New research out investigating the development of aperiodic activity (1/f) in the human brain using a large developmental iEEG dataset (>100 patients, age range 6 - 54 years, 5000+ electrodes), spearheaded by Zach Cross and Lisa Johnson from Northwestern University.

https://www.nature.com/articles/s41562-025-02270-x

#neuroscience #development #ieeg #memory

🧒 Quick #introduction, my name is Etienne Combrisson, I'm a postdoc researcher at the #INT in Marseille - France

👾 I'm interested in understanding the #emergence of cognitive functions such as learning and decision-making using human intracranial EEG #ieeg. I'm looking at dynamic and cognitively driven modulations of large scale Functional Connectivity ( #FC ). I'm developing open-source neuroinformatic tools 💻, mainly in #python 🐍. The last one is called #frites 🍟 and can be used for group-level statistics and #FC estimations on neurophysiological data: https://brainets.github.io/frites/index.html

🔮 For the future, I'm trying to get a permanent researcher position, but as you know, it's not easy! 𝗜 𝘄𝗼𝘂𝗹𝗱 𝗹𝗼𝘃𝗲 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗲 𝗱𝗼𝗶𝗻𝗴 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝗶𝗻 𝗮𝗻 𝗲𝗴𝗼- 𝗮𝗻𝗱 𝘀𝘁𝗿𝗲𝘀𝘀-𝗳𝗿𝗲𝗲 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗻𝗲𝗺𝗲𝗻𝘁

Out of science and academy 🌎 I'm a #runner 🏃, I like playing pool (3 cushions) 🎱, I used to play & produce music a lot 🎸 and I love hiking in my snowy mountains ❄️

Hello everyone.

BIDS is the Brain Imaging Data Structure.

A simple and intuitive way to organize and describe your neuroimaging and behavioral data.

It supports data for #EEG, #MRI, #fMRI, #MEG, #iEEG, #PET, #microscopy, #NIRS and behavioral data.

The latest version of the specification can be found here: https://bids-specification.readthedocs.io/en/latest/

The data validator is here: https://bids-standard.github.io/bids-validator/

RT by @EegManyPipes

Preprint alarm📢 Do you care about an inclusive and fair science culture, good scientific practices, and reproducible and reliable results? Or perhaps about the brain or #EEG (or #MEG, #iEEG, #ephys, etc.)?

Then check out our latest preprint👇
https://osf.io/preprints/metaarxiv/jq342/ 🧵

#arxivfeed :

"Improved high-dimensional multivariate autoregressive model estimation of human electrophysiological data using fMRI priors"
https://www.biorxiv.org/content/10.1101/2022.11.18.516669v1

#Neuroscience #Neuro #Brain #Network #Neuroimaging #Connectivity #MVAR #fMRI #iEEG #EEG

@texasmemory have you done (or do you know of) any #iEEG studies of the #testing #effect or paradigms that could have looked at #retrieval practice vs #study ?

[hashtagging the hell out of this: #episodic #memory]