BACKGROUND: Findings from postmortem studies suggest reduced prefrontal cortical thickness in schizophrenia; however, cortical thickness in first-episode schizophrenia has not been evaluated using magnetic resonance imaging (MRI).
METHODS: Prefrontal cortical thickness was measured using MRI in first-episode schizophrenia patients (n = 17), first-episode affective psychosis patients (n = 17), and normal control subjects (n = 17); subjects were age-matched within 2 years and within a narrow age range (18-29 years). A previous study using the same subjects reported reduced prefrontal gray matter volume in first-episode schizophrenia. Manual editing was performed on those prefrontal segmentations before cortical thickness was measured.
RESULTS: Prefrontal cortical thickness was not significantly different among groups. Prefrontal gray matter volume and thickness were, however, positively correlated in both schizophrenia and control subjects. The product of boundary complexity and thickness, an alternative measure of volume, was positively correlated with volume for all three groups. Finally, age and age at first medication were negatively correlated with prefrontal cortical thickness only in first-episode schizophrenia.
CONCLUSIONS: This study demonstrates the potential usefulness of MRI for the study of cortical thickness abnormalities in schizophrenia. Correlations between cortical thickness and age and between cortical thickness and age at first medication suggest that the longer the schizophrenic process has been operative, the thinner the prefrontal cortex, although this needs confirmation in a longitudinal study.
RATIONALE AND OBJECTIVES: Both single-shot diffusion-weighted echo-planar imaging (EPI) and line scan diffusion imaging (LSDI) can be used to obtain magnetic resonance diffusion tensor data and to calculate directionally invariant diffusion anisotropy indices, ie, indirect measures of the organization and coherence of white matter fibers in the brain. To date, there has been no comparison of EPI and LSDI. Because EPI is the most commonly used technique for acquiring diffusion tensor data, it is important to understand the limitations and advantages of LSDI relative to EPI.
MATERIALS AND METHODS: Five healthy volunteers underwent EPI and LSDI diffusion on a 1.5 Tesla magnet (General Electric Medical Systems, Milwaukee, WI). Four-mm thick coronal sections, covering the entire brain, were obtained. In addition, one subject was tested with both sequences over four sessions. For each image voxel, eigenvectors and eigenvalues of the diffusion tensor were calculated, and fractional anisotropy (FA) was derived. Several regions of interest were delineated, and for each, mean FA and estimated mean standard deviation were calculated and compared.
RESULTS: Results showed no significant differences between EPI and LSDI for mean FA for the five subjects. When intersession reproducibility for one subject was evaluated, there was a significant difference between EPI and LSDI in FA for the corpus callosum and the right uncinate fasciculus. Moreover, errors associated with each FA measure were larger for EPI than for LSDI.
CONCLUSION: Results indicate that both EPI- and LSDI-derived FA measures are sufficiently robust. However, when higher accuracy is needed, LSDI provides smaller error and smaller inter-subject and inter-session variability than EPI.
Placental insufficiency with fetal intrauterine growth restriction (IUGR) is an important cause of perinatal mortality and morbidity and is subsequently associated with significant neurodevelopmental impairment in cognitive function, attention capacity, and school performance. The underlying biologic cause for this association is unclear. Twenty-eight preterm infants (gestational age 32.5 +/- 1.9 wk) were studied by early and term magnetic resonance imaging (MRI). An advanced quantitative volumetric three-dimensional MRI technique was used to measure brain tissue volumes in 14 premature infants with placental insufficiency, defined by abnormal antenatal Doppler measurements and mean birth weights <10(th) percentile (1246 +/- 299 g) (IUGR) and in 14 preterm infants matched for gestational age with normal mean birth weights 1843 +/- 246 g (control). Functional outcome was measured at term in all infants by a specialized assessment scale of preterm infant behavior. Premature infants with IUGR had a significant reduction in intracranial volume (mean +/- SD: 253.7 +/- 29.9 versus 300.5 +/- 43.5 mL, p < 0.01) and in cerebral cortical gray matter (mean +/- SD: 77.2 +/- 16.3 versus 106.8 +/- 24.6 mL, p < 0.01) when measured within the first 2 wk of life compared with control premature infants. These findings persisted at term with intracranial volume (mean +/- SD: 429.3 +/- 47.9 versus 475.9 +/- 53.4 mL, p < 0.05) and cerebral cortical gray matter (mean +/- SD: 149.3 +/- 29.2 versus 189 +/- 34.2 mL, p < 0.01). Behavioral assessment at term showed a significantly less mature score in the subsystem of attention-interaction availability in IUGR infants (p < 0.01). Cerebral cortical gray matter volume at term correlated with attention-interaction capacity measured at term (r = 0.45, p < 0.05). These results suggest that placental insufficiency with IUGR have specific structural and functional consequences on cerebral cortical brain development. These findings may provide insight into the structural-functional correlate for the developmental deficits associated with IUGR.
