Topological FDR for neuroimaging.

In this technical note, we describe and validate a topological false discovery rate (FDR) procedure for statistical parametric mapping. This procedure is designed to deal with signal that is continuous and has, in principle, unbounded spatial support. We therefore infer on topological features of the signal, such as the existence of local maxima or peaks above some threshold. Using results from random field theory, we assign a p-value to each maximum in an SPM and identify an adaptive threshold that controls false discovery rate, using the Benjamini and Hochberg (BH) procedure (1995). This provides a natural complement to conventional family wise error (FWE) control on local maxima. We use simulations to contrast these procedures; both in terms of their relative number of discoveries and their spatial accuracy (via the distribution of the Euclidian distance between true and discovered activations). We also assessed two other procedures: cluster-wise and voxel-wise FDR procedures. Our results suggest that (a) FDR control of maxima or peaks is more sensitive than FWE control of peaks with minimal cost in terms of false-positives, (b) voxel-wise FDR is substantially less accurate than topological FWE or FDR control. Finally, we present an illustrative application using an fMRI study of visual attention.

Longitudinal and cross-sectional analysis of atrophy in pharmacoresistant temporal lobe epilepsy.

BACKGROUND: Whether recurrent epileptic seizures induce brain damage is debated. Disease progression in epilepsy has been evaluated only in a few community-based studies involving patients with seizures well controlled by medication. These studies concluded that epilepsy does not inevitably lead to global cerebral damage. OBJECTIVE: To track the progression of neocortical atrophy in pharmacoresistant temporal lobe epilepsy (TLE) using longitudinal and cross-sectional designs. METHODS: Using a fully automated measure of cortical thickness on MRI, we studied a homogeneous sample of patients with pharmacoresistant TLE. In the longitudinal analysis (n = 18), fixed-effect models were used to quantify cortical atrophy over a mean interscan interval of 2.5 years (range = 7 to 90 months). In the cross-sectional analysis (n = 121), we correlated epilepsy duration and thickness. To dissociate normal aging from pathologic progression, we compared aging effects in TLE to healthy controls. RESULTS: The longitudinal analysis mapped progression in ipsilateral temporopolar and central and contralateral orbitofrontal, insular, and angular regions. In patients with more than 14 years of disease, atrophy progressed more rapidly in frontocentral and parietal regions that in those with shorter duration. The cross-sectional study showed progressive atrophy in the mesial and superolateral frontal, and parietal cortices. CONCLUSIONS: Our combined cross-sectional and longitudinal analysis in patients with pharmacoresistant temporal lobe epilepsy demonstrated progressive neocortical atrophy over a mean interval of 2.5 years that is distinct from normal aging, likely representing seizure-induced damage. The cumulative character of atrophy underlies the importance of early surgical treatment in this group of patients.

Episodic memory impairment in patients with Alzheimer's disease is correlated with entorhinal cortex atrophy. A voxel-based morphometry study.

The aims of this study were to investigate the pattern of cortical atrophy and the relationships between memory performances and the brain regions in Alzheimer's Disease (AD). optimized voxel-based morphometry (VBM) was applied to the MRI brain images of 18 probable AD and 18 healthy subjects (HS). Patients performed verbal and visuo-spatial episodic and shortterm memory tests. Contrasting of AD group with HS, and anatomobehavioural correlations were carried out in order to identify regional atrophic changes and neuro-cognitive aspects in AD group. We found evidence of gray matter (GM) volume reduction in AD in the medial temporal, parietal and frontal areas bilaterally and in the left anterior thalamic nuclei. Performance on the episodic memory delayed recall tests co-varied with GM volume in the left entorhinal cortex. The pattern of cortical atrophy likely reflects the heterogeneous level of dementia severity in our AD group. The anatomical region affected in the left hemisphere indicates a sufferance at multiple levels of the Polysynaptic Hippocampal Pathway, which is involved in declarative memory. Findings on the entorhinal cortex and the delayed memory scores support the role of the entorhinal cortex in episodic memory. Damage to the entorhinal cortex, deafferenting the hippocampus from neocortical inputs, interferes with episodic memory consolidation in AD patients.

Inference for magnitudes and delays of responses in the FIAC data using BRAINSTAT/FMRISTAT.

We used straightforward linear mixed effects models as described in Worsley et al. together with recent advances in smoothing to control the degrees of freedom, and random field theory based on discrete local maxima. This has been implemented in BRAINSTAT, a Python version of FMRISTAT. Our main novelty is voxel-wise inference for both magnitude and delay (latency) of the hemodynamic response. Our analysis appears to be more sensitive than that of Dehaene-Lambertz et al. Our main findings are greater magnitude (1.08% +/- 0.17%) and delay (0.153 +/- 0.035 s) for different sentences compared to same sentences, together with a smaller but still significantly greater magnitude for different speaker compared to same speaker (0.47% +/- 0.08%).

