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Reposted by Francesca Fardo

Heejung Jung @jungheejung.bsky.social · Sep 4

New Open dataset alert:
🧠 Introducing "Spacetop" – a massive multimodal fMRI dataset that bridges naturalistic and experimental neuroscience!

N = 101 x 6 hours each = 606 functional iso-hours combining movies, pain, faces, theory-of-mind and other cognitive tasks!

🧵below

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Francesca Fardo @francescafardo.bsky.social · Mar 19

I'm deeply grateful to everyone who contributed! Especially first-author, Jesper Fischer Ehmsen, and co-last author @micahgallen.com, whose brilliance, dedication, and generosity brought our vision to life in ways far beyond what a single tweet could capture!

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Francesca Fardo @francescafardo.bsky.social · Mar 19

Huge thanks to our funders @erc.europa.eu and @lundbeckfonden.bsky.social for supporting this project. Thanks also to our research centre, CFIN, without whom we could not have collected this data. This work was the result of incredible teamwork between my lab and @the-ecg.bsky.social.

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Francesca Fardo @francescafardo.bsky.social · Mar 19

Key takeaways: (1) the brain interprets sensations based on expectations, making harmless inputs painful when uncertainty is high. (2) Individual computational fingerprints during thermosensory learning reflect distinct brain microstructural correlates.

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Francesca Fardo @francescafardo.bsky.social · Mar 19

Using quantitative MRI, we linked thermal learning parameters to brain microstructure. Notably, decision temperature correlated with iron content in the Subnucleus Reticularis Dorsalis (SRD), a key structure in descending pain modulation.

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Francesca Fardo @francescafardo.bsky.social · Mar 19

In contrast, second-order uncertainty, reflecting how unsure you are about the precision of your own predictions, actually intensified the illusion of pain. This means that when thermal expectations are uncertain, the brain interprets these ambiguous stimuli as more painful!

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Francesca Fardo @francescafardo.bsky.social · Mar 19

We compared multiple computational models and found a two-level Hierarchical Gaussian Filter best explained our data. First-level uncertainty shaped temperature sensations: stronger expectations of cold amplified cold feelings, while stronger warm predictions enhanced warmth.

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Francesca Fardo @francescafardo.bsky.social · Mar 19

We manipulated uncertainty in two ways: (1) continuously changing cue-stimulus associations via probabilistic reversals; (2) introducing ambiguous trials combining harmless cool & warm stimuli, eliciting the "thermal grill illusion" of pain!

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Francesca Fardo @francescafardo.bsky.social · Mar 19

To answer this question, over 250 participants learned associations between auditory cues and warm or cold stimuli, and rated their experiences of cold, warmth, and pain.

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Francesca Fardo @francescafardo.bsky.social · Mar 19

We know expectations shape how we experience temperature and pain. But what happens when we're unsure what's coming next? Our key question: can uncertainty about expectations lead the brain to mistakenly perceive harmless temperatures as painful?

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Francesca Fardo @francescafardo.bsky.social · Mar 19

Excited to share our latest publication, out now in @ScienceAdvances: “Thermosensory predictive coding underpins an illusion of pain.” www.science.org/doi/10.1126/.... Read the full thread for details!

Thermosensory predictive coding underpins an illusion of pain

Computational modeling reveals how uncertainty transforms harmless stimuli into perceptions of pain.

www.science.org

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Reposted by Francesca Fardo

Massi(e)h Moayedi — مسیح مویدی @massih.bsky.social · Feb 14

My brilliant student Matt Cormie became interested in the thermal grill, and designed a study to see if the TG can elicit primary/secondary hyperalgesia and/or brush allodynia. These could help us parse out the mechanisms involved.

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

The Thermal Grill Elicits Central Sensitization

The thermal grill, in which innocuous warm and cool stimuli are interlaced, can produce a paradoxical burning pain sensation—the thermal grill illusion (TGI). While the mechanisms underlying TGI remai...

www.biorxiv.org

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Reposted by Francesca Fardo

Gerardo Salvato @gerardosalvato.bsky.social · Jan 10

Cold hands, altered ownership? We link disturbed body ownership in stroke patients to reduced hand temperature and impaired thermoception. The right Insula & Parietal hubs are key players. We are excited to share our new findings
@naturecomms.bsky.social
▶️ rdcu.be/d5SDU

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Francesca Fardo @francescafardo.bsky.social · Apr 16

All data, code, and materials will soon be available here: github.com/Body-Pain-Pe... Thanks for reading, and we are excited to hear your thoughts on the study!

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Francesca Fardo @francescafardo.bsky.social · Apr 16

This was a monumental effort - the culmination of a decade of collaboration with @micahgallen.com, and our first shared last-author paper. It would not have been possible without the amazing work of @jesperfischer.bsky.social!

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Francesca Fardo @francescafardo.bsky.social · Apr 16

In summary: we learn about thermal contingencies using hierarchical Bayesian mechanisms, where the precision of our expectations guides both veridical pain sensation and illusions of pain. Individual differences in this precision weighting are linked to TGI response and brain microstructure!

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Francesca Fardo @francescafardo.bsky.social · Apr 16

We were intrigued to find that R2* in bilateral, basolateral amygdala was related to the uncertainty modulation of the thermal grill index. This region is important for affect and pain conditioning, and could help explain how we infer pain sensations from harmless inputs.

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Francesca Fardo @francescafardo.bsky.social · Apr 16

We find that individual variance in thermosensory computations (e.g., decision temperature and hierarchical uncertainty) index the microstructural features of the somatosensory cortex, insula, amygdala, and the brainstem Subnucleus Reticularis Dorsalis (SRD).

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Francesca Fardo @francescafardo.bsky.social · Apr 16

Next, leveraging our large sample, we conducted a whole brain quantitative MRI analysis using the MPM sequences developed collaborators at UCL, relating individual fingerprints of thermosensory computations to brain maps indexing cortical myelination and iron.

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Francesca Fardo @francescafardo.bsky.social · Apr 16

This means that the more you incorporate uncertainty into your illusory pain ratings, the more sensitive to the TGI you are in general! In the future, uncertainty-weighted TGI may provide a useful way to quantify disordered pain.

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Francesca Fardo @francescafardo.bsky.social · Apr 16

Clinically, responsiveness to the TGI is often used as an indicator of thermo-nociceptive function. We calculated an uncertainty modulation of thermal grill illusion index (UMTI), and found that it was highly correlated with individual differences in TGI response.

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Francesca Fardo @francescafardo.bsky.social · Apr 16

So cool and warm percepts are precision-weighted, but what about the illusion? Remarkably, we find that the intensity of the TGI is specifically increased by estimation uncertainty. When we are unable to make reliable thermal predictions, we infer greater pain.

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Francesca Fardo @francescafardo.bsky.social · Apr 16

Next, using our hierarchical ZOIB regression approach, we predicted VAS ratings using trial by trial variance in prediction and estimation uncertainty. We find that the precision (i.e., inverse uncertainty) of thermosensory predictions increases felt cold and warm sensations.

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Francesca Fardo @francescafardo.bsky.social · Apr 16

The model does an excellent job of explaining trial by trial learning, where the 1st level encodes thermal prediction uncertainty (e.g., probability of warm vs cold stimuli) and the 2nd encodes estimation uncertainty (e.g., uncertainty about cue-stimulus associations).

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Francesca Fardo @francescafardo.bsky.social · Apr 16

To develop a precision-weighted predictive coding model of thermosensation, we adapted the hierarchical gaussian filter (HGF) to our task. Bayesian model comparison, posterior predictive checks, and cross-validation all supported a 2-level learning model.

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