Abstract: Exact timing is essential for functional MRI data analysis. Datasets are commonly measured using repeated 2D imaging methods, resulting in a temporal offset between slices. To compensate for this timing difference, slice-timing correction (i.e. temporal data interpolation) has been used as an fMRI pre-processing step for more than fifteen years. However, there has been an ongoing debate about the effectiveness and applicability of this method. This paper presents the first elaborated analysis of the impact of the slice-timing effect on simulated data for different fMRI paradigms and measurement parameters, taking into account data noise and smoothing effects. Here we show, depending on repetition time and paradigm design, slice-timing effects can significantly impair fMRI results and slice-timing correction methods can successfully compensate for these effects and therefore increase the robustness of the data analysis. In addition, our results from simulated data were supported by empirical in vivo datasets. Our findings suggest that slice-timing correction should be included in the fMRI pre-processing pipeline.
Abstract: Exploratory analysis of functional MRI data allows activation to be detected even if the time course differs from that which is expected. Independent Component Analysis (ICA) has emerged as a powerful approach, but current extensions to the analysis of group studies suffer from a number of drawbacks: they can be computationally demanding, results are dominated by technical and motion artefacts, and some methods require that time courses be the same for all subjects or that templates be defined to identify common components. We have developed a group ICA (gICA) method which is based on single-subject ICA decompositions and the assumption that the spatial distribution of signal changes in components which reflect activation is similar between subjects. This approach, which we have called Fully Exploratory Network Independent Component Analysis (FENICA), identifies group activation in two stages. ICA is performed on the single-subject level, then consistent components are identified via spatial correlation. Group activation maps are generated in a second-level GLM analysis. FENICA is applied to data from three studies employing a wide range of stimulus and presentation designs. These are an event-related motor task, a block-design cognition task and an event-related chemosensory experiment. In all cases, the group maps identified by FENICA as being the most consistent over subjects correspond to task activation. There is good agreement between FENICA results and regions identified in prior GLM-based studies. In the chemosensory task, additional regions are identified by FENICA and temporal concatenation ICA that we show is related to the stimulus, but exhibit a delayed response. FENICA is a fully exploratory method that allows activation to be identified without assumptions about temporal evolution, and isolates activation from other sources of signal fluctuation in fMRI. It has the advantage over other gICA methods that it is computationally undemanding, spotlights components relating to activation rather than artefacts, allows the use of familiar statistical thresholding through deployment of a higher level GLM analysis and can be applied to studies where the paradigm is different for all subjects.
Abstract: Mouse units (mU) are used for quantification of the biological activity of botulinum A and B toxin preparations. However, in human tissue, mU values between preparations are not equivalent and lack of clarity concerning efficacy and safety remains with regard to their respective potencies, duration of drug effect and diffusion qualities.
Abstract: Social anxiety disorder patients suffer from excessive anxious responses in social interaction leading to avoidance behavior and social impairment. Although the amygdala has a central role in perception and processing of threatening cues, little is known about the involved networks and corresponding dysfunctions in social anxiety. Therefore, this study aims to investigate the functional connectivity network of the amygdala in patients with social anxiety disorder and to identify regions that might influence amygdalar reactivity via modulatory pathways. Ten patients with anxiety disorders (social and/or panic) and 27 healthy controls underwent a facial emotion processing task as well as 6-min functional MRI at resting state. Individual voxel-wise functional connectivity maps were calculated using the amygdala as seed region. Group comparisons were done by random-effects analysis in SPM. Patients exhibited an amygdala hyperactivation during the emotional task and decreased functional coupling of the left amygdala with the medial orbitofrontal cortex and the posterior cingulate cortex/precuneus. The strength of this functional connectivity showed a negative association with the severity of state anxiety. In addition, an exploratory analysis revealed further reduced functional connectivity and a marked functional separation between the medial orbitofrontal and anterior cingulate cortices in the patient group. Our results suggest alterations within the amygdalar functional connectivity network in social anxiety disorder. Combined with the amygdalar hyperactivation our findings corroborate the proposed dysfunction of the fronto-amygdalar inhibition in anxiety disorders and indicate a modulatory influence of the anterior and posterior cingulate cortices on threat perception and processing.
Abstract: Functional magnetic resonance imaging (fMRI) is currently the primary method for non-invasive functional localization in the brain. With the emergence of MR systems with field strengths of 4 Tesla and above, neuronal activation may be studied with unprecedented accuracy. In this article we present different approaches to use the improved sensitivity and specificity for expanding current fMRT resolution limits in space and time based on several 7 Tesla studies. In addition to the challenges that arise with ultra-high magnetic fields possible solutions will be discussed.
Abstract: Currently, most studies that employ dynamic causal modeling (DCM) use random-effects (RFX) analysis to make group inferences, applying a second-level frequentist test to subjects' parameter estimates. In some instances, however, fixed-effects (FFX) analysis can be more appropriate. Such analyses can be implemented by combining the subjects' posterior densities according to Bayes' theorem either on a multivariate (Bayesian parameter averaging or BPA) or univariate basis (posterior variance weighted averaging or PVWA), or by applying DCM to time-series averaged across subjects beforehand (temporal averaging or TA). While all these FFX approaches have the advantage of allowing for Bayesian inferences on parameters a systematic comparison of their statistical properties has been lacking so far. Based on simulated data generated from a two-region network we examined the effects of signal-to-noise ratio (SNR) and population heterogeneity on group-level parameter estimates. Data sets were simulated assuming either a homogeneous large population (N=60) with constant connectivities across subjects or a heterogeneous population with varying parameters. TA showed advantages at lower SNR but is limited in its applicability. Because BPA and PVWA take into account posterior (co)variance structure, they can yield non-intuitive results when only considering posterior means. This problem is relevant for high SNR data, pronounced parameter interdependencies and when FFX assumptions are violated (i.e. inhomogeneous groups). It diminishes with decreasing SNR and is absent for models with independent parameters or when FFX assumptions are appropriate. Group results obtained with these FFX approaches should therefore be interpreted carefully by considering estimates of dependencies among model parameters.
