Research

This research dives deep into one of the most universal human experiences — the emotion we feel through music. By exploring how real-time neurochemical fluctuations and neural activity evolve during naturalistic music listening, this project investigates the hidden biological dynamics that shape emotional experience.

Combining advanced brain imaging with real-time emotion tracking and powerful computational tools, the study looks at how internal mood states may alter our emotional responses to music. What happens in the brain — not just electrically, but chemically — when music moves us? And how might this knowledge help harness music for mental and neural health?

Positioned at the crossroads of neuroscience, psychology, and music, this project opens up new avenues in emotion science and translational neurotechnology.

       Lead Researcher: Arijit Bhattacharya

While research on Major Depressive Disorder (MDD) has long focused on mood, cognition, and affective processing, the body—our fundamental interface with the world—has received comparatively little attention. Sensorimotor alterations, such as changes in posture, movement, and bodily awareness, are increasingly recognized as integral to the experience of depression, yet remain a relatively neglected dimension in both research and clinical practice. 

This project seeks to shift that focus. It asks: What if understanding depression requires us to move beyond the brain’s higher cognitive functions and pay closer attention to the body’s subtle signals? And can immersive, interactive technologies like Virtual Reality (VR) offer a more ecologically valid lens through which to observe these embodied features? 

By leveraging VR, we aim to create life like environments that retain experimental control while allowing individuals to move, perceive, and interact more naturally. These settings enable richer, more dynamic assessments of sensorimotor function than traditional lab-based tasks can offer. In doing so, this research not only explores sensorimotor disruptions as potential markers of MDD but also positions VR as a transformative tool for studying and intervening in affective disorders. 

Through this work, we hope to reframe how depression is understood—bridging the gap between mind and body, and between clinical theory and lived experience. 

       Lead Researcher: Urvashi Jinwal 

The search for reliable neurobiological markers of mental health conditions has remained elusive. Conventionally, the psychological components of mental health conditions-maladaptive rumination, cognitive control over emotional processing, negative expectations, and so on-have been the primary focus of neurobiological research. However, mental health conditions are embodied phenomena and are associated with alterations in several sensorimotor functions. Persons suffering from depression, for example, are known to experience a lack of sharpness in their vision. In other words, depression literally makes the world look dull and grey. Depression is also associated with other sensory and motor changes like unexplained chronic back pain, slowed speech, body immobility, and loss of facial expression. A significant number of individuals with anxiety disorders consistently display a heightened sensitivity to subtle stimuli, an elevated heartbeat perception, dizziness, motor restlessness, and elevated muscle tension. Apart from the recognition of bodily components of mental health conditions, our work is also motivated by the paradigm shift that is taking place regarding our understanding of the sensory and motor function of the brain. Spearheaded by predictive coding and related theoretical frameworks, there is an emerging consensus among neuroscientists that perception is not a passive bottom-up mechanism of progressive integration of visual information. Top-down connections in brain are involved in proecces like attention and prediction and play crucial role in perception. Overall, Bottom-up, top-down, and intrinsic connections play distinct but crucial roles in both perception and motor functions. Motivated by these insights, we analyzed effective connectivity [spectral dynamic causal modeling ] in resting-state functional MRI data among hierarchical sensorimotor regions in individuals with mental health conditions and neurotypical individuals. We found that top-down and bottom-up effective connectivity in sensory and motor cortices is altered with increasing depression and anxiety severity in a way that is consistent with their symptoms. More importantly, the alteration is of an effect size large enough to predict whether someone has mild or severe depression/anxiety and was found robust in cross-validation analyses. These findings open up a new line of inquiry into the neurological basis of mental health issues.

       Lead Researcher: Dipanjan Ray

There is so much more research on vision than on any other sensory modality. Despite that, there are several unanswered questions relating to visual processing in the brain. One such long-held debate is about the dual stream theory. The theory proposes that two neuronal streams (ventral and dorsal) connect the primary visual cortex (V1) to higher-level visual areas. However, two prominent models of visual dual stream diverge regarding the basis of functional specialisation of the two streams. One model proposes an input-based dissociation depending on the stimulus level information (what vs where information) while the other model suggests that the dissociation depends on the purpose of processing - mere perceiving vs using the information for a visually guided task- i.e., an output-based dissociation. We addressed these gaps in the visual dual stream theory by employing a series of perceptual and motor tasks within an fMRI scanner. The study supported an input-based functional specialization in the dual stream, however, the dominant dual-stream theories could not explain the pattern of BOLD activations and network-level observations. Overall, the findings point towards the existence of more intricate context-driven functional networks selective of ``what" and ``where" information processing and likely breakdown of the parallel architecture underlying the processing of visual information. The mechanism of predictive coding also emerged as a guiding principle to interpret the brain dynamics in dorsal and ventral stream areas.

       Lead Researcher: Dipanjan Ray

Vectorcardiography is a method of recording cardiac electric signals in three dimensions, unlike the more popular ECG which records cardiac activity in two dimensions. VCG was invented in the 1950s and is known to be more sensitive than ECG in detecting several cardiac conditions. However, as the method of recording VCG is cumbersome it is much less used in clinical practice. We exploited the recent advances in digital signal processing methods and developed a novel approach to (re)construct VCGs from three simultaneously recorded quasi-orthogonal ECG leads. We demonstrated that the spatial QRS loops constructed by our method were essentially planar in normal controls and the planarity was lost in patients with acute myocardial infarction (STEMI): thus establishing its clinical translatability. 

       Lead Researcher: Dipanjan Ray