Milena Korostenskaja, PhD
Neuroscience Educator, Career Consultant & Coach | Neurocareers Podcast Host | Founder, Director at The Institute of Neuroapproaches
In August 2019, Dr. K founded The Institute of Neuroapproaches that provides services in three major areas:
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Career consulting and coaching for people in neuroscience and neurotech ("NeuroCareers");
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Neuroscience & neurotechnologies education ("NeuroEdu");
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Neuroscience & neurotechnologies research ("NeuroResearch").
Dr. K is fluent in three languages (English, Lithuanian, Russian) and offers her services in any of these languages, based on the demand from her clients and customers.
Dr. K holds three post-graduate degrees: M.Sc in Neurobiology (Biology), Doctorate in Biophysics (Biomedical Sciences), and Doctorate in Psychology (Behavioral Sciences).
Dr K completed her post-doctoral training in neurophysiological research at the Department of Neurology, Cincinnati Children Hospital Medical Center (CCHMC). Later, at CCHMC, she received her first Faculty position as an Assistant Professor of Pediatric Neurology Research supported by both CCHMC and the University of Cincinnati.
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Neuroscience Career Coaching ("NeuroCareers"):
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Dr. K is a Certified Life Coach and Brain-Based Coach (Applied Neuroscience Coaching). She received her certification from The Life Coach School and Brain First Institute. Dr. K is also trained in leadership and career coaching (coaching for improvement, coaching for success).
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Dr. K utilizes best practices in applied neuroscience and causal life coaching to help people in in neuroscience-related professions achieve their career and business goals. Dr. K develops unique innovative neuroscience-based methodologies that provide unprecedented value to her customers.
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Neuroscience Education ("NeuroEdu"):
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In The Institute of Neuroapproaches, Dr. K offers "Adaptive Neurotechnologies" (Brain-Computer Interfaces - BCIs) course for those, who wants to begin their journey into the field of neurotexchnologies.
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Dr. K has extensive expertise in teaching neuroscience-based courses. For many years, she has been teaching in the Associate Professor capacity at Vilnius University as well as a Part-time Faculty at the University of North Florida and AdventHealth University.
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Dr. K. offers her classes both in-person and on-line. She is a certified on-line course delivery instructor and Quality Matters reviewer.
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Dr. K is a frequent invited guest of scientific and educational venues. She gave over 80 public presentations and workshops worldwide, including USA, Canada, Finland, France, Germany, South Korea, Lithuania, and others.
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Neuroscience research ("NeuroResearch")
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Dr. K leads Brain-Computer Interface (BCI) program to help patients suffering from neurological disorders, such as epilepsy and stroke. This includes real-time functional mapping for epilepsy surgery and motor rehabilitation in patients status post-stroke. Dr. K also develops innovative BCI approaches to help healthy people maximize their brain potential by applying BCI technology.
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Dr. K authored and co-authored over 50 scientific publications, including journal articles and book chapters on the application of brain imaging techniques and BCI approaches in clinical practice for patients with neurological and mental disorders.
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Dr. K developed skills and gained experience working with various neuroimaging techniques and BCIs from a number of the top research and clinical centers world-wide:
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The Visual Sciences Laboratory, U.M.I.S.T., Manchester, UK;
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BioMag Laboratory, Helsinki University Central Hospital, Helsinki, Finland;
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Department of Electrophysiological Investigations, Vilnius Republican Psychiatric Hospital, Vilnius, Lithuania;
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Department of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA;
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Neuroscience Institute, AdventHelath Orlando, Orlando, Florida, USA.
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Dr. K has developed fruitful collaborations with scientists and clinicians around the globe. This includes artificial intelligence specialists, physical therapists, neurosurgeons, neuroengineers, and cognitive neuroscientists.
