Characterization of BOLD-fMRI signal during a verbal fluency paradigm in patients with intracerebral tumors affecting the frontal lobe

Previous studies have indicated that the BOLD-fMRI signal can be modified by tumor processes in close vicinity to functional brain areas. This effect has been investigated primarily for the perirolandic area but there is only a limited number of studies concerning frontal cortical regions. Therefore, the aim of the current study was to characterize BOLD-fMRI signal and activation patterns in patients with frontal brain tumors while performing a verbal fluency task. Six patients (ages 31-56 years) suffering from frontal (5 left sided and 1 right sided) intracerebral tumors were examined with fMRI while performing a verbal fluency task in a blocked paradigm design. Eight healthy volunteers served as the control group. The patients (5 right and 1 left handed) demonstrated left frontal activation which could be clearly located outside the tumor area and adjacent edema with varying degrees of additional right frontal activation. In the predominant left frontal activation cluster, the mean voxel based z-score and cluster size were not statistically different between patients and controls. The present fMRI study is indicating that language related BOLD signal changes in the frontal cortex of patients with tumors close to functional areas were comparable to the signal in normal controls. Additionally, the temporal hemodynamic response characteristic was comparable in both groups. This is an important finding consistent with PET results and corroborates the feasibility of functional mapping approaches in patients with tumors affecting the frontal lobe. Additional studies investigating alterations of the hemodynamic response depending on tumor location and histology are required in order to further elucidate the association between pathophysiology and BOLD fMRI signal.     

Neural mechanisms underlying the facilitation of naming in aphasia using a semantic task: an fMRI study

ABSTRACT: BACKGROUND: Previous attempts to investigate the effects of semantic tasks on picture naming in both healthy controls and people with aphasia have typically been confounded by inclusion of the phonological word form of the target item. As a result, it is difficult to isolate any facilitatory effects of a semantically-focused task to either lexical-semantic or phonological processing. This functional magnetic resonance imaging (fMRI) study examined the neurological mechanisms underlying short-term (within minutes) and long-term (within days) facilitation of naming from a semantic task that did not include the phonological word form, in both participants with aphasia and age-matched controls. RESULTS: Behavioral results showed that a semantic task that did not include the phonological word form can successfully facilitate subsequent picture naming in both healthy controls and individuals with aphasia. The whole brain neuroimaging results for control participants identified a repetition enhancement effect in the short-term, with modulation of activity found in regions that have not traditionally been associated with semantic processing, such as the right lingual gyrus (extending to the precuneus) and the left inferior occipital gyrus (extending to the fusiform gyrus). In contrast, the participants with aphasia showed significant differences in activation over both the short- and the long-term for facilitated items, predominantly within either left hemisphere regions linked to semantic processing or their right hemisphere homologues. CONCLUSIONS: For control participants in this study, the short-lived facilitation effects of a prior semantic task that did not include the phonological word form were primarily driven by object priming and episodic memory mechanisms. However, facilitation effects appeared to engage a predominantly semantic network in participants with aphasia over both the short- and the long-term. The findings of the present study also suggest that right hemisphere involvement may be supportive rather than maladaptive, and that a large distributed perisylvian network in both cerebral hemispheres supports the facilitation of naming in individuals with aphasia.     

Postprocessing of functional MRI data of motor cortex stimulation measured with a standard 1.5 T imager

Functional magnetic resonance imaging (fMRI) is usually based on acquisition of alternating series of images under rest and an activation task (stimulus). Brain activation maps can be generated from fMRI data sets by applying several mathematical methods. Two methods of image postprocessing have been compared: (i) simple difference of mean values between rest and stimulation, and (ii) Student's t-test. The comparison shows that the difference method is very sensitive to arbitrary signal fluctuations as seen mainly in large vessels (e.g., in the sagittal sinus), leading to insignificantly activated spots in brain activation maps. In contrary, Student's t-test maps show strongly reduced sensitivity for fluctuations and have the advantage of giving activation thresholds by setting significance levels. This allows the comparison of activation strength between patient collectives by using a grid overlay technique leading to an observer independent quantification of the stimulation effects. The method was able to reproduce previous findings of activation differences between healthy volunteers and schizophrenic patients. Moreover, a simple algorithm for the correction of slight head movements during the functional imaging task is presented. The algorithm is based on shifting the fMRI data set relative to a reference image by maximizing the linear correlation coefficients. This leads to a further reduction of insignificant brain activation and to an improvement in brain activation map quality.     