Abstract: Area-specific and stimulation-dependent changes of human brain activation by selective serotonin reuptake inhibitors (SSRI) are an important issue for improved understanding of treatment mechanisms, given the frequent prescription of these drugs in depression and anxiety disorders. The aim of this neuroimaging study was to investigate differences in BOLD-signal caused by administration of the SSRIs escitalopram and citalopram using pharmacological functional magnetic resonance imaging (pharmaco-fMRI). Eighteen healthy subjects participated in a placebo-controlled, randomized, double-blind study in cross-over repeated measures design. Each volunteer performed facial emotional discrimination and a sensorimotor control paradigm during three scanning sessions. Citalopram (20 mg/d), escitalopram (10 mg/d) and placebo were administered for 10 days each with a drug-free period of at least 21 days. Significant pharmacological effects on BOLD-signal were found in the amygdala, medial frontal gyrus, parahippocampal, fusiform and middle temporal gyri. Post-hoc t-tests revealed decreased BOLD-signal in the right amygdala and left parahippocampal gyrus in both pharmacological conditions, compared to placebo. Escitalopram, compared to citalopram, induced a decrease of BOLD-signal in the medial frontal gyrus and an increase in the right fusiform and left parahippocampal gyri. Drug effects were concentrated in brain regions with dense serotonergic projections. Both escitalopram and citalopram attenuated BOLD-signal in the amygdala and parahippocampal cortex to emotionally significant stimuli compared to control stimuli. We believe that reduced reactivity in the medial frontal gyrus found for escitalopram compared to citalopram administration might explain the response differences between study drugs as demonstrated in previous clinical trials.
Abstract: Throughout recent years there has been an increasing interest in studying unconscious visual processes. Such conditions of unawareness are typically achieved by either a sufficient reduction of the stimulus presentation time or visual masking. However, there are growing concerns about the reliability of the presentation devices used. As all these devices show great variability in presentation parameters, the processing of visual stimuli becomes dependent on the display-device, e.g. minimal changes in the physical stimulus properties may have an enormous impact on stimulus processing by the sensory system and on the actual experience of the stimulus. Here we present a custom-built three-way LC-shutter-tachistoscope which allows experimental setups with both, precise and reliable stimulus delivery, and millisecond resolution. This tachistoscope consists of three LCD-projectors equipped with zoom lenses to enable stimulus presentation via a built-in mirror-system onto a back projection screen from an adjacent room. Two high-speed liquid crystal shutters are mounted serially in front of each projector to control the stimulus duration. To verify the intended properties empirically, different sequences of presentation times were performed while changes in optical power were measured using a photoreceiver. The obtained results demonstrate that interfering variabilities in stimulus parameters and stimulus rendering are markedly reduced. Together with the possibility to collect external signals and to send trigger-signals to other devices, this tachistoscope represents a highly flexible and easy to set up research tool not only for the study of unconscious processing in the brain but for vision research in general.
Abstract: Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (<0.1 Hz) fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individual's "functional connectome." Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individual's functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain-behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. High-throughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at www.nitrc.org/projects/fcon_1000/.
Abstract: Independent component analysis (ICA) is one of the most valuable explorative methods for analyzing resting-state networks (RSNs) in fMRI, representing a data-driven approach that enables decomposition of high-dimensional data into discrete components. Extensions to a group-level suffer from the drawback of evaluating single-subject resting-state components of interest either using a predefined spatial template or via visual inspection. FENICA introduced in the context of group ICA methods is based solely on spatially consistency across subjects directly reflecting similar networks. Therefore, group data can be processed without further visual inspection of the single-subject components or the definition of a template (Schöpf et al., 2009). In this study FENICA was applied to fMRI resting-state data from 28 healthy subjects resulting in eight group RSNs. These RSNs resemble the spatial patterns of the following previously described networks: (1) visual network, (2) default mode network, (3) sensorimotor network, (4) dorsolateral prefrontal network, (5) temporal prefrontal network, (6) basal ganglia network, (7) auditory processing network, and (8) working memory network. This novel analysis approach for identifying spatially consistent networks across a group of subjects does not require manual or template-based selection of single-subject components and, therefore, offers a truly explorative procedure of assessing RSNs.
Abstract: In spite of long-standing evidence showing that the hypothalamus is instrumental in generating behaviors associated with positive and negative emotions, little is known about the role of the hypothalamus in normal human emotional processing. Recent findings have suggested that the hypothalamus plays a role beyond mere control of HPA-axis function; this is also supported by the existence of rich anatomical connections between the hypothalamus and the amygdala, a region known for its important role in emotional processing. However, evidence of emotion-induced hypothalamic activity from neuroimaging studies has been inconsistent, possibly due to methodological limitations (e.g., low spatial resolution). Taking advantage of recent improvements in fMRI technology we set out to explore a possible valence-dependent modulation of hypothalamic activity. Using second order parametric analysis of high-resolution BOLD fMRI, we assessed hypothalamic activation patterns during passive viewing of visual stimuli of varying valence, and compared the results with the activity pattern in the amygdalae, i.e. nuclei with known valence-dependent activity profiles. We show that both hypothalamic and amygdalar activation is modulated by the second-order stimulus valence term, i.e., there is increased neural activity following the processing of both positive and negative stimuli. Our results suggest that the hypothalamus may serve a role in generating emotions broader than generally assumed.
Abstract: Independent component analysis (ICA) has proven its applicability in both standard and resting-state fMRI. While there is consensus on single-subject ICA methodology, the extension to group ICA is more complex and a number of approaches have been suggested. Currently, two software packages are most frequently used for ICA group analysis: (1) GIFT introduced by Calhoun et al., and (2) PICA, proposed by Beckmann et al.. Both methods are based on the assumption of statistical independence of the extracted component maps ("spatial ICA"). Group maps are estimated via ICA on pre-calculated group data sets.
Abstract: Magnetic resonance (MR) methods are non-invasive techniques to provide detailed, multi-parametric information on human anatomy, function and metabolism. Sensitivity, specificity, spatial and temporal resolution may, however, vary depending on hardware (e.g., field strength, gradient strength and speed) and software (optimised measurement protocols and parameters for the various techniques). Furthermore, multi-modality imaging may enhance specificity to better characterise complex disease patterns.
Abstract: Neuroimaging studies have documented modulation of the activity of the amygdala - a key node in the neural network underlying emotion perception and processing, and one that has also been associated with regulating aggression - by exogenous testosterone. However, results on the impact of normal range testosterone levels on explicit emotion recognition as a prerequisite for social interaction and amygdala activation in healthy young males are missing. Hence, we performed functional MRI at 3T in a group of 21 healthy males during explicit emotion recognition with a protocol specifically optimized to reliably detect amygdala activation. We observed similar amygdala activation to all emotions presented without any effect of gender of poser or laterality. Reaction times to fearful male faces were found negatively correlated to testosterone concentration, while no significant effects emerged for other emotions and neutral expressions. Correlation analyses revealed a significant positive association between testosterone levels and amygdala response to fearful and angry facial expressions, but not to other expressions. Hence, our results demonstrate that testosterone levels affect amygdala activation and also behavioral responses particularly to threat-related emotions in healthy young males. We conclude that these findings add to our understanding of emotion processing and its modulation by neuroendocrine factors.