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ACTIVITIES
"Advancing the field of pre-surgical functional brain mapping: a multidisciplinary approach with incorporated machine learning” invited talk at the Lithuanian Department of Statistics, Lithuanian Association of Statistics, Vilnius, Lithuania
November 26, 2019
Adaptive neurotechnologies enable real-time interactions with the nervous system. The primary goals of adaptive neurotechnologies include facilitation of changes in neuroplasticity; enhancement, restoration or replacement of neuromuscular functions; evaluation, and localization of brain activity. During Dr. Korostenskaja's talk, the current state of pre-surgical functional brain mapping and its evolution into an adaptive neurotechnology approach werediscussed. Examples of adaptive neurotechnology research conducted at the Functional Brain Mapping and Brain-Computer Interface Lab (FBM-BCI Lab), AdventHealth Orlando were presented, such as real-time functional brain mapping to prevent post-surgical deficits in epilepsy patients. Current gaps in this approach were demonstrated. The vision of integrating machine learning algorithms to advance functional brain mapping were provided. A potential application of machine learning for automatic brain signal classification will be discussed. The importance of multi-disciplinary efforts between neuroscientists and computer science, mathematics, and signal processing specialists were emphasized.
PUBLISHED WORK
Here you will find information about the research publications authored by Dr. Milena Korostenskaja (Dr. K) - the Founder and Director of Research at The Institute of Neuroapproaches. Dr. K has established long-lasting fruitful collaborations with top scientists around the world. We are proud to showcase the results of this productive work with the list of publications below.
2022
October 2, 2022
Clark, E., Czaplewski, A., Nguyen, K., Pasciucco, P., Rios, M., & Korostenskaja, M.
In: Lecture Notes in Computer Science book series. 2022, LNCS Volume 13519, p. 371-390.
This work was prepared in collaboration with Dr. Elizabeth Clark and graduate students from the Department of Physical Therapy at Advent Health University. The project aims at improving functional motor outcomes in patients status post-stroke.
Abstract
Stroke is a major cause of disability resulting in multiple system impairments. Limited extended care resulted in prioritizing high level repetitions of task-specific activities to improve function. One such modality is BCI to drive motor rehabilitation. While several systematic reviews and meta-analyses highlight the benefits of utilizing BCIs to enhance motor recovery, it is still unclear how these interventions facilitate rehabilitation of motor function in individuals post-stroke. This systematic review analyzed outcome measures and type of feedback during BCI interventions to inform future protocol development. Included articles were held to rigorous criteria, and potential studies were assessed for methodological quality using the PEDro Scale. Only articles that scored six or greater were included for analysis, and nine randomized controlled trials were included. In brief, the randomized controlled trials demonstrated that BCI enhanced the motor function of the upper extremity as measured by the FMA UE, however no other consistent outcome measures of function or self-efficacy were reported. EEG and ERD of the affected sensorimotor cortices were significantly enhanced in the BCI groups (p < 0.05). For those studies that measured retention of function, long-lasting improvements were noted, and BCI coupled to FES elicited significant, clinically relevant motor recovery. Somatosensory/motor and visual feedback were the most common across reviewed studies. While each of the studies had a wide variety of methods, all the evidence suggested that subjects improved. These findings suggest that the most important concept in protocol development may have been the incorporation of principles of motor learning.
October 2, 2022
Demchenko, T. & Korostenskaja, M.
In: Lecture Notes in Computer Science book series. 2022, LNCS Volume 13519, p. 391-414.
This work was prepared with Taisija Demchenko - Dr. K's graduate student at that time from Vilnius University, as a part of Taisija's Master's Thesis.
Abstract
Dreams are often forgotten despite their impact on our emotions and memory. In our pursuit of developing an objective dream-content recording methodology (reaDream), we focused on the motor imagery (MI)-related dream component, which is reported to be present in dreams along with other sensory, perceptual, and cognitive phenomena. It has been shown that brain activation during dreamed actions corresponds to the brain activation for the same actions in a wakeful state. This allows one to decode electrocorticographic (ECoG) brain activity during sleep using a machine learning (ML) model trained on wakeful data. ECoG data is very specific to each individual and not generalized between subjects; deep ML models are prone to overfit on small amounts of data. We propose to generalize ECoG data by combining recordings from several subjects. For that, we developed a Convolutional Neural Network (CNN)-based classifier that discriminates between hand and tongue movements in different subjects. We tested a hypothesis on whether a MI classifier can be trained on motor execution (ME) data. We demonstrate that ME types are easier to distinguish compared to MI. We showed that power features are more informative than temporal features. Finally, we demonstrated how our trained models could be used to predict MI during Rapid Eye Movement (REM) sleep.
