dipanjanr

I am excited to share that our paper is now published in Nature Human Behavior! Check it out here: rdcu.be/e6pzS. Thanks to my PI, Katherine Duncan, and to my collaborators for their support on this journey! Stay tuned for an iEEG follow up 🧠

What a fascinating finding showing human insula contains effector specific connectome map to primary motor cortex. It is quite likely that in the encoding of somatotopic representation Thalamo-Insular connectome plays an important role as a causal hub

Deciphering multiway multiscale brain network connectivity from birth to 6 months: Communications Biology, Published online: 16 January 2026; doi:10.1038/s42003-026-09549-3Multiscale and multiway brain network interactions during the first six months of… dlvr.it/TR5GBr

New hi-res subcortical atlas defined at 100 microns! Manually delineated using MRI, fibre tracking and histology biorxiv.org/content/10.648… datadryad.org/dataset/doi:10… @andreashorn_ Helen Friedrich & team

Nature Building compositional tasks with shared neural subspaces nature.com/articles/s4158…

@GhoshRoyLab @singhpallavi96 @DBT_NBRC Congrats @GhoshRoyLab and Pallavi. Very best for the future endeavours

Congratulations to @singhpallavi96 for a successful defence of her thesis ✨🎉🎈🥳…A thesis loaded with lots of good stuff.. new methods, discovery and lots of new questions for the field.. a paper on the way (waiting to see online) 🤞 @DBT_NBRC

𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝘂𝗹𝘁𝗿𝗮𝘀𝗼𝘂𝗻𝗱 𝗶𝗺𝗮𝗴𝗶𝗻𝗴 Fantastic study of the functional organization of the mouse cortex with focused ultrasound, including dynamics doi.org/10.64898/2026.…

Interested in cortical wave dynamics? Check out our review on the physics, physiology, and psychology of cortical waves led by J Cruddas with @jchrispang Out now in @NeuroCellPress sciencedirect.com/science/articl…

Check out our February issue! nature.com/neuro/volumes/…

@PessoaBrain It is kind of frustrating. We cannot do much but he is a wonderful human being and an exceptional talent.

@dynamicdip Google got him, what can we do??? 😕

𝗧𝗵𝗿𝗲𝗮𝘁/𝗲𝗺𝗼𝘁𝗶𝗼𝗻 𝗶𝘀 𝗱𝘆𝗻𝗮𝗺𝗶𝗰 So we model the dynamics. Version of record of our paper is available. elifesciences.org/articles/102539

@arpansview @partikasiwatch @DBT_NBRC @DBTIndia Wonderful! Many Congrats to both of you. Wish you all the best for your future endeavours @partikasiwatch

It is always an end of an era when a PhD student defends thesis. Fortunate to mentor 8th PhD student as supervisor Dr @partikasiwatch towards successful defense in the area of language cognition. We learnt a lot about language, ageing, AI and GAMM together! @DBT_NBRC @DBTIndia

@iamvivekt Many thanks @iamvivekt

@dynamicdip Congratulations Dipanjan and Team. Wonderful work.

New paper out in Cerebral Cortex! One of the many works that is going to appear in 2026. This one is an exciting collaboration with PhD student Anirban and Prof. Srinivasa Chakravarthy from IIT Madras. In this paper, we introduce the first whole-brain model that can reliably learn and reconcile multiple simultaneous EEG–fMRI signals and the underlying brain dynamics observed across diverse experimental paradigms. Our article, titled “A mechanistic whole-brain model to capture simultaneous EEG-fMRI data”, is now published and freely accessible at the link below: lnkd.in/gbzzvuuA

@regularjoy Thanks @regularjoy

@dynamicdip Congratulations! reading your article. awesome.

@PessoaBrain Thanks @PessoaBrain Looking forward to your feedback

@dynamicdip Looks great!

A recent work by Mondal, M. et al. Patterns0, 101427 (2026) was covered by Nature India yesterday where I commented on this exciting brain research findings based on network topological organisation. In case interested please read the details here, nature.com/articles/d4415…

New preprint alert! A tour de force by Suman Saha and a former CBDL colleague with fantastic collaborators Chittaranjan Hens, Syamal Dana, @Priyankame1, @arpansview, Gustavo Deco, and me introducing a new framework that generalizes classic ecological scaling laws to complex neural systems with both positive and negative signals, enabling a principled way to quantify how coordination and synchrony shape large-scale brain dynamics. Using RMS-based scaling. In fMRI from 840 adults (18–88 yrs), we find: stronger neural synchrony → lower scaling exponent. With aging, resting-state scaling is stable, but task-related scaling shifts, especially in limbic, subcortical & cerebellar networks. In ADHD, this synchrony–scaling coupling breaks down, pointing to a promising systems-level biomarker of brain health. For an early look at new findings, see here 👇 biorxiv.org/cgi/content/sh… A generalized scaling law to characterize coordinated brain activity