Tessera

New paper in Nature Aging led by PhD student Lina Lu! We mapped how APOE ε4 and APOE ε2 shape molecular changes in blood and cerebrospinal fluid across Alzheimer’s disease. www.nature.com/articles/s43...

Proteomic signatures of the APOE ε4 and APOE ε2 genetic variants and Alzheimer’s disease - Nature Aging

Apolipoprotein E (APOE) is the strongest genetic influence in Alzheimer’s disease (AD). Compared to the most frequent allele, ε3, the ε4 allele increases AD risk, and the ε2 allele is protective. Here...

nature.com

Lijun An @anlijuncn.bsky.social · 3mo ago

If you want to learn proteomics signatures of APOE genetic variants on a massive sample from multi-chort, you should not miss this tour de force! Huge congrats to Lu Lina and Niklas!

@biofinder.bsky.social · 3mo ago

New paper in Nature Aging led by PhD student Lina Lu! We mapped how APOE ε4 and APOE ε2 shape molecular changes in blood and cerebrospinal fluid across Alzheimer’s disease. www.nature.com/articles/s43...

Evan Gordon @gordonneuro.bsky.social · 4mo ago

I always assumed that brain function had to line up with cytoarchitectonics. It turns out I was wrong. Human cortex, especially PFC, is tiled by chains of functional patches that subdivide and interlink architectonic areas into parallel processing streams. www.biorxiv.org/content/10.6...

Healthcare in Europe @healthcare-europe.bsky.social · 4mo ago

Researchers led by Jacob Vogel and @anlijuncn.bsky.social from 🇸🇪 @lund-university.bsky.social, with @biofinder.bsky.social and @neuroproteome.bsky.social have developed an #AI model to detect different neurodegenerative diseases like #Alzheimers and Lewy body disease from a single blood sample.

New AI model detects multiple brain diseases from a single blood sample

The symptom profiles of different neurodegenerative diseases often overlap, and diagnosing age-related cognitive symptoms is complex. A patient may have multiple overlapping disease processes in the brain at the same time.

healthcare-in-europe.com

Thomas Yeo @bttyeo.bsky.social · 4mo ago

We develop a new TMS targeting algorithm and test it in an open label trial in a treatment-resistant depression population with high comorbidities. Preprints by @rubykong92.bsky.social Phern-Chern Tor 1. doi.org/10.1101/2025... 2. doi.org/10.64898/202... Our new approach ...

Jake Vogel @jwvogel.bsky.social · 4mo ago

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

Lijun An@anlijuncn.bsky.social · 4mo ago

Our work is now out in @natmed.nature.com www.nature.com/articles/s41... We developed a state-of-the-art AI-proteomics model for diagnosing multiple neurodegenerative diseases from blood plasma. ProtAIDe-Dx now outperformed both classic machine learning models and even foundation models like TabPFN!

Elvisha Dhamala @elvisha.bsky.social · 4mo ago

Our latest work looking at the neuroanatomical basis of impulsivity in youth is out now in Molecular Psychiatry!

Neuroanatomy reflects individual variability in impulsivity in youth - Molecular Psychiatry

Molecular Psychiatry - Neuroanatomy reflects individual variability in impulsivity in youth

nature.com

Lijun An @anlijuncn.bsky.social · 4mo ago

Our work is now out in @natmed.nature.com www.nature.com/articles/s41... We developed a state-of-the-art AI-proteomics model for diagnosing multiple neurodegenerative diseases from blood plasma. ProtAIDe-Dx now outperformed both classic machine learning models and even foundation models like TabPFN!

Lijun An@anlijuncn.bsky.social · last yr.

Can AI reveal risk & co-pathology of multiple neurodegenerative diseases from a single blood sample? We explored AI-based diagnostic power on high rank plasma proteomics (N=17,170). www.medrxiv.org/content/10.1... #neuroskyence #neurosky #Alzheimer #compneuro #AI #datascience #neurology

Mark A. Hanson @hansonmark.bsky.social · 9mo ago

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/...

