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• A high-level overview of how tractography is used to enable quantitative analysis of the brains structural connectivity. • Review methodology involved for tractography correction, segmentation and quantification. • Review studies using quantitative tractography approaches to study the brains white matter in health and disease. Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications. [ABSTRACT FROM AUTHOR]

In activation-induced manganese-enhanced MRI (AIM-MRI) experiments, differential accumulation of Mn in activated and silent brain areas is generally assessed using T1-weighted images and quantified by the enhancement of signal intensity (SI), calculated with reference to SI before Mn administration or to SI of brain regions unaffected by the specific stimulus. However, SI enhancement can be unreliable when animals are removed from and reinserted into the magnet. We have developed an experimental protocol based on repeated intraperitoneal (i.p.) injections of Mn, quantitative determination of T1, and coregistration of images to a rat brain atlas that allows absolute quantification of Mn concentration in selected brain areas. Results showed that interanimal variability of postcontrast T1 values was very low (compared to the experimental error in T1 determinations) allowing detection of differential regional Mn uptake in stimulated and unstimulated animals. In addition we have determined in vivo relaxivity of Mn in brain tissue and its frequency dependence. Magn Reson Med, 2009. © 2009 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

The article focuses on the proposed method of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) techniques for cancer area classification. It says that machine learning techniques are aimed to guide medical researchers in examining biomedical data. It mentions that the proposed approach consists of a three-step procedure including robust feature extraction and voxel segmentation and classification. In the first step, robust features that concisely represent the response of the tissue to the DCE-MRI techniques are computed. The second and third step provide a division basing on the mean shift paradigm and a support vector machine trained to classify voxels. The study suggests that the proposed method makes the analysis of DCE-MRI data more precise and fast.

Abstract Object  Use of polyethylenimines (PEIs) of different molecular weight and selected carboxylated-PEI derivatives (PEI-COOH) in the synthesis and stabilization of iron oxide nanoparticles, to obtain possible multifunctional contrast agents. Materials and methods  Oxidation of Fe(II) at slightly elevated pH and temperature resulted in the formation of highly soluble and stable nanocomposites of iron oxides and polymer. Composites were characterized and studied by atomic force microscopy (AFM), transmission electron microscopy (TEM), X-ray diffractometry, AC and DC magnetometry, NMR relaxometry and magnetic resonance imaging (MRI). Results  From AFM the dimensions of the aggregates were found to be in the ~150–250 nm size region; the mean diameter of the magnetic core of the compounds named PEI-25, PEI-500 and PEI-COOH60 resulted d ~20  ± 5 nm for PEI-25, d ~9.5  ± 1.0 nm for PEI-500 and d ~6.8  ± 1.0 nm for PEI-COOH60. In PEI-COOH60 TEM and X-ray diffractometry revealed small assemblies of mineral magnetic cores with clear indications that the main constituents are maghemite and/or magnetite as confirmed by AC and DC SQUID magnetometry. For PEI-COOH60, the study of NMR-dispersion profiles revealed r 1 and r 2 relaxivities comparable to superparamagnetic iron-oxide commercial compounds in the whole investigated frequency range 7 ≤ ν ≤ 212 MHz. Conclusion  PEI-25 was studied as possible MRI contrast agent (CA) to map the cerebral blood volume (CBV) and cerebral blood flow (CBF) in an animal model obtaining promising results. The reported compounds may be further functionalized to afford novel multifunctional systems for biomedical applications. [ABSTRACT FROM AUTHOR]

Background: Autosomal Recessive Spastic Ataxia of Charlevoix-Saguenay (ARSACS) and Spastic Paraplegia Type 7 (SPG7) are paradigmatic spastic ataxias (SPAX) with suggested white matter (WM) involvement. Aim of this work was to thoroughly disentangle the degree of WM involvement in these conditions, evaluating both macrostructure and microstructure via the analysis of diffusion MRI (dMRI) data. Material and methods: In this multi-center prospective study, ARSACS and SPG7 patients and Healthy Controls (HC) were enrolled, all undergoing a standardized dMRI protocol and a clinimetrics evaluation including the Scale for the Assessment and Rating of Ataxia (SARA). Differences in terms of WM volume or global microstructural WM metrics were probed, as well as the possible occurrence of a spatially defined microstructural WM involvement via voxel-wise analyses, and its correlation with patients' clinical status. Results: Data of 37 ARSACS (M/F = 21/16; 33.4 ± 12.4 years), 37 SPG7 (M/F = 24/13; 55.7 ± 10.7 years), and 29 HC (M/F = 13/16; 42.1 ± 17.2 years) were analyzed. While in SPG7, only a mild mean microstructural damage was found compared to HC, ARSACS patients present a severe WM involvement, with a reduced global volume (p < 0.001), an alteration of all microstructural metrics (all with p < 0.001), without a spatially defined pattern of damage but with a prominent involvement of commissural fibers. Finally, in ARSACS, a correlation between microstructural damage and SARA scores was found (p = 0.004). Conclusion: In ARSACS, but not SPG7 patients, we observed a complex and multi-faced involvement of brain WM, with a clinically meaningful widespread loss of axonal and dendritic integrity, secondary demyelination and, overall, a reduction in cellularity and volume. [ABSTRACT FROM AUTHOR]