Imaging treatment effects in Alzheimer's disease

Alzheimer's disease (AD) is the commonest form of degenerative dementia and is characterised by progressive cognitive decline. Despite extensive research, the cause of AD is unknown and there is no cure at present. Of the deficits found in AD, that affecting the cholinergic neurotransmitter system is the best established and the only one translated into symptomatic treatment. Cholinergic enhancement with cholinesterase inhibitor (ChEI) drugs has been achieved and their efficacy and safety ascertained by conventional clinical trials. The mechanism of action of these drugs, however, is not well understood. Imaging with SPECT, PET, MRI and fMRI after treatment has clarified what happens in the brains of those AD patients treated with ChEI drugs. Studies with these techniques have identified increases in brain blood flow and glucose metabolism, restoration of nicotinic receptor function and re-establishment of task-related regional brain activation in response to cognitive stimulation after treatment. Structural MRI studies have explained, to some degree, why only a proportion of patients benefits from ChEI treatment and there is some evidence that some ChEI drugs might be neuroprotective. There are, however, many unsolved problems. Timing of treatment intervention to obtain maximum response and the determinants of treatment response are mostly unknown. It is also unclear whether administration of treatment in those patients who have no potential for response accelerates disease progression. These issues cannot be solved by conventional clinical trials. Pharmacoimaging studies could assist the development and refinement of drugs to treat those diseases, such as AD, which affect the central nervous system.     

Empirical optimization of ASL data analysis using an ASL data processing toolbox: ASLtbx

Arterial spin labeling (ASL) perfusion fMRI data differ in important respects from the more familiar blood oxygen level-dependent (BOLD) fMRI data and require specific processing strategies. In this paper, we examined several factors that may influence ASL data analysis, including data storage bit resolution, motion correction, preprocessing for cerebral blood flow (CBF) calculations and nuisance covariate modeling. Continuous ASL data were collected at 3 T from 10 subjects while they performed a simple sensorimotor task with an epoch length of 48 s. These data were then analyzed using systematic variations of the factors listed above to identify the approach that yielded optimal signal detection for task activation. Improvements in statistical power were found for use of at least 10 bits for data storage at 3 T. No significant difference was found in motor cortex regarding using simple subtraction or sinc subtraction, but the former presented minor but significantly (P<.024) larger peak t value in visual cortex. While artifactual head motion patterns were observed in synthetic data and background-suppressed ASL data when label/control images were realigned to a common target, independent realignment of label and control images did not yield significant improvements in activation in the sensorimotor data. It was also found that CBF calculations should be performed prior to spatial normalization and that modeling of global fluctuations yielded significantly increased peak t value in motor cortex. The implementation of all ASL data processing approaches is easily accomplished within an open-source toolbox, ASLtbx, and is advocated for most perfusion fMRI data sets.     

Activation mapping as a percentage of local excitation: fMRI stability within scans, between scans and across field strengths

Functional magnetic resonance imaging (fMRI) does not typically yield highly reproducible maps of brain activation. Maps can vary significantly even with constant scanning parameters and consistent task performance conditions (Liu et al., Magn. Reson. Med., 2004, 52:751-760). Reproducibility is even more of a problem when comparing fMRI signal magnitude and spatial extent of activation across scans involving different task performance levels, scan durations, pulse sequences or magnetic field strengths. In this report, the consistency of fMRI was reexamined by considering the relative spatial and temporal distribution of fMRI blood oxygen level dependent (BOLD) activation signals separately from the absolute magnitude of the activation signal in each brain area. Subjects repeatedly performed the same simple motor task but under a variety of imaging conditions, using both spiral and standard echo-planar pulse sequences and at 1.5- and 4.0-T magnetic field strengths. The results demonstrate that the absolute amplitude of BOLD statistical activation signals varied significantly across time and scanning conditions, but the relative spatial pattern of BOLD activation was highly reproducible across all conditions. Analysis of realistic simulated fMRI data sets indicates that stability of relative activation patterns could provide a useful tool for assessing the accuracy of fMRI maps.     

