Fuzzy Fibers: Uncertainty in dMRI Tractography

Fiber tracking based on diffusion weighted Magnetic Resonance Imaging (dMRI) allows for noninvasive reconstruction of fiber bundles in the human brain. In this chapter, we discuss sources of error and uncertainty in this technique, and review strategies that afford a more reliable interpretation of the results. This includes methods for computing and rendering probabilistic tractograms, which estimate precision in the face of measurement noise and artifacts. However, we also address aspects that have received less attention so far, such as model selection, partial voluming, and the impact of parameters, both in preprocessing and in fiber tracking itself. We conclude by giving impulses for future research

Test-retest reliability of structural brain networks from diffusion MRI

Structural brain networks constructed from diffusion MRI (dMRI) and tractography have been demonstrated in healthy volunteers and more recently in various disorders affecting brain connectivity. However, few studies have addressed the reproducibility of the resulting networks. We measured the test–retest properties of such networks by varying several factors affecting network construction using ten healthy volunteers who underwent a dMRI protocol at 1.5 T on two separate occasions. Each T1-weighted brain was parcellated into 84 regions-of-interest and network connections were identified using dMRI and two alternative tractography algorithms, two alternative seeding strategies, a white matter waypoint constraint and three alternative network weightings. In each case, four common graph-theoretic measures were obtained. Network properties were assessed both node-wise and per network in terms of the intraclass correlation coefficient (ICC) and by comparing within- and between-subject differences. Our findings suggest that test–retest performance was improved when: 1) seeding from white matter, rather than grey; and 2) using probabilistic tractography with a two-fibre model and sufficient streamlines, rather than deterministic tensor tractography. In terms of network weighting, a measure of streamline density produced better test–retest performance than tract-averaged diffusion anisotropy, although it remains unclear which is a more accurate representation of the underlying connectivity. For the best performing configuration, the global within-subject differences were between 3.2% and 11.9% with ICCs between 0.62 and 0.76. The mean nodal within-subject differences were between 5.2% and 24.2% with mean ICCs between 0.46 and 0.62. For 83.3% (70/84) of nodes, the within-subject differences were smaller than between-subject differences. Overall, these findings suggest that whilst current techniques produce networks capable of characterising the genuine between-subject differences in connectivity, future work must be undertaken to improve network reliability

Inter subject variability and reproducibility of diffusion tensor imaging within and between different imaging sessions.

The aim of these studies was to provide reference data on intersubject variability and reproducibility of diffusion tensor imaging. Healthy volunteers underwent imaging on two occasions using the same 3T Siemens Verio magnetic resonance scanner. At each session two identical diffusion tensor sequences were obtained along with standard structural imaging. Fractional anisotropy, apparent diffusion coefficient, axial and radial diffusivity maps were created and regions of interest applied in normalised space. The baseline data from all 26 volunteers were used to calculate the intersubject variability, while within session and between session reproducibility were calculated from all the available data. The reproducibility of measurements were used to calculate the overall and within session 95% prediction interval for zero change. The within and between session reproducibility data were lower than the values for intersubject variability, and were different across the brain. The regional mean (range) coefficient of variation figures for within session reproducibility were 2.1 (0.9-5.5%), 1.2 (0.4-3.9%), 1.2 (0.4-3.8%) and 1.8 (0.4-4.3%) for fractional anisotropy, apparent diffusion coefficient, axial and radial diffusivity, and were lower than between session reproducibility measurements (2.4 (1.1-5.9%), 1.9 (0.7-5.7%), 1.7 (0.7-4.7%) and 2.4 (0.9-5.8%); p<0.001). The calculated overall and within session 95% prediction intervals for zero change were similar. This study provides additional reference data concerning intersubject variability and reproducibility of diffusion tensor imaging conducted within the same imaging session and different imaging sessions. These data can be utilised in interventional studies to quantify change within a single imaging session, or to assess the significance of change in longitudinal studies of brain injury and disease.RCUK, Wellcome, OtherThis is the published version. It was originally published by PLoS in PLoS ONE here: http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0065941

Reproducible protocol to obtain and measure first-order relay human thalamic white-matter tracts

