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Fabrizio Musacchio
Fabrizio Musacchio
@pixeltracker@sigmoid.social  ·  activity timestamp 2 weeks ago

🧠 New paper by Huang et al.: By using #pharmacological #fMRI and dynamic #connectome-based #PredictiveModeling, they show how #cortisol reshapes whole-brain #NetworkDynamics during emotional memory encoding. Trial-level analyses reveal distinct but increasingly integrated #arousal and #memory networks under #stress, supporting a hormonally driven "memory formation mode".

🌍 https://doi.org/10.1126/sciadv.adz4143

#Neuroscience #CognitiveNeuroscience #BrainNetworks #CogSci

Dynamic brain mechanisms supporting salient memories under cortisol

Fig. 2. Analysis design. (A) Schematic of the trial-level phase synchrony extraction approach. (B) Schematic of dCPM. Edges that are significantly correlated with memory/arousal are selected. A linear model is then trained to predict memory/arousal on the left-out trial. This model is separately applied to all four study conditions (pill × emotionality) to yield four predictive networks. R, remembered.
Fig. 2. Analysis design. (A) Schematic of the trial-level phase synchrony extraction approach. (B) Schematic of dCPM. Edges that are significantly correlated with memory/arousal are selected. A linear model is then trained to predict memory/arousal on the left-out trial. This model is separately applied to all four study conditions (pill × emotionality) to yield four predictive networks. R, remembered.
Fig. 2. Analysis design. (A) Schematic of the trial-level phase synchrony extraction approach. (B) Schematic of dCPM. Edges that are significantly correlated with memory/arousal are selected. A linear model is then trained to predict memory/arousal on the left-out trial. This model is separately applied to all four study conditions (pill × emotionality) to yield four predictive networks. R, remembered.
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bk
bk
@knutson_brain@sfba.social  ·  activity timestamp 6 months ago

Other key points :
• Neural predictors of risky choice can be triply dissociated from neural correlates of sensory input and motor output.
• Predictors generalize across samples, tasks, and analyses (a priori and post-hoc)
• Predictors also account for individual differences in risky choice within the lab, as well as debt beyond the lab.
• Findings robustly support a #BrainAsPredictor account of risky choice of gambles (if you don't blow out your signal in the middle of the brain with #multiband#FMRI acquisition, see Study 4)

bk
bk
@knutson_brain@sfba.social replied  ·  activity timestamp 6 months ago

Spearheaded by the unstoppable Leili Mortazavi with Elnaz Ghasemi and Charlene C. Wu , this culminates > 15 years of work!
The findings imply that some #FMRI findings are more robust than commonly thought (with respect to reliability, validity, and generalizability).
Special thanks to the editors and reviewers at PNAS Nexus and for support from Stanford University 's #Neurochoice Initiative and the #ToyotaResearchInstitute ! (#AffectiveNeuroscience , #Neuroeconomics )

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bk
bk
@knutson_brain@sfba.social  ·  activity timestamp 6 months ago

Now out in @pnasnexus.org@bsky.com :
“Deconstructing neural predictors of risky choice”!
Take-home point :
At least two systems (not one) predict risky choice — but both are affective! ( #neuroscience )

https://academic.oup.com/pnasnexus/advance-article/doi/10.1093/pnasnexus/pgaf204/8168828

bk
bk
@knutson_brain@sfba.social replied  ·  activity timestamp 6 months ago

Other key points :
• Neural predictors of risky choice can be triply dissociated from neural correlates of sensory input and motor output.
• Predictors generalize across samples, tasks, and analyses (a priori and post-hoc)
• Predictors also account for individual differences in risky choice within the lab, as well as debt beyond the lab.
• Findings robustly support a #BrainAsPredictor account of risky choice of gambles (if you don't blow out your signal in the middle of the brain with #multiband#FMRI acquisition, see Study 4)

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