Tootfinder

This article is a good example of why I’m fed up with the “if used responsibly” argument:
https://thejournal.com/articles/2025/08/07/the-brain-drain-how-overreliance-on-ai-may-erode-creativity-and-critical-thin…

BrainATCL: Adaptive Temporal Brain Connectivity Learning for Functional Link Prediction and Age Estimation
Yiran Huang, Amirhossein Nouranizadeh, Christine Ahrends, Mengjia Xu
https://arxiv.org/abs/2508.07106

BrainATCL: Adaptive Temporal Brain Connectivity Learning for Functional Link Prediction and Age Estimation
Functional Magnetic Resonance Imaging (fMRI) is an imaging technique widely used to study human brain activity. fMRI signals in areas across the brain transiently synchronise and desynchronise their activity in a highly structured manner, even when an individual is at rest. These functional connectivity dynamics may be related to behaviour and neuropsychiatric disease. To model these dynamics, temporal brain connectivity representations are essential, as they reflect evolving interactions betwe…

Modality-Agnostic Input Channels Enable Segmentation of Brain lesions in Multimodal MRI with Sequences Unavailable During Training
Anthony P. Addison, Felix Wagner, Wentian Xu, Natalie Voets, Konstantinos Kamnitsas
https://arxiv.org/abs/2509.09290

Modality-Agnostic Input Channels Enable Segmentation of Brain lesions in Multimodal MRI with Sequences Unavailable During Training
Segmentation models are important tools for the detection and analysis of lesions in brain MRI. Depending on the type of brain pathology that is imaged, MRI scanners can acquire multiple, different image modalities (contrasts). Most segmentation models for multimodal brain MRI are restricted to fixed modalities and cannot effectively process new ones at inference. Some models generalize to unseen modalities but may lose discriminative modality-specific information. This work aims to develop a m…

This article is a good example of why I’m fed up with the “if used responsibly” argument:
https://thejournal.com/articles/2025/08/07/the-brain-drain-how-overreliance-on-ai-may-erode-creativity-and-critical-thin…

This article is a good example of why I’m fed up with the “if used responsibly” argument:
https://thejournal.com/articles/2025/08/07/the-brain-drain-how-overreliance-on-ai-may-erode-creativity-and-critical-thin…

🧠 Brain-enriched microRNA shows promise for protecting neurons in depression
https://medicalxpress.com/news/2025-07-brain-enriched-microrna-neurons-depression.html

Brain-enriched microRNA shows promise for protecting neurons in depression
A Henan University of Science and Technology team reports that boosting miR-542-3p thwarts stress-related cell loss in the hippocampus and eases depressive-like behavior in mice.

Fears of brain drain grow as Trump cuts take a toll on UCLA
Research that was years in the making
— into diabetes, cancer, heart disease, strokes, PTSD and fighting antibiotic-resistant infections
— is on pause at UCLA, nearly a month after UCLA saw around $500 million in federal grants suspended.
While a federal judge ordered the restorationof 114 National Science Foundation grants in early August, grants from the National Institutes of Health remain suspended.
S…

Fears of brain drain grow as Trump cuts take a toll on UCLA
Several UCLA scientists tell LAist that their experiments on cancer, heart disease and other ailments are imperiled, but they’re more worried that young academics will abandon the field and that humanity will be worse off for it.

Validation of a CT-brain analysis tool for measuring global cortical atrophy in older patient cohorts
Sukhdeep Bal, Emma Colbourne, Jasmine Gan, Ludovica Griffanti, Taylor Hanayik, Nele Demeyere, Jim Davies, Sarah T Pendlebury, Mark Jenkinson
https://arxiv.org/abs/2509.08012

Validation of a CT-brain analysis tool for measuring global cortical atrophy in older patient cohorts
Quantification of brain atrophy currently requires visual rating scales which are time consuming and automated brain image analysis is warranted. We validated our automated deep learning (DL) tool measuring the Global Cerebral Atrophy (GCA) score against trained human raters, and associations with age and cognitive impairment, in representative older (>65 years) patients. CT-brain scans were obtained from patients in acute medicine (ORCHARD-EPR), acute stroke (OCS studies) and a legacy sample. …