Available online 13 August 2022The “primary ”or “first-order relay ”nuclei of the thalamus feed the cerebral cortex with information about on- going activity in the environment or the subcortical motor systems. Because of the small size of these nuclei and the high specificity of their input and output pathways, new imaging protocols are required to investigate thala- mocortical interactions in human perception, cognition and language. The goal of the present study was twofold: I) to develop a reconstruction protocol based on in vivo diffusion MRI to extract and measure the axonal fiber tracts that originate or terminate specifically in individual first-order relay nuclei; and, II) to test the reliability of this reconstruction protocol. In left and right hemispheres, we investigated the thalamocortical/corticothalamic axon bundles linking each of the first-order relay nuclei and their main cortical target areas, namely, the lateral geniculate nucleus (optic radiation), the medial geniculate nucleus (acoustic radiation), the ventral posterior nu- cleus (somatosensory radiation) and the ventral lateral nucleus (motor radiation). In addition, we examined the main subcortical input pathway to the ventral lateral posterior nucleus, which originates in the dentate nucleus of the cerebellum. Our protocol comprised three components: defining regions-of-interest; preprocessing diffu- sion data; and modeling white-matter tracts and tractometry. We then used computation and test-retest methods to check whether our protocol could reliably reconstruct these tracts of interest and their profiles. Our results demonstrated that the protocol had nearly perfect computational reproducibility and good-to-excellent test-retest reproducibility. This new protocol may be of interest for both basic human brain neuroscience and clinical studies and has been made publicly available to the scientific community.This work was supported by grants from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie (grant agreement No. 713673 ), and from “la Caixa ”Foundation (grant No. 11660016 ) to M.L.; grants from the Span- ish Ministerio de Ciencia e Innovación ( IJC2020-042887-I ; PID2021- 123577NA-I00 ) to G.L.-U.; grants from the European Union ’s Horizon 2020 Research and Innovation Program, European Commission (grant agreement No. 945539 - HBP SGA3 ) and from the Ministerio de Ciencia e Innovación FLAG-ERA grant NeuronsReunited ( MICINN-AEI PCI2019-111900-2 ) to F.C.; and grants from the Ministerio de Ciencia e Innovación ( PGC2018-093408-B-I00 ; PID2021-123574NB-I00 ), Neuro- science projects from the Fundación Tatiana Pérez de Guzmán el Bueno , Basque Government ( PIBA-2021-1-0003 ), and a grant from “la Caixa ”Banking Foundation under the project code LCF/PR/HR19/52160002 to P.M.P.-A. BCBL acknowledges support by the Basque Government through the BERC 2022-2025 program and by the S panish State Re- search Agency through BCBL Severo Ochoa excellence accreditation CEX2020-001010-S

Longitudinal Reproducibility of Neurite Orientation Dispersion and Density Imaging (NODDI) Derived Metrics in the White Matter

Diffusion-weighted magnetic resonance imaging (DWI) is undergoing constant evolution with the ambitious goal of developing in-vivo histology of the brain. A recent methodological advancement is Neurite Orientation Dispersion and Density Imaging (NODDI), a histologically validated multi-compartment model to yield microstructural features of brain tissue such as geometric complexity and neurite packing density, which are especially useful in imaging the white matter. Since NODDI is increasingly popular in clinical research and fields such as developmental neuroscience and neuroplasticity, it is of vast importance to characterize its reproducibility (or reliability). We acquired multi-shell DWI data in 29 healthy young subjects twice over a rescan interval of 4 weeks to assess the within-subject coefficient of variation (CVWS), between-subject coefficient of variation (CVBS) and the intraclass correlation coefficient (ICC), respectively. Using these metrics, we compared regional and voxel-by-voxel reproducibility of the most common image analysis approaches (tract-based spatial statistics [TBSS], voxel-based analysis with different extents of smoothing [“VBM-style”], ROI-based analysis). We observed high test–retest reproducibility for the orientation dispersion index (ODI) and slightly worse results for the neurite density index (NDI). Our findings also suggest that the choice of analysis approach might have significant consequences for the results of a study. Collectively, the voxel-based approach with Gaussian smoothing kernels of ≥4 mm FWHM and ROI-averaging yielded the highest reproducibility across NDI and ODI maps (CVWS mostly ≤3%, ICC mostly ≥0.8), respectively, whilst smaller kernels and TBSS performed consistently worse. Furthermore, we demonstrate that image quality (signal-to-noise ratio [SNR]) is an important determinant of NODDI metric reproducibility. We discuss the implications of these results for longitudinal and cross-sectional research designs commonly employed in the neuroimaging field

Tracking dynamic interactions between structural and functional connectivity : a TMS/EEG-dMRI study

