CP 9: Understanding the Neurocognitive Mechanisms of Alpha-band Brain Oscillations Using Concurrent EEG-fMRI Recordings

CP9 aims to use EEG data to track neural processing efficiency in real-time. The ultimate goal is to establish biological targets for treatment and monitoring strategies, addressing a critical gap in ADHD research, where current measures e.g., response time variability (RTV), lack precision.

Attention-system impairments are a robust indicator across neurodevelopmental disorders linked to negative life outcomes, such as weaker academic achievement. Critically, these are not effectively resolved given current treatments. For instance, attention-deficit/ hyperactivity disorder (ADHD), which is highly prevalent (2-7% in 20181), has a significant effect on the quality of life and, in particular, on educational outcomes. This project aims to establish the neural mechanisms of intra-individual variability in responding, a robust characteristic of ADHD. Secondary data analyses of three existing datasets will be performed to identify a biological measure of neural processing efficiency and to link this measure with both variable responding and underlying network dynamics. Appropriate biological targets for treatment and monitoring strategies in disease states, such as ADHD, where intra-individual response variability interferes with cognition and daily performance, will be identified as a result of this study. Increased intraindividual variability (IIV) in task performance is a crucial symptom of ADHD, but its neural basis remains unclear. Current measures of IIV, like response time variability (RTV), lack precision due to sparse sampling and the aggregation of time, limiting their translational value. This project proposes using neurophysiological measures derived from EEG to continuously track neural processing efficiency through critical metrics like low-frequency power and network stability. These continuous measures will bridge the gap between behavioral summary statistics and underlying neural dynamics, providing a more specific index of IIV in ADHD.

CP9 will use the biomarkers of motion from wearables developed by TR&D3 so it can be combined with neurophysiological measurements to quantify motivation and arousal, which are crucial for interpreting attention states and predicting behavioral outcomes like school performance.

TR&D3 is challenged to integrate LiDAR and mmWave sensing technology with wearables and EEG caps to capture motion patterns and brain signals in real-time without compromising privacy.

CP9 will obtain the most advanced quantification of motivation and attention states, contributing to a more precise understanding of IIV in ADHD.

Despite vigorous research in the area, there still exists a significant need to improve the translational impact of laboratory findings in ADHD, and this conclusion is exemplary of neurodevelopmental disorder. TR&D3 researchers are collaborating with the PI to assess the utility of other sensor modalities in assessing intra-individual variability. It is essential to understand the contributions of the neural, arousal, and motivational systems to accurately interpret attention states and, ultimately, predict behavioral outcomes (e.g., school performance). This knowledge will have an immediate practical impact by enabling the identification of causal factors of inattention, thereby supporting more targeted interventions. In alignment of TR&D3 Aim1 and Aim3, we are working in developing sensor systems for quantifying motivation. Quantifying motivation is challenging but can be approached by measuring posture, orientation, and physical interactions using skeletal models. While video-based methods compromise privacy and motion capture reflectors are impractical in natural settings, a potential solution is integrating LiDAR and mmWave sensing technology with wearable sensors. LiDAR provides high-resolution spatial data but struggles with clutter and occlusions, while mmWave radar adds the ability to track sub-centimeter motion and velocity in real-time. Combining data from LiDAR, mmWave radar, and wearable sensors, this multimodal approach can accurately capture human pose and motion patterns while maintaining privacy. In December 2023, we conducted a joint pilot session with the PI’s team to combine neurophysiology, behavior and heart rate, motion, and LiDAR/MmWave sensing via a server-based synchronization system to guide our development.