Article

Within-subject template estimation for unbiased longitudinal image analysis.

Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA.
NeuroImage (Impact Factor: 6.13). 03/2012; 61(4):1402-18. DOI: 10.1016/j.neuroimage.2012.02.084
Source: PubMed

ABSTRACT 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.

0 Bookmarks
 · 
150 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Healthy aging is associated with cognitive declines typically accompanied by increased task-related brain activity in comparison to younger counterparts. The Scaffolding Theory of Aging and Cognition (STAC) (Park and Reuter-Lorenz, 2009; Reuter-Lorenz and Park, 2014) posits that compensatory brain processes are responsible for maintaining normal cognitive performance in older adults, despite accumulation of aging-related neural damage. Cross-sectional studies indicate that cognitively intact elders at genetic risk for Alzheimer's disease (AD) demonstrate patterns of increased brain activity compared to low risk elders, suggesting that compensation represents an early response to AD-associated pathology. Whether this compensatory response persists or declines with the onset of cognitive impairment can only be addressed using a longitudinal design. The current prospective, 5-year longitudinal study examined brain activation in APOE ε4 carriers (N=24) and non-carriers (N=21). All participants, ages 65-85 and cognitively intact at study entry, underwent task-activated fMRI, structural MRI, and neuropsychological assessments at baseline, 18, and 57months. fMRI activation was measured in response to a semantic memory task requiring participants to discriminate famous from non-famous names. Results indicated that the trajectory of change in brain activation while performing this semantic memory task differed between APOE ε4 carriers and non-carriers. The APOE ε4 group exhibited greater activation than the Low Risk group at baseline, but they subsequently showed a progressive decline in activation during the follow-up periods with corresponding emergence of episodic memory loss and hippocampal atrophy. In contrast, the non-carriers demonstrated a gradual increase in activation over the 5-year period. Our results are consistent with the STAC model by demonstrating that compensation varies with the severity of underlying neural damage and can be exhausted with the onset of cognitive symptoms and increased structural brain pathology. Our fMRI results could not be attributed to changes in task performance, group differences in cerebral perfusion, or regional cortical atrophy. Copyright © 2015 Elsevier Inc. All rights reserved.
    NeuroImage 02/2015; 111. · 6.13 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The objective of this longitudinal magnetic resonance (MR) imaging study was to examine the effects of endurance training on hippocampal and grey matter volumes in schizophrenia patients and healthy controls. 20 chronic schizophrenia patients and 21 age- and gender-matched healthy controls underwent 3months of endurance training (30 min, 3 times perweek). 19 additionally recruited schizophrenia patients played table soccer (“foosball” in the USA) over the same period. MR imaging with 3D-volumetric T1-weighted sequences was performed on a 3 T MR scanner at baseline, after 6 weeks and after the 3-month intervention and 3 additional training-free months. In addition to voxel-based morphometry (VBM), we performed manual and automatic delineation of the hippocampus and its substructures. Endurance capacity and psychopathological symptoms were measured as secondary endpoints. No significant increases in the volumes of the hippocampus or hippocampal substructures were observed in schizophrenia patients or healthy controls. However, VBM analyses displayed an increased volume of the left superior, middle and inferior anterior temporal gyri compared to baseline in schizophrenia patients after the endurance training, whereas patients playing table soccer showed increased volumes in the motor and anterior cingulate cortices. After the additional training-free period, the differenceswere no longer present.While endurance capacity improved in exercising patients and healthy controls, psychopathological symptoms did not significantly change. The subtle changes in the left temporal cortex indicate an impact of exercise on brain volumes in schizophrenia. Subsequent studies in larger cohorts are warranted to address the question of response variability of endurance training.
    Schizophrenia Research 02/2015; · 4.43 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We introduce BrainPrint , a compact and discriminative representation of brain morphology. BrainPrint captures shape information of an ensemble of cortical and subcortical structures by solving the eigenvalue problem of the 2D and 3D Laplace-Beltrami operator on triangular (boundary) and tetrahedral (volumetric) meshes. This discriminative characterization enables new ways to study the similarity between brains; the focus can either be on a specific brain structure of interest or on the overall brain similarity. We highlight four applications for BrainPrint in this article: (i) subject identification, (ii) age and sex prediction, (iii) brain asymmetry analysis, and (iv) potential genetic influences on brain morphology. The properties of BrainPrint require the derivation of new algorithms to account for the heterogeneous mix of brain structures with varying discriminative power. We conduct experiments on three datasets, including over 3000 MRI scans from the ADNI database, 436 MRI scans from the OASIS dataset , and 236 MRI scans from the VETSA twin study. All processing steps for obtaining the compact representation are fully automated, making this processing framework particularly attractive for handling large datasets. Copyright © 2015 Elsevier Inc. All rights reserved.
    NeuroImage 01/2015; · 6.13 Impact Factor

Full-text (2 Sources)

Download
40 Downloads
Available from
May 30, 2014