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Background: Computed Tomography (CT) contributes up to 50% of the medical exposure to the United States population. Children are considered to be at higher risk of developing radiation-induced tumors due to the young age of exposure and increased tissue radiosensitivity. Organ dose estimation is essential for pediatric and adult patient cancer risk assessment. The objective of this study is to validate the VirtualDose software in comparison to currently available software and methods for pediatric and adult CT organ dose estimation., Methods: Five age groups of pediatric patients and adult patients were simulated by three organ dose estimators. Head, chest, abdomen-pelvis, and chest-abdomen-pelvis CT scans were simulated, and doses to organs both inside and outside the scan range were compared. For adults, VirtualDose was compared against ImPACT and CT-Expo. For pediatric patients, VirtualDose was compared to CT-Expo and compared to size-based methods from literature. Pediatric to adult effective dose ratios were also calculated with VirtualDose, and were compared with the ranges of effective dose ratios provided in ImPACT., Results: In-field organs see less than 60% difference in dose between dose estimators. For organs outside scan range or distributed organs, a five times' difference can occur. VirtualDose agrees with the size-based methods within 20% difference for the organs investigated. Between VirtualDose and ImPACT, the pediatric to adult ratios for effective dose are compared, and less than 21% difference is observed for chest scan while more than 40% difference is observed for head-neck scan and abdomen-pelvis scan. For pediatric patients, 2 cm scan range change can lead to a five times dose difference in partially scanned organs., Conclusions: VirtualDose is validated against CT-Expo and ImPACT with relatively small discrepancies in dose for organs inside scan range, while large discrepancies in dose are observed for organs outside scan range. Patient-specific organ dose estimation is possible using the size-based methods, and VirtualDose agrees with size-based method for the organs investigated. Careful range selection for CT protocols is necessary for organ dose optimization for pediatric and adult patients.

Gaucher disease results from GBA1 mutations that lead to defective acid β-glucosidase (GCase) mediated cleavage of glucosylceramide (GC) and glucosylsphingosine as well as heterogeneous manifestations in the viscera and CNS. The mutation, tissue, and age-dependent accumulations of different GC species were characterized in mice with Gba1 missense mutations alone or in combination with isolated saposin C deficiency (C*). Gba1 heteroallelism for D409V and null alleles (9V/null) led to GC excesses primarily in the visceral tissues with preferential accumulations of lung GC24∶0, but not in liver, spleen, or brain. Age-dependent increases of different GC species were observed. The combined saposin C deficiency (C*) with V394L homozygosity (4L;C*) showed major GC18:0 degradation defects in the brain, whereas the analogous mice with D409H homozygosity and C* (9H;C*) led to all GC species accumulating in visceral tissues. Glucosylsphingosine was poorly degraded in brain by V394L and D409H GCases and in visceral tissues by D409V GCase. The neonatal lethal N370S/N370S genotype had insignificant substrate accumulations in any tissue. These results demonstrate age, organ, and mutation-specific quantitative differences in GC species and glucosylsphingosine accumulations that can have influence in the tissue/regional expression of Gaucher disease phenotypes.

Accumulating evidence suggests that altered cerebral white matter (WM) influences normal aging, and further that WM degeneration may modulate the clinical expression of Alzheimer's disease (AD). Here we conducted a study of differences in WM volume across the adult age span and in AD employing a newly developed, automated method for regional parcellation of the subcortical WM that uses curvature landmarks and gray matter (GM)/WM surface boundary information. This procedure measures the volume of gyral WM, utilizing a distance constraint to limit the measurements from extending into the centrum semiovale. Regional estimates were first established to be reliable across two scan sessions in 20 young healthy individuals. Next, the method was applied to a large clinically-characterized sample of 299 individuals including 73 normal older adults and 91 age-matched participants with very mild to mild AD. The majority of measured regions showed a decline in volume with increasing age, with strong effects found in bilateral fusiform, lateral orbitofrontal, superior frontal, medial orbital frontal, inferior temporal, and middle temporal WM. The association between WM volume and age was quadratic in many regions suggesting that WM volume loss accelerates in advanced aging. A number of WM regions were further reduced in AD with parahippocampal, entorhinal, inferior parietal and rostral middle frontal WM showing the strongest AD-associated reductions. There were minimal sex effects after correction for intracranial volume, and there were associations between ventricular volume and regional WM volumes in the older adults and AD that were not apparent in the younger adults. Certain results, such as the loss of WM in the fusiform region with aging, were unexpected and provide novel insight into patterns of age associated neural and cognitive decline. Overall, these results demonstrate the utility of automated regional WM measures in revealing the distinct patterns of age and AD associated volume loss that may contribute to cognitive decline.

