An increase in ipsilateral descending motor pathway activity has been reported following hemiparetic stroke. In axial muscles, increased ipsilateral cortical activity has been correlated with good recovery whereas in distal arm muscles it is correlated with poor recovery. Currently, little is known about the control of proximal upper limb muscles following stroke. This muscle group is less impaired than the distal arm muscles following stroke, yet contributes to the abnormal motor coordination patterns associated with movements of the arm which can severely impair reaching ability. This study used transcranial magnetic stimulation (TMS) to evaluate the presence and magnitude of ipsilateral and contralateral projections to the pectoralis major (PMJ) muscle in stroke survivors. A laterality index (LI) was used to investigate the relationship between ipsilateral and contralateral projections and strength, clinical impairment level, and the degree of abnormal coordination expressed in the arm. The ipsilateral and contralateral hemispheres were stimulated using 90% TMS intensity while the subject generated shoulder adduction torques in both arms. Motor evoked potentials (MEPs) were measured in the paretic and non-paretic PMJ. The secondary torque at the elbow was measured during maximal adduction as an indicator of the degree of extensor synergy. Ipsilateral MEPs were most common in stroke survivors with moderate to severe motor deficits. The LI was correlated with clinical impairment level (P = 0.05) and the degree of extension synergy expressed in the arm (P = 0.03). The LI was not correlated with strength. These results suggest that increased excitability of ipsilateral pathways projecting to the proximal upper arm may contribute to the expression of the Correspondence to: Susan Schwerin, sc.schwerin@yahoo.com. extension synergy following stroke. These findings are discussed in relation to a possible unmasking or upregulation of oligosynaptic cortico-bulbospinal pathways following stroke. NIH Public Access

PurposeThis study was a systematic evaluation across different and prominent diffusion MRI models to better understand the ways in which scalar metrics are influenced by experimental factors, including experimental design (diffusion‐weighted imaging [DWI] sampling) and noise.MethodsFour diffusion MRI models—diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator MRI (MAP‐MRI), and neurite orientation dispersion and density imaging (NODDI)—were evaluated by comparing maps and histogram values of the scalar metrics generated using DWI datasets obtained in fixed mouse brain with different noise levels and DWI sampling complexity. Additionally, models were fit with different input parameters or constraints to examine the consequences of model fitting procedures.ResultsExperimental factors affected all models and metrics to varying degrees. Model complexity influenced sensitivity to DWI sampling and noise, especially for metrics reporting non‐Gaussian information. DKI metrics were highly susceptible to noise and experimental design. The influence of fixed parameter selection for the NODDI model was found to be considerable, as was the impact of initial tensor fitting in the MAP‐MRI model.ConclusionAcross DTI, DKI, MAP‐MRI, and NODDI, a wide range of dependence on experimental factors was observed that elucidate principles and practical implications for advanced diffusion MRI. Magn Reson Med 78:1767–1780, 2017. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

This article provides a review of brain tissue alterations that may be detectable using diffusion MRI (dMRI) approaches and an overview and perspective on the modern dMRI toolkits for characterizing alterations that follow traumatic brain injury (TBI). Non-invasive imaging is a cornerstone of clinical treatment of TBI and has become increasingly used for pre-clinical and basic research studies. In particular, quantitative MRI methods have the potential to distinguish and evaluate the complex collection of neurobiological responses to TBI arising from pathology, neuroprotection and recovery. Diffusion MRI provides unique information about the physical environment in tissue and can be used to probe physiological, architectural and microstructural features. While well-established approaches such as diffusion tensor imaging (DTI) are known to be highly sensitive to changes in the tissue environment, more advanced diffusion MRI techniques have been developed that may offer increased specificity or new information for describing abnormalities. These tools are promising, but incompletely understood in the context of TBI. Furthermore, model dependencies and relative limitations may impact the implementation of these approaches and the interpretation of abnormalities in their metrics. The objective of this paper is to present a basic review and comparison across diffusion MRI methods as they pertain to the detection of the most commonly observed tissue and cellular alterations following TBI.

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