New publication out in Brain! 📢We developed a data-driven multimodal biomarker framework to characterize a memory clinic cohort based on the presence, extent, and sequence of common pathologies. This framework may support diagnosis and trial selection.
🔗https://doi.org/10.1093/brain/awaf411
Incredibly excited that the lab's first papers is now published! We use AI to simultaneously predict multiple neurodegenerative disease diagnoses from plasma proteomics. Congrats @anlijuncn.bsky.social !
#MedSky #neuroskyence #neurosky #alzsky #compneuro #ai #datascience #bioinformatics #neurology
Going to ADPD? Come see our work! We would love to share it with you and chat about it if you are interested. See below for a schedule of our sessions, which cover fMRI, proteomics, transcriptomics and AI, led by brilliant lab members @anlijuncn.bsky.social @jorittmo.bsky.social and others
I couldn't find a tool to plot different #neuroimaging data in one consistent style, so I made one! Meet yabplot (yet another brain plot) - a #Python package for (sub)cortex & tracts.🧠
- Simple API
- Built-in atlases
- Custom atlas support
🔗 github.com/teanijarv/ya... (drop a ⭐️!)
A table showing profit margins of major publishers. A snippet of text related to this table is below. 1. The four-fold drain 1.1 Money Currently, academic publishing is dominated by profit-oriented, multinational companies for whom scientific knowledge is a commodity to be sold back to the academic community who created it. The dominant four are Elsevier, Springer Nature, Wiley and Taylor & Francis, which collectively generated over US$7.1 billion in revenue from journal publishing in 2024 alone, and over US$12 billion in profits between 2019 and 2024 (Table 1A). Their profit margins have always been over 30% in the last five years, and for the largest publisher (Elsevier) always over 37%. Against many comparators, across many sectors, scientific publishing is one of the most consistently profitable industries (Table S1). These financial arrangements make a substantial difference to science budgets. In 2024, 46% of Elsevier revenues and 53% of Taylor & Francis revenues were generated in North America, meaning that North American researchers were charged over US$2.27 billion by just two for-profit publishers. The Canadian research councils and the US National Science Foundation were allocated US$9.3 billion in that year.
A figure detailing the drain on researcher time. 1. The four-fold drain 1.2 Time The number of papers published each year is growing faster than the scientific workforce, with the number of papers per researcher almost doubling between 1996 and 2022 (Figure 1A). This reflects the fact that publishers’ commercial desire to publish (sell) more material has aligned well with the competitive prestige culture in which publications help secure jobs, grants, promotions, and awards. To the extent that this growth is driven by a pressure for profit, rather than scholarly imperatives, it distorts the way researchers spend their time. The publishing system depends on unpaid reviewer labour, estimated to be over 130 million unpaid hours annually in 2020 alone (9). Researchers have complained about the demands of peer-review for decades, but the scale of the problem is now worse, with editors reporting widespread difficulties recruiting reviewers. The growth in publications involves not only the authors’ time, but that of academic editors and reviewers who are dealing with so many review demands. Even more seriously, the imperative to produce ever more articles reshapes the nature of scientific inquiry. Evidence across multiple fields shows that more papers result in ‘ossification’, not new ideas (10). It may seem paradoxical that more papers can slow progress until one considers how it affects researchers’ time. While rewards remain tied to volume, prestige, and impact of publications, researchers will be nudged away from riskier, local, interdisciplinary, and long-term work. The result is a treadmill of constant activity with limited progress whereas core scholarly practices – such as reading, reflecting and engaging with others’ contributions – is de-prioritized. What looks like productivity often masks intellectual exhaustion built on a demoralizing, narrowing scientific vision.
A table of profit margins across industries. The section of text related to this table is below: 1. The four-fold drain 1.1 Money Currently, academic publishing is dominated by profit-oriented, multinational companies for whom scientific knowledge is a commodity to be sold back to the academic community who created it. The dominant four are Elsevier, Springer Nature, Wiley and Taylor & Francis, which collectively generated over US$7.1 billion in revenue from journal publishing in 2024 alone, and over US$12 billion in profits between 2019 and 2024 (Table 1A). Their profit margins have always been over 30% in the last five years, and for the largest publisher (Elsevier) always over 37%. Against many comparators, across many sectors, scientific publishing is one of the most consistently profitable industries (Table S1). These financial arrangements make a substantial difference to science budgets. In 2024, 46% of Elsevier revenues and 53% of Taylor & Francis revenues were generated in North America, meaning that North American researchers were charged over US$2.27 billion by just two for-profit publishers. The Canadian research councils and the US National Science Foundation were allocated US$9.3 billion in that year.
The costs of inaction are plain: wasted public funds, lost researcher time, compromised scientific integrity and eroded public trust. Today, the system rewards commercial publishers first, and science second. Without bold action from the funders we risk continuing to pour resources into a system that prioritizes profit over the advancement of scientific knowledge.
We wrote the Strain on scientific publishing to highlight the problems of time & trust. With a fantastic group of co-authors, we present The Drain of Scientific Publishing:
a 🧵 1/n
Drain: arxiv.org/abs/2511.04820
Strain: direct.mit.edu/qss/article/...
