White matter integrity measured by fractional anisotropy correlates poorly with actual individual fiber anisotropy

Fractional anisotropy (FA), a very widely used measure of fiber integrity based on diffusion tensor imaging (DTI), is a problematic concept as it is influenced by several quantities including the number of dominant fiber directions within each voxel, each fiber's anisotropy, and partial volume effects from neighboring gray matter. High-angular resolution diffusion imaging (HARDI) can resolve more complex diffusion geometries than standard DTI, including fibers crossing or mixing. The tensor distribution function (TDF) can be used to reconstruct multiple underlying fibers per voxel, representing the diffusion profile as a probabilistic mixture of tensors. Here we found that DTIderived mean diffusivity (MD) correlates well with actual individual fiber MD, but DTI-derived FA correlates poorly with actual individual fiber anisotropy, and may be suboptimal when used to detect disease processes that affect myelination. Analysis of the TDFs revealed that almost 40% of voxels in the white matter had more than one dominant fiber present. To more accurately assess fiber integrity in these cases, we here propose the differential diffusivity (DD), which measures the average anisotropy based on all dominant directions in each voxel.

Tracking the arcuate fasciculus in patients with aphasia

The arcuate fasciculus (AF), a white matter tract linking temporal and inferior frontal language cortices, can be disrupted in stroke patients suffering from aphasia. Using diffusion tensor imaging (DTI) tractography it is possible to track AF connections to neural regions associated with either phonological or semantic linguistic processing. The aim of the current study is to investigate the relationship between integrity of white matter microstructure and specific linguistic deficits.

Visualization tools for high angular resolution diffusion imaging

There is a major effort in medical imaging to develop algorithms to extract information from DTI and HARDI, which provide detailed information on brain integrity and connectivity. As the images have recently advanced to provide extraordinarily high angular resolution and spatial detail, including an entire manifold of information at each point in the 3D images, there has been no readily available means to view the results. This impedes developments in HARDI research, which need some method to check the plausibility and validity of image processing operations on HARDI data or to appreciate data features or invariants that might serve as a basis for new directions in image segmentation, registration, and statistics. We present a set of tools to provide interactive display of HARDI data, including both a local rendering application and an off-screen renderer that works with a web-based viewer. Visualizations are presented after registration and averaging of HARDI data from 90 human subjects, revealing important details for which there would be no direct way to appreciate using conventional display of scalar images.

Investigating brain connectivity heritability in a twin study using diffusion imaging data

Heritability of brain anatomical connectivity has been studied with diffusion-weighted imaging (DWI) mainly by modeling each voxel's diffusion pattern as a tensor (e.g., to compute fractional anisotropy), but this method cannot accurately represent the many crossing connections present in the brain. We hypothesized that different brain networks (i.e., their component fibers) might have different heritability and we investigated brain connectivity using High Angular Resolution Diffusion Imaging (HARDI) in a cohort of twins comprising 328 subjects that included 70 pairs of monozygotic and 91 pairs of dizygotic twins. Water diffusion was modeled in each voxel with a Fiber Orientation Distribution (FOD) function to study heritability for multiple fiber orientations in each voxel. Precision was estimated in a test-retest experiment on a sub-cohort of 39 subjects. This was taken into account when computing heritability of FOD peaks using an ACE model on the monozygotic and dizygotic twins. Our results confirmed the overall heritability of the major white matter tracts but also identified differences in heritability between connectivity networks. Inter-hemispheric connections tended to be more heritable than intra-hemispheric and cortico-spinal connections. The highly heritable tracts were found to connect particular cortical regions, such as medial frontal cortices, postcentral, paracentral gyri, and the right hippocampus.

How does angular resolution affect diffusion imaging measures?