The aim of this study was to determine the role of regional cortical thickness in recall of verbal material over an extended time period. MRI scans of healthy adults of varying ages were obtained. Two scans were averaged per person to achieve high spatial resolution, and a semi-automated method for continuous measurement of thickness across the entire cortical mantle was employed. Verbal memory tests assessing recall after 5 min, 30 min, and a mean interval of 83 days were administered. A general linear model (GLM) of the effects of thickness at each vertex on the different memory indices was computed, controlling for gender, age, IQ, and intracranial volume. These analyses were repeated with hippocampal volume as an additional variable to be controlled for, to assess to which extent effects of cortical thickness were independent of hippocampal size. Minute effects of cortical thickness were observed with regard to shorter time intervals (5 and 30 min). However, even when controlling for the effects of hippocampal volume, higher recall across months was associated with thicker cortex of distinct areas including parts of the gyrus rectus, the middle frontal gyrus, the parieto-occipital sulcus and the lingual gyrus of both hemispheres. In addition, hemisphere-specific associations were found in parts of the right temporal and parietal lobe as well as parts of the left precuneus. This supports a unique and critical role of the thickness of distinct cortical areas in recall after months, more than after minutes.

In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.

The aim of this study was to identify cortical areas important for optimal cognitive aging. 74 participants (20-88 years) went through neuropsychological tests and two MR sessions. The sample was split into two age groups. In each, every participant was classified as "high" or "average" on fluid ability tests and on neuropsychological tests related to executive function. The groups were compared with regard to thickness on a point-by-point basis across the entire cortical mantle. The old high fluid performers had thicker cortex than the average performers in large areas of cortex, while there was minimal difference between the groups of high vs. average executive function. Furthermore, the old group with high fluid function had thicker cortex than the young participants in the posterior cingulate and adjacent areas. Further analyses showed that the latter was a result of a complex aging pattern, differing between the two performance groups, with decades of cortical thickening and subsequent thinning.

The effect of age was investigated in and compared across 16 automatically segmented brain measures: cortical gray matter, cerebral white matter, hippocampus, amygdala, thalamus, the accumbens area, caudate, putamen, pallidum, brainstem, cerebellar cortex, cerebellar white matter, the lateral ventricle, the inferior lateral ventricle, and the 3rd and 4th ventricle. Significant age effects were found for all volumes except pallidum and the 4th ventricle. Heterogeneous age responses were seen in that age relationships for cortex, amygdala, thalamus, the accumbens area, and caudate were linear, while cerebral white matter, hippocampus, brainstem, cerebellar white, and gray matter, as well as volume of the lateral, inferior lateral, and 3rd ventricles showed curvilinear relationships with age. In general, the findings point to global and large effects of age across brain volumes.

The aim of the study was to investigate whether age affects visual memory retention across extended time intervals. In addition, we wanted to study how memory capabilities across different time intervals are related to the volume of different neuroanatomical structures (right hippocampus, right cortex, right white matter). One test of recognition (CVMT) and one test of recall (Rey-Osterrieth Complex Figure Test) were administered, giving measures of immediate recognition/recall, 20-30 min recognition/recall, and recognition/recall at a mean of 75 days. Volumetric measures of right hemisphere hippocampus, cortex, and white matter were obtained through an automated labelling procedure of MRI recordings. Results did not demonstrate a steeper rate of forgetting for older participants when the retention intervals were increased, indicating that older people have spared ability to retain information in the long-term store. Differences in neuroanatomical volumes could explain up to 36% of the variance in memory performance, but were not significantly related to rates of forgetting. Cortical volume and hippocampal volume were in some cases independent as predictors of memory function. Generally, cortical volume was a better predictor of recognition memory than hippocampal volume, while the 2 structures did not differ in their predictive power of recall abilities. While neuroanatomical volumetric differences can explain some of the differences in memory functioning between younger and older persons, the hippocampus does not seem to be unique in this respect.

