Modeling & Analysis

2003 NeuroImage  
Introduction MR images of geriatric subjects do no match properly with the Montreal Neurological Institute (MNI) templates for three main reasons: brain atrophy, presence of large white matter lesions and lower contrast between white and grey matter. Most of the automatic brain image segmentation algorithms are using the MNI templates, which have been generated from MR images of young healthy individuals (mean age 23.4+-4.1 years). The aim of this work was to generate new Leiden University
more » ... al Center (LUMC) templates that represent the geriatric subjects in the perspective of improving subsequent automatic image segmentation techniques. Methods 527 healthy elderly subjects, aged between 70-83 years, were recruited for the PROspective Study Of Pravastatin in the Elderly at Risk (PROSPER) at the LUMC. MRI was performed with a 1.5 Tesla Philips system (Best, The Netherlands). Dual fast spin-echo imaging (TE 27/120ms, TR 3000 ms, echo train length factor 10, 48 contiguous 3mm slices, matrix 256x256, FOV 220) were obtained. All scans were semi-automatically segmented using in-house software. Binary masks of Intra-Cranial (IC), CSF Peri-Venticular Lesion (PVL) and SubCortical Lesions (SCL) where generated and manually corrected. LUMC template generation consisted of three steps. (1) Normalization: Proton Density (PD) weighted images were automatically registered to the MNI PD template using a 12 parameter affine transformation1; for each patient, the transformation matrix resulting from this affine registration was applied to reslice the corresponding PD and T2 weighted images as well as the IC,CSF, PVL and SCL masks. Tri-cubic interpolation was used while reslicing the images. (2) Expert quality control: Normalized PD images (n=527) were reviewed by an expert. Brains that were not properly reoriented in standard space were excluded. 432 brains were averaged for the LUMC template. (3) Template generation: Average PD and T2 images as well as prior distribution maps of CSF, PVL and SCL were computed for the categories (i) whole database; (ii) gender; (iii) age-intervals for the whole data base and (iv) age-intervals for each gender. Results In an elderly population, the LUMC template compared to the MNI template accounts for (i) enlarged ventricle size (~260% compared to the MNI template) in elderly subjects. (ii) reduced grey-white matter contrast in the elderly, (iii) atrophy ( 0.73 for the LUMC template and 0.98 for the MNI template) and (iv) higher lesion loads in females in the gender specific templates. Abstract Introduction Non-parametric permutation tests offer some advantages over traditional parametric methods [1]. Analyses of perfusion fMRI time-series suggest that, unlike BOLD fMRI, these data do not posses significant temporal autocorrelation under the null-hypothesis [2], and therefore might be approached with permutation methods. Tests upon null-hypothesis, perfusion fMRI data Null-hypothesis data (resting quietly, eyes open) were obtained from 10 subjects. Each subject was scanned for 8 minutes (TR=3 sec, 8 slices, 1.5 Tesla) using modified FAIR/EPI [3]. Data were pre-processed as previously described [2]. We examined three hypothetical, box-car experimental designs, with 4 minute epochs, 2 minute epochs, and 30 second epochs. General linear models were constructed using these designs and movement covariates of no-interest were included. Permutation analyses consisted of 1000 random permutations of the data, each yielding a t-statistic map. The p value for each voxel was the obtained by comparing the t-statistic for the original time-series to the distribution of t-statistics for the permuted time-series. Uniform distributions of p values were observed for all three designs, again supporting the validity of the method. Non-uniform distributions were obtained when movement covariates were not included. Map-wise false-positive rate The maximum map t-value was obtained for each permutation, and a critical map-wise t threshold corresponding to an alpha=0.05 was obtained. For the 30 second epoch hypothetical design, there were 0/10 false positive maps, while for the 2 minute and 4 minute epoch designs there were 1/10 and 2/10 false positive maps respectively. In each case, one or two voxels in areas of high susceptibility were responsible for the false-positive result. Therefore, while the false positive rate exceeded tabular values for the lowest frequency designs, the supra-threshold voxels would be unlikely to be mistaken for true experimental effect. Tests upon experimental, perfusion fMRI data Five, 8 minute scans were obtained from each of ten subjects using CASL perfusion. During scanning, subjects experienced alternating 31 second periods of darkness and visual stimulation (data previously reported in [2]). Permutation analyses conducted as above. The average map-wise threshold (for alpha=0.05, 3 voxel FWHM smooth) was 4.06 for the permutation method, 4.40 for random field theory [4] and 4.36 for Bonferroni correction. On average, 20 more voxels (16% of activated volume) were identified using the permutation test. Conclusions Non-parametric permutation tests appear valid for application to perfusion fMRI data that have been appropriately pre-processed, although the elevated map-wise false positive rate seen for very low hypothetical designs is concerning. The permutation approach afforded greater statistical power in the example, experimental dataset. Abstract Introduction This study aimed at merging approaches based on (very) low frequency correlation analysis and spectral coherence analysis. Typical correlation studies (1) involve low-pass filtering fMRI time series (e.g. 0.08 Hz), and computing correlation coefficients between the time series of an ROI and the remaining brain voxels, yielding plausible connectivity patterns. Studies involving coherence spectra, common in EEG/MEG litterature, have seldom been applied to fMRI (2). This approach amounts to computing phase-free correlations at any chosen frequency, eliminating the need to apply temporal filtering; moreover, information about phase can be retrieved. A key feature of the present study is the ability to detect statistically significant departures from white noise in the fMRI spectrum (using the Lomb periodogram), and to apply coherence analysis to frequencies of interest thus selected. Methods Eight subjects were scanned while performing a task that involved hearing a sentence and making a syntax-related decision. (This acquisition was part of a wider study whose aim was to assess the effect of an anesthetic; only data from control subjects were used in the study described herein.) An SPM99 analysis (3) was performed to detect regions engaged by this paradigm. 