Annie G. Bryant

Thank you all for the great chats about this work and the inspiring ideas for where to go next with this project!

And a big thank you to @bendfulcher.bsky.social and @macshine.bsky.social for your guidance and support throughout this project, as with all others of my PhD 😊

#OHBM2025 was a blast -- the perfect way to celebrate submitting my PhD thesis with y'all 🎉
It was an honor (and so fun!) to represent the Aussie imaging community alongside a fab panel in the LOC Symposium, sharing my final thesis work on homotopic connectivity:

www.biorxiv.org/content/10.1...

New preprint!

Why are long-range connectomic interactions in the cortex dominant in shaping dynamics in some experiments but apparently negligible in others?

We (w/ R Maran, @elimuller.bsky.social) address this question by studying a new hybrid model of cortical dynamics.

arxiv.org/abs/2506.19800

‼️ATTENTION‼️

Social night is tonight 8pm at Paddo Tavern!!

Come for a relaxing night and unwind after a brain stimulating day! 🫠

All are welcome!! #OHBM2025

Big shout-out to @jlizier.bsky.social, whose patience, dedication, and guidance in supervising me through this project have been invaluable 🙏 Thank you also to our other fab co-authors for their insightful input as always: Oliver Cliff, @macshine.bsky.social, and @bendfulcher.bsky.social!

I learned so much through working on this piece! My goal with this is to share a beginner-friendly and intuitive guide for all my fellow visual learners out there, that also doesn’t shy away from the maths and important nitty-gritty details 😊

Unbiased opinion, but I’m really interested in how we can get the most bang for our info theory buck in computational neuroscience 🧠 So, we demo the implementation and interpretation of all 11 measures with an illustrative BOLD fMRI case study:

We organized 11 measures into 6 categories according to temporal dependency and the type(s) of dynamical properties each measure captures. These measures are summarized in one large ‘pocket reference’ schematic, and we then zoom in to discuss the ‘what/how/why’ of each measure individually 🔎

Info theory offers powerful measures for capturing complexity & interaction among elements of a complex system, like the brain! 🧠 Here's our new unified reference for key info-theoretic time series measures ft. 📊 visuals, ➗equations, & 💬descriptions:

arxiv.org/abs/2505.13080

New preprint!: "Using matrix-product states for time-series machine learning".
arxiv.org/abs/2412.15826
Quick summary below 👇

🎨🧑‍🎨 Looking for a tool to visualize subcortical/thalamic data in 2D? Check out this python-based package I put together (subcortex-visualization on PyPI), plus a guide for creating your own custom atlas meshes and vector graphics! All feedback/tips welcome 😊

anniegbryant.github.io/subcortex_vi...

I am so very proud announce my lab's first preprint!!

@xiaoyucaly.bsky.social delves into the mechanisms driving tau distribution:
* Tau sequence driven by connectivity dynamics
* Tau load driven by local factors

Check out her thread below

We are hiring!

@aurinaarn.bsky.social and I are jointly looking for a talented Postdoc in Systems and Comp Neuro at Monash (AUS).

If you like neuroimaging 🧠, comp models 💻, genetics 🧬, brain disorders 🤕, and/or evolution 🐵🐭, this could be the job for you!!

careers.pageuppeople.com/513/cw/en/jo...

The molecular and cellular underpinnings of human brain lateralization Hemispheric specialization is a fundamental characteristic of human brain organization, where most individuals exhibit left-hemisphere dominance for language and right-hemisphere dominance for visuosp...

🚨 New #preprint: The molecular and cellular underpinnings of human brain lateralization

🔗 doi.org/10.1101/2025...

🧠 We identify an acetylcholine-norepinephrine axis underlying functional lateralization, along with mitochondrial and cellular correlates. 🧵1/9👇

#neuroskyence

Postdoctoral Research Associate (Physics) Full time, 2-year fixed term position with potential to extend. Located on the Camperdown Campus at the School of Physics. Exciting opportunity to conduct research in a world-leading interdisciplinary...

An opportunity to *join our group* as a postdoc researcher analyzing complex physical systems, and the time-varying data they produce (with applications to sleep)!

Join us @sydneyphysics.bsky.social on a great campus in a beautiful city!

