Johan Nakuci
@johannakuci.bsky.social
Neuroscientist on the job market
Formerly at ARL, Georgia Tech, UB
Formerly at ARL, Georgia Tech, UB
Overall I find these approaches very interesting...My one concern is that no one checks if the reconstruction maintains the signal associated with the task or if it's only reconstructing the intrinsic signal. This is an easy test since you can do a GLM on the reconstructed signal.
November 11, 2025 at 10:07 AM
Overall I find these approaches very interesting...My one concern is that no one checks if the reconstruction maintains the signal associated with the task or if it's only reconstructing the intrinsic signal. This is an easy test since you can do a GLM on the reconstructed signal.
Agree! and chemoarchitecture is a better predictor of brain connectivity than structure, at least the mesoscale .
September 6, 2025 at 11:03 PM
Agree! and chemoarchitecture is a better predictor of brain connectivity than structure, at least the mesoscale .
Does a question like this even make sense for a system that is constantly receiving external inputs (eg. the brain)?
August 5, 2025 at 5:49 PM
Does a question like this even make sense for a system that is constantly receiving external inputs (eg. the brain)?
Lastly, we extend the framework to provide insight into the neuroreceptors underlying alterations in brain activity in Schizophrenia, Bipolar Disorder and ADHD
July 30, 2025 at 5:20 PM
Lastly, we extend the framework to provide insight into the neuroreceptors underlying alterations in brain activity in Schizophrenia, Bipolar Disorder and ADHD
Moreover, we recover the binding profiles of LSD - 5-HT1a, 5-HT1b, 5-HT2a, and D2 - and Modafinil - D2, 5-HT1a, 5-HT2a, and NET -, demonstrating consistency with known pharmacological and neurobiological associations.
July 30, 2025 at 5:18 PM
Moreover, we recover the binding profiles of LSD - 5-HT1a, 5-HT1b, 5-HT2a, and D2 - and Modafinil - D2, 5-HT1a, 5-HT2a, and NET -, demonstrating consistency with known pharmacological and neurobiological associations.
In terms of how results can be biased (Marks blog), I agree with you completely. If the issue is inherent in the data, then it's difficult to see how an encoding model overcomes this. Overall, I see what you are trying to address.
June 19, 2025 at 3:55 AM
In terms of how results can be biased (Marks blog), I agree with you completely. If the issue is inherent in the data, then it's difficult to see how an encoding model overcomes this. Overall, I see what you are trying to address.
In the simplest sense, I can fit a linear model to just Y ~ noise, and get a beta-value. However, that beta value is pointless without knowing how well the model fits the data. I could just be misinterpreting what you meant?
June 19, 2025 at 3:07 AM
In the simplest sense, I can fit a linear model to just Y ~ noise, and get a beta-value. However, that beta value is pointless without knowing how well the model fits the data. I could just be misinterpreting what you meant?
I agree with that but I am still not sure how the encoding model overcomes the issue? If noise > signal (x) then the model would be learning the noise. B/c without the decoding, then how do you know that what is being encoded reflects x?
June 19, 2025 at 3:05 AM
I agree with that but I am still not sure how the encoding model overcomes the issue? If noise > signal (x) then the model would be learning the noise. B/c without the decoding, then how do you know that what is being encoded reflects x?
I think I am missing something...are you saying that the noise level in the data is the issue or that if the image naturalistic (contains horse, barn and cat) then it is difficult to decode? My concern is that how can you then "determine" then the encoding model is reflecting the inputs?
June 18, 2025 at 11:59 PM
I think I am missing something...are you saying that the noise level in the data is the issue or that if the image naturalistic (contains horse, barn and cat) then it is difficult to decode? My concern is that how can you then "determine" then the encoding model is reflecting the inputs?
Applying this framework, we identify distinct biophysical drivers of FC alterations in schizophrenia, bipolar disorder, and ADHD, offering insights into the unique characteristics of these disorders.
