12/ Synergistic effort 🙏
Huge thanks to my co-authors @jordytasserie.bsky.social, Lynn Uhrig, & the incredible teams across 3 continents! 🇬🇧🇫🇷🇮🇹🇩🇪🇯🇵🇺🇸🇨🇦
@frosas.bsky.social Pedro Mediano, @parkersingleton.bsky.social, @zhenqi.bsky.social,
@bechir-jarraya.bsky.social @gozziale.bsky.social
11/ Summing Up
We identified evolutionarily conserved principles of mammalian 🧠 architecture
Neural information integration is governed by a specific synergy between connectomics and transcriptomics, with PVALB inhibitory gradients and central-thalamic switch acting as key gatekeepers 🤝
10/ Working Model of Thalamic Restoration
Species-specific biophysical modelling of macaque 🧠 successfully recapitulates restoration of integration (ΦR) by central but not ventral thalamus DBS: connectivity profile is key!
Next: model-based virtual clinical trial in disorder of consciousness?
9/ Inhibition Controls Controllability
Species-specific models with heterogeneous inhibition also show that PVALB is anatomically well-suited to tune 🧠 controllability
Consciousness may rely on conserved interplay between the brain's wiring diagram and its molecular tuning🧬
8/ Biophysical Modelling
To go beyond correlation, we built computational models 💻🔬 integrating species-specific anatomical wiring with 🧬 gradients for humans, macaques, & mice
Only models with PVALB-driven inhibition successfully recapitulate breakdown of ΦR/integration
7/ PVALB Inhibition?
We mapped the regional breakdown of ΦR against species-specific gene expression 🧬 in 3 species 👱🐒🐭
Spatial topography of ΦR breakdown coincides with PVALB gene expression (parvalbumin interneurons).
This suggests a role for PV-mediated inhibition in governing integration
6/ Out of Control
As the system disintegrates, the brain's dynamics become significantly harder to "control" in a network-theoretic sense 🌀
Loss of controllability scales with breakdown of integration (& is restored by CT-DBS)
5/ Tracking Behaviour
Dominance Analysis shows that ΦR outperforms traditional Φ and other proposed information-theoretic measures of "integration" 📊⚖️ for tracking DBS-induced changes in behavioural arousal
4/ Reversing the Collapse via Thalamus⚡️🧠
When macaque🐒 is re-awakened by deep-brain stimulation of central thalamus (CT), we also re-ignite ΦR & the brain’s integrative capacity 🔥
Not so for DBS of a control site!
CT is a local switch for the global informational architecture
3/ Convergent Breakdown
The results were striking: across 4 mammalian species 👱🐒🐵🐭 and 6 molecularly diverse drugs, anaesthetic-induced disconnection from environment is marked by a convergent collapse of ΦR 📉 (but *not* traditional Φ or related measures) - rising again upon recovery 📈
2/ From Φ to ΦR 🔥
How do you measure "integration"? Information decomposition reveals that influential measures of "whole-minus-sum-of-parts" double-count the redundancy in the parts.
Our revised Φ (ΦR) is a principled solution📊⚖️
1/ The Big Picture
We combined fMRI, information theory, & species-specific computational modelling in human, macaque, marmoset and mouse 👱🐒🐵🐭 to ask: does anaesthesia reduce the 🧠's capacity to be "more than the sum of its parts"?
The Mammalian Architecture of Information Integration🧠🧬
For #BrainAwarenessWeek, excited to share our latest work about #Neuroscience of #Consciousness in @nathumbehav.nature.com
www.nature.com/articles/s41... 🧵👇
11/ Thanks 🙏
Extremely grateful to Hana & also Yoni Sanz Perl, Jakub Vohryzek, @frosas.bsky.social @gozziale.bsky.social, @misicbata.bsky.social, Morten Kringelbach, Gustavo Deco & all other co-authors, for making this team effort possible!
10/ Open code!