OBJECTIVE: To investigate the effects of early experience on brain function and structure.
METHODS: A randomized clinical trial tested the neurodevelopmental effectiveness of the Newborn Individualized Developmental Care and Assessment Program (NIDCAP). Thirty preterm infants, 28 to 33 weeks' gestational age (GA) at birth and free of known developmental risk factors, participated in the trial. NIDCAP was initiated within 72 hours of intensive care unit admission and continued to the age of 2 weeks, corrected for prematurity. Control (14) and experimental (16) infants were assessed at 2 weeks' and 9 months' corrected age on health status, growth, and neurobehavior, and at 2 weeks' corrected age additionally on electroencephalogram spectral coherence, magnetic resonance diffusion tensor imaging, and measurements of transverse relaxation time.
RESULTS: The groups were medically and demographically comparable before as well as after the treatment. However, the experimental group showed significantly better neurobehavioral functioning, increased coherence between frontal and a broad spectrum of mainly occipital brain regions, and higher relative anisotropy in left internal capsule, with a trend for right internal capsule and frontal white matter. Transverse relaxation time showed no difference. Behavioral function was improved also at 9 months' corrected age. The relationship among the 3 neurodevelopmental domains was significant. The results indicated consistently better function and more mature fiber structure for experimental infants compared with their controls.
CONCLUSIONS: This is the first in vivo evidence of enhanced brain function and structure due to the NIDCAP. The study demonstrates that quality of experience before term may influence brain development significantly.
Although functional MRI (fMRI) has shown to be a tool with great potential to study the normal and diseased human brain, the large variability in the detected hemodynamic responses across sessions and across subjects hinders a wider application. To investigate the long-term reproducibility of fMRI activation of verbal working memory (WM), eight normal subjects performed an auditory version of the 2-back verbal WM task while fMRI images were acquired. The experiment was repeated nine times with the same settings for image acquisition and fMRI task. Data were analyzed using SPM99 program. Single-session activation maps and multi-subject session-specific activation maps were generated. Regions of interest (ROIs) associated to specific components of verbal WM were defined based on the voxels' coordinates in Talairach space. Visual observation of the multi-subject activation maps showed similar activation patterns, and quantitative analysis showed small coefficients of variance of activation within ROIs over time, suggesting small longitudinal variability of activation. Visual observation of the activation maps of individual sessions demonstrated striking variation of activation across sessions and across subjects, and quantitative analysis demonstrated larger contribution from between-subject variation to overall variation than that from within-subject variation. We concluded that by multi-subject analysis of data from a relatively small number of subjects, reasonably reproducible activation for the 2-back verbal WM paradigm can be achieved. The level of reproducibility encourages the application of this fMRI paradigm to the evaluation of cognitive changes in future investigations. The quantitative estimation of the proportions of within-subject and between-subject variabilities in the overall variability may be helpful for the design of future studies.