LITcoder: A General-Purpose Library for Building and Comparing Encoding Models
Taha Binhuraib, Ruimin Gao, Anna A. Ivanova
https://arxiv.org/abs/2509.09152 https://

LITcoder: A General-Purpose Library for Building and Comparing Encoding Models
We introduce LITcoder, an open-source library for building and benchmarking neural encoding models. Designed as a flexible backend, LITcoder provides standardized tools for aligning continuous stimuli (e.g., text and speech) with brain data, transforming stimuli into representational features, mapping those features onto brain data, and evaluating the predictive performance of the resulting model on held-out data. The library implements a modular pipeline covering a wide array of methodological…

(2015) "Visual" cortex responds to spoken language in blind children https://www.jneurosci.org/content/35/33/11674 Now what will the "visual" cortex of blind children do once trained with live imagery (camera views) encoded in sound by The vOICe?

“Visual” Cortex Responds to Spoken Language in Blind Children
Plasticity in the visual cortex of blind individuals provides a rare window into the mechanisms of cortical specialization. In the absence of visual input, occipital (“visual”) brain regions respond to sound and spoken language. Here, we examined the time course and developmental mechanism of this plasticity in blind children. Nineteen blind and 40 sighted children and adolescents (4–17 years old) listened to stories and two auditory control conditions (unfamiliar foreign speech, and musi…

Computational modelling of Parkinson's disease: A multiscale approach with deep brain stimulation and stochastic noise
Aaron Herrera, Hina Shaheen
https://arxiv.org/abs/2509.08179

Computational modelling of Parkinson's disease: A multiscale approach with deep brain stimulation and stochastic noise
Multiscale modelling presents a multifaceted perspective into understanding the mechanisms of the brain and how neurodegenerative disorders like Parkinson's disease (PD) manifest and evolve over time. In this study, we propose a novel co-simulation multiscale approach that unifies both micro- and macroscales to more rigorously capture brain dynamics. The presented design considers the electrodiffusive activity across the brain and in the network defined by the cortex, basal ganglia, and thalamu…

CognitiveArm: Enabling Real-Time EEG-Controlled Prosthetic Arm Using Embodied Machine Learning
Abdul Basit, Maha Nawaz, Saim Rehman, Muhammad Shafique
https://arxiv.org/abs/2508.07731

CognitiveArm: Enabling Real-Time EEG-Controlled Prosthetic Arm Using Embodied Machine Learning
Efficient control of prosthetic limbs via non-invasive brain-computer interfaces (BCIs) requires advanced EEG processing, including pre-filtering, feature extraction, and action prediction, performed in real time on edge AI hardware. Achieving this on resource-constrained devices presents challenges in balancing model complexity, computational efficiency, and latency. We present CognitiveArm, an EEG-driven, brain-controlled prosthetic system implemented on embedded AI hardware, achieving real-t…

Having just one alcoholic drink every day is linked to brain damage - Earth.com
https://www.earth.com/news/having-just-one-alcoholic-drink-every-day-linked-to-damaged-brain-vessels/

Functional Connectivity Graph Neural Networks
Yang Li, Luopeiwen Yi, Tananun Songdechakraiwut
https://arxiv.org/abs/2508.05786 https://arxiv.org/pdf/2508.0…

Functional Connectivity Graph Neural Networks
Real-world networks often benefit from capturing both local and global interactions. Inspired by multi-modal analysis in brain imaging, where structural and functional connectivity offer complementary views of network organization, we propose a graph neural network framework that generalizes this approach to other domains. Our method introduces a functional connectivity block based on persistent graph homology to capture global topological features. Combined with structural information, this fo…

:brain_nou:

Mechanistic Learning with Guided Diffusion Models to Predict Spatio-Temporal Brain Tumor Growth
Daria Laslo, Efthymios Georgiou, Marius George Linguraru, Andreas Rauschecker, Sabine Muller, Catherine R. Jutzeler, Sarah Bruningk
https://arxiv.org/abs/2509.09610

Mechanistic Learning with Guided Diffusion Models to Predict Spatio-Temporal Brain Tumor Growth
Predicting the spatio-temporal progression of brain tumors is essential for guiding clinical decisions in neuro-oncology. We propose a hybrid mechanistic learning framework that combines a mathematical tumor growth model with a guided denoising diffusion implicit model (DDIM) to synthesize anatomically feasible future MRIs from preceding scans. The mechanistic model, formulated as a system of ordinary differential equations, captures temporal tumor dynamics including radiotherapy effects and es…

