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A fundamental tenet of neuroscience is that cortical functional differentiation is related to the cross-areal differences in cyto-, receptor-, and myeloarchitectonics that are observed in ex-vivo preparations. An ongoing challenge is to create noninvasive magnetic resonance (MR) imaging techniques that offer sufficient resolution, tissue contrast, accuracy and precision to allow for characterization of cortical architecture over an entire living human brain. One exciting development is the advent of fast, high-resolution quantitative mapping of basic MR parameters that reflect cortical myeloarchitecture. Here, we outline some of the theoretical and technical advances underlying this technique, particularly in terms of measuring and correcting for transmit and receive radio frequency field inhomogeneities. We also discuss new directions in analytic techniques, including higher resolution reconstructions of the cortical surface. We then discuss two recent applications of this technique. The first compares individual and group myelin maps to functional retinotopic maps in the same individuals, demonstrating a close relationship between functionally and myeloarchitectonically defined areal boundaries (as well as revealing an interesting disparity in a highly studied visual area). The second combines tonotopic and myeloarchitectonic mapping to localize primary auditory areas in individual healthy adults, using a similar strategy as combined electrophysiological and post-mortem myeloarchitectonic studies in non-human primates. •We present an overview of high-resolution quantitative R1 (1/T1) mapping techniques.•High R1 cortical areas colocalize with functionally defined highly myelinated areas.•In-vivo R1 maps give promising insights into individual anatomical differences.

Neuroscience and clinical researchers are increasingly interested in quantitative magnetic resonance imaging (qMRI) due to its sensitivity to micro-structural properties of brain tissue such as axon, myelin, iron and water concentration. We introduce the hMRI-toolbox, an open-source, easy-to-use tool available on GitHub, for qMRI data handling and processing, presented together with a tutorial and example dataset. This toolbox allows the estimation of high-quality multi-parameter qMRI maps (longitudinal and effective transverse relaxation rates R1 and R2⋆, proton density PD and magnetisation transfer MT saturation) that can be used for quantitative parameter analysis and accurate delineation of subcortical brain structures. The qMRI maps generated by the toolbox are key input parameters for biophysical models designed to estimate tissue microstructure properties such as the MR g-ratio and to derive standard and novel MRI biomarkers. Thus, the current version of the toolbox is a first step towards in vivo histology using MRI (hMRI) and is being extended further in this direction. Embedded in the Statistical Parametric Mapping (SPM) framework, it benefits from the extensive range of established SPM tools for high-accuracy spatial registration and statistical inferences and can be readily combined with existing SPM toolboxes for estimating diffusion MRI parameter maps. From a user's perspective, the hMRI-toolbox is an efficient, robust and simple framework for investigating qMRI data in neuroscience and clinical research. [Display omitted]

The Rubber Hand Illusion (RHI) is an established paradigm for studying body ownership, and several studies have implicated premotor and temporo-parietal brain regions in its neuronal foundation. Here we used an automated setup to induce a novel multi-site version of the RHI in healthy human participants inside an MR-scanner, with a RHI and control condition that were matched in terms of synchrony of visual and tactile stimulation. Importantly, as previous research has shown that most of the ownership-related brain areas also respond to observed human actions and touch, or body parts of others, here such potential effects of the experimenter were eliminated by the automated procedure. The RHI condition induced a strong ownership illusion; we found correspondingly stronger brain activity during the RHI versus control condition in contralateral middle occipital gyrus (mOCG) and bilateral anterior insula, which have previously been related to illusory body ownership. Using independent functional localizers, we confirmed that the activity in mOCG was located within the body-part selective extrastriate body area (EBA). Crucially, activity differences in participants' peak voxels within the left EBA correlated strongly positively with their behavioral illusion scores. Thus EBA activity also reflected interindividual differences in the experienced intensity of illusory limb ownership. Moreover, psychophysiological interaction analyses (PPI) revealed that contralateral primary somatosensory cortex had stronger brain connectivity with EBA during the RHI versus control condition, while EBA was more strongly interacting with temporo-parietal multisensory regions. In sum, our findings demonstrate a direct involvement of EBA in limb ownership. •We induce the Rubber Hand Illusion using a novel, fully automated fMRI-setup.•We use a somatotopical design to match RHI and control in visuo-tactile synchrony.•We functionally localize the body-part selective extrastriate body area and MT+.•Brain activity in EBA and anterior insulae is stronger during RHI versus control.•Reported intensity of illusory ownership is positively correlated with EBA activity.