June 5, 2025 at 11:00 PM
Applying this framework, we identify distinct biophysical drivers of FC alterations in schizophrenia, bipolar disorder, and ADHD, offering insights into the unique characteristics of these disorders.
Our findings reveal that neuroreceptor congruence play a dominant role in shaping FC network features, while structural connectivity has a surprisingly minor influence.
June 5, 2025 at 10:59 PM
Our findings reveal that neuroreceptor congruence play a dominant role in shaping FC network features, while structural connectivity has a surprisingly minor influence.
Lastly, it's not my intension to make sweeping generalization about group-level analyses. (All typically developing individuals have a visual system, etc). However, it's hard to make nuanced points in 250 characters.
March 9, 2025 at 8:48 PM
Lastly, it's not my intension to make sweeping generalization about group-level analyses. (All typically developing individuals have a visual system, etc). However, it's hard to make nuanced points in 250 characters.
I am referring to brain-behavior or brain-pathology and etc relationships where individual heterogeneity is a "huge" factor. More importantly, to me group-level findings tend to be weak at best or simply noise at the worst case scenario. I know this is an old and on-going debate.
March 9, 2025 at 8:48 PM
I am referring to brain-behavior or brain-pathology and etc relationships where individual heterogeneity is a "huge" factor. More importantly, to me group-level findings tend to be weak at best or simply noise at the worst case scenario. I know this is an old and on-going debate.
Let me rephrase my initial thoughts. I would prefer that we collect 1000 trials vs 1000 subjects (or longer resting-state, etc). I lean toward focusing on the the individual ("deep sampling" and etc). Group-level analyses have their place, but I am not sure how "meaningful" the results are.
March 9, 2025 at 8:48 PM
Let me rephrase my initial thoughts. I would prefer that we collect 1000 trials vs 1000 subjects (or longer resting-state, etc). I lean toward focusing on the the individual ("deep sampling" and etc). Group-level analyses have their place, but I am not sure how "meaningful" the results are.
Can you convince with besides the hypothetical "sure"? Overall, I think this kind of mechanism might work for language learning for instance in infants and/or for fine tuning in other instances, but in general I think it too slow.
March 6, 2025 at 3:47 AM
Can you convince with besides the hypothetical "sure"? Overall, I think this kind of mechanism might work for language learning for instance in infants and/or for fine tuning in other instances, but in general I think it too slow.
Can learning via GD reach optimal weights sufficiently fast enough so that an organism doesn't get eaten (or fall off a cliff, literally)?
March 6, 2025 at 1:33 AM
Can learning via GD reach optimal weights sufficiently fast enough so that an organism doesn't get eaten (or fall off a cliff, literally)?
By the way, the multiple patterns of activation are present in EEG too. See doi.org/10.1016/j.ne...
Redirecting
doi.org
March 5, 2025 at 9:48 PM
By the way, the multiple patterns of activation are present in EEG too. See doi.org/10.1016/j.ne...
As we mentioned in the discussion section, if the subtypes reflected mind wandering or inattention, then this would presumably be reflected in the behavioral performance (RT), but the behaivoral performace was almost identical across subtypes.
February 26, 2025 at 5:39 PM
As we mentioned in the discussion section, if the subtypes reflected mind wandering or inattention, then this would presumably be reflected in the behavioral performance (RT), but the behaivoral performace was almost identical across subtypes.
To clarify, the subtypes are not driven by experimental factors (task conditions, etc). However, across the cortex, the brain-behavior correlation improves when subtypes are factored in. This is the "sensitvity" analysis (Figure 7). Overall, we can only speculate on what the patterns mean.
February 25, 2025 at 1:29 AM
To clarify, the subtypes are not driven by experimental factors (task conditions, etc). However, across the cortex, the brain-behavior correlation improves when subtypes are factored in. This is the "sensitvity" analysis (Figure 7). Overall, we can only speculate on what the patterns mean.