Quantification Of Fibers Research Articles

Quantitative Assessment of Asbestos Fibers in Normal and Pathological Pleural Tissue-A Scoping Review.

Background: pleural mesothelioma is a rare cancer in the general population, but it is more common in subjects occupationally exposed to asbestos. Studies with asbestos fiber quantification in pleural tissue are scarce: for this reason, we aimed at undertaking a scoping review to summarize the evidence provided by studies in which asbestos fibers were determined by electron microscopy (SEM or TEM) in human pleural tissues, whether normal or pathologic. Materials and methods: A scoping review of articles that quantified asbestos fibers in human pleural tissue (normal or pathologic) by electron microscopy (SEM or TEM), in subjects with asbestos exposure (if any) was performed. Results: The 12 studies selected comprised 137 cases, out of which 142 samples were analyzed. Asbestos fibers were detected in 111 samples (78%) and were below the detectable limit in 31 samples (22%). The concentration of asbestos fibers detected in the positive samples was distributed from as low as 0.01 mfgdt (millions of fibers per gram of dry tissue) up to 240 mfgdt. However, the minimum concentration of fibers overlaps in the three types of tissues (normal pleura, pleural plaque, mesothelioma) in terms of magnitude; therefore, it is not possible to distinguish a definite pattern which differentiates one tissue from the other. Conclusions: The studies included were heterogeneous as to the representativeness of the samples and analytical techniques; the possibility of false negatives must be considered. It would be desirable to systematically search for asbestos fibers to fill the knowledge gap about the presence of asbestos fibers in normal or pathological pleural tissue in order to better understand the development of the different pleural diseases induced by this mineral.

Learning white matter subject-specific segmentation from structural MRI.

Mapping brain white matter (WM) is essential for building an understanding of brain anatomy and function. Tractography-based methods derived from diffusion-weighted MRI (dMRI) are the principal tools for investigating WM. These procedures rely ontime-consuming dMRI acquisitions thatmay not always be available, especially for legacy or time-constrained studies. To address this problem, we aim to generate WM tracts from structural magnetic resonance imaging (MRI) image by deep learning. Following recently proposed innovations in structural anatomical segmentation, we evaluate the feasibility of training multiply spatial localized convolution neural networks to learn context from fixed spatial patches from structural MRI on standard template. We focus on six widely used dMRI tractography algorithms (TractSeg, RecoBundles, XTRACT, Tracula, automated fiber quantification (AFQ), and AFQclipped) and train 125 U-Net models to learn these techniques from 3870 T1-weighted images from the Baltimore Longitudinal Study of Aging, the Human Connectome Project S1200 release, and scans acquired at Vanderbilt University. The proposed framework identifies fiber bundles with high agreement against tractography-based pathways with a median Dice coefficient from 0.62 to 0.87 on a test cohort, achieving improved subject-specific accuracy when compared to population atlas-based methods. We demonstrate the generalizabilityof the proposed framework on three externally available datasets. We show that patch-wise convolutional neural network can achieve robust bundle segmentation from T1w. We envision the use of this framework for visualizing the expected course of WM pathways when dMRI is not available.

Anterior cruciate ligament innervation in primary knee osteoarthritis.

To relate the Anterior Cruciate Ligament (ACL) innervation and histologic degeneration status to the knee osteoarthritis radiologic and functional status. Prospective observational study including 30 consecutive patients affected by primary knee osteoarthritis undergoing Total Knee Arthroplasty (TKA). All patients suffering secondary knee osteoarthritis, an antecedent of an infectious process, malignant process, autoimmune disorder, or previous knee surgery were excluded. We recorded biodemographic, clinical, and radiologic variables of all participants previous to the TKA procedure. ACL tissue was harvested during TKA standard procedure and the obtained sample was fixed in 4% formalin and paraffin-embedded. ACL cross-sections were stained by haematoxylin-eosin and Gallego staining for elastic and collagen fibers, and Sevier-Munger silver staining for nervous tissue. ACL samples histologic degeneration classification reported 15.4% normal, 23.1% slight, 26.9% mild, 11.5% moderate and 23.1% marked. We noted 46.2% large nervous fascicles, 15.4% medium fascicles, 3.8% small fascicles, and no nerve fibers were found in 34.6% ACL samples. No significant correlation was found between the histologic degeneration and the nervous fiber quantification (p>0.05, in all cases). We noted a significant histologic degeneration inverse correlation with the VAS scale (p=0.016), and nervous fiber quantification correlation with Lequesne maximum distance walked punctuation (p=0.043). We also noted greater nervous fiber quantification with minor radiological knee osteoarthritis (Kellgren-Lawrence grade II). ACL degeneration and innervation deficit may play a role in primary knee osteoarthritis onset, but the lack of a defining relationship among the different parameters assessed justifies further research in greater populations.