A key question in diffusion imaging is how many diffusion-weighted images suffice to provide adequate signal-to-noise ratio (SNR) for studies of fiber integrity. Motion, physiological effects, and scan duration all affect the achievable SNR in real brain images, making theoretical studies and simulations only partially useful. We therefore scanned 50 healthy adults with 105-gradient high-angular resolution diffusion imaging (HARDI) at 4T. From gradient image subsets of varying size (6 ≤ N ≤ 94) that optimized a spherical angular distribution energy, we created SNR plots (versus gradient numbers) for seven common diffusion anisotropy indices: fractional and relative anisotropy (FA, RA), mean diffusivity (MD), volume ratio (VR), geodesic anisotropy (GA), its hyperbolic tangent (tGA), and generalized fractional anisotropy (GFA). SNR, defined in a region of interest in the corpus callosum, was near-maximal with 58, 66, and 62 gradients for MD, FA, and RA, respectively, and with about 55 gradients for GA and tGA. For VR and GFA, SNR increased rapidly with more gradients. SNR was optimized when the ratio of diffusion-sensitized to non-sensitized images was 9.13 for GA and tGA, 10.57 for FA, 9.17 for RA, and 26 for MD and VR. In orientation density functions modeling the HARDI signal as a continuous mixture of tensors, the diffusion profile reconstruction accuracy rose rapidly with additional gradients. These plots may help in making trade-off decisions when designing diffusion imaging protocols.

A novel measure of fractional anisotropy based on the tensor distribution function

Fractional anisotropy (FA), a very widely used measure of fiber integrity based on diffusion tensor imaging (DTI), is a problematic concept as it is influenced by several quantities including the number of dominant fiber directions within each voxel, each fiber's anisotropy, and partial volume effects from neighboring gray matter. With High-angular resolution diffusion imaging (HARDI) and the tensor distribution function (TDF), one can reconstruct multiple underlying fibers per voxel and their individual anisotropy measures by representing the diffusion profile as a probabilistic mixture of tensors. We found that FA, when compared with TDF-derived anisotropy measures, correlates poorly with individual fiber anisotropy, and may sub-optimally detect disease processes that affect myelination. By contrast, mean diffusivity (MD) as defined in standard DTI appears to be more accurate. Overall, we argue that novel measures derived from the TDF approach may yield more sensitive and accurate information than DTI-derived measures.

Analyzing multi-fiber reconstruction in high angular resolution diffusion imaging using the tensor distribution function

High-angular resolution diffusion imaging (HARDI) can reconstruct fiber pathways in the brain with extraordinary detail, identifying anatomical features and connections not seen with conventional MRI. HARDI overcomes several limitations of standard diffusion tensor imaging, which fails to model diffusion correctly in regions where fibers cross or mix. As HARDI can accurately resolve sharp signal peaks in angular space where fibers cross, we studied how many gradients are required in practice to compute accurate orientation density functions, to better understand the tradeoff between longer scanning times and more angular precision. We computed orientation density functions analytically from tensor distribution functions (TDFs) which model the HARDI signal at each point as a unit-mass probability density on the 6D manifold of symmetric positive definite tensors. In simulated two-fiber systems with varying Rician noise, we assessed how many diffusionsensitized gradients were sufficient to (1) accurately resolve the diffusion profile, and (2) measure the exponential isotropy (EI), a TDF-derived measure of fiber integrity that exploits the full multidirectional HARDI signal. At lower SNR, the reconstruction accuracy, measured using the Kullback-Leibler divergence, rapidly increased with additional gradients, and EI estimation accuracy plateaued at around 70 gradients.

Multiple stages classification of Alzheimer’s disease based on structural brain networks using generalized low rank approximations (GLRAM)

To classify each stage for a progressing disease such as Alzheimer’s disease is a key issue for the disease prevention and treatment. In this study, we derived structural brain networks from diffusion-weighted MRI using whole-brain tractography since there is growing interest in relating connectivity measures to clinical, cognitive, and genetic data. Relatively little work has usedmachine learning to make inferences about variations in brain networks in the progression of the Alzheimer’s disease. Here we developed a framework to utilize generalized low rank approximations of matrices (GLRAM) and modified linear discrimination analysis for unsupervised feature learning and classification of connectivity matrices. We apply the methods to brain networks derived from DWI scans of 41 people with Alzheimer’s disease, 73 people with EMCI, 38 people with LMCI, 47 elderly healthy controls and 221 young healthy controls. Our results show that this new framework can significantly improve classification accuracy when combining multiple datasets; this suggests the value of using data beyond the classification task at hand to model variations in brain connectivity.