The watershed transform has interesting properties that make it useful for many different image segmentation applications: it is simple and intuitive, can be parallelized, and always produces a complete division of the image. However, when applied to medical image analysis, it has important drawbacks (oversegmentation, sensitivity to noise, poor detection of thin or low signal to noise ratio structures). We present an improvement to the watershed transform that enables the introduction of prior information in its calculation. We propose to introduce this information via the use of a previous probability calculation. Furthermore, we introduce a method to combine the watershed transform and atlas registration, through the use of markers. We have applied our new algorithm to two challenging applications: knee cartilage and gray matter/white matter segmentation in MR images. Numerical validation of the results is provided, demonstrating the strength of the algorithm for medical image segmentation.
We created a spatial probability atlas of schizophrenia to provide information about the neuroanatomic variability of brain regions of patients with the disorder. Probability maps of 16 regions of interest (ROIs) were constructed by taking manually parcellated ROIs from subjects' magnetic resonance images (MRIs) and linearly transforming them into Talairach space using the Montreal Neurological Institute (MNI) template. ROIs included temporal, parietal, and prefrontal cortex subregions, with a principal focus on temporal lobe structures. Subject Ns ranged from 11 to 28 for the different ROIs. Our global measure of the spatial distribution of the transformed ROI was the sum of voxels with 50% overlap among subjects. The superior temporal gyrus (STG) and fusiform gyrus (FG) had lower values for schizophrenic subjects than for normal controls, suggestive of greater spatial variability for these ROIs in schizophrenic subjects. For the computation of statistical significance of group differences in portions of the ROI, we used voxel-wise comparisons and Fisher's exact test. First-episode schizophrenic patients compared with controls showed lower probability (P < 0.05) at dorso-posterior areas of planum temporale and Heschl's gyrus, lateral and anterior regions in the left hippocampus (HIPP), and dorsolateral regions of fusiform gyrus. Importantly, most ROIs of schizophrenic subjects showed a significantly lower spatial overlap than controls, even after nonlinear spatial normalization, suggesting a greater heterogeneity in the spatial distribution of ROIs. There is consequently a need for caution in neuroimaging studies where data from schizophrenic subjects are normalized to a particular stereotaxic coordinate system based on healthy controls. Apparent group differences in activation may simply reflect a greater heterogeneity of spatial distribution in schizophrenia.
BACKGROUND: We measured the shape of the head of the caudate nucleus with a new approach based on magnetic resonance imaging (MRI) in schizotypal personality disorder (SPD) subjects in whom we previously reported decreased caudate nucleus volume. We believe MRI shape analysis complements traditional MRI volume measurements. METHODS: Magnetic resonance imaging scans were used to measure the shape of the caudate nucleus in 15 right-handed male subjects with SPD, who had no prior neuroleptic exposure, and in 14 matched normal comparison subjects. With MRI processing tools, we measured the head of the caudate nucleus using a shape index, which measured how much a given shape deviates from a sphere. RESULTS: In relation to comparison subjects, neuroleptic never-medicated SPD subjects had significantly higher (more "edgy") head of the caudate shape index scores, lateralized to the right side. Additionally, for SPD subjects, higher right and left head of the caudate SI scores correlated significantly with poorer neuropsychological performance on tasks of visuospatial memory and auditory/verbal working memory, respectively. CONCLUSIONS: These data confirm the value of measuring shape, as well as volume, of brain regions of interest and support the association of intrinsic pathology in the caudate nucleus, unrelated to neuroleptic medication, with cognitive abnormalities in the schizophrenia spectrum.
