Translational Neuroscience and Technology Lab

On closed loop intervention for Mood disorders:

• Using whole person cognitive electrophysiology to develop a predictive tool for identifying of treatment success, and guide recommendation of precise and personalized treatment strategy in Depression.

We address the problem of the need to arrive at efficient personalized medication strategy in depression, much sooner than the standard trial and error investigation.

We propose that cognitive physiological signals can help predict treatment outcome in depression and guide individualized medication strategy, as early as within 10 days in contrast to classical 4-6 weeks depression requirement.

We also work on using the principles of cognitive dynamics for seizure prediction and guide the treatment strategy

Investigating brain and gut physiology and its coupling effects on cognition and behavior across cultures/personality and wellness

·  Investigating the utility of cognitive agency features from narratives for mental health

• Utility of games, behavior and daily prompts to predict mood swings in Bipolar disorder (mania, depression)

• Developing a multi-level tele-counseling platform with biofeedback (speech, chat, visual modalities, avatar based)

• Understanding cognitive coupling effects on TMS efficiency using TMS-EEG

On Whole body cognition and Parkinson's Disease:

• Using whole person cognition and motor evaluation for recommending medicine dosage, and building computational models of non-invasive brain stimulation like intervention in Parkinson’s Disease

• Quantifying gut instincts for understanding medication related cognitive and motor abnormalities

·      We address the need to understand the brain and gut electrophysiology and their relationship with the underlying microbiome through various stages of Parkinson's Disease and Depression

• Quantifying gut traveling waves and development of a meso-level oscillator model of gut-brain functions accounting for multiple facets of cortico-basal ganglia system in choice selection and executive control

On cognitive BCI and non-invasive stimulation for motor complications:

• Developing a closed loop cognitively adaptable brain machine (and computer) interface for upper and lower extremity balance and motor coordination

  • Using network codes to inform about cognitive control and reward processes for adaptive upper extremity BCI

• Using the principles of brain entrainment to design multimodal cues and non-invasive stimulation designs

·      We work on the principles of brain entrainment to suppress tremor through multimodal cues and peripheral nervous stimulation designs for tremor

·      We also design multimodal cues and tasks for rehabilitation in stroke

On understanding mental load effects predictive utility for Alzheimer's severity and management:

• Characterizing brain and body physiological patterns of mental overload

·      Cognitive overload and distress worsen the progression of dementia. We address the need to extract markers of mental overload and cognitive choking for intervening in mild cognitive impaired patients.

• Developing AR interfaces to assist with memory impairments

On closed loop intervention for Autism:

• Understanding the effects of planning and rehearsal on stimming and social cognition in autism, using a virtual scheduling and (avatar based) communication interface.

  • Developing immersive AI-AR based interfaces for sensori-motor and cognitive training

  • Developing predictive interfaces for repetitive behavior disorders

More on computational cognitive science:

• Investigating the kinship between accept-reject decisions with stopping decisions.

  ·      We investigate the shared and distinct representations of inhibitory control and choice using single unit electrophysiological data collected from macaque Orbitofrontal cortices. We use the principles of computational cognitive neuroscience to understand the parallel distributed computing in the brain.

• Understanding the brain-body coupling along with social cognition using simple multi-agent reinforcement learning models

(In collaboration with NIMHANS)

• Effect of Virechana Karma on Vagal tone, microbiome, and metabolic parameters in Schizophrenia

You can read about our earlier work here.

PROTOTYPES 

• An interface for whole person cognition adaptable brain machine interface 

• Cognitive closed loop neurostimulation as a novel treatment for Depression

  • Treatment outcome prediction and designing of treatment strategy in Depression

  • A multi-level tele-counseling platform

  • A tool for cogntive calibration in rTMS

• Epileptic focus localization and seizure prediction

• Characterizing the dynamics of bipolar oscillations for precision medicine

• Developing MulSeMedia devices for Stroke Rehabilitation

• Affordable cognitive and sensorimotor symptom diagnostics in Parkinson’s Disease 

• CLEAR-PATH: A Smoking Assessment and Cessation Program Recommender

Courses offered at IIT Kanpur

2022-23

CGS641: Topics in Translational Neuroscience 

CGS613: Basics of Electroencephalography

2023-24

CGS600: Computational Tools for Cognitive Science 

CGS786: Computational Cognitive Science 

CGS613: Basics of Electroencephalography

2024-25

CGS641: Topics in Translational Neuroscience 

CGS616: Human Centered Computing

CGS613m: Basics of Electroencephalography

  CODE OF CONDUCT

We pledge that despite the diversity in our interests and ways to pursue scientific research and developing technology, we will adhere to research integrity and keep our values, ethics, rigor, relevance, transparency, respect, impartiality, and accountability. We will conduct our research in a systematic and methodologically rigorous fashion and carefully draw conclusions that can be traced to the research. 

We will maintain good documentation in the form of laboratory notes, research journals, or field notes for keeping track of our research progress. This record of the processes and procedures, including information on data sources, their quality, storage, and retrieval, is necessary to document proper research practice and address the veracity of results. 

We will secure participant consent and all requisite permissions for research. We will store research data and related files securely during all phases of the research process. We will ensure clear data ownership and accountability, access restrictions with appropriate protocols to ensure safety and privacy, data integrity by using a copy of the original data, regular back-ups, and careful and reliable data collection, storage, and retrieval. 

It is our responsibility to avoid plagiarism, falsification, fabrication, or misrepresentation and to report such misdemeanors if they are observed or suspected. 

To minimize the potential for any conflicts, agreements regarding roles and responsibilities, authorship, ownership of intellectual property, and other arrangements, especially in collaborative research, will be clarified at the outset.

(Adapted from University Grants Commission Guidance Document on Good Academic Research Practices, Sept. 2020)