AI-focused hedge funds raise billions, such as ex-OpenAI researcher Leopold Aschenbrenner's Situational Awareness that amassed $1.5B for a "brain trust on AI" (Peter Rudegeair/Wall Street Journal)
https://www.wsj.com/finance/investing/bill…

All You Need Is A Fuzzing Brain: An LLM-Powered System for Automated Vulnerability Detection and Patching
Ze Sheng, Qingxiao Xu, Jianwei Huang, Matthew Woodcock, Heqing Huang, Alastair F. Donaldson, Guofei Gu, Jeff Huang
https://arxiv.org/abs/2509.07225

All You Need Is A Fuzzing Brain: An LLM-Powered System for Automated Vulnerability Detection and Patching
Our team, All You Need Is A Fuzzing Brain, was one of seven finalists in DARPA's Artificial Intelligence Cyber Challenge (AIxCC), placing fourth in the final round. During the competition, we developed a Cyber Reasoning System (CRS) that autonomously discovered 28 security vulnerabilities - including six previously unknown zero-days - in real-world open-source C and Java projects, and successfully patched 14 of them. The complete CRS is open source at https://github.com/o2lab/afc-crs-all-you-ne…

Machine Learning-Based Prediction of Speech Arrest During Direct Cortical Stimulation Mapping
Nikasadat Emami, Amirhossein Khalilian-Gourtani, Jianghao Qian, Antoine Ratouchniak, Xupeng Chen, Yao Wang, Adeen Flinker
https://arxiv.org/abs/2509.08703

Machine Learning-Based Prediction of Speech Arrest During Direct Cortical Stimulation Mapping
Identifying cortical regions critical for speech is essential for safe brain surgery in or near language areas. While Electrical Stimulation Mapping (ESM) remains the clinical gold standard, it is invasive and time-consuming. To address this, we analyzed intracranial electrocorticographic (ECoG) data from 16 participants performing speech tasks and developed machine learning models to directly predict if the brain region underneath each ECoG electrode is critical. Ground truth labels indicating…

"Sixteen-year-old Reuben Nsemoh was playing soccer for his school in Gwinnett County, Georgia last month when he was kicked in the head and ended up in a #coma. Despite the severity of Reuben's brain injury, he regained consciousness after a few days. But what happened next came as a complete surprise to everyone around him.
The boy—a monolingual speaker of English—was suddenly able to s…

GmSLM : Generative Marmoset Spoken Language Modeling
Talia Sternberg, Michael London, David Omer, Yossi Adi
https://arxiv.org/abs/2509.09198 https://arxiv.…

GmSLM : Generative Marmoset Spoken Language Modeling
Marmoset monkeys exhibit complex vocal communication, challenging the view that nonhuman primates vocal communication is entirely innate, and show similar features of human speech, such as vocal labeling of others and turn-taking. Studying their vocal communication offers a unique opportunity to link it with brain activity-especially given the difficulty of accessing the human brain in speech and language research. Since Marmosets communicate primarily through vocalizations, applying standard L…

Determining the acceleration field of a rigid body using three accelerometers and one gyroscope, with applications in mild traumatic brain injury
Yang Wan, Benjamin E. Grossman-Ponemona, Haneesh Kesari
https://arxiv.org/abs/2508.07464

Determining the acceleration field of a rigid body using three accelerometers and one gyroscope, with applications in mild traumatic brain injury
Mild traumatic brain injury (mTBI) often results from violent head motion or impact. Most prevention strategies explicitly or implicitly rely on motion- or deformation-based injury criteria, both of which require accurate measurements of head motion. We present an algorithm for reconstructing the full acceleration field of a rigid body from measurements obtained by three tri-axial accelerometers and one tri-axial gyroscope. Unlike traditional gyroscope-based methods, which require numerically d…

I was looking into the #DANDI archive, following a post of @… that mentioned it as a possibility for #Neuroscience data storage.. It looks like it could be useful i…

Deep Learning-Based Desikan-Killiany Parcellation of the Brain Using Diffusion MRI
Yousef Sadegheih, Dorit Merhof
https://arxiv.org/abs/2508.07815 https://…

