Tomo Hiroyasu @HTechLab

Congrats to Longzhou @xlzhope on this exciting new work: "Long-range white-matter pathways enable efficient spontaneous neural activity propagation in the human brain", now out in J. Neurosci. @SfNJournals: jneurosci.org/content/early/…

SfN Journals @SfNJournals

a day ago

#JNeurosci: Findings from @xlzhope, @zaixucui et al. provide insight into how the brain supports efficient large-scale communication and link brain structure to fast neural dynamics. doi.org/10.1523/JNEURO…

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New article in @PNASNews: We all know that ChatGPT loves to delve, bolster, leverage, encompass, showcase, underscore, et cetera. I analyzed full text of 7.3 million journal articles published 2020-2025, hunting for 228 words that spiked after ChatGPT launched in late 2022.

[Aging] Akbaş et al.: "The brain-heart interconnection may alter through aging and cognitive impairment." doi.org/10.1016/j.clin…

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Minogue & Zouridakis et al. studied the brains of people with dementia and FTLD-PSP or FTLD-CBD. They found that PSP and CBD show distinct patterns of selective vulnerability, with tau pathology predominantly affecting glia in PSP and neurons in CBD. shorturl.at/WPMzh

Don't miss our work presented at #ICRA2026 in Vienna 🇦🇹 📅 June 4, 12:00-12:10, Hall A2 "Congestion Mitigation Path Planning for Large-Scale Multi-Agent Navigation in Dense Environments" (RA-L) T Kato, KO, Y Sasaki & N Yokomachi arxiv.org/abs/2508.05253

Over at @Neuro_Skeptic, there's details of a "fun" new paper that looks at the changing landscape of #neuroimaging research. Check it out here: bit.ly/2NmCtuA #neuroscience

The dynamic epigenetic landscape of the retina and the accompanying cloud based data resource @StJude pecan.stjude.org/#/proteinpaint…

New paper in Imaging Neuroscience by SungJun Cho, Mark W. Woolrich, et al: Modelling discrete states and long-term dynamics in functional brain networks doi.org/10.1162/IMAG.a…

"La codificación predictiva plantea el autismo y la esquizofrenia como desequilibrios entre el procesamiento descendente (top-down) y ascendente (bottom-up). El autismo podría favorecer la entrada sensorial ascendente, lo que llevaría a una mayor atención a los detalles y a sobrecarga sensorial. La esquizofrenia podría favorecer las creencias descendentes, lo que conduciría a alucinaciones y delirios" *Otra vez, predictive coding en acción!

Mitchell B Slapik @mslapik

2 months ago

Predictive coding frames autism and schizophrenia as imbalances in top-down vs. bottom-up processing. Autism may favor bottom-up sensory input, leading to a focus on details and sensory overload. Schizophrenia may favor top-down beliefs, leading to hallucinations and delusions.

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The mclust R package is a widely used tool for model-based clustering, classification, and density estimation. It fits Gaussian finite mixture models and can automatically select the best model and number of clusters using the Bayesian Information Criterion (BIC). It also supports hierarchical model-based clustering and provides tools for classification of new observations. ✔️ Reveals patterns in complex data sets ✔️ Produces probabilistic cluster assignments for improved interpretation ✔️ Provides uncertainty measures to assess classification reliability ✔️ Offers rich visualization options for exploring clustering structure The visualizations below are from the mclust package website and show example plots generated with mclust. For more details, visit the package website: cran.r-project.org/web/packages/m… Want more practical tips and examples on Statistics, Data Science, R, and Python? Subscribe to my email newsletter for exclusive insights. For more information, visit this link: statisticsglobe.com/newsletter #Rpackage #database #RStats #R #DataViz #statisticians #DataVisualization

New paper in Imaging Neuroscience by Aliza Brzezinski-Rittner, Mahsa Dadar, et al: Intracranial volume: To adjust or not to adjust? It is not a matter of if, but how doi.org/10.1162/IMAG.a…