Please share 🙂

usyd.wd105.myworkdayjobs.com/USYD_EXTERNA...

Thank you very much to the COGITATE Consortium for openly sharing the MEG data we used in our analyses, and to our supervisors @bendfulcher.bsky.social and @macshine.bsky.social for supporting our first fully independent PhD project. Hope you enjoy, and we’re very open to feedback! 😊

@christopherjwhyte.bsky.social and I introduce a novel data-driven framework to evaluate >200 connectivity-based neural correlates of consciousness! Results are used to quantitatively compare predictions from neurodynamical models tailored to theoretic predictions.

www.biorxiv.org/content/10.1...

GitHub - brendanjohnharris/Cosyne_2025: Poster file for Cosyne 2025 Poster file for Cosyne 2025. Contribute to brendanjohnharris/Cosyne_2025 development by creating an account on GitHub.

Going to #cosyne2025? Wondering whether higher cortical regions are closer to a critical point? Stop by poster [1-117] on the 27th March to chat about detecting #criticality in noisy systems like the #brain!

Poster and related links are up now at: github.com/brendanjohnh...

All the data viz code is in our repo (though it's currently still a bit of spaghetti code while I finish organizing my bits 🍝): github.com/DynamicsAndN...

And lastly I combine all my figures in Inkscape!

Thank you, Tara! I swear by the R package "ggseg" (led by @drmowinckels.io) for brain map visualization and the python package @networkx.bsky.social for the circular network connectivity plots 😊

Thank you to @aditijh.bsky.social for welcoming me to join her in this project this last year, to our supervisors @bendfulcher.bsky.social, @alexfornito.bsky.social, @lawraga.bsky.social & fantastic co-authors Patrick Cahill, Brandon Lam, @stuartoldham.bsky.social, and @aurinaarn.bsky.social!

Together with new biological insights into the brain’s overlapping structural architecture, this work introduces a generalizable method for systematically comparing overlapping community detection algorithms to an ensemble of simulated networks tailored to your particular dataset at hand:

In contrast to Louvain clustering, which doesn’t allow overlapping assignments, OSLOM can disentangle communities bridged by two key regions, with the flexibility to capture the brain as a complex system characterized by fluid distributed interactions:

These 15 nodes generally (though not always!) sit at the apex of the topographical hierarchy of the cortex quantified by the first principal gradient (PG) of functional connectivity (shoutout to Dan Margulies for sharing the data via neuromaps from Ross and Justine in @misicbata.bsky.social lab):

After pinpointing OSLOM (order statistics local optimization method) as the top-performing algorithm, we identified seven structural communities in the right cortex collectively bridged by fifteen overlapping nodes with demonstrated cross-community connectivity:

First figure of our preprint, depicting brain maps of seven identified structural connectivity communities bridged by fifteen overlapping nodes at the spatial interface between communities. Structural communities are depicted as nodes on a circular graph while the overlapping regions between the corresponding communities are highlighted along the edges. The seven communities are: (1) Dorsomedial cortex; (2) Limbic; (3) Visual; (4) Ventral attention and visual stream; (5) Somatomotor; (6) Insula; and (7) Frontal pole.

📣🎉 Excited to share work co-led with @aditijh.bsky.social, developing a data-driven selection technique for overlapping community detection algorithms, applied to the human structural connectome! 📣🎉

www.biorxiv.org/content/10.1...

New year, new preprint (+ last thesis paper!)

Macroscale gradients of intracranial EEG power spectra follow familiar axes: sensory-motor and unimodal-transmodal 🕵️‍♀️
Gradients show overall stronger association to distance, but multimodal information was best in transmodal areas 🧠

tinyurl.com/ykacndd5

Massive thank you to @bendfulcher.bsky.social for guiding me throughout this project, and the incredible support from @alexfornito.bsky.social @lindenmp.bsky.social & @kevinaquino.bsky.social 😊 Wishing everyone a happy and healthy new year!

📣 First three years of my PhD have culminated in this work now published at PLoS Computational Biology! 🥳 We take a deep dive into all the many ways we can slice n dice fMRI dynamics to quantify biologically relevant properties 🧠🧮

doi.org/10.1371/jour...