The application of advances in biomedical computing to medical imaging research is enabling scientists to conduct quantitative clinical imaging studies using data collected across multiple sites to test new hypotheses on larger cohorts, increasing the power to detect subtle effects. Given that many research groups have valuable existing (legacy) data, one goal of the Morphometry Biomedical Informatics Research Network (BIRN) Testbed is to assess the feasibility of pooled analyses of legacy structural neuroimaging data in normal aging and Alzheimer's disease. The present study examined whether such data could be meaningfully reanalyzed as a larger combined data set by using rigorous data curation, image analysis, and statistical modeling methods; in this case, to test the hypothesis that hippocampal volume decreases with age and to investigate findings of hippocampal asymmetry. This report describes our work with legacy T1-weighted magnetic resonance (MR) and demographic data related to normal aging that have been shared through the BIRN by three research sites. Results suggest that, in the present application, legacy MR data from multiple sites can be pooled to investigate questions of scientific interest. In particular, statistical analyses suggested that a mixed-effects model employing site as a random effect best fits the data, accounting for site-specific effects while taking advantage of expected comparability of age-related effects. In the combined sample from three sites, significant age-related decline of hippocampal volume and right-dominant hippocampal asymmetry were detected in healthy elderly controls. These expected findings support the feasibility of combining legacy data to investigate novel scientific questions.

Due to the increasing need for subject privacy, the ability to deidentify structural MR images so that they do not provide full facial detail is desirable. A program was developed that uses models of nonbrain structures for removing potentially identifying facial features. When a novel image is presented, the optimal linear transform is computed for the input volume (Fischl et al. [2002]: Neuron 33:341-355; Fischl et al. [2004]: Neuroimage 23 (Suppl 1):S69-S84). A brain mask is constructed by forming the union of all voxels with nonzero probability of being brain and then morphologically dilated. All voxels outside the mask with a nonzero probability of being a facial feature are set to 0. The algorithm was applied to 342 datasets that included two different T1-weighted pulse sequences and four different diagnoses (depressed, Alzheimer's, and elderly and young control groups). Visual inspection showed none had brain tissue removed. In a detailed analysis of the impact of defacing on skull-stripping, 16 datasets were bias corrected with N3 (Sled et al. [1998]: IEEE Trans Med Imaging 17:87-97), defaced, and then skull-stripped using either a hybrid watershed algorithm (Ségonne et al. [2004]: Neuroimage 22:1060-1075, in FreeSurfer) or Brain Surface Extractor (Sandor and Leahy [1997]: IEEE Trans Med Imaging 16:41-54; Shattuck et al. [2001]: Neuroimage 13:856-876); defacing did not appreciably influence the outcome of skull-stripping. Results suggested that the automatic defacing algorithm is robust, efficiently removes nonbrain tissue, and does not unduly influence the outcome of the processing methods utilized; in some cases, skull-stripping was improved. Analyses support this algorithm as a viable method to allow data sharing with minimal data alteration within large-scale multisite projects.

Abstract: There is a lack of studies mapping electrophysiological event-related potentials (ERPs) to structural neuroanatomical characteristics. The aim of the present study was to integrate electrophysiological memory-related activity with cortical and hippocampal volume, as well as psychometric memory performance, in a life-span sample. More specifically, we wanted to investigate the functional significance of the often-observed frontal shift of ERP amplitude with increasing age and whether neuroanatomical characteristics can explain this shift. Sixty six healthy participants (20–78 years) went through a neuropsychological examination, MRI scans, and a visual recognition ERP task with verbal stimuli. The results showed that ERPs elicited in the recognition memory task (the old/new effect) correlated significantly with cortical volume, but not with hippocampal volume. Large cortex predicted more differentiated ERP activity and not just larger amplitude in general, implying more distinct and efficient retrieval. Furthermore, ERP amplitude, cortical volume, and hippocampal volume all predicted scores on a composite memory scale. All these relationship were dependent upon the common influence of age. Finally, the participants with the most anterior distribution of activity showed the poorest recognition memory performance. Neither cortical nor hippocampal volume were related to this frontal shift. It is concluded that the distribution of activity along the anterior–posterior axis in a memory paradigm may have functional but not neuroanatomical volumetric correlates. The functional correlates need not be restricted to the older age groups. [Copyright &y& Elsevier]

Abstract: The rationale for the present study was to investigate the relationship between cortical volume, the latency of the ERP component P3a (as a measure of speed-of-processing), and performance intelligence (not adjusted for age differences). Seventy-one participants aged 20–88 years underwent a visual 3-stimuli oddball ERP task, an MRI-scan, and intelligence testing. P3a latency and cortical volume shared 9% variance (p <.05) and both were significantly related to performance intelligence (R 2 =.26 and .40, respectively). The amount of explained variance increased significantly (to R 2 =.51) when both measures were used as simultaneous predictors. When a path diagram was constructed including age as an exogenous variable, P3a latency and cortical volume both significantly predicted performance intelligence, but were no longer related to one another. The main conclusion from the study is that speed and size are complementary in prediction of performance intelligence, and the theoretical implications are discussed. [Copyright &y& Elsevier]