Right arcuate fasciculus and left uncinate fasciculus abnormalities in young smoker.

Previous diffusion tensor imaging (DTI) studies had investigated the white matter (WM) integrity abnormalities in smokers. Exposure to nicotine disrupts neurodevelopment during adolescence, possibly by disrupting the trophic effects of acetylcholine. However, little is known about the diffusion parameters of specific fibre bundles at multiple locations in young smokers. Thirty-seven young smokers and 29 age-, education- and gender-matched healthy non-smokers participated in this study. Automated Fibre Quantification (AFQ) was employed to investigate the WM microstructure in young smokers by integrating multiple indices. Diffusion parameters, that is, fractional anisotropy (FA), axial diffusion (AD), radial diffusion (RD) and mean diffusion (MD), were calculated at 100 points along the length of 18 major brain tracts. The relationships between neuroimaging differences and smoking behaviours were explored, including Fagerström Test of Nicotine Dependence (FTND) and pack-years. Compared with non-smokers, young smokers showed significantly increased FA, AD and decreased RD in the left uncinate fasciculus (UF) and right thalamic radiation (TR), increased AD, RD and decreased FA in the right arcuate fasciculus (Arc). Correlation analyses revealed that FA values of the left UF and RD values of the right Arc were negatively correlated with FTND score in smokers and FA values of the right Arc were positively correlated with FTND scores. Positive correlation was observed between AD values of the left UF and pack-years in smokers. The findings enhanced our understanding of the potential effect of adolescent smoking on WM microstructure.

Spatiotemporal changes in along-tract profilometry of cerebellar peduncles in cerebellar mutism syndrome

Cerebellar mutism syndrome, characterised by mutism, emotional lability and cerebellar motor signs, occurs in up to 39% of children following resection of medulloblastoma, the most common malignant posterior fossa tumour of childhood. Its pathophysiology remains unclear, but prior studies have implicated damage to the superior cerebellar peduncles. In this study, the objective was to conduct high-resolution spatial profilometry of the cerebellar peduncles and identify anatomic biomarkers of cerebellar mutism syndrome.In this retrospective study, twenty-eight children with medulloblastoma (mean age 8.8 ± 3.8 years) underwent diffusion MRI at four timepoints over one year. Forty-nine healthy children (9.0 ± 4.2 years), scanned at a single timepoint, served as age- and sex-matched controls. Automated Fibre Quantification was used to segment cerebellar peduncles and compute fractional anisotropy (FA) at 30 nodes along each tract.Thirteen patients developed cerebellar mutism syndrome. FA was significantly lower in the distal third of the left superior cerebellar peduncle pre-operatively in all patients compared to controls (FA in proximal third 0.228, middle and distal thirds 0.270, p = 0.01, Cohen’s d = 0.927). Pre-operative differences in FA did not predict cerebellar mutism syndrome. However, post-operative reductions in FA were highly specific to the distal left superior cerebellar peduncle, and were most pronounced in children with cerebellar mutism syndrome compared to those without at the 1–4 month follow up (0.325 vs 0.512, p = 0.042, d = 1.36) and at the 1-year follow up (0.342, vs 0.484, p = 0.038, d = 1.12).High spatial resolution cerebellar profilometry indicated a site-specific alteration of the distal segment of the superior cerebellar peduncle seen in cerebellar mutism syndrome which may have important surgical implications in the treatment of these devastating tumours of childhood.