7x7x7 (mm3) cubic boxes centered at the SPM maxima were chosen as ROIs for a subsequent coherence analysis. The functional data were spatially smoothed (6.5 mm FWHM). Low frequencies associated with statistically significant signal power were chosen as frequencies of interest. Maps of coherence values and phase shifts between the ROIs and the remaining brain voxels were created. Results The SPM99 analysis yielded highly significant bilateral superior temporal activations, as expected. A strong coherence (at the paradigm frequency) was observed between the ROIs and the contralateral homologous region. Compared with the SPM analysis, additional activations were highlighted, e.g. occipital cortex. At lower frequencies, coherence maps showed a similar bilateral pattern, but involving also other structures (e.g. ant. cingulate gyrus, precuneus). Joint analysis of the coherence maps and phase maps showed, in 5 subjects, a phase gradient that suggests sub-second spread of activation-related hemodynamic response across the superior temporal lobes. Conclusions Abstracts presented at the 9th International Conference on Functional Mapping of the Human Brain, June 19OE22, 2003 i) Coherence analysis is a more versatile technique than simple correlation plus low-pass filtering approaches, allowing phase and phase-free frequency relationships to be analyzed, and thus providing a way to gain insight about low frequency fluctuations underlying correlation between functionally related brain regions. ii) Coherence analysis highlights paradigm-activated regions undetected by the SPM99 analysis, probably due to a significant time lag that precludes detection by SPM without affecting coherence. iii) Phase analysis provides information about the temporal sequences of cortical events. This study indicates spread of BOLD-related activity consistent with the distinction between primary and association auditory areas. These results are in agreement with previous findings (4) that relied on time domain frequencies. Abstract Introduction The frequency profile of fMRI noise is still not well understood. Notably, high power at very low frequencies (below 0.1 Hz) cannot be explained only by physiological artefacts (1). Possible causes for very low frequency drifts are movement-related noise, instrumental instability, magnetic field changes; metabolic, vascular and neuronal causes have also been suggested. Whereas attempts have been made to probe deeper into the nature of these drifts (2) , knowledge about their tissue and frequency specificity is still very limited. The main goal of this study was to investigate the tissue specificity of noise frequency profiles. Methods Resting state EPI functional acquisitions were performed in two volunteers (3 10-minute runs per subject, TR=2 s or 2.3 s, 64x64 matrix, 17 contiguous slices). Structural (SPGR) images and field maps (needed to assess EPI distortion) were also acquired. The functional images were realigned using SPM99 (3). The anatomical images were segmented into grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) using SPM99. The EPI undistortion tool described in (4) was applied in reverse, so that the estimated distortion parameters were applied to the probabilistic images resulting from segmentation, yielding spatially matching EPI and anatomical segmented images. Power spectra were computed for all voxels. Statistically significant departures from white noise were detected through the use of Lomb periodograms. Frequency ranges of interest were thus selected, notably a "very low frequency" (VLF) and a "low-frequency" (LF) range (0-0.01 Hz and 0.01-0.05 Hz respectively). Voxel-by-voxel values of power in frequencies of interest were saved as images. Results The global frequency for each tissue type was similar; however, power at the LF range was considerably lower in WM when compared with other tissues. The distribution of high-power voxels was fairly uniform in WM, across all frequency ranges. In contrast, in GM there was a clear spatial segregation between voxels with high power at the VLF range (mainly frontal regions) and voxels with high power at the LF range (left occipital and temporal structures), in both subjects. Higher frequencies did not show significant spatial segregation. Conclusions Abstracts presented at the 9th International Conference on Functional Mapping of the Human Brain, June 19OE22, 2003 Clearly, further studies are needed to substantiate the present findings. However, these results strongly suggest that several sources play an important role in fMRI low-frequency drifts. The main observation (spatial and tissue specificity of different frequency ranges within the typical "1/f" profile) hints at an important physiological component, and provides a link with connectivity studies based on low-frequency drifts (5), and the considerable body of work related to residual activity during rest due to involuntary cognitive processing. Abstract The cerebral cortex is not a uniform layer of gray matter, but is striped by a distinct internal lamination. Such stratified structural design can be shown to vary locally on the surface of the hemispheres and can be related to specific function. We describe an automatic and reproducible method to analyze the histological design of the cortex as applied to sections stained to reveal myelinated fibers. The technique provides an evaluation of the distribution of myelination across the width of the cortical mantle in accordance with a model of its curvature and its intrinsic geometry. The profile lines along which the density of staining is measured are generated from the solution of a partial differential equation that models the intermediate layers of the cortex. Cortical profiles are classified according to significant components that emerge from wavelet analysis. The borders between several visual areas (V1, V2, V3 and V5) were adequately localized by our automated classification algorithm and validated by a blinded rater. The intensity profiles that are grouped into distinct architectonic classes are normalized and averaged to produce area-specific templates of cortical myelo-architecture. Understanding the relationship between structure and function in the cerebral cortex is both a classification and a localization problem. The definition of unambiguous architectonic templates is the prerequisite for topographic histological surveys and hence for comparative studies and for the generalization of architectonic maps to a population. Abstract Introduction The understanding and quantifying of cerebral energy metabolism coupling between neurons and astrocytes are necessary to the interpretation of functional brain imaging data, especially fMRI, MRS and PET. Furthermore, the balance of oxidative and non-oxidative metabolism, two mechanisms involved in the ATP regeneration during neural activation, may be correlated to the kind of information processing (pre-or post-synaptic) executed in the brain (Gjedde et al., J. Cereb. Blood Flow Metab. 22: 1-14, 2002). We develop a previous mathematical model of the coupling between brain electrical activity, metabolism, and hemodynamics (Aubert and Costalat, NeuroImage, 17: 1162-1181, by taking into account the compartmentalization between neurons and astrocytes. Method The model includes two cellular compartments, neuron and astrocyte (refered to as glial cell), and an extracellular compartment which exchanges both with the cells and a capillary compartment. Furthermore, venous dilatation processes are taken into account using the Balloon model of Buxton et al. (Magn. Reson. Med. 39: 855-864, 1998). The model describes the possible disparities of metabolic fluxes magnitudes between neurons and astrocytes, including regeneration of ATP via phosphocreatine buffer effect, consumption of glucose, production of lactate, consumption of pyruvate by mitochondria, and lactate exchanges through cell membranes. The increase of ATP consumption during neural activation is assumed to result from: (i) the activation of the Na+,K+-ATPase in response to the sodium-glutamate cotransport in astrocytes, excitatory post-synaptic potentials and action potentials in neurons; (ii) to a lesser extent, ATP-consuming metabolic reactions, especially synthesis of glutamine from glutamate in astrocytes. Results and Discussion The model allows to test specific hypotheses. At steady state, we can assume that (1) neurons have a net production of lactate which is released in extracellular space, (2) neurons consume lactate produced by astrocytes, (3) neurons globally neither produce nor consume lactate. During a stimulation, the extra pyruvate consumed by neuronal mitochondria can be issued from the lactate produced by astrocytes (Magistretti et al., Science 283: 496-497, 1999) or from neuronal glycolysis (Gjedde and Marrett, J. Cereb. Blood Flow Metab. 