Oligopoly: direct.mit.edu/qss/article/...
🚨New paper alert! Our study led by @teanijarv.bsky.social
investigated why some individuals with Alzheimer’s disease (AD) develop hemispheric asymmetry in tau pathology and what drives this phenomenon.
Out now in Nature Communications! 🔗Full article: doi.org/10.1038/s414...
A thread🧵👇
Really compelling evidence in humans for network propagation hypothesis of tau spread: individualized connectivity helps resolve individualized tau patterns.
#MedSky #neuroskyence #neurosky #alzsky #compneuro #ai #datascience #bioinformatics #neurology
I'm thrilled that our paper "Personalised regional modelling predicts tau progression in the human brain" is finally published in PLoS Biology!
Here's a short thread about the main findings of the paper...
🧵How to write and manage your first research budgets
The point of funding is to convert it into quality research. A well-spent research budget should fund the idea it was raised on, plus revision experiments, plus preliminary data for the next grant. So you need to spend, while avoiding waste.
Sure -- i guess the idea/hope here is that the information itself might actually be in the 3T image, but in such a manner that is not perceptible by humans, or quantifiable using current approaches. We've already seen some evidence of that, but we def need more clinical data to figure it out
Many included participants had fairly extensive brain atrophy due to old age and/or neurodegenerative disease. Or did you mean something else?
We need higher resolution brain scans for people with #MyalgicEncephalomyelitis. This is an exciting development that could lead to better clinical understanding for many of us. #Neuroskyence #medsky
If clinically validated, it could democratize access to advanced imaging, improve neuro-diagnostic precision, and streamline both research and patient care without requiring costly hardware upgrades.
A win for everyone
Led by brilliant MSc student Malo Gicquel and co-supervised by equally brilliant Gabrielle Flood. We also got great contributions from @anikawuestefeld.bsky.social @xiaoyucaly.bsky.social @rikossenkoppele.bsky.social @lemwisse.bsky.social @biofinder.bsky.social @davidberron.bsky.social
and others!
We are working on assessing utility of this 3T->7T model in clinical situations, improving it, making it more generalizable, and extending it to other sequences. Please get in contact if you have some paired 3T-7T data, even if its a small dataset!
This is super important — it means increasing the quality of the image is not just eye candy. It might actually improve some of the downstream tasks we use the MRI for, like segmentation and visual reads.
Most impressively, when we ran automated amygdala segmentation on the real 3T and synthetic 7T, the segmentations from the synthetic 7T better matched manual segmentations of the amygdala!!
Our models outperform existing models on this task in terms of traditional metrics. However, a set of four blinded radiologists and MRI professionals also subjectively rated the synthetic images to be of higher visual quality than the images they were synthesized from.
Shockingly, when applied to unseen 3T scans, our models produce synthetic scans that resemble the original images, but sharper and with better contrast, while avoiding common artifacts seen on 7T scans.
We exploited a valuable dataset of 172 cognitively impaired and unimpaired older people from
@biofinder.bsky.social with paired 3T and a 7T T1-weighted scans. To this dataset, we apply our own specialized U-Net and GAN U-Net model, as well as some previously described models.
To improve accessibility of these high quality scans, we look to synthetic AI “super-resolution”, which has already been successful in brining lower field (0.5T - 1.5T) to 3T resolution.
7 Tesla (7T) MRI is important for studying fine anatomical morphology, and has found important applications in clinical research centers focusing on epilepsy and MS. But, its ceiling is underexplored; <150 7T scanners exist worldwide!
7T world map: google.com/maps/d/u/0/v...
‼️NEW PREPRINT‼️
What if you could take a normal 3T T1w MRI and make it look like it was acquired from a 7T scanner?
That's exactly what we do using AI in our new preprint!
Link: arxiv.org/abs/2507.13782
#neuroskyence #neurosky #compneuro #AI #datascience #neurology #mrisky #neuroimaging
Incredibly excited for this new work from our lab. We test the potential of AI-based neurodegenerative disease diagnostics using plasma proteomics data from n>17,000 people, led by the brilliant and indefatigable @anlijuncn.bsky.social Check it out!👇
Have recent changes led to uncertainty in your future scientific career?
Wonder it's like in Australia?
Good news!
Monash's is seeking to hire talented EMCRs from other countries.
Come join a wonderful community of brain mappers & modellers!
www.monash.edu/research/eme...
‼️Preprint alert‼️
Star PhD student @jorittmo.bsky.social uses functional gradients as a framework to study longitudinal brain functional reorganization in aging and AD.
Check out the thread
👇🏼
#MedSky #neuroskyence #neurosky #alzsky #compneuro #MRI #neuroimaging
#neurology
My med school textbook says stimulants like Ritalin treat hyperactivity by “stimulating” the brain’s attention and cognitive control systems. We studied children taking stimulants in the ABCD Study, and the largest differences were actually in arousal and reward networks! Check out our preprint!
