Spatio-Semantic Graphs From Picture Description: Applications to Detection of Cognitive Impairment

Clinical assessments often use complex picture description tasks to elicit natural speech patterns and magnify changes occurring in brain regions implicated in Alzheimer's disease and dementia. As The Cookie Theft picture description task is used in the largest Alzheimer's disease and dementia cohort studies available, we aimed to create algorithms that could characterize the visual narrative path a participant takes in describing what is happening in this image. We proposed spatio-semantic graphs, models based on graph theory that transform the participants' narratives into graphs that retain semantic order and encode the visuospatial information between content units in the image. The resulting graphs differ between Cognitively Impaired and Unimpaired participants in several important ways. Cognitively Impaired participants consistently scored higher on features that are heavily associated with symptoms of cognitive decline, including repetition, evidence of short-term memory lapses, and generally disorganized narrative descriptions, while Cognitively Unimpaired participants produced more efficient narrative paths. These results provide evidence that spatio-semantic graph analysis of these tasks can generate important insights into a participant's cognitive performance that cannot be generated from semantic analysis alone.

Divisive normalization unifies disparate response signatures throughout the human visual hierarchy

Neural processing is hypothesized to apply the same mathematical operations in a variety of contexts, implementing so-called canonical neural computations. Divisive normalization (DN) is considered a prime candidate for a canonical computation. Here, we propose a population receptive field (pRF) model based on DN and evaluate it using ultra-high-field functional MRI (fMRI). The DN model parsimoniously captures seemingly disparate response signatures with a single computation, superseding existing pRF models in both performance and biological plausibility. We observe systematic variations in specific DN model parameters across the visual hierarchy and show how they relate to differences in response modulation and visuospatial information integration. The DN model delivers a unifying framework for visuospatial responses throughout the human visual hierarchy and provides insights into its underlying information-encoding computations. These findings extend the role of DN as a canonical computation to neuronal populations throughout the human visual hierarchy.

Signatures of visuospatial information foraging during learning of complex environments

Many real world environments contain latent features that animals must learn in order to accomplish their goals. Animals often learn these environments over sequences of choices, but the behavioral mechanisms underlying this complex cognitive competence remain poorly characterized. We investigate this sophisticated behavior in two primate species, humans and macaque monkeys, with a task in which subjects searched for shapes hidden on a grid. Both primate species were adept learners, quickly learning the subset of shapes out of the numerous possibilities. Subjects tended to select tiles earlier in trials that were informative in the past about the hidden shape than those that were rewarding. In addition, we found a surprising signature of foraging behavior over sequences of choices during trials, with human subjects searching local areas of the board until information dropped below the average across all choices, at which time they jumped to a different part of the board. This pattern of choices was not evident for rewards in humans. In contrast, the sequences of choices of monkeys were equally well-described as information or reward foraging. Finally, the rate at which humans learned shapes could be predicted by how well their choice sequences matched foraging behavior. These findings suggest that humans are more tuned to the search for information than reward than monkeys and that foraging competence predicts the capacity to learn complex environments.

3D Printing of Human Microbiome Constituents to Understand Spatial Relationships & Shape Parameters in Bacteriology

Effective laboratory and classroom demonstration of microbiome size and shape, diversity, and ecological relationships is hampered by a lack of high-resolution, easy-to-use, readily accessible physical or digital models for use in teaching. Three-dimensional (3D) representations are, overall, more effective in communicating visuospatial information, allowing for a better understanding of concepts not directly observable with the unaided eye. Published morphology descriptions and microscopy images were used as the basis for designing 3D digital models, scaled at 20,000×, using computer-aided design software (CAD) and generating printed models of bacteria on mass-market 3D printers. Sixteen models are presented, including rod-shaped, spiral, flask-like, vibroid, and filamentous bacteria as well as different arrangements of cocci. Identical model scaling enables direct comparison as well as design of a wide range of educational plans.

A Spelling Paradigm With an Added Red Dot Improved the P300 Speller System Performance

The traditional P300 speller system uses the flashing row or column spelling paradigm. However, the classification accuracy and information transfer rate of the P300 speller are not adequate for real-world application. To improve the performance of the P300 speller, we devised a new spelling paradigm in which the flashing row or column of a virtual character matrix is covered by a translucent green circle with a red dot in either the upper or lower half (GC-RD spelling paradigm). We compared the event-related potential (ERP) waveforms with a control paradigm (GC spelling paradigm), in which the flashing row or column of a virtual character matrix was covered by a translucent green circle only. Our experimental results showed that the amplitude of P3a at the parietal area and P3b at the frontal–central–parietal areas evoked by the GC-RD paradigm were significantly greater than those induced by the GC paradigm. Higher classification accuracy and information transmission rates were also obtained in the GC-RD system. Our results indicated that the added red dots increased attention and visuospatial information, resulting in an amplitude increase in both P3a and P3b, thereby improving the performance of the P300 speller system.

The Time-Based Resource-Sharing Model of Working Memory

The time-based resource-sharing model considers working memory as the workspace in which mental representations are built, maintained, and transformed for completing goal-oriented tasks. Its main component is made of an episodic buffer and a procedural system that form an executive loop in which processing and storage share domain-general attentional resources on a temporal basis. Because working memory representations decay with time when attention is diverted, the cognitive load of a given activity is the proportion of time during which it occupies attention and prevents it from counteracting this decay through attentional refreshing. Consequently, recall in working memory tasks is an inverse function of the cognitive load of concurrent processing. Besides this system, an independent domain-specific maintenance system exists for verbal, but not visuospatial, information. Within this framework, working memory development mainly results from increasing processing speed that affects both the duration of the distraction of attention by concurrent tasks and refreshing efficiency.