@dasasgue.bsky.social
Reposted
We believe our results may (partly) reframe how we interpret fMRI connectivity
▶️ fMRI connectivity ≠direct communication strength
▶️ fMRI connectivity is supported by distributed slow neuronal coupling
▶️ Hyper/hypoconnectivity (eg., in brain disorders) may reflect cortical hypo/hyperexcitability
16/n
▶️ fMRI connectivity ≠direct communication strength
▶️ fMRI connectivity is supported by distributed slow neuronal coupling
▶️ Hyper/hypoconnectivity (eg., in brain disorders) may reflect cortical hypo/hyperexcitability
16/n
March 18, 2026 at 12:15 PM
We believe our results may (partly) reframe how we interpret fMRI connectivity
▶️ fMRI connectivity ≠direct communication strength
▶️ fMRI connectivity is supported by distributed slow neuronal coupling
▶️ Hyper/hypoconnectivity (eg., in brain disorders) may reflect cortical hypo/hyperexcitability
16/n
▶️ fMRI connectivity ≠direct communication strength
▶️ fMRI connectivity is supported by distributed slow neuronal coupling
▶️ Hyper/hypoconnectivity (eg., in brain disorders) may reflect cortical hypo/hyperexcitability
16/n
Reposted
They may also have implications for brain stimulation. For example: if we increase excitability in a cortical area (with TMS) we may see a decrease in its fMRI connectivity. What we like here is that these are testable hypotheses: and so we will soon see if (any of) this holds in humans!
17/n
17/n
March 18, 2026 at 12:15 PM
They may also have implications for brain stimulation. For example: if we increase excitability in a cortical area (with TMS) we may see a decrease in its fMRI connectivity. What we like here is that these are testable hypotheses: and so we will soon see if (any of) this holds in humans!
17/n
17/n
Reposted
Thus our work suggests that
1️⃣ cortical excitability inversely modulates fMRI connectivity
2️⃣ fMRI coupling rests on distributed, slow neuronal fluctuations (i.e. QPPs, CAPs, neuromodulation pulses..)
3️⃣cortical excitability gates local coupling by weakening or facilitating that slow synchrony
15/n
1️⃣ cortical excitability inversely modulates fMRI connectivity
2️⃣ fMRI coupling rests on distributed, slow neuronal fluctuations (i.e. QPPs, CAPs, neuromodulation pulses..)
3️⃣cortical excitability gates local coupling by weakening or facilitating that slow synchrony
15/n
March 18, 2026 at 12:15 PM
Thus our work suggests that
1️⃣ cortical excitability inversely modulates fMRI connectivity
2️⃣ fMRI coupling rests on distributed, slow neuronal fluctuations (i.e. QPPs, CAPs, neuromodulation pulses..)
3️⃣cortical excitability gates local coupling by weakening or facilitating that slow synchrony
15/n
1️⃣ cortical excitability inversely modulates fMRI connectivity
2️⃣ fMRI coupling rests on distributed, slow neuronal fluctuations (i.e. QPPs, CAPs, neuromodulation pulses..)
3️⃣cortical excitability gates local coupling by weakening or facilitating that slow synchrony
15/n
Reposted
Notably, biophysical modelling supports this framework. Using a simple three-node model we found that local excitability changes are sufficient to reproduce the direction of the low-frequency coherence effects across perturbations.
This offers a plausible mechanistic account of our results!
14/n
This offers a plausible mechanistic account of our results!
14/n
March 18, 2026 at 12:15 PM
Notably, biophysical modelling supports this framework. Using a simple three-node model we found that local excitability changes are sufficient to reproduce the direction of the low-frequency coherence effects across perturbations.
This offers a plausible mechanistic account of our results!
14/n
This offers a plausible mechanistic account of our results!
