Luke Bloy

Unsupervised White Matter Fiber Clustering and Tract Probability Map Generation: Applications of a Gaussian Process framework for White Matter Fibers

With the increasing importance of fiber tracking in diffusion tensor images for clinical needs, there has been a growing demand for an objective mathematical framework to perform quantitative analysis of white matter fiber bundles incorporating their underlying physical significance. This article presents such a novel mathematical framework that facilitates mathematical operations between tracts using an inner product between fibres. Such inner product operation, based on Gaussian processes, spans a metric space. This metric facilitates combination of fiber tracts, rendering operations like tract membership to a bundle or bundle similarity simple. Based on this framework, we have designed an automated unsupervised atlas-based clustering method that does not require manual initialization nor an a priori knowledge of the number of clusters. Quantitative analysis can now be performed on the clustered tract volumes across subjects, thereby avoiding the need for point parameterization of these fibers, or the use of medial or envelope representations as in previous work. Experiments on synthetic data demonstrate the mathematical operations. Subsequently, the applicability of the unsupervised clustering framework has been demonstrated on a 21-subject dataset.

Diffusion based abnormality markers of pathology: toward learned diagnostic prediction of ASD.

This paper presents a paradigm for generating a quantifiable marker of pathology that supports diagnosis and provides a potential biomarker of neuropsychiatric disorders, such as autism spectrum disorder (ASD). This is achieved by creating high-dimensional nonlinear pattern classifiers using support vector machines (SVM), that learn the underlying pattern of pathology using numerous atlas-based regional features extracted from diffusion tensor imaging (DTI) data. These classifiers, in addition to providing insight into the group separation between patients and controls, are applicable on a single subject basis and have the potential to aid in diagnosis by assigning a probabilistic abnormality score to each subject that quantifies the degree of pathology and can be used in combination with other clinical scores to aid in diagnostic decision. They also produce a ranking of regions that contribute most to the group classification and separation, thereby providing a neurobiological insight into the pathology. As an illustrative application of the general framework for creating diffusion based abnormality classifiers we create classifiers for a dataset consisting of 45 children with ASD (mean age 10.5 ± 2.5 yr) as compared to 30 typically developing (TD) controls (mean age 10.3 ± 2.5 yr). Based on the abnormality scores, a distinction between the ASD population and TD controls was achieved with 80% leave one out (LOO) cross-validation accuracy with high significance of p<0.001, ~84% specificity and ~74% sensitivity. Regions that contributed to this abnormality score involved fractional anisotropy (FA) differences mainly in right occipital regions as well as in left superior longitudinal fasciculus, external and internal capsule while mean diffusivity (MD) discriminates were observed primarily in right occipital gyrus and right temporal white matter.

On Computing the Underlying Fiber Directions from the Diffusion Orientation Distribution Function

In this work, a novel method for determining the principal directions (maxima) of the diffusion orientation distribution function (ODF) is proposed. We represent the ODF as a symmetric high-order Cartesian tensor restricted to the unit sphere and show that the extrema of the ODF are solutions to a system of polynomial equations whose coefficients are polynomial functions of the tensor elements. In addition to demonstrating the ability of our methods to identify the principal directions in real data, we show that this method correctly identifies the principal directions under a range of noise levels. We also propose the use of the principal curvatures of the graph of the ODF function as a measure of the degree of diffusion anisotropy in that direction. We present simulated results illustrating the relationship between the mean principal curvature, measured at the maxima, and the fractional anisotropy of the underlying diffusion tensor.

Spatial sensitivity and temporal response of spin echo and gradient echo bold contrast at 3 T using peak hemodynamic activation time

Recent theoretical and experimental work has suggested that spin echo (SE) functional MRI (fMRI) has improved localization of neural activity compared to gradient echo (GE) fMRI at high field strengths, albeit with a decrease in blood oxygenation level-dependent (BOLD) contrast. The present study investigated spatial and temporal variations in GE and SE fMRI at 3 T in response to a brief visual stimulus. The results demonstrate that SE BOLD contrast reaches its maximum amplitude more quickly than does GE contrast at long echo times. We have called this metric the peak hemodynamic activation time (PHAT). Because BOLD changes in response to increased neuronal activity occur earlier in the microvasculature and then later propagate into the venous compartment, these results provide further evidence that SE-based BOLD contrast provides superior localization to the site of activation at 3 T. Spatial overlay of SE and GE PHAT maps onto structural images reveal markedly different spatial profiles and further support the interpretation that shorter peak times correlate to improved spatial sensitivity.