High spatial resolution BOLD fMRI using simultaneous multislice excitation with echo-shifting gradient echo at 7 Tesla

We introduce an accelerated gradient echo (GRE) sequence combining simultaneous multislice excitation (SMS) with echo-shifting technique for high spatial resolution blood oxygen level dependent (BOLD) functional MRI (fMRI). The simulation was conducted to optimize scan parameters. To validate the feasibility of the proposed technique, the visual and motor task experiments were performed at 7.0 Tesla (T). The single-shot EPI sequence was also applied in comparison with the proposed technique. The simulation results showed that an optimized flip angle of 9° provided maximal BOLD contrast for our scanning scheme, allowing low power deposition and SMS acceleration factor of 5. Additionally, parallel acquisition imaging with acceleration factor of 2 was utilized, which allowed a total acceleration factor of 10 in volunteer study. The experiment results showed that geometric distortion-free BOLD images with voxel size of 1.0 × 1.0 × 2.5 mm³ were obtained. Significant brain activation was identified in both visual and motor task experiments, which were in accordance with previous investigations. The proposed technique has potential for high spatial resolution fMRI at ultra-high field because of its sufficient BOLD sensitivity as well as improved acquisition speed over conventional GRE-based techniques.     

Neural correlates of a clinical continuous performance test

Functional magnetic resonance imaging (fMRI) was performed in 30 healthy adults to identify the location, magnitude, and extent of activation in brain regions that are engaged during the performance of Conners' Continuous Performance Test (CPT). Performance on the task during fMRI was highly correlated with performance on the standard Conners' CPT in the behavioral testing laboratory. An extensive neural network was activated during the task that included the frontal, cingulate, parietal, temporal, and occipital cortices; the cerebellum and the basal ganglia. There was also a network of brain regions which were more active during fixation than task. The magnitude of activation in several regions was correlated with reaction time. Among regions that were more active during task, the overall volume of supratentorial activation and cerebellar activation was greater in the left hemisphere. Frontal activation was greater in dorsal than in ventral regions, and dorsal frontal activation was bilateral. Ventral frontal region and parietal lobe activation were greater in the right hemisphere. The volume of clusters of activation in the extrastriate ventral visual pathway was greater in the left hemisphere. This network is consistent with existing models of motor control, visual object processing and attentional control and may serve as a basis for hypothesis-driven fMRI studies in clinical populations with deficits in Conners' CPT performance.     

A preliminary study of functional abnormalities in aMCI subjects during different episodic memory tasks

Functional magnetic resonance imaging (fMRI) is an important imaging modality to understand the neurodegenerative course of mild cognitive impairment (MCI) and early Alzheimer's disease (AD), because the memory dysfunction may occur before structural degeneration is obvious. In this research, we investigated the functional abnormalities of subjects with amnestic MCI (aMCI) using three episodic memory paradigms that are relevant to different memory domains in both encoding and recognition phases. Both whole-brain analysis and region-of-interest (ROI) analysis of the medial temporal lobes (MTL), which are central to the memory formation and retrieval, were used to compare the efficiency of the different memory paradigms and the functional difference between aMCI subjects and normal control subjects. We also investigated the impact of using different functional activation measurements in ROI analysis. This pilot study could facilitate the use of fMRI activations in the MTL as a marker for early detection and monitoring progression of AD.     

Multisubject activation detection in fMRI by testing correlation of data with a signal subspace

In this article, a generalized likelihood ratio test is proposed to assess the correlation between multisubject functional MRI (fMRI) time series and bases of a signal subspace for detecting the existence of group activation in each voxel of the brain. The signal subspace is generated by a design matrix using the time series of the desired effects. The proposed method leads to testing the product of eigenvalues of a specific matrix. The eigenvector corresponding to the largest eigenvalue is the weighting vector for the linear combination of time series of various subjects that has the maximum correlation with the signal subspace. In another method, namely, canonical correlation analysis, the largest eigenvalue of the above matrix is tested for activation detection. Surrogate data on resting state (no activation) are generated by randomization and used to estimate the statistical distribution of these parameters under the null hypothesis condition. A postprocessing step is applied to prevent false detection of voxels that are not sufficiently active (among subjects) by defining a minimum ratio for the active population. The proposed methods are applied on simulated and experimental fMRI data, and the results are compared with those of the general linear model (GLM; using the SPM and FMRISTAT toolboxes). The proposed methods showed higher detection sensitivity as compared with the GLM for activation detection in simulated data. Similarly, they detected more activated regions than did the GLM from multisubject experimental fMRI data on a visual (sensorimotor) event-related task.     