October 2, 2022
Meyer, J., Cass, D., Korostenskaja, M., Ethridge, L., Harris, E., Basu, I,
In: Biological Psychiatry. 2022, Volume 91, Issue 9, Supplement S 276.
This work was prepared in collaboration with Elana Harris, MD, PhD from Cincinnati Children's Hospital Medical Center (CCHMC) and a group of scientists, lead by Ishita Basu, PhD from University of Cincinnati (UC). This is a continuation of the previous collaborative Dr. K's work with Dr. Harris (project PI) aiming at establishing neurophysiological biomarkers for obsessive-compulsive disorder (OCD).
Abstract
Obsessive compulsive disorder (OCD) is a common psychiatric disorder that may be related to an impaired inhibitory process. Reduced local circuit inhibition leading to an inability to synchronize neural responses to sensory input is hypothesized to lead to increased gamma activity. Oscillating auditory stimuli have previously been used to demonstrate a reduced ability of subjects with other developmental disorders (e.g., Autism Spectrum Disorder) to synchronize neural activity at higher gamma frequencies. Because there may be a common developmental phenotype amongst these disorders, we hypothesized that subjects with OCD may exhibit similar deficits in neural synchronization.
2019
July 6, 2019
Clark, E., Czaplewski, A., Dourney, S., Gadelha, A., Nguyen, K., Pasciucco, P., Rios, M., Stuart, R., Castillo, E., Korostenskaja, M.
In: Communications in Computer and Information Science. 2019, CCIS Volume 1032, p. 243-254.
This work was prepared in collaboration with Dr. Elizabeth Clark and graduate students from the Department of Physical Therapy at Advent Health University. The project aims at improving functional motor outcomes in patients status post-stroke.
Abstract
Stroke is the fifth leading cause of death and disability in the United States with approximately 6.8 million people living with residual deficits and approximately $34 billion spent on treatment annually. Simultaneously, dramatic healthcare shifts have limited extended care accessibility, with many individuals discharged from restorative therapy by three-months post-stroke. Decreased access and increased costs have led clinicians, and scientists to investigate more effective and efficient interventions to improve the function of the hemiparetic upper extremity of individuals post-stroke. One such modality is a brain-computer interface (BCI) technology that utilizes brain signals to drive rehabilitation of motor function. Emerging data suggests the use of BCI for motor rehabilitation post-stroke, facilitating an individual’s return to function and improving quality of life. Specifically, integration of virtual reality (VR) and functional electrical stimulation (FES) components is an innovative rehabilitation strategy with a strong potential to reinstitute central motor programs specific to hand function in patients’ status post-stroke. By utilizing the Fugl-Meyer Assessment (FMA), researchers can monitor the motor function of the hemiparetic upper extremity pre/post-intervention, objectively quantifying the effectiveness of BCI for the restoration of upper extremity motor function. Neurophysiological brain imaging techniques allow tracking changes in the neural substrates of motor function due to BCI intervention. Therefore, the purpose of our study is to demonstrate the utility of BCI-VR-FES intervention for motor rehabilitation of upper extremity, based upon the theory of neuroplasticity, in individuals’ post-stroke by using functional (FMA) and neurophysiological outcome measures.
July 9, 2019
Korostenskaja, M., RaviPrakash, H., Bagci, U., Lee, K.H., Chen, P.C., Salinas, C., Baumgartner, J., Castillo, E.M.
In: Brain-Computer Interface Research: A State-of-the-Art Summary 6, C. Guger, B. Allison, and J. Ushiba, Editors. 2019, Springer International Publishing, p. 11-29.
This work is a part of unique colalboratibe endavour with Artificial Intelligence in Medicine (AIM) specialists from the Center of Research in Computer Vision at the University of Central Florida - Dr. Ulas Bagci and his teammates. The project uses cutting-edge computer science approaches (machine learning, deep learning) to prevent functional morbidity of epilepsy surgery.
