Andrew E. Papale

I wonder if coupling with amygdala or (anterior vs posterior hippocampus) during rumination messes up the relationship between DMN and mind wandering. Why would the brain have redundant circuits for conceptually similar processes?

So, practically, direct attention externally switching to CTR/DAN etc circuits, to break out of repetitive thought cycles? Shouldn't DMN be negatively correlated with success?

And now for something completely different!

In this new lab preprint led by Ceci Westbrook, we find a link between successful control of repetitive thought and increased activity in attention-related brain networks. By contrast, default mode activity was not associated with regulatory success.

It's publication day for 📘Elusive Cures. What a moment! 1st book, 1st time on Mindscape (which, as many of you know, is one of my favorite podcasts).

Here, @seanmcarroll.bsky.social and I have a wide-ranging conversation around: Why are brain and mental disorders so hard to understand and treat?

Thank you for speaking out. Hoping for flat funding at FY 24 levels. When is the vote? Reps probably deserve a call on this one.

During inevitable conflict, is Exponential backoff key to deescalation? Are dysfunctional relationships characterized by deviations in these network patterns?

Reading the "Networking" chapter about the internet in "Algorithms to Live By." Are healthy relationships in social psychology characterized by interactions that mirror TCP packets in Additive Increase, Multiplicative Decrease (AIMD) algorithms?

AIMD = Additive Increase, Multiplicative Decrease

does nature, in the form of neural ensembles, prefer winner take all (first past the post) voting or ranked choice voting? Making an analogy to Democratic systems on this primary election day. Can a neuron represent multiple simultaneous and competing options?

By state I mean capturing the internal dynamics of your brain while you are integrating a new fact about a complex idea such as the reputation of a person. But put simply, it could mean are you relaxed or vigilant.

Given the forces acting on a golf ball during a putt and the shape of the green, how many possible paths can it take to get to the hole? On any particular path, what is the margin of error on the magnitude and direction of the force applied by the club? ⛳️

Hey folks! I'll be teaching two workshops in May on Structural Equation Modeling!

One course is introductory (May 7-9): smart-workshops.com/intro-sem-info

The other is on longitudinal models (May 14-16): smart-workshops.com/long-sem-info

🙏Please RT and share! 🙏

Let met tell you more...

We often use discretization to approximate continuous laws of physics, but it also goes the other way:

You can use continuous equations to approximate the behavior of discrete systems!

Here we'll see how electrical circuits can be modeled using the Laplace equation Δφ=0. [1/n]

Has anyone applied big O notation to derive constraints on decision-making algorithms on the timescale of neural processing? 🤔

Prefrontal default-mode network interactions with posterior hippocampus during exploration Hippocampal maps and ventral prefrontal cortex (vPFC) value and goal representations support foraging in continuous spaces. How might hippocampal-vPFC interactions control the balance between behavior...

Thanks for reading, here's a link to the preprint, feel free to reach out with any questions! 15/end.
www.biorxiv.org/content/10.1...

What does this mean? Compressed representations may facilitate strategic exploration on a complex task like the clock task. There is evidence that entropy and value maxima are represented in vPFC. These representations may drive biological solutions to E/E problems. 14/15

Finally, we look at the effects of this connectivity analysis on exploration behavior. We find that following rewarded trials, higher entropy modulation of posterior hippocampal-DMN connectivity predicted subsequent exploration. Maybe these signals are related to choice. 13/15

We next examine interactions between vPFC and hippocampus using the deconvolved MLM framework. We do this by using HC as a regressor and vPFC as an outcome. A key result from this analysis is that hippocampal-DMN connectivity is higher when entropy is higher. 12/15

We find robust encoding of the value maximum in vPFC, strongest in region 14m25/32 of the DMN and in the LIM network. This is consistent with the neuroeconomic literature that suggests ventromedial PFC encodes scalar values. 11/15

We find that DMN responds when entropy is lower, when there is likely one prominent global value maximum that is easier to exploit. We use deconvolved BOLD aligned to trial onset in a MLM. This allows examination of the time course of signals w.r.t. an event (like a PETH). 10/15

Entropy is an emergent property that captures the complexity of the value function on a given trial: higher entropy indicates many local value maxima (complex value landscape) and lower entropy typically indicates one prominent global value maximum (simple value landscape). 9/15

An RL model is fit to subjects’ behavior, and the value maximum and entropy of the value distribution are calculated on each trial. We can then examine neural correlates with predictions from the model. 8/15

Subjects explore and exploit on a clock task where a dot rotates 360 deg, and responding at different locations gives probabilistic rewards. Hidden underlying value distributions are learned through exploration. Subjects play this task during an fMRI scan. 7/15

Here we extend these findings to ventral prefrontal cortex (vPFC), and interactions between hippocampus and vPFC. We examine 3 resting state networks, the control (CTR), default mode (DMN) and limbic (LIM). Importantly, we replicate key findings out-of-sample. 6/15

In prior work, we found that compressing out some of the information the RL agent could have learned better explained people’s choices. Compressed (lower-entropy) maps of valuable options were found in the anterior hippocampus with fMRI. 5/15

a black and white image of a brain with a blue line going through it ALT: a black and white image of a brain with a blue line going through it

One of the more exciting prospects in the computational neuroscience of decision-making is that these algorithms can shed light on how humans solve the explore exploit dilemma. But all RL algorithms are not built the same. 4/15

We encounter a similar problem when we decide whether to stick with known good options or explore unknown but possibly better alternatives. Reinforcement learning (RL) algorithms are often used to solve this so-called explore/exploit (E/E) dilemma. 3/15

During WW2, Alan Turing decoded the German Enigma using an early computer. He used common phrases and statistical techniques to limit the number of possible solutions, eliminating extraneous alternatives. Does our brain do something similar? 2/15

Excited to share a summary of a new preprint looking at exploration in ventral prefrontal cortex and hippocampus using fMRI. A 🧵...1/15

Temporal fMRI Dynamics Map Dopamine Physiology Spatial variations in dopamine function are linked to cognition and substance use disorders but are challenging to characterize with current methods. Because dopamine influences blood vessel dilation,...

Interested in dopamine? Have fMRI data? We’ve identified a temporal BOLD feature that carries rich information about dopamine physiology. This measure, obtainable from resting-state and task fMRI, opens new ways to indirectly probe dopamine’s role in cognition and disease. 1/n tinyurl.com/bddyz67b