Deep Learning-Based Desikan-Killiany Parcellation of the Brain Using Diffusion MRI
Accurate brain parcellation in diffusion MRI (dMRI) space is essential for advanced neuroimaging analyses. However, most existing approaches rely on anatomical MRI for segmentation and inter-modality registration, a process that can introduce errors and limit the versatility of the technique. In this study, we present a novel deep learning-based framework for direct parcellation based on the Desikan-Killiany (DK) atlas using only diffusion MRI data. Our method utilizes a hierarchical, two-stage…

Pyanodons hurts my brain. So many things to keep track of. They don't fit on the screen. And I don't have construction robots (yet).

AutoMashup: Automatic Music Mashups Creation
Marine Delabaere (IMT Atlantique), L\'ea Miqueu (IMT Atlantique), Michael Moreno (IMT Atlantique), Gautier Bigois (IMT Atlantique), Hoang Duong (IMT Atlantique), Ella Fernandez (IMT Atlantique), Flavie Manent (IMT Atlantique), Maria Salgado-Herrera (IMT Atlantique), Bastien Pasdeloup (Lab\_STICC\_BRAIn, IMT Atlantique - MEE, IMT Atlantique), Nicolas Farrugia (Lab\_STICC\_BRAIn, IMT Atlantique - MEE, IMT Atlantique), Axel Marmoret (Lab\_S…

AutoMashup: Automatic Music Mashups Creation
We introduce AutoMashup, a system for automatic mashup creation based on source separation, music analysis, and compatibility estimation. We propose using COCOLA to assess compatibility between separated stems and investigate whether general-purpose pretrained audio models (CLAP and MERT) can support zero-shot estimation of track pair compatibility. Our results show that mashup compatibility is asymmetric -- it depends on the role assigned to each track (vocals or accompaniment) -- and that cur…

The invisible plastic threat you can finally see #environment

The invisible plastic threat you can finally see
Researchers in Germany and Australia have created a simple but powerful tool to detect nanoplastics—tiny, invisible particles that can slip through skin and even the blood-brain barrier. Using an "optical sieve" test strip viewed under a regular microscope, these particles reveal themselves through striking color changes.

Gut-Brain Axis as a Closed-Loop Molecular Communication Network
Beyza E. Ortlek, Ozgur B. Akan
https://arxiv.org/abs/2509.07911 https://arxiv.org/pdf/2509.…

Gut-Brain Axis as a Closed-Loop Molecular Communication Network
Molecular communication (MC) provides a quantitative framework for analyzing information transfer within biological systems. This paper introduces a novel and comprehensive MC framework for the gut-brain axis (GBA) as a system of six coupled, nonlinear delay differential equations (DDEs). The proposed model defines a bidirectional feedback loop with a gut-to-brain inflammatory channel and a brain-to-gut neuroendocrine channel. Under prolonged stress, this feedback loop becomes self-perpetuating…

Jerry Jones shuns media after Cowboys loss; Brian Schottenheimer defends DC Matt Eberflus https://cowboyswire.usatoday.com/story/sports/nfl/cowboys/2025/10/12/cowboys-jerry-jones-matt-eberflus…

Network-Specific Models for Multimodal Brain Response Prediction
Andrea Corsico, Giorgia Rigamonti, Simone Zini, Luigi Celona, Paolo Napoletano
https://arxiv.org/abs/2508.06499 …

Network-Specific Models for Multimodal Brain Response Prediction
In this work, we present a network-specific approach for predicting brain responses to complex multimodal movies, leveraging the Yeo 7-network parcellation of the Schaefer atlas. Rather than treating the brain as a homogeneous system, we grouped the seven functional networks into four clusters and trained separate multi-subject, multi-layer perceptron (MLP) models for each. This architecture supports cluster-specific optimization and adaptive memory modeling, allowing each model to adjust tempo…

Identifying Neural Signatures from fMRI using Hybrid Principal Components Regression
Jared Rieck, Julia Wrobel, Joshua L. Gowin, Yue Wang, Martin Paulus, Ryan Peterson
https://arxiv.org/abs/2509.07300 …