A statistical fMRI model for differential T2* contrast incorporating T1 and T2* of gray matter

Relaxation parameter estimation and brain activation detection are two main areas of study in magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI). Relaxation parameters can be used to distinguish voxels containing different types of tissue whereas activation determines voxels that are associated with neuronal activity. In fMRI, the standard practice has been to discard the first scans to avoid magnetic saturation effects. However, these first images have important information on the MR relaxivities for the type of tissue contained in voxels, which could provide pathological tissue discrimination. It is also well-known that the voxels located in gray matter (GM) contain neurons that are to be active while the subject is performing a task. As such, GM MR relaxivities can be incorporated into a statistical model in order to better detect brain activation. Moreover, although the MR magnetization physically depends on tissue and imaging parameters in a nonlinear fashion, a linear model is what is conventionally used in fMRI activation studies. In this study, we develop a statistical fMRI model for Differential T₂^? ConTrast Incorporating T₁ and T₂^? of GM, so-called DeTeCT-ING Model, that considers the physical magnetization equation to model MR magnetization; uses complex-valued time courses to estimate T₁ and T₂^? for each voxel; then incorporates gray matter MR relaxivities into the statistical model in order to better detect brain activation, all from a single pulse sequence by utilizing the first scans.     

Top-down and bottom-up modulation of language related areas – An fMRI Study

Background  

One major problem for cognitive neuroscience is to describe the interaction between stimulus and task driven neural modulation. We used fMRI to investigate this interaction in the human brain. Ten male subjects performed a passive listening and a semantic categorization task in a factorial design. In both tasks, words were presented auditorily at three different rates.     

The effect of task block arrangement on the detectability of activation in fMRI

The effect of task block arrangements on the detection of brain activation was investigated. Sessions of functional magnetic resonance imaging (fMRI) including the same number of two different task conditions but with different arrangements were compared. The two task conditions were, A) Ellipse-shaped black and white checkerboard flicker stimulation at 4.2 Hz covering the bilateral visual field, and B) the same flicker stimuli covering only the left visual field. In the rest blocks (0), the subjects looked at a fixation point. Four different task block arrangements were compared, 1) A0 (0A0A0A0) and B0 (0B0B0B0), 2) A0B0 (0A0B0A0B0A0B0), 3) AB0 (0AB0AB0AB0) and 4) AB (0ABABAB). Bilateral V1, V2, V3 and the left V5 were activated by condition A, and the right V1 and V2 by B. The activation in the left visual field by A0 was larger than in the other three conditions. In a differential analysis between conditions A and B, activation in the left V3 and V5 was declined by AB0 or AB. When rest blocks were located in the post-stimulus undershoot phase, the % signal change of the BOLD signal was emphasized, which caused augmented significance in the detection of the activity. It was indicated that the outcome of the activation map was influenced by the arrangement of task blocks, even though the same number of task blocks were repeated within the sessions. In fMRI studies, task conditions should be carefully compared within or across sessions considering the characteristics of hemodynamic response functions.     

Assessing functional connectivity in the human brain by fMRI

Functional magnetic resonance imaging (fMRI) is widely used to detect and delineate regions of the brain that change their level of activation in response to specific stimuli and tasks. Simple activation maps depict only the average level of engagement of different regions within distributed systems. FMRI potentially can reveal additional information about the degree to which components of large-scale neural systems are functionally coupled together to achieve specific tasks. In order to better understand how brain regions contribute to functionally connected circuits, it is necessary to record activation maps either as a function of different conditions, at different times or in different subjects. Data obtained under different conditions may then be analyzed by a variety of techniques to infer correlations and couplings between nodes in networks. Several multivariate statistical methods have been adapted and applied to analyze variations within such data. An approach of particular interest that is suited to studies of connectivity within single subjects makes use of acquisitions of runs of MRI images obtained while the brain is in a so-called steady state, either at rest (i.e., without any specific stimulus or task) or in a condition of continuous activation. Interregional correlations between fluctuations of MRI signal potentially reveal functional connectivity. Recent studies have established that interregional correlations between different components of circuits in each of the visual, language, motor and working memory systems can be detected in the resting state. Correlations at baseline are changed during the performance of a continuous task. In this review, various methods available for assessing connectivity are described and evaluated.     