The role of the temporal pole in temporal lobe epilepsy: A diffusion kurtosis imaging study

This study aimed to evaluate the use of diffusion kurtosis imaging (DKI) to detect microstructural abnormalities within the temporal pole (TP) and its temporopolar cortex in temporal lobe epilepsy (TLE) patients. DKI quantitative maps were obtained from fourteen lesional TLE and ten non-lesional TLE patients, along with twenty-three healthy controls. Data collected included mean (MK); radial (RK) and axial kurtosis (AK); mean diffusivity (MD) and axonal water fraction (AWF). Automated fiber quantification (AFQ) was used to quantify DKI measurements along the inferior longitudinal (ILF) and uncinate fasciculus (Unc). ILF and Unc tract profiles were compared between groups and tested for correlation with disease duration. To characterize temporopolar cortex microstructure, DKI maps were sampled at varying depths from superficial white matter (WM) towards the pial surface. Patients were separated according to the temporal lobe ipsilateral to seizure onset and their AFQ results were used as input for statistical analyses. Significant differences were observed between lesional TLE and controls, towards the most temporopolar segment of ILF and Unc proximal to the TP within the ipsilateral temporal lobe in left TLE patients for MK, RK, AWF and MD. No significant changes were observed with DKI maps in the non-lesional TLE group. DKI measurements correlated with disease duration, mostly towards the temporopolar segments of the WM bundles. Stronger differences in MK, RK and AWF within the temporopolar cortex were observed in the lesional TLE and noticeable differences (except for MD) in non-lesional TLE groups compared to controls. This study demonstrates that DKI has potential to detect subtle microstructural alterations within the temporopolar segments of the ILF and Unc and the connected temporopolar cortex in TLE patients including non-lesional TLE subjects. This could aid our understanding of the extrahippocampal areas, more specifically the temporal pole role in seizure generation in TLE and might inform surgical planning, leading to better seizure outcomes.

Enrichment of tetracycline residues from honey samples using carrier-mediated hollow fibre liquid-phase micro-extraction and quantification by LC-Q-TOF/MS.

In this study, development and validation of a simple, miniaturized and, environmentally friendly carrier-mediated three-phase hollow-fibre liquid-phase micro-extraction (HFLPME) technique was investigated for the enrichment of tetracycline residues in honey samples. The extracts were analysed using UV-visible spectrophotometry and liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-Q-TOF/MS). Parameters affecting the extraction efficiency of HFLPME such as pH of the donor and acceptor solutions, salt addition, agitation speed and extraction time were optimized. The calibration curves showed good linearity, in the range of 1-100 μg kg-1 with correlation coefficients ranging between 0.9943 and 0.9992, under the optimized conditions. Recoveries of blank honey samples at three spiking levels (1, 10 and 20 μg kg-1 ) ranged from 81.2% to 107.5%. Relative standard deviations for the precision of the method were less than 15.0%. Limits of detection and limits of quantification were in the range of 0.0861-0.2628 μg kg-1 and 0.2610-0.7964 μg kg-1 , respectively. Finally, the proposed method was successfully applied in the extraction of five tetracyclines from honey samples. Doxycycline residue detected in one of the commercial honey samples was below the limit of quantification. Because of the advantages offered by HFLPME, this method can be employed as an alternative to conventional extraction techniques for the clean-up and pre-concentration of antibiotics in complex matrices, including food samples. © 2021 Society of Chemical Industry.

Segmental Abnormalities of White Matter Microstructure in End-Stage Renal Disease Patients: An Automated Fiber Quantification Tractography Study.

Background and Purpose: End-stage renal disease (ESRD) results in extensive white matter abnormalities, but the specific damage segment cannot be identified. This study aimed to determine the segmental abnormalities of white matter microstructure in ESRD and its relationship with cognitive and renal function indicators.Methods: Eighteen ESRD patients and 19 healthy controls (HCs) were prospectively recruited. All participants underwent DTI and clinical assessments. Automatic fiber quantification (AFQ) was applied to generate bundle profiles along 16 main white matter tracts. We compared the DTI parameters between groups. Besides, we used partial correlation and multiple linear regression analyses to explore the associations between white matter integrity and cognitive performance as well as renal function indicators.Results: In the global tract level, compared to HCs, ESRD patients had greater MD, AD, and RD values and lower FA value in several fibers (P < 0.05, FDR correction). In the point-wise level, extensive damage existed in specific locations of different fiber tracts, particularly in the left hemisphere (P < 0.05, FDR correction). Among these tracts, the mean AD values of the left cingulum cingulate correlated negatively with MoCA score. Urea and UA level were independent predictors of the AD value of superior component of the left corticospinal. Besides, urea level was the independent predictors of mean MD value of left anterior thalamic radiation (ATR).Conclusion: White matter fiber tract damage in ESRD patients may be characterized by abnormalities in its specific location, especially in the left hemisphere. Aberrational specific located fibers were related to cognitive impairment and renal dysfunction.

