Function-based Intersubject Alignment of Human Cortical Anatomy

Making conclusions about the functional neuroanatomical organization of the human brain requires methods for relating the functional anatomy of an individual's brain to population variability. We have developed a method for aligning the functional neuroanatomy of individual brains based on the patterns of neural activity that are elicited by viewing a movie. Instead of basing alignment on functionally defined areas, whose location is defined as the center of mass or the local maximum response, the alignment is based on patterns of response as they are distributed spatially both within and across cortical areas. The method is implemented in the two-dimensional manifold of an inflated, spherical cortical surface. The method, although developed using movie data, generalizes successfully to data obtained with another cognitive activation paradigm—viewing static images of objects and faces—and improves group statistics in that experiment as measured by a standard general linear model (GLM) analysis

Learning to rank from medical imaging data

Medical images can be used to predict a clinical score coding for the severity of a disease, a pain level or the complexity of a cognitive task. In all these cases, the predicted variable has a natural order. While a standard classifier discards this information, we would like to take it into account in order to improve prediction performance. A standard linear regression does model such information, however the linearity assumption is likely not be satisfied when predicting from pixel intensities in an image. In this paper we address these modeling challenges with a supervised learning procedure where the model aims to order or rank images. We use a linear model for its robustness in high dimension and its possible interpretation. We show on simulations and two fMRI datasets that this approach is able to predict the correct ordering on pairs of images, yielding higher prediction accuracy than standard regression and multiclass classification techniques

Finding a balance: A systematic review of the biomechanical effects of vestibular prostheses on stability in humans

This is the final version. Available on open access from MDPI via the DOI in this recordThe vestibular system is located in the inner ear and is responsible for maintaining balance in humans. Bilateral vestibular dysfunction (BVD) is a disorder that adversely affects vestibular function. This results in symptoms such as postural imbalance and vertigo, increasing the incidence of falls and worsening quality of life. Current therapeutic options are often ineffective, with a focus on symptom management. Artificial stimulation of the vestibular system, via a vestibular prosthesis, is a technique being explored to restore vestibular function. This review systematically searched for literature that reported the effect of artificial vestibular stimulation on human behaviours related to balance, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) technique. A total of 21 papers matched the inclusion criteria of the literature search conducted using the PubMed and Web of Science databases (February 2019). The populations for these studies included both healthy adults and patients with BVD. In every paper, artificial vestibular stimulation caused an improvement in certain behaviours related to balance, although the extent of the effect varied greatly. Various behaviours were measured such as the vestibulo-ocular reflex, postural sway and certain gait characteristics. Two classes of prosthesis were evaluated and both showed a significant improvement in at least one aspect of balance-related behaviour in every paper included. No adverse effects were reported for prostheses using noisy galvanic vestibular stimulation, however, prosthetic implantation sometimes caused hearing or vestibular loss. Significant heterogeneity in methodology, study population and disease aetiology were observed. The present study confirms the feasibility of vestibular implants in humans for restoring balance in controlled conditions, but more research needs to be conducted to determine their effects on balance in non-clinical settings

Beyond brain reading: randomized sparsity and clustering to simultaneously predict and identify

International audienceThe prediction of behavioral covariates from functional MRI (fMRI) is known as brain reading. From a statistical standpoint, this challenge is a supervised learning task. The ability to predict cognitive states from new data gives a model selection criterion: prediction accu- racy. While a good prediction score implies that some of the voxels used by the classifier are relevant, one cannot state that these voxels form the brain regions involved in the cognitive task. The best predictive model may have selected by chance non-informative regions, and neglected rele- vant regions that provide duplicate information. In this contribution, we address the support identification problem. The proposed approach relies on randomization techniques which have been proved to be consistent for support recovery. To account for the spatial correlations between voxels, our approach makes use of a spatially constrained hierarchical clustering algorithm. Results are provided on simulations and a visual experiment

Beliefs about the Minds of Others Influence How We Process Sensory Information

Attending where others gaze is one of the most fundamental mechanisms of social cognition. The present study is the first to examine the impact of the attribution of mind to others on gaze-guided attentional orienting and its ERP correlates. Using a paradigm in which attention was guided to a location by the gaze of a centrally presented face, we manipulated participants' beliefs about the gazer: gaze behavior was believed to result either from operations of a mind or from a machine. In Experiment 1, beliefs were manipulated by cue identity (human or robot), while in Experiment 2, cue identity (robot) remained identical across conditions and beliefs were manipulated solely via instruction, which was irrelevant to the task. ERP results and behavior showed that participants' attention was guided by gaze only when gaze was believed to be controlled by a human. Specifically, the P1 was more enhanced for validly, relative to invalidly, cued targets only when participants believed the gaze behavior was the result of a mind, rather than of a machine. This shows that sensory gain control can be influenced by higher-order (task-irrelevant) beliefs about the observed scene. We propose a new interdisciplinary model of social attention, which integrates ideas from cognitive and social neuroscience, as well as philosophy in order to provide a framework for understanding a crucial aspect of how humans' beliefs about the observed scene influence sensory processing

Social presence and dishonesty in retail

Self-service checkouts (SCOs) in retail can benefit consumers and retailers, providing control and autonomy to shoppers independent from staff, together with reduced queuing times. Recent research indicates that the absence of staff may provide the opportunity for consumers to behave dishonestly, consistent with a perceived lack of social presence. This study examined whether a social presence in the form of various instantiations of embodied, visual, humanlike SCO interface agents had an effect on opportunistic behaviour. Using a simulated SCO scenario, participants experienced various dilemmas in which they could financially benefit themselves undeservedly. We hypothesised that a humanlike social presence integrated within the checkout screen would receive more attention and result in fewer instances of dishonesty compared to a less humanlike agent. This was partially supported by the results. The findings contribute to the theoretical framework in social presence research. We concluded that companies adopting self-service technology may consider the implementation of social presence in technology applications to support ethical consumer behaviour, but that more research is required to explore the mixed findings in the current study.<br/

Anatomical connectivity patterns predict face selectivity in the fusiform gyrus

A fundamental assumption in neuroscience is that brain structure determines function. Accordingly, functionally distinct regions of cortex should be structurally distinct in their connections to other areas. We tested this hypothesis in relation to face selectivity in the fusiform gyrus. By using only structural connectivity, as measured through diffusion-weighted imaging, we were able to predict functional activation to faces in the fusiform gyrus. These predictions outperformed two control models and a standard group-average benchmark. The structure–function relationship discovered from the initial participants was highly robust in predicting activation in a second group of participants, despite differences in acquisition parameters and stimuli. This approach can thus reliably estimate activation in participants who cannot perform functional imaging tasks and is an alternative to group-activation maps. Additionally, we identified cortical regions whose connectivity was highly influential in predicting face selectivity within the fusiform, suggesting a possible mechanistic architecture underlying face processing in humans.United States. Public Health Service (DA023427)National Institute of Mental Health (U.S.) (F32 MH084488)National Eye Institute (T32 EY013935)Poitras FoundationSimons FoundationEllison Medical Foundatio