Cortex-based independent component analysis of fMRI time series

The cerebral cortex is the main target of analysis in many functional magnetic resonance imaging (fMRI) studies. Since only about 20% of the voxels of a typical fMRI data set lie within the cortex, statistical analysis can be restricted to the subset of the voxels obtained after cortex segmentation. While such restriction does not influence conventional univariate statistical tests, it may have a substantial effect on the performance of multivariate methods.

Here, we describe a novel approach for data-driven analysis of single-subject fMRI time series that combines techniques for the segmentation and reconstruction of the cortical surface of the brain and the spatial independent component analysis (sICA) of the functional time courses (TCs). We use the mesh of the white matter/gray matter boundary, automatically reconstructed from high-spatial-resolution anatomical MR images, to limit the sICA decomposition of a coregistered functional time series to those voxels which are within a specified region with respect to the cortical sheet (cortex-based ICA, or cbICA). We illustrate our analysis method in the context of fMRI blocked and event-related experimental designs and in an fMRI experiment with perceptually ambiguous stimulation, in which an a priori specification of the stimulation protocol is not possible.

A comparison between cbICA and conventional hypothesis-driven statistical methods shows that cortical surface maps and component TCs blindly obtained with cbICA reliably reflect task-related spatiotemporal activation patterns. Furthermore, the advantages of using cbICA when the specification of a temporal model of the expected hemodynamic response is not straightforward are illustrated and discussed. A comparison between cbICA and anatomically unconstrained ICA reveals that — beside reducing computational demand — the cortex-based approach improves the fitting of the ICA model in the gray matter voxels, the separation of cortical components and the estimation of their TCs, particularly in the case of fMRI data sets with a complex spatiotemporal statistical structure.     

Rate dependence of human visual cortical response due to brief stimulation: an event-related fMRI study

The human brain response to a wide range of visual stimulus rates presented over a prolonged time period has been investigated by various neuroimaging techniques. However, to date, no imaging study has been performed to study the dynamic human brain response to various stimulus rates when presented in a short time. This report describes activation in the human brain due to brief visual stimulus presentation (1 s) for stimulus rates varying from 1 to 20 Hz using event-related functional MRI (fMRI). Our results show that the amplitude of the fMRI response increases with the stimulus frequency and plateaus at 6 Hz. This finding differs slightly from the results of previous blocked task paradigm experiments (with a longer time of stimulus presentation), in which the response peaks at approximately 8 Hz and then decreases. Our results are in close agreement with previously published psychophysical studies, suggesting that the fMRI signal in this experiment is indicative of cortical activity related to visual processing.     

A novel local PCA-based method for detecting activation signals in fMRI.

A novel local principal component analysis (LPCA) technique is presented for activation signal detection in functional magnetic resonance imaging (fMRI) without explicit knowledge about the shape of the model activation signal. Unlike the traditional PCA methods, our LPCA algorithm is based on a measure of separation between two clusters formed by the signal segments in active periods and inactive periods, which is computed in an eigen-subspace. In addition, we only applied PCA to the temporal sequence of each individual voxel instead of applying PCA to the fMRI data set. In our algorithm, we first applied a linear regression procedure to alleviate the baseline drift artifact. Then, the baseline-corrected temporal signals were partitioned into active and inactive segments according to the paradigm used for the fMRI data acquisition. Principal components were computed from all these segments for each voxel by PCA. By projecting the segments of each voxel onto a linear subspace formed by the corresponding most dominant principal components, two separate clusters were formed from active and inactive segments. An activation measure was defined based on the degree of separation between these two clusters in the projection space. We show experimental results on the activation signal detection from various sets of fMRI data with different types of stimulation by using the proposed LPCA algorithm and the standard t-test method for comparison. Our experiments indicate that the LPCA algorithm in general provides substantial signal-to-noise ratio improvement over the t-test method.     

