Biased competition in semantic representation during natural visual search

Humans divide their attention among multiple visual targets in daily life, and visual search can get more difficult as the number of targets increases. The biased competition hypothesis (BC) has been put forth as an explanation for this phenomenon. BC suggests that brain responses during divided attention are a weighted linear combination of the responses during search for each target individually. This combination is assumed to be biased by the intrinsic selectivity of cortical regions. Yet, it is unknown whether attentional modulation of semantic representations are consistent with this hypothesis when viewing cluttered, dynamic natural scenes. Here, we investigated whether BC accounts for semantic representation during natural category-based visual search. Subjects viewed natural movies, and their whole-brain BOLD responses were recorded while they attended to “humans”, “vehicles” (i.e. single-target attention tasks), or “both humans and vehicles” (i.e. divided attention) in separate runs. We computed a voxelwise linearity index to assess whether semantic representation during divided attention can be modeled as a weighted combination of representations during the two single-target attention tasks. We then examined the bias in weights of this linear combination across cortical ROIs. We find that semantic representations of both target and nontarget categories during divided attention are linear to a substantial degree, and that they are biased toward the preferred target in category-selective areas across ventral temporal cortex. Taken together, these results suggest that the biased competition hypothesis is a compelling account for attentional modulation of semantic representations

Model-based dynamic off-resonance correction for improved accelerated fMRI in awake behaving non-human primates

Purpose To estimate dynamic off-resonance due to vigorous body motion in accelerated fMRI of awake behaving non-human primates (NHPs) using the standard EPI 3-line navigator, in order to attenuate the effects of time-varying off-resonance on the reconstruction. Methods In NHP fMRI the animal’s head is usually head-posted, and the dynamic off-resonance is mainly caused by motion in body parts that are distant from the brain and have low spatial frequency. Hence, off-resonance at each frame can be approximated as a spatially linear perturbation of the off-resonance at a reference frame, and is manifested as a relative linear shift in k-space. Using GRAPPA operators, we estimated these shifts by comparing the 3-line navigator at each time frame with that at the reference frame. Estimated shifts were then used to correct the data at each frame. The proposed method was evaluated in phantom scans, simulations, and in vivo data. Results The proposed method is shown to successfully estimate low-spatial order dynamic off-resonance perturbations, including induced linear off-resonance perturbations in phantoms, and is able to correct retrospectively corrupted data in simulations. Finally, it is shown to reduce ghosting artifacts and geometric distortions by up to 20% in simultaneous multi-slice in vivo acquisitions in awake-behaving NHPs. Conclusion A method is proposed that does not need any sequence modification or extra acquisitions and makes accelerated awake behaving NHP imaging more robust and reliable, reducing the gap between what is possible with NHP protocols and state-of-the-art human imaging.</p

Prior-guided image reconstruction for accelerated multi-contrast MRI via generative adversarial networks

Multi-contrast MRI acquisitions of an anatomy enrich the magnitude of information available for diagnosis. Yet, excessive scan times associated with additional contrasts may be a limiting factor. Two mainstream frameworks for enhanced scan efficiency are reconstruction of undersampled acquisitions and synthesis of missing acquisitions. Recently, deep learning methods have enabled significant performance improvements in both frameworks. Yet, reconstruction performance decreases towards higher acceleration factors with diminished sampling density at high-spatial-frequencies, whereas synthesis can manifest artefactual sensitivity or insensitivity to image features due to the absence of data samples from the target contrast. Here we propose a new approach for synergistic recovery of undersampled multi-contrast acquisitions based on conditional generative adversarial networks. The proposed method mitigates the limitations of pure learning-based reconstruction or synthesis by utilizing three priors: shared high-frequency prior available in the source contrast to preserve high-spatial-frequency details, low-frequency prior available in the undersampled target contrast to prevent feature leakage/loss, and perceptual prior to improve recovery of high-level features. Demonstrations on brain MRI datasets from healthy subjects and patients indicate the superior performance of the proposed method compared to pure reconstruction and synthesis methods. The proposed method can help improve the quality and scan efficiency of multi-contrast MRI exams

