A Computerized Approach for Morphological Analysis of the Corpus Callosum

Davatzikos, Christos; Vaillant, Marc; Resnick, Susan M.; Prince, Jerry L.; Letovsky, Stanley; Bryan, R. Nick

doi:10.1097/00004728-199601000-00017

Cited by 293 publications

(214 citation statements)

References 14 publications

Supporting

Mentioning

205

Contrasting

Unclassified

Order By: Relevance

“…Jacobian determinant values derived from these tensor fields indicate the fractional volume expansion and contraction at each voxel (e.g., Chung et al, 2001;Davatzikos et al, 1996). In other words, the resulting Jacobian map quantifies the magnitude of regional volume alterations required to match the template.…”

Section: Image Analysismentioning

confidence: 99%

Structural consequences of diffuse traumatic brain injury: A large deformation tensor-based morphometry study

et al. 2008

View full text Add to dashboard Cite

Traumatic brain injury (TBI) is one of the most common causes of long-term disability. Despite the importance of identifying neuropathology in individuals with chronic TBI, methodological challenges posed at the stage of inter-subject image registration have hampered previous voxel-based MRI studies from providing a clear pattern of structural atrophy after TBI. We used a novel symmetric diffeomorphic image normalization method to conduct a tensor-based morphometry (TBM) study of TBI. The key advantage of this method is that it simultaneously estimates an optimal template brain and topology preserving deformations between this template and individual subject brains. Detailed patterns of atrophies are then revealed by statistically contrasting control and subject deformations to the template space. Participants were 29 survivors of TBI and 20 control subjects who were matched in terms of age, gender, education, and ethnicity. Localized volume losses were found most prominently in white matter regions and the subcortical nuclei including the thalamus, the midbrain, the corpus callosum, the mid-and posterior cingulate cortices, and the caudate. Significant voxel-wise volume loss clusters were also detected in the cerebellum and the frontal/ temporal neocortices. Volume enlargements were identified largely in ventricular regions. A similar pattern of results was observed in a subgroup analysis where we restricted our analysis to the 17 TBI participants who had no macroscopic focal lesions (total lesion volume> 1.5 cm 3 ). The current study confirms, extends, and partly challenges previous structural MRI studies in chronic TBI. By demonstrating that a large deformation image registration technique can be successfully combined with TBM to identify TBI-induced diffuse structural changes with greater precision, our approach is expected to increase the sensitivity of future studies examining brain-behavior relationships in the TBI population.

show abstract

Section: Image Analysismentioning

confidence: 99%

Structural consequences of diffuse traumatic brain injury: A large deformation tensor-based morphometry study

et al. 2008

View full text Add to dashboard Cite

show abstract

“…Therefore, measuring volumes or average PET signals of a few structures cannot capture Voxel-based morphometry (VBM) has been proposed by a number of investigators as a more comprehensive way of measuring the spatial distribution of brain atrophy in MCI and AD, by evaluating images region by region instead of making a priori assumptions about specific ROIs. A variety of VBM-type methods exist (Ashburner and Friston, 2000;Chung et al, 2001;Davatzikos et al, 2001;Davatzikos et al, 1996;Thompson et al, 2001), which generally fall under the umbrella of the field of computational neuroanatomy (Ashburner et al, 2003;Miller et al, 1997). VBM studies have confirmed that complex spatial patterns of brain atrophy can be measured in MCI and AD (Bozzali et al, 2006;Chetelat et al, 2002; Davatzikos et al, in press, 2006;Karas et al, 2004;Pennanen et al, 2005a;Saykin et al, 2006;Thompson et al, 2001;Whitwell et al, 2007;Xie et al, 2006).…”