Detecting fMRI activation allowing for unknown latency of the hemodynamic response.

Several authors have suggested allowing for unknown latency of the hemodynamic response by incorporation of hemodynamic derivative terms into the linear model for the statistical analysis of fMRI data. In this paper, we show how to use random field theory to provide a P value for local maxima of two test statistics that have been recently proposed for detecting activation based on this analysis.

An improved theoretical P value for SPMs based on discrete local maxima.

We present a new continuity correction to the P value for local maxima of a statistical parametric map that bridges the gap between small FWHM, when the Bonferroni correction is accurate, and large FWHM, when random field theory is accurate. The new method, based on discrete local maxima, is always an upper bound (like the Bonferroni), but lower and hence more accurate for large FWHM, without increasing false positives. It resulted in P values that were approximately 43% lower than the best of Bonferroni or random field theory methods when applied to a typical fMRI data set.

Spatial smoothing of autocorrelations to control the degrees of freedom in fMRI analysis.

In the statistical analysis of fMRI data, the parameter of primary interest is the effect of a contrast; of secondary interest is its standard error, and of tertiary interest is the standard error of this standard error, or equivalently, the degrees of freedom (df). In a ReML (Restricted Maximum Likelihood) analysis, we show how spatial smoothing of temporal autocorrelations increases the effective df (but not the smoothness of primary or secondary parameter estimates), so that the amount of smoothing can be chosen in advance to achieve a target df, typically 100. This has already been done at the second level of a hierarchical analysis by smoothing the ratio of random to fixed effects variances (Worsley, K.J., Liao, C., Aston, J.A.D., Petre, V., Duncan, G.H., Morales, F., Evans, A.C., 2002. A general statistical analysis for fMRI data. NeuroImage, 15:1-15); we now show how to do it at the first level, by smoothing autocorrelation parameters. The proposed method is extremely fast and it does not require any image processing. It can be used in conjunction with other regularization methods (Gautama, T., Van Hulle, M.M., in press. Optimal spatial regularisation of autocorrelation estimates in fMRI analysis. NeuroImage.) to avoid unnecessary smoothing beyond 100 df. Our results on a typical 6-min, TR = 3, 1.5-T fMRI data set show that 8.5-mm smoothing is needed to achieve 100 df, and this results in roughly a doubling of detected activations.

Functional abnormalities in symptomatic concussed athletes: an fMRI study.

Our aim was to quantify with functional magnetic resonance imaging (fMRI) changes in brain activity in concussed athletes and compare the results with those of normal control subjects. Regional brain activations associated with a working memory task were obtained from a group of concussed athletes (15 symptomatic, 1 asymptomatic) and eight matched control subjects, using blood oxygen level dependent (BOLD) fMRI. The average percent signal change from baseline to working memory condition in each region of interest was computed. Symptomatic concussed athletes demonstrated task-related activations in some but not all the regions of interest, even when they performed as well as the control subjects. Furthermore, several concussed athletes had additional increases in activity outside the regions of interest, not seen in the control group. Quantitative analysis of BOLD signals within regions of interest revealed that, in general, concussed athletes had different BOLD responses compared to the control subjects. The task-related activation pattern of the one symptom-free athlete was comparable to that of the control group. We also repeated the study in one athlete whose symptoms had resolved. On the first study, when he was still symptomatic, less task-related activations were observed. On follow-up, once his symptoms had disappeared, the task-related activations became comparable to those of the control group. These results demonstrate the potential of fMRI, in conjunction with the working memory task, to identify an underlying pathology in symptomatic concussed individuals with normal structural imaging results.

Random field-union intersection tests for EEG/MEG imaging.

Electrophysiological (EEG/MEG) imaging challenges statistics by providing two views of the same spatiotemporal data: topographic and tomographic. Until now, statistical tests for these two situations have developed separately. This work introduces statistical tests for assessing simultaneously the significance of spatiotemporal event-related potential/event-related field (ERP/ERF) components and that of their sources. The test for detecting a component at a given time instant is provided by a Hotelling's T(2) statistic. This statistic is constructed in such a manner to be invariant to any choice of reference and is based upon a generalized version of the average reference transform of the data. As a consequence, the proposed test is a generalization of the well-known Global Field Power statistic. Consideration of tests at all time instants leads to a multiple comparison problem addressed by the use of Random Field Theory (RFT). The Union-Intersection (UI) principle is the basis for testing hypotheses about the topographic and tomographic distributions of such ERP/ERF components. The performance of the method is illustrated with actual EEG recordings obtained from a visual experiment of pattern reversal stimuli.

Detecting activation in fMRI data.