RATIONALE AND OBJECTIVES: To examine a statistical validation method based on the spatial overlap between two sets of segmentations of the same anatomy. MATERIALS AND METHODS: The Dice similarity coefficient (DSC) was used as a statistical validation metric to evaluate the performance of both the reproducibility of manual segmentations and the spatial overlap accuracy of automated probabilistic fractional segmentation of MR images, illustrated on two clinical examples. Example 1: 10 consecutive cases of prostate brachytherapy patients underwent both preoperative 1.5T and intraoperative 0.5T MR imaging. For each case, 5 repeated manual segmentations of the prostate peripheral zone were performed separately on preoperative and on intraoperative images. Example 2: A semi-automated probabilistic fractional segmentation algorithm was applied to MR imaging of 9 cases with 3 types of brain tumors. DSC values were computed and logit-transformed values were compared in the mean with the analysis of variance (ANOVA). RESULTS: Example 1: The mean DSCs of 0.883 (range, 0.876-0.893) with 1.5T preoperative MRI and 0.838 (range, 0.819-0.852) with 0.5T intraoperative MRI (P < .001) were within and at the margin of the range of good reproducibility, respectively. Example 2: Wide ranges of DSC were observed in brain tumor segmentations: Meningiomas (0.519-0.893), astrocytomas (0.487-0.972), and other mixed gliomas (0.490-0.899). CONCLUSION: The DSC value is a simple and useful summary measure of spatial overlap, which can be applied to studies of reproducibility and accuracy in image segmentation. We observed generally satisfactory but variable validation results in two clinical applications. This metric may be adapted for similar validation tasks.
The injury of Phineas Gage has fueled research on and fascination with the localization of cerebral functions in the past century and a half. Most physicians and anatomists believed that Gage sustained a largely bilateral injury to the frontal lobes. However, previous studies seem to have overlooked a few less obvious, but essential details. This has led us to reanalyze the injury using three-dimensional reconstruction and quantitative computer-aided techniques and to propose a new biomechanical model, in order to determine the location and extent of the injury and explain Gage's improbable survival. Unlike previous studies on this subject, our findings are based on computer-generated three-dimensional reconstructions of a thin-slice computed tomography scan (CAT) of Phineas Gage's skull. The results of our image analysis were corroborated with the clinical findings, thoroughly recorded by Dr. Harlow in 1848, as well as with a systematic examination of the original skull specimen. Our results show that the cerebral injury was limited to the left frontal lobe, did not extend to the contralateral side, did not affect the ventricular system, and did not involve vital intracranial vascular structures. Although modern neuroscience has perhaps outgrown the speculations prompted by this famous case, it is still a living part of the medical folklore and education. Setting the record straight based on clinical reasoning, observation of the physical evidence, and sound quantitative computational methods is more than mere minutia and of interest for the broad medical community.
The validity of brain tumour segmentation is an important issue in image processing because it has a direct impact on surgical planning. We examined the segmentation accuracy based on three two-sample validation metrics against the estimated composite latent gold standard, which was derived from several experts' manual segmentations by an EM algorithm. The distribution functions of the tumour and control pixel data were parametrically assumed to be a mixture of two beta distributions with different shape parameters. We estimated the corresponding receiver operating characteristic curve, Dice similarity coefficient, and mutual information, over all possible decision thresholds. Based on each validation metric, an optimal threshold was then computed via maximization. We illustrated these methods on MR imaging data from nine brain tumour cases of three different tumour types, each consisting of a large number of pixels. The automated segmentation yielded satisfactory accuracy with varied optimal thresholds. The performances of these validation metrics were also investigated via Monte Carlo simulation. Extensions of incorporating spatial correlation structures using a Markov random field model were considered.
In the longitudinal study of multiple sclerosis (MS) lesions, varying position of the patient inside the MRI scanner is one of the major sources of assessment errors. We propose to use analytical indices that are invariant to spatial orientation to describe the lesions, rather than focus on patient repositioning or image realignment. Studies were made on simulated lesions systematically rotated, from in vitro MS lesions scanned on different days, and from in vivo MS lesions from a patient that was scanned five times the same day with short intervals of time between scans. Each of the lesions' 3D surfaces was approximated using spherical harmonics, from which indices that are invariant to space rotation were derived. From these indices, an accurate and highly reproducible volume estimate can be derived, which is superior to the common approach of 2D slice stacking. The results indicate that the suggested approach is useful in reducing part of the errors that affect the analysis of changes of MS lesions during follow-up studies. In conclusion, our proposed method circumvents the need for precise patient repositioning and can be advantageous in MRI longitudinal studies of MS patients.