Impairment and Plasticity of Language-Related White Matter in Patients With Brain Arteriovenous Malformations

Language dysfunction is rarely seen in patients with unruptured brain arteriovenous malformation (AVM) albeit the AVM nidus involving language areas, which provides a unique disease model to study language reorganization. The objective of this study was to investigate the impairment and reorganization patterns and characteristics of language-related white matter in AVMs located at different brain areas. Thirty-three patients with AVMs involving language areas were prospectively enrolled. Patients were categorized into 3 groups according to the lesion locations: the frontal (14 patients), temporal (15 patients), and parietal groups (4 patients). Thirty age- and sex-matched healthy controls were enrolled as comparison. All participants underwent diffusion tensor imaging scans, and automated fiber quantification method was applied to quantitatively study the difference of segmented language-related white matter connectivity between 3 AVM groups and control group. Language functions were normal in all subjects according to Western Aphasia Battery test. In the frontal group, fractional anisotropy (FA) value decreased in the left arcuate fascicle and increased in left superior longitudinal fasciculus and uncinate fascicle; in the temporal group, FA values decreased in left inferior fronto-occipital fascicle and inferior longitudinal fascicle and increased in right anterior thalamic radiation and uncinate fascicle; in the parietal group, FA values decreased in left arcuate fascicle and inferior longitudinal fascicle and increased in bilateral anterior thalamic radiations and uncinate fascicles and right inferior fronto-occipital fascicle. In fascicles with decreased FA values, the increase of radial diffusivity was common, and fascicles with increased FA values usually presented along with increased axial diffusivity values. Remodeling of language-related white matter occurs when traditional language areas are involved by AVM nidus, and its reorganization patterns vary with locations of AVM nidus. Fascicle impairment is mainly caused by the myelin deficits, and its plasticity may be dominated by the axon remodeling procedure.

Psychoradiological abnormalities in treatment-naive noncomorbid patients with posttraumatic stress disorder.

BackgroundNeuroimaging studies in posttraumatic stress disorder (PTSD) have identified various alterations in white matter (WM) microstructural organization. However, it remains unclear whether these are localized to specific regions of fiber tracts, and what diagnostic value they might have. This study set out to explore the spatial profile of WM abnormalities along defined fiber tracts in PTSD.MethodsDiffusion tensor images were obtained from 77 treatment‐naive noncomorbid patients with PTSD and 76 demographically matched trauma‐exposed non‐PTSD (TENP) controls. Using automated fiber quantification, tract profiles of fractional anisotropy, axial diffusivity, mean diffusivity, and radial diffusivity were calculated to evaluate WM microstructural organization. Results were analyzed by pointwise comparisons, by correlation with symptom severity, and for diagnosis‐by‐sex interactions. Support vector machine analyses assessed the ability of tract profiles to discriminate PTSD from TENP.ResultsCompared to TENP, PTSD showed lower fractional anisotropy accompanied by higher radial diffusivity and mean diffusivity in the left uncinate fasciculus, and lower fractional anisotropy accompanied by higher radial diffusivity in the right anterior thalamic radiation. Tract profile alterations were correlated with symptom severity, suggesting a pathophysiological relevance. There were no significant differences in diagnosis‐by‐sex interaction. Tract profiles allowed individual classification of PTSD versus TENP with significant accuracy, of potential diagnostic utility.ConclusionsThese findings add to the knowledge of the neuropathological basis of PTSD. WM alterations based on a tract‐profile quantification approach are a potential biomarker for PTSD.

