Quantifying cerebral changes in adolescence with MRI and deformation based morphometry

Brain MRI Radiomics Analysis of School-Aged Children with Tetralogy of Fallot

Introduction

Radiomics could be potential imaging biomarkers by capturing and analyzing the features. Children and adolescents with CHD have worse neurodevelopmental and functional outcomes compared with their peers. Early diagnosis and intervention are the necessity to improve neurological outcomes in CHD patients.

Methods

School-aged TOF patients and their healthy peers were recruited for MRI and neurodevelopmental assessment. LASSO regression was used for dimension reduction. ROC curve graph showed the performance of the model.

Results

Six related features were finally selected for modeling. The final model AUC was 0.750. The radiomics features can be potential significant predictors for neurodevelopmental diagnoses.

Conclusion

The radiomics on the conventional MRI can help predict the neurodevelopment of school-aged children and provide parents with rehabilitation advice as early as possible.

A new data analysis approach for measuring longitudinal changes of metabolism in cognitively normal elderly adults

INTRODUCTION

Previously, we discussed several critical barriers in including [18F] fluorodeoxyglucose positron emission tomography ([18F]FDG-PET) imaging of preclinical Alzheimer's disease (AD) subjects. These factors included the reference region selection and intensity normalization of PET images and the within- and across-subject variability of affected brain regions. In this study, we utilized a novel FDG-PET analysis, the regional FDG time correlation coefficient, rFTC, that can address and resolve these barriers and provide a more sensitive way of monitoring longitudinal changes in metabolism of cognitively normal elderly adults. The rFTC analysis captures the within-subject similarities between baseline and follow-up regional radiotracer distributions.

METHODS

The rFTC trajectories of 27 cognitively normal subjects were calculated to identify 1) trajectories of rFTC decline in individual cognitively normal subjects; 2) how these trajectories correlate with the subjects' cognitive test scores, baseline cerebrospinal fluid (CSF) levels of amyloid beta (Aβ), and apolipoprotein E4 (APOE-E4) status; and 3) whether similar trajectories are observed in regional/composite standardized uptake value ratio (SUVR) values.

RESULTS

While some of the subjects maintained a stable rFTC trajectory, other subjects had declining and fluctuating rFTC values. We found that the rFTC decline was significantly higher in APOE-E4 carriers compared to noncarriers (p=0.04). We also found a marginally significant association between rFTC decline and cognitive decline measured by Alzheimer's Disease Assessment Scale - cognitive subscale (ADAS_cog) decline (0.05). In comparison to the rFTC trajectories, the composite region of interest (ROI) SUVR trajectories did not change in any of the subjects. No individual/composite ROI SUVR changes contributed significantly to explaining changes in ADAS_cog, conversion to mild cognitive impairment (MCI), or any general changes in clinical symptoms.

CONCLUSION

The rFTC decline may serve as a new biomarker of early metabolic changes before the MCI stage.

Modeling clustered activity increase in amyloid-beta positron emission tomographic images with statistical descriptors

Background

Amyloid-beta (Aβ) imaging with positron emission tomography (PET) holds promise for detecting the presence of Aβ plaques in the cortical gray matter. Many image analyses focus on regional average measurements of tracer activity distribution; however, considerable additional information is available in the images. Metrics that describe the statistical properties of images, such as the two-point correlation function (S₂), have found wide applications in astronomy and materials science. S₂ provides a detailed characterization of spatial patterns in images typically referred to as clustering or flocculence. The objective of this study was to translate the two-point correlation method into Aβ-PET of the human brain using 11C-Pittsburgh compound B (11C-PiB) to characterize longitudinal changes in the tracer distribution that may reflect changes in Aβ plaque accumulation.

Methods

We modified the conventional S₂ metric, which is primarily used for binary images and formulated a weighted two-point correlation function (wS₂) to describe nonbinary, real-valued PET images with a single statistical function. Using serial 11C-PiB scans, we calculated wS₂ functions from two-dimensional PET images of different cortical regions as well as three-dimensional data from the whole brain. The area under the wS₂ functions was calculated and compared with the mean/median of the standardized uptake value ratio (SUVR). For three-dimensional data, we compared the area under the wS₂ curves with the subjects’ cerebrospinal fluid measures.

