Visual fMRI Research Articles

Alterations in cortical activity of male methamphetamine abusers performing an empathy task: fMRI study

We investigate possible differences in neural correlates of empathy processing between abstinent methamphetamine (MA) abusers and healthy subjects using functional magnetic resonance imaging (fMRI). Nineteen abstinent MA abusers (mean age of 36.06 years, range 31-52 years) and 19 healthy subjects (mean age of 37.05 years, range 33-42 years) participated in this study. A visual fMRI activation paradigm was used, comprising a series of cartoons, each depicting a short story. There were two categories of stories: empathy (Empathy) and Physical causality (Physical). fMRI images were acquired using a 3.0 T whole-body scanner. All fMRI data were analyzed using MATLAB v. 7.2 and SPM5. Both MA subjects and controls exhibited activation in the dorsomedial prefrontal cortex. Despite this similarity in activation patterns, we found that the two groups differed in the activation of several cortical regions associated with the processing of empathy information. Hypoactivations of the orbitofrontal cortex, temporal poles, and hippocampus in MA abusers relative to healthy subjects suggests that the ability of empathic response could be compromised in abstinent MA abusers (p < 0.05, corrected for a small volume). Functional impairments in the empathic neural network caused by MA may contribute to the misunderstanding of others and to the erosion of social interactions in MA abusers.

Exploratory fMRI analysis without spatial normalization.

We present an exploratory method for simultaneous parcellation of multisubject fMRI data into functionally coherent areas. The method is based on a solely functional representation of the fMRI data and a hierarchical probabilistic model that accounts for both intersubject and intra-subject forms of variability in fMRI response. We employ a Variational Bayes approximation to fit the model to the data. The resulting algorithm finds a functional parcellation of the individual brains along with a set of population-level clusters, establishing correspondence between these two levels. The model eliminates the need for spatial normalization while still enabling us to fuse data from several subjects. We demonstrate the application of our method on a visual fMRI study.

Frequency stabilization using infinite impulse response filtering for SSFP fMRI at 3T

The steady-state free precession (SSFP) method has been shown to exhibit strong potential for distortion-free functional magnetic resonance imaging (fMRI). One major challenge of SSFP fMRI is that the frequency band corresponding to the highest functional sensitivity is extremely narrow, leading to substantial loss of functional contrast in the presence of magnetic field drifts. In this study we propose a frequency stabilization scheme whereby an RF pulse with small flip angle is applied before each image scan, and the initial phase of the free induction decay (FID) signals is extracted to reflect temporal field drifts. A simple infinite impulse response (IIR) filter is further employed to obtain a low-pass-filtered estimate of the central reference frequency for the upcoming scan. Experimental results suggest that the proposed scheme can stabilize the frequency settings in accordance with field drifts, with oscillation amplitudes of <0.5 Hz. Phantom studies showed that both slow drifts and fast fluctuations were prominently reduced, resulting in less than 5% signal variations. Visual fMRI at submillimeter in-plane resolution further demonstrated 15% activation signals that were nicely registered in the microvessels within the sulci. It is concluded that the IIR-filtered frequency stabilization is an effective technique for achieving reliable SSFP fMR images at high field strengths.

Non-Inferential Multi-Subject Study of Functional Connectivity during Visual Stimulation

Independent component analysis (ICA) is a powerful technique for the multivariate, non-inferential, data-driven analysis of functional magnetic resonance imaging (fMRI) data-sets. The non-inferential nature of ICA makes this a suitable technique for the study of complex mental states whose temporal evolution would be difficult to describe analytically in terms of classical statistical regressors. Taking advantage of this feature, ICA can extract a number of functional connectivity patterns regardless of the task executed by the subject. The technique is so powerful that functional connectivity patterns can be derived even when the subject is just resting in the scanner, opening the opportunity for functional investigation of the human mind at its basal "default" state, which has been proposed to be altered in several brain disorders. However, one major drawback of ICA consists in the difficulty of managing its results, which are not represented by a single functional image as in inferential studies. This produces the need for a classification of ICA results and exacerbates the difficulty of obtaining group "averaged" functional connectivity patterns, while preserving the interpretation of individual differences. Addressing the subject-level variability in the very same framework of "grouping" appears to be a favourable approach towards the clinical evaluation and application of ICA-based methodologies. Here we present a novel strategy for group-level ICA analyses, namely the self-organizing group-level ICA (sog-ICA), which is used on visual activation fMRI data from a block-design experiment repeated on six subjects. We propose the sog-ICA as a multi-subject analysis tool for grouping ICA data while assessing the similarity and variability of the fMRI results of individual subject decompositions.

