Neuroanatomy in virtual reality: Development and pedagogical evaluation of photogrammetry-based 3D brain models.

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.

Generalizing prediction of task-evoked brain activity across datasets and populations.

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.

The Prediction of Brain Activity from Connectivity: Advances and Applications.

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.

Act natural: Functional connectivity from naturalistic stimuli fMRI outperforms resting-state in predicting brain activity.

The search for an 'ideal' approach to investigate the functional connections in the human brain is an ongoing challenge for the neuroscience community. While resting-state functional magnetic resonance imaging (fMRI) has been widely used to study individual functional connectivity patterns, recent work has highlighted the benefits of collecting functional connectivity data while participants are exposed to naturalistic stimuli, such as watching a movie or listening to a story. For example, functional connectivity data collected during movie-watching were shown to predict cognitive and emotional scores more accurately than resting-state-derived functional connectivity. We have previously reported a tight link between resting-state functional connectivity and task-derived neural activity, such that the former successfully predicts the latter. In the current work we use data from the Human Connectome Project to demonstrate that naturalistic-stimulus-derived functional connectivity predicts task-induced brain activation maps more accurately than resting-state-derived functional connectivity. We then show that activation maps predicted using naturalistic stimuli are better predictors of individual intelligence scores than activation maps predicted using resting-state. We additionally examine the influence of naturalistic-stimulus type on prediction accuracy. Our findings emphasize the potential of naturalistic stimuli as a promising alternative to resting-state fMRI for connectome-based predictive modelling of individual brain activity and cognitive traits.

Predicting individual traits from unperformed tasks.

Relating individual differences in cognitive traits to brain functional organization is a long-lasting challenge for the neuroscience community. Individual intelligence scores were previously predicted from whole-brain connectivity patterns, extracted from functional magnetic resonance imaging (fMRI) data acquired at rest. Recently, it was shown that task-induced brain activation maps outperform these resting-state connectivity patterns in predicting individual intelligence, suggesting that a cognitively demanding environment improves prediction of cognitive abilities. Here, we use data from the Human Connectome Project to predict task-induced brain activation maps from resting-state fMRI, and proceed to use these predicted activity maps to further predict individual differences in a variety of traits. While models based on original task activation maps remain the most accurate, models based on predicted maps significantly outperformed those based on the resting-state connectome. Thus, we provide a promising approach for the evaluation of measures of human behavior from brain activation maps, that could be used without having participants actually perform the tasks.

Alterations in Network Connectivity after Traumatic Brain Injury in Mice.

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.

Short-term plasticity following motor sequence learning revealed by diffusion magnetic resonance imaging.

Current noninvasive methods to detect structural plasticity in humans are mainly used to study long-term changes. Diffusion magnetic resonance imaging (MRI) was recently proposed as a novel approach to reveal gray matter changes following spatial navigation learning and object-location memory tasks. In the present work, we used diffusion MRI to investigate the short-term neuroplasticity that accompanies motor sequence learning. Following a 45-min training session in which participants learned to accurately play a short sequence on a piano keyboard, changes in diffusion properties were revealed mainly in motor system regions such as the premotor cortex and cerebellum. In a second learning session taking place immediately afterward, feedback was given on the timing of key pressing instead of accuracy, while participants continued to learn. This second session induced a different plasticity pattern, demonstrating the dynamic nature of learning-induced plasticity, formerly thought to require months of training in order to be detectable. These results provide us with an important reminder that the brain is an extremely dynamic structure. Furthermore, diffusion MRI offers a novel measure to follow tissue plasticity particularly over short timescales, allowing new insights into the dynamics of structural brain plasticity.