The subthalamic nucleus (STN) is a small, glutamatergic nucleus situated in the diencephalon. A critical component of normal motor function, it has become a key target for deep brain stimulation in the treatment of Parkinson's disease. Animal studies have demonstrated the existence of three functional sub-zones but these have never been shown conclusively in humans. In this work, a data driven method with diffusion weighted imaging demonstrated that three distinct clusters exist within the human STN based on brain connectivity profiles. The STN was successfully sub-parcellated into these regions, demonstrating good correspondence with that described in the animal literature. The local connectivity of each sub-region supported the hypothesis of bilateral limbic, associative and motor regions occupying the anterior, mid and posterior portions of the nucleus respectively. This study is the first to achieve in-vivo, non-invasive anatomical parcellation of the human STN into three anatomical zones within normal diagnostic scan times, which has important future implications for deep brain stimulation surgery. ► Three distinct sub-regions within the human STN are demonstrated in vivo using DWI. ► Limbic, associative and motor zones are labelled based on the regional connectivity. ► The findings agree with previous results from the animal literature. ► A somatotopic arrangement of STN projections to subcortical structures is shown. ► An overlap between motor STN projections and extra-STN hemiballismus is demonstrated.

Measuring the structural composition of the cortex is critical to understanding typical development, yet few investigations in humans have charted markers in vivo that are sensitive to tissue microstructural attributes. Here, we used a well-validated quantitative MR protocol to measure four parameters (R1, MT, R2*, PD*) that differ in their sensitivity to facets of the tissue microstructural environment (R1, MT: myelin, macromolecular content; R2*: myelin, paramagnetic ions, i.e., iron; PD*: free water content). Mapping these parameters across cortical regions in a young adult cohort (18–39 years, N = 93) revealed expected patterns of increased macromolecular content as well as reduced tissue water content in primary and primary adjacent cortical regions. Mapping across cortical depth within regions showed decreased expression of myelin and related processes – but increased tissue water content – when progressing from the grey/white to the grey/pial boundary, in all regions. Charting developmental change in cortical microstructure cross-sectionally, we found that parameters with sensitivity to tissue myelin (R1 & MT) showed linear increases with age across frontal and parietal cortex (change 0.5–1.0% per year). Overlap of robust age effects for both parameters emerged in left inferior frontal, right parietal and bilateral pre-central regions. Our findings afford an improved understanding of ontogeny in early adulthood and offer normative quantitative MR data for inter- and intra-cortical composition, which may be used as benchmarks in further studies. •We mapped multi-parameter maps (MPMs) across and within cortical regions.•We charted age effects (ages 18–39) on myelin and related processes.•MPMs sensitive to myelin (R1, MT) showed elevated values in primary areas over most cortical depths.•R2* map foci tended to overlap MPMs sensitive to myelin (R1, MT).•R1 and MT increased with age (0.5–1.0% per year) at mid-depth in frontal and parietal cortex.

Quantitative mapping of the longitudinal relaxation rate (R1=1/T1) in the human brain enables the investigation of tissue microstructure and macroscopic morphology which are becoming increasingly important for clinical and neuroimaging applications. R1 maps are now commonly estimated from two fast high-resolution 3D FLASH acquisitions with variable excitation flip angles, because this approach is fast and does not rely on special acquisition techniques. However, these R1 maps need to be corrected for bias due to RF transmit field (B1+) inhomogeneities, requiring additional B1+ mapping which is usually time consuming and difficult to implement. We propose a technique that simultaneously estimates the B1+ inhomogeneities and R1 values from the uncorrected R1 maps in the human brain without need for B1+ mapping. It employs a probabilistic framework for unified segmentation based correction of R1 maps for B1+ inhomogeneities (UNICORT). The framework incorporates a physically informed generative model of smooth B1+ inhomogeneities and their multiplicative effect on R1 estimates. Extensive cross-validation with the established standard using measured B1+ maps shows that UNICORT yields accurate B1+ and R1 maps with a mean deviation from the standard of less than 4.3% and 5%, respectively. The results of different groups of subjects with a wide age range and different levels of atypical brain anatomy further suggest that the method is robust and generalizes well to wider populations. UNICORT is easy to apply, as it is computationally efficient and its basic framework is implemented as part of the tissue segmentation in SPM8. ►UNICORT estimates RF transmit inhomogeneities and corrected R1 values from data. ►Performance similar to widely used corrections without need for special sequences. ►Allows for high-resolution R1 mapping with standard tools. ►R1 mapping enables the investigation of tissue microstructure.

