Within-Subject Template Estimation for Unbiased Longitudinal Image Analysis

Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA.
NeuroImage (Impact Factor: 6.36). 03/2012; 61(4):1402-18. DOI: 10.1016/j.neuroimage.2012.02.084
Source: PubMed


Longitudinal image analysis has become increasingly important in clinical studies of normal aging and neurodegenerative disorders. Furthermore, there is a growing appreciation of the potential utility of longitudinally acquired structural images and reliable image processing to evaluate disease modifying therapies. Challenges have been related to the variability that is inherent in the available cross-sectional processing tools, to the introduction of bias in longitudinal processing and to potential over-regularization. In this paper we introduce a novel longitudinal image processing framework, based on unbiased, robust, within-subject template creation, for automatic surface reconstruction and segmentation of brain MRI of arbitrarily many time points. We demonstrate that it is essential to treat all input images exactly the same as removing only interpolation asymmetries is not sufficient to remove processing bias. We successfully reduce variability and avoid over-regularization by initializing the processing in each time point with common information from the subject template. The presented results show a significant increase in precision and discrimination power while preserving the ability to detect large anatomical deviations; as such they hold great potential in clinical applications, e.g. allowing for smaller sample sizes or shorter trials to establish disease specific biomarkers or to quantify drug effects.

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Available from: Nick Schmansky, Jan 06, 2014
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    • "Briefly, T1-weighted 3D images were preprocessed with intensity variations correction, normalization, affine registration to the Talairach atlas, skull stripping, and segmentation of grey and white matter. The pipeline for longitudinal processing has been used that includes the creation of an unbiased within-subject template using robust, inverse consistent registration [9]. For cortical thickness , we used surface-based analysis of thickness values at each vertex. "
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    ABSTRACT: The preclinical stage of frontotemporal lobar degeneration (FTLD) is not well characterized. We conducted a brain metabolism (FDG-PET) and structural (cortical thickness) study to detect early changes in asymptomatic GRN mutation carriers (aGRN+) that were evaluated longitudinally over a 20-month period. At baseline, a left lateral temporal lobe hypometabolism was present in aGRN+ without any structural changes. Importantly, this is the first longitudinal study and, across time, the metabolism more rapidly decreased in aGRN+ in lateral temporal and frontal regions. The main structural change observed in the longitudinal study was a reduction of cortical thickness in the left lateral temporal lobe in carriers. A limit of this study is the relatively small sample (n = 16); nevertheless, it provides important results. First, it evidences that the pathological processes develop a long time before clinical onset, and that early neuroimaging changes might be detected approximately 20 years before the clinical onset of disease. Second, it suggests that metabolic changes are detectable before structural modifications and cognitive deficits. Third, both the baseline and longitudinal studies provide converging results implicating lateral temporal lobe as early involved in GRN disease. Finally, our study demonstrates that structural and metabolic changes could represent possible biomarkers to monitor the progression of disease in the presymptomatic stage toward clinical onset.
    Journal of Alzheimer's disease: JAD 09/2015; 47(3):751-759. DOI:10.3233/JAD-150270 · 4.15 Impact Factor
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    • "Analyses were performed to determine the association between regional cortical thickness and cognitive function. For the longitudinal analyses of cortical thinning, vertex-wise comparisons of per cent change of cortical thickness among the diagnostic groups were analysed using the longitudinal two-stage GLM in Freesurfer (Reuter et al., 2012). In the longitudinal analysis, the per cent change of cortical thickness was the dependent factor and the diagnostic group was the independent factor. "
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    ABSTRACT: BACKGROUND Mild cognitive impairment in Parkinson’s disease (PD-MCI) is associated with progression to dementia (PDD) in a majority of patients. Determining structural imaging biomarkers associated with prodromal PDD may allow for the earlier identification of those at risk, and allow for targeted disease modifying therapies METHODS 105 non-demented subjects with newly diagnosed idiopathic Parkinson’s disease (PD) and 37 healthy matched controls had serial 3T structural MRI scans with clinical and neuropsychological assessments at baseline which were repeated after18 months. The MDS Task Force criteria were used to classify the PD subjects into PD-MCI (n=39) and PD with no cognitive impairment (PD-NC) (n=66). Freesurfer image processing software was used to measure cortical thickness and subcortical volumes at baseline and follow-up. We compared regional percentage change of cortical thinning and subcortical atrophy over 18 months. RESULTS At baseline, PD-MCI cases demonstrated widespread cortical thinning relative to controls and atrophy of the nucleus accumbens compared to both controls and PD-NC. Regional cortical thickness at baseline was correlated with global cognition in the combined PD cohort. Over 18 months, PD-MCI demonstrated more severe cortical thinning in frontal and temporo-parietal cortices, including hippocampal atrophy, relative to PD-NC and healthy controls, while PD-NC showed more severe frontal cortical thinning compared to healthy controls. At baseline, PD-NC converters showed bilateral temporal cortex thinning relative to the PD-NC stable subjects. CONCLUSION Although loss of both cortical and subcortical volume occurs in non-demented PD, our longitudinal analyses revealed that PD-MCI shows more extensive atrophy and greater percentage of cortical thinning comparing to PD-NC. In particular, an extension of cortical thinning in the temporo-parietal regions in addition to frontal atrophy could be a biomarker in therapeutic studies of PD-MCI for progression towards dementia.
    Brain 06/2015; DOI:10.1093/brain/awv211 · 9.20 Impact Factor
    • "Traditional cortical thickness calculations are the result of postmortem observations, but Magnetic Resonance Imaging (MRI) allows for in vivo measurements to be made. Using MRI, regional measures can then be either determined manually [Hermoye et al., 2004; Hutton et al., 2009; Jou et al., 2005; Peterson et al., 2009] or automatically [Dale et al., 1999; Hutton et al., 2009; Liem et al., 2015; Reuter et al., 2012; Sereno et al., 1995; Storsve et al., 2014; Tustison et al., 2014]. As the human cortex consists of many layers and folds of sheets of neurons, manual estimation is challenging and time consuming. "
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    ABSTRACT: In the last decade, many studies have used automated processes to analyze magnetic resonance imaging (MRI) data such as cortical thickness, which is one indicator of neuronal health. Due to the convenience of image processing software (e.g., FreeSurfer), standard practice is to rely on automated results without performing visual inspection of intermediate processing. In this work, structural MRIs of 40 healthy controls who were scanned twice were used to determine the test-retest reliability of FreeSurfer-derived cortical measures in four groups of subjects-those 25 that passed visual inspection (approved), those 15 that failed visual inspection (disapproved), a combined group, and a subset of 10 subjects (Travel) whose test and retest scans occurred at different sites. Test-retest correlation (TRC), intraclass correlation coefficient (ICC), and percent difference (PD) were used to measure the reliability in the Destrieux and Desikan-Killiany (DK) atlases. In the approved subjects, reliability of cortical thickness/surface area/volume (DK atlas only) were: TRC (0.82/0.88/0.88), ICC (0.81/0.87/0.88), PD (0.86/1.19/1.39), which represent a significant improvement over these measures when disapproved subjects are included. Travel subjects' results show that cortical thickness reliability is more sensitive to site differences than the cortical surface area and volume. To determine the effect of visual inspection on sample size required for studies of MRI-derived cortical thickness, the number of subjects required to show group differences was calculated. Significant differences observed across imaging sites, between visually approved/disapproved subjects, and across regions with different sizes suggest that these measures should be used with caution. Hum Brain Mapp, 2015. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.
    Human Brain Mapping 05/2015; 36(9). DOI:10.1002/hbm.22856 · 5.97 Impact Factor
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