Latencies in fMRI time-series: effect of slice acquisition order and perception

Depressive symptoms in prodromal Huntington's Disease correlate with Stroop-interference related functional connectivity in the ventromedial prefrontal cortex

Huntington's Disease (HD) is a neurodegenerative disorder caused by a cytosine-adenine-guanine (CAG) triplet repeat-expansion in the Huntingtin (HTT) gene. Diagnosis of HD is classically defined by the presence of motor symptoms; however, cognitive and depressive symptoms frequently precede motor manifestations, and may occur early in the prodromal phase. There are sparse data so far on functional brain correlates of depressive symptoms in prodromal HD. A Stroop color-naming test was administered to 32 subjects in the prodromal phase of HD and 52 expansion-negative controls while performing functional magnetic resonance imaging at 3Tesla. Networks of functional connectivity were identified using group independent component analysis, followed by an analysis of functional network interactions. A contrast of temporal regression-based beta-weights was calculated as a reflection of Stroop-interference related activity and correlated with Center for Epidemiologic Studies Depression (CES-D) scores. For secondary analysis, patients were stratified into two subgroups by median split of CAG repeat-length. Stroop performance was independent of HTT mutation-carrier status and CES-D score. Stroop-interference-related activity of the ventromedial prefrontal cortex-node of the default-mode network, calculated by temporal-regression beta-weights, was more highly correlated with depressive symptoms in subjects in the prodromal phase of HD than in controls, differing significantly. The strength of this correlation and its difference from controls increased when a subgroup of patients with longer CAG repeat lengths was analyzed. These findings suggest that depressive symptoms in prodromal HD subjects may reflect altered functional brain network activity in the context of early HD-related brain alterations.

Mapping brain activity induced by olfaction of virgin olive oil aroma

The difficulty of explaining sensory descriptors of virgin olive oil aroma by the analysis of volatile compounds is partially due to the subjective opinions of panelists and the lack of information of the neural mechanisms that ultimately produce a sensory perception. In this study the technique of functional magnetic resonance imaging (fMRI) has been applied to study brain activity during the smelling of virgin olive oil of different qualities. The volatile compounds of the samples were analyzed by solid-phase microextraction gas chromatography to explain the differences in the aromas presented to the subjects during the fMRI experiments. Comparing the pleasant and unpleasant aromas, the most evident differences in brain activity were found at the anterior cingulate gyrus (Brodmann area 32) and at the temporal lobe (Brodmann area 38). The activations were also observed when subjects smelled dilutions of heptanal and hexanoic acid, both compounds being responsible for off-flavors. Other areas were inherent to the olfaction task (e.g., Brodmann area 10) and to the intensity of the aroma (Brodmann area 6).

Localization of brain activation by umami taste in humans

There are no credible data to support the notion that individual taste qualities have dedicated pathways leading from the tongue to the end of the pathway in the brain. Moreover, the insular cortex is activated not only by taste but also by non-taste information from oral stimuli. These responses are invariably excitatory, and it is difficult to determine whether they are sensory, motor, or proprioceptive in origin. Furthermore, umami is a more unfamiliar and complex taste than other basic tastes. Considering these issues, it may be effective to minimize somatosensory stimuli, oral movement, and psychological effects in a neuroimaging study to elicit cerebral activity by pure umami on the human tongue. For this purpose, we developed an original taste delivery system for functional magnetic resonance imaging (fMRI) studies for umami. Then, we compared the results produced by two authorized models, namely, the block design model and event-related design model, to decide the appropriate model for detecting activation by umami. Activation by the umami taste was well localized in the insular cortex using our new system and block design model analysis. The peaks of the activated areas in the middle insular cortex by umami were very close to another prototypical taste quality (salty). Although we have to carefully interpret the perceiving intensities and brain activations by taste from different sessions, this study design might be effective for detecting the accession area in the cortex of pure umami taste on the tongue.