Joint analysis of band-specific functional connectivity and signal complexity in autism.

Examination of resting state brain activity using electrophysiological measures like complexity as well as functional connectivity is of growing interest in the study of autism spectrum disorders (ASD). The present paper jointly examined complexity and connectivity to obtain a more detailed characterization of resting state brain activity in ASD. Multi-scale entropy was computed to quantify the signal complexity, and synchronization likelihood was used to evaluate functional connectivity (FC), with node strength values providing a sensor-level measure of connectivity to facilitate comparisons with complexity. Sensor level analysis of complexity and connectivity was performed at different frequency bands computed from resting state MEG from 26 children with ASD and 22 typically developing controls (TD). Analyses revealed band-specific group differences in each measure that agreed with other functional studies in fMRI and EEG: higher complexity in TD than ASD, in frontal regions in the delta band and occipital-parietal regions in the alpha band, and lower complexity in TD than in ASD in delta (parietal regions), theta (central and temporal regions) and gamma (frontal-central boundary regions); increased short-range connectivity in ASD in the frontal lobe in the delta band and long-range connectivity in the temporal, parietal and occipital lobes in the alpha band. Finally, and perhaps most strikingly, group differences between ASD and TD in complexity and FC appear spatially complementary, such that where FC was elevated in ASD, complexity was reduced (and vice versa). The correlation of regional average complexity and connectivity node strength with symptom severity scores of ASD subjects supported the overall complementarity (with opposing sign) of connectivity and complexity measures, pointing to either diminished connectivity leading to elevated entropy due to poor inhibitory regulation or chaotic signals prohibiting effective measure of connectivity.

T1ρ Contrast in Functional Magnetic Resonance Imaging

The application of T1 in the rotating frame (T1ρ) to functional MRI in humans was studied at 3 T. Increases in neural activity increased parenchymal T1ρ. Modeling suggested that cerebral blood volume mediated this increase. A pulse sequence named spin‐locked echo planar imaging (SLEPI) that produces both T1ρ and T2* contrast was developed and used in a visual functional MRI (fMRI)experiment. Spin‐locked contrast significantly augments the T2* blood oxygen level‐dependent (BOLD) contrast in this sequence. The total functional contrast generated by the SLEPI sequence (1.31%) was 54% larger than the contrast (0.85%) obtained from a conventional gradient‐echo EPI sequence using echo times of 30 ms. Analysis of image SNR revealed that the spin‐locked preparation period of the sequence produced negligible signal loss from static dephasing effects. The SLEPI sequence appears to be an attractive alternative to conventional BOLD fMRI, particularly when long echo times are undesirable, such as when studying prefrontal cortex or ventral regions, where static susceptibility gradients often degrade T2*‐weighted images. Magn Reson Med, 2005.

Alpha-to-Gamma Phase-Amplitude Coupling Methods and Application to Autism Spectrum Disorder

Adult studies have shown that a basic property of resting-state (RS) brain activity is the coupling of posterior alpha oscillations (alpha phase) to posterior gamma oscillations (gamma amplitude). The present study examined whether this basic RS process is present in children. Given reports of abnormal parietal-occipital RS alpha in children with autism spectrum disorder (ASD), the present study examined whether RS alpha-to-gamma phase-amplitude coupling (PAC) is disrupted in ASD. Simulations presented in this study showed limitations with traditional PAC analyses. In particular, to avoid false-positive PAC findings, simulations showed the need to use a unilateral passband to filter the upper frequency band as well as the need for longer epochs of data. For the human study, eyes-closed RS magnetoencephalography data were analyzed from 25 children with ASD and 18 typically developing (TD) children with at least 60 sec of artifact-free data. Source modeling provided continuous time course data at a midline parietal-occipital source for PAC analyses. Greater alpha-to-gamma PAC was observed in ASD than TD (p<0.005). Although children with ASD had higher PAC values, in both groups gamma activity increased at the peak of the alpha oscillation. In addition, an association between alpha power and alpha-to-gamma PAC was observed in both groups, although this relationship was stronger in ASD than TD (p<0.05). Present results demonstrated that although alpha-to-gamma PAC is present in children, this basic RS process is abnormal in children with ASD. Finally, simulations and the human data highlighted the need to consider the interplay between alpha power, epoch length, and choice of signal processing methods on PAC estimates.

