Background and Purpose-Functional MRI is a powerful tool to investigate recovery of brain function in patients with stroke. An inherent assumption in functional MRI data analysis is that the blood oxygenation level-dependent (BOLD) signal is stable over the course of the examination. In this study, we evaluated the validity of such assumption in patients with chronic stroke. Methods-Fifteen patients performed a simple motor task with repeated epochs using the paretic and the unaffected hand in separate runs. The corresponding BOLD signal time courses were extracted from the primary and supplementary motor areas of both hemispheres. Statistical maps were obtained by the conventional General Linear Model and by a parametric General Linear Model. Results-Stable BOLD amplitude was observed when the task was executed with the unaffected hand. Conversely, the BOLD signal amplitude in both primary and supplementary motor areas was progressively attenuated in every patient when the task was executed with the paretic hand. The conventional General Linear Model analysis failed to detect brain activation during movement of the paretic hand. However, the proposed parametric General Linear Model corrected the misdetection problem and showed robust activation in both primary and supplementary motor areas. Conclusions-The use of data analysis tools that are built on the premise of a stable BOLD signal may lead to misdetection of functional regions and underestimation of brain activity in patients with stroke. The present data urge the use of caution when relying on the BOLD response as a marker of brain reorganization in patients with stroke. (Stroke. 2010; 41:1921-1926.); Brazilian Financial Agencies FAPESP[05/03225-7]; CNPq; CAPES[PROCAD-NF: 23/2010]; FINEP; National Institutes of Health...
Attention deficit hyperactivity disorder (ADHD) affects about 5% of school-aged child. Previous published works using different techniques of magnetic resonance imaging (MRI) have demonstrated that there may be some differences between the brain of people with and without this condition. This review aims at providing neurologists, pediatricians and psychiatrists an update on the differences between the brain of children with and without ADHD using advanced techniques of magnetic resonance imaging such as diffusion tensor imaging, brain volumetry and cortical thickness, spectroscopy and functional MRI. Data was obtained by a comprehensive, non-systematic review of medical literature. The regions with a greater number of abnormalities are splenium of the corpus callosum, cingulated girus, caudate nucleus, cerebellum, striatum, frontal and temporal cortices. The brain regions where abnormalities are observed in studies of diffusion tensor, volumetry, spectroscopy and cortical thickness are the same involved in neurobiological theories of ADHD coming from studies with functional magnetic resonance imaging.
Functional MRI was used to identify cortical areas involved in category learning by prototype abstraction. Participants studied 40 dot patterns that were distortions of an underlying prototype and then, while functional MRI data were collected, made yes-no category judgments about new dot patterns. The dot patterns alternated between ones mostly requiring a “yes” response and ones mostly requiring a “no” response. Activity in four cortical areas correlated with the category judgment task. A sizeable posterior occipital cortical area (BA 17/18) exhibited significantly less activity during processing of the categorical patterns than during processing of noncategorical patterns. Significant increases in activity during processing the categorical patterns were observed in left and right anterior frontal cortex (BA 10) and right inferior lateral frontal cortex (BA 44/47). Decreases in activation of visual cortex when categorical patterns were being evaluated suggest that these patterns could be processed in a more rapid or less effortful manner after the prototype had been learned. Increases in prefrontal activity associated with processing categorical patterns could be related to any of several processes involved in retrieving information about the learned exemplars.
A method is given for determining the time course and spatial
extent of consistently and transiently task-related activations from
other physiological and artifactual components that contribute to
functional MRI (fMRI) recordings. Independent component analysis (ICA)
was used to analyze two fMRI data sets from a subject performing 6-min
trials composed of alternating 40-sec Stroop color-naming and control
task blocks. Each component consisted of a fixed three-dimensional
spatial distribution of brain voxel values (a “map”) and an
associated time course of activation. For each trial, the algorithm
detected, without a priori knowledge of their spatial or
temporal structure, one consistently task-related component activated
during each Stroop task block, plus several transiently task-related
components activated at the onset of one or two of the Stroop task
blocks only. Activation patterns occurring during only part of the fMRI
trial are not observed with other techniques, because their time
courses cannot easily be known in advance. Other ICA components were
related to physiological pulsations, head movements, or machine noise.
