Can we ‘see’ #autism in the brain? And is there a specific brain signature for the condition?

This is what we found by comparing brain connectivity maps across 16 mouse autism models

➡️Majestically led 👏👏👏by @Valerio_Zerbi @NCMLabETH

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Autism is diagnosed based on behavior - but is this condition associated with a specific brain signature of circuit dysfunction?
Despite decades of work, brain imaging has failed to identify such signature as we have evidence of under-connectivity...


or both (e.g. a mosaic pattern)…

So the question now is: what is the origin and significance of these heterogeneous findings?

A common (if not prevailing) interpretation of these findings is ➡️there MUST be an autism-specific brain imprint so the fact we have NOT found it YET implicates that brain imaging is a noisy and an unreliable method to map circuit dysfunction in complex brain disorders

However, an equally plausible alternative explanation could be that no autism-specific signature of brain dysfunction has been found.....because there is no such thing!

This would actually be consistent with the large etiological heterogeneity of the spectrum...

...and so heterogeneous imaging findings in autism would be a reflection of underlying etiological heterogeneity.
But how can we rigorously test this hypothesis? Clinical studies are complicated by genetic heterogeneity
and confounding environmental contributions

So we turned to mouse🐁 imaging
➡️we established a bi-center study to model the etiological "spectrum" in the 🐁collecting #16 mutations/etiologies
➡️we next mapped and compared the corresponding functional connectivity signature of each mutations using rsfMRI

We found that
➡️Most individual scans can be clustered together etiology-wise: so rsfMRI is not so noisy after all..
➡️Autism-related etiologies define a pseudo-continuous connectivity landscape - this argues AGAINST a *unique* signature of circuit dysfunction for autism

What differentiates these connectivity signatures? And can we leverage this heterogeneity to identify cross-etiological connectivity subtypes?
To addressed these question we used unsupervised clustering of connectivity across etiologies

and found enlightening results:
➡️No single dysconnectivity signature but n=4 cross-etiological connectivity subtypes with anatomically-specific patterns of dysconnectivity!
➡️some of these subtypes (i.e. #1 & #4) show *opposing* patterns of under- vs- over-connectivity!

Taken together our results suggest that
➡️Etiological variability is a *key* determinant of connectivity heterogeneity in autism
➡️Diverging connectivity patterns may explain & reconcile conflicting findings in clinical studies

➡️Last but not least: etiologically-relevant connectivity subtypes could help parse autism heterogeneity
Check-out recent example here⬇️

⚠️Note we did not emphasize role of grouped etiologies here as N=16 is too low to speculate about pathways⚠️

So that's all. Many thanks & kudos to @Valerio_Zerbi for promoting and flawlessly executing the project @MarcoPagani1985 for crucial input + all the stellar collaborators for sharing mouse lines and helping with interpretation @SFARIorg & all funding agencies involved

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More from @Gozzi_Ale

9 Oct
#Autism is often associated with an excess of synapses:
but how does this trait affect large-scale circuit function?

Here's what we found by modelling autism-related pruning deficits across-species🧠🐭

By @MarcoPagani1985 @mvlombardo & al.

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Postmortem investigations in idiopathic #Autism have consistently revealed an excess of excitatory synapses

Seminal work form the #Sulzer_Lab @Columbia has shown that postmortem synaptic surplus in #Autism is associated with hyperactive mTOR signalling
➡️this is a molecular pathway often dysregulated in autism and a key point of convergence of many autism-risk genes

Read 19 tweets
7 Aug
Wondering whether axonal input *drives* fMRI-based functional connectivity? Us too!

Here is what we found using #chemogenetic deconstruction of rsfMRI in the mouse🐁🐭

➡️Cortical silencing results in paradoxical fMRI overconnectivity

Thread below 1/n
Computational and empirical evidence suggest that structural and functional connectivity are robustly related

➡️ this recent review from @richardfbetzel @misicbata summarizes it all

⚠️A key prediction of structurally based models of fMRI coupling is that *inactivation* of a brain node would result in reduced rsfMRI connectivity with its targets⚠️

But is that really the case?

Read 19 tweets
15 May
**New preprint from the lab**

Regional, layer and cell-class specific connectivity of the mouse default mode network

Conceived & majestically led by @DrJigsaww @harrisjuliea @AllenInstitute

Thread covering DMN basics + implications of findings
1/n 👇
What is the Default Mode Network (DMN)?

👉Network identified in human PET/fMRI studies
👉Active and strongly synchronized during rest
👉Desynchronized by goal-oriented tasks
👉Encompasses associative cortices - Prefrontal, Cingulate, Retrosplenial, Parietal, Temporal

Why study the DMN?

👉Most prominent large-scale network of human brain
👉Pivotal substrate for higher-order cognitive and social functions
👉Key point of vulnerability for autism, schizophrenia, Alzhemier's and other brain disorders (seminal feature review below)

Read 13 tweets

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