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The application of voxel-level three-dimensional registration to serial magnetic resonance imaging (MRI) is described. This fluid registration determines deformation fields modeling brain change, which are consistent with a model describing a viscous fluid. The objective was to validate the measurement of hippocampal volumetric change by fluid registration in Alzheimer's disease (AD) against current methodologies. The hippocampus was chosen for this study because it is difficult to measure reproducibly by manual segmentation and is widely studied; however, the technique is applicable to any structure which can be delineated on a scan. First, suitable values for the viscosity-body-force-ratio, alpha (0.01), and the number of iterations (300), were established and the convergence, repeatability, linearity, and accuracy investigated and compared with expert manual segmentation. A simple model of hippocampal atrophy was used to compare simulated volumetric change against that obtained by fluid registration. Finally the serial segmentation was compared with the current gold standard technique-expert human labeling with a volume repeatability of approximately 4%-in 27 subjects (15 normal controls, 12 clinically diagnosed with Alzheimer's disease). The scan-rescan volumetric consistency of serial segmentation by fluid-registration was shown to be superior to human serial segmentors ( approximately 2%). The mean absolute volume difference between fluid and manual segmentation was 0.7%. Fluid registration has potential importance for tracking longitudinal structural changes in brain particularly in the context of the clinical trial where large numbers of subjects may have multiple MR scans.
We describe the application of statistical shape analysis to homologous landmarks on the cortical surface of the adult human brain. Statistical shape analysis has a sound theoretical basis. Landmarks are identified on the surface of a 3-D reconstruction of the segmented cortical surface from magnetic resonance image (MRI) data. Using publicly available software (morphologika) the location and size dependence of the landmarks are removed and the differences in landmark distribution across subjects are analysed using principal component analysis. These differences, representing shape differences between subjects, can be visually assessed using wireframe models and transformation grids. The MRI data of 58 adult brains (27 female and 15 left handed) were examined. Shape differences in the whole brain are described which concern the relative orientation of frontal lobe sulci. Analysis of all 116 hemispheres revealed a statistically significant difference (P < 0.001) between left and right hemispheres. This finding was significant for right- but not left-handed subjects alone. No other significant age, gender, handedness, or brain-size correlations with shape differences were found.
Voxel-based morphometry has recently been used successfully to detect gray matter volume reductions in schizophrenic patients. The aim of the present study was to confirm the findings on gray-matter changes and to complement these by applying the methodology to CSF-differences. Also, we wanted to determine whether a correlation exists between a clinically defined parameter of disease severity and brain morphology in schizophrenic patients. We investigated 48 schizophrenic patients and compared them with 48 strictly age- and sex-matched controls. High-resolution whole-brain MR-images were segmented and analyzed using SPM99. In a further analysis, the covariate effect of the global assessment of functioning-score (GAF) was calculated. Main findings were (i) left-dominant frontal, temporal, and insular GM-reductions and (ii) GM-increases in schizophrenic patients in the right basal ganglia and bilaterally in the superior cerebellum; (iii) CSF-space increases in patients complementary to some GM-reductions; (iv) a correlation between the GAF-score and local GM-volume in the left inferior frontal and inferior parietal lobe of schizophrenic patients. This study confirms and extends some earlier findings on GM-reduction and detected distinct GM-increases in schizophrenic patients. These changes were corroborated by complementary CSF-increases. Most importantly, a correlation could be established between two particular gray matter-regions and the overall disease severity, with more severely ill patients displaying a local GM-deficit. These findings may be of potentially large importance for both the future interpretation and design of neuroimaging studies in schizophrenia and the further elucidation of possible pathophysiological processes occurring in this disease.
Learning curves are presented as an unbiased means for evaluating the performance of models for neuroimaging data analysis. The learning curve measures the predictive performance in terms of the generalization or prediction error as a function of the number of independent examples (e.g., subjects) used to determine the parameters in the model. Cross-validation resampling is used to obtain unbiased estimates of a generic multivariate Gaussian classifier, for training set sizes from 2 to 16 subjects. We apply the framework to four different activation experiments, in this case [(15)O]water data sets, although the framework is equally valid for multisubject fMRI studies. We demonstrate how the prediction error can be expressed as the mutual information between the scan and the scan label, measured in units of bits. The mutual information learning curve can be used to evaluate the impact of different methodological choices, e.g., classification label schemes, preprocessing choices. Another application for the learning curve is to examine the model performance using bias/variance considerations enabling the researcher to determine if the model performance is limited by statistical bias or variance. We furthermore present the sensitivity map as a general method for extracting activation maps from statistical models within the probabilistic framework and illustrate relationships between mutual information and pattern reproducibility as derived in the NPAIRS framework described in a companion paper.
