Baptiste Couvy-Duchesne

What other criteria should we use to benchmark processing? Elise also quantified the carbon footprint associated with computation in her PhD thesis.

Still, we hope our results can help researchers make informed decisions when selecting a processing. And that it can encourage others to evaluate other image processing options (e.g., different software versions, non-default settings).

Many questions remain. Would we see the same results in other age groups (e.g., ABCD data) or in other databases (we used @ukbiobank.bsky.social)?

To make things even more complicated, it may also depend on the trait you are interested in. On average FSL VBM does a good job, but for some traits like Alzheimer's disease or maternal smoking, FreeSurfer may yield more significant associations.

We also demonstrated that each processing step captures a unique signal. So we cannot claim that a single processing (among the ones we considered) is the best. Some are better, but none is perfect.

Elise's results also confirm that the choices of processing have important consequences on the results, which contributes to the reproducibility crisis. See results below for the same individuals and same trait (maternal smoking around birth - associated regions vary depending on processing)

Choice of Processing Pipelines for T1-Weighted Brain MRI Impacts Association and Prediction Analyses - PubMed The growing availability of large neuroimaging datasets, such as the UK Biobank, provides new opportunities to improve robustness and reproducibility in brain imaging research. However, little is known about the extent to which MRI processing pipelines influence results. Using 39,655 T1-weighted MRI …

Results (in brief) show that FSL VBM performs very well, capturing the most signal (morphometricity), yielding performant predictors, maximising power to detect associated brain regions, and yielding replicable results.
pubmed.ncbi.nlm.nih.gov/41163627/

I have always wondered if there is a best method/software to process T1w image (at a vertex/voxel wise level). Elise Delzant has some answers in her first published paper from her PhD. She compared FreeSurfer, CAT12 and FSL across several traits and analyses (association, prediction).

Post-doc job offer with Sonia Shah in Brisbane, working in statistical genetics within a great lab and ecosystem.
www.linkedin.com/jobs/view/41...

@institutducerveau.bsky.social

Lastly, we showed that the brain-based predictor of Alzheimer's could also predict early AD, mild cognitive impairment, cognition, tau levels or genetic risk. Our predictor is on par with some of the best ones that can predict progression to AD, which could help with early intervention

Interestingly, the brain regions associated with Alzheimer's, disease progression and cognition overlapped suggesting some of the same regions are implicated. Some regions stand out being associated with many traits.

We systematically attempted to replicate the results also showed the identified regions could help predict disease, progression and cognition in independent samples.

We have also estimated that there should be at least 5 times more regions that remain to be identified (difference between the morphometricity and variance currently explained by the identified regions). Even more data will be needed!

Even better with a GIF (showing the regions with reduced thickness associated with Alzheimer's across the left subcortical structures)! Made with our R package brainMapR. github.com/baptisteCD/b...

Our high-resolution approach makes it possible to identify sub-regions of the hippocampus (or of the amygdala) that are involved, which can orient research towards specific brain regions/networks or cell populations.

This is the largest study of its kind in terms of the number of participants (>9,000 subjects from 10 clinical cohorts+ the UK Biobank), and we have identified 103 grey matter regions associated with Alzheimer's, including some in ‘known’ regions, such as the hippocampus.

Grey‐Matter Structure Markers of Alzheimer's Disease, Alzheimer's Conversion, Functioning and Cognition: A Meta‐Analysis Across 11 Cohorts We meta-analysed results from 11 neuroimaging cohorts of elderly participants, to estimate the morphometricity (total association with brain measurements) of 17 traits of interest, and to identify th...

Our latest paper is out! A well powered high resolution mapping of the grey-matter regions associated with Alzheimer's, progression to Alzheimer's disease, cognition and functioning. @drbreaky.bsky.social @barkhof.bsky.social @michelleklupton.bsky.social

At this point, we do not know if the identified drugs or conditions directly cause neurodegenerative disorders or whether they are early symptoms of the disease or reflect more complex aetiology. However, the results could be useful for personalising and accelerating referrals to neurologists.

Likewise for medical conditions, which are associated with future risk of neurodegenerative disorders.

Here is a summary of the drug classes, whose frequency of use differs in individuals who are going to receive a neurodegenerative diagnosis within 5-10 years.

We focused on associations that were consistent across the different countries - hence, they cannot be attributed to specific health systems, bias in the data or the type of medical records (primary, hospital).

Using medical records from Australia, France, the UK and Sweden, we identified several drug usage, conditions and biomarkers associated up to 10 years before diagnoses.

Ten years preceding a diagnosis of neurodegenerative disease in Europe and Australia: medication use, health conditions, and biomarkers associated with Alzheimer's disease, Parkinson's disease, and am... Many studies have investigated early predictors for Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS). Howev…

Here is our latest publication (the first from our Lemerend consortium) that looks at early risk factors of neurodegenerative disorders.
Thank you to all the authors, and in particular the fantastic team of analysts!