Transcranial magnetic stimulation (TMS) in combination with neuroimaging techniques allows to measure the effects of a direct perturbation of the brain. When coupled with high-density electroencephalography (TMS/hd-EEG), TMS pulses revealed electrophysiological signatures of different cortical modules in health and disease. However, the neural underpinnings of these signatures remain unclear. Here, by applying multimodal analyses of cortical response to TMS recordings and diffusion magnetic resonance imaging (dMRI) tractography, we investigated the relationship between functional and structural features of different cortical modules in a cohort of awake healthy volunteers. For each subject, we computed directed functional connectivity interactions between cortical areas from the source-reconstructed TMS/hd-EEG recordings and correlated them with the correspondent structural connectivity matrix extracted from dMRI tractography, in three different frequency bands (alpha, beta, gamma) and two sites of stimulation (left precuneus and left premotor). Each stimulated area appeared to mainly respond to TMS by being functionally elicited in specific frequency bands, that is, beta for precuneus and gamma for premotor. We also observed a temporary decrease in the whole-brain correlation between directed functional connectivity and structural connectivity after TMS in all frequency bands. Notably, when focusing on the stimulated areas only, we found that the structure-function correlation significantly increases over time in the premotor area controlateral to TMS. Our study points out the importance of taking into account the major role played by different cortical oscillations when investigating the mechanisms for integration and segregation of information in the human brain

In utero diffusion tensor imaging of the fetal brain: a reproducibility study

Our purpose was to evaluate the within-subject reproducibility of in utero diffusion tensor imaging (DTI) metrics and the visibility of major white matter structures. Images for 30 fetuses (20-33. postmenstrual weeks, normal neurodevelopment: 6 cases, cerebral pathology: 24 cases) were acquired on 1.5 T or 3.0 T MRI. DTI with 15 diffusion-weighting directions was repeated three times for each case, TR/TE: 2200/63 ms, voxel size: 1 ∗ 1 mm, slice thickness: 3-5 mm, b-factor: 700 s/mm(2). Reproducibility was evaluated from structure detectability, variability of DTI measures using the coefficient of variation (CV), image correlation and structural similarity across repeated scans for six selected structures. The effect of age, scanner type, presence of pathology was determined using Wilcoxon rank sum test. White matter structures were detectable in the following percentage of fetuses in at least two of the three repeated scans: corpus callosum genu 76%, splenium 64%, internal capsule, posterior limb 60%, brainstem fibers 40% and temporooccipital association pathways 60%. The mean CV of DTI metrics ranged between 3% and 14.6% and we measured higher reproducibility in fetuses with normal brain development. Head motion was negatively correlated with reproducibility, this effect was partially ameliorated by motion-correction algorithm using image registration. Structures on 3.0 T had higher variability both with- and without motion correction. Fetal DTI is reproducible for projection and commissural bundles during mid-gestation, however, in 16-30% of the cases, data were corrupted by artifacts, resulting in impaired detection of white matter structures. To achieve robust results for the quantitative analysis of diffusivity and anisotropy values, fetal-specific image processing is recommended and repeated DTI is needed to ensure the detectability of fiber pathways

Human thalamocortical connections and their involvement in language systems.

139 p.During evolution the expansion of the neocortex has been linked with the emergence of higher level cognitive functions, such as reasoning, abstract thinking, or language in human beings. Current research on cognitive neuroscience is mainly focused on the cerebral cortex. Whereas the thalamus is a structure that has extensive white-matter connections with the cerebral cortex, its expansion during evolution is parallel to the expansion of the neocortex. The thalamocortical connections are involved in communication between cortical areas. Thus, to fully understand the neural basis of cognition, a better understanding of the role of the thalamus in cortical function is necessary. The present doctoral dissertation is focused on the structure and function of the thalamus: the first study proposes a reproducible protocol to reconstruct the first-order thalamic white-matter tracts from diffusion-weighted imaging data; the second study investigates the higher-order thalamic white-matter tracts and a similar protocol is proposed to reconstruction those tracts; the third study uses task-based fMRI to examine the involvement of first-order thalamic nuclei in the main language systems.the current dissertation successfully reconstructed first-order and higher-order thalamic white-matter tracts from DWI data, and has proved high reproducibility of the reconstruction protocol. This protocol could benefit the tractography community to better understand the structural connectivity of the thalamus with cortical and subcortical structures and facilitate the research on thalamocortical pathways in humans. We also found evidence for differences in the processing of linguistic and nonlinguistic stimuli in first-order thalamic nuclei through a task-based fMRI study. These results suggest that the first-order thalamic nuclei play roles in human language that are beyond relaying sensory information from periphery to cerebral cortex. These findings are important to push forward our understanding on the role of subcortical structures, such as the thalamus, in human language functions, and to urge a revisitation of existing language models taking the thalamus into consideration