Check out our code for the cooperative-cpmpetitive Hopf model, courtesy of the amazing Hana Ali
👉 github.com/Hana-Ali/com...
9/ Outlook 🔭
Competitive interactions are 🗝️ for realistic computational models
Macroscale competition may be a conserved principle of mammalian 🧠 organization
Implications for more personalised digital twins in medicine ♊️, & designing biologically inspired AI systems
8/ Computational consequences
Competitive interactions improve computational capacity, when the model’s generative connectivity is used as the wiring diagram for a connectome-based reservoir network 🤖
Could competition lie behind the efficiency of mammalian👱🐒🐭 brains? 🤔
7/ Not just "more parameters"...
Allowing negative connections provides greater performance gains than doubling (!) the number of model parameters.
Want more? Check out our Supplementary for extensive validations & more sanity checks
6/ Consistent network topology of competition
Competitive connections are not randomly distributed.
In each species🐒🐭, negative connections tend to be longer-range than positive ones, and less modular & locally clustered
5/ Biological annotations
Across species 🐭🐒, competitive interactions are grounded in 🧠 biology
They link regions at opposite ends of the cortical hierarchy, with opposite molecular 🧬 and cytoarchitectonic profiles.
Competition may help segregate functionally diverse systems
4/ Dynamical consequences
It is not just FC that improves, either. After all, that was the model objective.
But by allowing competitive interactions, we also get more realistic 🧠 dynamics : synergy, local-global broadcasting, metastability, and irreversibility. For free!
3/ From distant cousins to digital twins ♊️
Each 🧠 has a unique fingerprint.
But traditional cooperative-only models miss its essence: your model and a random stranger's are easily confused. Hardly "personalised"...
Allow competition, and it becomes clear which model was yours!
2/ Improved fit
Competitive interactions dramatically improve model fit to human, 🐒 & 🐭 FC.
The model does not *have to* include negatives – but it chooses to!
In humans, competition improves model "cognitive matching" to >100 meta-analytic patterns from NeuroSynth
1/ Introducing competition
Most whole-brain models assume that regions cooperate (as A goes up, so does B)
Our generative model also allows competitive (negative-sign) interactions. As A gets more active, it can suppress B
We test if this improves model fit in human, 🐒 & 🐭
Just out in @natneuro.nature.com! 🧠
“Competitive interactions shape mammalian brain network dynamics and computation”
www.nature.com/articles/s41...
Is large-scale brain communication purely cooperative — or is competition a core organizing principle?
We built 🧠 models to find out: read on🧵👇
9/ Wonderful collaboration with extremely talented colleagues: Hana Ali, @zhenqi.bsky.social , Filip Milisav, @gozziale.bsky.social , Danilo Bzdok, & @misicbata.bsky.social (& thanks to our AI neuroscience experts 🤖 of course!)
8/ The Big Picture
To sum up: AI-powered synthesis of the neuroscience literature brings together a scattered literature to identify emergent patterns across disparate subfields, modalities, and species.
Try it yourself with our GitHub repo: github.com/Hana-Ali/neu...
7/ Mapping disorder involvement ⚕️
We derive regional risk maps for 30+ brain disorders 🧠🩹. Their clustering aligns with official ICD-11 clinical classification 🩺 better than clustering maps of risk-gene expression 🧬, & recover symptom co-morbidities
6/ From shared function to shared co-activation
Regions’ LLM-derived cognitive similarity predicts functional co-activation from fMRI, and effects of direct stimulation ⚡️ better than anatomical connectivity, molecular profile, or spatial proximity - also in 🐭&🐒
5/ Conserved molecular circuits for cognition 🧬🧠
We can also extend to macaque🐒 & mouse🐭 previous human studies based on NeuroSynth (Hansen 2021 Nat Hum Behav; Luppi 2024 Nat Biomed Eng). Integration w/ species-specific gene expression 🧬 reveals a cross-species molecular circuit for cognition