21: 1384-1392 Chih et al., Trends in Neurosciences 24: 573-578, 2001), depending on critical parameters pertaining to the regulation of glycolysis and cell respiration, lactate transport and possibly lactate dehydrogenase catalytic properties. Our results show that the orientation of LDH-catalysed reactions and lactate transport can change Abstract Recent studies have shown a spatial correspondence between functional magnetic resonance (fMR) images of blood oxygenation dependent (BOLD) signal change and magnetoencephalograhic (MEG) measures of induced oscillatory power changes [1]. In this work we examine measures with which to quantify this similarity. Typically, we wish to compare images of BOLD signal change with Synthetic Aperture Magnetometry (SAM) images of band-limited power change [2] for the same subject in the same experimental paradigm. Electrically, these changes can be decreases in power, termed event related desynchronistaion (ERD), or increases, termed event related synchronisation (ERS), with respect to baseline; a typical activation time-series will consist of both ERD and ERS phases [3]. The time-courses of these power changes are however not homogeneous (ranging over the order of seconds) across frequency bands, nor across the cortex. That is, for a given time-window it is not uncommon to see significant ERD in one active area of cortex and ERS in another; both areas will however show positive BOLD signal change. In such cases, linear measures of similarity such as correlation fail to show the true dependence between images from the two modalities. We examine measures of mutual information and entropy as an objective way of quantifying the relationship between image sets. For example, initial results, based on biological motion data for a single subject, suggest that the mutual information between the fMRI and MEG data sets is maximal in the 5-15Hz band. We suggest this method may be extended further to the examination of the similarity between fMRI and MEG (beamformer estimates of) voxel time-series. Abstract A new neural network algorithm has been developed for the automatic visual segmentation of T1-weighted 3-D head magnetic resonance images. Our first experiments give good performances in segmenting skull, brain in all its ramifications, as other structures within the skull, like cerebellum and brain stem. The network is effective in segmenting gray and white matter too. The 3-D segmentation results can be used to generate surface and volume tessellations suitable for FEM (Finite Element Method) forward field calculations, such as, for instance, in magnetoencephalography source modeling. We have applied the algorithm to several MRI Data Sets. Despite the diversity of the images the neural network shows good robustness. Abstract We introduce a set of new fMRI experiments inspired by , where the degree of spatial and temporal independence of two task-related components is parametrically controlled in each run. We then present the results of the application of Independent Component Analysis (ICA), and measure if, and how accurately, ICA separates the two components. The stimuli presented to the subjects consist of the superposition of two spatio-temporal patterns F 1 (s,t) = S 1 (s) x T 1 (t) and F 2 = S 2 (s) x T 2 (t). The two temporal functions T 1 and T 2 are simple "on/off" sequences. The two spatial functions S 1 and S 2 are flickering checkerboards at 8 Hz presented on one half of the visual field. The function F 1 (s,t) is fixed for all runs, while F 2 (s,t) is different for each run: it changes according to two parameters which control its degrees of spatial and temporal independence with the first one. α controls the degree of temporal independence of the two patterns by indexing T 2 α (t), and β controls their degree of spatial independence by indexing S 2 β (s). This is summarized in Figure 1 . In the experiment, α varies among 1/8, 1/4, 3/8 and 1/2 , and β among 0, π/4, π/2 and 3π/4. A total of 16 experimental runs per subject are acquired, spanning all possible values of the pair (α, β). Five additional control runs are acquired with a single spatio-temporal pattern, allowing estimating separately each spatial function (S 1 and S 2 β for all four values of β): these are then used as gold standard to measure the accuracy of the separation. Abstract Introduction In classical second-level analysis, the choice of experimental design and filtering strategy are not considered important for the outcome, since it is assumed that the inter-subject variance (random effect) dominates the mixed effect variance [1]. If this assumption is to hold then the efficiency at the second-level should be independent of differences in design and first-level filtering. In the present work we set out to analyse these assumptions more closely. We acquired event-related fMRI data for designs with various first-level efficiencies and analysed these with different filtering strategies. The resulting data were evaluated in terms of first-and second-level efficiency and the ratio between intra-subject and inter-subject variance. Abstract Background noise in MEG/EEG-measurements is correlated both in space and in time. Incorporating the spatial covariance into the localization of equivalent current dipole sources improves in general the accuracy of the estimated source parameters. Models for this spatial covariance have been developed [1]. Recently it has been shown that also the temporal covariance yields an improvement of the parameters when this covariance is taken into account in the source localization [2,3]. In these recent approaches the spatiotemporal covariance matrix was modeled as a Kronecker Product of a spatial and a temporal covariance matrix in order to reduce its dimensionality. Furthermore the two matrices are estimated in an iterative Maximum Likelihood (ML) procedure. When the number of time samples is larger than say T=500, this ML-estimation is too time consuming to be useful on a routine basis (typically 46 hours for 151 channels, 1000 time samples and 500 trials on a P3-800MHz). For that reason we studied several temporal covariance matrices of different kinds of MEG-data of different subjects to see if, similar to the spatial covariance, further parameterization beyond the Kronecker product is possible. The temporal covariance vanishes for large time lag. Moreover it shows a clear alpha oscillation, which gives rise to separating the temporal background noise into two components: alpha activity and remaining random noise. The alpha activity is modeled as randomly occurring waves with random phase in each trial and the covariance of the random noise is modeled as exponentially decreasing with lag. This model requires only six parameters (three non-linear) instead of T(T+1)/2. Theoretically, this model is stationary but in practice the stationarity of the matrix is hampered by the baseline correction (BC). This effect is illustrated in figure 1 : when the average alpha activity over the BC-window is not zero, the correction introduces a vertical shift in the signals. This yields an extra variance that varies over time (i.e. non-stationarity). To obtain a stationary structure the length of the BC-window should equal a multiple of alpha periods. Abstract In general the inverse problem (IP) in MEG is ill posed: extra constraints are necessary to solve and stabilize the IP. One way of solving this problem is to make extra assumptions in order to reduce the dimensionality of the parameter space. Another way is adding more data sets and assuming some parameters to have fixed values in all data sets. This second idea leads to an integrated model, in which multiple data sets are investigated simultaneously. Abstract Segmentation of brain/non-brain tissue is traditionally one of the more time-consuming