By using higher-order statistics to specify stricter criteria for
spatial independence between component maps...
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. The
potential clinical usefulness of visual functional MRI data during
resective surgery was evaluated in patients with occipital lobe tumours. METHODS—Eleven
patients with various tumours and visual field defects and 12 normal
subjects were studied by fMRI using bimonocular or monocular repetitive
photic stimulation (8 Hz). The data obtained were analyzed with the
statistical parametric maps software (p<10-8) and were
compared with the results of Goldmann visual field perimetric
evaluation. In patients with occipital brain tumours undergoing
surgery, the functional data were registered in a frameless stereotactic device and the images fused into anatomical three standard
planes and three dimensional reconstructions of the brain surface. RESULTS—Two studies of
patients were discarded, one because of head motion and the other
because of badly followed instructions. On the remaining patients the
functional activations found in the visual cortex were consistent with
the results of perimetric evaluation in all but one of the patients and
all the normal subjects although the results of fMRI were highly
dependent on the choices of the analysis thresholds. Visual functional
MRI image guided data were used in five patients with occipital brain
tumours. No added postoperative functional field defect was detected. CONCLUSIONS—There was
a good correspondence between fMRI data and the results of perimetric
evaluation although dependent on the analysis thresholds. Visual fMRI
data registered into a frameless stereotactic device may be useful in
surgical planning and tumour removal.
In this study, an implicit reference group-wise (IRG) registration with a small deformation, linear elastic model was used to jointly estimate correspondences between a set of MRI images. The performance of pair-wise and group-wise registration algorithms was evaluated for spatial normalization of structural and functional MRI data. Traditional spatial normalization is accomplished by group-to-reference (G2R) registration in which a group of images are registered pair-wise to a reference image. G2R registration is limited due to bias associated with selecting a reference image. In contrast, implicit reference group-wise (IRG) registration estimates correspondences between a group of images by jointly registering the images to an implicit reference corresponding to the group average. The implicit reference is estimated during IRG registration eliminating the bias associated with selecting a specific reference image. Registration performance was evaluated using segmented T1-weighted magnetic resonance images from the Nonrigid Image Registration Evaluation Project (NIREP), DTI and fMRI images. Implicit reference pair-wise (IRP) registration—a special case of IRG registration for two images—is shown to produce better relative overlap than IRG for pair-wise registration using the same small deformation...
Estimating the effective signal dimension of resting-state functional MRI (fMRI) data sets (i.e., selecting an appropriate number of signal components) is essential for data-driven analysis. However, current methods are prone to overestimate the dimensions, especially for concatenated group data sets. This work aims to develop improved dimension estimation methods for group fMRI data generated by data reduction and grouping procedure at multiple levels. We proposed a “noise-blurring” approach to suppress intragroup signal variations and to correct spectral alterations caused by the data reduction, which should be responsible for the group dimension overestimation. This technique was evaluated on both simulated group data sets and in vivo resting-state fMRI data sets acquired from 14 normal human subjects during five different scan sessions. Reduction and grouping procedures were repeated at three levels in either “scan–session–subject” or “scan–subject–session” order. Compared with traditional estimation methods, our approach exhibits a stronger immunity against intragroup signal variation, less sensitivity to group size and a better agreement on the dimensions at the third level between the two grouping orders.
The traditional approach to functional image analysis models images as matrices of raw voxel intensity values. Although such a representation is widely utilized and heavily entrenched both within neuroimaging and in the wider data mining community, the strong interactions among space, time, and categorical modes such as subject and experimental task inherent in functional imaging yield a dataset with “high-order” structure, which matrix models are incapable of exploiting. Reasoning across all of these modes of data concurrently requires a high-order model capable of representing relationships between all modes of the data in tandem. We thus propose to model functional MRI data using tensors, which are high-order generalizations of matrices equivalent to multidimensional arrays or data cubes. However, several unique challenges exist in the high-order analysis of functional medical data: naïve tensor models are incapable of exploiting spatiotemporal locality patterns, standard tensor analysis techniques exhibit poor efficiency, and mixtures of numeric and categorical modes of data are very often present in neuroimaging experiments. Formulating the problem of image clustering as a form of Latent Semantic Analysis and using the WaveCluster algorithm as a baseline...