Shifting from region of interest (ROI) to voxel-based analysis in human brain mapping

Current clinical studies involve multidimensional high-resolution images containing an overwhelming amount of structural and functional information. The analysis of such a wealth of information is becoming increasingly difficult yet necessary in order to improve diagnosis, treatment and healthcare. Voxel-wise analysis is a class of modern methods of image processing in the medical field with increased popularity. It has replaced manual region of interest (ROI) analysis and has provided tools to make statistical inferences at voxel level. The introduction of voxel-based analysis software in all modern commercial scanners allows clinical use of these techniques. This review will explain the main principles, advantages and disadvantages behind these methods of image analysis.

Feature-based fusion of medical imaging data

The acquisition of multiple brain imaging types for a given study is a very common practice. There have been a number of approaches proposed for combining or fusing multitask or multimodal information. These can be roughly divided into those that attempt to study convergence of multimodal imaging, for example, how function and structure are related in the same region of the brain, and those that attempt to study the complementary nature of modalities, for example, utilizing temporal EEG information and spatial functional magnetic resonance imaging information. Within each of these categories, one can attempt data integration (the use of one imaging modality to improve the results of another) or true data fusion (in which multiple modalities are utilized to inform one another). We review both approaches and present a recent computational approach that first preprocesses the data to compute features of interest. The features are then analyzed in a multivariate manner using independent component analysis. We describe the approach in detail and provide examples of how it has been used for different fusion tasks. We also propose a method for selecting which combination of modalities provides the greatest value in discriminating groups. Finally, we summarize and describe future research topics.

On-line psychophysical data acquisition and event-related fMRI protocol optimized for the investigation of brain activation in response to gustatory stimuli

An experimental method for event-related functional magnetic resonance imaging that allows for the presentation of several chemosensory stimuli in the oral cavity during the same run, the collection of psychophysical measures (intensity or pleasantness) during the presentation of the stimuli, and the analysis of the data in an event-related fashion are described. The automatic pumps used to present taste stimuli allowed for multiple tastes to be delivered in small amounts under computer control. Psychophysical ratings of pleasantness or intensity were collected after each presentation of a taste stimulus and water, with the general labeled magnitude scale, using a joystick that controlled the movement of an arrow on the visual display. Performing these cognitive tasks required that the participant remained focused, and aided in the interpretation of the data collected. The perceived pleasantness differed across stimuli for all conditions; however, pleasantness ratings for the same stimulus displayed consistency, over the duration of the run and before each scan on separate days. Activation in response to sucrose and caffeine while the participant rated pleasantness was found in the insula, frontal operculum, rolandic operculum and orbitofrontal cortex which is consistent with previous taste fMRI studies.

Dissociable effects of phonetic competition and category typicality in a phonetic categorization task: an fMRI investigation

The current study used fMRI to explore the extent to which neural activation patterns in the processing of speech are driven by the quality of a speech sound as a member of its phonetic category, that is, its category typicality, or by the competition inherent in resolving the category membership of stimuli which are similar to other possible speech sounds. Subjects performed a phonetic categorization task on synthetic stimuli ranging along a voice-onset time continuum from [da] to [ta]. The stimulus set included sounds at the extreme ends of the voicing continuum which were poor phonetic category exemplars, but which were minimally competitive, stimuli near the phonetic category boundary, which were both poor exemplars of their phonetic category and maximally competitive, and stimuli in the middle of the range which were good exemplars of their phonetic category. Results revealed greater activation in bilateral inferior frontal areas for stimuli with the greatest degree of competition, consistent with the view that these areas are involved in selection between competing alternatives. In contrast, greater activation was observed in bilateral superior temporal gyri for the least prototypical phonetic category exemplars, irrespective of competition, consistent with the view that these areas process the acoustic-phonetic details of speech to resolve a token's category membership. Taken together, these results implicate separable neural regions in two different aspects of phonetic categorization.

How does spatial extent of fMRI datasets affect independent component analysis decomposition?

