Amelie Metz

Neuroscience PhD Candidate @mcgillu 🧠 | enthusiastic about dementia and neuroimaging, speech and language pathology, yoga, cats, and coffee | 🥨@🇨🇦

[Mahsa Dadar @DadarMahsa]

Job Alert! 📢📢📢 We're looking for a motivated RA with experience in histology/IHC processing: https://t.co/UowGrNHgKF

[Mahsa Dadar @DadarMahsa]

We are excited to launch The Data Quartet Challenge, recognizing student contributions to projects that integrate data from at least 2 of 4 FRQ supported Quebec Alzheimer’s and Aging cohorts (PREVENT-AD, CIMA-Q, NuAge, TRIAD), with 3 prizes of 5000$
https://t.co/jUYizVxi2u

[Ting Qiu @Ting12460123]

🧠 Villeneuve Lab at #AAIC2025 July 26-31
Amyloid, tau, neuroimaging, fluid biomarkers, and clinical trials — we’re bringing it all!
Come see our talks + posters at AIC and AAIC! @alzassociation ⬇️

[Amelie Metz @ameliebuh]

Hey #ADPD2025 , I'm happy to present our work on characterizing brain atrophy and cerebrovascular pathology in Frontotemporal Dementia subtypes!

Tomorrow, April 5, 9:40am, Hall F2, session: Mechanisms and Clinical Trials in FTD, ALS, and Neurodegeneration 02 @adpdnet

[@BrainComms @braincomms]

Metz et al. report that a study combining MRI brain atrophy and clinical data using machine learning achieved 89% accuracy in classifying frontotemporal dementia subtypes. Read at:https://t.co/XKXCFJRJrA @DadarMahsa @YasharZeighami @sylv_villeneuve @sducharme66

[Amelie Metz @ameliebuh]

10/ Big THANK YOU to my advisors
@DadarMahsa and @sylv_villeneuve and everyone who contributed to this work (aka @YasharZeighami and @sducharme66 ) for the ideas, feedback, and encouragement that made this possible! Make sure to check out the full paper 😄

[Amelie Metz @ameliebuh]

Excited to share that the first project of my PhD was published in 🧠 @braincomms 🧠 !! We showed that machine learning can effectively differentiate frontotemporal dementia subtypes combining brain atrophy and cognition 🎉
https://t.co/M7hydjtfUZ (1/10)

[Amelie Metz @ameliebuh]

9/ TAKEAWAY
🧠We can use the mapping between neurodegeneration and cognitive decline of FTD subtypes for phenotyping patients according to clinical variants.
🧠The inclusion of DBM measures adds to the classification precision in the absence of extensive clinical testing.

[Mahsa Dadar @DadarMahsa]

Frontotemporal dementia subtyping using machine learning, multivariate statistics and neuroimaging https://t.co/zSqi3hDs3x

[Aliza Brzezinski 🟣 @AlBRzez]

It’s out! 🎉 Check our paper “Disentangling the effect of sex from brain size on brain organization and cognitive functioning” 🧠, where we explored sex differences in the brain while removing the effect of brain size differences. 1/10

[Amelie Metz @ameliebuh]

@DouglasResearch @FdnDouglas @ciusss_ouestmtl Congrats @AlBRzez 🎉

[Amelie Metz @ameliebuh]

In other exciting news, our pre-print is out too!!

https://t.co/TVDYnVBKrs

[Amelie Metz @ameliebuh]

Hey #OHBM2024!

Are you interested in FTD and how we can use MRI/DBM and multivariate statistics for subtype classification?

Come chat with me at poster 1464 Wednesday and Thursday 😋