Background Integrity of functional brain networks is closely associated with maintained cognitive performance at old age. Consistently, both carrier status of Apolipoprotein E ε4 allele (APOE4), and age-related aggregation of Alzheimer’s disease (AD) pathology result in altered brain network connectivity. The posterior cingulate and precuneus (PCP) is a node of particular interest due to its role in crucial memory processes. Moreover, the PCP is subject to the early aggregation of AD pathology. The current study aimed at characterizing brain network properties associated with unimpaired cognition in old aged adults. To determine the effects of age-related brain change and genetic risk for AD, pathological proteins β-amyloid and tau were measured by Positron-emission tomography (PET), PCP connectivity as a proxy of cognitive network integrity, and genetic risk by APOE4 carrier status. Methods Fifty-seven cognitively unimpaired old-aged adults (MMSE = 29.20 ± 1.11; 73 ± 8.32 years) were administered 11C PittsbuAPOE4. Additional longitudinal studies may determine protective connectivity patterns associated with healthy aging trajectories of AD-pathology aggregation. Copyright © 2020 Quevenco, van Bergen, Treyer, Studer, Kagerer, Meyer, Gietl, Kaufmann, Nitsch, Hock and Unschuld.Monte-Carlo Diffusion Simulations (MCDS) have been used extensively as a ground truth tool for the validation of microstructure models for Diffusion-Weighted MRI. However, methodological pitfalls in the design of the biomimicking geometrical configurations and the simulation parameters can lead to approximation biases. Such pitfalls affect the reliability of the estimated signal, as well as its validity and reproducibility as ground truth data. In this work, we first present a set of experiments in order to study three critical pitfalls encountered in the design of MCDS in the literature, namely, the number of simulated particles and time steps, simplifications in the intra-axonal substrate representation, and the impact of the substrate’s size on the signal stemming from the extra-axonal space. The results obtained show important changes in the simulated signals and the recovered microstructure features when changes in those parameters are introduced. Thereupon, driven by our findings from the first studies, we outline a general framework able to generate complex substrates. We show the framework’s capability to overcome the aforementioned simplifications by generating a complex crossing substrate, which preserves the volume in the crossing area and achieves a high packing density. The results presented in this work, along with the simulator developed, pave the way toward more realistic and reproducible Monte-Carlo simulations for Diffusion-Weighted MRI. Copyright © 2020 Rafael-Patino, Romascano, Ramirez-Manzanares, Canales-Rodríguez, Girard and Thiran.Automatic segmentation of Multiple Sclerosis (MS) lesions from Magnetic Resonance Imaging (MRI) images is essential for clinical assessment and treatment planning of MS. Recent years have seen an increasing use of Convolutional Neural Networks (CNNs) for this task. Although these methods provide accurate segmentation, their applicability in clinical settings remains limited due to a reproducibility issue across different image domains. MS images can have highly variable characteristics across patients, MRI scanners and imaging protocols; retraining a supervised model with data from each new domain is not a feasible solution because it requires manual annotation from expert radiologists. FK506 molecular weight In this work, we explore an unsupervised solution to the problem of domain shift. We present a framework, Seg-JDOT, which adapts a deep model so that samples from a source domain and samples from a target domain sharing similar representations will be similarly segmented. We evaluated the framework on a multi-site dataset, MICCAI 2016, and showed that the adaptation toward a target site can bring remarkable improvements in a model performance over standard training. Copyright © 2020 Ackaouy, Courty, Vallée, Commowick, Barillot and Galassi.For more than 30 years, deep brain stimulation (DBS) has been used to target the symptoms of a number of neurological disorders and in particular movement disorders such as Parkinson’s disease (PD) and essential tremor (ET). It is known that the loss of dopaminergic neurons in the substantia nigra leads to PD, while the exact impact of this on the brain dynamics is not fully understood, the presence of beta-band oscillatory activity is thought to be pathological. The cause of ET, however, remains uncertain, however pathological oscillations in the thalamocortical-cerebellar network have been linked to tremor. Both of these movement disorders are treated with DBS, which entails the surgical implantation of electrodes into a patient’s brain. While DBS leads to an improvement in symptoms for many patients, the mechanisms underlying this improvement is not clearly understood, and computational modeling has been used extensively to improve this. Many of the models used to study DBS and its effect on the human brai induced by DBS, and allow us to optimize this clinical therapy, particularly in terms of target selection and parameter setting. Copyright © 2020 Yousif, Bain, Nandi and Borisyuk.The effects of action observation (AO) on motor performance can be modulated by instruction. The effects of two top-down aspects of the instruction on motor performance have not been fully resolved those related to attention to the observed task and the incorporation of motor imagery (MI) during AO. In addition, the immediate vs. 24-h retention test effects of those instruction’s aspects are yet to be elucidated. Forty-eight healthy subjects were randomly instructed to (1) observe reaching movement (RM) sequences toward five lighted units with the intention of reproducing the same sequence as fast and as accurate as possible (Intentional + Attentional group; AO+At); (2) observe the RMs sequence with the intention of reproducing the same sequence as fast and as accurate as possible and simultaneously to the observation to imagine performing the RMs (Intentional + attentional + MI group; AO+At+MI); and (3) observe the RMs sequence (Passive AO group). Subjects’ performance was tested before and immediately after the AO and retested after 24 h.