Spatial independent component analysis (sICA) of functional magnetic resonance imaging (fMRI) time series can generate meaningful activation maps and associated descriptive signals, which are useful to evaluate datasets of the entire brain or selected portions of it. Besides computational implications, variations in the input dataset combined with the multivariate nature of ICA may lead to different spatial or temporal readouts of brain activation phenomena. By reducing and increasing a volume of interest (VOI), we applied sICA to different datasets from real activation experiments with multislice acquisition and single or multiple sensory-motor task-induced blood oxygenation level-dependent (BOLD) signal sources with different spatial and temporal structure. Using receiver operating characteristics (ROC) methodology for accuracy evaluation and multiple regression analysis as benchmark, we compared sICA decompositions of reduced and increased VOI fMRI time-series containing auditory, motor and hemifield visual activation occurring separately or simultaneously in time. Both approaches yielded valid results; however, the results of the increased VOI approach were spatially more accurate compared to the results of the decreased VOI approach. This is consistent with the capability of sICA to take advantage of extended samples of statistical observations and suggests that sICA is more powerful with extended rather than reduced VOI datasets to delineate brain activity.

Using Virtual Reality to Study Alcohol Intoxication Effects on the Neural Correlates of Simulated Driving

The use of virtual reality in the form of simulated tasks can provide a realistic environment in which to study complex naturalistic behaviors. Many of the behavioral effects of alcohol intoxication are well known, but there is relatively little imaging evidence examining how alcohol exposure might transiently modulate brain function, especially in the context of task performance. In this review, we provide a brief synopsis of previous work using functional magnetic resonance imaging (fMRI) to study the neural correlates of alcohol intoxication. We describe in detail two studies from our published work, the first involving a visual perception paradigm, and the second involving virtual reality through a naturalistic behavior; simulated driving. Participants received single-blind individualized doses of beverage alcohol designed to produce blood alcohol content (BAC) of 0.04 and 0.08 or placebo. Subjects were fMRI scanned after training to asymptote performance. In both studies we found specific circuits that were differentially modulated by alcohol, we revealed both global and local effects of alcohol, and we examined relationships between behavior, brain function, and alcohol blood levels.

Functional MRI Detection of Activation in the Primary Gustatory Cortices in Humans

Magnetoencephalography (MEG) has recently revealed that the transitions between the parietal operculum (Pop) and the insula (area G) and the ventral end of the central sulcus (cs) were activated with the shortest latency by instrumental gustatory stimulation, which suggests that the location of the primary gustatory area is in these two regions. However, studies using other noninvasive brain-imaging methods such as positron-emission tomography or functional magnetic resonance imaging (fMRI) with manual application of tastants into the mouth have been unable to confirm this. The present study examined cortical activation by repetitive stimulation of the tongue tip with 1 M NaCl with a computer-controlled stimulator and used fMRI to detect it. In individual brains, activations were detected with multiple comparisons (false discovery rate) across the whole brain corrected (threshold at P < 0.05) at both area G and frontal operculum (Fop) in 8 of 11 subjects and at the rolandic operculum (Rop) in 7 subjects. Activations were also found at the ventral end of the cs (n = 3). Group analysis with random-effect models (multiple comparison using familywise error in regions of interest, P < 0.02) revealed activation at area G in both hemispheres and in the Fop, Rop, and ventral end of the cs on the left side. The present study revealed no activation on the gyrus of the external cerebral surface except for the Rop. Taking MEG findings into consideration, the present findings strongly indicate that the primary gustatory area is present at both the transition between the Pop and insula and the Rop including the gray matter within a ventral part of the cs.