We present a simple approach to the analysis of fMRI data collected from several runs, sessions and subjects. We take advantage of the spatial nature of the data to reduce the noise in certain key parameters, achieving an increase in degrees of freedom for a mixed effects analysis. Our main interest is the analysis of the resulting images of test statistics using the geometry of random fields. We show how the Euler characteristic of the excursion set plays a key role in setting the threshold of the image to detect regions of the brain activated by a stimulus.

Estimating the delay of the fMRI response.

We propose a fast, efficient, general, simple, valid, and robust method of estimating and making inference about the delay of the fMRI response modeled as a temporal shift of the hemodynamic response function (HRF). We estimate the shift unbiasedly using two optimally chosen basis functions for a spectrum of time shifted HRFs. This is done at every voxel, to create an image of estimated delays and their standard deviations. This can be used to compare delays for the same stimulus at different voxels, or for different stimuli at the same voxel. Our method is compared to other alternatives and validated on an fMRI data set from an experiment in pain perception.

Unmyelinated tactile afferents signal touch and project to insular cortex.

There is dual tactile innervation of the human hairy skin: in addition to fast-conducting myelinated afferent fibers, there is a system of slow-conducting unmyelinated (C) afferents that respond to light touch. In a unique patient lacking large myelinated afferents, we found that activation of C tactile (CT) afferents produced a faint sensation of pleasant touch. Functional magnetic resonance imaging (fMRI) analysis during CT stimulation showed activation of the insular region, but not of somatosensory areas S1 and S2. These findings identify CT as a system for limbic touch that may underlie emotional, hormonal and affiliative responses to caress-like, skin-to-skin contact between individuals.

A general statistical analysis for fMRI data.

We propose a method for the statistical analysis of fMRI data that seeks a compromise between efficiency, generality, validity, simplicity, and execution speed. The main differences between this analysis and previous ones are: a simple bias reduction and regularization for voxel-wise autoregressive model parameters; the combination of effects and their estimated standard deviations across different runs/sessions/subjects via a hierarchical random effects analysis using the EM algorithm; overcoming the problem of a small number of runs/session/subjects using a regularized variance ratio to increase the degrees of freedom.

Structural asymmetries in the human brain: a voxel-based statistical analysis of 142 MRI scans.

The use of computational approaches in the analysis of high resolution magnetic resonance images (MRI) of the human brain provides a powerful tool for in vivo studies of brain anatomy. Here, we report results obtained with a voxel-wise statistical analysis of hemispheric asymmetries in regional 'amounts' of gray matter, based on MRI scans obtained in 142 healthy young adults. Firstly, the voxel-wise analysis detected the well-known frontal (right > left) and occipital (left > right) petalias. Secondly, our analysis confirmed the presence of left-greater-than-right asymmetries in several posterior language areas, including the planum temporale and the angular gyrus; no significant asymmetry was detected in the anterior language regions. We also found previously described asymmetries in the cingulate sulcus (right > left) and the caudate nucleus (right > left). Finally, in some brain regions we observed highly significant asymmetries that were not reported before, such as in the anterior insular cortex (right > left). The above asymmetries were observed in men and women. Our results thus provide confirmation of the known structural asymmetries in the human brain as well as new findings that may stimulate further research of hemispheric specialization.

Wisconsin Card Sorting revisited: distinct neural circuits participating in different stages of the task identified by event-related functional magnetic resonance imaging.

The Wisconsin Card Sorting Task (WCST) has been used to assess dysfunction of the prefrontal cortex and basal ganglia. Previous brain imaging studies have focused on identifying activity related to the set-shifting requirement of the WCST. The present study used event-related functional magnetic resonance imaging (fMRI) to study the pattern of activation during four distinct stages in the performance of this task. Eleven subjects were scanned while performing the WCST and a control task involving matching two identical cards. The results demonstrated specific involvement of different prefrontal areas during different stages of task performance. The mid-dorsolateral prefrontal cortex (area 9/46) increased activity while subjects received either positive or negative feedback, that is at the point when the current information must be related to earlier events stored in working memory. This is consistent with the proposed role of the mid-dorsolateral prefrontal cortex in the monitoring of events in working memory. By contrast, a cortical basal ganglia loop involving the mid-ventrolateral prefrontal cortex (area 47/12), caudate nucleus, and mediodorsal thalamus increased activity specifically during the reception of negative feedback, which signals the need for a mental shift to a new response set. The posterior prefrontal cortex response was less specific; increases in activity occurred during both the reception of feedback and the response period, indicating a role in the association of specific actions to stimuli. The putamen exhibited increased activity while matching after negative feedback but not while matching after positive feedback, implying greater involvement during novel than routine actions.

A unified statistical approach to deformation-based morphometry.

