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Focal cortical dysplasia (FCD) is the most common cause of pediatric epilepsy and the third most common lesion in adults with treatment-resistant epilepsy. Advances in MRI have revolutionized the diagnosis of FCD, resulting in higher success rates for resective epilepsy surgery. However, many patients with histologically confirmed FCD have normal presurgical MRI studies ('MRI-negative'), making presurgical diagnosis difficult. The purpose of this study was to test whether a novel MRI postprocessing method successfully detects histopathologically verified FCD in a sample of patients without visually appreciable lesions. We applied an automated quantitative morphometry approach which computed five surface-based MRI features and combined them in a machine learning model to classify lesional and nonlesional vertices. Accuracy was defined by classifying contiguous vertices as "lesional" when they fell within the surgical resection region. Our multivariate method correctly detected the lesion in 6 of 7 MRI-positive patients, which is comparable with the detection rates that have been reported in univariate vertex-based morphometry studies. More significantly, in patients that were MRI-negative, machine learning correctly identified 14 out of 24 FCD lesions (58%). This was achieved after separating abnormal thickness and thinness into distinct classifiers, as well as separating sulcal and gyral regions. Results demonstrate that MRI-negative images contain sufficient information to aid in the in vivo detection of visually elusive FCD lesions., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Magnetic resonance imaging has revolutionized the detection of structural abnormalities in patients with epilepsy. However, many focal abnormalities remain undetected in routine visual inspection. Here we use an automated, surface-based method for quantifying morphometric features related to epileptogenic cortical malformations to detect abnormal cortical thickness and blurred gray-white matter boundaries. Using MRI morphometry at 3T with surface-based spherical averaging techniques that precisely align anatomical structures between individual brains, we compared single patients with known lesions to a large normal control group to detect clusters of abnormal cortical thickness, gray-white matter contrast, local gyrification, sulcal depth, jacobian distance and curvature. To assess the effects of threshold and smoothing on detection sensitivity and specificity, we systematically varied these parameters with different thresholds and smoothing levels. To test the effectiveness of the technique to detect lesions of epileptogenic character, we compared the detected structural abnormalities to expert-tracings, intracranial EEG, pathology and surgical outcome in a homogeneous patient sample. With optimal parameters and by combining thickness and GWC, the surface-based detection method identified 92% of cortical lesions (sensitivity) with few false positives (96% specificity), successfully discriminating patients from controls 94% of the time. The detected structural abnormalities were related to the seizure onset zones, abnormal histology and positive outcome in all surgical patients. However, the method failed to adequately describe lesion extent in most cases. Automated surface-based MRI morphometry, if used with optimized parameters, may be a valuable additional clinical tool to improve the detection of subtle or previously occult malformations and therefore could improve identification of patients with intractable focal epilepsy who may benefit from surgery.

Automated MRI-derived measurements of in-vivo human brain volumes provide novel insights into normal and abnormal neuroanatomy, but little is known about measurement reliability. Here we assess the impact of image acquisition variables (scan session, MRI sequence, scanner upgrade, vendor and field strengths), FreeSurfer segmentation pre-processing variables (image averaging, B1 field inhomogeneity correction) and segmentation analysis variables (probabilistic atlas) on resultant image segmentation volumes from older (n=15, mean age 69.5) and younger (both n=5, mean ages 34 and 36.5) healthy subjects. The variability between hippocampal, thalamic, caudate, putamen, lateral ventricular and total intracranial volume measures across sessions on the same scanner on different days is less than 4.3% for the older group and less than 2.3% for the younger group. Within-scanner measurements are remarkably reliable across scan sessions, being minimally affected by averaging of multiple acquisitions, B1 correction, acquisition sequence (MPRAGE vs. multi-echo-FLASH), major scanner upgrades (Sonata-Avanto, Trio-TrioTIM), and segmentation atlas (MPRAGE or multi-echo-FLASH). Volume measurements across platforms (Siemens Sonata vs. GE Signa) and field strengths (1.5 T vs. 3 T) result in a volume difference bias but with a comparable variance as that measured within-scanner, implying that multi-site studies may not necessarily require a much larger sample to detect a specific effect. These results suggest that volumes derived from automated segmentation of T1-weighted structural images are reliable measures within the same scanner platform, even after upgrades; however, combining data across platform and across field-strength introduces a bias that should be considered in the design of multi-site studies, such as clinical drug trials. The results derived from the young groups (scanner upgrade effects and B1 inhomogeneity correction effects) should be considered as preliminary and in need for further validation with a larger dataset.

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 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., ((c) 2007 Wiley-Liss, Inc.)

We compared cortical folding patterns between patients with schizophrenia and demographically-matched healthy controls in prefrontal and temporal regions of interest. Using the Freesurfer (http://surfer.nmr.mgh.harvard.edu) cortical surface-based reconstruction methodology, we indirectly ascertained cortical displacement and convolution, together, by measuring the degree of metric distortion required to optimally register cortical folding patterns to an average template. An area within the pars triangularis of the left inferior frontal gyrus (Broca's area) showed significantly reduced metric distortion in the patient group relative to the control group (p=0.0352). We discuss these findings in relation to the neurodevelopmental hypothesis and language dysfunction in schizophrenia.