Abstract: Cultural differences in emotion recognition performance have frequently been reported, whereby duration of stay in a foreign culture seems to be a crucial factor. Furthermore, cultural aspects influence the neural correlates of face and emotion processing thereby also affecting the response of the amygdala. Here, the exposure to a foreign culture and its influence on the cerebral correlates of facial emotion recognition were examined in 24 Asian and 24 age-matched European males. Subjects performed an explicit emotion recognition task and were imaged with a 3 T MR-scanner. Results demonstrate a significant cultural influence on the specific recognition of disgust and anger, with higher accuracy among the Europeans, while the functional data indicate generally elevated amygdala activation in Asians compared to Europeans. Moreover, a significant inverse correlation between duration of stay and amygdala response emerged, with stronger activation in those subjects with shorter duration of stay in Europe. The observed amygdala hyperactivation in Asians may reflect novelty aspects but might also be associated with greater effort and motivation in immigrants, thus it possibly reflects one neural correlate of the "alien-effect". We conclude that exposure to a foreign culture and duration of stay affect the behavioral and neural response to facial expressions of emotions.
Abstract: Resting-state data sets contain coherent fluctuations unrelated to neural processes originating from residual motion artefacts, respiration and cardiac action. Such confounding effects may introduce correlations and cause an overestimation of functional connectivity strengths. In this study we applied several multidimensional linear regression approaches to remove artificial coherencies and examined the impact of preprocessing on sensitivity and specificity of functional connectivity results in simulated data and resting-state data sets from 40 subjects. Furthermore, we aimed at clarifying possible causes of anticorrelations and test the hypothesis that anticorrelations are introduced via certain preprocessing approaches, with particular focus on the effects of regression against the global signal. Our results show that preprocessing in general greatly increased connection specificity, in particular correction for global signal fluctuations almost doubled connection specificity. However, widespread anticorrelated networks were only found when regression against the global signal was applied. Results in simulated data sets compared with result of human data strongly suggest that anticorrelations are indeed introduced by global signal regression and should therefore be interpreted very carefully. In addition, global signal regression may also reduce the sensitivity for detecting true correlations, i.e. increase the number of false negatives. Concluding from our results we suggest that is highly recommended to apply correction against realignment parameters, white matter and ventricular time courses, as well as the global signal to maximize the specificity of positive resting-state correlations.
Abstract: The ability to recognize emotions in facial expressions relies on an extensive neural network with the amygdala as the key node as has typically been demonstrated for the processing of fearful stimuli. A sufficient characterization of the factors influencing and modulating amygdala function, however, has not been reached now. Due to lacking or diverging results on its involvement in recognizing all or only certain negative emotions, the influence of gender or ethnicity is still under debate. This high-resolution fMRI study addresses some of the relevant parameters, such as emotional valence, gender and poser ethnicity on amygdala activation during facial emotion recognition in 50 Caucasian subjects. Stimuli were color photographs of emotional Caucasian and African American faces.
Abstract: Although motor imagery is widely used for motor learning in rehabilitation and sports training, the underlying mechanisms are still poorly understood. Based on fMRI data sets acquired with very high temporal resolution (300 ms) under motor execution and imagery conditions, we utilized Dynamic Causal Modeling (DCM) to determine effective connectivity measures between supplementary motor area (SMA) and primary motor cortex (M1). A set of 28 models was tested in a Bayesian framework and the by-far best-performing model revealed a strong suppressive influence of the motor imagery condition on the forward connection between SMA and M1. Our results clearly indicate that the lack of activation in M1 during motor imagery is caused by suppression from the SMA. These results highlight the importance of the SMA not only for the preparation and execution of intended movements, but also for suppressing movements that are represented in the motor system but not to be performed.
Abstract: Conventional fMRI analyses assess the summary of temporal information in terms of the coefficients of temporal basis functions. Based on established finite impulse response (FIR) analysis methodology we show how spatiotemporal statistical parametric maps may be concatenated to form Brain Activation Movies (BAMs), dynamic activation maps representing the temporal evolution of brain activation throughout task performance. These BAMs enable comprehensive assessment of the dynamics in functional topology without restriction to predefined regions and without detailed information on the stimulus paradigm. We apply BAM visualization to two fMRI studies demonstrating the additional spatiotemporal information available compared to standard fMRI result presentation. Here we show that BAMs allow for unbiased data visualization providing dynamic activation maps without assumptions on the neural activity except reproducibility across trials. It may thus be useful in proceeding from static to dynamic brain mapping, widening the range of fMRI in neuroscience. In addition, BAMs might be helpful tools in visualizing the temporal evolution of activation in "real-time" for better and intuitive understanding of temporal processes in the human brain.
Abstract: Converging evidence has accumulated that menstrual cycle and thus hormonal levels can affect emotional behavior, in particular facial emotion recognition. Here we explored the association of ovarian hormone levels and amygdala activation during an explicit emotion recognition task in two groups of healthy young females: one group was measured while in their follicular phase (n=11) and the other during their luteal phase (n=11). Using a 3T scanner in combination with a protocol specifically optimized to reliably detect amygdala activation we found significantly stronger amygdala activation in females during their follicular phase. Also, emotion recognition performance was significantly better in the follicular phase. We observed significant negative correlations between progesterone levels and amygdala response to fearful, sad and neutral faces, further supporting a significant modulation of behavior and neural response by hormonal changes during the menstrual cycle. From an evolutionary point of view this significant influence of ovarian hormone level on emotion processing and an important neural correlate, the amygdala, may enable a higher social sensitivity in females during their follicular phase, thus facilitating socio-emotional behavior (and social interaction) which may possibly facilitate mating behavior as well.
Abstract: Receptor distribution patterns of neurotransmitters and distinct functional fields of the human brain appear to be tightly connected with respect to their topological allocation along the cerebral cortex. There is, however, considerable lack of human data directly demonstrating this association in vivo. Here, we assessed the relationship between the distribution of the major inhibitory serotonergic neurotransmitter receptor, the 5-HT(1A) subtype, and the functional organization within early visual cortex defined by retinotopic mapping. The 5-HT(1A) receptor-binding potential was quantified by positron emission tomography (PET) using the highly selective and specific radioligand [carbonyl-(11)C]WAY-100635 in seven healthy subjects. The retinotopic maps and borders determined by functional magnetic resonance imaging (fMRI) were compared to the receptor distribution employing surface-based region of interest analysis in each of these subjects. We found a significant difference in receptor-binding potential in the functionally defined primary (V1) compared to secondary (V2) visual area, as V1 exhibits only 68% of receptor binding found in V2 in both hemispheres, which is consistent with postmortem data. Our in vivo findings clearly support prior assumptions of a link between receptor distribution and functional fields of the human cortex.