Visual fMRI Responses in Human Superior Colliculus Show a Temporal–Nasal Asymmetry That Is Absent in Lateral Geniculate and Visual Cortex

Eye patching has revealed enhanced saccadic latencies or attention effects when orienting toward visual stimuli presented in the temporal versus nasal hemifields of humans. Such behavioral advantages have been tentatively proposed to reflect possible temporal-nasal differences in the retinotectal pathway to the superior colliculus, rather than in the retinogeniculate pathway or visual cortex. However, this has not been directly tested with physiological measures in humans. Here, we examined responses of the human superior colliculus (SC) to contralateral visual field stimulation, using high spatial resolution fMRI, while manipulating which hemifield was stimulated and orthogonally which eye was patched. The SC responded more strongly to visual stimulation when eye-patching made this stimulation temporal rather than nasal. In contrast, the lateral geniculate nucleus (LGN) plus retinotopic cortical areas V1-V3 did not show any temporal-nasal differences and differed from the SC in this respect. These results provide the first direct physiological demonstration in humans that SC shows temporal-nasal differences that LGN and early visual cortex apparently do not. This may represent a temporal hemifield bias in the strength of the retinotectal pathway, leading to a preference for the contralateral hemifield in the contralateral eye.

An improved temporal clustering analysis method applied to whole-brain data in fMRI study

Temporal clustering analysis (TCA) has been proposed as a method to detect the brain responses of an fMRI time series when the time and location of the activation are completely unknown. But TCA is still incompetent in dealing with the time series of the whole brain due to the existence of many inactive pixels. If only active pixels are considered, the sensitivity of TCA will be improved greatly and it could be applied to the whole brain. In this study, some modifications were made to TCA to remove inactive pixels, and the applicability of the modified TCA to the whole brain was validated with a set of visual fMRI data. Based on the time series of the modified TCA, activations of the whole brain corresponding to the visual stimulation were detected. Compared with the previous TCA, the modified TCA method shows a significant improvement in the sensitivity to detect activation peaks of the whole brain.

An adaptive filter for suppression of cardiac and respiratory noise in MRI time series data

The quality of MRI time series data, which allows the study of dynamic processes, is often affected by confounding sources of signal fluctuation, including the cardiac and respiratory cycle. An adaptive filter is described, reducing these signal fluctuations as long as they are repetitive and their timing is known. The filter, applied in image domain, does not require temporal oversampling of the artifact-related fluctuations. Performance is demonstrated for suppression of cardiac and respiratory artifacts in 10-minute brain scans on 6 normal volunteers. Experimental parameters resemble a typical fMRI experiment (17 slices; 1700 ms TR). A second dataset was acquired at a rate well above the Nyquist frequency for both cardiac and respiratory cycle (single slice; 100 ms TR), allowing identification of artifacts specific to the cardiac and respiratory cycles, aiding assessment of filtering performance. Results show significant reduction in temporal standard deviation (SDt) in all subjects. For all 6 datasets with 1700 ms TR combined, the filtering method resulted in an average reduction in SDt of 9.2% in 2046 voxels substantially affected by respiratory artifacts, and 12.5% for the 864 voxels containing substantial cardiac artifacts. The maximal SDt reduction achieved was 52.7% for respiratory and 55.3% for cardiac filtering. Performance was found to be at least equivalent to the previously published RETROICOR method. Furthermore, the interaction between the filter and fMRI activity detection was investigated using Monte Carlo simulations, demonstrating that filtering algorithms introduce a systematic error in the detected BOLD-related signal change if applied sequentially. It is demonstrated that this can be overcome by combining physiological artifact filtering and detection of BOLD-related signal changes simultaneously. Visual fMRI data from 6 volunteers were analyzed with and without the filter proposed here. Inclusion of the cardio-respiratory regressors in the design matrix yielded a 4.6% t-score increase and 4.0% increase in the number of significantly activated voxels.