We introduce a data-analysis framework and performance metrics for evaluating and optimizing the interaction between activation tasks, experimental designs, and the methodological choices and tools for data acquisition, preprocessing, data analysis, and extraction of statistical parametric maps (SPMs). Our NPAIRS (nonparametric prediction, activation, influence, and reproducibility resampling) framework provides an alternative to simulations and ROC curves by using real PET and fMRI data sets to examine the relationship between prediction accuracy and the signal-to-noise ratios (SNRs) associated with reproducible SPMs. Using cross-validation resampling we plot training-test set predictions of the experimental design variables (e.g., brain-state labels) versus reproducibility SNR metrics for the associated SPMs. We demonstrate the utility of this framework across the wide range of performance metrics obtained from [(15)O]water PET studies of 12 age- and sex-matched data sets performing different motor tasks (8 subjects/set). For the 12 data sets we apply NPAIRS with both univariate and multivariate data-analysis approaches to: (1) demonstrate that this framework may be used to obtain reproducible SPMs from any data-analysis approach on a common Z-score scale (rSPM[Z]); (2) demonstrate that the histogram of a rSPM[Z] image may be modeled as the sum of a data-analysis-dependent noise distribution and a task-dependent, Gaussian signal distribution that scales monotonically with our reproducibility performance metric; (3) explore the relation between prediction and reproducibility performance metrics with an emphasis on bias-variance tradeoffs for flexible, multivariate models; and (4) measure the broad range of reproducibility SNRs and the significant influence of individual subjects. A companion paper describes learning curves for four of these 12 data sets, which describe an alternative mutual-information prediction metric and NPAIRS reproducibility as a function of training-set sizes from 2 to 18 subjects. We propose the NPAIRS framework as a validation tool for testing and optimizing methodological choices and tools in functional neuroimaging.
The hippocampal formation of eight perfusion-fixed human brains was examined using new methods according to stereotactic and morphometric principles (macrovibratome and computer-aided 3D reconstruction). The reconstructions form part of a neuroanatomical reference system (NeuRef). This reference system allows for 3D visualisation of the brain and its components on a computer graphic workstation, as well as for the presentation of the union set based on a neuroanatomical structure taken from this sample of brains. This retrievable knowledge of neurofunctional systems is important for the preoperative planning of neurosurgeons and the adjustment of radiotherapy.
Magnetic source imaging is the reconstruction of the current source distribution inside an inaccessible volume from magnetic field measurements made outside the volume. It is possible in many applications to estimate, from prior physiological and anatomical knowledge, the source positions, amplitudes, and correlations, as well as the noise amplitudes and correlations. The optimal constrained linear inverse method (OCLIM) uses this prior knowledge to obtain a minimum mean-square error estimate of the current distribution. OCLIM can be efficiently computed using the Cholesky decomposition, taking about a second on a workstation-class computer for a problem with 64 sources and 144 detectors. Any source and detector configuration is allowed as long as their positions are fixed a priori. Correlations among source and noise amplitudes are permitted. OCLIM reduces to the optimally weighted pseudoinverse method of Shim and Cho if the source amplitudes are independent and identically distributed and to the minimum-norm least-squares estimate in the limit of no measurement noise or no prior knowledge of the source amplitudes. In the general case, OCLIM has better mean-square error than either previous method. OCLIM appears well suited to magnetic imaging, since it exploits prior information, provides the minimum reconstruction error, and is inexpensive to compute.
BACKGROUND: The movement of a rigid body through space may be comprehensively described by constructing a "finite helical axis" (FHA). The rigid body carries out a rotation around this axis for discrete periods of time while at the same time moving along this axis. MATERIAL AND METHODS: The free opening and closing movement of the mouth was registered in eight asymptomatic test persons, using the CADIAX electronic axiography system (GAMMA-DENTAL, Klosterneuburg, Austria). Subsequently, the position of the FHA in space, the angle of rotation around this axis, and the amount of translation along the FHA were determined for each subject, using the coordinates of the measuring styluses. RESULTS: During the initial phase of the mouth-opening movement, the FHAs were near the condyle. Over the course of the opening movement, they moved toward downward backward, downward and downward forward, and finally to forward upward toward the condyle upon reaching maximum mouth opening. During the closing movement, the FHAs shifted back in the reverse direction. During both the opening and the closing movement, there was a slight translational movement along the FHAs, which provided an indication of mandibular deviation.