Abstract: Emotion recognition is essential for social interaction and communication and is a capacity in which the amygdala plays a central role. So far, neuroimaging results have been inconsistent as to whether the amygdala is more active during explicit or incidental facial emotion processing. In consideration of its functionality in fast automatic evaluation of stimuli and involvement in higher-order conscious processing, we hypothesize a similar response to the emotional faces presented regardless of attentional focus. Using high field functional magnetic resonance imaging (fMRI) specifically optimized for ventral brain regions we show strong and robust amygdala activation for explicit and implicit processing of emotional facial expressions in 29 healthy subjects. Bilateral amygdala activation was, however, significantly greater when subjects were asked to recognize the emotion (explicit condition) than when required to discern the age (implicit condition). A significant correlation between amygdala activation and emotion recognition, but not age discrimination performance, emphasizes the amygdala's enhanced role during conscious emotion processing.
Abstract: Subjects deciding whether two objects presented at angular disparity are identical or mirror versions of each other usually show response times that linearly increase with the angle between objects. This phenomenon has been termed mental rotation. While there is widespread agreement that parietal cortex plays a dominant role in mental rotation, reports concerning the involvement of motor areas are less consistent. From a theoretical point of view, activation in motor areas suggests that mental rotation relies upon visuo-motor rather than visuo-spatial processing alone. However, the type of information that is processed by motor areas during mental rotation remains unclear. In this study we used event-related fMRI to assess whether activation in parietal and dorsolateral premotor areas (dPM) during mental rotation is distinctively related to processing spatial orientation information. Using a newly developed task paradigm we explicitly separated the processing steps (encoding, mental rotation proper and object matching) required by mental rotation tasks and additionally modulated the amount of spatial orientation information that had to be processed. Our results show that activation in dPM during mental rotation is not strongly modulated by the processing of spatial orientation information, and that activation in dPM areas is strongest during mental rotation proper. The latter finding suggests that dPM is involved in more generalized processes such as visuo-spatial attention and movement anticipation. We propose that solving mental rotation tasks is heavily dependent upon visuo-motor processes and evokes neural processing that may be considered as an implicit simulation of actual object rotation.
Abstract: Whenever we plan, imagine, or observe an action, the motor systems that would be involved in preparing and executing that action are similarly engaged. The way in which such common motor activation is formed, however, is likely to differ depending on whether it arises from our own intentional selection of action or from the observation of another's action. In this study, we use time-resolved event-related functional MRI to tease apart neural processes specifically related to the processing of observed actions, the selection of our own intended actions, the preparation for movement, and motor response execution. Participants observed a finger gesture movement or a cue indicating they should select their own finger gesture to perform, followed by a 5-s delay period; participants then performed the observed or self-selected action. During the preparation and readiness for action, prior to initiation, we found activation in a common network of higher motor areas, including dorsal and ventral premotor areas and the pre-supplementary motor area (pre-SMA); the more caudal SMA showed greater activation during movement execution. Importantly, the route to this common motor activation differed depending on whether participants freely selected the actions to perform or whether they observed the actions performed by another person. Observation of action specifically involved activation of inferior and superior parietal regions, reflecting involvement of the dorsal visual pathway in visuomotor processing required for planning the action. In contrast, the selection of action specifically involved the dorsal lateral prefrontal and anterior cingulate cortex, reflecting the role of these prefrontal areas in attentional selection and guiding the selection of responses.
Abstract: The supplementary motor area (SMA) is thought to play in important role in the preparation and organisation of voluntary movement. It has long been known that cortical activity begins to increase up to 2s prior to voluntary self-initiated movement. This increasing premovement activity measured in EEG is known as the Bereitschaftspotential or readiness potential. Modern functional brain imaging methods, using event-related and time-resolved functional MRI techniques, are beginning to reveal the role of the SMA, and in particular the more anterior pre-SMA, in premovement activity associated with the readiness for action. In this paper we review recent studies using event-related time-resolved fMRI methods to examine the time-course of activation changes within the SMA throughout the preparation, readiness and execution of action. These studies suggest that the pre-SMA plays a common role in encoding or representing actions prior to our own voluntary self-initiated movements, during motor imagery, and from the observation of others' actions. We suggest that the pre-SMA generates and encodes motor representations which are then maintained in readiness for action.
Abstract: The optimum parameters for single-shot gradient-recalled (GR) EPI-based fMRI studies of the limbic region are systematically established at 3 T via their ability to mitigate intravoxel dephasing-measured via SNR and T2* in the amygdalae-and their implications for temporal resolution (or brain coverage). Conventional imaging parameters (64 x 64 matrix size and 4-6 mm thick slices) are confirmed to be inadequate for functional studies at 3 T. Measurements of main magnetic field variations across the amygdalae suggest that such variations are equal in the craniocaudal and anterior-posterior directions, and slightly lower in the mediolateral direction, with this and other considerations leading us to conclude an oblique axial orientation to be most suitable. In-plane resolution of approximately 1.7 mm was sufficient to recover signal in the area of the amygdalae. SNR was found to peak at a slice thickness of between 2.0 and 2.5 mm, dependent on the subject. T2* time in the amygdalae was measured with a standard EPI protocol to be 22 +/- 3 ms. Using the optimized (high resolution) EPI protocol proposed here, the measured T2* time increased to 48 +/- 2 ms (compared with 43 +/- 3 ms for a reference FLASH scan), only slightly lower than the cortex (49 +/- 2 ms measured with optimized EPI and 52 +/- 2 ms with FLASH). The FLASH measurement of 43 ms is taken to be a suitable effective echo time (TE(eff)) to achieve maximum BOLD sensitivity in the amygdalae. Time series data acquired with these parameters showed a 60% increase in SNR in the amygdala over that obtained with a standard low-resolution protocol and suggest sufficient SNR and BOLD sensitivity to make functional studies feasible. Arteries, but no substantial draining veins, were found in high-resolution BOLD venograms of the region. Our results indicate that EPI protocols need to be carefully optimized for structures of interest if reliable results from single subjects are to be established in this brain region.