Identifying Neural Signatures from fMRI using Hybrid Principal Components Regression
Recent advances in neuroimaging analysis have enabled accurate decoding of mental state from brain activation patterns during functional magnetic resonance imaging scans. A commonly applied tool for this purpose is principal components regression regularized with the least absolute shrinkage and selection operator (LASSO PCR), a type of multi-voxel pattern analysis (MVPA). This model presumes that all components are equally likely to harbor relevant information, when in fact the task-related si…

Improving Continuous Grasp Force Decoding from EEG with Time-Frequency Regressors and Premotor-Parietal Network Integration
Parth G. Dangi, Yogesh Kumar Meena
https://arxiv.org/abs/2508.07677

Improving Continuous Grasp Force Decoding from EEG with Time-Frequency Regressors and Premotor-Parietal Network Integration
Brain-machine interfaces (BMIs) have significantly advanced neuro-rehabilitation by enhancing motor control. However, accurately decoding continuous grasp force remains a challenge, limiting the effectiveness of BMI applications for fine motor tasks. Current models tend to prioritise algorithmic complexity rather than incorporating neurophysiological insights into force control, which is essential for developing effective neural engineering solutions. To address this, we propose EEGForceMap, an…

#neuroscience #news

Natural Compound Combo Restores Aging Brain Cells - Neuroscience News
Neuroscience News provides research news for neuroscience, neurology, psychology, AI, brain science, mental health, robotics and cognitive sciences.

fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…

Symmetry Interactive Transformer with CNN Framework for Diagnosis of Alzheimer's Disease Using Structural MRI
Zheng Yang, Yanteng Zhang, Xupeng Kou, Yang Liu, Chao Ren
https://arxiv.org/abs/2509.08243

Symmetry Interactive Transformer with CNN Framework for Diagnosis of Alzheimer's Disease Using Structural MRI
Structural magnetic resonance imaging (sMRI) combined with deep learning has achieved remarkable progress in the prediction and diagnosis of Alzheimer's disease (AD). Existing studies have used CNN and transformer to build a well-performing network, but most of them are based on pretraining or ignoring the asymmetrical character caused by brain disorders. We propose an end-to-end network for the detection of disease-based asymmetric induced by left and right brain atrophy which consist of 3D CN…

So: played through 5 Starship songs and 6 Kansas songs today.
Regression in a couple spots as my art brain wrestles with my chart brain, but what has come into focus is what needs really close attention.
The 12 Temptations songs are more straightforward and I can read them if necessary, but the rock stuff needs more muscle memory and commitment to come alive.
Tomorrow I review:
- live endings for the Temps
- Smash Mouth tunes, incl charting them

In Vivo Wideband MR Elastography for Assessing Age-Related Viscoelastic Changes of the Human Brain
Jakob Schattenfroh, Tom Meyer, Hossein S. Aghamiry, Noah Jaitner, Michael Fedders, Steffen G\"orner, Helge Herthum, Stefan Hetzer, Melanie Estrella, Guillaume Fl\'e, Paul Steinmann, Jing Guo, Ingolf Sack
https://arxiv.org/abs/2509.08…

In Vivo Wideband MR Elastography for Assessing Age-Related Viscoelastic Changes of the Human Brain
Magnetic Resonance Elastography (MRE) provides maps of brain biomechanics and is highly sensitive to alterations associated with aging and neurodegenerative disease. Most implementations use a single frequency or a narrow frequency band, limiting analysis of frequency-dependent viscoelastic parameters. We developed a dual-actuator wideband MRE (5-50 Hz) protocol and acquired wavefields at 13 frequencies in 24 healthy adults (young: 23-39 years; old: 50-63 years). Shear wave speed (SWS) maps wer…

Forbes: The past, present and future of brain-computer interfaces https://www.forbes.com/sites/robtoews/2025/10/05/these-are-the-startups-merging-your-brain-with-ai/ Will Part 2 address The vOICe non-invasive vision BCI for the blind? …

These Are The Startups Merging Your Brain With AI
Brain-computer interfaces (BCI) sound like science fiction to most people. But this technology is getting real, quickly.