preprocessing steps performed in neuroimaging laboratories. Several brain extraction algorithms (BEAs) have been developed recently to perform this step automatically. While automated BEAs speed up overall image processing, their output can greatly affect the results of image analysis. We therefore compared the performance of three BEAs against manual brain extraction using a high-resolution set of T1-weighted MRI brain volumes. Methods Sixteen T1-weighted MRI scans of normal subjects were acquired during an fMRI static force experiment [1]; voxel dimensions were 0.86 x 0.86 x 1mm. Three algorithms for brain/non-brain segmentation were evaluated: (i) Brain Surface Extractor ( BSE), v. 2.99.8 [2], (ii) Brain Extraction Tool (BET), v. 1.2 [3], and (iii) Minneapolis Consensus Strip (MCS) [4] . Manual brain extraction was performed by one of the authors (KR). BSE and BET are software packages with parameters that may be adjusted by the user; for each algorithm parameters were tuned on two training volumes, and the set resulting in the "best" strip (removal of skull, CSF and dura with preservation of brain tissue) was applied to all 16 brain volumes. In order to perform adequately, BSE required manual cropping of the brain with a bounding cube. MCS was initialized with a warp mask and incorporated both intensity thresholding and BSE. MCS masks were created in a separate experiment and were optimized for the entire 16-volume dataset. The following performance metrics were calculated: (i) processing time and (ii) number of misclassified voxels relative to the manually-stripped "gold standard." In order to assess the influence of edge effects on the misclassification metrics the manual mask was dilated and eroded by 1 (thin) and 2 (thick) voxels. Results and Conclusions The average time required to process a single brain volume was 1 minute for BSE (exclusive of manual cropping), 40 seconds for BET, and 75 minutes for MCS on a 500 MHz Linux workstation. The performance of each algorithm with respect to the gold standard is summarized in Table 1 . "Missed" voxels are voxels classified as brain by the manual strip and non-brain by the candidate algorithm, whereas "extra" voxels are voxels classified as non-brain by the manual strip and brain by the candidate algorithm. Misclassified voxels are expressed as a percentage of total brain voxels. One volume that could not be satisfactorily stripped by any of the BEAs was excluded from the averages reported in Table 1 . MCS, though slower, consistently outperformed BSE and BET (see Table 1 and Figure 1 ). In the future, we will develop additional metrics, including the effect of masking on subsequent data analysis and will extend our evaluation to include additional algorithms. Abstract The analysis of imaging data, from PET or functional MRI, involves the detection of which areas of the brain activate during a cognitive task of interest. In addition, it is possible through various techniques, such as functional and effective connectivity, to calculate the interaction between two regions involved in a cognitive task. The computation of the functional interactions through correlation coefficients (functional connectivity) or through linear regression (effective connectivity) are based on an average over many subjects (in the case of positron emission tomography data) or an average over a run (as can be the case with functional MRI data sets). A new method is developed using Kalman filters to quantify the interaction between regions of a neural network with functional MRI data. The Kalman filter is a recursive process where new information (such as a data point from an functional MRI time series) is added to estimate the linear interaction between two regions. The Kalman filter allows one to calculate the linear interaction between two regions at each time point in the functional MRI time series or as an average value over the entire time series. The filter is modified with a diffuse filter to optimize the estimates of the linear relationship at the beginning of the time series. In addition, the filter is modified with a smoothing step, so that information from later periods of the time series can be used in quantifying the linear relationship. The Kalman filter is embedded within a maximum likelihood estimator to optimize the variance estimates of the linear relationship. The method is demonstrated through (a) simulation studies and (b) a task using attention to modulate the interaction between regions. Supported by the Volkswagen Stiftung. Abstract Objectives: Elucidating the nature of dynamic interactions between brain regions requires the accurate description of functional correlations in brain imaging data [1]. Two steps are required: 1. A suitable method of quantifying correlations in spatio-temporal data, and 2. A technique of estimating the distribution of such correlations under the null hypothesis that they reflect only trivial (stochastic) correlations in the data [2]. The aim of the present study was to extend existing wavelet-based methods [3] in order to achieve this objective. Method Both steps require multi-scale transformation of the (four-dimensional) data into the wavelet domain. For the first step a 'coarse-grained representation' [4] of the data is obtained. Correlations between different spatial locations within the same, or across different scales, are calculated by integrating the inner product of their coarse-grained fields with respect to time. The second step repeats this procedure on 'surrogate data' obtained by extending wavelet resampling techniques [3] from the temporal to the spatio-temporal domain with constraints so that 1. Only intracranial data are permuted, 2. Spatio-temporal correlations between planar slices are preserved, and 3. Spatial scales influenced predominantly by extracranial artefact can be excluded. A non-parametric test allows identification of statistically significant correlations in the experimental data. The method is demonstrated in standard IEEE test images and then applied to motion-corrected fMRI data collected from 8 healthy subjects viewing block-design checkerboard stimuli [5]. Results Step 1: Strong positive correlations between right and left extrastriate visual cortex were observed to occur within the same scale and across scales. Strong negative correlations between signal fluctuations on different scales were also observed. Strong correlations were maximum with zero time-difference. Step 2: Fig. 1 presents a standard IEEE test image (panel a), surrogate data constructed by resampling only the finer scales of this image (panel b), only the coarse-grained scales (panel c) and all scales, but constrained to a central ellipse (panel d). Spectral analysis revealed that each surrogate image contained the same correlations as the original data. Extension to multiple images and irregular intracranial domains was also achieved. When applied to fMRI data, this allowed identification of which of the correlations calculated in step 1 were statistically significant. This method permits identification of functional correlations in fMRI data occurring between sites at the same, or different scales, with or without time delays, and with linear or nonlinear structure. Modification of the technique (e.g. Spatial integration of the inner product, decomposition of the correlation matrix into symmetric and anti-symmetric parts) permits analysis of information 'flow' across scales and between brain regions. The method may be superior to existing techniques in that the data, rather than the design of the scanner, 'chooses' the spatial scale at which the analysis is optimized. Abstract Event related (ER) designs have become standard in recent FMRI studies. Although they have major benefits in terms of the range of phenomena that can be