Abstract: The aim of this work is to compare the efficiency and power of several cluster analysis techniques on fully artificial (mathematical) and synthesized (hybrid) functional magnetic resonance imaging (fMRI) data sets. The clustering algorithms used are hierarchical, crisp (neural gas, self-organizing maps, hard competitive learning, k-means, maximin-distance, CLARA) and fuzzy (c-means, fuzzy competitive learning). To compare these methods we use two performance measures, namely the correlation coefficient and the weighted Jaccard coefficient (wJC). Both performance coefficients (PCs) clearly show that the neural gas and the k-means algorithm perform significantly better than all the other methods using our setup. For the hierarchical methods the ward linkage algorithm performs best under our simulation design. In conclusion, the neural gas method seems to be the best choice for fMRI cluster analysis, given its correct classification of activated pixels (true positives (TPs)) whilst minimizing the misclassification of inactivated pixels (false positives (FPs)), and in the stability of the results achieved.
Abstract: To establish a fast and robust technique for generating magnetic field maps for the correction of geometric distortions in echo-planar magnetic resonance (MR) images.
Abstract: Functional magnetic resonance imaging (fMRI) time series are investigated with a multifractal method based on the Wavelet Modulus Maxima (WTMM) method to extract local singularity ("fractal") exponents. The spectrum of singularity exponents of each fMRI time series is quantified by spectral characteristics including its maximum and the corresponding dimension. We found that the range of Hölder exponents in voxels with activation is close to 1, whereas exponents are close to 0.5 in white matter voxels without activation. The maximum dimension decreases going from white matter to gray matter, and is lower still for activated time series. The full-width-at-half-maximum of the spectra is higher in activated areas. The proposed method becomes particularly effective when combining these spectral characteristics into a single parameter. Using these multifractal parameters, it is possible to identify activated areas in the human brain in both hybrid and in vivo fMRI data sets without knowledge of the stimulation paradigm applied.
Abstract: The functional role of human premotor and primary motor cortex during mental rotation has been studied using functional MRI at 3 T. Fourteen young, male subjects performed a mental rotation task in which they had to decide whether two visually presented cubes could be identical. Exploratory Fuzzy Cluster Analysis was applied to identify brain regions with stimulus-related time courses. This revealed one dominant cluster which included the parietal cortex, premotor cortex, and dorsolateral prefrontal cortex that showed signal enhancement during the whole stimulus presentation period, reflecting cognitive processing. A second cluster, encompassing the contralateral primary motor cortex, showed activation exclusively after the button press response. This clear separation was possible in 3 subjects only, however. Based on these exploratory results, the hypothesis that primary motor cortex activity was related to button pressing only was tested using a parametric approach via a random-effects group analysis over all 14 subjects in SPM99. The results confirmed that the stimulus response via button pressing causes activation in the primary motor cortex and supplementary motor area while parietal cortex and mesial regions rostral to the supplementary motor area are recruited for the actual mental rotation process.
Abstract: Osteoporosis is characterized by low bone mass and inferior structural competence. In this study we introduce the bone homogeneity factor (BHF) as a quantitative measurement of bone structure, which could be equally important as bone mineral density.
Abstract: Activity within motor areas of the cortex begins to increase 1 to 2 s prior to voluntary self-initiated movement (termed the Bereitschaftspotential or readiness potential). There has been much speculation and debate over the precise source of this early premovement activity as it is important for understanding the roles of higher order motor areas in the preparation and readiness for voluntary movement. In this study, we use high-field (3-T) event-related fMRI with high temporal sampling (partial brain volumes every 250 ms) to specifically examine hemodynamic response time courses during the preparation, readiness, and execution of purely self-initiated voluntary movement. Five right-handed healthy volunteers performed a rapid sequential finger-to-thumb movement performed at self-determined times (12-15 trials). Functional images for each trial were temporally aligned and the averaged time series for each subject was iteratively correlated with a canonical hemodynamic response function progressively shifted in time. This analysis method identified areas of activation without constraining hemodynamic response timing. All subjects showed activation within frontal mesial areas, including supplementary motor area (SMA) and cingulate motor areas, as well as activation in left primary sensorimotor areas. The time courses of hemodynamic responses showed a great deal of variability in shape and timing between subjects; however, four subjects clearly showed earlier relative hemodynamic responses within SMA/cingulate motor areas compared with left primary motor areas. These results provide further evidence that the SMA and cingulate motor areas are major contributors to early stage premovement activity and play an important role in the preparation and readiness for voluntary movement.
Abstract: Functional magnetic resonance imaging (fMRI) based on blood-oxygen level dependent (BOLD) contrast today is an established brain research method and quickly gains acceptance for complementary clinical diagnosis. However, neither the basic mechanisms like coupling between neuronal activation and haemodynamic response are known exactly, nor can the various artifacts be predicted or controlled. Thus, modeling functional signal changes is non-trivial and exploratory data analysis (EDA) may be rather useful. In particular, identification and separation of artifacts as well as quantification of expected, i.e. stimulus correlated, and novel information on brain activity is important for both, new insights in neuroscience and future developments in functional MRI of the human brain. After an introduction on fuzzy clustering and very high-field fMRI we present several examples where fuzzy cluster analysis (FCA) of fMRI time series helps to identify and locally separate various artifacts. We also present and discuss applications and limitations of fuzzy cluster analysis in very high-field functional MRI: differentiate temporal patterns in MRI using (a) a test object with static and dynamic parts, (b) artifacts due to gross head motion artifacts. Using a synthetic fMRI data set we quantitatively examine the influences of relevant FCA parameters on clustering results in terms of receiver-operator characteristics (ROC) and compare them with a commonly used model-based correlation analysis (CA) approach. The application of FCA in analyzing in vivo fMRI data is shown for (a) a motor paradigm, (b) data from multi-echo imaging, and (c) a fMRI study using mental rotation of three-dimensional cubes. We found that differentiation of true "neural" from false "vascular" activation is possible based on echo time dependence and specific activation levels, as well as based on their signal time-course. Exploratory data analysis methods in general and fuzzy cluster analysis in particular may help to identify artifacts and add novel and unexpected information valuable for interpretation, classification and characterization of functional MRI data which can be used to design new data acquisition schemes, stimulus presentations, neuro(physio)logical paradigms, as well as to improve quantitative biophysical models.