An Integrated Neighborhood Correlation and Hierarchical Clustering Approach of Functional MRI

Clustering analysis is a promising data-driven method for the analysis of functional magnetic resonance imaging (fMRI) time series, however, the huge computation load makes it difficult for practical use. In this paper, neighborhood correlation (NC) and hierarchical clustering (HC) methods are integrated as a new approach where fMRI data are processed first by NC to get a preliminary image of brain activations, and then by HC to remove some noises. In HC, to better use spatial and temporal information in fMRI data, a new spatio-temporal measure is introduced. A simulation study and an application to visual fMRI data show that the brain activations can be effectively detected and that different response patterns can be discriminated. These results suggest that the proposed new integrated approach could be useful in detecting weak fMRI signals.

Neural correlates of crossmodal visual-tactile extinction and of tactile awareness revealed by fMRI in a right-hemisphere stroke patient

We used fMRI to study the neural correlates of crossmodal, visual-tactile extinction in a single case (patient GK). GK has chronic extinction after a lesion centred on right inferior parietal cortex, and has previously been investigated extensively in purely visual fMRI studies [e.g. Rees, G., Wojciulik, E., Clarke, K., Husain, M., Frith, C., & Driver, J. (2000). Unconscious activation of visual cortex in the damaged right hemisphere of a parietal patient with extinction. Brain, 123(Pt 8), 1624–1633; Rees, G., Kreiman, G., & Koch, C. (2002). Neural correlates of consciousness in humans. Nature Reviews Neuroscience, 3(4), 261–270]. With concurrent stimulation of the right visual field plus left index finger, GK showed crossmodal extinction of left touch on approximately half of such trials here (reflecting impaired sensitivity, i.e. lowered d-prime), albeit becoming aware of left touch on the other half. fMRI revealed activation of contralateral primary somatosensory cortex on crossmodal trials when touch was extinguished from awareness, suggesting unconscious residual processing there. When GK became aware of the left touch, additional activation was found in surviving right parietal cortex, and in frontal regions; moreover, functional coupling was enhanced with a region of frontal cortex implicated in awareness by previous work. Finally, on trials where crossmodal extinction arose, surviving right parietal cortex showed stronger functional coupling with the left visual and right somatosensory regions driven by the competing stimuli, indicating that crossmodal extinction arises when inputs to separate modalities interact competitively via multimodal cortex.

Physical and physiological consequences of passive intra-oral shimming

Imaging the human orbitofrontal cortex (OFC) with fMRI is problematic due to the proximity of this region to the air-filled sinuses, which causes susceptibility artifacts. Placing a strongly diamagnetic material into the mouth (‘mouthshim’) of a human volunteer can significantly reduce the artifacts in this region. Using the same combined olfactory and visual fMRI paradigm, we compared brain activation and static B 0 field maps of participants being scanned both with and without the ‘mouthshim’. Results demonstrate that the device improves the B 0 field homogeneity within OFC, resulting in significantly stronger BOLD activation in this region. However, the device also caused both increased head motion and reduced activation in insular cortices due to more frequent swallowing and tactile stimulation of the tongue. The ‘mouthshim’ should only, therefore, be used where sensitivity in OFC regions is paramount.

Adaptive cortical plasticity in higher visual areas after acute optic neuritis

The ability to distinguish adaptive cortical reorganization may help to target future therapeutic strategies after neurological insult. We investigated cortical plasticity by prospectively applying visual functional magnetic resonance imaging (fMRI) and optic nerve MRI to 20 patients with acute optic neuritis at baseline, 1, 3, 6, and 12 months. We performed three types of correlation analyses to investigate the relationships between fMRI activity, clinical function, and optic nerve structure. The first analysis directly correlated the fMRI response to clinical function or optic nerve structure and found dynamic relations especially within the first 3 months. The second analysis used a novel technique that modeled the fMRI response and optic nerve structure together with clinical function, to determine the contribution fMRI made to clinical function after accounting for structural factors. Significant effects were found at baseline only, within the right peristriate cortex, and bilaterally in the lateral occipital complexes, which are normally involved in higher order visual processing. The third analysis investigated the relation between the modeled visual recovery rate and fMRI response but found no significant effects. The key findings of this study are from the second analysis and suggest a genuine adaptive role for cortical reorganization within extrastriate visual areas early after optic neuritis.