Effect of rTMS on fractional anisotropy, radial diffusivity, and axonal volume in select fiber tracts in patients with traumatic brain injury

Abstract Traumatic brain injury (TBI) frequently involves damage to the white matter tracts in the brain. Repetitive transcranial magnetic stimulation (rTMS) can improve many of the sequelae of TBI, including depression and executive order dysfunction. This raises the question of whether rTMS treatment may result in measurable changes in the diffusion properties of white matter tracts, especially in those proximal to the rTMS treatment target location. In this study, 33 Veterans diagnosed with mild or moderate TBI (age 20-69 years; mild (n = 27) and moderate (n = 6) underwent the rTMS protocol that is FDA approved for major depression, receiving one treatment per day, five days per week, for four weeks (either treatment (n=17) or sham (n=16) version). Each participant underwent cognitive testing, as well as T1-weighted (T1W) and diffusion weighted (DWI) MR imaging scans, at the baseline, immediate post-treatment, and one month follow up timepoints. DWIs were processed using the Automated Fiber Quantification (AFQ) pipeline (Yeatman et al. 2012). This procedure identifies 20 white matter fiber tracts in the brain and obtain the diffusion parameters, e.g. fractional anisotropy (FA) and radial diffusivity (RD), at the100 discrete segments along the central portions of the fiber tracts. AFQ outcomes from each timepoint will be subjected to a linear mixed effects analysis to determine what effect rTMS treatment has on the diffusion properties in fibers proximal to the target, especially the superior longitudinal fasciculus. We will control for age, years of education, and baseline diffusivity values. Diffusion parameters of interest include fractional anisotropy, radial diffusivity, and one non-diffusivity variable, tract volume. We also plan to correlate these outcomes with performance on an executive function task. Keywords: DTI, AFQ, LME, rTMS

Altered Connection and Diagnosis Utility of White Matter in Alzheimer's Disease: A Multi-site Automated Fiber Quantification Study.

Alzheimer's disease (AD) is a typical neurodegenerative disease that is associated with cognitive decline, memory loss, and functional disconnection. Diffusion tensor imaging (DTI) has been widely used to investigate the integrity and degeneration of white matter in AD. In this study, with one of the world's largest DTI biobanks (865 individuals), we aim to explore the diagnosis utility and stability of tractbased features (extracted by automated fiber quantification (AFQ) pipeline) in AD. First, we studied the clinical association of tract-based features by detecting AD-associated alterations of diffusion properties along fiber bundles. Then, a binary classification experiment between AD and normal controls was performed using tract-based diffusion properties as features and support vector machine (SVM) as a classifier with an independent site cross-validation strategy. The average accuracy of 77.90% (the highest was 88.89%) showed that white matter properties as biomarkers had a relatively stable role in the clinical diagnosis of AD.Clinical Relevance- White matter characteristics are valid and robust biomarkers of AD, which have high accuracy and generalizability in the AD diagnosis in a large multi-site dataset.

Spatiotemporal changes in along-tract profilometry of cerebellar peduncles in cerebellar mutism syndrome