Scalable Sample-to-Population Estimation of Hyperbolic Space Models for Hypergraphs
Cornelius Fritz, Yubai Yuan, Michael Schweinberger
https://arxiv.org/abs/2509.07031 https://

Scalable Sample-to-Population Estimation of Hyperbolic Space Models for Hypergraphs
Hypergraphs are useful mathematical representations of overlapping and nested subsets of interacting units, including groups of genes or brain regions, economic cartels, political or military coalitions, and groups of products that are purchased together. Despite the vast range of applications, the statistical analysis of hypergraphs is challenging: There are many hyperedges of small and large sizes, and hyperedges can overlap or be nested. We develop a novel statistical approach to hypergraphs…

ADHDeepNet From Raw EEG to Diagnosis: Improving ADHD Diagnosis through Temporal-Spatial Processing, Adaptive Attention Mechanisms, and Explainability in Raw EEG Signals
Ali Amini, Mohammad Alijanpour, Behnam Latifi, Ali Motie Nasrabadi
https://arxiv.org/abs/2509.08779

ADHDeepNet From Raw EEG to Diagnosis: Improving ADHD Diagnosis through Temporal-Spatial Processing, Adaptive Attention Mechanisms, and Explainability in Raw EEG Signals
Attention Deficit Hyperactivity Disorder (ADHD) is a common brain disorder in children that can persist into adulthood, affecting social, academic, and career life. Early diagnosis is crucial for managing these impacts on patients and the healthcare system but is often labor-intensive and time-consuming. This paper presents a novel method to improve ADHD diagnosis precision and timeliness by leveraging Deep Learning (DL) approaches and electroencephalogram (EEG) signals. We introduce ADHDeepNet…

Low-Cost and Detunable Wireless Resonator Glasses for Enhanced Eye MRI with Concurrent High-Quality Whole Brain MRI
Ming Lu, Xiaoyue Yang, Jason Moore, Pingping Li, Adam W. Anderson, John C. Gore, Seth A. Smith, Xinqiang Yan
https://arxiv.org/abs/2509.08797

Low-Cost and Detunable Wireless Resonator Glasses for Enhanced Eye MRI with Concurrent High-Quality Whole Brain MRI
Purpose: To develop and evaluate a wearable wireless resonator glasses design that enhances eye MRI signal-to-noise ratio (SNR) without compromising whole-brain image quality at 7 T. Methods: The device integrates two detunable LC loop resonators into a lightweight, 3D-printed frame positioned near the eyes. The resonators passively couple to a standard 2Tx/32Rx head coil without hardware modifications. Bench tests assessed tuning, isolation, and detuning performance. B1$^+$ maps were measure…

Brain Co., co-founded by Jared Kushner, Elad Gil, and others to help enterprises and governments use AI, comes out of stealth with a $30M Series A (Forbes)
http://www.forbes.com/sites/giacomotognini/2025/09/10/jared-…

Meet Jared Kushner’s New AI Startup, Brain Co
Kushner teamed up with prolific solo investor Elad Gil and Luis Videgaray, the former Mexican foreign minister, to launch Brain Co., which helps businesses and governments apply AI to their operations.

Ensemble-Based Graph Representation of fMRI Data for Cognitive Brain State Classification
Daniil Vlasenko, Vadim Ushakov, Alexey Zaikin, Denis Zakharov
https://arxiv.org/abs/2508.06118

Ensemble-Based Graph Representation of fMRI Data for Cognitive Brain State Classification
Understanding and classifying human cognitive brain states based on neuroimaging data remains one of the foremost and most challenging problems in neuroscience, owing to the high dimensionality and intrinsic noise of the signals. In this work, we propose an ensemble-based graph representation method of functional magnetic resonance imaging (fMRI) data for the task of binary brain-state classification. Our method builds the graph by leveraging multiple base machine-learning models: each edge wei…

Looking for recommended reading/listening when it comes to addressing the claims of neural networks being similar to how the human brain works. Planning to write an instructive essay on this next month and need to start my research 😊

💥 Amateur soccer headers and brain health: Study finds changes within folds of the brain
https://medicalxpress.com/news/2025-09-amateur-soccer-headers-brain-health.html

Amateur soccer headers and brain health: Study finds changes within folds of the brain
In amateur soccer players, more frequent heading, or using the head to control or pass the ball, is linked to alterations within the folds of the brain, according to a study published in Neurology. The study does not prove that soccer heading causes brain changes, it only shows an association.

