COVER ILLUSTRATION Evidence for short-term, motor learning-related structural plasticity in the adult human brain was revealed using diffusion MRI. Structural changes were detectable in cortical and sub-cortical brain regions of non-musicians after only 45 minutes of pianotraining. This work highlights the dynamic nature of of the adult human brain; and the use of diffusion MRI as a rapid and sensitive biomarker for gray-matter learning-driven plasticity.
Abstract Anatomy studies are an essential part of medical training. The study of neuroanatomy in particular presents students with a unique challenge of three‐dimensional spatial understanding. Virtual Reality (VR) has been suggested to address this challenge, yet the majority of previous reports have implemented computer‐generated or imaging‐based models rather than models of real brain specimens. Using photogrammetry of real human bodies and advanced editing software, we developed 3D models of a real human brain at different stages of dissection. Models were placed in a custom‐built virtual laboratory, where students can walk around freely, explore, and manipulate (i.e., lift the models, rotate them for different viewpoints, etc.). Sixty participants were randomly assigned to one of three learning groups: VR, 3D printed models or read‐only, and given 1 h to study the white matter tracts of the cerebrum, followed by theoretical and practical exams and a learning experience questionnaire. We show that following self‐guided learning in virtual reality, students demonstrate a gain in spatial understanding and an increased satisfaction with the learning experience, compared with traditional learning approaches. We conclude that the models and virtual lab described in this work may enhance learning experience and improve learning outcomes.
Abstract How does our brain transform when we encounter a new task? To fully answer this question, comparing brain states before and after learning may not be enough, but rather an on-going, continuous monitoring of brain changes during learning is required. While such continuous examinations of functional learning-induced changes are widely available using functional magnetic resonance imaging (fMRI), a continuous investigation of microstructural brain modifications during learning is yet to be reported. Here, we continuously acquire diffusion MRI images during task performance. We then compute the mean diffusivity (MD) using a sliding-window approach, resulting in a continuous measure of microstructural changes throughout learning. We demonstrate the utility of this method on a motor sequence learning (finger tapping) task (n=58). MD decrease was detected in task-related brain regions, including the parahippocampal gyrus, hippocampus, inferior temporal gyrus, and cerebellum. Analysis of the temporal patterns of decrease revealed a rapid MD reduction in the right temporal gyrus after 11 minutes of learning, with additional decrease in the right parahippocampal gyrus and left cerebellum after 22 minutes. We further computed “neuroplasticity networks” of brain areas showing similar change patterns and detected similarities between these networks and canonical functional connectivity networks. Our findings offer novel insights on the spatio-temporal dynamics of microstructural neuroplasticity by demonstrating continuous modifications during the encoding phase of learning itself, rather than comparing pre- and post-learning states.
Faces convey rich information including identity, gender and expression. Current neural models of face processing suggest a dissociation between the processing of invariant facial aspects such as identity and gender, that engage the fusiform face area (FFA) and the processing of changeable aspects, such as expression and eye gaze, that engage the posterior superior temporal sulcus face area (pSTS-FA). Recent studies report a second dissociation within this network such that the pSTS-FA, but not the FFA, shows much stronger response to dynamic than static faces. The aim of the current study was to test a unified model that accounts for these two functional characteristics of the neural face network. In an fMRI experiment, we presented static and dynamic faces while subjects judged an invariant (gender) or a changeable facial aspect (expression). We found that the pSTS-FA was more engaged in processing dynamic than static faces and changeable than invariant aspects, whereas the OFA and FFA showed similar response across all four conditions. These findings support an integrated neural model of face processing in which the ventral areas extract form information from both invariant and changeable facial aspects whereas the dorsal face areas are sensitive to dynamic and changeable facial aspects.