Abstract
RATIONALE: Electrocorticography-based functional language mapping (ECoG-FLM) utilizes an ECoG signal paired with simultaneous language task presentation to create functional maps of the eloquent language cortex in patients selected for resective epilepsy or tumor surgery. At present, the concordance of functional maps derived by ECoG-FLM and electrical cortical stimulation mapping (ESM) remains rather low. This impedes the transition of ECoG-FLM into an independent functional mapping modality. As ESM is considered the gold standard of functional mapping, we aimed to use it in combination with machine learning (ML) approaches ("ESM-ML guide"), to improve the accuracy of ECoG-FLM.
METHODS: The ECoG data were collected from 6 patients (29.67±12.5 yrs; 19-52 yrs; 3 males, 3 females). Patient ECoG activity was recorded (g.USBamp, g.tec, Austria) during administration of language tasks. For data analysis: (1) All ECoG sites were divided into ESM positive (ESM (+)) and ESM negative (ESM(-)); (2) Features of ESM(+) and ESM(-) sites in the ECoG signal were determined by analyzing the signal in the frequency domain; (3) ML classifiers (Random Forest (RF) and Deep Learning (DL)) were trained to identify these features in language-related ECoG activity; (4) The accuracy of the ESM-ML guided classification was compared with the accuracy of the conventional ECoG-FLM.
RESULTS: The conventional approach demonstrated: 58% accuracy, 22% sensitivity, and 78% specificity. The "ESM-ML guide" approach with RF classifier demonstrated: 76.2% accuracy, 73.6% sensitivity, and 78.78% specificity. The DL classifier achieved the highest performances compared to all others with 83% accuracy, 84% sensitivity and 83% specificity.
CONCLUSION: ECoG-FLM accuracy can be improved by using an "ESM-ML guide", making the use of ECoG-FLM feasible as a stand-alone methodology. The long-term goal is to create a tool-box with "ready to use an ESM-ML guide" algorithm trained to provide high accuracy ECoG-FLM results by classifying between ESM(+) and ESM(-) contacts in prospective sets of language-related ECoG data and, thus, contribute towards improved surgical outcomes.
February 8, 2019
Salillas, E., Korostenskaja, M., Kleineschay, T., Mehta, S., Vega, A., Castillo, E.
Frontiers in Psychology, section Cognitive Science, 2019, 10: 139 E-collection: https://doi.org/10.3389/fpsyg.2019.00139
Abstract
A common magnitude system for the processing of time and numerosity, supported by areas in the posterior parietal cortex, has been proposed by some authors. The present study aims to investigate possible intersections between the neural processing of non-numerical (time) and numerical magnitudes in the posterior parietal lobe. Using Magnetoencephalography for the comparison of brain source activations during the processing of duration and numerosity contrasts, we demonstrate parietal overlap as well as dissociations between these two dimensions. Within the parietal cortex, the main areas of overlap were bilateral precuneus, bilateral intraparietal sulci, and right supramarginal gyrus. Interestingly, however, these regions did not equivalently correlate with the behavior for the two dimensions: left and right precuneus together with the right supramarginal gyrus accounted functionally for durational judgments, whereas numerosity judgments were accounted by the activation pattern in the right intraparietal sulcus. Present results, indeed, demonstrate an overlap between the neural substrates for processing duration and quantity. However, the functional relevance of parietal overlapping areas for each dimension is not the same. In fact, our data indicate that the same parietal sites rule differently non-numerical and numerical dimensions, as parts of broader networks.
NEWS
Making Headlines
January 3, 2020
Students, neurology residents, and doctoral students at the Faculty of Medicine of Vilnius University (VU) had the opportunity to discuss the latest topic in neurobiology, directly contributing to the successful execution of the Systems Biology program with a guest lecturer Dr Milena Korostenskaja, who is a much accomplished scientist in this field.
Professor Audronė Jakaitienė, coordinator of the Systems Biology Programme, also participated in the discussion – “The brain–computer interface and modelling of this interface were the subject of many questions and computational suggestions not only from students, but also from the Department of Statistics. VU scientists had the opportunity to broaden and deepen their knowledge of recent advances in computer-to-brain interface modelling. We are grateful to the VU Strategic Foundation, which contributed to the funding of this initiative,” she said.
After her visit, Dr Korostenskaja discussed the possibility of teaching in the Systems Biology programme again. Having this in mind, let us find out more about Dr Milena Korostenskaja...