A high prevalence of abnormal cavum septi pellucidi (CSP) in schizophrenia may reflect neurodevelopmental abnormalities in midline structures of the brain. The relationship, however, between abnormal CSP and clinical symptoms, and with abnormalities in other limbic structures remains unclear, as does the question of whether a similar abnormality is present in affective psychosis. Seventy-four patients at their first hospitalization, 33 with schizophrenia and 41 with affective (mainly manic) psychosis, and 56 healthy control subjects underwent high-spatial-resolution magnetic resonance imaging (MRI). CSP on six slices or more on 0.9375-mm resampled coronal images was categorized as abnormal. The prevalence of abnormal CSP in both schizophrenic patients (26.1%) and affective psychosis patients (18.2%) was significantly higher than was observed in control subjects (8.2%). In schizophrenic patients only, larger CSP was significantly associated with more severe thinking disturbance and smaller left parahippocampal gyrus gray matter volumes. While the relationships between CSP ratings and clinical symptoms did not significantly differ between the two psychosis groups as assessed by the comparison of regression slopes, the association with limbic volumes appeared to be specific to schizophrenic patients. These results suggest that psychosis associated with schizophrenia and affective disorder share, at least to some extent, neurodevelopmental abnormalities involving midline structures and associated psychopathological consequences. However, the association between abnormal CSP and limbic systems may be more specific to schizophrenia.
In this paper we present a framework for unsupervised segmentation of white matter fiber traces obtained from diffusion weighted MRI data. Fiber traces are compared pairwise to create a weighted undirected graph which is partitioned into coherent sets using the normalized cut (N cut) criterion. A simple and yet effective method for pairwise comparison of fiber traces is presented which in combination with the N cut criterion is shown to produce plausible segmentations of both synthetic and real fiber trace data. Segmentations are visualized as colored stream-tubes or transformed to a segmentation of voxel space, revealing structures in a way that looks promising for future explorative studies of diffusion weighted MRI data.
Changes in mean magnetic resonance imaging (MRI)-derived measurements between patient groups are often used to determine outcomes in therapeutic trials and other longitudinal studies of multiple sclerosis (MS). However, in day-to-day clinical practice the changes within individual patients may also be of interest In this paper, we estimated the measurement error of an automated brain tissue quantification algorithm and determined the thresholds for statistically significant change of MRI-derived T2 lesion volume and brain atrophy in individual patients. Twenty patients with MS were scanned twice within 30 min. Brain tissue volumes were measured using the computer algorithm. Brain atrophy was estimated by calculation of brain parenchymal fraction. The threshold of change between repeated scans that represented statistically significant change beyond measurement error with 95% certainty was 0.65 mL for T2 lesion burden and 0.0056 for brain parenchymal fraction. Changes in lesion burden and brain atrophy below these thresholds can be safely (with 95% certainty) explained by measurement variability alone. These values provide clinical neurologists with a useful reference to interpret MRI-derived measures in individual patients.
A novel method for the visual and quantitative analysis of the geometrical relationship between the vascular architecture of the brain and white matter pathology is presented. The cerebro vascular system is implicated in the pathogenesis of many diseases of the cerebral white matter, for example, stroke, microcerebrovascular disease, and multiple sclerosis (MS). In our work, white matter lesions and vessels are depicted using magnetic resonance imaging (MRI) and extracted using image analysis techniques. We focus on measuring distance relationships between white matter lesions and vessels, and distribution of lesions with respect to vessel caliber. Vascular distance maps are generated by computing for each voxel the Euclidean distance to the closest vessel. Analogously, radius maps assign the radius of the closest vessel to each voxel in the image volume. The distance and radius maps are used to analyze the distribution of lesions with respect to the vessels' locations and their calibers. The method was applied to three MS patients to demonstrate its functionality and feasibility. Preliminary findings indicate that larger MS lesions tend to be farther from detected vessels and that the caliber of the vessels nearest to larger lesions tends to be smaller, suggesting a possible role of relative hypoperfusion or hypoxia in lesion formation.
