⬇️ Check out this great paper by Jingnan Du et al. It shows you can use fMRI task data to estimate networks as reliably as with rest data! A game changer to increase statistical power in regions with low SNR and leverage datasets that may only have task data.
📸 Bonus: A snapshot of three Center for Brain Recovery alumni — each from a different era — all in one photo! With the wonderful Brandi BeCoats. (@swathikiran.bsky.social)
5/5
Special thanks to Sofia Vallila Rohter for being such a generous, thoughtful, and inspiring mentor.
💡For any student reading this: build your network of mentors early on by applying to the programs offered at your university/conferences/societies. It will be invaluable help in the future.
4/n
Huge thanks to The American Speech-Language-Hearing Association (ASHA) for organizing such a thoughtful and practical career development program. I highly recommend the Pathways program to anyone at the PhD, postdoc, or early career investigator stage in CSD research!
3/n
The past two days were a powerful reminder of that — filled with insightful conversations with mentors, joyful reunions with old friends, and the excitement of meeting new inspiring colleagues.
2/n
Amid the current challenges we’re all facing as scientists, one thing keeps me grounded and hopeful: the incredible humans who show up for each other.
1/n
Very excited to see this great team work on semantic therapy in a trilingual individual with aphasia published! Open access article available in the link below 👇
At #CNS2025 and interested in whether and how we can dissociate higher-order cognitive functions within DLPFC at the individual level with precision fMRI? 🧐 Let’s chat at my poster B42 tomorrow from 8 to 10 am! 🧠
This work is also relevant for anyone doing TMS targeting the DLPFC in patients! 🧲⚡️
I know this site only reaches a subset of neuro/ cog professors. If you know a young professor in the area who would benefit from a week of targeted learning about doing good science, good mentoring, networking etc, it would be great if you could forward them: compneurosci.com/Neuro4Pros/i...
Our language neuroscience lab (evlab.mit.edu) is looking for a new lab manager/FT RA to start in the summer. Apply here: tinyurl.com/3r346k66 We'll start reviewing apps in early Mar. (Unfortunately, MIT does not sponsor visas for these positions, but OPT works.)
A tremendous endeavor: sign up today! I am very much looking forward to being in the virtual audience!
TODAY CBR Seminar Series featuring Sofia Vallila Rohter
WHEN: Monday, January 27th
4:00pm-5:00pm EST
WHERE: 111 Cummington Mall Suite 280 OR on Zoom: bostonu.zoom.us/meet...
#brainhealth #brainresearch #rehabilitation #aphasia 🧠
Thanks for sharing, Claudia!
7/ In sum, this study underscores the importance of individual-level functional localization in characterizing age-related differences among adjacent networks and highlights the need for more nuanced models of brain aging that consider the distinct trajectories of brain networks.
6/ These findings challenge the idea of uniform brain aging, showing different networks age at different rates. Functions that decline with age show neural changes, while functions that are well preserved (e.g., language comprehension) show stable neural activity patterns.
5/ In addition, the language-selective and MD networks remain robustly dissociated with age, maintain strong within-network synchronization and minimal between-network synchronization, and do not show increased overlap in their activation patterns.
4/ In contrast, the language network remained remarkably stable in older adults. It responded to language as strongly and selectively as in younger adults, with no changes in lateralization or internal synchronization.
3/ Results showed significant changes in the MD network: weaker activation during a working memory task and reduced within-network synchronization, consistent with the decline in executive functions in older adults.
2/ Using precision fMRI and robust localizers, we compared two key networks in young adults (YA, n=470, 19-39 years old; average=27.8) and two cohorts of older adults (OA, n=77, age range: 40-80; average=57.9): the MD network & the language-selective network.
1/ In this study, we show #aging affects brain networks differently. While the Multiple Demand (MD) network, supporting executive functions, shows age-related decline, the language network (which supports comprehension and production) remains stable. Let’s dive into the findings! 🧠👇
[NEW PREPRINT!] The language network ages well!
biorxiv.org/cgi/content/...
Co-led with Niharika Jhingan, in collaboration with @evfedorenko.bsky.social, @swathikiran.bsky.social, Maria Varkanitsa, @ryskin.bsky.social & Idan Blank.
🧵 Details 👇
Thanks @claudiapenaloza.bsky.social !
Looking forward to interacting with scientists and clinicians here! It was great to reconnect with and meet new colleagues at SNL in Marseille! Let’s keep the fun and inspiring discussions going here 🧠🙌
First post here 🚀 after the first day of #SNL2023 in Marseille! A dynamic blend of honoring the past’s breakthroughs that led us to better understand the neural basis of language processing + forging a visionary path forward inspired by different methods (e.g, LLM) + 🥐. Excited for what's next!