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.
Studies of functional MRI data are increasingly concerned with the estimation of differences in spatio-temporal networks across groups of subjects or experimental conditions. Unsupervised clustering and independent component analysis (ICA) have been used to identify such spatio-temporal networks. While these approaches have been useful for estimating these networks at the subject-level, comparisons over groups or experimental conditions require further methodological development. In this paper, we tackle this problem by showing how self-organizing maps (SOMs) can be compared within a Frechean inferential framework. Here, we summarize the mean SOM in each group as a Frechet mean with respect to a metric on the space of SOMs. The advantage of this approach is twofold. Firstly, it allows the visualization of the mean SOM in each experimental condition. Secondly, this Frechean approach permits one to draw inference on group differences, using permutation of the group labels. We consider the use of different distance functions, and introduce one extension of the classical sum of minimum distance (SMD) between two SOMs, which take into account the spatial pattern of the fMRI data. The validity of these methods is illustrated on synthetic data. Through these simulations...
Raumforderungen der Zentralregion stellen für die neurochirurgische Behandlung eine besondere Herausforderung dar. Magnetresonanz- tomographische (MRT) Bildgebungsmodalitäten haben das Potential, als nicht-invasive Hirnkartierungsmethoden zur präoperative Planung und operativen Risikoverminderung beizutragen. Dabei bietet die funktionelle Magnetresonanztomographie (fMRT) wichtige Informationen über die kortikale Funktion, während die Diffusions-Tensor-Magnetresonanztomographie (DTI) die subkortikale Faserbahnarchitektur darstellt. Eine systematische Kombination dieser beiden kernspintomographischen Modalitäten für die neurochirurgische Operationsplanung bei Patienten mit Raumforderungen der Zentralregion steht aus.
Durchführung und Visualisierung von strukturellen, funktionellen und diffusionsgewichteten MRT-Aufnahmen bei Probanden und neurochirurgischen Patienten mit kortikalen und subkortikalen Raumforderungen der Zentralregion. Akquirierung von fMRT-Daten mit motorischen Paradigmen zur Darstellung des primären, motorischen Kortex und von DTI-Daten zur Traktographie des motorischen Faserbahnsystems (Pyramidenbahn). Durchführung einer klassischen Traktographie mittels, vom Untersucher vordefinierter...
Les lésions de la moelle épinière ont un impact significatif sur la qualité de la vie car elles peuvent induire des déficits moteurs (paralysie) et sensoriels. Ces déficits évoluent dans le temps à mesure que le système nerveux central se réorganise, en impliquant des mécanismes physiologiques et neurochimiques encore mal connus. L'ampleur de ces déficits ainsi que le processus de réhabilitation dépendent fortement des voies anatomiques qui ont été altérées dans la moelle épinière. Il est donc crucial de pouvoir attester l'intégrité de la matière blanche après une lésion spinale et évaluer quantitativement l'état fonctionnel des neurones spinaux. Un grand intérêt de l'imagerie par résonance magnétique (IRM) est qu'elle permet d'imager de façon non invasive les propriétés fonctionnelles et anatomiques du système nerveux central. Le premier objectif de ce projet de thèse a été de développer l'IRM de diffusion afin d'évaluer l'intégrité des axones de la matière blanche après une lésion médullaire. Le deuxième objectif a été d'évaluer dans quelle mesure l'IRM fonctionnelle permet de mesurer l'activité des neurones de la moelle épinière. Bien que largement appliquées au cerveau, l'IRM de diffusion et l'IRM fonctionnelle de la moelle épinière sont plus problématiques. Les difficultés associées à l'IRM de la moelle épinière relèvent de sa fine géométrie (environ 1 cm de diamètre chez l'humain)...