Functional MRI changes in the central motor system in myotonic dystrophy type 1

Myotonic dystrophy type 1 (DM1) is a multisystemic disease involving multiple organ systems including central nervous system (CNS) and muscles. Few studies have focused on the central motor system in DM1, pointing to a subclinical abnormality in the CNS. The aim of our study was to investigate patterns of cerebral activation in DM1 during a motor task using functional MRI (fMRI). Fifteen DM1 patients, aged 20 to 59 years, and 15 controls of comparable age were scanned during a self-paced sequential finger-to-thumb opposition task of their dominant right hand. Functional MRI images were analyzed using SPM99. Patients underwent clinical and genetic assessment; all subjects underwent a conventional MR study. Myotonic dystrophy type 1 patients showed greater activation than controls in bilateral sensorimotor areas and inferior parietal lobules, basal ganglia and thalami, in the ipsilateral premotor area, insula and supplementary motor area (corrected P<.05). Analysis of the interaction between disease and age showed that correlation with age was significantly greater in patients than in controls in bilateral sensorimotor areas and in contralateral parietal areas. Other clinical and MR characteristics did not correlate with fMRI. Functional changes in DM1 may represent compensatory mechanisms such as reorganization and redistribution of functional networks to compensate for ultrastructural and neurochemical changes occurring as part of the accelerated aging process.     

Spatially aggregated multiclass pattern classification in functional MRI using optimally selected functional brain areas

In previous works, boosting aggregation of classifier outputs from discrete brain areas has been demonstrated to reduce dimensionality and improve the robustness and accuracy of functional magnetic resonance imaging (fMRI) classification. However, dimensionality reduction and classification of mixed activation patterns of multiple classes remain challenging. In the present study, the goals were (a) to reduce dimensionality by combining feature reduction at the voxel level and backward elimination of optimally aggregated classifiers at the region level, (b) to compare region selection for spatially aggregated classification using boosting and partial least squares regression methods and (c) to resolve mixed activation patterns using probabilistic prediction of individual tasks. Brain activation maps from interleaved visual, motor, auditory and cognitive tasks were segmented into 144 functional regions. Feature selection reduced the number of feature voxels by more than 50%, leaving 95 regions. The two aggregation approaches further reduced the number of regions to 30, resulting in more than 75% reduction of classification time and misclassification rates of less than 3%. Boosting and partial least squares (PLS) were compared to select the most discriminative and the most task correlated regions, respectively. Successful task prediction in mixed activation patterns was feasible within the first block of task activation in real-time fMRI experiments. This methodology is suitable for sparsifying activation patterns in real-time fMRI and for neurofeedback from distributed networks of brain activation.     

Changes in cerebral activity after decreased upper-limb hypertonus: an EMG-fMRI study

Objective

Whereas several studies have used functional magnetic resonance imaging (fMRI) to investigate motor recovery, whether therapy to decrease post-stroke hypertonus alters central motor patterns remains unclear. In this study, we used continuous electromyography (EMG)-fMRI to investigate possible changes in movement-related brain activation in patients receiving Botulinum toxin (BoNT-A) for hand-muscle hypertonus after chronic stroke.

Methods

We studied eight stroke patients all of whom had hemiparesis and associated upper-limb hypertonus. All patients underwent an fMRI-EMG recording and clinical-neurological assessment before BoNT-A and 5 weeks thereafter. The handgrip motor task during imaging was fixed across both patients and controls. The movements were metronome paced, movement amplitude and force were controlled with a plastic orthosis, dynamometer and EMG recording. An age-matched control group was recruited from among healthy volunteers underwent the same fMRI-EMG recording.

Results

Before BoNT-A, while patients moved the paretic hand, fMRI detected wide bilateral activation in the sensorymotor areas (SM1), in the supplementary motor area (SMA) and cerebellum. After BoNT-A blood oxygenation level-dependent (BOLD) activation decreased in ipsilateral and contralateral motor areas and became more lateralized. BOLD activation decreased also in ipsilateral cerebellar regions and in the SMA.

Conclusion

Changes in peripheral upper-limb hypertonus after BoNT-A were associated to an improvement in active movements and more lateralized and focalized activation of motor areas. The clinical and EMG-fMRI coregistration technique we used to study hand-muscle hypertonus in patients receiving BoNT-A after chronic stroke should be useful in future studies seeking improved strategies for post-stroke neurorehabilitation.