Abstract: Studies of functional brain imaging in humans and single cell recordings in monkeys have generally shown preferential involvement of the medially located supplementary motor area (SMA) in self-initiated movement and the lateral premotor cortex in externally cued movement. Studies of event-related cortical potentials recorded during movement preparation, however, generally show increased cortical activity prior to self-initiated movements but little activity at early stages prior to movements that are externally cued at unpredictable times. In this study, the spatial location and relative timing of activation for self-initiated and externally triggered movements were examined using rapid event-related functional MRI. Twelve healthy right-handed subjects were imaged while performing a brief finger sequence movement (three rapid alternating button presses: index-middle-index finger) made either in response to an unpredictably timed auditory cue (between 8 to 24 s after the previous movement) or at self-paced irregular intervals. Both movement conditions involved similar strong activation of medial motor areas including the pre-SMA, SMA proper, and rostral cingulate cortex, as well as activation within contralateral primary motor, superior parietal, and insula cortex. Activation within the basal ganglia was found for self-initiated movements only, while externally triggered movements involved additional bilateral activation of primary auditory cortex. Although the level of SMA and cingulate cortex activation did not differ significantly between movement conditions, the timing of the hemodynamic response within the pre-SMA was significantly earlier for self-initiated compared with externally triggered movements. This clearly reflects involvement of the pre-SMA in early processes associated with the preparation for voluntary movement.
Abstract: Recently, the technique of single-trial fMRI was introduced, which allows the assessment of hemodynamic responses to single task executions (e.g. sensory, motor, or cognitive). In this study, single-trial fMRI was used to examine regional differences in the inter-trial consistency (ITC) of brain activity related to the processing of a dynamic visuo-spatial imagery task. For every single trial, a t-statistic assessing task-related activation was calculated and thresholded at a p-value of P < or =l0.05 (uncorrected). The percentage of trials with t-values above the threshold was used to assess differences in the consistency of brain activity in occipital, parietal, premotor and prefrontal regions of interest. While most of these regions showed activation which was highly consistent across trials, the consistency of activation was significantly reduced in the dorsolateral prefrontal cortex. We hypothesize that the consistency of activation across trials may be interpreted as an indicator of the functional relevance of a brain region for processing and solving a cognitive task. Thus, the analysis concept presented here has the potential to provide new insights into the neuro-cognitive mechanisms of human information processing. In addition, the results of this study confirm that averaging across trials might result in a significant loss of information about functional neuroanatomy. Regions which are active in some trials only, which show only weak activity increases, or whose activity is not constant and time-locked, might not show up in averaged activation maps and could thus erroneously be interpreted as irrelevant for task processing.
Abstract: BOLD-based functional MRI (fMRI) can be used to explicitly measure hemodynamic aspects and functions of human neuro-physiology. As fMRI measures changes in regional cerebral blood flow and volume as well as blood oxygenation, rather than neuronal brain activity directly, other processes that may change the above parameters have to be examined closely to assess sensitivity and specificity of fMRI results. Physiological processes that can cause artifacts include cardiac action, breathing and vasomotion. Although there has been substantial research on physiological artifacts and appropriate compensation methods, controversy still remains on the mechanisms that cause the fMRI signal fluctuations. Respiratory-correlated fluctuations may either be induced by changes of the magnetic field homogeneity due to moving organs, intra-thoracic pressure differences, respiration-dependent vasodilation or oxygenation differences. The aim of this study was to characterize the impact of different breathing patterns by varying respiration frequency and/or tidal volume on EPI time courses of the resting human brain. The amount of respiration-related oscillations during three respiration patterns was quantified, and statistically significant differences were obtained in white matter only: p < 0.03 between 6 vs. 12 ml/kg body weight end tidal volume at a respiration frequency of 15/min, p < 0.03 between 12 vs. 6 ml/kg body weight and 15 vs. 10 respiration cycles/min. There was no significant difference between 15 vs. 10 respiration cycles/min at an end tidal volume of 6 ml/kg body weight (p = 0.917). In addition, the respiration-affected brain regions were very similar with EPI readout in the a-p and l-r direction. Based on our results and published literature we hypothesize that venous oxygenation oscillations due to changing intra-thoracic pressure represent a major factor for respiration-related signal fluctuations and increase significantly with increasing end tidal volume in white matter only.
Abstract: The spatial distribution of cancer foci of prostate carcinomas with negative initial biopsies was compared to that of prostate carcinomas with positive initial biopsies to detect areas in which carcinomas were more frequently located when the initial biopsy was negative.
Abstract: Although qualitative reports about somatotopic representation of fingers in the human motor cortex exist, up to now no study could provide clear statistical evidence. The goal of the present study was to reinvestigate finger motor somatotopy by means of a thorough investigation of standardized movements of the index and little finger of the right hand. Using high resolution fMRI at 3 Tesla, blood oxygenation level-dependent (BOLD) responses in a group of 26 subjects were repeatedly measured to achieve reliable statistical results. The center of mass of all activated voxels within the primary motor cortex was calculated for each finger and each run. Results of all runs were averaged to yield an individual index and little finger representation for each subject. The mean center of mass localizations for all subjects were then submitted to a paired t test. Results show a highly significant though small scale somatotopy of fingerspecific activation patterns in the order indicated by Penfields motor homunculus. In addition, considerable overlap of finger specific BOLD responses was found. Comparing various methods of analysis, the mean center of mass distance for the two fingers was 2--3 mm with overlapping voxels included and 4--5 mm with overlapping voxels excluded. Our data may be best understood in the context of the work of Schieber (1999) who recently described overlapping somatotopic gradients in lesion studies with humans.
Abstract: A strong correspondence has been repeatedly observed between actually performed and mentally imagined object rotation. This suggests an overlap in the brain regions involved in these processes. Functional neuroimaging studies have consistently revealed parietal and occipital cortex activity during dynamic visuospatial imagery. However, results concerning the involvement of higher-order cortical motor areas have been less consistent. We investigated if and when premotor structures are active during processing of a three-dimensional cube comparison task that requires dynamic visuospatial imagery. In order to achieve a good temporal and spatial resolution, single-trial functional magnetic resonance imaging (fMRI) and scalp-recorded event-related slow cortical potentials (SCPs) were recorded from the same subjects in two separate measurement sessions. In order to reduce inter-subject variability in brain activity due to individual differences, only male subjects (n = 13) with high task-specific ability were investigated. Functional MRI revealed consistent bilateral activity in the occipital (Brodmann area BA18/19) and parietal cortex (BA7), in lateral and medial premotor areas (BA6), the dorsolateral prefrontal cortex (BA9), and the anterior insular cortex. The time-course of SCPs indicated that task-related activity in these areas commenced approximately 550-650 ms after stimulus presentation and persisted until task completion. These results provide strong and consistent evidence that the human premotor cortex is involved in dynamic visuospatial imagery.
