Spatial attention enhances object coding in local and distributed representations of the lateral occipital complex

The modulation of neural activity in visual cortex is thought to be a key mechanism of visual attention. The investigation of attentional modulation in high-level visual areas, however, is hampered by the lack of clear tuning or contrast response functions. In the present functional magnetic resonance imaging study we therefore systematically assessed how small voxel-wise biases in object preference across hundreds of voxels in the lateral occipital complex were affected when attention was directed to objects. We found that the strength of attentional modulation depended on a voxel's object preference in the absence of attention, a pattern indicative of an amplificatory mechanism. Our results show that such attentional modulation effectively increased the mutual information between voxel responses and object identity. Further, these local modulatory effects led to improved information-based object readout at the level of multi-voxel activation patterns and to an increased reproducibility of these patterns across repeated presentations. We conclude that attentional modulation enhances object coding in local and distributed object representations of the lateral occipital complex

Sensory processing and categorization in cortical and deep neural networks

Many recent advances in artificial intelligence (AI) are rooted in visual neuroscience. However, ideas from more complicated paradigms like decision-making are less used. Although automated decision-making systems are ubiquitous (driverless cars, pilot support systems, medical diagnosis algorithms etc.), achieving human-level performance in decision making tasks is still a challenge. At the same time, these tasks that are hard for AI are easy for humans. Thus, understanding human brain dynamics during these decision-making tasks and modeling them using deep neural networks could improve AI performance. Here we modelled some of the complex neural interactions during a sensorimotor decision making task. We investigated how brain dynamics flexibly represented and distinguished between sensory processing and categorization in two sensory domains: motion direction and color. We used two different approaches for understanding neural representations. We compared brain responses to 1) the geometry of a sensory or category domain (domain selectivity) and 2) predictions from deep neural networks (computation selectivity). Both approaches gave us similar results. This confirmed the validity of our analyses. Using the first approach, we found that neural representations changed depending on context. We then trained deep recurrent neural networks to perform the same tasks as the animals. Using the second approach, we found that computations in different brain areas also changed flexibly depending on context. Color computations appeared to rely more on sensory processing, while motion computations more on abstract categories. Overall, our results shed light to the biological basis of categorization and differences in selectivity and computations in different brain areas. They also suggest a way for studying sensory and categorical representations in the brain: compare brain responses to both a behavioral model and a deep neural network and test if they give similar results

CEREBELLUM-SEEDED FUNCTIONAL CONNECTIVITY CHANGES IN TRAIT-ANXIOUS INDIVIDUALS UNDERGOING ATTENTION BIAS MODIFICATION TRAINING

Anxiety and anxiety related disorders are increasing at a drastic rate in the past decade, with the NIMH reporting that 31.1% of U.S. adults will experience an anxiety disorder at some point in their lives. Anxiety is commonly characterized by increased attention bias to threat. Attention Bias Modification (ABM) is a new treatment used to reduce individual’s attention bias towards threat. The extent to which ABM leads to underlying neural changes is still unknown. The cerebellum is a neglected brain structure, with new research provides evidence that cerebellum’s functional connectivity and shared networks with threat processing regions has a direct impact on anxiety etiology and symptomology. Therefore, the current study assessed functional connectivity changes seeded in cerebellum as an outcome of ABM training. The experiment consists of a 6-week ABM or control training period bookended by pre and post resting state functional magnetic resonance imaging (rsfMRI) sessions. Heightened trait anxiety was correlated with heightened connectivity from the cerebellum to threat processing regions. (i.e., the amygdala, ACC, and the thalamus). Decreased cerebellar connectivity to threat processing regions (i.e., the amygdala, ACC, and the thalamus) was observed following ABM training. This suggests that ABM may underly neural changes within the cerebellum—resulting in decreased attention bias. This also suggests the cerebellum may contribute to the etiology and maintenance of anxiety and attention bias. Limitations and future directions concerned with both ABM and the functional role of the cerebellum are discussed