Source density-driven independent component analysis approach for fMRI data

Independent component analysis (ICA) has become a popular tool for functional magnetic resonance imaging (fMRI) data analysis. Conventional ICA algorithms including Infomax and FAST-ICA algorithms employ the underlying assumption that data can be decomposed into statistically independent sources and implicitly model the probability density functions of the underlying sources as highly kurtotic or symmetric. When source data violate these assumptions (e.g., are asymmetric), however, conventional ICA methods might not work well. As a result, modeling of the underlying sources becomes an important issue for ICA applications. We propose a source density-driven ICA (SD-ICA) method. The SD-ICA algorithm involves a two-step procedure. It uses a conventional ICA algorithm to obtain initial independent source estimates for the first-step and then, using a kernel estimator technique, the source density is calculated. A refitted nonlinear function is used for each source at the second step. We show that the proposed SD-ICA algorithm provides flexible source adaptivity and improves ICA performance. On SD-ICA application to fMRI signals, the physiologic meaningful components (e.g., activated regions) of fMRI signals are governed typically by a small percentage of the whole-brain map on a task-related activation. Extra prior information (using a skewed-weighted distribution transformation) is thus additionally applied to the algorithm for the regions of interest of data (e.g., visual activated regions) to emphasize the importance of the tail part of the distribution. Our experimental results show that the source density-driven ICA method can improve performance further by incorporating some a priori information into ICA analysis of fMRI signals.

Alcohol intoxication effects on simulated driving: exploring alcohol-dose effects on brain activation using functional MRI

Driving while intoxicated is a major public health problem. We investigated impaired driving using a simulated driving skill game that presents an 'in-car' view of a road and a readout of speed. We explored brain activation and behavioral alterations from baseline at two blood alcohol concentrations (BACs). Participants received single-blind individualized doses of beverage alcohol designed to produce blood alcohol content (BAC) of 0.04 and 0.08 or placebo. Scanning occurred on a 1.5 Tesla Philips MRI scanner after training to asymptote performance. Analysis was performed using independent component analysis (ICA) to isolate systematically nonoverlapping 'networks' and their time courses. Imaging results revealed seven separate driving-related brain networks with different time courses. Several significant findings were observed for the imaging data. First, dose-dependent functional magnetic resonance imaging (fMRI) changes were revealed in orbitofrontal (OF) and motor (but not cerebellar) regions; visual and medial frontal regions were unaffected. Second, cerebellar regions were significantly associated with driving behavior in a dose-dependent manner. Finally, a global disruptive effect of alcohol on the ICA time courses was observed with highly significant differences in OF and motor regions. Alcohol thus demonstrated some behavioral effects and unique, disruptive, dose-dependent effects on fMRI signal within several brain circuits. The fMRI data also suggest that the deficits observed in alcohol intoxication may be modulated primarily through OF/anterior cingulate, motor and cerebellar regions as opposed to attentional areas in frontoparietal cortex.

A method for comparing group fMRI data using independent component analysis: application to visual, motor and visuomotor tasks

Independent component analysis (ICA) is an approach for decomposing fMRI data into spatially independent maps and time courses. We have recently proposed a method for ICA of multisubject data; in the current paper, an extension is proposed for allowing ICA group comparisons. This method is applied to data from experiments designed to stimulate visual cortex, motor cortex or both visual and motor cortices. Several intergroup and intragroup metrics are proposed for assessing the utility of the components for comparisons of group ICA data. The proposed method may prove to be useful in answering questions requiring multigroup comparisons when a flexible modeling approach is desired.

Improvement of fMRI data processing of olfactory responses with a perception-based template

Neuroimaging and in particular functional magnetic resonance imaging (fMRI) of olfactory function relies on the ability to model the time course of brain responses elicited by odor stimuli. In this study we compared two templates of olfactory brain activation by comparing levels of correlation in regions critical to olfactory processing with either a stimulation-based template or a perception-based template, derived from perception profiles acquired off-line during a simulated fMRI session. fMRI signal was more correlated with the perception-based template than with the stimulation-based template in all regions. This effect was not observed when comparing correlations obtained with the exact same templates shifted in time by 12 s. Therefore, the improvement due to the use of the perception-based template was not only caused by a difference of shape between the stimulation-based and the perception-based template but was specifically related to the olfactory stimulation performed. These results suggest that the perception-based template better represents brain activity in response to olfactory stimulation and might help improve data processing of fMRI studies investigating olfactory function.

