Anne Billot, PhD, @annebillot.bsky.social
@Annel3illoT
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Postdoctoral Researcher, Harvard University/MGH Personalized rehab, NIBS & neuroplasticity in neurological and psychiatric disorders. 🇺🇸🇫🇷 (she/her)
Boston, MA
Joined April 2017
[NEW PREPRINT!] The language network ages well Co-led with Niharika Jhingan, in collaboration with @ev_fedorenko, @AphasiaLab, @MVarkanitsa, @raryksin, & Idan Blank. 🧵 Details 👇.
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RT @mcxfrank: We're still accepting apps for our research scientist position with LEVANTE (. This position is ideal….
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RT @OHBM_Trainees: OHBM Lunch with Mentors will be happening in Brisbane! Fill out the registration form below to enter a lottery draw. Reg….
www.ohbmtrainees.com
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RT @jchrispang: We are hiring!. @AurinaArn and I are jointly looking for a talented Postdoc in Systems and Comp Neuro at Monash (AUS). If….
careers.pageuppeople.com
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At #CNS2025 and interested in 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! 🧠.Also relevant for anyone doing TMS targeting DLPFC in patients! 🧲⚡️.
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RT @BlancoElorrieta: JOB OPENING: I'm looking to hire a full-time RA! If you know someone who interested in language and the brain and woul….
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Check out this other great study from the Buckner Lab led by Max Elliott using multiple very short T1 to track brain aging at the individual level!.
We found a solution - cluster scanning. Utilizing the latest advances in scan acceleration we collected several short, 1-minute long T1s to drive to measurement error within individuals . and it worked!
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Great work from my colleague Jingnan Du et al.👇.
Our results suggest that there is an underlying, stable network architecture that is idiosyncratic to the individual and persists across task states. For further discussion on the implications for task design and analysis, read the preprint! (6/6).
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RT @jingnandu049: Our findings indicate that functional correlation matrices derived from task data are highly similar to those derived fro….
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RT @jingnandu049: [PREPRINT❗️❗️] Very excited to share our latest work on precision mapping of brain networks exclusively using task data!….
www.biorxiv.org
Precision mapping of brain networks within individuals has become a widely used tool that prevailingly relies on functional connectivity analysis of resting-state data. Here we explored whether...
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RT @max12elliott: New preprint!!!. We can reliably detect brain changes in individuals in just a year by collecting several rapid 1-minute….
www.medrxiv.org
Individual differences in human brain aging are difficult to estimate over short intervals because of measurement error. Using a cluster scanning approach that reduces error by densely repeating...
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RT @GrattonCaterina: [Taking a break from bouts of 🤯 😑😩 to be. ].Happy that our paper is now officially out in the latest issue of TICS:.h….
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Like @TheRealDrDukes says, let’s give each other extra kindness and care these days 🧡.
My mental health is not in a good place right now. But I did what I could for my students by extending their exams and encouraging them to take care of themselves 💞. This email gave me a little hope back that our small efforts can still help
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RT @BrielleStarkPhD: #aphasia colleagues, please pass this on! What an awesome experience to work with @yinaquique at UNM Speech & Hearing….
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RT @rafeequemavoor: Free scientific illustrations for biologists! 😍. @NIH has released a library of 500+ free scientific illustrations to….
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RT @OHBM_Trainees: 📣❗️Applications for the SP-SIG’s Mentoring and Fellowship programmes close on Thursday 31st October - apply now! ❗️📣.
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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.
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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.
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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.
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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.
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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.
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