Sonnet 122 - CXXII
Thy gift, thy tables, are within my brain
Full charactered with lasting memory,
Which shall above that idle rank remain,
Beyond all date, even to eternity:
Or, at the least, so long as brain and heart
Have faculty by nature to subsist;
Till each to razed oblivion yield his part
Of thee, thy record never can be missed.
That poor retention could not so much hold,
Nor need I tallies thy dear love to score;
Ther…

ASLSL: Adaptive shared latent structure learning with incomplete multi-modal physiological data for multi-dimensional emotional feature selection
Xueyuan Xu, Tianze Yu, Wenjia Dong, Fulin Wei, Li Zhuo
https://arxiv.org/abs/2508.05934

ASLSL: Adaptive shared latent structure learning with incomplete multi-modal physiological data for multi-dimensional emotional feature selection
Recently, multi-modal physiological signals based emotion recognition has garnered increasing attention in the field of brain-computer interfaces. Nevertheness, the associated multi-modal physiological features are often high-dimensional and inevitably include irrelevant, redundant, and noisy representation, which can easily lead to overfitting, poor performance, and high computational complexity in emotion classifiers. Feature selection has been widely applied to address these challenges. Howe…

Resource-Efficient Glioma Segmentation on Sub-Saharan MRI
Freedmore Sidume, Oumayma Soula, Joseph Muthui Wacira, YunFei Zhu, Abbas Rabiu Muhammad, Abderrazek Zeraii, Oluwaseun Kalejaye, Hajer Ibrahim, Olfa Gaddour, Brain Halubanza, Dong Zhang, Udunna C Anazodo, Confidence Raymond
https://arxiv.org/abs/2509.09469

Resource-Efficient Glioma Segmentation on Sub-Saharan MRI
Gliomas are the most prevalent type of primary brain tumors, and their accurate segmentation from MRI is critical for diagnosis, treatment planning, and longitudinal monitoring. However, the scarcity of high-quality annotated imaging data in Sub-Saharan Africa (SSA) poses a significant challenge for deploying advanced segmentation models in clinical workflows. This study introduces a robust and computationally efficient deep learning framework tailored for resource-constrained settings. We leve…

Everybody with half a brain knows that Trump's threat "We'll fire more workers because we can't get a #budget" is BS. They would have #fired those civil servants anyway, sooner or later.
"If you don't eat what your mother has cooked, the Sun will set tonight!!!&quot…

My neurons are tingling that "world war is coming" within the next decade or two. It will begin as regional - perhaps EU (or Poland Ukraine) vs Russia.
My brain is suggesting that this war will not be a classic "us versus them" war but will, instead, be more like a slowly expanding brawl with many countries and groups. All kinds of strange transient alliances may come and go - such as Israel Saudi vs Iran.
My sense is that the US would enter with unclear goa…

Waiting for the Neuralink Blindsight brain implant PR BS tsunami after the first implant? Better form your own opinion first, based on what is already known https://www.artificialvision.com/neuralink.htm
ChatGPT 5:

Computing with Canonical Microcircuits
PK Douglas
https://arxiv.org/abs/2508.06501 https://arxiv.org/pdf/2508.06501…

Computing with Canonical Microcircuits
The human brain represents the only known example of general intelligence that naturally aligns with human values. On a mere 20-watt power budget, the brain achieves robust learning and adaptive decision-making in ways that continue to elude advanced AI systems. Inspired by the brain, we present a computational architecture based on canonical microcircuits (CMCs) - stereotyped patterns of neurons found ubiquitously throughout the cortex. We implement these circuits as neural ODEs comprising spi…

Invisible Attributes, Visible Biases: Exploring Demographic Shortcuts in MRI-based Alzheimer's Disease Classification
Akshit Achara, Esther Puyol Anton, Alexander Hammers, Andrew P. King
https://arxiv.org/abs/2509.09558

Invisible Attributes, Visible Biases: Exploring Demographic Shortcuts in MRI-based Alzheimer's Disease Classification
Magnetic resonance imaging (MRI) is the gold standard for brain imaging. Deep learning (DL) algorithms have been proposed to aid in the diagnosis of diseases such as Alzheimer's disease (AD) from MRI scans. However, DL algorithms can suffer from shortcut learning, in which spurious features, not directly related to the output label, are used for prediction. When these features are related to protected attributes, they can lead to performance bias against underrepresented protected groups, such …

🧠 Imaging tech promises deepest looks yet into living brain tissue at single-cell resolution
https://medicalxpress.com/news/2025-08-imaging-tech-deepest-brain-tissue.html

Imaging tech promises deepest looks yet into living brain tissue at single-cell resolution
Both for research and medical purposes, researchers have spent decades pushing the limits of microscopy to produce ever deeper and sharper images of brain activity, not only in the cortex but also in regions underneath such as the hippocampus. In a new study, a team of MIT scientists and engineers demonstrates a new microscope system capable of peering exceptionally deep into brain tissues to detect the molecular activity of individual cells by using sound.