Alcohol intoxication effects on visual perception: an fMRI study

We examined the effects of two doses of alcohol (EtOH) on functional magnetic resonance imaging (fMRI) activation during a visual perception task. The Motor-Free Visual Perception Test-Revised (MVPT-R) provides measures of overall visual perceptual processing ability. It incorporates different cognitive elements including visual discrimination, spatial relationships, and mental rotation. We used the MVPT-R to study brain activation patterns in healthy controls (1) sober, and (2) at two doses of alcohol intoxication with event-related fMRI. The fMRI data were analyzed using a general linear model approach based upon a model of the time course and a hemodynamic response estimate. Additionally, a correlation analysis was performed to examine dose-dependent amplitude changes. With regard to alcohol-free task-related brain activation, we replicate our previous finding in which SPM group analysis revealed robust activation in visual and visual association areas, frontal eye field (FEF)/dorsolateral prefrontal cortex (DLPFC), and the supplemental motor area (SMA). Consistent with a previous study of EtOH and visual stimulation, EtOH resulted in a dose-dependent decrease in activation amplitude over much of the visual perception network and in a decrease in the maximum contrast-to-noise ratio (in the lingual gyrus). Despite only modest behavior changes (in the expected direction), significant dose-dependent activation increases were observed in insula, DLPFC, and precentral regions, whereas dose-dependent activation decreases were observed in anterior and posterior cingulate, precuneus, and middle frontal areas. Some areas (FEF/DLPFC/SMA) became more diffusely activated (i.e., increased in spatial extent) at the higher dose. Alcohol, thus, appears to have both global and local effects upon the neural correlates of the MVPT-R task, some of which are dose dependent.

Latency (in)sensitive ICA

Independent component analysis (ICA), a data-driven approach utilizing high-order statistical moments to find maximally independent sources, has found fruitful application in functional magnetic resonance imaging (fMRI). A limitation of the standard fMRI ICA model is that a given component's time course is required to have the same delay at every voxel. As spatially varying delays (SVDs) may be found in fMRI data, using an ICA model with a fixed temporal delay for each source will have two implications. Larger SVDs can result in the splitting of regions with different delays into different components. Second, smaller SVDs can result in a biased ICA amplitude estimate due to only a slight delay difference. We propose a straightforward approach for incorporating this prior temporal information and removing the limitation of a fixed source delay by performing ICA on the amplitude spectrum of the original fMRI data (thus removing latency information). A latency map is then estimated for each component using the resulting component images and the raw data. We show that voxels with similar time courses, but different delays, are grouped into the same component. Additionally, when using traditional ICA, the amplitudes of motor areas are diminished due to systematic delay differences between visual and motor areas. The amplitudes are more accurately estimated when using a latency-insensitive ICA approach. The resulting time courses, the component maps, and the latency maps may prove useful as an addition to the collection of methods for fMRI data analysis.

Tracking cognitive processes with functional MRI mental chronometry

Functional magnetic resonance imaging (fMRI) is used widely to determine the spatial layout of brain activation associated with specific cognitive tasks at a spatial scale of millimeters. Recent methodological improvements have made it possible to determine the latency and temporal structure of the activation at a temporal scale of few hundreds of milliseconds. Despite the sluggishness of the hemodynamic response, fMRI can detect a cascade of neural activations - the signature of a sequence of cognitive processes. Decomposing the processing into stages is greatly aided by measuring intermediate responses. By combining event-related fMRI and behavioral measurement in experiment and analysis, trial-by-trial temporal links can be established between cognition and its neural substrate.