Abstract Aims Cerebellar mutism syndrome occurs in 25% of children following resection of posterior fossa tumours. Characterised by mutism, emotional lability and cerebellar motor signs, the syndrome is usually reversible over weeks to months. Its pathophysiology remains unclear, but evidence from diffusion MRI studies has implicated damage to the superior cerebellar peduncles in the development of this condition. The objective of this study was to describe the application of automated tractography of the cerebellar peduncles to provide a high-resolution spatiotemporal profile of diffusion MRI changes in cerebellar mutism syndrome. Method A retrospective case-control study was performed at Lucille Packard Children’s Hospital, Stanford University. Thirty children with midline medulloblastoma (mean age ± standard deviation 8.8 ± 3.8 years) underwent volumetric T1-weighted and diffusion MRI at four timepoints over one year. Forty-nine healthy children (9.0 ± 4.2 years), scanned at a single timepoint, were included as age- and sex-matched controls. Cerebellar mutism syndrome status was determined by contemporaneous casenote review. Automated Fibre Quantification was used to segment each subject’s cerebellar peduncles (Figure 1), and fractional anisotropy was computed at 30 nodes along each tract. A non-parametric permutation-based method was used to generate a critical cluster size and p-value for by-node ANOVA group comparisons. Z-scores for patients’ fractional anisotropy at each node were calculated based on values from controls’ corresponding nodes; these were analysed using mixed ANOVA with post-hoc false discovery rate-corrected pairwise t-tests. Results 13 patients developed cerebellar mutism syndrome. Automated fibre segmentation successfully identified the cerebellar peduncles in the majority of participants, but was more robust at follow-up timepoints (78.7% vs. 44.7% pre-operatively). Fractional anisotropy was significantly lower in the distal regions of the left superior cerebellar peduncle pre-operatively (p=0.0137) in patients compared to controls, although patients could not be distinguished pre-operatively with respect to cerebellar mutism syndrome status (Figure 2). Post-operative reductions in fractional anisotropy in children with cerebellar mutism syndrome were highly specific to the distal left superior cerebellar peduncle, and were most pronounced at follow-up timepoints (p=0.006; Figure 3). There were no significant differences in other cerebellar peduncles, either in along-tract fractional anisotropy or Z-scores, with respect to cerebellar mutism syndrome status. Conclusion A novel application of an automated tool to extract diffusion MRI data along the length of the cerebellar peduncles is described in a longitudinal retrospective cohort of paediatric medulloblastoma. Changes in fractional anisotropy in the cerebellar peduncles following tumour resection are described in a heretofore unprecedented level of spatiotemporal detail. In particular, children with post-operative cerebellar mutism syndrome show changes in the distal regions of the left superior cerebellar peduncle, and these changes persist up to a year post-operatively. These findings will have direct clinical implications for neurosurgeons performing resection of midline paediatric posterior fossa tumours.

Aberrant White Matter Microstructure in Depressed Patients with Suicidality.

Depression is a common psychiatric disorder affecting 264 million people globally, and the worst outcome is suicide. While regional brain alterations in depressed suicidal brain have previously been reported, knowledge about white matter (WM) microstructure is limited. Automated fiber quantification (AFQ) acquired by magnetic resonance imaging was used to calculate diffusion properties of fiber tracks to explore the structural alteration of WM associated with suicidality in depressive patients. Cross-sectional. Forty-five depressive patients without suicidality (DS- group, 60.00% females), 53 depressed patients with suicidality (DS+ group, 66.04% females), and 59 healthy controls (HC group, 67.80% females). 3.0 T; single-shot echo-planar imaging sequence. The point-wise group difference of the fiber tracts was determined by diffusion properties including fractional anisotropy, mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) of 18 specific WM tracts. Analysis of covariance (ANCOVA) and partial correlation analysis were used. A threshold of P < 0.05 was considered statistically significant. The significantly different diffusion properties were found in callosum forceps, left inferior fronto-occipital fasciculus (IFOF), right anterior thalamic radiation (ATR), and left cingulum cingulate in DS- and DS+ groups. The correlation analysis results showed that MD of right ATR was significantly positively correlated with Hamilton Depression Rating Scale (HAMD) scores (r=0.363). In addition, AD of right ATR (r=0.372), MD of callosum forceps minor (r=0.511), RD of left IFOF (r=0.429), and RD of callosum forceps minor (r=0.515) were significantly positively correlated with suicide item scores of HAMD. Our demonstration of decreased WM tract integrity including callosum forceps, IFOF, and ATR confirms the central involvement of the frontal cortex and limbic system with suicidality in depression. 3 TECHNICAL EFFICACY STAGE: 3.

Fiber tract integrity in patients with brain injury and chronic health symptoms

Patients with traumatic brain injuries (TBI), especially those with chronic health symptoms, often experience behavioral impairments attributable to white matter (WM) microstructural degeneration. Diffusion tensor imaging (DTI) is sensitive to these WM abnormalities, which are frequently a consequence of diffuse axonal injuries precipitated by the original trauma. In this study, tract profile analyses using an automated fiber quantification (AFQ) procedure compared diffusion properties along 20 major fiber tracts in a total of 46 participants, including individuals with TBI, who either have (n = 17) or did not have (n = 16) chronic symptoms, and a control group (n = 13). The tract profile analyses indicated that fractional anisotropy (FA) and radial diffusivity (RD) are more sensitive biomarkers of WM integrity than other commonly used diffusion measures, such as the linear anisotropy coefficient (CL) and mean diffusivity (MD). For all participants with TBI, FA was significantly lower and RD significantly higher in the middle portion of the left cingulum hippocampus (as compared to the control group). Additionally, FA was significantly lower in the inside segment of the left uncinate and the posterior segment of the inferior longitudinal fasciculus for TBI participants with chronic symptoms compared to the controls, but this was not the case for individuals with TBI without chronic symptoms. TBI participants with chronic symptoms also exhibited significantly lower fiber volume (VOL) in three other fiber tracts. This study provides evidence that the diffusion properties of discrete segments along the major fiber tracts, as measured by AFQ, can serve as biomarkers of WM abnormalities, especially for individuals with TBI and chronic health symptoms.