We evaluated the performance of an optical camera based prospective motion correction (PMC) system in improving the quality of 3D echo-planar imaging functional MRI data. An optical camera and external marker were used to dynamically track the head movement of subjects during fMRI scanning. PMC was performed by using the motion information to dynamically update the sequence's RF excitation and gradient waveforms such that the field-of-view was realigned to match the subject's head movement. Task-free fMRI experiments on five healthy volunteers followed a 2 × 2 × 3 factorial design with the following factors: PMC on or off; 3.0 mm or 1.5 mm isotropic resolution; and no, slow, or fast head movements. Visual and motor fMRI experiments were additionally performed on one of the volunteers at 1.5 mm resolution comparing PMC on vs PMC off for no and slow head movements. Metrics were developed to quantify the amount of motion as it occurred relative to k-space data acquisition. The motion quantification metric collapsed the very rich camera tracking data into one scalar value for each image volume that was strongly predictive of motion-induced artifacts. The PMC system did not introduce extraneous artifacts for the no motion conditions and improved the time series temporal signal-to-noise by 30% to 40% for all combinations of low/high resolution and slow/fast head movement relative to the standard acquisition with no prospective correction. The numbers of activated voxels (p < 0.001...
An emerging application of resting-state functional MRI (rs-fMRI) is the study of patients with disorders of consciousness (DoC), where integrity of default-mode network (DMN) activity is associated to the clinical level of preservation of consciousness. Due to the inherent inability to follow verbal instructions, arousal induced by scanning noise and postural pain, these patients tend to exhibit substantial levels of movement. This results in spurious, non-neural fluctuations of the rs-fMRI signal, which impair the evaluation of residual functional connectivity. Here, the effect of data preprocessing choices on the detectability of the DMN was systematically evaluated in a representative cohort of 30 clinically and etiologically heterogeneous DoC patients and 33 healthy controls. Starting from a standard preprocessing pipeline, additional steps were gradually inserted, namely band-pass filtering (BPF), removal of co-variance with the movement vectors, removal of co-variance with the global brain parenchyma signal, rejection of realignment outlier volumes and ventricle masking. Both independent-component analysis (ICA) and seed-based analysis (SBA) were performed, and DMN detectability was assessed quantitatively as well as visually. The results of the present study strongly show that the detection of DMN activity in the sub-optimal fMRI series acquired on DoC patients is contingent on the use of adequate filtering steps. ICA and SBA are differently affected but give convergent findings for high-grade preprocessing. We propose that future studies in this area should adopt the described preprocessing procedures as a minimum standard to reduce the probability of wrongly inferring that DMN activity is absent.
Subject motion has long since been known to be a major confound in functional MRI studies of the human brain. For resting-state functional MRI in particular, data corruption due to motion artefacts has been shown to be most relevant. However, despite 6 parameters (3 for translations and 3 for rotations) being required to fully describe the head's motion trajectory between timepoints, not all are routinely used to assess subject motion. Using structural (n = 964) as well as functional MRI (n = 200) data from public repositories, a series of experiments was performed to assess the impact of using a reduced parameter set (translationonly and rotationonly) versus using the complete parameter set. It could be shown that the usage of 65 mm as an indicator of the average cortical distance is a valid approximation in adults, although care must be taken when comparing children and adults using the same measure. The effect of using slightly smaller or larger values is minimal. Further, both translationonly and rotationonly severely underestimate the full extent of subject motion; consequently, both translationonly and rotationonly discard substantially fewer datapoints when used for quality control purposes (“motion scrubbing”). Finally...
Neuroimaging community usually employs spatial smoothing to denoise magnetic resonance imaging (MRI) data, e.g., Gaussian smoothing kernels. Such an isotropic diffusion (ISD) based smoothing is widely adopted for denoising purpose due to its easy implementation and efficient computation. Beyond these advantages, Gaussian smoothing kernels tend to blur the edges, curvature and texture of images. Researchers have proposed anisotropic diffusion (ASD) and non-local diffusion (NLD) kernels. We recently demonstrated the effect of these new filtering paradigms on preprocessing real degraded MRI images from three individual subjects. Here, to further systematically investigate the effects at a group level, we collected both structural and functional MRI data from 23 participants. We first evaluated the three smoothing strategies' impact on brain extraction, segmentation and registration. Finally, we investigated how they affect subsequent mapping of default network based on resting-state functional MRI (R-fMRI) data. Our findings suggest that NLD-based spatial smoothing maybe more effective and reliable at improving the quality of both MRI data preprocessing and default network mapping. We thus recommend NLD may become a promising method of smoothing structural MRI images of R-fMRI pipeline.
