White Matter Fiber Architecture Research Articles

The impact of input node placement in the controllability of structural brain networks

Network controllability refers to the ability to steer the state of a network towards a target state by driving certain nodes, known as input nodes. This concept can be applied to brain networks for studying brain function and its relation to the structure, which has numerous practical applications. Brain network controllability involves using external signals such as electrical stimulation to drive specific brain regions and navigate the neurophysiological activity level of the brain around the state space. Although controllability is mainly theoretical, the energy required for control is critical in real-world implementations. With a focus on the structural brain networks, this study explores the impact of white matter fiber architecture on the control energy in brain networks using the theory of how input node placement affects the LCC (the longest distance between inputs and other network nodes). Initially, we use a single input node as it is theoretically possible to control brain networks with just one input. We show that highly connected brain regions that lead to lower LCCs are more energy-efficient as a single input node. However, there may still be a need for a significant amount of control energy with one input, and achieving controllability with less energy could be of interest. We identify the minimum number of input nodes required to control brain networks with smaller LCCs, demonstrating that reducing the LCC can significantly decrease the control energy in brain networks. Our results show that relying solely on highly connected nodes is not effective in controlling brain networks with lower energy by using multiple inputs because of densely interconnected brain network hubs. Instead, a combination of low and high-degree nodes is necessary.

Evaluation of tractogram filtering methods using human-like connectome phantoms

Tractography algorithms are prone to reconstructing spurious connections. The set of streamlines generated with tractography can be post-processed to retain the streamlines that are most biologically plausible. Several microstructure-informed filtering algorithms are available for this purpose, however, the comparative performance of these methods has not been extensively evaluated. In this study, we aim to evaluate streamline filtering and post-processing algorithms using simulated connectome phantoms. We first establish a framework for generating connectome phantoms featuring brain-like white matter fiber architectures. We then use our phantoms to systematically evaluate the performance of a range of streamline filtering algorithms, including SIFT, COMMIT, and LiFE. We find that all filtering methods successfully improve connectome accuracy, although filter performance depends on the complexity of the underlying white matter fiber architecture. Filtering algorithms can markedly improve tractography accuracy for simple tubular fiber bundles (F-measure deterministic– unfiltered: 0.49 and best filter: 0.72; F-measure probabilistic– unfiltered: 0.37 and best filter: 0.81), but for more complex brain-like fiber architectures, the improvement is modest (F-measure deterministic– unfiltered: 0.53 and best filter: 0.54; F-measure probabilistic– unfiltered: 0.46 and best filter: 0.50). Overall, filtering algorithms have the potential to improve the accuracy of connectome mapping pipelines, particularly for weighted connectomes and pipelines using probabilistic tractography methods. Our results highlight the need for further advances tractography and streamline filtering to improve the accuracy of connectome mapping.

KS05.5.A Alterations in white matter fiber density associated with structural MRI and metabolic PET lesions following multimodal therapy in glioma patients

Abstract Background In glioma patients, multimodal therapy and recurrent tumor result in local brain tissue changes, characterized by pathologic findings in structural MRI and metabolic PET images. Little is known about these different lesion types’ impact on the local white matter fiber architecture and clinical outcome. Patients and Methods This study included data from 121 pretreated patients (median age, 52 years; ECOG, 01) with histomolecularly characterized glioma (WHO grade IV glioblastoma, n=81; WHO grade III anaplastic astrocytoma, n=28; WHO grade III anaplastic oligodendroglioma, n=12), who had a resection, radiotherapy, alkylating chemotherapy, or combinations thereof. After a median time of 14 months (range, 1-214 months), post-therapeutic structural and metabolic findings were evaluated using anatomical MRI and O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET acquired on a 3T hybrid PET/MR scanner. Local fiber density was estimated from tractography based on highangular resolution diffusion-weighted imaging. A cohort of 121 healthy subjects selected from the 1000BRAINS study and matched for age, gender and education served as a control group. Results The median volume of resection cavities, contrast-enhancing regions, regions with pathologically increased FET uptake, and T2/FLAIR hyperintense regions amounted to 20.9, 7.9, 30.3, and 53.4 mL, respectively. Compared to the control group, the average local fiber density in these regions was significantly reduced (p<0.001). Resection cavities showed the highest reduction, followed by contrast-enhancing lesions and metabolically active tumors on FET PET (relative fiber density reduction, -87%, -65%, -55%, respectively). The local fiber density was inversely related (p=0.005) to the FET uptake in recurrent tumors. T2/FLAIR hyperintense lesions, either assigned to peritumoral edema in recurrent glioma or radiation-induced gliosis, had a comparable impact on reducing fiber density (48% and 41%, respectively). The total fiber loss (average fiber loss multiplied by lesion volume) associated with contrast-enhancing lesions (p=0.006) and T2/FLAIR hyperintense lesions (p=0.013) had a significant impact on the general performance status of the patients (ECOG score). Conclusions Our results suggest that apart from resection cavities, reduction in local fiber density is greatest in contrast-enhancing recurrent tumors, but total fiber loss induced by edema or gliosis has an equal detrimental effect on the patients’ performance due to the larger volume affected. Funding Funded by the 1000BRAINS study (INM, Research Centre Juelich, Germany), Horizon 2020 (Grant No. 945539 (HBP SGA3; SC)), and Heinz Nixdorf Foundation.

