Researchers from National Institute of Mental Health and Neurosciences and Indian Institute of Technology Bhubaneswar have developed a novel method to study Schizophrenia by analysing how brain signals evolve over time. By applying principles of Chaos Theory, the study offers fresh insights into disease progression and highlights how treatment responses may vary across individuals. Ultimately, this approach aims to improve both diagnosis and personalized treatment strategies.
A Growing Mental Health Challenge
Globally, mental health conditions affect nearly 15% of the population, with schizophrenia ranking among the most severe. Typically emerging during late adolescence or early adulthood, the disorder often disrupts crucial years of personal and professional development. Therefore, researchers emphasize that a deeper understanding of brain function is essential to develop more effective treatments.
Using Advanced Brain Imaging Techniques
To explore brain activity, scientists employed Functional MRI (fMRI), which measures changes in blood oxygen levels. Specifically, they focused on resting-state signals—brain activity when a person is not engaged in any task. This approach allowed them to examine how different brain regions communicate in individuals with schizophrenia.
Applying Chaos Theory to Brain Function
Moreover, the team designed a specialized analytical system based on Chaos Theory. Instead of viewing schizophrenia as a static condition, this framework treats it as a dynamic disorder where brain activity, behavior, and thought patterns follow complex, non-linear trajectories. As a result, researchers could track how these patterns shift over time and respond to treatments such as medication, transcranial magnetic stimulation, and electroconvulsive therapy.
Introducing the Chaotic Dynamics Marker
One of the study’s key breakthroughs is the development of the Chaotic Dynamics Marker (CDM). According to Brahma Deo, this marker enables doctors to measure patient recovery more accurately and refine treatment decisions. Furthermore, the findings suggest that certain treatments may produce different effects beyond specific thresholds, reinforcing the need for personalized care.
Visual Models to Track Disease Progression
In addition, researchers created an innovative model known as U-KBBC, which generates a visual pattern called ‘Sudarshan’. This pattern dynamically changes shape based on a patient’s brain signals. Consequently, it helps scientists monitor disease progression and recovery in real time. The system also produces patient-specific indicators, including CDM and a synchronization measure (SyncSZ), enabling precise tracking of clinical outcomes.
Towards Real-World Clinical Applications
To extend these findings beyond the laboratory, the team developed a portable electronic device named ‘Chinmoy’, embedded with the U-KBBC system. This innovation enhances the potential for real-world clinical use. Furthermore, NIMHANS Bengaluru and IIT Bhubaneswar have jointly filed a patent to protect the technology.
Collaborative Effort Driving Innovation
As reported by TOI, the study represents a collaborative effort involving researchers such as Urvakhsh Meherwan Mehta, Kousik Samanta, Barathram Ramkumar, and Chinmoy Raj Hota, alongside Brahma Deo. Moving forward, the team plans to expand this research across more medical institutions, thereby advancing the understanding and management of schizophrenia on a broader scale.