Motor sequences; separating the sequence from the motor: A longitudinal rsfMRI study

In motor learning, sequence specificity, i.e. the learning of specific sequential associations, has predominantly been studied using task-based fMRI paradigms. However, offline changes in resting state functional connectivity after sequence-specific motor learning are less well understood. Previous research has established that plastic changes following motor learning can be divided into stages including fast learning, slow learning and retention. A description of how resting state functional connectivity after sequence-specific motor sequence learning (MSL) develops across these stages is missing. This study aimed to identify plastic alterations in whole-brain functional connectivity after learning a complex motor sequence by contrasting an active group who learned a complex sequence with a control group who performed a control task matched for motor execution. Resting state fMRI and behavioural performance were collected in both groups over the course of 5 consecutive training days and at follow-up after 12 days to encompass fast learning, slow learning, overall learning and retention. Between-group interaction analyses showed sequence-specific decreases in functional connectivity during overall learning in the right supplementary motor area (SMA). We found that connectivity changes in a key region of the motor network, the superior parietal cortex (SPC) were not a result of sequence-specific learning but were instead linked to motor execution. Our study confirms the sequence-specific role of SMA that has previously been identified in online task-based learning studies, and extends it to resting state network changes after sequence-specific MSL

Time-lapse geophysical assessment of agricultural practices on soil moisture dynamics

Geophysical surveys are now commonly used in agriculture for mapping applications. High-throughput collection of geophysical properties such as electrical conductivity (inverse of resistivity), can be used as a proxy for soil properties of interest (e.g. moisture, texture, salinity). Most applications only rely on a single geophysical survey at a given time. However, time-lapse geophysical surveys have greater capabilities to characterize the dynamics of the system, which is the focus of this work. Assessing the impact of agricultural practices through the growth season can reveal important information for the crop production. In this work, we demonstrate the use of time-lapse electrical resistivity tomography (ERT) and electromagnetic induction (EMI) surveys through a series of three case studies illustrating common agricultural practices (cover crops, compaction with irrigation, tillage with nitrogen fertilization). In the first case study, time-lapse EMI reveals the initial effect of cover crops on soil drying and the absence of effect on the subsequent main crop. In the second case study, compaction, leading to a shallower drying depth for potatoes was imaged by time-lapse ERT. In the third case study, larger change in electrical conductivity over time were observed in conventional tillage compared to direct drill using time-lapse EMI. In addition, different nitrogen application rates had significant effect on the yield and leaf area index but only ephemeral effects on the dynamics of electrical conductivity mainly after the first application. Overall, time-lapse geophysical surveys show great potential for monitoring the impact of different agricultural practices that can influence crop yield

Nighres: Processing tools for high-resolution neuroimaging

With recent improvements in human magnetic resonance imaging (MRI) at ultra-high fields, the amount of data collected per subject in a given MRI experiment has increased considerably. Standard image processing packages are often challenged by the size of these data and dedicated methods are needed to leverage their extraordinary spatial resolution. Here we introduce a flexible Python toolbox which implements a set of advanced techniques for high-resolution neuroimaging. With these tools, segmentation and laminar analysis of cortical MRI data can be performed at resolutions up to 500 μm in reasonable times. Comprehensive online documentation makes the toolbox easy to use and install. An extensive developer’s guide encourages contributions of other researchers that will help to accelerate progress in the promising field of high-resolution neuroimaging

Gradients of functional connectivity in the mouse cortex reflect neocortical evolution

Contains fulltext : 228744.pdf (publisher's version ) (Open Access