Different activation dynamics in multiple neural systems during simulated driving

Driving is a complex behavior that recruits multiple cognitive elements. We report on an imaging study of simulated driving that reveals multiple neural systems, each of which have different activation dynamics. The neural correlates of driving behavior are identified with fMRI and their modulation with speed is investigated. We decompose the activation into interpretable pieces using a novel, generally applicable approach, based upon independent component analysis. Some regions turn on or off, others exhibit a gradual decay, and yet others turn on transiently when starting or stopping driving. Signal in the anterior cingulate cortex, an area often associated with error monitoring and inhibition, decreases exponentially with a rate proportional to driving speed, whereas decreases in frontoparietal regions, implicated in vigilance, correlate with speed. Increases in cerebellar and occipital areas, presumably related to complex visuomotor integration, are activated during driving but not associated with driving speed.

fMRI activation in a visual-perception task: network of areas detected using the general linear model and independent components analysis

The Motor-Free Visual Perception Test, revised (MVPT-R), provides a measure of visual perceptual processing. It involves different cognitive elements including visual discrimination, spatial relationships, and mental rotation. We adapted the MVPT-R to an event-related functional MRI (fMRI) environment to investigate the brain regions involved in the interrelation of these cognitive elements. Two complementary analysis methods were employed to characterize the fMRI data: (a) a general linear model SPM approach based upon a model of the time course and a hemodynamic response estimate and (b) independent component analysis (ICA), which does not constrain the specific shape of the time course per se, although we did require it to be at least transiently task-related. Additionally, we implemented ICA in a novel way to create a group average that was compared with the SPM group results. Both methods yielded similar, but not identical, results and detected a network of robustly activated visual, inferior parietal, and frontal eye-field areas as well as thalamus and cerebellum. SPM appeared to be the more sensitive method and has a well-developed theoretical approach to thresholding. The ICA method segregated functional elements into separate maps and identified additional regions with extended activation in response to presented events. The results demonstrate the utility of complementary analyses for fMRI data and suggest that the cerebellum may play a significant role in visual perceptual processing. Additionally, results illustrate functional connectivity between frontal eye fields and prefrontal and parietal regions.

A method for making group inferences from functional MRI data using independent component analysis

Independent component analysis (ICA) is a promising analysis method that is being increasingly applied to fMRI data. A principal advantage of this approach is its applicability to cognitive paradigms for which detailed models of brain activity are not available. Independent component analysis has been successfully utilized to analyze single-subject fMRI data sets, and an extension of this work would be to provide for group inferences. However, unlike univariate methods (e.g., regression analysis, Kolmogorov-Smirnov statistics), ICA does not naturally generalize to a method suitable for drawing inferences about groups of subjects. We introduce a novel approach for drawing group inferences using ICA of fMRI data, and present its application to a simple visual paradigm that alternately stimulates the left or right visual field. Our group ICA analysis revealed task-related components in left and right visual cortex, a transiently task-related component in bilateral occipital/parietal cortex, and a non-task-related component in bilateral visual association cortex. We address issues involved in the use of ICA as an fMRI analysis method such as: (1) How many components should be calculated? (2) How are these components to be combined across subjects? (3) How should the final results be thresholded and/or presented? We show that the methodology we present provides answers to these questions and lay out a process for making group inferences from fMRI data using independent component analysis.

Spatial and temporal independent component analysis of functional MRI data containing a pair of task-related waveforms

Independent component analysis (ICA) is a technique that attempts to separate data into maximally independent groups. Achieving maximal independence in space or time yields two varieties of ICA meaningful for functional MRI (fMRI) applications: spatial ICA (SICA) and temporal ICA (TICA). SICA has so far dominated the application of ICA to fMRI. The objective of these experiments was to study ICA with two predictable components present and evaluate the importance of the underlying independence assumption in the application of ICA. Four novel visual activation paradigms were designed, each consisting of two spatiotemporal components that were either spatially dependent, temporally dependent, both spatially and temporally dependent, or spatially and temporally uncorrelated, respectively. Simulated data were generated and fMRI data from six subjects were acquired using these paradigms. Data from each paradigm were analyzed with regression analysis in order to determine if the signal was occurring as expected. Spatial and temporal ICA were then applied to these data, with the general result that ICA found components only where expected, e.g., S(T)ICA "failed" (i.e., yielded independent components unrelated to the "self-evident" components) for paradigms that were spatially (temporally) dependent, and "worked" otherwise. Regression analysis proved a useful "check" for these data, however strong hypotheses will not always be available, and a strength of ICA is that it can characterize data without making specific modeling assumptions. We report a careful examination of some of the assumptions behind ICA methodologies, provide examples of when applying ICA would provide difficult-to-interpret results, and offer suggestions for applying ICA to fMRI data especially when more than one task-related component is present in the data.