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.

Thomas Yeo @bttyeo.bsky.social · 2y ago

🚨 Predicting Alzheimer's Progression 🚨 A thread 🧵 1/ Accurate prediction of Alzheimer’s progression is critical for early intervention. How can we make predictions more precise and generalizable? 🧠✨ 📝 Read the preprint led by @chen-zhang.bsky.social : doi.org/10.1101/2024...

Thomas Yeo @bttyeo.bsky.social · 9mo ago

Preprint is now published! doi.org/10.1002/hbm.... Thanks to co-authors @chen-zhang.bsky.social @anlijuncn.bsky.social @csabaorban.bsky.social

Cross‐Dataset Evaluation of Dementia Longitudinal Progression Prediction Models

We introduced a simplified variant of FROG, the winning algorithm of the TADPOLE challenge, for predicting dementia progression using multimodal longitudinal data. Combining the longitudinal-to-cross...

doi.org

Thomas Yeo@bttyeo.bsky.social · 2y ago

🚨 Predicting Alzheimer's Progression 🚨 A thread 🧵 1/ Accurate prediction of Alzheimer’s progression is critical for early intervention. How can we make predictions more precise and generalizable? 🧠✨ 📝 Read the preprint led by @chen-zhang.bsky.social : doi.org/10.1101/2024...

Lijun An @anlijuncn.bsky.social · last yr.

Check out lab's latest preprint for MRI super resolution!!!

Jake Vogel@jwvogel.bsky.social · last yr.

‼️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

Jake Vogel @jwvogel.bsky.social · last yr.

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!👇

Lijun An@anlijuncn.bsky.social · last yr.

Can AI reveal risk & co-pathology of multiple neurodegenerative diseases from a single blood sample? We explored AI-based diagnostic power on high rank plasma proteomics (N=17,170). www.medrxiv.org/content/10.1... #neuroskyence #neurosky #Alzheimer #compneuro #AI #datascience #neurology

Thomas Yeo @bttyeo.bsky.social · last yr.

1/11 Excited to share our @Naturestudy led by @leonooi.bsky.social @csabaorban.bsky.social @shaoshiz.bsky.social AI performance is known to scale with logarithm of sample size (Kaplan 2020), but in many domains, sample size can be # participants or # measurements... doi.org/10.1038/s415...

Lijun An @anlijuncn.bsky.social · last yr.

Can AI reveal risk & co-pathology of multiple neurodegenerative diseases from a single blood sample? We explored AI-based diagnostic power on high rank plasma proteomics (N=17,170). www.medrxiv.org/content/10.1... #neuroskyence #neurosky #Alzheimer #compneuro #AI #datascience #neurology

Benchmarking the AI-based diagnostic potential of plasma proteomics for neurodegenerative disease in 17,170 people

Co-pathology is a common feature of neurodegenerative diseases that complicates diagnosis, treatment and clinical management. However, sensitive, specific and scalable biomarkers for in vivo pathologi...

medrxiv.org

Lijun An @anlijuncn.bsky.social · last yr.

Check out Xiaoyu’s fantastic work!!

Yu Xiao (肖燏)@xiaoyucaly.bsky.social · last yr.

🧠New preprint! What drives tau, the pathological protein in AD, to spread? We found that WHERE tau appears and HOW MUCH accumulates are governed by different mechanisms. Check it out: www.biorxiv.org/content/10.1...  #MedSky #neuroskyence #neurosky #alzsky #compneuro #MRI #neuroimaging #neurology

Yu Xiao (肖燏)@xiaoyucaly.bsky.social · last yr.

🧵15/ Huge thanks to our amazing team and coauthors! Endless thanks to @jwvogel.bsky.social for guiding and supporting this work from day one. To our amazing team DeMON lab, especially @anlijuncn.bsky.social for enormous support.