Abstract: This study provides the first investigation of supplementary motor area (SMA) and primary motor cortex (MI) activation with similar movements differing only in subjective difficulty of motor control. Brain activation with simple tapping of the right index finger (well trained during daily life and easy to perform) was compared with tapping of the little finger (less trained and difficult to perform) using functional magnetic resonance imaging at 3 Tesla. Due to optimised movement standardisation, extrinsic influences on activation levels such as movement complexity, amplitude and frequency were minimised. Fifth finger tapping significantly increased the number of activated SMA voxels by 450% whereas MI activation showed no significant difference between fingers. We conclude that with similar movements the degree of subjective difficulty specifically modifies SMA but not MI activation.
Abstract: Accurate co-registration of MRI and EEG data is indispensable for the correct interpretation of EEG maps or source localizations in relation to brain anatomy derived from MRI. In this study, a method for the co-registration of EEG and MRI data is presented. The method consists of an iterative matching of EEG-electrode based reconstructions of the scalp surface to scalp-segmented MRIs. EEG-electrode based surface reconstruction is achieved via spline interpolation of individually digitized 3D-electrode coordinates. In contrast to other approaches, neither fiducial determination nor any additional provisions (such as bite bars, other co-registration devices or head shape digitization) are required, and co-registration errors associated with inaccurate fiducial determination are avoided. The accuracy of the method was estimated by calculating the root-mean-square (RMS) deviation of spline interpolated and MRI-segmented surface reconstructions in 20 subjects. In addition, the distance between co-registered and genuine electrode coordinates was assessed via a simulation study, in which surface reconstruction was based on virtual electrodes determined on the scalp surface of a high-resolution MRI data set. The mean RMS deviation of surface reconstructions was 2.43 mm, and the maximal distance between any two matched surface points was 5.06 mm. The simulated co-registration revealed a mean deviation of genuine and co-registered electrode coordinates of 0.61 mm. It is concluded that surface matching using spline interpolated reconstructions of scalp surfaces is a precise and highly practicable method to co-register EEG and MRI data.
Abstract: A combination of multiple gradient-echo imaging and exploratory data analysis (EDA), i.e. fuzzy cluster analysis (FCA), is proposed for separation and characterization of BOLD activation in single-shot spiral functional magnetic resonance imaging (fMRI) experiments at 3 T. Differentiation of functional activation using FCA is performed by clustering pixel signal changes (DeltaS) as a function of echo time (TE). Further vascular classification is supported by the localization of activation and the comparison with a single-exponential decay model. In some subjects, an additional indication for large vessels within a voxel was found as oscillation of the fMRI signal difference vs echo time (TE). Such large vessels may be separated from small vessel activation and, therefore, our proposed procedure might prove useful if a more specific functional localization is desired in fMRI. In addition to the signal change DeltaS, DeltaT(2)*/T(2)* is significantly different between activated regions. Averaged over all eight subjects DeltaT(2)* is 1.7 +/- 0.2 ms in ROIs with the highest signal change characterized as containing large vessels, whereas in ROIs corresponding to microvascular environment average DeltaT(2)* values are 0.8 +/- 0.1 ms.
Abstract: Despite the fact that the knowledge about the structure and the function of the supplementary motor area (SMA) is steadily increasing, the role of the SMA in the human brain, e.g., the contribution of the SMA to the Bereitschaftspotential, still remains unclear and controversial. The goal of this study was to contribute further to this discussion by taking advantage of the increased spatial information of a whole-scalp magnetoencephalography (MEG) system enabling us to record the magnetic equivalent of the Bereitschaftspotential 1, the Bereitschaftsfeld 1 (BF 1) or readiness field 1. Five subjects performed a complex, and one subject a simple, finger-tapping task. It was possible to record the BF 1 for all subjects. The first appearance of the BF 1 was in the range of -1.9 to -1.7 s prior to movement onset, except for the subject performing the simple task (-1 s). Analysis of the development of the magnetic field distribution and the channel waveforms showed the beginning of the Bereitschaftsfeld 2 (BF 2) or readiness field 2 at about -0.5 s prior to movement onset. In the time range of BF 1, dipole source analysis localized the source in the SMA only, whereas dipole source analysis containing also the time range of BF 2 resulted in dipole models, including dipoles in the primary motor area. In summary, with a whole-head MEG system, it was possible for the first time to detect SMA activity in healthy subjects with MEG.
Abstract: Magnetic resonance (MR) images acquired with fast measurement often display poor signal-to-noise ratio (SNR) and contrast. With the advent of high temporal resolution imaging, there is a growing need to remove these noise artifacts. The noise in magnitude MR images is signal-dependent (Rician), whereas most de-noising algorithms assume additive Gaussian (white) noise. However, the Rician distribution only looks Gaussian at high SNR. Some recent work by Nowak employs a wavelet-based method for de-noising the square magnitude images, and explicitly takes into account the Rician nature of the noise distribution. In this article, we apply a wavelet de-noising algorithm directly to the complex image obtained as the Fourier transform of the raw k-space two-channel (real and imaginary) data. By retaining the complex image, we are able to de-noise not only magnitude images but also phase images. A multiscale (complex) wavelet-domain Wiener-type filter is derived. The algorithm preserves edges better when the Haar wavelet rather than smoother wavelets, such as those of Daubechies, are used. The algorithm was tested on a simulated image to which various levels of noise were added, on several EPI image sequences, each of different SNR, and on a pair of low SNR MR micro-images acquired using gradient echo and spin echo sequences. For the simulated data, the original image could be well recovered even for high values of noise (SNR approximately 0 dB), suggesting that the present algorithm may provide better recovery of the contrast than Nowak's method. The mean-square error, bias, and variance are computed for the simulated images. Over a range of amounts of added noise, the present method is shown to give smaller bias than when using a soft threshold, and smaller variance than a hard threshold; in general, it provides a better bias-variance balance than either hard or soft threshold methods. For the EPI (MR) images, contrast improvements of up to 8% (for SNR = 33 dB) were found. In general, the improvement in contrast was greater the lower the original SNR, for example, up to 50% contrast improvement for SNR of about 20 dB in micro-imaging. Applications of the algorithm to the segmentation of medical images, to micro-imaging and angiography (where the correct preservation of phase is important for flow encoding to be possible), as well as to de-noising time series of functional MR images, are discussed.