A weighted least-squares algorithm for estimation and visualization of relative latencies in event-related functional MRI

The properties of the hemodynamic latencies in functional maps have been relatively unexplored. Accurate methods of estimating hemodynamic latencies are needed to take advantage of this feature of fMRI. A fully automated, weighted least-squares (WLS) method for estimating temporal latencies is reported. Using a weighted linear model, the optimal latency and amplitude of the fMRI response can be determined for those voxels that pass a detection threshold. There is evidence from previous studies that the hemodynamic response may be time-locked to the stimulus within certain limits, less variable earlier in its evolution, and able to resolve information about relative hemodynamic timing. This information can be used to test hypotheses about the sequence and spatial distribution of neural activity. The method can be used to weight the earliest evolution of the hemodynamic response more heavily and decrease bias resulting from the hemodynamic response function. Additionally, the WLS method can control for varying response shapes across the brain and improve latency comparisons between brain regions. The WLS method was developed to study the properties of hemodynamic latencies, which may be increasingly important as event-related fMRI continues to be advanced.

Cerebral hemodynamics in CADASIL before and after acetazolamide challenge assessed with MRI bolus tracking

BACKGROUND

White matter lesions in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) are underlaid by severe ultrastructural changes of the arteriolar wall. Although chronic ischemia is presumed to cause the tissue lesions, the pattern of perfusion abnormalities and hemodynamic reserve in CADASIL, particularly within the white matter, remains unknown.

METHODS

We used the MRI bolus tracking method in 15 symptomatic patients with CADASIL (5 with dementia) and 10 age-matched control subjects before and 20 minutes after the intravenous injection of acetazolamide (ACZ, 17 mg/kg). Cerebral blood flow (CBF), blood volume (CBV), and mean transit time (MTT) were calculated both in the cortex and in the white matter according to the singular value decomposition technique. Perfusion parameters were obtained in regions of hyperintensities and within the normal-appearing white matter as observed on T2-weighted images. Analysis was performed with both absolute and relative (region/whole brain) values.

RESULTS

A significant reduction in absolute and relative CBF and CBV was found within areas of T2 hyperintensities in white matter in the absence of significant variations of MTT. This reduction was more severe in demented than in nondemented patients. No significant change in absolute CBF and CBV values was observed in the cortex of patients with CADASIL. A decrease in relative CBF and CBV values was detected in the occipital cortex. After ACZ administration, CBF and CBV increased significantly in both the cortex and white matter of affected subjects, but the increase in absolute CBF was lower within areas of increased signal on T2-weighted images in patients than in the white matter of control subjects.

CONCLUSIONS

In CADASIL, both basal perfusion and hemodynamic reserve are decreased in areas of T2 hyperintensities in the white matter. This hypoperfusion appears to be related to the clinical severity. The significant effect of ACZ on CBF and CBV suggests that cerebral perfusion might be increased using pharmacological vasodilation in CADASIL.

Human taste cortical areas studied with functional magnetic resonance imaging: evidence of functional lateralization related to handedness

Whole-brain functional magnetic resonance imaging was used to detect local hemodynamic changes reflecting cortical activation in five left handed and five right handed human subjects during bilateral stimulation of the tongue with various tastes. Activation was found bilaterally in the insula and the perisylvian region. These regions correspond to the primary taste cortical areas identified with electrophysiological recordings in monkeys and suggested from former clinical observations in human subjects. Moreover, a unilateral projection was described for the first time in the inferior part of the insula of the dominant hemisphere, according to the subject's handedness.