Functional MRI using regularized parallel imaging acquisition

Parallel MRI techniques reconstruct full-FOV images from undersampled k-space data by using the uncorrelated information from RF array coil elements. One disadvantage of parallel MRI is that the image signal-to-noise ratio (SNR) is degraded because of the reduced data samples and the spatially correlated nature of multiple RF receivers. Regularization has been proposed to mitigate the SNR loss originating due to the latter reason. Since it is necessary to utilize static prior to regularization, the dynamic contrast-to-noise ratio (CNR) in parallel MRI will be affected. In this paper we investigate the CNR of regularized sensitivity encoding (SENSE) acquisitions. We propose to implement regularized parallel MRI acquisitions in functional MRI (fMRI) experiments by incorporating the prior from combined segmented echo-planar imaging (EPI) acquisition into SENSE reconstructions. We investigated the impact of regularization on the CNR by performing parametric simulations at various BOLD contrasts, acceleration rates, and sizes of the active brain areas. As quantified by receiver operating characteristic (ROC) analysis, the simulations suggest that the detection power of SENSE fMRI can be improved by regularized reconstructions, compared to unregularized reconstructions. Human motor and visual fMRI data acquired at different field strengths and array coils also demonstrate that regularized SENSE improves the detection of functionally active brain regions.

Adaptive cortical plasticity in higher visual areas after acute optic neuritis

The ability to distinguish adaptive cortical reorganization may help to target future therapeutic strategies after neurological insult. We investigated cortical plasticity by prospectively applying visual functional magnetic resonance imaging (fMRI) and optic nerve MRI to 20 patients with acute optic neuritis at baseline, 1, 3, 6, and 12 months. We performed three types of correlation analyses to investigate the relationships between fMRI activity, clinical function, and optic nerve structure. The first analysis directly correlated the fMRI response to clinical function or optic nerve structure and found dynamic relations especially within the first 3 months. The second analysis used a novel technique that modeled the fMRI response and optic nerve structure together with clinical function, to determine the contribution fMRI made to clinical function after accounting for structural factors. Significant effects were found at baseline only, within the right peristriate cortex, and bilaterally in the lateral occipital complexes, which are normally involved in higher order visual processing. The third analysis investigated the relation between the modeled visual recovery rate and fMRI response but found no significant effects. The key findings of this study are from the second analysis and suggest a genuine adaptive role for cortical reorganization within extrastriate visual areas early after optic neuritis.

Visual functional magnetic resonance imaging in patients with Sturge-Weber syndrome

The purpose of this study is to report different patterns of visual cortex activation in patients with Sturge-Weber syndrome as compared with healthy control subjects. Utilizing a visual paradigm of flashing lights, three children with Sturge-Weber syndrome were studied with functional magnetic resonance imaging. The results are compared with those documented in eight normal sedated children, and six young adult awake volunteers, using the same paradigms. All adult volunteers manifested bilateral activation in primary visual cortex (Brodmann's 17 and 18 areas). Two of them also had activation in secondary visual cortex (Brodmann's 19 area). In the eight sedated normal children, seven manifested activation in primary visual areas. The last exhibited no activation. The patients with Sturge-Weber syndrome demonstrated in the affected occipital lobe increased activation in one patient (11 months old), no activation in the second (12 years of age), and abnormal distribution of the activation in the third (11 months old). This report demonstrates that the vascular malformation of Sturge-Weber syndrome does not necessarily prevent cortical activation in the expected occipital cortex and may be associated with different patterns of abnormal activation. Assessing cortical function with functional magnetic resonance imaging in patients with Sturge-Weber syndrome may be helpful in decisions of surgical management and counseling. Sturge-Weber syndrome.

An extended convolution dynamic model of fMRI BOLD response

Blood oxygenation level dependent (BOLD) contrast based functional magnetic resonance imaging (fMRI) can be used to detect brain neural activities and to reveal hemodynamic characteristics of various brain regions activated by a stimulus. In this paper, an extended convolution dynamic model of a BOLD signal is proposed as a new BOLD model for the interpretation of the fMRI signal. The model introduces a convolution between a cerebral blood flow kernal function and the perfusion function of neural response to a stimulus. Moreover, our model has extended previous models by adding a baseline and a shift of the baseline. In a visual evoked fMRI experiment, the parameters of the model are estimated with a nonlinear optimal algorithm. The results show that the extended convolution model fits the fMRI data well, and that the parameters are different in the left and right occipital region of the primary visual cortex. This finding indicates that the dynamic performances are different in various cerebral functional regions.