Shaoshi Zhang @shaoshiz.bsky.social · last yr.

Check our latest preprint led by the amazing @tianchu.bsky.social and @tianfang.bsky.social where we speed up the tedious parameter optimization process for biophysical modelling

Thomas Yeo@bttyeo.bsky.social · last yr.

While the world burns, we cook up a new preprint! doi.org/10.1101/2025... Biophysical modeling is a key tool to derive mechanistic insights into the brain. These models are governed by biologically meaningful parameters (unlike artificial neural networks), but the dirty secret ... 1/N

Lianglong Sun @longonga.bsky.social · last yr.

Happy to share that our article “Human lifespan changes in the brain’s functional connectome” is now published online at Nature Neuroscience @natureneuro.bsky.social ! Many thanks to all collaborators & data contributors, and the editor team & reviewers! www.nature.com/articles/s41...

Human lifespan changes in the brain’s functional connectome - Nature Neuroscience

Sun et al. report human lifespan changes in the brain’s functional connectome in 33,250 individuals, which highlights critical growth milestones and distinct maturation patterns and offers a normative...

nature.com

Thomas Yeo @bttyeo.bsky.social · last yr.

While the world burns, we cook up a new preprint! doi.org/10.1101/2025... Biophysical modeling is a key tool to derive mechanistic insights into the brain. These models are governed by biologically meaningful parameters (unlike artificial neural networks), but the dirty secret ... 1/N

Thomas Yeo @bttyeo.bsky.social · last yr.

Updated preprint for those who might be interested: doi.org/10.1101/2024...

Longer scans boost prediction and cut costs in brain-wide association studies

A pervasive dilemma in brain-wide association studies (BWAS) is whether to prioritize functional MRI (fMRI) scan time or sample size. We derive a theoretical model showing that individual-level phenot...

doi.org

Thomas Yeo@bttyeo.bsky.social · 2y ago

Thanks to everyone's feedback, we have updated our calculator to optimize sample size N & scan time T for fMRI studies: leonoqr.github.io/ORSP_Calcula... The first new feature is that users can explore how different N & T leads to different accuracy, e.g., N=1000 & T=30min => 81% max accuracy. 🧵

Lijun An @anlijuncn.bsky.social · last yr.

Excellent work by Ruby!!

Thomas Yeo@bttyeo.bsky.social · last yr.

It's finally here! Use the Network Correspondence Toolbox to help contextualize your neuroimaging findings 🧠

Hu Chuan-Peng @hcp4715.bsky.social · 2y ago

a cool new study "established in India & Tanzania, with appropriate training, structured teams, & daily automated analysis & feedback, non-specialists can reliably collect EEG data alongside various survey & assessments w/ consistently high throughput & quality. " www.biorxiv.org/content/10.1...

EEG data quality in large scale field studies in India and Tanzania

There is a growing imperative to understand the neurophysiological impact of our rapidly changing and diverse technological, social, chemical, and physical environments. To untangle the multidimension...

biorxiv.org

Emily Finn @esfinn.bsky.social · 2y ago

This paper sets up a bit of a straw man in that I don't think most people who use movies and stories as fMRI stimuli assume that all movies will (or should) evoke the same response. In a naturalistic neuroimaging expt, the movie *is* the task...

Between-movie variability severely limits generalizability of “naturalistic” neuroimaging

“Naturalistic imaging” paradigms, where participants watch movies during fMRI, have gained popularity over the past two decades. Many movie-watching studies measure inter-subject correlation (ISC), wh...

biorxiv.org

Thomas Yeo @bttyeo.bsky.social · 2y ago

🚨 Brain Age vs Direct Models in Alzheimer’s disease (AD) 🚨 A thread 🧵 1/ Brain age is a powerful indicator of general brain health, trained on massive datasets. But does this translate to better prediction for specific outcomes, like AD? Preprint by @twktan.bsky.social : doi.org/10.1101/2024...