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It is unclear if adjusting cortical thickness for changes in gray/white matter intensity ratio improves discrimination between normal aging, MCI, and AD.

ABSTRACT: The contrast between gray and white matter in MRI is critical for accurately measuring cortical thickness. The gray/white matter intensity ratio (GWIR) has been proposed to be an important adjustment factor for cortical thickness measures in Alzheimer's disease (AD) and mild cognitive impairment (MCI). This study examined the GWIR and its influence on cortical thickness in normal aging, mild cognitive impairment (MCI), and AD. The ability for GWIR to discriminate between these groups was assessed on its own and as an adjustment factor for cortical thickness. Minimal age- and AD-related changes in GWIR were observed. GWIR was not able to differentiate between normal aging, MCI, and AD. However, adjusting cortical thickness for GWIR slightly improved the ability to discriminate between groups and the effect size of cortical thickness increased after adjusting for GWIR. This work demonstrates the ambiguity in adjusting cortical thickness measures for GWIR, particularly when attempting to discriminate between normal aging, MCI, and AD groups.

SUBMITTER: Bauer CM

PROVIDER: S-EPMC3930075 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

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It is unclear if adjusting cortical thickness for changes in gray/white matter intensity ratio improves discrimination between normal aging, MCI, and AD.

Bauer Corinna M CM Cabral Howard J HJ Killiany Ronald J RJ

Brain imaging and behavior 20140301 1

The contrast between gray and white matter in MRI is critical for accurately measuring cortical thickness. The gray/white matter intensity ratio (GWIR) has been proposed to be an important adjustment factor for cortical thickness measures in Alzheimer's disease (AD) and mild cognitive impairment (MCI). This study examined the GWIR and its influence on cortical thickness in normal aging, mild cognitive impairment (MCI), and AD. The ability for GWIR to discriminate between these groups was assesse ...[more]

PMID: 24535034

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Project description:ObjectiveHere we aimed to explore the differences in individual gray matter (GM) networks at baseline in mild cognitive impairment patients who converted to Alzheimer's disease (AD) within 3 years (MCI-C) and nonconverters (MCI-NC).Materials and methodsData from 461 MCI patients (180 MCI-C and 281 MCI-NC) were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). For each subject, a GM network was constructed using 3D-T1 imaging and the Kullback-Leibler divergence method. Gradient and topological analyses of individual GM networks were performed, and partial correlations were calculated to evaluate relationships among network properties, cognitive function, and apolipoprotein E (APOE) €4 alleles. Subsequently, a support vector machine (SVM) model was constructed to discriminate the MCI-C and MCI-NC patients at baseline.ResultsThe gradient analysis revealed that the principal gradient score distribution was more compressed in the MCI-C group than in the MCI-NC group, with scores for the left lingual gyrus, right fusiform gyrus and left middle temporal gyrus being increased in the MCI-C group (p < 0.05, FDR corrected). The topological analysis showed significant differences in nodal efficiency in four nodes between the two groups. Furthermore, the regional gradient scores or nodal efficiency were found to be significantly related to the neuropsychological test scores, and the left middle temporal gyrus gradient scores were positively associated with the number of APOE €4 alleles (r = 0.192, p = 0.002). Ultimately, the SVM model achieved a balanced accuracy of 79.4% in classifying MCI-C and MCI-NC patients (p < 0.001).ConclusionThe whole-brain GM network hierarchy in the MCI-C group was more compressed than that in the MCI-NC group, suggesting more serious cognitive impairments in the MCI-C group. The left middle temporal gyrus gradient scores were related to both cognitive function and APOE €4 alleles, thus serving as potential biomarkers distinguishing MCI-C from MCI-NC at baseline.

Project description:IntroductionWe performed a whole-transcriptome correlation analysis, followed by the pathway enrichment and testing of innate immune response pathway analyses to evaluate the hypothesis that transcriptional activity can predict cortical gray matter thickness (GMT) variability during normal cerebral aging.MethodsTranscriptome and GMT data were available for 379 individuals (age range=28-85) community-dwelling members of large extended Mexican American families. Collection of transcriptome data preceded that of neuroimaging data by 17 years. Genome-wide gene transcriptome data consisted of 20,413 heritable lymphocytes-based transcripts. GMT measurements were performed from high-resolution (isotropic 800 ?m) T1-weighted MRI. Transcriptome-wide and pathway enrichment analysis was used to classify genes correlated with GMT. Transcripts for sixty genes from seven innate immune pathways were tested as specific predictors of GMT variability.ResultsTranscripts for eight genes (IGFBP3, LRRN3, CRIP2, SCD, IDS, TCF4, GATA3, and HN1) passed the transcriptome-wide significance threshold. Four orthogonal factors extracted from this set predicted 31.9% of the variability in the whole-brain and between 23.4 and 35% of regional GMT measurements. Pathway enrichment analysis identified six functional categories including cellular proliferation, aggregation, differentiation, viral infection, and metabolism. The integrin signaling pathway was significantly (p<10(-6)) enriched with GMT. Finally, three innate immune pathways (complement signaling, toll-receptors and scavenger and immunoglobulins) were significantly associated with GMT.ConclusionExpression activity for the genes that regulate cellular proliferation, adhesion, differentiation and inflammation can explain a significant proportion of individual variability in cortical GMT. Our findings suggest that normal cerebral aging is the product of a progressive decline in regenerative capacity and increased neuroinflammation.

Project description:Results from studies that have examined age-related changes in gray matter based on structural MRI scans have not always been consistent. Reasons for this variability likely include small or unevenly-distributed samples, different methods for tissue class segmentation and spatial normalization, and the use of different statistical models. Particularly relevant to the latter is the method of adjusting for global (total) gray matter when making inferences about regionally-specific changes. In the current study, we use voxel-based morphometry (VBM) to explore the impact of these methodological choices in assessing age-related changes in gray matter volume in a sample of 420 adults evenly distributed between the ages of 18-77 years. At a broad level, we replicate previous findings, showing age-related gray matter decline in nearly all parts of the brain, with particularly rapid decline in inferior regions of frontal cortex (e.g., insula and left inferior frontal gyrus) and the central sulcus. Segmentation was improved by increasing the number of tissue classes and using less age-biased templates, and registration was improved by using a diffeomorphic flow-based algorithm (DARTEL) rather than a "constrained warp" approach. Importantly, different approaches to adjusting for global effects--not adjusting, Local Covariation, Global Scaling, and Local Scaling--significantly affected regionally-specific estimates of age-related decline, as demonstrated by ranking age effects across anatomical ROIs. Split-half cross-validation showed that, on average, Local Covariation explained a greater proportion of age-related variance across these ROIs than did Global Scaling. Nonetheless, the appropriate choice for global adjustment depends on one's assumptions and specific research questions. More generally, these results emphasize the importance of being explicit about the assumptions underlying key methodological choices made in VBM analyses and the inferences that follow.

Dataset Information

It is unclear if adjusting cortical thickness for changes in gray/white matter intensity ratio improves discrimination between normal aging, MCI, and AD.

Publications

It is unclear if adjusting cortical thickness for changes in gray/white matter intensity ratio improves discrimination between normal aging, MCI, and AD.

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