A dynamic system model-based technique for functional MRI data analysis

Signals in functional magnetic resonance imaging (fMRI) are influenced by physiological fluctuations in addition to local brain activity. We have proposed a dynamic system model-based technique for separation of signal changes related to brain activation inputs from those related to physiological fluctuations. We applied this technique to a visual fMRI experiment to determine the validity and feasibility of this technique for fMRI data analyses. Gradient-echo echo planar images were obtained from 12 healthy volunteers with a Siemens ALLEGRA operating at 3 T, with a repetition time of 500 ms, echo time of 20 ms, field of view of 200–210 mm, matrix size of 64 × 64, and slice thickness of 5 mm. Twelve runs with two stimulation periods of varied duration (2–8 s) with 8-Hz flickering illumination were obtained for each subject. Local signal changes were modeled by an autoregressive model with two exogenous inputs, a visual stimulation input and a global reference signal. Local signal changes were appropriately predicted not only for stimulation periods but also resting periods. A significant linear relationship was found between model static gain based on the dynamic system modeling and beta coefficient based on a general linear model (GLM) analysis for active voxels in the primary visual cortex (analysis of covariance [ANCOVA], P < 0.001; estimated parameter, 0.967; 95% confidence interval, 0.734–1.201). This dynamic system model-based technique is sufficiently accurate and feasible for use in extracting signal changes related to brain activation inputs from measured signals with physiological fluctuations.

Brain activation in sedated children: auditory and visual functional MR imaging.

To map developing areas of activation with functional magnetic resonance (MR) imaging in sedated children by using passive auditory and visual tasks. Forty children between 2 months and 9 years old were examined and grouped according to age. Children were selected from patients referred to undergo brain MR imaging. Patients received pentobarbital (3.0-7.0 mg per kilogram of body weight) or chloral hydrate (50-75 mg/kg). The functional MR imaging study was performed at the end of the examination. Paradigms consisted of flashing lights at 8 Hz displayed on special goggles and a prerecorded mother's voice presented through headphones. Activation maps were obtained from a paired t test with a P value of.0005 (uncorrected). The visual stimulus produced statistically significant negative values in the rostral aspect of the primary visual area (28 [90%] of 31 patients). The auditory paradigm activated either temporal or frontal areas in 26 (68%) of 31 patients. There was more frontal activation in the older children. Visual and auditory cortices can be activated in children who have been sedated. Visual responses show negative values in the rostral visual cortex, independent of age. Auditory activation is seen in temporal and frontal lobes.

Perimetric visual field and functional MRI correlation: implications for image-guided surgery in occipital brain tumours

OBJECTIVETo compare the results of visual functional MRI with those of perimetric evaluation in patients with visual field defects and retrochiasmastic tumours and in normal subjects without visual field defect....

Functional MRI in Patients with Band Heterotopia

Functional activation associated with a motor task (fist movements) was studied in three patients with band heterotopias by fMRI. In two patients, additional visual fMRI studies were performed using a flickering checkerboard stimulus. In all patients activation of the outer cortex and of the inner neuronal band could be found during performance of the motor task. Visual stimulation elicited a normal activation pattern without activation of the ectopic neuronal layer in one patient; in another patient activation extended toward the ventricular wall, i.e., along the route of embryonic neuronal migration. The potential participation of ectopic neuronal tissue in physiologic cerebral functions is of clinical impact in patients with neuronal heterotopias suffering from medically intractable seizures prior to epilepsy surgery.

On Multivariate Spectral Analysis of fMRI Time Series

Most of functional magnetic resonance imaging (fMRI) time series analysis is based on single voxel data evaluation using parametric statistical tests. The result of such an analysis is a statistical parametric map. Voxels with a high significance value in the parametric test are interpreted as activation regions stimulated by the experimental task. However, for the investigation of functional connectivities it would be interesting to get some detailed information about the temporal dynamics of the blood oxygen level-dependent (BOLD) signal. For investigating that behavior, a method for fMRI data analysis has been developed that is based on Wiener theory of spectral analysis for multivariate time series. Spectral parameters such as coherence measure and phase lead can be estimated. The resulting maps give detailed information on brain regions that belong to a network structure and also show the temporal behavior of the BOLD response function. This paper describes the method and presents a visual fMRI experiment as an example to demonstrate the results.