•Novel sequential learning task to dissociate model-free from model-based signals.•Human behavior best explained by the Actor-critic framework.•Approximate Bayesian approach for surprise-based learning of the world-model.•Neural signatures of a surprise-modulated Actor-critic algorithm.•Evidence for temporal-difference, policy gradient, and surprise in the human brain. Learning how to reach a reward over long series of actions is a remarkable capability of humans, and potentially guided by multiple parallel learning modules. Current brain imaging of learning modules is limited by (i) simple experimental paradigms, (ii) entanglement of brain signals of different learning modules, and (iii) a limited number of computational models considered as candidates for explaining behavior. Here, we address these three limitations and (i) introduce a complex sequential decision making task with surprising events that allows us to (ii) dissociate correlates of reward prediction errors from those of surprise in functional magnetic resonance imaging (fMRI); and (iii) we test behavior against a large repertoire of model-free, model-based, and hybrid reinforcement learning algorithms, including a novel surprise-modulated actor-critic algorithm. Surprise, derived from an approximate Bayesian approach for learning the world-model, is extracted in our algorithm from a state prediction error. Surprise is then used to modulate the learning rate of a model-free actor, which itself learns via the reward prediction error from model-free value estimation by the critic. We find that action choices are well explained by pure model-free policy gradient, but reaction times and neural data are not. We identify signatures of both model-free and surprise-based learning signals in blood oxygen level dependent (BOLD) responses, supporting the existence of multiple parallel learning modules in the brain. Our results extend previous fMRI findings to a multi-step setting and emphasize the role of policy gradient and surprise signalling in human learning.

•Projecting the voxel myelin index on bundles using tractometry provides biased results when more than one bundle crosses a voxel.•Microstructure informed tractography can deconvolve myelin markers derived from voxel-wise maps on bundles to provide Bundle-specific myelin fractions (BMFs).•Myelin streamline decomposition (MySD) derived values projected on cortex show a pattern in line with literature.•BMFs estimated with MySD are consistent across two different myelin sensitive maps. To date, we have scarce information about the relative myelination level of different fiber bundles in the human brain. Indirect evidence comes from postmortem histology data but histological stainings are unable to follow a specific bundle and determine its intrinsic myelination. In this context, quantitative MRI, and diffusion MRI tractography may offer a viable solution by providing, respectively, voxel-wise myelin sensitive maps and the pathways of the major tracts of the brain. Then, “tractometry” can be used to combine these two pieces of information by averaging tissue features (obtained from any voxel-wise map) along the streamlines recovered with diffusion tractography. Although this method has been widely used in the literature, in cases of voxels containing multiple fiber populations (each with different levels of myelination), tractometry provides biased results because the same value will be attributed to any bundle passing through the voxel. To overcome this bias, we propose a new method - named “myelin streamline decomposition” (MySD) - which extends convex optimization modeling for microstructure informed tractography (COMMIT) allowing the actual value measured by a microstructural map to be deconvolved on each individual streamline, thereby recovering unique bundle-specific myelin fractions (BMFs). We demonstrate the advantage of our method with respect to tractometry in well-studied bundles and compare the cortical projection of the obtained bundle-wise myelin values of both methods. We also prove the stability of our approach across different subjects and different MRI sensitive myelin mapping approaches. This work provides a proof-of-concept of in vivo investigations of entire neuronal pathways that, to date, are not possible.

Survival in biological environments requires learning associations between predictive sensory cues and threatening outcomes. Such aversive learning may be implemented through reinforcement learning algorithms that are driven by the signed difference between expected and encountered outcomes, termed prediction errors (PEs). While PE-based learning is well established for reward learning, the role of putative PE signals in aversive learning is less clear. Here, we used functional magnetic resonance imaging in humans (21 healthy men and women) to investigate the neural representation of PEs during maintenance of learned aversive associations. Four visual cues, each with a different probability (0, 33, 66, 100%) of being followed by an aversive outcome (electric shock), were repeatedly presented to participants. We found that neural activity at omission (US-) but not occurrence of the aversive outcome (US+) encoded PEs in the medial prefrontal cortex. More expected omission of aversive outcome was associated with lower neural activity. No neural signals fulfilled axiomatic criteria, which specify necessary and sufficient components of PE signals, for signed PE representation in a whole-brain search or in a-priori regions of interest. Our results might suggest that, different from reward learning, aversive learning does not involve signed PE signals that are represented within the same brain region for all conditions.

In-vivo whole brain mapping of the radio frequency transmit field B(1) (+) is a key aspect of recent method developments in ultra high field MRI. We present an optimized method for fast and robust in-vivo whole-brain B(1) (+) mapping at 7T. The method is based on the acquisition of stimulated and spin echo 3D EPI images and was originally developed at 3T. We further optimized the method for use at 7T. Our optimization significantly improved the robustness of the method against large B(1) (+) deviations and off-resonance effects present at 7T. The mean accuracy and precision of the optimized method across the brain was high with a bias less than 2.6 percent unit (p.u.) and random error less than 0.7 p.u. respectively.