Results

Overall, the longitudinal changes in wS₂ correlated with the increase in mean SUVR but showed lower variance. The whole brain results showed a higher inverse correlation between the cerebrospinal Aβ and wS₂ than between the cerebrospinal Aβ and SUVR mean/median. We did not observe any confounding of wS₂ by region size or injected dose.

Conclusion

The wS₂ detects subtle changes and provides additional information about the binding characteristics of radiotracers and Aβ accumulation that are difficult to verify with mean SUVR alone.

Longitudinal progression of cognitive decline correlates with changes in the spatial pattern of brain 18F-FDG PET

Evaluating the symptomatic progression of mild cognitive impairment (MCI) caused by Alzheimer disease (AD) is practically accomplished by tracking performance on cognitive tasks, such as the Alzheimer Disease Assessment Scale's cognitive subscale (ADAS_cog), the Mini-Mental Status Examination (MMSE), and the Functional Activities Questionnaire (FAQ). The longitudinal relationships between cognitive decline and metabolic function as assessed using (18)F-FDG PET are needed to address both the cognitive and the biologic progression of disease state in individual subjects. We conducted an exploratory investigation to evaluate longitudinal changes in brain glucose metabolism of individual subjects and their relationship to the subject's changes of cognitive status.

METHODS

We describe a method to determine correlations in (18)F-FDG spatial distribution over time. This parameter is termed the regional (18)F-FDG time correlation coefficient (rFTC). By using linear mixed-effects models, we determined the difference in the rFTC decline rate between controls and subjects at high risk of developing AD, such as individuals with MCI or the presence of apolipoprotein E (APOE)-ε4 allele. The association between each subject's rFTC and performance on cognitive tests (ADAS_cog, MMSE, and FAQ) was determined with 2 different correlation methods. All subject data were downloaded from the Alzheimer Disease Neuroimaging Initiative.

RESULTS

The rFTC values of controls remained fairly constant over time (-0.003 annual change; 95% confidence interval, -0.010-0.004). In MCI patients, the rFTC declined faster than in controls by an additional annual change of -0.02 (95% confidence interval, -0.030 to -0.010). In MCI patients, the decline in rFTC was associated with cognitive decline (ADAS_cog, P = 0.011; FAQ, P = 0.0016; MMSE, P = 0.004). After a linear effect of time was accounted for, visit-to-visit changes in rFTC correlated with visit-to-visit changes in all 3 cognitive tests.

CONCLUSION

Longitudinal changes in rFTC detect subtle metabolic changes in individuals associated with variations in their cognition. This analytic tool may be useful for a patient-based monitoring of cognitive decline.

Investigating the 'latent' deficit hypothesis: age at time of head injury, implicit and executive functions and behavioral insight

This study investigated the 'latent deficit' hypothesis in two groups of head-injured patients with predominantly frontal lesions, those injured prior to steep morphological and corresponding functional maturational periods for frontal networks (<or=age 25), and those injured>28 years. The latent deficit hypothesis proposes that early injuries produce enduring cognitive deficits manifest later in the lifespan with graver consequences for behavior than adult injuries, particularly after frontal pathology (Eslinger, Grattan, Damasio & Damasio, 1992). Implicit and executive deficits both contribute to behavioral insight after frontal head injury (Barker, Andrade, Romanowski, Morton, & Wasti, 2006). On the basis of morphological and behavioral data, we hypothesized that early injury would confer greater vulnerability to impairment on tasks associated with frontal regions than later injury. Patients completed experimental tasks of implicit cognition, executive function measures and the DEX measure of behavioral insight (Behavioral Assessment of the Dysexecutive Syndrome: Wilson, Alderman, Burgess, Emslie, & Evans, 1996). The Early Injury group were more impaired on implicit cognition tasks compared to controls that Late Injury patients. There were no marked group differences on most executive function measures. Executive ability only contributed to behavioral awareness in the Early Injury Group. Findings showed that age at injury moderates the relationship between executive and implicit cognition and behavioral insight and that early injuries result in long-standing deficits to functions associated with frontal regions partially supporting the latent deficit hypothesis.