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(1) Background: Growing evidence indicates that inflammation can induce neural circuit dysfunction and plays a vital role in the pathogenesis of major depressive disorder (MDD). Nevertheless, whether inflammation affects the integrity of white matter pathways is only beginning to be explored. (2) Methods: We computed quantitative anisotropy (QA) from diffusion magnetic resonance imaging as an index of white matter integrity and regressed QA on C-reactive protein (CRP), controlling for age, sex, and BMI, in 176 participants with MDD. (3) Results: The QA values of several white matter tracts were negatively correlated with CRP concentration (standardized beta coefficient = -0.22, 95%CI = -0.38--0.06, FDR < 0.05). These tracts included the bilateral cortico-striatal tracts, thalamic radiations, inferior longitudinal fasciculi, corpus callosum (the forceps minor portion and the tapetum portion), cingulum bundles, and the left superior longitudinal fasciculus III. Importantly, the association remained robust after regressing up to twelve potential confounders. The bilateral fornix and a small portion of the thalamic radiation showed a positive association with CRP levels, but these associations did not remain significant after adjusting for confounders. (4) Conclusions: Peripheral inflammation may contribute to the etiology of MDD by impacting the microstructural integrity of brain corticolimbic white matter pathways.

• Using diffusion MRI on the white matter tracts of the late-life depressed older adults in the community, those with depression had lower quantitative anisotropy (QA) in left superior longitudinal Fasciculus-II, but those with greater perceived physical fatigability had lower QA in more widespread brain regions. • For tracts associated physical fatigability, lower QA in the left acoustic radiation and left superior thalamic radiation negatively correlated with high blood sugar. • For tracts associated with depression, lower QA in the left SLF-II correlated with higher bilirubin level. • Depression and fatigability are associated with differential changes in white matter integrity. Nevertheless, they may share a common underlying inflammatory mechanism. Fatigability is prevalent in older adults. However, it is often associated with depressed mood. We aim to investigate these two psychobehavioral constructs by examining their underpinning of white matter structures in the brain and their associations with different medical conditions. Twenty-seven older adults with late-life depression (LLD) and 34 cognitively normal controls (CN) underwent multi-shell diffusion MRI. Fatigability was measured with the Pittsburgh Fatigability Scale. We examined white matter integrity by measuring the quantitative anisotropy (QA), a fiber tracking parameter with better accuracy than the traditional imaging technique. We found those with LLD had lower QA in the 2nd branch of the left superior longitudinal fasciculus (SLF-II), and those with more physical fatigability had lower QA in more widespread brain regions. In tracts associated with more physical fatigability, the lower QA in left acoustic radiation and left superior thalamic radiation correlated with higher blood glucose (r = - 0.46 and - 0.49). In tracts associated with depression, lower QA in left SLF-II correlated with higher bilirubin level (r = - 0.58). Depression and fatigability were associated with various white matter integrity changes, which correlated with biochemistry biomarkers all related to inflammation. [ABSTRACT FROM AUTHOR]

Diffusion tensor imaging (DTI) has been employed for over 2 decades to noninvasively quantify central nervous system diseases/injuries. However, DTI is an inadequate simplification of diffusion modeling in the presence of coexisting inflammation, edema and crossing nerve fibers. We employed a tissue phantom using fixed mouse trigeminal nerves coated with various amounts of agarose gel to mimic crossing fibers in the presence of vasogenic edema. Diffusivity measures derived by DTI and diffusion basis spectrum imaging (DBSI) were compared at increasing levels of simulated edema and degrees of fiber crossing. Furthermore, we assessed the ability of DBSI, diffusion kurtosis imaging (DKI), generalized q‐sampling imaging (GQI), q‐ball imaging (QBI) and neurite orientation dispersion and density imaging to resolve fiber crossing, in reference to the gold standard angles measured from structural images. DTI‐computed diffusivities and fractional anisotropy were significantly confounded by gel‐mimicked edema and crossing fibers. Conversely, DBSI calculated accurate diffusivities of individual fibers regardless of the extent of simulated edema and degrees of fiber crossing angles. Additionally, DBSI accurately and consistently estimated crossing angles in various conditions of gel‐mimicked edema when compared with the gold standard (r2 = 0.92, P = 1.9 × 10−9, bias = 3.9°). Small crossing angles and edema significantly impact the diffusion orientation distribution function, making DKI, GQI and QBI less accurate in detecting and estimating fiber crossing angles. Lastly, we used diffusion tensor ellipsoids to demonstrate that DBSI resolves the confounds of edema and crossing fibers in the peritumoral edema region from a patient with lung cancer metastasis, while DTI failed. In summary, DBSI is able to separate two crossing fibers and accurately recover their diffusivities in a complex environment characterized by increasing crossing angles and amounts of gel‐mimicked edema. DBSI also indicated better angular resolution compared with DKI, QBI and GQI. [ABSTRACT FROM AUTHOR]