Everyone is fine-tuning LLMs. Almost nobody understands what is actually being updated inside the model. Here are 5 techniques that change how you think about model adaptation, and what each one is actually doing to the weights: 1./ LoRA - Learn the update, not the weights The pretrained weight W is frozen. Completely untouched. Instead of updating W directly, two small matrices are trained => A ∈ ℝʳˣᵈ and B ∈ ℝᵈˣʳ, where r ≪ d The weight update is: ΔW = BA Effective weight: W' = W + BA The entire adaptation happens in a tiny low-rank space. W never changes. 2./ LoRA-FA - What if we freeze even more? Same structure as LoRA. One change. A is frozen alongside W. Only B is trained. Effective weight: W' = W + BA (A is fixed) Half the trainable matrices of LoRA. Same core idea. Fewer parameters. 3./ VeRA - What if the matrices don't need to be learned at all? This is where it gets interesting. A and B are both frozen, and randomly initialized. What gets trained are just two tiny scaling vectors => b ∈ ℝʳ and d ∈ ℝʳ Instead of learning the low-rank matrices themselves, VeRA keeps them frozen and learns small scaling vectors that modulate their contribution. Initialization => b = 0, d = 1 You're not learning matrices. You're learning how to scale them. One of the most parameter-efficient techniques on this list. 4./ Delta-LoRA - What if W itself learns from the low-rank updates? This one is fundamentally different. Unlike standard LoRA, the base weight W is not fully frozen. It is updated through low-rank delta propagation at every step => W^(t+1) = W^t + c(B_(t+1)A_(t+1) − B_t A_t) Where c is a scaling factor. A and B are trainable. W evolves, but guided entirely by low-rank changes. 5./ LoRA+ - Same structure. Smarter learning rates. Identical to LoRA, freeze W, train A and B. One change => B is assigned a larger learning rate than A. η_B > η_A A ← A − η_A · ∂J/∂A B ← B − η_B · ∂J/∂B A small optimization change that can make LoRA training more effective. The core idea running through all five: You do not always need full fine-tuning to adapt a model. LoRA updates two matrices. LoRA-FA updates one. LoRA+ updates two at different speeds. Delta-LoRA lets W evolve - guided by low-rank deltas. VeRA updates two vectors. Same goal. Five different answers to the same question: => What is the minimum we actually need to learn? That is the core idea behind parameter-efficient fine-tuning. And now you know what is actually happening inside the model.

In this study, Martins and colleagues provide an integrated characterization of the human brain histaminergic system, using transcriptomic and neuroimaging datasets to describe the molecular organization of this system and its relevance to #mentalhealth. nature.com/articles/s4422…

New paper in Imaging Neuroscience by Dalin Yang, Adam T. Eggebrecht, et al: Mapping brain function underlying naturalistic motor observation and imitation using high-density diffuse optical tomography doi.org/10.1162/IMAG.a…

Benchmarking methods for mapping functional connectivity in the brain | doi.org/10.1038/s41592… What is the best FC metric? led by @liuzhenqi0303 in @naturemethods ⤵️

Mapping cerebral blood perfusion and its links to multi-scale brain organization across the human lifespan | doi.org/10.1371/journa… How does blood perfusion map onto canonical features of brain structure and function? @AsaFarahani investigates @PLOSBiology ⤵️

A new atlas reveals that functional connectivity patterns in the human neocortex shift dramatically from birth through old age, organizing brain regions along three dominant axes: sensory-to-association, visual-to-somatosensory, and modulation-to-representation. nature.com/articles/s4158…

政府が始めたAI for Scienceはすでに時代遅れ...

石部統久 @mototchen

2 weeks ago

@ykfrs1217 Natureの『AIと科学』３論文が言っていること・言っていないこと 論文を読むと印象が少し変わるかもしれません。

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Many of you asked how we create those pretty whole-brain plots with the cortical outline. We just made it available as a Python package: pni-lab.github.io/quickbrain/

Benchmarking methods for mapping functional connectivity in the brain | doi.org/10.1101/2024.0… What is the best FC metric? Led by @liuzhenqi0303 avec @loopyluppi @JustineYHansen @yetianmed @AndrewZalesky @bttyeo @bendfulcher ⤵️