Retrograde drilling of osteochondral lesions has obtained acceptable results in the initial stage. Intraoperatively not all lesions are accessible with the arthroscopic technique, despite being readily identifiable with modern imaging preoperatively. As an alternative, open surgical treatment is recommended to achieve good results. The use of computer-assisted navigated retrograde drilling of osteochondral lesions has been described with promising results as a new technique. Computed tomography (CT)- and fluoroscopy-based navigation systems in current use are limited in their flexibility. The drawbacks of fluoroscopy are lack of three-dimensional imaging intraoperatively. CT-based navigation still requires intraoperative cumbersome registration, extra preoperative planning, and imaging with further technical resources. In the current case report, we describe a patient with an osteochondral lesion of the posteromedial talus. In addition to the current method of arthroscopic evaluation and treatment, we also introduce an alternative technique of using Iso-C(3D)-based navigation-assisted retrograde drilling of the lesion. The advantages of this technique are an actual intraoperative three-dimensional imaging for the use of navigation without the need for anatomical registration and an immediate postoperative control of surgical treatment. The results of this case report demonstrate accurately navigated drilling with the described system. The accuracy was confirmed with immediate intraoperative Iso-C(3D) and postoperative CT scans. Our results indicate that the use of an Iso-C(3D) navigation system is a possible alternative to arthroscopic or open drilling for osteochondral lesions of the talus. To provide further evidence for the use of Iso-C (3D)-based drilling, current studies will start at our institution.
The output of the sensitivity analysis system, SENSEN, described in the preceding two papers is applied to an optimization study for the VESPERS model of the spread of infection with influenza virus in a community. We know of no similar studies. In this application, constrained, non-linear optimization routines in IMSL are used. The optimization study attempts to answer whether behavioural modification (modeled as discrete interventions) is necessary to represent simultaneously the age-specific attack rates and the epidemic curve. The results presented here indicate that such model interventions are necessary.
The Johnson-Neyman (JN) procedure, as originally formulated (Stat Res Mem, 1 (1936) 57-93), applies to a situation in which measurements on 1 dependent (response) variable, X, and 2 independent (predictor) variables, Z1 and Z2, are available for the members of 2 groups. The expected value of X is assumed to be a linear function of Z1 and Z2, but not necessarily the same function for both groups. The JN technique is used to obtain a set of values for the Z variables for which one would reject, at a specified level of significance alpha (e.g., alpha = 0.05), the hypothesis that the 2 groups have the same expected X values. This set of values, or 'region of significance,' may then be plotted to obtain a convenient description of those values of Z1 and Z2 for which the 2 groups differ. The technique can thus be described as a generalization of the analysis of covariance (ANCOVA) which does not make the assumption that the regression coefficients for the regression of X on the covariates, Z1 and Z2, are equal in the groups being compared. In this paper we describe, illustrate and make available a menu-driven PC program (TXJN2) implementing the JN procedure.
We investigated the prevalence and characteristics of intrapulmonary shunt using contrast echocardiography with harmonic imaging in 130 liver transplant candidates. We found a high prevalence of intrapulmonary shunts and a significant correlation between the degree of intrapulmonary shunt and the Child-Pugh classification score.
UNLABELLED: The hippocampus is discussed as one of the key regions in the pathogenesis of Posttraumatic Stress Disorder (PTSD). MRI results concerning the volume of the hippocampus are, however, inconsistent. This may be due to the heterogeneity of patients' traumata or postprocessing of the imaging data. To overcome these problems, the present study investigates volume changes in well-characterized chronic PTSD patients in comparison to controls using two different evaluation methods. MATERIAL AND METHODS: 15 patients with chronic PTSD, traumatized at the same air show plane crash in 1988 (Ramstein, Germany), and 15 matched healthy controls participated in this study. All patients suffered from significant impairment by the PTSD; none had a history of drug or alcohol abuse. Hippocampus volume changes were processed by a semi-automated standard procedure performed with BRAINS2 as well as the voxel based morphometry (VBM) using SPM2. RESULTS: No differences in total brain grey or white matter were detected between patients and controls. No differences in total hippocampal volume or in right and left parts were seen, even when hippocampal volumes were corrected by total brain volume or correlated with clinical data. Finally, no significant differences were detected between patients and controls in hippocampal regions using VBM. DISCUSSION: This is the first study examining long-term changes in hippocampal volumes in chronic PTSD patients compared to matched controls using two different evaluation methods. Neither conventional volumetry nor VBM could detect any differences in the volume and structure. This supports the hypothesis that previously described hippocampal volume reduction is not necessarily due to PTSD or at least that, after 15 years, volume changes have been restored or have not yet developed.
