BrainPrint: A discriminative characterization of brain morphology

Wachinger, Christian; Golland, Polina; Kremen, William S.; Fischl, Bruce; Reuter, Martin

doi:10.1016/j.neuroimage.2015.01.032

Cited by 151 publications

(206 citation statements)

References 84 publications

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“…Currently, there are multiple methods of deriving a brain age estimate which are applicable for identifying differential rates of aging (Bunge and Whitaker, 2012; Franke et al, 2010; Wachinger et al, 2015), tracking recovery from traumatic brain injuries (Cole et al, 2015) and in measuring brain development in patients with multiple sclerosis (Aubert-Broche et al, 2014). When predicting chronological age, other methods have explained more variance in their data then in the methods employed in this work (Cole et al, 2015; Franke et al, 2010; Wang et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Differences between chronological and brain age are related to education and self-reported physical activity

Steffener

Habeck

O’Shea

et al. 2016

Neurobiology of Aging

207

168

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This study investigated the relationship between education and physical activity and the difference between a physiological prediction of age and chronological age. Cortical and subcortical grey matter regional volumes were calculated from 331 healthy adults (range: 19-79 years). Multivariate analyses identified a covariance pattern of brain volumes best predicting chronological age (CA)(R2 = 47%). Individual expression of this brain pattern served as a physiologic measure of brain age (BA). The difference between CA and BA was predicted by education and self-report measures of physical activity. Education and the daily number of flights of stairs climbed were the only two significant predictors of decreased brain age. Effect sizes demonstrated that brain age decreased by 0.95 years for each year of education and by 0.58 years for one additional daily FOSC. Effects of education and FOSC on regional brain volume were largely driven by temporal and subcortical volumes. These results demonstrate that higher levels of education and daily FOSC are related to larger brain volume than predicted by chronological age which supports the utility of regional grey matter volume as a biomarker of healthy brain aging.

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Section: Discussionmentioning

confidence: 99%

Differences between chronological and brain age are related to education and self-reported physical activity

Steffener

Habeck

O’Shea

et al. 2016

Neurobiology of Aging

207

168

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“…Based on these meshes, we compute compact shape representations for all structures, constituting the BrainPrint (Wachinger et al, 2015). The shapeDNA (Reuter et al, 2006) is used as shape descriptor, which performed among the best in a comparison of methods for non-rigid 3D shape retrieval (Lian et al, 2012).…”

Section: Methodsmentioning

confidence: 99%

“…The extensive list of articles discussed in the review illustrates the wide interest in the research field. In this work, we introduce an algorithm for AD classification that is based on BrainPrint (Wachinger et al, 2015) for quantifying brain morphology, which naturally extends the region of interest (ROI)-based volume and thickness analysis with shape information (Reuter et al, 2006). Anatomical shape features contribute valuable information to the characterization of brain structures, which are only coarsely represented by their volume.…”

Section: Introductionmentioning

confidence: 99%

Domain adaptation for Alzheimer's disease diagnostics

2016

Self Cite

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With the increasing prevalence of Alzheimer’s disease, research focuses on the early computer-aided diagnosis of dementia with the goal to understand the disease process, determine risk and preserving factors, and explore preventive therapies. By now, large amounts of data from multi-site studies have been made available for developing, training, and evaluating automated classifiers. Yet, their translation to the clinic remains challenging, in part due to their limited generalizability across different datasets. In this work, we describe a compact classification approach that mitigates overfitting by regularizing the multinomial regression with the mixed ℓ1/ℓ2 norm. We combine volume, thickness, and anatomical shape features from MRI scans to characterize neuroanatomy for the three-class classification of Alzheimer’s disease, mild cognitive impairment and healthy controls. We demonstrate high classification accuracy via independent evaluation within the scope of the CADDementia challenge. We, furthermore, demonstrate that variations between source and target datasets can substantially influence classification accuracy. The main contribution of this work addresses this problem by proposing an approach for supervised domain adaptation based on instance weighting. Integration of this method into our classifier allows us to assess different strategies for domain adaptation. Our results demonstrate (i) that training on only the target training set yields better results than the naïve combination (union) of source and target training sets, and (ii) that domain adaptation with instance weighting yields the best classification results, especially if only a small training component of the target dataset is available. These insights imply that successful deployment of systems for computer-aided diagnostics to the clinic depends not only on accurate classifiers that avoid overfitting, but also on a dedicated domain adaptation strategy.

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“…In the computational analysis of brain anatomy, volumetric or surface feature measures of structures identified on 3D MRI have been used to study group differences in brain structure and also to predict diagnosis (Botino et al, 2002; Li et al, 2014). In research studies that analyze brain morphometry, many shape analysis methods have been proposed, such as spherical harmonic analysis (SPHARM) (Gerig et al, 2001; Chung et al, 2008), medial representations (M-reps) (Pizer et al, 1999), point distribution models (PDM) (Cootes et al, 1995; Shen et al, 2004), minimum description length approaches (Davies et al, 2003), spectral methods (Shi et al, 2010; Seo and Chung, 2011; Wachinger et al, 2015) and cortical thickness and gyrification indices (Batchelor et al, 2002; Luders et al, 2006), etc. These methods may be applied to analyze shape changes or abnormalities in brain cortical and subcortical structures.…”

Section: Introductionmentioning

confidence: 99%

Conformal invariants for multiply connected surfaces: Application to landmark curve-based brain morphometry analysis

Shi

Zhang

Tang

et al. 2017

Medical Image Analysis

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Landmark curves were widely adopted in neuroimaging research for surface correspondence computation and quantified morphometry analysis. However, most of the landmark based morphometry studies only focused on landmark curve shape difference. Here we propose to compute a set of conformal invariant-based shape indices, which are associated with the landmark curve induced boundary lengths in the hyperbolic parameter domain. Such shape indices may be used to identify which surfaces are conformally equivalent and further quantitatively measure surface deformation. With the surface Ricci flow method, we can conformally map a multiply connected surface to the Poincaré disk. Our algorithm provides a stable method to compute the shape index values in the 2D (Poincaré Disk) parameter domain. The proposed shape indices are succinct, intrinsic and informative. Experimental results with synthetic data and 3D MRI data demonstrate that our method is invariant under isometric transformations and able to detect brain surface abnormalities. We also applied the new shape indices to analyze brain morphometry abnormalities associated with Alzheimer’s disease (AD). We studied the baseline MRI scans of a set of healthy control and AD patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI: 30 healthy control subjects vs. 30 AD patients). Although the lengths of the landmarks in Euclidean space, cortical surface area, and volume features did not differ between the two groups, our conformal invariant based shape indices revealed significant differences by Hotelling’s T2 test. The novel conformal invariant shape indices may offer a new sensitive biomarker and enrich our brain imaging analysis toolset for studying diagnosis and prognosis of AD.

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BrainPrint: A discriminative characterization of brain morphology

Cited by 151 publications

References 84 publications

Differences between chronological and brain age are related to education and self-reported physical activity

Differences between chronological and brain age are related to education and self-reported physical activity

Domain adaptation for Alzheimer's disease diagnostics

Conformal invariants for multiply connected surfaces: Application to landmark curve-based brain morphometry analysis

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