Abstract: Recent functional magnetic resonance imaging (FMRI) replication studies show a high variability of active voxels within subjects and across runs - a potentially harmful situation for clinical applications. We tried to reduce these uncertainties inherent in current presurgical FMRI. For this, a new high quality head fixation device was used to detect reliably activated voxels over repeated measurements. In addition high correlation thresholds were applied to define the areas with highest probability of activation. The results show a focussing of such functional high risk areas to only a few voxels which localized close to intraoperative cortical stimulation. The generation of such FMRI risk maps may improve validity of clinical localization and facilitate the development of currently missing standards for maximized but still safe tumor resection.
Abstract: Single-shot echo planar imaging (EPI) is one of the most suitable techniques for very fast image acquisition, especially in functional MRI. In standard EPI schemes the k-space center is sampled in the middle of the acquisition train. This leads to longer echo times for higher spatial resolutions, as well as reduced signal intensity and signal-to-noise ratio. Therefore, echo shifting to lower echo times is often used. After a brief overview on the theoretical background of various point-spread-functions (PSF) computational simulations are presented, which quantify the modulation amplitude of a binary test object sampled with either standard, zero-filled or shifted k-space acquisition. The results suggest that echo-shifting with zero-filling is not advantageous, not even with small matrix sizes, and that echo-shifting with additional acquisition of outer k-space lines decreases the modulation amplitude only slightly. Simulations were also performed on noise-corrupted test objects, indicating that the use of the echo-shifted scheme causes resolution loss of up to 30% compared to the standard scheme for a 128 by 128 pixel matrix at a noise level of 20%. Finally, in vivo experiments using different echo shifts are presented and the characteristics of signal and noise with varying TE are quantified.
Abstract: Magnetic resonance images acquired with high temporal resolution often exhibit large noise artifacts, which arise from physiological sources as well as from the acquisition hardware. These artifacts can be detrimental to the quality and interpretation of the time-course data in functional MRI studies. A class of wavelet-domain de-noising algorithms estimates the underlying, noise-free signal by thresholding (or 'shrinking') the wavelet coefficients, assuming the underlying temporal noise of each pixel is uncorrelated and Gaussian. A Wiener-type shrinkage algorithm is developed in this paper, for de-noising either complex- or magnitude-valued image data sequences. Using the de-correlation properties of the wavelet transform, as elucidated by Johnstone and Silverman, the assumption of i.i.d. Gaussian noise can be abandoned, opening up the possibility of removing colored noise. Both wavelet- and wavelet-packet based algorithms are developed, and the Wiener method is compared to the traditional Hard and Soft wavelet thresholding methods of Donoho and Johnstone. The methods are applied to two types of data sets. In the first, an artificial set of complex-valued images was constructed, in which each pixel has a simulated bimodal time-course. Gaussian noise was added to each of the real and imaginary channels, and the noise removed from the complex image sequence as well as the magnitude image sequence (where the noise is Rician). The bias and variance between the original and restored paradigms was estimated for each method. It was found that the Wiener method gives better balance in bias and variance than either Hard or Soft methods. Furthermore, de-noising magnitude data provides comparable accuracy of the restored images to that obtained from de-noising complex data. In the second data set, an actual in vivo complex image sequence containing unknown physiological and instrumental noise was used. The same bimodal paradigm as in the first data set was added to pixels in a small localized region of interest. For the paradigm investigated here, the smooth Daubechies wavelets provide better de-noising characteristics than the discontinuous Haar wavelets. Also, it was found that wavelet packet de-noising offers no significant improvement over the computationally more efficient wavelet de-noising methods. For the in vivo data, it is desirable that the groups of "activated" time-courses are homogeneous. It was found that the internal homogeneity of the group of time-courses increases when de-noising is applied. This suggests using de-noising as a pre-processing tool for both exploratory and inferential data analysis methods in fMRI.
Abstract: In light of artifact-induced high variability of activation in fMRI repeat studies, we developed and tested a clinically useful plaster cast head holder (PCH) with improved immobilization, repositioning, and comfort. With PCH, there were considerably lower levels of translational and rotational head motion components compared to head fixation with conventional restraining straps (CRS). Rotational components cannot be fully compensated by realignment and lead to "false activations." In addition, task-correlated head motion, which highly increases the risk of artifacts, was considerably reduced with PCH, especially in a motion prone subject. Compared with PCH, head motion was 133% larger with CRS in a highly cooperative subject. With a motion prone subject, head motion range was increased by 769% (PCH: 0.9 mm, CRS: 7.8 mm), which may indicate the usefulness of PCH for restless patients. In functional activation maps, PCH alone yielded fewer residual motion artifacts than CRS + image registration. Subject tolerance of the head holder during the long measurement times of up to 2.5 hr was good, and slice orientation on different days confirmed the quality of repositioning.
Abstract: Fractal geometry is a tool used to characterize irregularly shaped and complex figures. It can be used not only to generate biological structures (e.g., the human renal artery tree), but also to derive parameters such as the fractal dimension in order to quantify the shapes of structures. As such, it allows user-independent evaluation and does not rely on the experience level of the examiner.
Abstract: The potential of functional MRI (fMRI) data analysis using the paradigm independent fuzzy cluster analysis (FCA) applied in the time domain compared to frequently used paradigm based correlation analysis (CA) was studied with simulated and in vivo fMRI data. The performance of FCA and CA was investigated in a typical contrast-to-noise range for fMRI, ranging from 1.33 to 3.33. Using simulated fMRI data the methods were quantitatively compared in terms of generation of true positives, false positives, and the corresponding signal enhancement. Even without prior knowledge about the stimulation paradigm and the actual hemodynamic response function the performance of FCA was comparable to that of CA where extensive prior knowledge has to be added. Furthermore, discrimination of nonanticipated hemodynamic responses by FCA, such as different levels of activation and delayed response, are demonstrated in simulated and in vivo fMRI data. We demonstrate that using CA one cannot differentiate between these responses at least without extensive prior knowledge, i.e., FCA yields a more particular description of fMRI data. This may be worthwhile for analysis and optimization of data quality in fMRI as well as in the final data analysis.
Abstract: Fractal analysis techniques are common tools in physics and image processing. In the past few years they have gained increasing attention in medical sciences, e.g., cardiology, pathology and radiology, respectively. This article intends to describe this new technique by applying fractal analysis to the instant of a breast carcinoma. One of the advantages of fractal analysis is the ability to describe an irregular and complex object by a measureable value, called the fractal dimension. The fractal dimension can be determined by using the box-counting method. We applied this technique to the mammography as well as to the histologic section of a breast carcinoma. The application of fractal analysis provides a specific measureable value of the growth pattern of a tumor. Thus, the fractal dimension serves in addition to the common used metric diameter.