studied, they can be complicated to analyze and interpret. Recent theoretical studies have addressed power in ER studies, and have suggested that frequent events with random inter-stimulus intervals (ISIs) give high power. However, these simulations have required several assumptions, including independence between events. Therefore we have attempted to replicate the results of these simulations in a real FMRI study. Methods 6 Subjects were scanned on a 3T Bruker scanner at the Wolfson Brain Imaging Centre in Cambridge, using a standard EPI protocol, collecting 16 slices with a 2 second TR. Events were flashes of a visual checkerboard lasting 0.5 sec, to which the subject had to respond with a button press with the right hand. ISIs were generated from an exponential distribution with a minimum of 0.6 sec, and means of 1, 2, 3, 4, 6, 8 and 10 seconds, to give 7 different stimulus sets. Subjects were scanned in each of the 7 ISI conditions, for 280 seconds per session. The order of the ISI conditions was randomized across subjects. We used SPM99 to correct the images for slice time offset, realign to the first image in the series, and smooth to 8mm FWHM. In order to identify the visual cortex we performed a standard SPM analysis of the mean ISI=3 session (60s high pass filter, haemodynamic response function (HRF) low-pass filter). We used HRF and temporal derivative (TD) convolution for event modeling, and an F test on both parameters to test for task effects. From this analysis we selected the maximum activated cluster in the occipital cortex with voxels at p<0.0001 uncorrected. The region thus identified was used to extract mean time courses in the other ISI sessions. We analyzed these time courses with a similar model, but without low-pass filtering, using the MarsBar SPM toolbox. Results To study the effect of different ISIs, we analyzed the model parameters for the HRF and TD regressors. We also calculated the F statistic for the two regressors, the t statistic for the HRF only, and the root mean squared residual error (RMSE). There was a significant linear trend for the estimated HRF parameter to increase with longer ISI, reflecting apparent greater effect size with longer ISI (p<0.001); the effect size for ISI=1 was 60% of that for effect size of ISI=10. The parameter for the temporal derivative showed a complex effect of ISI (p<0.05), which was mainly quadratic, with higher values for intermediate ISIs. As for the simulations, there was a trend for F and t statistics to decrease linearly with increasing ISI. The RMSE increased linearly with increasing ISI. Conclusion These results suggest that very short ISIs do result in an increase in detection power, but with a reduction in measured effect size. Error appears to increase with longer ISI. We will discuss the implications of these results for the design and analysis of ER FMRI. Abstract A key point underlying brain mapping is the study of inter-individual variability of various structures after spatial normalization, leading to the development of Statistical Parametric Anatomical Maps (SPAM) [1]. Usually, these maps rely on a 3D coordinate system related to the Talairach atlas. More recently, lineic maps have been proposed to study the variability of the localization of the trace of some cortical sulci in the 3D proportional system [2]. In this abstract, we propose the development of surfacic SPAMs of the sulcus bottoms embedded into a 2D coordinate system based on the cortical surface. We propose also an algorithm providing automatic parcellation of the cortex into surfaces of interest (SOI) related to gyri, in order to extend the standard Volume of Interest (VOI) approaches to morphometric studies. Cortical thickness analysis is the 2D analog of Voxel Based Morphometry on tissue density maps. It relies on a one-to-one mapping between individual cortical surfaces and a spherical coordinate system [3,4]. Various mapping strategies can be devised. In the following, each brain is first 3D normalized using the MNI 305 average template. Then the grey/white interface is extracted via the iterative deformations of a spherical mesh, which provides the mapping [4]. For each brain, the main cortical sulci are automatically extracted and recognized using a set of processing tools freely available on "http://anatomist.info" [5]. Then, the sulcus bottom lines are topologically defined and projected onto the spherical mesh [6]. The subjects processed were 151 unselected normal volunteers used to compute the MNI template. The surfacic SPAMs of the localization of the bottom lines were computed sulcus-by-sulcus and mapped on the average cortical mesh [4] (see Fig.left ). These SPAMs provide the remaining inter-individual variability after the spherical mapping. Therefore, their dispersion could be used to drive some improvements of the spherical mapping algorithms. Moreover they can be compared across populations or hemispheres. For instance, the dispersion of the superior temporal sulcus is more important in the left hemisphere, which may be a clue to higher variability of the left sulcus related to the development of language areas. Once the sulcus bottom lines have been projected, they can be used to define gyral based SOIs, using the computation of Voronoi diagrams, which stem from successive dilations of a set of seeds aiming at filling a domain. A first diagram is computed for the projected sulcal lines, which parcellates the cortical surface into sulcal areas. Some boundaries of this first diagram are used as gyral seeds for the computation of a second diagram related to the standard anatomical gyri (see Fig.right ). The gyral parcellations can be used for morphometric studies of the gyral areas or more complex descriptors. Gyral SPAMs could also be computed for some applications. Abstract Constrained Principal Component Analysis (CPCA) is introduced as a correlation-based method of identifying (a) connectivity between neuronal structures and (b) functional interactions between neuronal systems. As for typical principal component analyses (PCA), this method derives eigenimages from singular-value decomposition of voxel-level correlation matrices of brain activations. However, in contrast with typical PCA methods, CPCA allows the separate analysis of portions of the overall variance, defined by contrasts of interest. In the present analysis, we employed CPCA to identify the brain systems involved in general and load-dependent working memory operations. We employed a visually presented digit-working-memory task with encoding, maintenance, and retrieval epochs under four different load conditions. Three separate analyses were conducted, in which the variance submitted to CPCA was constrained to the activation elicited by encoding and maintenance, retrieval and maintenance, and all three classes of operations, respectively. Across analyses, we identified a load-dependent occipital/parietal/premotor system active during encoding, but not during maintenance and retrieval. In addition, the results showed that parietal activity was negatively correlated with occipital activity. During maintenance and retrieval, but not during encoding, a superior parietal/anterior cingulate system was activated and again negatively correlated with occipital activity. These findings are consistent with the results of conventional image analysis (SPM99), and they demonstrate that CPCA is a robust method for the examination of the connectivity within, and the interactions between, neuronal systems. Abstract Introduction Independent component analysis (ICA) has been shown to be useful for characterizing data sets for which a specific a priori model is not available [1]. A limitation of ICA of fMRI models is that a given component's associated time course is required to be identical (except in