Heritability of brain network topology in 853 twins and siblings

Anatomical brain networks change throughout life and with diseases. Genetic analysis of these networks may help identify processes giving rise to heritable brain disorders, but we do not yet know which network measures are promising for genetic analyses. Many factors affect the downstream results, such as the tractography algorithm used to define structural connectivity. We tested nine different tractography algorithms and four normalization methods to compute brain networks for 853 young healthy adults (twins and their siblings). We fitted genetic structural equation models to all nine network measures, after a normalization step to increase network consistency across tractography algorithms. Probabilistic tractography algorithms with global optimization (such as Probtrackx and Hough) yielded higher heritability statistics than 'greedy' algorithms (such as FACT) which process small neighborhoods at each step. Some global network measures (probtrackx-derived GLOB and ST) showed significant genetic effects, making them attractive targets for genome-wide association studies.

Improving fault detection and reliability in FDDI networks

FDDI (Fibre Distributed Data Interface) is a 100 Mbit/s token ring network with two counter rotating optical rings. In this paper various possible faults (like lost token, link failures, etc.) are considered, and fault detection and the ring recovery process in case of a failure and the reliability mechanisms provided are studied. We suggest a new method to improve the fault detection and ring recovery process. The performance improvement in terms of station queue length and the average delay is compared with the performance of the existing fault detection and ring recovery process through simulation. We also suggest a modification for the physical configuration of the FDDI networks within the guidelines set by the standard to make the network more reliable. It is shown that, unlike the existing FDDI network, full connectivity is maintained among the stations even when multiple single link failures occur. A distributed algorithm is proposed for link reconfiguration of the modified FDDI network when many successive as well as simultaneous link failures occur. The performance of the modified FDDI network under link failures is studied through simulation and compared with that of the existing FDDI network.

Increasing top-down suppression from prefrontal cortex facilitates tactile working memory

Navigated transcranial magnetic stimulation (TMS) combined with diffusion-weighted magnetic resonance imaging (DW-MRI) and tractography allows investigating functional anatomy of the human brain with high precision. Here we demonstrate that working memory (WM) processing of tactile temporal information is facilitated by delivering a single TMS pulse to the middle frontal gyrus (MFG) during memory maintenance. Facilitation was obtained only with a TMS pulse applied to a location of the MFG with anatomical connectivity to the primary somatosensory cortex (S1). TMS improved tactile WM also when distractive tactile stimuli interfered with memory maintenance. Moreover, TMS to the same MFG site attenuated somatosensory evoked responses (SEPs). The results suggest that the TMS-induced memory improvement is explained by increased top-down suppression of interfering sensory processing in S1 via the MFG-S1 link. These results demonstrate an anatomical and functional network that is involved in maintenance of tactile temporal WM. (C) 2009 Elsevier Inc. All rights reserved.

Effects of chronic mild traumatic brain injury on white matter integrity in iraq and afghanistan war veterans

Mild traumatic brain injury (TBI) is a common source of morbidity from the wars in Iraq and Afghanistan. With no overt lesions on structural MRI, diagnosis of chronic mild TBI in military veterans relies on obtaining an accurate history and assessment of behavioral symptoms that are also associated with frequent comorbid disorders, particularly posttraumatic stress disorder (PTSD) and depression. Military veterans from Iraq and Afghanistan with mild TBI (n = 30) with comorbid PTSD and depression and non-TBI participants from primary (n = 42) and confirmatory (n = 28) control groups were assessed with high angular resolution diffusion imaging (HARDI). White matter-specific registration followed by whole-brain voxelwise analysis of crossing fibers provided separate partial volume fractions reflecting the integrity of primary fibers and secondary (crossing) fibers. Loss of white matter integrity in primary fibers (P < 0.05; corrected) was associated with chronic mild TBI in a widely distributed pattern of major fiber bundles and smaller peripheral tracts including the corpus callosum (genu, body, and splenium), forceps minor, forceps major, superior and posterior corona radiata, internal capsule, superior longitudinal fasciculus, and others. Distributed loss of white matter integrity correlated with duration of loss of consciousness and most notably with "feeling dazed or confused," but not diagnosis of PTSD or depressive symptoms. This widespread spatial extent of white matter damage has typically been reported in moderate to severe TBI. The diffuse loss of white matter integrity appears consistent with systemic mechanisms of damage shared by blast- and impact-related mild TBI that involves a cascade of inflammatory and neurochemical events. © 2012 Wiley Periodicals, Inc.