mentioning

confidence: 99%

Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study

et al. 2008

Self Cite

View full text Add to dashboard Cite

This work builds upon previous studies that reported high sensitivity and specificity in classifying individuals with mild cognitive impairment (MCI), which is often a prodromal phase of Alzheimer's Disease (AD), via pattern classification of MRI scans. The current study integrates MRI and PET 15 O water scans from 30 participants in the Baltimore Longitudinal Study of Aging, and tests the hypothesis that joint evaluation of structure and function can yield higher classification accuracy than either alone. Classification rates of up to 100% accuracy were achieved via leave-one-out cross validation, whereas conservative estimates of generalization performance in new scans, evaluated via bagging cross-validation, yielded an area under the receiver operating characteristic (ROC) curve equal to 0.978 (97.8%), indicating excellent diagnostic accuracy. Spatial maps of regions determined to contribute the most to the classification implicated many temporal, prefrontal, orbitofrontal, and parietal regions. Detecting complex patterns of brain abnormality in early stages of cognitive impairment has pivotal importance for the detection and management of AD. KeywordsAlzheimer's Disease; MCI; high-dimensional Pattern Classification; MRI; PET; voxel-based analysis; diagnosis of AD Amnestic mild cognitive impairment (MCI) is often a prodromal stage to Alzheimer's Disease (AD), as individuals with MCI may convert to AD at an annual rate as high as 15% . Therefore, MCI is frequently considered to be a good target for early AD diagnosis, and for therapeutic interventions. MRI and PET imaging can potentially be used as diagnostic tools that assess brain structure and function in a direct and objective way and have potential utility for diagnosis, prognosis, and evaluation of disease progression and treatment effects.Many imaging studies have found loss of grey matter (GM) and metabolic abnormalities in MCI. Most of the earlier studies were based on volumetric measurements of regions of interest (ROI's) (Chetelat et al., 2002;Convit et al., 2000;Dickerson et al., 2001;Fox et al., 1996a;Kaye et al., 1997;Killiany et al., 2000), such as the hippocampus and the entorhinal cortex, and have confirmed GM atrophy in regions that are known to be affected by AD. However, the pattern of AD pathology is complex and evolves as the disease progresses, starting mainly in the hippocampus and entorhinal cortex, and subsequently spreading throughout temporal and orbitofrontal cortex, posterior cingulate, and association cortex generally (Braak et al., 1998). Therefore, measuring volumes or average PET signals of a few structures cannot capture Voxel-based morphometry (VBM) has been proposed by a number of investigators as a more comprehensive way of measuring the spatial distribution of brain atrophy in MCI and AD, by evaluating images region by region instead of making a priori assumptions about specific ROIs. A variety of VBM-type methods exist (Ashburner and Friston, 2000;Chung et al., 2001;Davatzikos et al., 2001;Davatzikos et al., 1996...

show abstract

“…This global transformation consists of a rigid component describing the patient positioning, and the remaining non-rigid components describing the global and local shape differences between each subject and the reference anatomy. To examine the shape differences only we take the approach of Davatzikos [7] and Machado [17] and examine the differential properties of the spatially normalising transformation through the Jacobian matrix, …”

Section: Clinical Hypothesis For Shape Differences In Ad and Ftldmentioning

confidence: 99%

“…To remove the influence of brain size from the analysis we have chosen to scale each Jacobian value by the average Jacobian of the subject. To detect differences in shape of the distributions for AD J RA (x), SD J RS (x) and FTD J RF (x) from the control group J RC (x) at each point, we have used the effect size as employed by Davatzikos et al [7] and Machado et al [17], defined between the AD and control group as:…”

Section: Clinical Hypothesis For Shape Differences In Ad and Ftldmentioning

confidence: 99%

“…Partially driven by the development of more accurate anatomical registration techniques, the field has expanded rapidly over the last few years. Much of the early work concentrated on specific anatomical regions such as the corpus callosum [7] which has remained an active area of methodological development [20]. Region based analysis techniques can rely on the identification and mapping of points on a specific anatomical boundary for statistical shape analysis [4,14] and has been used to analyze regional shape variation such as in the hippocampus in AD [6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detecting Spatially Consistent Structural Differences in Alzheimer’s and Fronto Temporal Dementia Using Deformation Morphometry

Studholme¹,

Cardenas²,

Schuff³

et al. 2001

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001

View full text Add to dashboard Cite

Abstract. Atrophy is known to occur at specific sites around the brain in both Alzheimer's disease (AD) and Fronto-Temporal Lobe Dementia (FTLD), inducing characteristic shape changes in brain anatomy. In this paper we employ an entropy driven fine lattice free form registration algorithm to investigate whole brain structural changes induced by these diseases relative to normal anatomy, using deformation morphometry. We focus on Alzheimer's disease (AD) and two common sub groups of FTLD: the frontal lobe variant (FTD) and semantic dementia (SD). The shape of each subject group was characterized at each point in the reference anatomy by the distribution of the determinant of the Jacobian of the transformation mapping the subject to a common reference anatomy. Statistical measures were then applied to locate points where voxel level differences in the Jacobian occur between a control group and each of the disease groups, indicating spatially consistent shape differences induced by a particular disease. Spatial maps of the statistical differences showed very different structural characteristics in each disease. AD was characterized by relative contractions in regions of the hippocampus and the parietal lobe and expansions of the ventricular CSF spaces. FTD was characterized by patterns of contraction in the frontal lobe. SD was characterized by large contractions in the temporal lobe, hippocampus and expansion of the ventricular CSF spaces.

show abstract

A Computerized Approach for Morphological Analysis of the Corpus Callosum

Abstract: The proposed methodology utilizes the full resolution of the data, rather than relying on global descriptions such as area measurements. The application of this methodology to an elderly group indicated sex-related differences in the callosal shape and size.

Cited by 293 publications

References 14 publications

Structural consequences of diffuse traumatic brain injury: A large deformation tensor-based morphometry study

Structural consequences of diffuse traumatic brain injury: A large deformation tensor-based morphometry study

Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study

Detecting Spatially Consistent Structural Differences in Alzheimer’s and Fronto Temporal Dementia Using Deformation Morphometry

Contact Info

Product

Resources

About