magnitude) for each and every voxel in the brain. Considerable variability of hemodynamic delays has been observed across different brain locations [2]. Such observations can only be captured by a model which allows for spatially varying delays. If they are instead modeled with a standard ICA approach, a large delay can result in regions being artifactually split into different components. We previously proposed a straightforward but effective approach for incorporating delays into an ICA model by performing the analysis in the frequency domain, on the power spectrum and we now extend this approach for use on multiple subjects [3]. Method Using a Philips NT 1.5 T scanner, BOLD scans were acquired (EPI, TR=1s, TE=39ms, fov=24cm, 64×64, st=5.5 mm, 18 slices) on eight subjects over a 3-min, 40-sec period. We designed a paradigm containing two identical, periodic, visual stimuli, shifted by 25 seconds from one another. This paradigm, when analyzed with standard ICA, separates into two different task-related components (one in left visual cortex, one in right visual cortex) [3]. The images were imported into SPM99 and normalized into a Tailarach template [4,5]. The power spectrum was calculated for the time course of each subjects' data, followed by single subject principle component analysis, then a second level group PCA, and (spatial) ICA estimation. Figure 1 shows two fMRI time courses from a single subject, one taken from the left visual cortex, one from the right visual cortex, with vastly different delays, but otherwise quite similar. Using our approach, a single ICA component captures both hemispheres into the same component. The latencies of these regions are then estimated [6] relative to the component voxel with the largest amplitude. The estimated latencies are depicted in Figure 2 . Results Discussion We demonstrate an approach for applying ICA to group fMRI data such that the delay of the hemodynamic response does not affect the separability of the sources. Voxels with time courses that differ in latency but are similar in other aspects will thus be grouped into the same component. This is potentially important for fMRI data analysis, because spatially varying delays have been observed. Such a model is also important to prevent artifactual separation of a source into multiple components due to the delay. Our model provides a computationally efficient approach for sub-TR latency estimation which involves only a fast Fourier transform, followed by standard group ICA unmixing, and then latency estimation. Abstract Order of appearance: 789 Abstract Background Magnetic resonance may provide image series describing cerebral functionalities correlated with external stimuli[1]. In particular brain activation is observed as a localized signal enhancement corresponding to a local increase in blood oxygenation [2]. The reliability of this signal variation may be assessed by means of different statistical approaches. By instance, SPM99 [3] performs voxel-based hypothesis tests where statistical parametric maps are image processes with voxel values that are, under the null hypothesis, distributed according to a gaussian density function. Here we apply two different statistical approaches to the analysis of functional volumes. The first method involves thresholding over correlation coefficients of the data with respect to a reference waveform followed by formation of a cross-correlation image [4]. A second even easier approach tests the independence of different voxel populations by means of a rank comparison approach where the computed test statistic is assessed according to the Spearman distribution [5]. Motivations We perform two simple fMRI experiments to validate the effectiveness of a software tool performing the analysis of dynamical scans by means of cross-correlation and rank comparison approaches. In the first experiment a tapping task is alternated with a rest period while in the second experiment the subjects are presented visual short meaningful words alternated with a uniformly grey control. Experimentsm The experiments are performed in the 1.5 T Philips Gyroscan at the Dipartimento di Fisiopatologia Clinica, Universita di Firenze. In the tapping experiment a rest epoch of 24 seconds is followed by a 24 seconds epoch of one-hand tapping task. This period is repeated six times; 48 functional volumes with 20 slices each (2x2x4.96 mm per voxel, 1 mm gap) are registered together with a high-resolution structural volume of 40 slices for each subject (1x1x2.96 mm per voxel, no gap). In the visual stimuli experiment, the subjects are presented patterns of stylized meaningful italian words according to an experimental paradigm based on four periods of two epochs each (control-word). The acquisition time for each volume is again 6 seconds and 4 volumes are acquired for each epoch; 32 dynamical Abstracts presented at the 9th International Conference on Functional Mapping of the Human Brain, June 19OE22, 2003 volumes of 20 slices are scanned together with a full anatomical volume. For both experiments we used eight collaborative italian speaking healthy subjects. Analysis And Results The functional volumes are analyzed by means of our software package and the activated clusters are neuroanatomically recognized in order to compare our results with the ones described in the vast tapping and reading tasks literature. A final assessment is performed by analyzing the same series by using a standard SPM99 approach. Abstract Introduction. In this study, a warped MRI-constructed brain template is used to localize cortical sites underlying (digitized) scalp points in subjects not having MR head images. Warping transformation of brain template is computed using 69 fiducial points from International 10-10 System, to preserve relationship between scalp point and underlying cortical structures and areas. Methods. About 50 scalp points and 4 skull landmarks (Nasion, Inion, left and right preauricolar points) digitized in the subject's head are used to determine 69 fiducial scalp points, distributed on a virtual reconstruction of the subject's scalp (spherical Spline interpolation), considering percentages distances between skull landmarks (S.I. 10-10) by means of an automatic procedure [1]. Analogously, 69 fiducial scalp points are computed on 21 MRI-construted scalp templates (including an "averaged" ones reconstructed using a set of MRIs of 152 subjects from Montréal Neurological Institute) and perpendicularly projected on the corresponding brain template, which is coded into the Talairach space. Most similar scalp template respect to subject's scalp is individuated on the basis of templates and subject fiducial data. Then, the most similar scalp template is warped to fit subject's scalp on the basis of fiducial data. The warping transformation is applied on the corresponding brain template. Finally, The warped brain template is used as reference to localize cortical structures and areas underlying digitized scalp point. The accuracy of the method was tested using individual 21 realistic MRI-constructed head models as gold standard. Talairach coordinates of scalp points projected into the realistic brain model were confronted with analogous coordinates obtained using warped brain template. Results and Conclusions. The mean difference between Talairach's coordinates computed into the realistic brain model and into the warped brain template model was less than 4,26 cm (SD ± 1,42 cm) in all subjects evaluated (Fig. 1) . The proposed method can be used in TMS studies to localize the stimulated cortical sites, and in high resolution EEG and MEG studies to supply tentative localization into Talairach space of source solutions obtained in subjects not having realistic MRI-constructed head models. Abstract INTRODUCTION With linear regression