Objective: The dentato‐thalamo‐cortical tract (DTT) is the main cerebellar efferent pathway. Degeneration of the DTT is a core feature of Friedreich ataxia (FRDA). However, it remains unclear whether DTT disruption is spatially specific, with some segments being more impacted than others. This study aimed to investigate microstructural integrity along the DTT in FRDA using a profilometry diffusion MRI (dMRI) approach. Methods: MRI data from 45 individuals with FRDA (mean age: 33.2 ± 13.2, Male/Female: 26/19) and 37 healthy controls (mean age: 36.5 ± 12.7, Male/Female:18/19) were included in this cross‐sectional multicenter study. A profilometry analysis was performed on dMRI data by first using tractography to define the DTT as the white matter pathway connecting the dentate nucleus to the contralateral motor cortex. The tract was then divided into 100 segments, and dMRI metrics of microstructural integrity (fractional anisotropy, mean diffusivity and radial diffusivity) at each segment were compared between groups. The process was replicated on the arcuate fasciculus for comparison. Results: Across all diffusion metrics, the region of the DTT connecting the dentate nucleus and thalamus was more impacted in FRDA than downstream cerebral sections from the thalamus to the cortex. The arcuate fasciculus was minimally impacted. Interpretation: Our study further expands the current knowledge about brain involvement in FRDA, showing that microstructural abnormalities within the DTT are weighted to early segments of the tract (i.e., the superior cerebellar peduncle). These findings are consistent with the hypothesis of DTT undergoing anterograde degeneration arising from the dentate nuclei and progressing to the primary motor cortex. [ABSTRACT FROM AUTHOR]

VERDICT (vascular, extracellular and restricted diffusion for cytometry in tumours) estimates and maps microstructural features of cancerous tissue non‐invasively using diffusion MRI. The main purpose of this study is to address the high computational time of microstructural model fitting for prostate diagnosis, while retaining utility in terms of tumour conspicuity and repeatability. In this work, we adapt the accelerated microstructure imaging via convex optimization (AMICO) framework to linearize the estimation of VERDICT parameters for the prostate gland. We compare the original non‐linear fitting of VERDICT with the linear fitting, quantifying accuracy with synthetic data, and computational time and reliability (performance and precision) in eight patients. We also assess the repeatability (scan‐rescan) of the parameters. Comparison of the original VERDICT fitting versus VERDICT‐AMICO showed that the linearized fitting (1) is more accurate in simulation for a signal‐to‐noise ratio of 20 dB; (2) reduces the processing time by three orders of magnitude, from 6.55 seconds/voxel to 1.78 milliseconds/voxel; (3) estimates parameters more precisely; (4) produces similar parametric maps and (5) produces similar estimated parameters with a high Pearson correlation between implementations, r2 > 0.7. The VERDICT‐AMICO estimates also show high levels of repeatability. Finally, we demonstrate that VERDICT‐AMICO can estimate an extra diffusivity parameter without losing tumour conspicuity and retains the fitting advantages. VERDICT‐AMICO provides microstructural maps for prostate cancer characterization in seconds. We adapt the accelerated microstructure imaging via convex optimization (AMICO) framework to linearize the estimation of the VERDICT parameters for prostate. Comparison of both fittings showed that, VERDICT‐AMICO, the linearized fitting (1) reduces the processing time by three orders of magnitude, around 2s per slice, (2) is more accurate in simulation and (3) estimates parameters more precisely without losing contrast between tumour and surrounding tissue. VERDICT‐AMICO provides microstructural maps for prostate cancer characterization in seconds while increasing precision and accuracy. [ABSTRACT FROM AUTHOR]