14/n
Reposted
So these results suggest that
▶️slow, shared LFP fluctuations provide a neuronal scaffold for fMRI connectivity
▶️cortical excitability gates how strongly regions participate on this process: shifts in cortical excitability weaken or facilitate this coupling, leading to
hypo/hyperconnectivity
13/n
▶️slow, shared LFP fluctuations provide a neuronal scaffold for fMRI connectivity
▶️cortical excitability gates how strongly regions participate on this process: shifts in cortical excitability weaken or facilitate this coupling, leading to
hypo/hyperconnectivity
13/n
March 18, 2026 at 12:15 PM
So these results suggest that
▶️slow, shared LFP fluctuations provide a neuronal scaffold for fMRI connectivity
▶️cortical excitability gates how strongly regions participate on this process: shifts in cortical excitability weaken or facilitate this coupling, leading to
hypo/hyperconnectivity
13/n
▶️slow, shared LFP fluctuations provide a neuronal scaffold for fMRI connectivity
▶️cortical excitability gates how strongly regions participate on this process: shifts in cortical excitability weaken or facilitate this coupling, leading to
hypo/hyperconnectivity
13/n
Reposted
By comparing fMRI to LFP coherence one thing stood out
▶️ low-frequency coherence (<4 Hz) consistently tracked fMRI effects across all manipulations! Higher frequencies also change (sometimes a lot), but they don’t covary with fMRI. So slow neuronal coupling is the common denominator of fMRI!
12/n
▶️ low-frequency coherence (<4 Hz) consistently tracked fMRI effects across all manipulations! Higher frequencies also change (sometimes a lot), but they don’t covary with fMRI. So slow neuronal coupling is the common denominator of fMRI!
12/n
March 18, 2026 at 12:15 PM
By comparing fMRI to LFP coherence one thing stood out
▶️ low-frequency coherence (<4 Hz) consistently tracked fMRI effects across all manipulations! Higher frequencies also change (sometimes a lot), but they don’t covary with fMRI. So slow neuronal coupling is the common denominator of fMRI!
12/n
▶️ low-frequency coherence (<4 Hz) consistently tracked fMRI effects across all manipulations! Higher frequencies also change (sometimes a lot), but they don’t covary with fMRI. So slow neuronal coupling is the common denominator of fMRI!
12/n
Reposted
Given that fMRI is a measure of how synchronous fMRI fluctuations are across regions, we thougth measures of interareal electrophysiological (LFP) coherence could shed light into rhythms underlying large-scale fMRI coupling
11/n
11/n
March 18, 2026 at 12:15 PM
Given that fMRI is a measure of how synchronous fMRI fluctuations are across regions, we thougth measures of interareal electrophysiological (LFP) coherence could shed light into rhythms underlying large-scale fMRI coupling
11/n
11/n
Reposted
This seem to hold across different manipulations, and different cortical areas. Which raises the obvious question: what neural signals/mechanism track the observed fMRI connectivity changes?
10/n
10/n
March 18, 2026 at 12:15 PM
This seem to hold across different manipulations, and different cortical areas. Which raises the obvious question: what neural signals/mechanism track the observed fMRI connectivity changes?
10/n
10/n
Reposted
However, fMRI revealed something very consistent
▶️higher excitability →reduced fMRI connectivity ▶️lower excitability →increased fMRI connectivity
So in our datasets more local activity ≠ more fMRI connectivity!
Rather, fMRI connectivity is inversely related to cortical excitability.
9/n
▶️higher excitability →reduced fMRI connectivity ▶️lower excitability →increased fMRI connectivity
So in our datasets more local activity ≠ more fMRI connectivity!
Rather, fMRI connectivity is inversely related to cortical excitability.
9/n
March 18, 2026 at 12:15 PM
However, fMRI revealed something very consistent
▶️higher excitability →reduced fMRI connectivity ▶️lower excitability →increased fMRI connectivity
So in our datasets more local activity ≠ more fMRI connectivity!
Rather, fMRI connectivity is inversely related to cortical excitability.
9/n
▶️higher excitability →reduced fMRI connectivity ▶️lower excitability →increased fMRI connectivity
So in our datasets more local activity ≠ more fMRI connectivity!
Rather, fMRI connectivity is inversely related to cortical excitability.
9/n
Reposted
As expected, these manipulations look very different spectrally.
So if fMRI connectivity depended on a complex mix of neuronal rhythms, we should see divergent effects once we map the effect of these manipulations on large-scale fMRI coupling.
8/n
So if fMRI connectivity depended on a complex mix of neuronal rhythms, we should see divergent effects once we map the effect of these manipulations on large-scale fMRI coupling.
8/n
March 18, 2026 at 12:15 PM
As expected, these manipulations look very different spectrally.
So if fMRI connectivity depended on a complex mix of neuronal rhythms, we should see divergent effects once we map the effect of these manipulations on large-scale fMRI coupling.