models of fMRI time series (e.g., SPM99), the asymptotic theory has been well studied. An asymptotic theory for convergence of Z-maps, which are commonly used in ICA of fMRI data, is unknown. Information about the rate of convergence of Z-maps is important. Convergence rates will help to answer questions such as, "How many time points will give a reasonable estimate of the true Z-map?" In this study, we empirically assess the convergence of Z-maps by investigating the effect of the number of trials in a simple motor paradigm on the Z-map error. MATERIALS AND METHODS The scan parameters: 1.5T GE Signa LX; standard head coil, single-shot gradient-recalled EPI pulse sequence for BOLD contrast; TR/TE 2000/40 ms; FOV 24cm; 64x64 pixel matrix; 22 coronal slices 6mm/1mm gap (whole brain coverage). During the fMRI experiment a healthy subject performed 15 bilateral finger tapping blocks. Each block consisted of 20s rest followed by 20s of tapping. The images were corrected for motion with SPM99 (WDCN). The data were then analyzed with spatial ICA (Bell and Sejnowski) a total of 15 times. The first analysis included only data from the first block. The second analysis included the first and second blocks, and so on up to the last analysis, which included all 15 blocks. In each analysis, the task component was identified and converted to a Z-map. Since it is impossible to know the true Z-map for this task, we assumed that the Z-map computed from 15 blocks represents the "truth." Then, we defined an error measure $E_i = \sum_{j=1}^v (Z_{i,j} -Z_{15,j})^2 / \sum_{j=1}^v Z_{15,j}^2$, where i indexes the Z-map with i blocks and j indexes the voxels in the Z-map, to compute the error as a function of the number of motor blocks included in the analysis. RESULTS An independent component corresponding to the bilateral finger tapping was identified in all 15 analyses. Even with just one block, a motor map was identified. The error decreases quite rapidly with an increasing number of blocks (Fig. 1A ). The red line shows a plot of 1/n (Fig. 1A) . The error follows this trend closely. Z-maps for 1, 7, and 15 blocks illustrate the rapid convergence of the maps (Fig. 1B) . DISCUSSION We demonstrate that error in Z-map estimates decreases rapidly (possibly 1/n) for a block paradigm. The errors appear to slightly differ from 1/n in a systematic way-possibly due to assuming that there is no error in Z_15, thereby forcing our error measure to zero for 15 blocks. One implication of our results is that a large number of blocks may not be necessary to obtain a good estimate of the true Z-map. We are currently repeating this analysis for random event-related and single trial designs. Preliminary results from a analogous study of an event-related motor paradigm suggest that 1/n does not fit the empirical data as well as the block data. A mathematical investigation of the asymptotics of Z-map estimates is a next logical step. Abstract Synopsis: A statistical model is used for the analysis of trial dependencies within auditory event-related fMRI experiments. A trial-dependent proportionality factor prevents underestimation of hemodynamic responses (HDR) that can occur with trial averaging. Analysis strongly suggests that dependencies exist between intra-run trials and that this is a result of a saturation effect. Abstract Order of appearance: 794 Abstract [Background] Cerebral coherence depicts association in (1) anatomic fibre tracks, (2) metabolic activation, and/or (3) electrophysilogical synchronisation of functional connectivity. Coherence per se calculates association strength of brain activities, such as magnitude and phase of EEG, between different sites. Hence, coherence measure is fundamental in unveiling the "binding problem" of perception (i.e. how various brain systems are in functional cooperating into genesis of conscious awareness). Traditional FFT calculates discrete EEG band activities within a time period, while recent use of wavelet analysis yields sontineuous activities over time. Van Doonik (ref.) has refined the wavelet method to calculate and display the instaneous coherence on a real-time basis. In this study, we reported the spatial characteriscs of coherence dynamics during and between "eyes-closed, eyes-open" conditions. [Methods] Subjects were 10 healthy right-handed males (age range 22-45 years, mean 29.5 years) and written informed consent was obtained from all subjects. Statistical analysis by Two-Way (stimulated hand by recording site) repeated measure ANOVA was conducted. Post hoc comparison of means by Tukey test was set at P<.05 for significance. [Results] Fig.1 demonstrates the special patterns of time-frequency coherence within and between "eyes-closed, eyes-open" conditions in a representative subject with high EEG power. Obviously, majority of high coherence (r>0.75, yellow intensity, p<.01) is distributed between 10-15 Hz. Not surprisingly, the posterior coherence activities were much more dominant than those in the anterior brain areas. However, two surpring findings stood out: (a) much higher coherence in the centro-parietal area than that in the occipital area, and (b) predominant right hemispheric centro-parietal coherence that that in the left hemisphere. These patterns are consistently observed across the 13 subjects (Wilcoxon tests, p<.05) in the group as a whole. Comparing the eye-closed and eyes-open conditions, no different in the occipital coherence was noted though great reduction in alpha band amplitude occurred (not shown). Given the steady-state, little time varying effect was observed in these resting states. Abstract [Background] We have advocated the use of 3D virtual reality Talairach modeling (VRTM) for topographic mapping of EEG/ERP activation and tomographic registration of PET/fMRI responses based on Talairach coordinates as a common anatomical standard. This 3D VRTM can facilitate visualization of (a) brain activation and (b) data organization for statistical evaluation. In this report, we carried out a systematic evaluation on the brain activation patterns between somatosensory perception of cold vs. heat as well as cold-pain vs. heat-pain. This work illustrates the use of this 3D Brain Model in examination of brain loci and organization associated with human pain. [Methods] We compiled activated loci from published PET and fMRI studies of heat and heat-pain (N= 18 reports, Ss= 163 subjects) vs. cold and cold-pain (N=7 reports, Ss= 83 subjects), systematically retrieved from Medline Index Medicus (Jan. 1991 to Dec. 2002 . This covered the first decade since the neuroimaging of human pain in the brain. This study was carried out not for stringent meta-analysis, but toward a 3D visualisation and data characterisation of their differential brain activations. Thus, it was inclusive than exclusively fully on "double-blind, placebo-controlled" experiments. A-priori defined ROI set included the major brain matrix in somatosensory and pain perception: Brain-Stem, SI, SII, Thalamus, Insular, Cingulate, Hippocampus/Amygdala, Temporal Lobe, Parietal Lobe, and Prefontal Lobe. Only reports with Talairach coordinates specified in these brain sites were included in the analyses. Statistics were carried out to (1) examine the magnitude % of activation in each ROI across the literature, (2) isolate the outliers, (3) characterize the central norms in and size/shape in each ROI, (4) compare the parameters between cold vs. heat as well as cold-pain vs. heat pain, and finally (5) identify brain regions of pain activation common to cold and heat modalities. For illustration, the site of activation (increased voxals only) for cold is denoted in blue: round as cold, diamond as cold-pain; head in red: round as heat, diamond as heat-pain for 4 conditions. [Results] As shown in Fig. 1 , w observed: (a) only