Alterations in white matter integrity and asymmetry in patients with benign childhood epilepsy with centrotemporal spikes and childhood absence epilepsy: An automated fiber quantification tractography study

PurposeTo investigate whether patients with benign childhood epilepsy with centrotemporal spikes (BECTS) and childhood absence epilepsy (CAE) show distinct patterns of white matter (WM) alterations and structural asymmetry compared with healthy controls and the relationship between WM alterations and epilepsy-related clinical variables. MethodsWe used automated fiber quantification to create tract profiles of fractional anisotropy (FA) and mean diffusivity (MD) in twenty-six patients with BECTS, twenty-nine patients with CAE, and twenty-four healthy controls. Group differences in FA and MD were quantified at 100 equidistant nodes along the fiber tract and these alterations and epilepsy-related clinical variables were correlated. A lateralization index (LI) representing the structural asymmetry of the fiber tract was computed and compared between both patient groups and controls. ResultsCompared with healthy controls, the BECTS group showed widespread FA reduction in 43.75% (7/16) and MD elevation in 50% (8/16) of identified fiber tracts, and the CAE group showed regional FA reduction in 31.25% (5/16) and MD elevation in 25% (4/16) of identified fiber tracts. In the BECTS group, FA and MD in the right anterior thalamic radiation positively and negatively correlated with the number of antiepileptic drugs, respectively, and MD in the right arcuate fasciculus (AF) positively correlated with seizure frequency. In the CAE group, the LI values were significantly lower in the inferior fronto-occipital fasciculus and the AF. ConclusionThe two childhood epilepsy syndromes display different patterns of WM alterations and structural asymmetry, suggesting that neuroanatomical differences may underlie the different profiles of BECTS and CAE.

Specific White Matter Tracts and Diffusion Properties Predict Conversion From Mild Cognitive Impairment to Alzheimer's Disease.

Identifying biomarkers that can assess the risk of developing Alzheimer’s Disease (AD) remains a significant challenge. In this study, we investigated the integrity levels of brain white matter in 34 patients with mild cognitive impairment (MCI) who later converted to AD and 53 stable MCI patients. We used diffusion tensor imaging (DTI) and automated fiber quantification to obtain the diffusion properties of 20 major white matter tracts. To identify which tracts and diffusion measures are most relevant to AD conversion, we used support vector machines (SVMs) to classify the AD conversion and non-conversion MCI patients based on the diffusion properties of each tract individually. We found that diffusivity measures from seven white matter tracts were predictive of AD conversion with axial diffusivity being the most predictive diffusion measure. Additional analyses revealed that white matter changes in the central and parahippocampal terminal regions of the right cingulate hippocampal bundle, central regions of the right inferior frontal occipital fasciculus, and posterior and anterior regions of the left inferior longitudinal fasciculus were the best predictors of conversion from MCI to AD. An SVM based on these white matter tract regions achieved an accuracy of 0.75. These findings provide additional potential biomarkers of AD risk in MCI patients.

