SUMMARY
Nuri F. Ince, Ph.D., studies basic and translational research in neural engineering and brain machine interfaces. Dr. Ince's research team develops new algorithms and machine learning techniques, particularly focusing on exploring large-scale neural activity recorded in clinical settings. His research not only contributes to the development of algorithms but also focuses on uncovering new methods and patterns for diagnosis and therapy applicable in clinical practice.
Dr. Ince collaborates with clinicians and researchers from diverse fields such as neuroscience, neurosurgery and neurology. His research team maintains close ties to other medical institutions, including Baylor College of Medicine, Department of Neurosurgery and Department of Neurology, MD Anderson Cancer Center and Texas Children's Hospital. Through this collaborative research, Dr. Ince and his team are uniquely positioned to promote clinical translation.
Focus areas
Deep-brain stimulation in Parkinson's disease. Dr. Ince is working to find neuro-biomarkers for closed-loop deep brain stimulation in movement disorders such as Parkinson's disease. The success of deep brain stimulation as a therapeutic approach hinges on precise placement of chronic leads into the target structure. Optimal selection of stimulation parameters — such as stimulation contacts and frequency — also is key.
Dr. Ince has demonstrated the predictability of the optimal stimulation contact by analyzing the distribution of excessive beta band (8-30Hz) activity in the subthalamic nucleus. With funding from a National Institutes of Health (NIH) R01 award, Dr. Ince's research team is now investigating high-frequency oscillations and evoked resonant neural activity. The goal of this research to more accurately place deep brain stimulation leads and personalize stimulation parameters for specific motor subtypes in people with Parkinson's disease.
Computational intelligence in neurology and epilepsy. Certain neuro-biomarkers are unique to the seizure-onset zone in people with refractory epilepsy. Discovering such neuro-biomarkers could enhance surgical planning and mitigate risks linked to extended invasive monitoring.
Dr. Ince's research team uses machine learning and sparse signal processing techniques to study high-frequency oscillations of 80 Hz to 500 Hz in intracranial electroencephalocardiograms. The team aims to use these high-frequency oscillations to swiftly pinpoint the location of the seizure-onset zone.
Dr. Ince has secured funding from the NIH Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative. With this funding, his research team is studying the use of high-frequency oscillations with a next-generation wireless brain implant to improve seizure control.
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Neural dynamics of complex hand function. An additional area of focus involves decoding oscillatory neural dynamics of complex hand function for closed-loop brain machine interfaces. Dr. Ince's neural engineering team has found that the broad-band modulations observed in micro-electrocorticography (ECoG) predict the derivative of grip force rather than the force itself. Additionally, the research group has gathered preliminary evidence suggesting that gamma band modulations in the somatosensory cortex, as assessed with a high-density ECoG grid, can differentiate between vibrotactile stimuli applied to individual fingers of the hand.
The research team is using high-density ECoG grids in awake brain surgeries to better understand the spatiotemporal dynamics of oscillatory activity in the sensorimotor cortex during sustained hand grasp. The team also is developing neural decoding strategies to recognize complex hand movements and differentiate received tactile stimuli.
Significance to patient care
The discovery of neuro-biomarkers associated with neurological disorders such as Parkinson's disease, essential tremor and epilepsy not only offers deeper insights into the underlying disease mechanisms but also provides crucial data for optimizing neuromodulation therapies.
Dr. Ince uses computational intelligence, including machine learning and sparse signal processing, to pinpoint biomarkers in extensive neural recordings. His ultimate goal is to integrate these biomarkers with device-based therapies. This integration is pivotal in the development of next-generation brain implants infused with computational intelligence.
Professional highlights
- Senior member, Engineering in Medicine and Biology Society, Institute of Electrical and Electronics Engineers, 2005-present.
- Senior member, Institute of Electrical and Electronics Engineers, 2004-present.