8/n
So if fMRI connectivity depended on a complex mix of neuronal rhythms, we should see divergent effects once we map the effect of these manipulations on large-scale fMRI coupling.
8/n
Reposted
Using these manipulations we sought to push cortical circuits into different firing-rate regimes.
▶️↑Excitation → higher firing
▶️↓Inhibition → higher firing
▶️ Silencing → lower firing
So we can now map what happens when we manipulate excitability via different circuit mechanisms
7/n
▶️↑Excitation → higher firing
▶️↓Inhibition → higher firing
▶️ Silencing → lower firing
So we can now map what happens when we manipulate excitability via different circuit mechanisms
7/n
March 18, 2026 at 12:15 PM
Using these manipulations we sought to push cortical circuits into different firing-rate regimes.
▶️↑Excitation → higher firing
▶️↓Inhibition → higher firing
▶️ Silencing → lower firing
So we can now map what happens when we manipulate excitability via different circuit mechanisms
7/n
▶️↑Excitation → higher firing
▶️↓Inhibition → higher firing
▶️ Silencing → lower firing
So we can now map what happens when we manipulate excitability via different circuit mechanisms
7/n
Reposted
We considered three manipulations (through novel or existing datasets)
1️⃣ increasing pyramidal-cell excitability (↑Excitation)
2️⃣ reducing PV interneuron activity (↓Inhibition)
3️⃣ pan-neuronal suppression (Silencing)
6/n
1️⃣ increasing pyramidal-cell excitability (↑Excitation)
2️⃣ reducing PV interneuron activity (↓Inhibition)
3️⃣ pan-neuronal suppression (Silencing)
6/n
March 18, 2026 at 12:15 PM
We considered three manipulations (through novel or existing datasets)
1️⃣ increasing pyramidal-cell excitability (↑Excitation)
2️⃣ reducing PV interneuron activity (↓Inhibition)
3️⃣ pan-neuronal suppression (Silencing)
6/n
1️⃣ increasing pyramidal-cell excitability (↑Excitation)
2️⃣ reducing PV interneuron activity (↓Inhibition)
3️⃣ pan-neuronal suppression (Silencing)
6/n
Reposted
To test our hypothesis, we combined (in the mouse PFC)
▶️Chemogenetics to manipulate excitability ▶️Electrophysiology (spikes + LFP)
▶️fMRI connectivity
But crucially: we combined the result of multiple manipulations into one unified framework, instead of interpreting each one in isolation
5/n
▶️Chemogenetics to manipulate excitability ▶️Electrophysiology (spikes + LFP)
▶️fMRI connectivity
But crucially: we combined the result of multiple manipulations into one unified framework, instead of interpreting each one in isolation
5/n
March 18, 2026 at 12:15 PM
To test our hypothesis, we combined (in the mouse PFC)
▶️Chemogenetics to manipulate excitability ▶️Electrophysiology (spikes + LFP)
▶️fMRI connectivity
But crucially: we combined the result of multiple manipulations into one unified framework, instead of interpreting each one in isolation
5/n
▶️Chemogenetics to manipulate excitability ▶️Electrophysiology (spikes + LFP)
▶️fMRI connectivity
But crucially: we combined the result of multiple manipulations into one unified framework, instead of interpreting each one in isolation
5/n
Reposted
In our work, cortical excitability = mean population firing. This is because in recurrent cortical networks, excitatory and inhibitory activity change in a coordinated way: if excitatory neurons fire more, inhibitory neurons also fire more (& viceversa - we show this using a simple model) 4/n
March 18, 2026 at 12:15 PM
In our work, cortical excitability = mean population firing. This is because in recurrent cortical networks, excitatory and inhibitory activity change in a coordinated way: if excitatory neurons fire more, inhibitory neurons also fire more (& viceversa - we show this using a simple model) 4/n
Reposted
The key idea here is
👉Cortical *excitability" might be a key hidden physiological variable controlling fMRI connectivity. This hypothesis stems from the observation that cortical excitability closely regulates rhythmic interareal coupling. So we thought this could apply to fMRI connectivity too
3/n
👉Cortical *excitability" might be a key hidden physiological variable controlling fMRI connectivity. This hypothesis stems from the observation that cortical excitability closely regulates rhythmic interareal coupling. So we thought this could apply to fMRI connectivity too
3/n
March 18, 2026 at 12:15 PM
The key idea here is
👉Cortical *excitability" might be a key hidden physiological variable controlling fMRI connectivity. This hypothesis stems from the observation that cortical excitability closely regulates rhythmic interareal coupling. So we thought this could apply to fMRI connectivity too
3/n
👉Cortical *excitability" might be a key hidden physiological variable controlling fMRI connectivity. This hypothesis stems from the observation that cortical excitability closely regulates rhythmic interareal coupling. So we thought this could apply to fMRI connectivity too
3/n
Reposted
fMRI connectivity is often interpreted as a proxy for direct interareal communication, but its physiology remains debated. In particular, it is unclear how local circuit activity maps onto fMRI connectivity.