Understanding the functional architecture of the brain in terms of networks
is becoming increasingly common. In most fMRI applications functional networks
are assumed to be stationary, resulting in a single network estimated for the
entire time course. However recent results suggest that the connectivity
between brain regions is highly non-stationary even at rest. As a result, there
is a need for new brain imaging methodologies that comprehensively account for
the dynamic (i.e., non-stationary) nature of the fMRI data. In this work we
propose the Smooth Incremental Graphical Lasso Estimation (SINGLE) algorithm
which estimates dynamic brain networks from fMRI data. We apply the SINGLE
algorithm to functional MRI data from 24 healthy patients performing a
choice-response task to demonstrate the dynamic changes in network structure
that accompany a simple but attentionally demanding cognitive task. Using graph
theoretic measures we show that the Right Inferior Frontal Gyrus, frequently
reported as playing an important role in cognitive control, dynamically changes
with the task. Our results suggest that the Right Inferior Frontal Gyrus plays
a fundamental role in the attention and executive function during cognitively
demanding tasks and may play a key role in regulating the balance between other
Studies of functional MRI data are increasingly concerned with the estimation
of differences in spatio-temporal networks across groups of subjects or
experimental conditions. Unsupervised clustering and independent component
analysis (ICA) have been used to identify such spatio-temporal networks. While
these approaches have been useful for estimating these networks at the
subject-level, comparisons over groups or experimental conditions require
further methodological development. In this paper, we tackle this problem by
showing how self-organizing maps (SOMs) can be compared within a Frechean
inferential framework. Here, we summarize the mean SOM in each group as a
Frechet mean with respect to a metric on the space of SOMs. We consider the use
of different metrics, and introduce two extensions of the classical sum of
minimum distance (SMD) between two SOMs, which take into account the
spatio-temporal pattern of the fMRI data. The validity of these methods is
illustrated on synthetic data. Through these simulations, we show that the
three metrics of interest behave as expected, in the sense that the ones
capturing temporal, spatial and spatio-temporal aspects of the SOMs are more
likely to reach significance under simulated scenarios characterized by
The anatomical structure of the brain can be observed via non-invasive
techniques such as diffusion imaging. However, these are imperfect because they
miss connections that are actually known to exist, especially long range
inter-hemispheric ones. In this paper we formulate the inverse problem of
inferring the structural connectivity of brain networks from experimentally
observed functional connectivity via functional Magnetic Resonance Imaging
(fMRI), by formulating it as a convex optimization problem. We show that
structural connectivity can be modeled as an optimal sparse representation
derived from the much denser functional connectivity in the human brain. Using
only the functional connectivity data as input, we present (a) an optimization
problem that models constraints based on known physiological observations, and
(b) an ADMM algorithm for solving it. The algorithm not only recovers the known
structural connectivity of the brain, but is also able to robustly predict the
long range inter-hemispheric connections missed by DSI or DTI, including a very
good match with experimentally observed quantitative distributions of the
weights/strength of anatomical connections. We demonstrate results on both
synthetic model data and a fine-scale 998 node cortical dataset...
Schizophrenia is a complex, chronic and disabling mental disorder that affects
about one percent of the adult population. The etiology of schizophrenia remains elusive
and to date there are no image based tools to diagnose it. Advancements in magnetic
resonance imaging (MRI) have enabled researchers to develop less invasive and in vivo
techniques, such as structural MRI (sMRI), functional MRI (fMRI) and diffusion tensor
imaging (DTI), to construct theories about the neural underpinnings of schizophrenia.
With sMRI, fMRI and DTI the distribution of tissues, the functional activity and the brain
network are imaged respectively. Subjects with schizophrenia (SZ) and healthy controls
(HC) are scanned with different modalities to identify differences, but the analysis of
each modality has traditionally been carried out separately. Data fusion of multimodal
data and an analysis of the joint information may hold the key to reveal hidden traces of
this subtle disorder.
In this work we develop techniques to correlate sMRI with fMRI, fMRI with
other fMRI and DTI with symptom scores. The brain is a highly interconnected organ
and local morphology can influence functional activity at distant regions. Through our
methods it is possible to perform a cross correlation analysis between modalities
incorporating all brain voxels. By reducing the large cross correlation matrix to useful
statistics new aspects of schizophrenia are revealed. The methods introduced are simple...