In vivo MRI-derived measurements of human cerebral cortex thickness are providing novel insights into normal and abnormal neuroanatomy, but little is known about their reliability. We investigated how the reliability of cortical thickness measurements is affected by MRI instrument-related factors, including scanner field strength, manufacturer, upgrade and pulse sequence. Several data processing factors were also studied. Two test-retest data sets were analyzed: 1) 15 healthy older subjects scanned four times at 2-week intervals on three scanners; 2) 5 subjects scanned before and after a major scanner upgrade. Within-scanner variability of global cortical thickness measurements was <0.03 mm, and the point-wise standard deviation of measurement error was approximately 0.12 mm. Variability was 0.15 mm and 0.17 mm in average, respectively, for cross-scanner (Siemens/GE) and cross-field strength (1.5 T/3 T) comparisons. Scanner upgrade did not increase variability nor introduce bias. Measurements across field strength, however, were slightly biased (thicker at 3 T). The number of (single vs. multiple averaged) acquisitions had a negligible effect on reliability, but the use of a different pulse sequence had a larger impact, as did different parameters employed in data processing. Sample size estimates indicate that regional cortical thickness difference of 0.2 mm between two different groups could be identified with as few as 7 subjects per group, and a difference of 0.1 mm could be detected with 26 subjects per group. These results demonstrate that MRI-derived cortical thickness measures are highly reliable when MRI instrument and data processing factors are controlled but that it is important to consider these factors in the design of multi-site or longitudinal studies, such as clinical drug trials.

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.

We present a set of techniques for embedding the physics of the imaging process that generates a class of magnetic resonance images (MRIs) into a segmentation or registration algorithm. This results in substantial invariance to acquisition parameters, as the effect of these parameters on the contrast properties of various brain structures is explicitly modeled in the segmentation. In addition, the integration of image acquisition with tissue classification allows the derivation of sequences that are optimal for segmentation purposes. Another benefit of these procedures is the generation of probabilistic models of the intrinsic tissue parameters that cause MR contrast (e.g., T1, proton density, T2*), allowing access to these physiologically relevant parameters that may change with disease or demographic, resulting in nonmorphometric alterations in MR images that are otherwise difficult to detect. Finally, we also present a high band width multiecho FLASH pulse sequence that results in high signal-to-noise ratio with minimal image distortion due to B0 effects. This sequence has the added benefit of allowing the explicit estimation of T2* and of reducing test-retest intensity variability.

The brain morphometry of 21 children, who were followed from birth and underwent structural brain magnetic resonance imaging at 8–10 years, was studied. This cohort included 11 children with prenatal cocaine exposure (CE) and 10 noncocaine-exposed children (NCE). We compared the CE versus NCE groups using FreeSurfer to automatically segment and quantify the volume of individual brain structures. In addition, we created a pediatric atlas specifically for this population and demonstrate the enhanced accuracy of this approach. We found an overall trend towards smaller brain volumes among CE children. The volume differences were significant for cortical gray matter, the thalamus and the putamen. Here, reductions in thalamic and putaminal volumes showed a robust inverse correlation with exposure levels, thus highlighting effects on dopamine-rich brain regions that form key components of brain circuitry known to play important roles in behavior and attention. Interestingly, head circumferences (HCs) at birth as well as at the time of imaging showed a tendency for smaller size among CE children. HCs at the time of imaging correlated well with the cortical volumes for all subjects. In contrast, HCs at birth were predictive of the cortical volume only for the CE group. A subgroup of these subjects (6 CE, 4 NCE) was also scanned at 13–15 years of age. In subjects who were scanned twice, we found that the trend for smaller structures continued into teenage years. We found that the differences in structural volumes between the CE and NCE groups are largely diminished when the HCs are controlled for or matched by study design. Participants in this study were drawn from a unique longitudinal cohort and, while the small sample size precludes strong conclusions regarding the longitudinal findings reported, the results point to reductions in HCs and in specific brain structures that persist through teenage years in children who were exposed to cocaine in utero. [ABSTRACT FROM AUTHOR]

Abstract: Functional neuroimaging implicates hyperactivity of amygdala-orbitofrontal circuitry as a common neurobiological mechanism underlying the development of anxiety. Less is known about anxiety-related structural differences in this network. In this study, a sample of healthy adults with no history of anxiety disorders completed a 3T MRI scan and self-report mood inventories. Post-processing quantitative MRI image analysis included segmentation and volume estimation of subcortical structures, which were regressed on anxiety inventory scores, with depression scores used to establish discriminant validity. We then used a quantitative vertex-based post-processing method to correlate (1) anxiety scores and (2) left amygdala volumes with cortical thickness across the whole cortical mantle. Left amygdala volumes predicted anxiety, with decreased amygdala volume associated with higher anxiety on both state and trait anxiety measures. A negative correlation between left amygdala volume and cortical thickness overlapped with a positive correlation between anxiety and cortical thickness in left lateral orbitofrontal cortex. These results suggest a structural anxiety network that corresponds with a large body of evidence from functional neuroimaging. Such findings raise the possibility that structural abnormalities may result in a greater vulnerability to anxiety or conversely that elevated anxiety symptoms may result in focal structural changes. [Copyright &y& Elsevier]