Score for neonatal acute physiology-II and neonatal pain predict corticospinal tract development in premature newborns

Premature infants are at risk for adverse motor outcomes, including cerebral palsy and developmental coordination disorder. The purpose of this study was to examine the relationship of antenatal, perinatal, and postnatal risk factors for abnormal development of the corticospinal tract, the major voluntary motor pathway, during the neonatal period. In a prospective cohort study, 126 premature neonates (24-32 weeks' gestational age) underwent serial brain imaging near birth and at term-equivalent age. With diffusion tensor tractography, mean diffusivity and fractional anisotropy of the corticospinal tract were measured to reflect microstructural development. Generalized estimating equation models examined associations of risk factors on corticospinal tract development. The perinatal risk factor of greater early illness severity (as measured by the Score for Neonatal Acute Physiology-II [SNAP-II]) was associated with a slower rise in fractional anisotropy of the corticospinal tract (P = 0.02), even after correcting for gestational age at birth and postnatal risk factors (P = 0.009). Consistent with previous findings, neonatal pain adjusted for morphine and postnatal infection were also associated with a slower rise in fractional anisotropy of the corticospinal tract (P = 0.03 and 0.02, respectively). Lessening illness severity in the first hours of life might offer potential to improve motor pathway development in premature newborns.

Tracking the source of cerebellar epilepsy: hemifacial seizures associated with cerebellar cortical dysplasia.

Traditionally, subcortical structures such as the cerebellum are supposed to exert a modulatory effect on epileptic seizures, rather than being the primary seizure generator. We report a 14-month old girl presenting, since birth, with seizures symptomatic of a right cerebellar dysplasia, manifested as paroxystic contralateral hemifacial spasm and ipsilateral facial weakness. Multimodal imaging was used to investigate both anatomical landmarks related to the cerebellar lesion and mechanisms underlying seizure generation. Electric source imaging (ESI) supported the hypothesis of a right cerebellar epileptogenic generator in concordance with nuclear imaging findings; subsequently validated by intra-operative intralesional recordings. Diffusion spectrum imaging-related tractography (DSI) showed severe cerebellar structural abnormalities confirmed by histological examination. We suggest that hemispheric cerebellar lesions in cases like this are likely to cause epilepsy via an effect on the facial nuclei through ipsilateral and contralateral aberrant connections.

Mutism and Amnesia following High-Voltage Electrical Injury: Psychogenic Symptomatology Triggered by Organic Dysfunction?

Background: Mutism and dense retrograde amnesia are found both in organic and dissociative contexts. Moreover, dissociative symptoms may be modulated by right prefrontal activity. A single case, M.R., developed left hemiparesis, mutism and retrograde amnesia after a high-voltage electric shock without evidence of lasting brain lesions. M.R. suddenly recovered from his mutism following a mild brain trauma 2 years later. Methods: M.R.'s neuropsychological pattern and anatomoclinical correlations were studied through (i) language and memory assessment to characterize his deficits, (ii) functional neuroimaging during a standard language paradigm, and (iii) assessment of frontal and left insular connectivity through diffusion tractography imaging and transcranial magnetic stimulation. A control evaluation was repeated after recovery. Findings: M.R. recovered from the left hemiparesis within 90 days of the accident, which indicated a transient right brain impairment. One year later, neurobehavioral, language and memory evaluations strongly suggested a dissociative component in the mutism and retrograde amnesia. Investigations (including MRI, fMRI, diffusion tensor imaging, EEG and r-TMS) were normal. Twenty-seven months after the electrical injury, M.R. had a very mild head injury which was followed by a rapid recovery of speech. However, the retrograde amnesia persisted. Discussion: This case indicates an interaction of both organic and dissociative mechanisms in order to explain the patient's symptoms. The study also illustrates dissociation in the time course of the two different dissociative symptoms in the same patient.