Victims of mild traumatic brain injury (mTBI) usually do not display clear morphological brain defects, but frequently have long-lasting cognitive deficits, emotional difficulties, and behavioral disturbances. In the present study we used diffusion magnetic resonance imaging (dMRI) combined with graph theory measurements to investigate the effects of mTBI on brain network connectivity. We employed a non-invasive closed-head weight-drop mouse model to produce mTBI. Mice were scanned at two time points, 24 h before the injury and either 7 or 30 days following the injury. Connectivity matrices were computed for each animal at each time point, and these were subsequently used to extract graph theory measures reflecting network integration and segregation, on both the global (i.e., whole brain) and local (i.e., single regions) levels. We found that cluster coefficient, reflecting network segregation, decreased 7 days post-injury and then returned to baseline level 30 days following the injury. Global efficiency, reflecting network integration, demonstrated opposite patterns in the left and right hemispheres, with an increase of right hemisphere efficiency at 7 days and then a decrease in efficiency following 30 days, and vice versa in the left hemisphere. These findings suggest a possible compensation mechanism acting to moderate the influence of mTBI on the global network. Moreover, these results highlight the importance of tracking the dynamic changes in mTBI over time, and the potential of structural connectivity as a promising approach for studying network integrity and pathology progression in mTBI.
The most dominant neural model of face processing posits that the face network is composed of two pathways that process different types of facial information (Haxby et al. 2000): A ventral pathway processes invariant aspects such as identity and gender and a dorsal pathway processes changeable aspects such as expression and gaze. This model is primarily based on studies that presented static images of faces. Recent studies that presented dynamic faces show that the dorsal stream is highly responsive to dynamic faces, whereas the ventral stream shows similar response to static and dynamic faces (Pitcher et al. 2011). These recent findings raise the question of what is the primary division of labor between the two pathways: is it to motion and form? to changeable and invariant facial aspects? or the interaction of both. To answer this question, we presented dynamic and static faces while subjects performed either an expression (positive/negative) or a gender (male/female) discrimination task. Univariate analysis revealed higher response to the dynamic than static faces in the dorsal pathway, whereas the ventral pathway responded similarly to the dynamic and static conditions. Both face streams showed no effect of task. Multivariate analysis further revealed that the dorsal but not the ventral regions carry information about motion, indicated by higher correlation within than between moving and static stimuli. Neither the ventral nor the dorsal streams carried information on whether subjects performed an expression or a gender task. Finally, the pattern of response in the motion area, MT, was correlated with the response of the dorsal face stream, further indicating its primary response to motion. These findings suggest that the primary division of labor between the two face streams is to motion and form rather than to changeable and invariant aspects, as current models posit. Meeting abstract presented at VSS 2016
The human brain is composed of multiple, discrete, functionally specialized regions that are interconnected to form large-scale distributed networks. Using advanced brain-imaging methods and machine-learning analytical approaches, recent studies have demonstrated that regional brain activity during the performance of various cognitive tasks can be accurately predicted from patterns of task-independent brain connectivity. In this review article, we first present evidence for the predictability of brain activity from structural connectivity (i.e., white matter connections) and functional connectivity (i.e., temporally synchronized task-free activations). We then discuss the implications of such predictions to clinical populations, such as patients diagnosed with psychiatric disorders or neurologic diseases, and to the study of brain-behavior associations. We conclude that connectivity may serve as an infrastructure that dictates brain activity, and we pinpoint several open questions and directions for future research.
Predictions of task-based functional magnetic resonance imaging (fMRI) from task-free resting-state (rs) fMRI have gained popularity over the past decade. This method holds a great promise for studying individual variability in brain function without the need to perform highly demanding tasks. However, in order to be broadly used, prediction models must prove to generalize beyond the dataset they were trained on. In this work, we test the generalizability of prediction of task-fMRI from rs-fMRI across sites, MRI vendors and age-groups. Moreover, we investigate the data requirements for successful prediction. We use the Human Connectome Project (HCP) dataset to explore how different combinations of training sample sizes and number of fMRI datapoints affect prediction success in various cognitive tasks. We then apply models trained on HCP data to predict brain activations in data from a different site, a different MRI vendor (Phillips vs. Siemens scanners) and a different age group (children from the HCP-development project). We demonstrate that, depending on the task, a training set of approximately 20 participants with 100 fMRI timepoints each yields the largest gain in model performance. Nevertheless, further increasing sample size and number of timepoints results in significantly improved predictions, until reaching approximately 450-600 training participants and 800-1000 timepoints. Overall, the number of fMRI timepoints influences prediction success more than the sample size. We further show that models trained on adequate amounts of data successfully generalize across sites, vendors and age groups and provide predictions that are both accurate and individual-specific. These findings suggest that large-scale publicly available datasets may be utilized to study brain function in smaller, unique samples.