Characterizing the performance of image segmentation approaches has been a persistent challenge. Performance analysis is important since segmentation algorithms often have limited accuracy and precision. Interactive drawing of the desired segmentation by human raters has often been the only acceptable approach, and yet suffers from intra-rater and inter-rater variability. Automated algorithms have been sought in order to remove the variability introduced by raters, but such algorithms must be assessed to ensure they are suitable for the task. The performance of raters (human or algorithmic) generating segmentations of medical images has been difficult to quantify because of the difficulty of obtaining or estimating a known true segmentation for clinical data. Although physical and digital phantoms can be constructed for which ground truth is known or readily estimated, such phantoms do not fully reflect clinical images due to the difficulty of constructing phantoms which reproduce the full range of imaging characteristics and normal and pathological anatomical variability observed in clinical data. Comparison to a collection of segmentations by raters is an attractive alternative since it can be carried out directly on the relevant clinical imaging data. However, the most appropriate measure or set of measures with which to compare such segmentations has not been clarified and several measures are used in practice. We present here an expectation-maximization algorithm for simultaneous truth and performance level estimation (STAPLE). The algorithm considers a collection of segmentations and computes a probabilistic estimate of the true segmentation and a measure of the performance level represented by each segmentation. The source of each segmentation in the collection may be an appropriately trained human rater or raters, or may be an automated segmentation algorithm. The probabilistic estimate of the true segmentation is formed by estimating an optimal combination of the segmentations, weighting each segmentation depending upon the estimated performance level, and incorporating a prior model for the spatial distribution of structures being segmented as well as spatial homogeneity constraints. STAPLE is straightforward to apply to clinical imaging data, it readily enables assessment of the performance of an automated image segmentation algorithm, and enables direct comparison of human rater and algorithm performance.
In this paper we present a new algorithm for 3D medical image segmentation. The algorithm is versatile, fast, relatively simple to implement, and semi-automatic. It is based on minimizing a global energy defined from a learned non-parametric estimation of the statistics of the region to be segmented. Implementation details are discussed and source code is freely available as part of the 3D Slicer project. In addition, a new unified set of validation metrics is proposed. Results on artificial and real MRI images show that the algorithm performs well on large brain structures both in terms of accuracy and robustness to noise.
Hemisphere asymmetry was explored in normal healthy subjects and in patients with schizophrenia using a novel voxel-based tensor analysis applied to fractional anisotropy (FA) of the diffusion tensor. Our voxel-based approach, which requires precise spatial normalization to remove the misalignment of fiber tracts, includes generating a symmetrical group average template of the diffusion tensor by applying nonlinear elastic warping of the demons algorithm. We then normalized all 32 diffusion tensor MRIs from healthy subjects and 23 from schizophrenic subjects to the symmetrical average template. For each brain, six channels of tensor component images and one T2-weighted image were used for registration to match tensor orientation and shape between images. A statistical evaluation of white matter asymmetry was then conducted on the normalized FA images and their flipped images. In controls, we found left-higher-than-right anisotropic asymmetry in the anterior part of the corpus callosum, cingulum bundle, the optic radiation, and the superior cerebellar peduncle, and right-higher-than-left anisotropic asymmetry in the anterior limb of the internal capsule and the anterior limb's prefrontal regions, in the uncinate fasciculus, and in the superior longitudinal fasciculus. In patients, the asymmetry was lower, although still present, in the cingulum bundle and the anterior corpus callosum, and not found in the anterior limb of the internal capsule, the uncinate fasciculus, and the superior cerebellar peduncle compared to healthy subjects. These findings of anisotropic asymmetry pattern differences between healthy controls and patients with schizophrenia are likely related to neurodevelopmental abnormalities in schizophrenia.
A new definition of affine invariant medial axis of planar closed curves is introduced. A point belongs to the affine medial axis if and only if it is equidistant from at least two points of the curve, with the distance being a minimum and given by the areas between the curve and its corresponding chords. The medial axis is robust, eliminating the need for curve denoising. In a dynamical interpretation of this affine medial axis, the medial axis points are the affine shock positions of the affine erosion of the curve. We propose a simple method to compute the medial axis and give examples. We also demonstrate how to use this method to detect affine skew symmetry in real images.