Lexical tone identification has a unique role in the perceptual processes of Chinese readers. Reduced lexical tone awareness, along with poor word-decoding abilities, is frequently observed in Chinese-speaking children with developmental dyslexia. However, whether this deficit is linked to reduced auditory processing and interrupted structural connectivity in the brain requires further investigation. This study therefore explores the white matter pathways associated with Chinese character recognition and auditory processing of pitch variations, with the objective of to identify the most representative neural correlates for Chinese developmental dyslexia. Diffusion magnetic resonance imaging and several behavior measures related to reading attainment and phonological awareness were acquired in twenty-four Chinese-speaking children with developmental dyslexia and twenty-two age-matched controls. We used diffusion magnetic resonance imaging connectometry to explore the relationships between behavior performance and specific white matter tracts. The results revealed significant correlations of the left inferior fronto-occipital fasciculus, cerebellar pathways, and thalamopontine tracts with Chinese character recognition (FDR = 0.03235). In addition, the posterior isthmus and anterior splenium of the corpus callosum correlated with auditory processing (FDR = 0.03980). The study provides evidence that the dysconnectivity on white matter pathways correlated with developmental dyslexia in Chinese-speaking children. Furthermore, the impairments of auditory temporal timing processing presented in poor readers with significant phonological deficits are likely to be a result of impoverished myelinization in sub-cortical tracts. Such findings may assist in the clinical identification of Chinese developmental dyslexia. [ABSTRACT FROM AUTHOR]

Abstract Purpose The diffusion MRI "b-vector" table describing the diffusion sensitization direction can be flipped and permuted in dimension due to different orientation conventions used in scanners and incorrect or improperly utilized file formats. This can lead to incorrect fiber orientation estimates and subsequent tractography failure. Here, we present an automated quality control procedure to detect when the b-table is flipped and/or permuted incorrectly. Methods We define a "fiber coherence index" to describe how well fibers are connected to each other, and use it to automatically detect the correct configuration of b-vectors. We examined the performance on 3981 research subject scans (Baltimore Longitudinal Study of Aging), 1065 normal subject scans of high image quality (Human Connectome Project), and 202 patient scans (Vanderbilt University Medical Center), as well as 9 in-vivo and 9 ex-vivo animal data. Results The coherence index resulted in a 99.9% (3979/3981) and 100% (1065/1065) success rate in normal subject scans, 98% (198/202) in patient scans, and 100% (18/18) in both in-vivo and ex-vivo animal data in detecting the correct gradient table in datasets without severe image artifacts. The four failing cases (4/202) in patient scans, and two failures in healthy subject scans (2/3981), all showed prominent motion or signal dropout artifacts. Conclusions The fiber coherence measure can be used as an automatic quality assurance check in any diffusion analysis pipeline. Additionally, the success of this fiber coherence measure suggests potential broader applications, including evaluating data quality, or even providing diagnostic value as a biomarker of white matter integrity. [ABSTRACT FROM AUTHOR]

Here we introduce the concept of the local connectome: the degree of connectivity between adjacent voxels within a white matter fascicle defined by the density of the diffusing spins. While most human structural connectomic analyses can be summarized as finding global connectivity patterns at either end of anatomical pathways, the analysis of local connectomes, termed connectometry, tracks the local connectivity patterns along the fiber pathways themselves in order to identify the subcomponents of the pathways that express significant associations with a study variable. This bottom-up analytical approach is made possible by reconstructing diffusion MRI data into a common stereotaxic space that allows for associating local connectomes across subjects. The substantial associations can then be tracked along the white matter pathways, and statistical inference is obtained using permutation tests on the length of coherent associations and corrected for multiple comparisons. Using two separate samples, with different acquisition parameters, we show how connectometry can capture variability within core white matter pathways in a statistically efficient manner and extract meaningful variability from white matter pathways, complements graph-theoretic connectomic measures, and is more sensitive than region-of-interest approaches. [ABSTRACT FROM AUTHOR]

Abstract: A diffusion deconvolution method is proposed to apply deconvolution to the diffusion orientation distribution function (dODF) and calculate the fiber orientation distribution function (fODF), which is defined as the orientation distribution of the fiber spin density. The dODF can be obtained from q-space imaging methods such as q-ball imaging (QBI), diffusion spectrum imaging (DSI), and generalized q-sampling imaging (GQI), and thus the method can be applied to various diffusion sampling schemes. A phantom study was conducted to compare the angular resolution of the fODF with the dODF, and the in vivo datasets were acquired using single-shell, two-shell, and grid sampling schemes, which were then reconstructed by QBI, GQI, and DSI, respectively. The phantom study showed that the fODF significantly improved the angular resolution over the dODF at 45- and 60-degree crossing angles. The in vivo study showed consistent fODF regardless of the applied sampling schemes and reconstruction methods, and the ability to resolve crossing fibers was improved in reduced sampling condition. The fiber spin density obtained from deconvolution showed a higher contrast-to-noise ratio than the fractional anisotropy (FA) mapping, and further application on tractography showed that the fiber spin density can be used to determine the termination of fiber tracts. In conclusion, the proposed deconvolution method is generally applicable to different q-space imaging methods. The calculated fODF improves the angular resolution and also provides a quantitative index of fiber spin density to refine fiber tracking. [Copyright &y& Elsevier]