A method for localizing microelectrode trajectories in the macaque brain using MRI

Magnetic resonance imaging (MRI) is often used by electrophysiologists to target specific brain regions for placement of microelectrodes. However, the effectiveness of this technique has been limited by few methods to quantify in three dimensions the relative locations of brain structures, recording chambers and microelectrode trajectories. Here we present such a method. After surgical implantation, recording chambers are fitted with a plastic cylinder that is filled with a high-contrast agent to aid in the segmentation of the cylinder from brain matter in an MRI volume. The resulting images of the filled cylinder correspond to a virtual cylinder that is projected along its long axis - parallel to the trajectories of microelectrodes advanced through the recording chamber - through the three-dimensional image of the brain. This technique, which does not require a stereotaxic coordinate system, can be used to quantify the coverage of an implanted recording chamber relative to anatomical landmarks at any depth or orientation. We have used this technique in conjunction with Caret [Van Essen DC, Drury HA, Dickson J, Harwell J, Hanlon D, Anderson CH. An integrated software suite for surface-based analyses of cerebral cortex. J Am Med Inform Assoc 2001;8:443-59] and AFNI [Cox RW. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 1996;29:162-73] brain-mapping software to successfully localize several regions of macaque cortex, including the middle temporal area, the lateral intraparietal area and the frontal eye field, and one subcortical structure, the locus coeruleus, for electrophysiological recordings.

Temporal resolving power of spin echo and gradient echo fMRI at 3T with apparent diffusion coefficient compartmentalization

The temporal resolving power of blood oxygenation level‐dependent (BOLD) functional magnetic resonance imaging (fMRI) at 3T was investigated in the visual and auditory cortices of the human brain. By using controlled temporal delays and selective visual hemifield stimulation, regions with similar (left vs. right occipital cortex) and different (occipital cortex vs. auditory cortex) vascular architectures were compared. Estimates of the time‐to‐peak (TTP) of the BOLD hemodynamic response function (hrf) were obtained using a spin echo (SE) sequence and compared to those acquired using a traditional gradient echo (GE) sequence. The hrf TTP in the visual cortex was found to be 4.73 s and 4.21 s for GE and SE, respectively. The auditory cortex response was significantly delayed, with TTPs of 4.95 s and 4.51 s for GE and SE, respectively. The GE response was able to resolve visual stimuli separated by 250 ms, whereas SE could resolve stimuli 500 ms apart. Apparent‐diffusion‐coefficient (ADC) compartmentalization of the BOLD signal was applied to restrict the vascular sensitivity of the SE and GE sequences. Limiting the response to voxels with ADCs < 0.8 × 10−3 mm2/s improved the temporal resolving power of GE and SE BOLD to 125 ms and 250 ms, respectively. Hum Brain Mapp 25:247–258, 2005.

Using Multiparametric Data with Missing Features for Learning Patterns of Pathology

The paper presents a method for learning multimodal classifiers from datasets in which not all subjects have data from all modalities. Usually, subjects with a severe form of pathology are the ones failing to satisfactorily complete the study, especially when it consists of multiple imaging modalities. A classifier capable of handling subjects with unequal numbers of modalities prevents discarding any subjects, as is traditionally done, thereby broadening the scope of the classifier to more severe pathology. It also allows design of the classifier to include as much of the available information as possible and facilitates testing of subjects with missing modalities over the constructed classifier. The presented method employs an ensemble based approach where several subsets of complete data are formed and trained using individual classifiers., The output from these classifiers is fused using a weighted aggregation step giving an optimal probabilistic score for each subject. The method is applied to a spatio-temporal dataset for autism spectrum disorders (ASD) (96 patients with ASD and 42 typically developing controls) that consists of functional features from magnetoencephalography (MEG) and structural connectivity features from diffusion tensor imaging (DTI). A clear distinction between ASD and controls is obtained with an average 5-fold accuracy of 83.3% and testing accuracy of 88.4%. The fusion classifier performance is superior to the classification achieved using single modalities as well as multimodal classifier using only complete data (78.3%). The presented multimodal classifier framework is applicable to all modality combinations.