Machine Learning and Mental Health Lab

If you’re a psychologist, psychiatrist or neuroscientist looking to learn about machine learning applications to brain disorders check out our new book! Written by experts in the field and with a tutorial in Python to get you started! elsevier.com/books/machine-…

Our review and meta-analysis on machine learning for predicting who will benefit from CBT has been just published in Clinical Psychology Review! @sandra___vieira @raquelg_18 sciencedirect.com/science/articl…

**New position** Our team is hiring a postdoc to work on a AI tool to detect brain-based disorders. If you're interested or have any questions please message me or the @mlmh_lab. Please share with anyone that may be interested! @KingsIoPPN @KingsJobs bit.ly/3mP0klT

The MLMH lab is hiring! We are looking for a postdoc interested in machine learning/deep learning applications to structural MRI in psychiatric and neurological disorders. Please rt. jobs.kcl.ac.uk/gb/en/job/0473…

If you're interested in machine learning and brain age check out our new paper led by Lea Baecker! It provides a state-of-the art review, introduces the main methods and discusses clinical applications @EBioMedicine @Don_Rafael @sandra___vieira @CScarpazza pubmed.ncbi.nlm.nih.gov/34614461/

Interested in brain age but not sure how to analyse your data? Our most recent paper in @OHBM tests different pipelines and provides recommendations! Check out the paper highlights by one of the study leads @Warvito (link to code included below!)

Our very own @sandra___vieira has been awarded the prestigious and highly competitive Sir Henry Wellcome Postdoctoral Fellowship from the @wellcometrust to develop predictive models in early psychosis. Congradualtions Sandra!🥳@KingsIoPPN @PSYSCANproject

I don't have a large twitter following, but some of you might be able to help spread the word of my experience getting the Pfizer Covid vaccine today.

The @PSYSCANproject is hiring! The data manager will have a key role in the project. Excellent opportunity to work in one of the largest and most exciting projects in psychosis. Pls RT jobs.kcl.ac.uk/gb/en/job/0127…

If you’re a psychologist, psychiatrist or neuroscientist looking to learn about machine learning applications to brain disorders check out our new book! Written by experts in the field and with a tutorial in Python to get you started! elsevier.com/books/machine-…

What a fantastic initiative form the @wellcometrust! First data challenge in mental health. We looking forward to knowing more about the challenge (and the prizes)!

Come and join us for 5-day winter school on modern statistical approaches to MRI data! Learn from world experts in lovely Padua and/or remotely (TBD) about MRI, fMRI, connectivty, machine learning and more! For more details see dpss.unipd.it/winter-school-… Pls RT

You can check out Neuroharmony at github.com/garciadias/Neu…

The final version of our new paper is now out @NeuroImage_EiC! Scanner Harmonization for unseen scanners. Well done @Don_Rafael! sciencedirect.com/science/articl…

PhD position in Oslo. Please share with interested Msc. students. jobbnorge.no/en/available-j…

1 week left to apply!

The Machine Learning Team at NIMH is hiring a machine learning research scientist: nih-fmrif.github.io/ml/index.html We help NIMH scientist use machine learning to solve research problems. We have amazing datasets, technical resources, and collaborators! Please RT or repost, thank you!

Several approaches have been developed to harmonise MRI data from different known scanners. But what about hamonising data from unknown and unseen scanners? Check-out our lab’s latest paper led by @Don_Rafael sciencedirect.com/science/articl…

Excited to advertise a 1 year Post-Doc position to develop markers of psychotic disorders from transcribed speech data. We'll be using Natural Language Processing & Network Science approaches. Please share/message me any qu.s! @turinginst @psychiatry_ucam jobs.cam.ac.uk/job/26048/

0/7 The preprint of our recent work on “Hierarchical Bayesian Regression for Multi-Site Normative Modeling of Neuroimaging Data” is available at arxiv.org/abs/2005.12055. #MICCAI2020 @amarquand @HesterHuijsdens @dinga92 @ThomasWolfers @MaartenMennes @oleaandreassen @larswestlye

6/7 Using a large dataset of 7499 participants aggregated across 33 scanners, we experimentally demonstrate the superiority of HBR in estimating the predictive posterior distribution compared to trivial pooling and ComBat harmonization.