White matter fiber tracking using diffusion magnetic resonance imaging (dMRI) provides a noninvasive approach to map brain connections, but improving anatomical accuracy has been a significant challenge since the birth of tractography methods. Utilizing tractography in brain studies therefore requires understanding of its technical limitations to avoid shortcomings and pitfalls. This review explores tractography limitations and how different white matter pathways pose different challenges to fiber tracking methodologies. We summarize the pros and cons of commonly-used methods, aiming to inform how tractography and its related analysis may lead to questionable results. Extending these experiences, we review the clinical utilization of tractography in patients with brain tumors and traumatic brain injury, starting from tensor-based tractography to more advanced methods. We discuss current limitations and highlight novel approaches in the context of these two conditions to inform future tractography developments. [ABSTRACT FROM AUTHOR]

Diffusion MRI tractography has been used to map the axonal structure of the human brain, but its ability to detect neuronal injury is yet to be explored. Here we report differential tractography, a new type of tractography that utilizes repeat MRI scans and a novel tracking strategy to map the exact segment of fiber pathways with a neuronal injury. We examined differential tractography on multiple sclerosis, Huntington's disease, amyotrophic lateral sclerosis, and epileptic patients. The results showed that the affected pathways shown by differential tractography matched well with the unique clinical symptoms of the patients, and the false discovery rate of the findings could be estimated using a sham setting to provide a reliability measurement. This novel approach enables a quantitative and objective method to monitor neuronal injury in individuals, allowing for diagnostic and prognostic evaluation of brain diseases. Image 1 • Conventional tractography maps all pathways and is insensitive to neuronal change. • Differential tractography compares two longitudinal scans to map neuronal injury. • Differential tractography tracks the exact segment with a decrease in anisotropy. • The injury pattern revealed can assist with diagnostic and prognostic evaluations. • The false discovery rate of the findings can be estimated using a sham analysis. Differential tractography utilizes repeat diffusion MRI scans to identify the exact segment of tracks with a neuronal injury. [ABSTRACT FROM AUTHOR]

Abstract: The optimum sequence parameters of diffusion spectrum MRI (DSI) on clinical scanners were investigated previously. However, the scan time of approximately 30min is still too long for patient studies. Additionally, relatively large sampling interval in the diffusion-encoding space may cause aliasing artifact in the probability density function when Fourier transform is undertaken, leading to estimation error in fiber orientations. Therefore, this study proposed a non-Cartesian sampling scheme, body-centered-cubic (BCC), to avoid the aliasing artifact as compared to the conventional Cartesian grid sampling scheme (GRID). Furthermore, the accuracy of DSI with the use of half-sphere sampling schemes, i.e. GRID102 and BCC91, was investigated by comparing to their full-sphere sampling schemes, GRID203 and BCC181, respectively. In results, smaller deviation angle and lower angular dispersion were obtained by using the BCC sampling scheme. The half-sphere sampling schemes yielded angular precision and accuracy comparable to the full-sphere sampling schemes. The optimum b max was approximately 4750s/mm2 for GRID and 4500s/mm2 for BCC. In conclusion, the BCC sampling scheme could be implemented as a useful alternative to the GRID sampling scheme. Combination of BCC and half-sphere sampling schemes, that is BCC91, may potentially reduce the scan time of DSI from 30min to approximately 14min while maintaining its precision and accuracy. [Copyright &y& Elsevier]

A comprehensive map of the structural connectome in the human brain has been a coveted resource for understanding macroscopic brain networks. Here we report an expert-vetted, population-averaged atlas of the structural connectome derived from diffusion MRI data (N = 842). This was achieved by creating a high-resolution template of diffusion patterns averaged across individual subjects and using tractography to generate 550,000 trajectories of representative white matter fascicles annotated by 80 anatomical labels. The trajectories were subsequently clustered and labeled by a team of experienced neuroanatomists in order to conform to prior neuroanatomical knowledge. A multi-level network topology was then described using whole-brain connectograms, with subdivisions of the association pathways showing small-worldness in intra-hemisphere connections, projection pathways showing hub structures at thalamus, putamen, and brainstem, and commissural pathways showing bridges connecting cerebral hemispheres to provide global efficiency. This atlas of the structural connectome provides representative organization of human brain white matter, complementary to traditional histologically-derived and voxel-based white matter atlases, allowing for better modeling and simulation of brain connectivity for future connectome studies. [ABSTRACT FROM AUTHOR]

Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods. [ABSTRACT FROM AUTHOR]