The glial framework reveals white matter fiber architecture in human and primate brains.

Uncovering the architecture of white matter axons is fundamental to the study of brain networks. We developed a method for quantifying axonal orientations at a resolution of ~15 micrometers. This method is based on the common Nissl staining technique for postmortem histological slices. Nissl staining reveals the spatial organization of glial cells along axons. Using structure tensor analysis, we leveraged this patterned organization to uncover local axonal orientation. We used Nissl-based structure tensor analysis to extract fine details of axonal architecture and demonstrated its applicability in multiple datasets of humans and nonhuman primates. Nissl-based structure tensor analysis can be used to compare fine-grained features of axonal architecture across species and is widely applicable to existing datasets.

Diffusion-regularized susceptibility tensor imaging (DRSTI) of tissue microstructures in the human brain.

Susceptibility tensor imaging (STI) has been proposed as an alternative to diffusion tensor imaging (DTI) for non-invasive in vivo characterization of brain tissue microstructure and white matter fiber architecture, potentially benefitting from its high spatial resolution. In spite of different biophysical mechanisms, animal studies have demonstrated white matter fiber directions measured using STI to be reasonably consistent with those from diffusion tensor imaging (DTI). However, human brain STI is hampered by its requirement of acquiring data at more than 10 head rotations and a complicated processing pipeline. In this paper, we propose a diffusion-regularized STI method (DRSTI) that employs a tensor spectral decomposition constraint to regularize the STI solution using the fiber directions estimated by DTI as a priori. We then explore the high-resolution DRSTI with MR phase images acquired at only 6 head orientations. Compared to other STI approaches, the DRSTI generated susceptibility tensor components, mean magnetic susceptibility (MMS), magnetic susceptibility anisotropy (MSA) and fiber direction maps with fewer artifacts, especially in regions with large susceptibility variations, and with less erroneous quantifications. In addition, the DRSTI method allows us to distinguish more structural features that could not be identified in DTI, especially in deep gray matters. DRSTI enables a more accurate susceptibility tensor estimation with a reduced number of sampling orientations, and achieves better tracking of fiber pathways than previous STI attempts on in vivo human brain.

Association of olfaction dysfunction with brain microstructure in prodromal Parkinson disease

Although olfaction dysfunction is now considered as an established clinical marker of prodromal Parkinson disease (PD), little is known about the neural underpinnings of olfaction dysfunction in the prodromal phase of PD. The aim of this study was to examine the microstructural association of olfaction in prodromal PD compared to early stage drug-naïve PD patients. Diffusion MRI connectometry was conducted on 18 early PD and 17 prodromal PD patients to investigate the differences in group in terms of altered connectivity, i.e., integrity of white matter tracts, and subsequently to study the correlation of University of Pennsylvania Smell Identification Test (UPSIT) score to white matter integrity in each group using a multiple regression model considering age, sex, RBD, and MoCA, as covariates. Individuals with prodromal PD had significantly higher quantitative anisotropy (QA) comparing with PD patients in bilateral middle cerebellar peduncles and right arcuate fasciculus. Multiple regression analysis in prodromal PD demonstrated positive association between UPSIT score and connectivity in left and right subgenual cingulum, right inferior fronto-occipital fasciculus, left corticospinal tract, left parietopontine, left corticothalamic tract, and the body and the splenium of corpus callosum. These results indicate that PD and prodromal PD patients, which were matched for sex, UPSIT, and MoCA scores, have different white matter fiber architecture. Thus, it is postulated that olfaction dysfunction in prodromal and early clinical phases of PD may involve distinct pathogenesis. Increased network connectivity in prodromal and early PD may suggest the neural compensation.