Intraepidermal Nerve fiber Density in healthy Subjects with Diabetic Family History. ¿Is Neuropathy an early Biomarker? A Pilot study

Objective: Evaluate the intraepidermal nerve fiber density in healthy subjects with diabetic family history compared with diabetic patients and controls. Introduction: Neuropathy is the most prevalent chronic complication of diabetes, presenting various symptoms that interfere with daily living activities, psychosocially disability, and reducing life quality. The skin biopsy is recognized as a minimally invasive procedure that allows morphometric quantification of intraepidermal nerve fibers and has made possible the study of peripheral neuropathies involving thin fibers that traditional methods cannot diagnose. Methods: Analytical cross-sectional observational pilot study with seven patients per group including healthy, diabetic, and healthy with diabetic family history subjects. For the statistical analysis, we used the R package, R software version 3.3.2, with a confidence level of 95%. The research was performed with ANOVA and Kruskal-Wallis test to test the primary objective. Results: The density of intraepidermal nerve fibers is similar between the group with diabetic family history 6.8 ± 2.1 (3.5 - 10.1) and diabetic patients 6.3 ± 2.9 (3.5 - 7.05) while the control group reported a density in parameters of normality of 10± 1.2 (8.2 - 10.1) with a p= 0.01 between the three groups. The decrease of intraepidermal nerve fibers showed a tendency to decrease with increasing age and BMI with a ratio coefficient for age of r= -0.342, 95% CI (-0.67 - 0.106), p= 0.129; and for BMI of r= -0.36, 95% CI (-0.685 - 0.0847), p= 0.109. Conclusion: Intraepidermal nerve fiber density is decreased in subjects with a family history of diabetes mellitus type 2 and even more so in diabetics, with no statistical difference.

Quantification of the collagen fibers of healthy and neoplastic mammary tissue in cats by the Picrossirius Red histochemical method

Although feline mammary carcinoma is not the most prevalent among the species, its aggressive behavior represents a low life expectancy, compared with most undifferentiated types of breast cancer. Tissue stiffness induced by the accumulation of collagen fibers is related to a risk factor for carcinogenesis in healthy women and aggressiveness in those with breast cancer, which can also occur in cats. The objective of this work is to identify the relationship between stromal collagen density and aggressiveness of mammary carcinoma in cats, according to the peripheral and central tissue distribution by the Picrossirius Red histochemical method. Image.J® and MatLab® software were used for digital image processing. The mean values of kurtosis and entropy attributes were ​​grouped into a control group, and low and high-grade carcinoma groups, analyzed with one-way ANOVA and Bonferroni’s multiple comparison test (p &lt;0.01). Interpretation of stromal dynamics is important to evaluate both central and peripheral locations. According to entropy, there was a significant increase in the peripheral density in the carcinoma groups in relation to the control group, which can be justified by blood support. The same can be said of the central region, with a significant gain in collagen fibers from the tumors, indicated by kurtosis. The results suggest the presence of increases in stromal density in mammary carcinomas of cats, regardless of their graduation, and occurring in both regions.

AI4AD: Artificial intelligence analysis for Alzheimer's disease classification based on a multisite DTI database

BackgroundDiffusion tensor imaging (DTI) has been widely used to identify structural integrity and to delineate white matter (WM) degeneration in Alzheimer's disease (AD). However, the validity and replicability of the ability to discriminate AD patients and normal controls (NCs) of WM measures are limited due to the use of small cohorts and diverse image processing methods. As yet, we still do not have a clear idea of whether WM characteristics are biomarkers for AD. MethodsWe conducted a competition with diffusion measurements along 18 fiber tracts as features extracted via the automated fiber quantification (AFQ) method based on one of the largest worldwide DTI multisite biobanks (862 individuals, consisting of 279 NCs, 318 ADs, and 265 MCIs). After quality control, 825 subjects (276 NCs, 294 ADs, and 255 MCIs) were divided into a public training set (N=700) and a private testing set (N=125). Forty-eight teams submitted 130 solutions that were estimated on the private testing samples. We reported the final results of the top ten models. ResultsThe performance of white matter features in AD classification was stable and generalizable, which indicated the potential of WM to be a biomarker for AD. The best model achieved a prediction accuracy of 82.35% (with a sensitivity of 86.36% and a specificity of 78.05%) on the private testing set. The average accuracy of the top ten solutions was over 80%. ConclusionsThe results of this competition demonstrated that DTI is a powerful tool to identify AD. A larger dataset and additional independent cohort cross-validation may improve the discriminant performance and generalization power of the classification models, thus revealing more precise disease severity factors associated with AD. For this purpose, we have released this database (https://github.com/YongLiuLab/AI4AD_AFQ) to the community, with the expectation of new solutions for the accurate diagnosis of AD.