Rajeev D. Raizada - US grants

With National Science Foundation support, Dr. Poldrack will conduct a year long investigation of a new approach to enhancing brain plasticity and increasing the speed of learning in adult humans. It has long been known that the brain changes extensively early in life, and that these changes are dependent upon particular experiences in the child's environment. However, more recent research has discovered that the brain continues to change throughout adulthood in response to experience. This ability to change is called plasticity, and is thought to underlie many forms of learning. Dr. Poldrack's project will explore an approach based upon results from studies of experimental animals, which have shown that plasticity in the cerebral cortex can be greatly enhanced by increasing the levels of the neurotransmitter acetylcholine. New drugs, known as cholinesterase inhibitors, that safely and effectively increase acetylcholine levels in humans have recently been developed and FDA-approved. The specific drug that Dr. Poldrack will use is galanthamine hydrobromide (tradename Reminyl). The effect of the drug on cortical plasticity will be assessed using both visual behavioral testing and functional magnetic resonance imaging, which is a non-invasive method for measuring the brain activity that occurs as a person performs a cognitive or perceptual task. The behavioral measure will be the rate at which the subjects learn to more accurately perform a simple visual perceptual learning task: learning to discriminate the orientation of a grating. The hypothesis to be tested is that learning of the visual task that takes place under the influence of the drug will proceed more quickly than learning that is paired with a placebo. Functional magnetic resonance imaging will be used to assess the effect of the drug on cortical plasticity, by comparing the pre-training versus post-training brain activation changes that are caused by learning the visual task while on the drug against those caused by learning the task on placebo.

If this new method of enhancing plasticity should turn out to be successful, it will provide fundamentally important and novel knowledge about the nature of plasticity in the adult human brain, and could also lead to a wide range of potential clinical and practical applications. Understanding how brain plasticity works in adult humans is of critical importance, because recent research suggests that plasticity can be capitalized upon in order to remediate neurological problems, such as movement disorders resulting from stroke or from repetitive strain injury, and reading and language disorders.

Research supported by this EAGER award is developing the NeuroPhone system, the first Brain-Mobile phone Interface (BMI) that enables neural signals from consumer-level wireless electroencephalography (EEG) headsets worn by people as they go about their everyday lives to be interfaced to mobile phones and combined with existing sensor streams on the phone (e.g., accelerometers, gyroscopes, GPS) to enable new forms of interaction, communications and human behavior modeling.

Specifically, this high-risk exploratory research is to:

1) study new energy-efficient techniques and algorithms for low-cost wireless EEG headsets and mobile phones for robust sensing, processing and duty cycling of neural signals using consumer devices;

2) develop new learning and classifications algorithms for the mobile phone to extract and infer cognitively informative signals (e.g., P300, N400, and neural synchrony) from EEG headsets in noisy mobile environments;

3) deploy networked NeuroPhone systems with a focus on real-time multi-party neural synchrony and the networking, privacy and sharing of neural signals between networked NeuroPhones; and

4) evaluate networked NeuroPhones applications, specifically, measuring teacher-student engagement in the classroom and measuring group level emotional state.

This interdisciplinary research opens up opportunities in education, teaching and outreach, in part because it focuses on an educational NeuroPhone application, which contributes new insights into cognitive engagements of students in the classroom as well as engages students from the Department of Computer Science and the Department of Psychological and Brain Sciences in the project. Results from this work will transform applications across diverse domains such as education, health monitoring, and social networking.

The brain uses similarity to generalize from the known to the unknown. For example, when a person encounters a new type of fruit and has to decide whether or not it is edible, the person must judge how similar it is to already known edible and inedible items. However, the type of similarity that is taken into account matters. A coconut can look like a rock (visual similarity), but for making a decision about edibility the fact that it hangs from a leafy tree (semantic similarity) is key. With funding from the National Science Foundation, Dr. Rajeev Raizada of Rochester University is investigating how the brain uses similarity to respond adaptively to changing circumstances. With an understanding of how types of similarity, such as visual and semantic similarity, are encoded in the brain, it should be possible to decode them from neural signals. In this project, Dr. Raizada is combining brain imaging with computational modeling and behavioral testing. He is developing novel methods of neural decoding to predict the similarity of brain patterns on the basis of computational models of the stimuli that people are perceiving. In addition, the methods are designed to investigate patterns across different people's brain activations.

The novel computational methods being developed in the project could have significant broader impacts, for example, such techniques underpin brain-computer interfaces that attempt to restore communication to locked-in patients. Moreover, the modeling of semantic similarity in the brain has implications for disorders such as semantic dementia. There are also possible implications for technology. The brain responds flexibly to changing circumstances, but artificial systems, in contrast, are all too often brittle. When confronted with circumstances similar, but not identical, to familiar ones, they break down. Insights into how the brain generalizes from the known to the unknown have the potential to transform our knowledge of how the brain achieves its adaptability, opening up new avenues for endowing artificial systems with similar skills.

Perhaps the most powerful aspect of the human brain, and also the least understood, is its ability to represent and understand language. The present research advances the field by developing mathematical models that try to capture an important aspect of that ability: how the brain represents individual words and how it combines them into phrases and sentences. By extracting measures of meaning from brain activation patterns, this research is of potential relevance to people with neurological language deficits who can represent meaning but who have problems expressing it. Beyond cognitive neuroscience, this work may also have application to improving computers' ability to process natural language, by building and testing more powerful computational models of meaning than are currently available. By bridging between Cognitive Neuroscience, Data Science and Linguistics this work also enables new interdisciplinary training of students.

To carry out this work, experimental and theoretical approaches are combined: functional magnetic resonance imaging (fMRI), behavioural testing and computational modeling. These approaches are brought together by using the shared framework of representing word meanings in what are known as "semantic spaces". In a semantic space, each word is represented as a vector, i.e. as an ordered list of numbers, where each such number quantifies a specific feature of the word's overall meaning. This sort of representation has structure: words with more similar meanings are closer together. The research uses models of semantic space to decode fMRI data, by finding mappings between the structure of the semantic model and the similarity-structure of distributed neural activation patterns. In particular, the work investigates whether greater understanding of neural representational structure can be achieved by combining two seemingly distinct types of semantic model: those derived from co-occurrence frequencies of words in large bodies of text, and those obtained from people's behaviourally measured ratings of features of a word's meaning. The research addresses this question not only for neural representations of isolated words, but also for adjective-noun phrases and for entire sentences. It also seeks to isolate purely meaning-related aspects of the neural signal by distinguishing between words which often co-occur with each other but which have distinct meanings, such as 'cup' and 'coffee', as opposed to words that have genuinely similar meanings, such as 'cup' and 'mug'. The work takes an additional approach to isolating meaning from lower-level features, by performing neural decoding across speakers of different languages, e.g. Chinese and English, which can represent the same meanings as each other but which differ greatly in their sound patterns and written visual appearance. Collectively, these lines of work enable progress on the fundamental problem of how the human brain understands language, by bringing together computation, psychology and neuroscience in novel ways.