyses such as multi-modality image fusion and intersubject image comparisons (Woods et al, 1998(Woods et al, , 1999; examination of the progression of brain disorders such as Alzheimer's Disease (Rusinek et al, 1991;Thompson et al, 2001), multiple sclerosis (Bermel et al, 2003;Horsfield et al, 2003;Zivadinov et al, 2004;Sharma et al, 2004) and schizophrenia Tanskanen et al, 2004); monitoring the development or aging of the brain (Jernigan et al, 2001;Blanton et al, 2004); and creating probabilistic atlases from large groups of subjects (Mazziotta et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

Skull-stripping magnetic resonance brain images using a model-based level set

2006

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The segmentation of brain tissue from nonbrain tissue in magnetic resonance (MR) images, commonly referred to as skull-stripping, is an important image processing step in many neuroimaging studies. A new mathematical algorithm, a model-based level set (MLS), was developed for controlling the evolution of the zero level curve that is implicitly embedded in the level set function. The evolution of the curve was controlled using two terms in the level set equation, whose values represented the forces that determined the speed of the evolving curve. The first force was derived from the mean curvature of the curve, and the second was designed to model the intensity characteristics of the cortex in MR images. The combination of these forces in a level set framework pushed or pulled the curve toward the brain surface. Quantitative evaluation of the MLS algorithm was performed by comparing the results of the MLS algorithm to those obtained using expert segmentation in 29 sets of pediatric brain MR images and 20 sets of young adult MR images. Another 48 sets of elderly adult MR images were used for qualitatively evaluating the algorithm. The MLS algorithm was also compared to two existing methods, the brain extraction tool (BET) and the brain surface extractor (BSE), using the data from the Internet brain segmentation repository (IBSR). The MLS algorithm provides robust skull-stripping results, making it a promising tool for use in large, multi-institutional, population-based neuroimaging studies.

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“…They used point counting method for the assessment of brain volume in the mentioned study. Bermel et al (2003) developed a semi-automated measure of brain parenchymal fraction using commercially available edge-finding and thresholding software. Their intra-rater variability was very low and they concluded their software could be used to quantify the brain structures within 30 minutes.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the Intra-rater Variation for the Estimation of Volume of Cerebral Structures Using the Cavalieri Principle on Magnetic Resonance Images

ELFAKİ¹,

OSMAN²,

ELSHEİKH³

et al. 2011

jecm

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Keywords:Stereology Cavalieri principle Planimetry Magnetic resonance imaging Brain Intra-rater variation ARTICLE INFO ABSTRACTMeasurement of brain volume is regarded as an objective marker of neurodegenerative diseases. The purpose of this study is to evaluate intra-rater variation for the estimation of volume of cerebral structures using the Cavalieri principle on magnetic resonance (MR) images to determine its reproducibility. The MR images of 30 cases were analyzed using the same standard protocols of the Cavalieri principle in two sessions with one month intervals. The structural MR images were analyzed using the ImageJ software by the same observer. The planimetry and threshold process was used for the cut surface area assessments. The volume of hemispheres, total brain, gray and white matters were and lateral ventricles estimated by means of the multiplication of cut surface are by the section interval. The same sections were used in both sessions. The results of two sessions were compared using Wilcoxon Signed Rank test. The mean total brain, right and left hemispheres volumes of first session were 1089.53, 544.82 and 544.71cm 3 , respectively. The mean total brain, right and left hemispheres volumes of second session were 1086.62, 543.99 and 542.63cm 3 , respectively. There was no statistically significant difference between the data (p>0.05). The mean total gray and white matters volumes were 553. 55, 535.98 and 549.32, 537.31cm 3 for the first and second sessions, respectively. There was no statistically significant difference for the gray and white matters results (p>0.05). Our results showed that the reproducibility of the obtained data is good. The volume of cerebral structures could be estimated using the Cavalieri principle on MR images for comparative studies. We are planning to evaluate inter-rater difference in advance.

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A semiautomated measure of whole-brain atrophy in multiple sclerosis

Cited by 114 publications

References 35 publications

Superior Cerebellar Peduncle Atrophy Predicts Cognitive Impairment in Relapsing Remitting Multiple Sclerosis Patients with Cerebellar Symptoms: A DTI Study

Superior Cerebellar Peduncle Atrophy Predicts Cognitive Impairment in Relapsing Remitting Multiple Sclerosis Patients with Cerebellar Symptoms: A DTI Study

Skull-stripping magnetic resonance brain images using a model-based level set

Evaluation of the Intra-rater Variation for the Estimation of Volume of Cerebral Structures Using the Cavalieri Principle on Magnetic Resonance Images

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