2/n
2/n
March 18, 2026 at 12:15 PM
fMRI connectivity is often interpreted as a proxy for direct interareal communication, but its physiology remains debated. In particular, it is unclear how local circuit activity maps onto fMRI connectivity.
2/n
2/n
Reposted
📢 New preprint from the lab🧠
▶️doi.org/10.64898/2026.03.12.710517
What does fMRI connectivity actually reflect at the neural level?
The natural intuition is: more neural activity = more connectivity! Using cortical perturbations we show this is not necessarily the case: sometimes less is more!
👇🧵
▶️doi.org/10.64898/2026.03.12.710517
What does fMRI connectivity actually reflect at the neural level?
The natural intuition is: more neural activity = more connectivity! Using cortical perturbations we show this is not necessarily the case: sometimes less is more!
👇🧵
March 18, 2026 at 12:15 PM
📢 New preprint from the lab🧠
▶️doi.org/10.64898/2026.03.12.710517
What does fMRI connectivity actually reflect at the neural level?
The natural intuition is: more neural activity = more connectivity! Using cortical perturbations we show this is not necessarily the case: sometimes less is more!
👇🧵
▶️doi.org/10.64898/2026.03.12.710517
What does fMRI connectivity actually reflect at the neural level?
The natural intuition is: more neural activity = more connectivity! Using cortical perturbations we show this is not necessarily the case: sometimes less is more!
👇🧵
🧠What if increasing neuronal firing could actually reduce fMRI connectivity?
📡What drives long-range fMRI connectivity?
I’m very excited to share that my PhD work is now out as a preprint
Check this out! 👇
doi.org/10.64898/202...
📡What drives long-range fMRI connectivity?
I’m very excited to share that my PhD work is now out as a preprint
Check this out! 👇
doi.org/10.64898/202...
March 17, 2026 at 7:51 PM
🧠What if increasing neuronal firing could actually reduce fMRI connectivity?
📡What drives long-range fMRI connectivity?
I’m very excited to share that my PhD work is now out as a preprint
Check this out! 👇
doi.org/10.64898/202...
📡What drives long-range fMRI connectivity?
I’m very excited to share that my PhD work is now out as a preprint
Check this out! 👇
doi.org/10.64898/202...
Reposted
🐭🧠 Can fUSI truly map canonical mouse resting-state networks — and how does it compare to fMRI?
I’m excited to share the work of my PhD in our new preprint!👉 doi.org/10.64898/202...
Go check it out — it’s time to expand your neuroimaging toolkit 🧠🚀
I’m excited to share the work of my PhD in our new preprint!👉 doi.org/10.64898/202...
Go check it out — it’s time to expand your neuroimaging toolkit 🧠🚀
February 11, 2026 at 6:09 PM
🐭🧠 Can fUSI truly map canonical mouse resting-state networks — and how does it compare to fMRI?
I’m excited to share the work of my PhD in our new preprint!👉 doi.org/10.64898/202...
Go check it out — it’s time to expand your neuroimaging toolkit 🧠🚀
I’m excited to share the work of my PhD in our new preprint!👉 doi.org/10.64898/202...
Go check it out — it’s time to expand your neuroimaging toolkit 🧠🚀
Reposted
Jane Goodall, known for her pioneering work with chimpanzees, has passed away aged 91
go.nature.com/46K10ja
go.nature.com/46K10ja
Jane Goodall’s legacy: three ways she changed science
The primatologist challenged what it meant to be a scientist.
go.nature.com
October 2, 2025 at 9:38 AM
Jane Goodall, known for her pioneering work with chimpanzees, has passed away aged 91
go.nature.com/46K10ja
go.nature.com/46K10ja