Jorge V. Jose

Bio

The research in the lab of Jorge V. Jose, Dr. Sc., goes from computational neuroscience, neuronal networks modeling, animal behaviors, motion studies of humans affected by neurodevelopmental disorders (NDD), including precision and computational psychiatry research applications. The lab collaborates with members of the Department of Psychiatry at Indiana University School of Medicine, as well as the Dallas School of Medicine. Critical neurodevelopmental cognitive landmarks sometimes fail to developed leading to NDDs. We study important NDD examples including Autism Spectrum Disorder (ASD), Attention Deficit Hyperactive Disorder, comorbid ASD+ADHD versus Typical Developing (TD). Current clinical diagnostic NDD surveys have focused almost exclusively on the cognitive deficits assessed by qualitative assessments aimed to improve the individual’s cognitive condition. We have taken advantage of recent advances in high precision wearable sensing devices. They have helped us bridge the gap between observational clinical practices to quantitative objective research outcomes. The sensors we use track the kinematics for different limbs, including also the eyes’ minute motions, lips, facial micro-expressions and body micro-movements. We have first studied the raw data sets collected by the sensors by using techniques developed in Deep Learning of Artificial Intelligence. This allowed us to diagnose new subjects just from measuring how they move naturally [1]. To further understand the meaning of these AI results we next filtered the raw data out of its electronic noise, leaving only the motion physiological noisy data. We developed new sets of statistical metrics by further using the Support Vector Machine techniques yielding quantitative biomarkers as a function of the individuals age [2]. Our new biometrics have allowed successful direct comparisons with a battery of clinical tests employed by psychiatrists [3-5]. Synchronization of inhibitory neurons as a possible mechanism for attentional gain modulation. Naturally occurring visual scenes contain large amounts of spatial and temporal information that are transduced into neuronal spike trains along the visual sensory pathway. Human psychophysics indicates that only a small part of that information is attended. We have developed Hodgkin-Huxley neuronal type models to analyze data obtained from electrophysiological experiments with nonhuman primates. We have suggested that attentional modulation of the synchrony of local interneuronal networks could potentially account for these experimental observations. We also considered the case when two stimuli are presented simultaneously. The neuronal response is in between those for each stimulus presented separately (stimulus competition) or when only one stimulus is attended. The neuronal response gets closer to the response to this stimulus presented alone (biased competition). When the stimulus contrast is varied, several types of gain responses have been found with attention. We further introduced a biophysical neural network model of V4, constraining it to reproduce the dynamics observed in the absence of attention. We were able to reproduce some of the detailed neural activity reported experimentally and the stimulus competition. We have explored the possibility that our model may provide a unified framework for attentional modulation in V4 [6-9]. Neuronal-hydrodynamic model describing larvae zebra fish rich swimming repertoire. Larval zebrafish (LZF) provide a unique opportunity to study realistic neuronal models since the fish is transparent and most of its neuronal properties can be measured explicitly. The LZF exhibits a variety of complex undulatory swimming behavioral patterns. This repertoire is controlled by 300 neurons projecting from brain stem into spinal cord. We developed a Hodgkin-Huxley segmental oscillator model (doing calculations using the NEURON program) to investigate this system’s behavior. By adjusting the NMDA strengths and glycinergic synapses, they produced the fish swimming oscillation (tail-beat) frequency patterns over the range exhibited experimentally. To describe visually the experimentally observed bending patterns we also developed a biomechanical-hydrodynamic model to better understand how those outputs are generated by the neuronal model we developed that compared positively to those seen experimentally [10,11]. 

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