We present a unified statistical framework for analyzing temporally varying brain morphology using the 3D displacement vector field from a nonlinear deformation required to register a subject's brain to an atlas brain. The unification comes from a single model for structural change, rather than two separate models, one for displacement and one for volume changes. The displacement velocity field rather than the displacement itself is used to set up a linear model to account for temporal variations. By introducing the rate of the Jacobian change of the deformation, the local volume change at each voxel can be computed and used to measure possible brain tissue growth or loss. We have applied this method to detecting regions of a morphological change in a group of children and adolescents. Using structural magnetic resonance images for 28 children and adolescents taken at different time intervals, we demonstrate how this method works.

Detection of fMRI activation using cortical surface mapping.

A methodology for fMRI data analysis confined to the cortex, Cortical Surface Mapping (CSM), is presented. CSM retains the flexibility of the General Linear Model based estimation, but the procedures involved are adapted to operate on the cortical surface, while avoiding to resort to explicit flattening. The methodology is tested by means of simulations and application to a real fMRI protocol. The results are compared with those obtained with a standard, volume-oriented approach (SPM), and it is shown that CSM leads to local differences in sensitivity, with generally higher sensitivity for CSM in two of the three subjects studied. The discussion provided is focused on the benefits of the introduction of anatomical information in fMRI data analysis, and the relevance of CSM as a step toward this goal.

A statistical method for the analysis of positron emission tomography neuroreceptor ligand data.

A method for voxel by voxel statistical inference of PET radioligand receptor studies is presented. This method is aimed at detecting differences in radioligand binding between baseline and activation scans. It uses nonlinear least squares theory to estimate the ligand-receptor model parameters and utilizes the residuals to calculate their associated variance. The approach both increases the degrees of freedom for statistical testing and produces more accurate estimates of the standard deviation of the parameters. This technique is applicable to any ligand with a validated compartmental model, whether reversibly or irreversibly bound. The method was investigated and compared with a simple voxel-wise t test. Both simulated and real PET data for the dopamine D(1) receptor ligand [(11)C]SCH 23390 were used to assess the method. The assumptions implicit in the residuals methods were validated. The residuals method was found to be more sensitive than a simple t test, while not producing false-positive results. In addition, we showed that this method reliably differentiates changes in radioligand binding from the effects of changes in cerebral blood flow.

Multiple sclerosis: magnetization transfer MR imaging of white matter before lesion appearance on T2-weighted images.

PURPOSE: To determine the evolution of magnetization transfer (MT) in white matter regions before and after plaque development in patients with multiple sclerosis (MS). MATERIALS AND METHODS: In a 5-year longitudinal evaluation, 30 patients with MS underwent conventional magnetic resonance (MR) imaging, MT MR imaging, and clinical assessment. Cross-sectional data in 12 healthy subjects were also collected. Semiautomated lesion classification with use of T2-weighted MR images was used to measure the time course of the MT ratio (calculated with MR data acquired without and with MT saturation) in every voxel and to help analyze the relationship with the status of lesions depicted on T2-weighted images. RESULTS: There was a significant (P <.001) temporal decline in lesion MT ratio after lesion appearance on T2-weighted images. A significant (P <. 001) progressive decline in MT ratio was also present in voxels that later became lesions, prior to initial detection on T2-weighted images. Even 1(1/2) years prior to lesion appearance, the MT ratio (33.3%) in regions destined to become such lesions was significantly (P <.001) lower than that in both white matter in healthy subjects (41.3%) and other normal-appearing white matter in patients with MS (38.1%). CONCLUSION: The MT ratio reveals progressive focal abnormalities in MS that antedate by up to 2 years the appearance of lesions on T2-weighted MR images.

Robust smoothness estimation in statistical parametric maps using standardized residuals from the general linear model.

The assessment of significant activations in functional imaging using voxel-based methods often relies on results derived from the theory of Gaussian random fields. These results solve the multiple comparison problem and assume that the spatial correlation or smoothness of the data is known or can be estimated. End results (i. e., P values associated with local maxima, clusters, or sets of clusters) critically depend on this assessment, which should be as exact and as reliable as possible. In some earlier implementations of statistical parametric mapping (SPM) (SPM94, SPM95) the smoothness was assessed on Gaussianized t-fields (Gt-f) that are not generally free of physiological signal. This technique has two limitations. First, the estimation is not stable (the variance of the estimator being far from negligible) and, second, physiological signal in the Gt-f will bias the estimation. In this paper, we describe an estimation method that overcomes these drawbacks. The new approach involves estimating the smoothness of standardized residual fields which approximates the smoothness of the component fields of the associated t-field. Knowing the smoothness of these component fields is important because it allows one to compute corrected P values for statistical fields other than the t-field or the Gt-f (e.g., the F-map) and eschews bias due to deviation from the null hypothesis. We validate the method on simulated data and demonstrate it using data from a functional MRI study.