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My scientific work centers on extracting insights from complex neuroscience data, especially high-dimensional time-series signals recorded from the brain. During my PhD, I developed strong analytical and quantitative research skills, strengthened through coursework in computer science (machine learning track).





This is how my data look like:
Real-time, simultaneous imaging of raw jRGECO1a fluorescence dynamics (with overlaid regions of interest), neural activity (∆F/F), and changes in hemodynamic signals. Neural activity ∆F/F signals from each ROI are plotted over time on the right.




I combine a deep understanding of brain physiology with advanced data science techniques, enabling me to turn raw biological signals into meaningful scientific insights. 



As an initial step, I designed, trained and validated machine learning / deep learning models to investigate how cortical activity encodes movement:
Neural activity recorded from 500 cortical regions (top right) was analyzed using both a linear model and a 1D convolutional neural network (1D-CNN) (top left) to decode mouse behavioral dynamics. Although the linear model performed adequately during active movement periods, the 1D-CNN significantly outperformed it during resting (non-locomoting) states (bottom left, bottom right). These results highlight that neural encoding of behavior involves distinct nonlinearities, especially prominent during periods without overt movement.
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Trained as an engineer, I believe that the best solutions are often the simplest, least expensive, and most straightforward. This principle has shaped my approach to scientific research, especially in addressing complex questions with limited resources or incomplete tools.




Activity is constrained by anatomical structure, and therefore, activity gives us a lens into its intrinsic organization. To infer cortical network, my research took an unconventional approach by analyzing the brain during rest. I found that spontaneous cortical activity during periods of quiet rest is not random noise but is instead rich with structured patterns. By applying correlation analysis to these intrinsic dynamics, I successfully quantified synchrony across the cortex and uncovered distinct functional ensembles:
Although appearing random, temporal fluctuations in each specific sensory region during rest are not strongly correlated to each other, instead exhibiting marked spatiotemporal heterogeneity (A and B). Sequential images of cortical activity during a resting period highlight that repeated activations of specific sensory regions are often accompanied by co-activation of discrete regions in frontal cortex (arrows in A). Using correlation maps seeded from sensory cortex (S1), I track the synchrony of spontaneous neural activity across the cortex (D). Each map clearly identifies distinct motor regions (solid outline in the medial frontal cortex, or secondary motor cortex M2) specifically correlated with their corresponding sensory areas (star markers in S1). Comparing across these maps demonstrates clear functional segregation for different body parts. The neural activity traces shown below each map are from the same brief resting-state period (6 seconds), illustrating tight sensory-motor coupling within the same body part and distinct patterns across different body parts. These body-part-specific topographies were consistently identified during awake resting fluctuations across all animals (E)..


Seeding from well-known sensory areas (S1), I found that spontaneous activity reliably revealed corresponding motor regions with clear, body-part-specific topographies. These maps are remarkably organized – even though no overt movement is present – demonstrating that the structure of motor control is embedded in the brain’s intrinsic dynamics. These insights were only possible because of the functional lens we applied and the careful, data-driven strategies we developed to interpret them.










When contours from correlation maps were overlaid, it reveals a homologous spatial arrangement between sensory (S1) and secondary motor cortex (M2):                            
Applying a simple spatial transformation (rotation and compression) aligned the sensory (S1) and motor (M2) maps, demonstrating a homologous organization between the two topographies.


Extending this analysis by placing seed regions uniformly across posterior cortical areas revealed a continuum of functional correlation between S1/V1 and M2: 
Seed regions used to compute correlation maps were color-coded according to their positions across S1 and visual cortex V1. Contours representing regions of high correlation are outlined with matching colors. Overlaying these contours highlights a continuous, graded pattern of connectivity between sensory/visual regions and M2 motor areas in the medial frontal cortex.
















Weihao Xu, ©2025