Fitting IVIM with Variable Projection and Simplicial Optimization

Fitting multi-exponential models to Diffusion MRI (dMRI) data has always been challenging due to various underlying complexities. In this work, we introduce a novel and robust fitting framework for the standard two-compartment IVIM microstructural model. This framework provides a significant improvement over the existing methods and helps estimate the associated diffusion and perfusion parameters of IVIM in an automatic manner. As a part of this work we provide capabilities to switch between more advanced global optimization methods such as simplicial homology (SH) and differential evolution (DE). Our experiments show that the results obtained from this simultaneous fitting procedure disentangle the model parameters in a reduced subspace. The proposed framework extends the seminal work originated in the MIX framework, with improved procedures for multi-stage fitting. This framework has been made available as an open-source Python implementation and disseminated to the community through the DIPY project

Quality Control of Motor Unit Number Index (MUNIX) Measurements in 6 Muscles in a Single-Subject “Round-Robin” Setup

Background Motor Unit Number Index (MUNIX) is a neurophysiological measure that provides an index of the number of lower motor neurons in a muscle. Its performance across centres in healthy subjects and patients with Amyotrophic Lateral Sclerosis (ALS) has been established, but inter-rater variability between multiple raters in one single subject has not been investigated. Objective To assess reliability in a set of 6 muscles in a single subject among 12 examiners (6 experienced with MUNIX, 6 less experienced) and to determine variables associated with variability of measurements. Methods Twelve raters applied MUNIX in six different muscles (abductor pollicis brevis (APB), abductor digiti minimi (ADM), biceps brachii (BB), tibialis anterior (TA), extensor dig. brevis (EDB), abductor hallucis (AH)) twice in one single volunteer on consecutive days. All raters visited at least one training course prior to measurements. Intra- and inter-rater variability as determined by the coefficient of variation (COV) between different raters and their levels of experience with MUNIX were compared. Results Mean intra-rater COV of MUNIX was 14.0% (±6.4) ranging from 5.8 (APB) to 30.3% (EDB). Mean inter-rater COV was 18.1 (±5.4) ranging from 8.0 (BB) to 31.7 (AH). No significant differences of variability between experienced and less experienced raters were detected. Conclusion We provide evidence that quality control for neurophysiological methods can be performed with similar standards as in laboratory medicine. Intra- and inter-rater variability of MUNIX is muscle-dependent and mainly below 20%. Experienced neurophysiologists can easily adopt MUNIX and adequate teaching ensures reliable utilization of this method

Stable Isotope Metabolic Labeling with a Novel 15N-Enriched Bacteria Diet for Improved Proteomic Analyses of Mouse Models for Psychopathologies

The identification of differentially regulated proteins in animal models of psychiatric diseases is essential for a comprehensive analysis of associated psychopathological processes. Mass spectrometry is the most relevant method for analyzing differences in protein expression of tissue and body fluid proteomes. However, standardization of sample handling and sample-to-sample variability are problematic. Stable isotope metabolic labeling of a proteome represents the gold standard for quantitative mass spectrometry analysis. The simultaneous processing of a mixture of labeled and unlabeled samples allows a sensitive and accurate comparative analysis between the respective proteomes. Here, we describe a cost-effective feeding protocol based on a newly developed 15N bacteria diet based on Ralstonia eutropha protein, which was applied to a mouse model for trait anxiety. Tissue from 15N-labeled vs. 14N-unlabeled mice was examined by mass spectrometry and differences in the expression of glyoxalase-1 (GLO1) and histidine triad nucleotide binding protein 2 (Hint2) proteins were correlated with the animals' psychopathological behaviors for methodological validation and proof of concept, respectively. Additionally, phenotyping unraveled an antidepressant-like effect of the incorporation of the stable isotope 15N into the proteome of highly anxious mice. This novel phenomenon is of considerable relevance to the metabolic labeling method and could provide an opportunity for the discovery of candidate proteins involved in depression-like behavior. The newly developed 15N bacteria diet provides researchers a novel tool to discover disease-relevant protein expression differences in mouse models using quantitative mass spectrometry

Fitting IVIM with Variable Projection and Simplicial Optimization

Fitting multi-exponential models to Diffusion MRI (dMRI) data has always been challenging due to various underlying complexities. In this work, we introduce a novel and robust fitting framework for the standard two-compartment IVIM microstructural model. This framework provides a significant improvement over the existing methods and helps estimate the associated diffusion and perfusion parameters of IVIM in an automatic manner. As a part of this work we provide capabilities to switch between more advanced global optimization methods such as simplicial homology (SH) and differential evolution (DE). Our experiments show that the results obtained from this simultaneous fitting procedure disentangle the model parameters in a reduced subspace. The proposed framework extends the seminal work originated in the MIX framework, with improved procedures for multi-stage fitting. This framework has been made available as an open-source Python implementation and disseminated to the community through the DIPY project

Mathematical methods for diffusion MRI processing

In this article, we review recent mathematical models and computational methods for the processing of diffusion Magnetic Resonance Images, including state-of-the-art reconstruction of diffusion models, cerebral white matter connectivity analysis, and segmentation techniques. We focus on Diffusion Tensor Images (DTI) and Q-Ball Images (QBI)

Implementing Motor Unit Number Index (Munix) in a Large Clinical Trial: Real World Experience From 27 Centres

Objective: Motor Unit Number Index (MUNIX) is a quantitative neurophysiological method that reflects loss of motor neurons in Amyotrophic Lateral Sclerosis (ALS) in longitudinal studies. It has been utilized in one natural history ALS study and one drug trial (Biogen USA) after training and qualification of raters. Methods: Prior to testing patients, evaluators had to submit test-retest data of 4 healthy volunteers. Twenty-seven centres with 36 raters measured MUNIX in 4 sets of 6 different muscles twice. Coefficient of variation of all measurements had to be \u3c20% to pass the qualification process. MUNIX COV of the first attempt, number of repeated measurements and muscle specific COV were evaluated. Results: COV varied considerably between raters. Mean COV of all raters at the first measurements was 12.9% ± 13.5 (median 8.7%). Need of repetitions ranged from 0 to 43 (mean 10.7 ± 9.1, median 8). Biceps and first dorsal interosseus muscles showed highest repetition rates. MUNIX variability correlated considerably with variability of compound muscle action potential. Conclusion: MUNIX revealed generally good reliability, but was rater dependent and ongoing support for raters was needed. Significance: MUNIX can be implemented in large clinical trials as an outcome measure after training and a qualification process

Correlation between duration of measurements and maximum stimulus intensity for all 6 muscles.

<p>Correlation between duration of measurements and maximum stimulus intensity for all 6 muscles.</p

Relative mean and standard deviation of MUNIX and CMAP measurements in individual muscles of the experienced group (filled circles) and less-experienced group (empty circles) compared to the hypothetical reference values, expressed as accuracy (%).

<p>Relative mean and standard deviation of MUNIX and CMAP measurements in individual muscles of the experienced group (filled circles) and less-experienced group (empty circles) compared to the hypothetical reference values, expressed as accuracy (%).</p

Characteristics of raters familiar (1 to 6) and less familiar (7 to12) with the MUNIX method.

<p># = number.</p

Coefficient of variation (COV) and variability () for MUNIX measurements in individual muscles in raters.

<p>Coefficient of variation (COV) and variability () for MUNIX measurements in individual muscles in raters.</p