less than 30% of reports showed non-painful cold and heat activation in brain areas, (b) above 60% of reports showed activation in thalamus, insular, cingulated, and frontal cortex under painful conditions, (c) SI and SII in about 40% of reports, and (d) brain-stem, hippocampus, and amygdala accounted to about 10% of reports under painful conditions. The major difference between cold-pain and heat-pain was in the thalamus, cingular and frontal cortex by the % of reports. For the Talairach coordinates at each condition/site, some outliers at the values of 2.5 s.d. was noted and these values were excluded from further Abstract When statistical maps are generated from functional brain images, it is important to address the possibility of Type 1 errors related to the number of regional comparisons in the brain volume of interest. Several methods have been proposed to address the problem of multiple comparisons such as those based on random field theory , the false discovery rates , and the non-parametric statistical mappings (Holmes et al, 1996) . Whereas these and other methods typically examine statistical maps in one direction (e.g., state-dependent increases in regional cerebral blood flow (CBF) or brain oxygenation or disease-dependent decreases in regional glucose metabolism), they do not simultaneously take into account information in both directions (e.g., both state-dependent increases and decreases in these measurements). We propose a strategy that addresses Type 1 errors in the direction of interest (e.g., state-dependent increases in regional blood flow), capitalizing on the absence of significant differences in the opposite direction. A computer Monte-Carlo software package which is based on SPM99 and is recently developed in our laboratory is used in this study. This package can calculate different kinds of set-level Type 1 errors, such as that associated with n statistical clusters, survived a magnitude threshold, whose sizes are not smaller than a spatial extent (Friston, et al., NeuroImage, 1995). In the case proposed here, this software incorporates information in the opposite direction (e.g., the absence of state-dependent decreases in regional CBF) to calculate the likelihood that the set of statistical clusters in the direction of interest (e.g., state-dependent increases in regional CBF) is attributable to a Type 1 error. To illustrate this strategy for cases that lack significant differences in the opposite direction, we compared it to the SPM99 multiple comparison correction method based on random field theory (at both the set-level and the voxel-level), when PET measurements from 7 healthy volunteers were used to characterize the changes in regional CBF associated with hand movement, which we have consistently found to be associated with CBF increases in the supplementary motor area, the left sensorimotor cortex and thalamus, and the right cerebellum. Searching the entire brain volume, SPM99 detected significant CBF increases (P<0.05 at both the voxel-level and the set-level) in left sensorimotor cortex, the supplementary area, and the right cerebellar vermis, but were unable to detect increases in the thalamus. When the Monte-Carlo simulation incorporated information about regional CBF decreases to correct for multiple comparisons in the map of CBF increases, we were able to detect significant CBF increases in all of expected regions, including the thalamus (p<0.05). Capitalizing on the empirically determined absence of significant differences in the opposite direction, the strategy proposed here may improve the power to detect changes in the direction of interest, while addressing the statistical problem of multiple comparisons in brain mapping studies. Abstract Traditionally, functional brain imaging data is analyzed by projecting activation data from a sequence of slices onto a standardized 3-dimensional anatomical space. However, the cerebral cortex is better modeled by a 2-dimensional sheet that is highly folded and curved. As such a 3D space may underestimate the "neural" distance between two points, particularly if the points lie on opposite sides of a sulcus. This anomaly has lead to the use of computer-based tools that create 2D cortical surfaces, which can be inflated, flattened, and overlaid with functional activation data. This review provides a discussion of two freely available cortical surface modeling packages that have gained wide use in the field of neuroimaging: FreeSurfer [1, 2] and SureFit [3]. Although in-depth descriptions of these tools have been provided by their respective authors, there has been to date no systematic qualitative or quantitative comparison between these tools. We provide here a qualitative comparison of these two packages. Methods The evaluation of FreeSurfer (October 2001 release) and SureFit (version 4.38, 2002) was based on a number of qualitative criteria, including ease of installation, difficulty of the manual editing procedure, quality of the documentation and tutorials that accompany each software package, graphical user interfaces, and overall ease of use. Results Installation of SureFit proceeded more smoothly than FreeSurfer, which required considerable alterations to the user environment. In terms of the processing sequence, FreeSurfer had the added advantage of a command line option that made the processing of volumes to create surfaces fairly automated and streamlined, eliminating constant user intervention and supervision. SureFit was usable only through the graphical user interface (GUI). We found the volume and surface GUIs within FreeSurfer to be less flexible and user-friendly than those within SureFit. However, the weakest element of SureFit was the cumbersome manual editing tools. Finally, while FreeSurfer provided tools to cut and flatten an inflated surface, these operations could not be performed from within SureFit and a sister program, known as Caret, had to be used. Conclusions Despite sharing similar underlying principles, the packages discussed here differ widely in their GUIs, editing tools, and general ease of use. Although SureFit received better marks for its GUIs and easy installation, FreeSurfer had far superior editing tools, a convenient command line option, and excellent documentation, giving it a higher rating overall. Nonetheless, it is up to the user to consider our comments to determine which package would be better suited to their particular application. Future work on this project will include a method to make quantitative comparisons between surfaces obtained from different surface modeling packages. Abstract Event related fMRI experiments involving finger tapping and alternating checker-boards indicate that inter-stimulus intervals (ISI) as short as 2-4 seconds result in approximately linear summation of BOLD responses. However, with more complex stimuli (faces), nonlinearities in response summation have been found even at ISI of 6s. Huettel et. al. showed that the second face in a pair of identical faces presented 6s apart elicited a BOLD signal that was only about 70% of the magnitude obtained from individual faces presented at ISI of 18s, and the extent of this "recovery" varied across cortical regions [1]. This additional variance in signal amplitude introduced by "response refractoriness" could theoretically compromise the discrimination of response magnitude between different experimental tasks. Moreover, the use of identical faces could result in repetition or adaptation (fMR-A) effects that could compound the intrinsic refractory effects. In this study, we presented similar and different faces at 3s and 6s to dissociate the relative contributions of fMR-A and refractory effects to signal attenuation in different cortical regions.
doi:10.1016/s1053-8119(05)70006-9 fatcat:zff2suxcofbxvetfrwfwcxi3zm