In this paper, a new procedure is presented which allows the estimation of the states and parameters of the hemodynamic approach from blood oxygenation level dependent (BOLD) responses. The proposed method constitutes an alternative to the recently proposed Friston [Neuroimage 16 (2002) 513] method and has some advantages over it. The procedure is based on recent groundbreaking time series analysis techniques that have been, in this case, adopted to characterize hemodynamic responses in functional magnetic resonance imaging (fMRI). This work represents a fundamental improvement over existing approaches to system identification using nonlinear hemodynamic models and is important for three reasons. First, our model includes physiological noise. Previous models have been based upon ordinary differential equations that only allow for noise or error to enter at the level of observation. Secondly, by using the innovation method and the local linearization filter, not only the parameters, but also the underlying states of the system generating responses can be estimated. These states can include things like a flow-inducing signal triggered by neuronal activation, de-oxyhemoglobine, cerebral blood flow and volume. Finally, radial basis functions have been introduced as a parametric model to represent arbitrary temporal input sequences in the hemodynamic approach, which could be essential to understanding those brain areas indirectly related to the stimulus. Hence, thirdly, by inferring about the radial basis parameters, we are able to perform a blind deconvolution, which permits both the reconstruction of the dynamics of the most likely hemodynamic states and also, to implicitly reconstruct the underlying synaptic dynamics, induced experimentally, which caused these states variations. From this study, we conclude that in spite of the utility of the standard discrete convolution approach used in statistical parametric maps (SPM), nonlinear BOLD phenomena and unspecific input temporal sequences must be included in the fMRI analysis.
The underlying neurobiology of autism, a severe pervasive developmental disorder, remains unknown. Few neocortical brain MRI abnormalities have been reported. Using rest functional brain imaging, two independent studies have described localized bilateral temporal hypoperfusion in children with primary autism. In order to search for convergent evidence of anatomical abnormalities in autistic children, we performed an anatomical MRI study using optimized whole-brain voxel-based morphometry (VBM). High-resolution 3-D T1-weighted MRI data sets were acquired in 21 children with primary autism (mean age 9.3 +/- 2.2 years) and 12 healthy control children (mean age 10.8 +/- 2.7 years). By comparing autistic children to normal children, we found bilaterally significant decreases of grey matter concentration located in superior temporal sulcus (STS) (P < 0.05 corrected, after small volume correction; SVC). Children with autism were also found to have a decrease of white matter concentration located in the right temporal pole and in cerebellum (P < 0.05, corrected) compared to normal children. These results suggest that autism is associated with bilateral anatomical abnormalities localized in the STS and are remarkably consistent with functional hypoperfusion previously reported in children with autism. The multimodal STS areas are involved in highest level of cortical integration of both sensory and limbic information. Moreover, the STS is now recognized as a key cortical area of the "social brain" and is implicated in social perceptual skills that are characteristically impaired in autism. Therefore, the convergent anatomical and functional temporal abnormalities observed in autism may be important in the understanding of brain behavior relationships in this severe developmental disorder.
A parametric functional MRI (fMRI) study with three levels of task difficulty was performed to determine the effect of practice and attentional load on brain activation during visual attention tasks. Brief practice during repeat fMRI scanning (20 min) did not change performance accuracy or reaction times (RT), but decreased activation bilaterally in the inferior, middle, and superior frontal gyri, superior temporal gyrus, thalamus, and cerebellum. Increased attentional load decreased performance accuracy but not RT, and increased activation bilaterally in the inferior, posterior, and superior parietal cortices, thalamus, cerebellum, and frontal gyri. These changes suggest that practice decreases dependency on thalamus, cerebellum, and the frontal cortices for controlled task processing possibly due to increased efficiency of the attentional network. Since short-term practice-effects in the prefrontal cortex may be similar to attentional load-effects, studies of attentional load need to take practice effects into account.
"Feeling-of-Knowing" (FOK) refers to the sense of what one knows and is a component of the human metamemory system. We investigated the neural correlates of the FOK induced by face-name associations using the Recall-Judgment-Recognition paradigm. Data were gathered using event-related functional magnetic resonance imaging (fMRI). We analyzed the fMRI data with parametric analyses of six FOK ratings while compensating for the effects of unbalanced response latencies among trials ("variable duration parametric analysis"). Regions showing a significant linear relationships with the FOK ratings (FOK regions) were the bilateral ventral, dorsal, and anterior prefrontal regions; the medial frontal regions; the medial surface regions; the left parietal regions; the bilateral superior temporal and nearby regions; the right anterior temporal region; and the bilateral thalami/basal ganglia. Most of the active areas in the prefrontal regions were common to those found in our previous FOK studies of general knowledge (Kikyo, H., Ohki, K., Miyashita, Y., 2002. Neural correlates for feeling-of-knowing: an fMRI parametric analysis. Neuron 36, 177-186). However, in this study, we found robust activations of the temporal regions near to the regions that were related to the higher-order information processing of face images or semantic information processing of the to-be-recalled person. Those results suggest that the information related to the higher-order visual features of a face, which was represented in the temporal cortex, was activated by the top-down signal from the prefrontal cortex, and that this cooperation between the temporal and prefrontal cortices may contribute to the FOK.