Quantitative magnetization transfer imaging (qMTI) increases specificity to macromolecular content in tissue by modeling the exchange process between the liquid and the macromolecular pool. However, its use has been mostly restricted to researchers that have developed these methods, in part due to the need to write complicated in‐house software for modeling and data analysis. We have developed a software package (qMTLab) with a simple and easy to use graphical user interface that unifies three of the most widely used qMTI methods: MT spoiled gradient echo (MT‐SPGR), MT balanced steady‐state free precession (MT‐bSSFP), and selective inversion recovery with fast spin echo (SIR‐FSE). qMTLab is free open‐source software that allows anyone interested in using these methods to easily simulate qMTI data, compare the performance of the methods under various experimental conditions, define new acquisition protocols, fit acquired data, and visualize the fitted parameters maps. By providing free software that gives end users a simple and easy to use graphical interface, we hope to make qMTI accessible to a greater number of investigators and facilitate the development, evaluation, and optimization of acquisition protocols and models. © 2016 Wiley Periodicals, Inc. Concepts Magn Reson Part A, 2016.

Magnetic resonance imaging (MRI) has revolutionized the way we look at the human body. However, conventional MR scanners are not measurement devices. They produce digital images represented by "shades of grey", and the intensity of the shades depends on the way the images are acquired. This is why it is difficult to compare images acquired at different clinical sites, limiting the diagnostic, prognostic, and scientific potential of the technology. Quantitative MRI (qMRI) aims to overcome this problem by assigning units to MR images, ensuring that the values represent a measurable quantity that can be reproduced within and across sites. While the vision for quantitative MRI is to overcome site-dependent variations, this is still a challenge due to variability in the hardware and software used by MR vendors to produce quantitative MRI maps.

Several theoretical models suggest that deciding about actions and executing them are not completely distinct neural mechanisms but instead two modes of an integrated dynamical system. Here, we investigate this proposal by examining how neural activity unfolds during a dynamic decision-making task within the high-dimensional space defined by the activity of cells in monkey dorsal premotor (PMd), primary motor (M1), and dorsolateral prefrontal cortex (dlPFC) as well as the external and internal segments of the globus pallidus (GPe, GPi). Dimensionality reduction shows that the four strongest components of neural activity are functionally interpretable, reflecting a state transition between deliberation and commitment, the transformation of sensory evidence into a choice, and the baseline and slope of the rising urgency to decide. Analysis of the contribution of each population to these components shows differences between regions but no distinct clusters within each region. During deliberation, cortical activity unfolds on a two-dimensional 'decision manifold' defined by sensory evidence and urgency, and falls off this manifold at the moment of commitment into a choice-dependent trajectory leading to movement initiation. The structure of the manifold varies between regions: In PMd it is curved, in M1 it is nearly perfectly flat, and in dlPFC it is almost entirely confined to the sensory evidence dimension. In contrast, pallidal activity during deliberation is primarily defined by urgency. We suggest that these findings reveal the distinct dynamics of different regions, supporting a unified recurrent attractor model of action selection and execution.

Recent theoretical models suggest that deciding about actions and executing them are not implemented by completely distinct neural mechanisms but are instead two modes of an integrated dynamical system. Here, we investigate this proposal by examining how neural activity unfolds during a dynamic decision-making task within the high-dimensional space defined by the activity of cells in monkey dorsal premotor (PMd), primary motor (M1), and dorsolateral prefrontal cortex (dlPFC) as well as the external and internal segments of the globus pallidus (GPe, GPi). Dimensionality reduction shows that the four strongest components of neural activity are functionally interpretable, reflecting a state transition between deliberation and commitment, the transformation of sensory evidence into a choice, and the baseline and slope of the rising urgency to decide. Analysis of the contribution of each population to these components shows meaningful differences between regions but no distinct clusters within each region, consistent with an integrated dynamical system. During deliberation, cortical activity unfolds on a two-dimensional “decision manifold” defined by sensory evidence and urgency and falls off this manifold at the moment of commitment into a choice-dependent trajectory leading to movement initiation. The structure of the manifold varies between regions: In PMd, it is curved; in M1, it is nearly perfectly flat; and in dlPFC, it is almost entirely confined to the sensory evidence dimension. In contrast, pallidal activity during deliberation is primarily defined by urgency. We suggest that these findings reveal the distinct functional contributions of different brain regions to an integrated dynamical system governing action selection and execution.

A new Q555X mutation on the SYN1 gene was recently found in several members of a family segregating dyslexia, epilepsy, and autism spectrum disorder. To describe the effects of this mutation on cortical gray matter microstructure, we performed a surface-based group study using novel diffusion and quantitative multiparametric imaging on 13 SYN1 mutation carriers and 13 age- and sex-matched controls. Specifically, diffusion kurtosis imaging (DKI) and neurite orientation and dispersion and density imaging (NODDI) were used to analyze multi-shell diffusion data and obtain parametric maps sensitive to tissue structure, while quantitative metrics sensitive to tissue composition (T1, T2* and relative proton density [PD]) were obtained from a multi-echo variable flip angle FLASH acquisition. Results showed significant microstructural alterations in several regions usually involved in oral and written language as well as dyslexia. The most significant changes in these regions were lowered mean diffusivity and increased fractional anisotropy. This study is, to our knowledge, the first to successfully use diffusion imaging and multiparametric mapping to detect cortical anomalies in a group of subjects with a well-defined genotype linked to language impairments, epilepsy and autism spectrum disorder (ASD).