Task-dependent warping of semantic representations during search for visual action categories

Object and action perception in cluttered dynamic natural scenes relies on efficient allocation of limited brain resources to prioritize the attended targets over distractors. It has been suggested that during visual search for objects, distributed semantic representation of hundreds of object categories is warped to expand the representation of targets. Yet, little is known about whether and where in the brain visual search for action categories modulates semantic representations. To address this fundamental question, we studied brain activity recorded from five subjects (1 female) via functional magnetic resonance imaging while they viewed natural movies and searched for either &lt;i&gt;communication&lt;/i&gt; or &lt;i&gt;locomotion&lt;/i&gt; actions. We find that attention directed to action categories elicits tuning shifts that warp semantic representations broadly across neocortex, and that these shifts interact with intrinsic selectivity of cortical voxels for target actions. These results suggest that attention serves to facilitate task performance during social interactions by dynamically shifting semantic selectivity towards target actions, and that tuning shifts are a general feature of conceptual representations in the brain.&lt;b&gt;SIGNIFICANCE STATEMENT&lt;/b&gt;The ability to swiftly perceive the actions and intentions of others is a crucial skill for humans, which relies on efficient allocation of limited brain resources to prioritise the attended targets over distractors. However, little is known about the nature of high-level semantic representations during natural visual search for action categories. Here we provide the first evidence showing that attention significantly warps semantic representations by inducing tuning shifts in single cortical voxels, broadly spread across occipitotemporal, parietal, prefrontal, and cingulate cortices. This dynamic attentional mechanism can facilitate action perception by efficiently allocating neural resources to accentuate the representation of task-relevant action categories

Projection onto Epigraph Sets for Rapid Self-Tuning Compressed Sensing MRI

The compressed sensing (CS) framework leverages the sparsity of MR images to reconstruct from the undersampled acquisitions. CS reconstructions involve one or more regularization parameters that weigh sparsity in transform domains against fidelity to acquired data. While parameter selection is critical for reconstruction quality, the optimal parameters are subject and dataset specific. Thus, commonly practiced heuristic parameter selection generalizes poorly to independent datasets. Recent studies have proposed to tune parameters by estimating the risk of removing significant image coefficients. Line searches are performed across the parameter space to identify the parameter value that minimizes this risk. Although effective, these line searches yield prolonged reconstruction times. Here, we propose a new self-tuning CS method that uses computationally efficient projections onto epigraph sets of the ${\ell }_{{1}}$ and total-variation norms to simultaneously achieve parameter selection and regularization. In vivo demonstrations are provided for balanced steady-state free precession, time-of-flight, and T1-weighted imaging. The proposed method achieves an order of magnitude improvement in computational efficiency over line-search methods while maintaining near-optimal parameter selection

Attentional Modulation of Hierarchical Speech Representations in a Multi-Talker Environment

Humans are remarkably adept in listening to a desired speaker in a crowded environment, while filtering out nontarget speakers in the background. Attention is key to solving this difficult cocktail-party task, yet a detailed characterization of attentional effects on speech representations is lacking. It remains unclear across what levels of speech features and how much attentional modulation occurs in each brain area during the cocktail-party task. To address these questions, we recorded whole-brain blood-oxygen-level-dependent (BOLD) responses while subjects either passively listened to single-speaker stories, or selectively attended to a male or a female speaker in temporally overlaid stories in separate experiments. Spectral, articulatory, and semantic models of the natural stories were constructed. Intrinsic selectivity profiles were identified via voxelwise models fit to passive listening responses. Attentional modulations were then quantified based on model predictions for attended and unattended stories in the cocktail-party task. We find that attention causes broad modulations at multiple levels of speech representations while growing stronger toward later stages of processing, and that unattended speech is represented up to the semantic level in parabelt auditory cortex. These results provide insights on attentional mechanisms that underlie the ability to selectively listen to a desired speaker in noisy multispeaker environments