Advances in myelin imaging with potential clinical application to pediatric imaging

White matter development and myelination are critical processes in neurodevelopment. Myelinated white matter facilitates the rapid and coordinated brain messaging required for higher-order cognitive and behavioral processing. Whereas several neurological disorders such as multiple sclerosis are associated with gross white matter damage and demyelination, other disorders such as epilepsy may involve altered myelination in the efferent or afferent white matter pathways adjoining epileptic foci. Current MRI techniques including T1 weighting, T2 weighting, FLAIR, diffusion tensor imaging, and MR spectroscopy permit visualization of gross white matter abnormalities and evaluation of underlying white matter fiber architecture and integrity, but they provide only qualitative information regarding myelin content. Quantification of these myelin changes could provide new insight into disease severity and prognosis, reveal information regarding spatial location of foci or lesions and the associated affected neural systems, and create a metric to evaluate treatment efficacy. Multicomponent analysis of T1 and T2 relaxation data, or multicomponent relaxometry (MCR), is a quantitative imaging technique that is sensitive and specific to myelin content alteration. In the past, MCR has been associated with lengthy imaging times, but a new, faster MCR technique (mcDESPOT) has made quantitative analysis of myelin content more accessible for clinical research applications. The authors briefly summarize traditional white matter imaging techniques, describe MCR and mcDESPOT, and discuss current and future clinical applications of MCR, with a particular focus on pediatric epilepsy.

3D fiber tractography with susceptibility tensor imaging

Gradient-echo MRI has revealed anisotropic magnetic susceptibility in the brain white matter. This magnetic susceptibility anisotropy can be measured and characterized with susceptibility tensor imaging (STI). In this study, a method of fiber tractography based on STI is proposed and demonstrated in the mouse brain. STI experiments of perfusion-fixed mouse brains were conducted at 7.0T. The magnetic susceptibility tensor was calculated for each voxel with regularization and decomposed into its eigensystem. The major eigenvector is found to be aligned with the underlying fiber orientation. Following the orientation of the major eigenvector, we are able to map distinctive fiber pathways in 3D. As a comparison, diffusion tensor imaging (DTI) and DTI fiber tractography were also conducted on the same specimens. The relationship between STI and DTI fiber tracts was explored with similarities and differences identified. It is anticipated that the proposed method of STI tractography may provide a new way to study white matter fiber architecture. As STI tractography is based on physical principles that are fundamentally different from DTI, it may also be valuable for the ongoing validation of DTI tractography.

Diffusion Tensor Imaging: Exploring the Motor Networks and Clinical Applications

With the advances in diffusion magnetic resonance (MR) imaging techniques, diffusion tensor imaging (DTI) has been applied to a number of neurological conditions because DTI can demonstrate microstructures of the brain that are not assessable with conventional MR imaging. Tractography based on DTI offers gross visualization of the white matter fiber architecture in the human brain in vivo. Degradation of restrictive barriers and disruption of the cytoarchitecture result in changes in the diffusion of water molecules in various pathological conditions, and these conditions can also be assessed with DTI. Yet many factors may influence the ability to apply DTI clinically, so these techniques have to be used with a cautious hand.

Brain Fiber Architecture in the Blind

Introduction: Blind people compensate for the loss of vision with enhanced abilities in other sensory and cognitive areas. These behavioral adaptations are often, at least in part, the result of cross-modal recruitment of visual cortices. Consequently, differences in white matter fiber architecture between blind and sighted individuals are of interest as they may reflect the brain’s capacity to adapt in response to chronic visual deprivation. Here we mapped the profile of differences in white matter fiber architecture between blind and sighted subjects using a new method that combines tensor-based morphometry and diffusion tensor imaging (DTI). Using a multivariate analysis of the full 6-dimensional tensor, group differences in fiber structure were more powerfully detected than with DTI-derived scalar statistics.