And another thing for those of you that get discouraged trying to learn music:
my brain is a weird old thing… The first day I try something new it's just a massive struggle but give it 24 hours to percolate, and the next day it's like 100% easier

Path to Intelligence: Measuring Similarity between Human Brain and Large Language Model Beyond Language Task
Doai Ngo, Mingxuan Sun, Zhengji Zhang, Ashwin G Ramayya, Mark Schnitzer, Zhe Zhao
https://arxiv.org/abs/2509.08831

Path to Intelligence: Measuring Similarity between Human Brain and Large Language Model Beyond Language Task
Large language models (LLMs) have demonstrated human-like abilities in language-based tasks. While language is a defining feature of human intelligence, it emerges from more fundamental neurophysical processes rather than constituting the basis of intelligence itself. In this work, we study the similarity between LLM internal states and human brain activity in a sensory-motor task rooted in anticipatory and visuospatial behavior. These abilities are essential for cognitive performance that cons…

Word2Spike: Poisson Rate Coding for Associative Memories and Neuromorphic Algorithms
Archit Kalra, Midhun Sadanand
https://arxiv.org/abs/2509.07361 https://

Word2Spike: Poisson Rate Coding for Associative Memories and Neuromorphic Algorithms
Spiking neural networks offer a promising path toward energy-efficient, brain-like associative memory. This paper introduces Word2Spike, a novel rate coding mechanism that combines continuous word embeddings and neuromorphic architectures. We develop a one-to-one mapping that converts multi-dimensional word vectors into spike-based attractor states using Poisson processes. Using BitNet b1.58 quantization, we maintain 97% semantic similarity of continuous embeddings on SimLex-999 while achieving…

Neuralink captures Wall Street's eye, sparks debate over brain interfaces and future "neuro elite" https://thedebrief.org/neuralink-captures-wall-streets-eye-sparks-debate-over-brain-interfaces-and-future-neuro-el…

Neuralink Captures Wall Street’s Eye, Sparks Debate Over Brain Interfaces and Future “Neuro Elite”
Science, Tech and Defense for the Rebelliously Curious.

cintestinalis: Tadpole larva brain (C. intestinalis)
Entire connectivity matrix for the complete brain of a larva of Ciona intestinalis. Each directed edge represents a synaptic connection from pre-synaptic cell i to post-synaptic cell j (may not be a neuron). Edge weights represent the cumulative depth of presynaptic contacts in µm.
This network has 205 nodes and 2903 edges.
Tags: Biological, Connectome, Weighted

NeuroGaze: A Hybrid EEG and Eye-Tracking Brain-Computer Interface for Hands-Free Interaction in Virtual Reality
Kyle Coutray, Wanyea Barbel, Zack Groth, Joseph J LaViola Jr
https://arxiv.org/abs/2509.07863

NeuroGaze: A Hybrid EEG and Eye-Tracking Brain-Computer Interface for Hands-Free Interaction in Virtual Reality
Brain-Computer Interfaces (BCIs) have traditionally been studied in clinical and laboratory contexts, but the rise of consumer-grade devices now allows exploration of their use in daily activities. Virtual reality (VR) provides a particularly relevant domain, where existing input methods often force trade-offs between speed, accuracy, and physical effort. This study introduces NeuroGaze, a hybrid interface combining electroencephalography (EEG) with eye tracking to enable hands-free interaction…

🧲 Coffee fortified with iron—new microparticles can be added to food and beverages to fight malnutrition
... who'd a thought metal organic frameworks could be edible?
https://phys.org/news/2025-08-coffee-fortified-iron-microparticles-added.html

Coffee fortified with iron—new microparticles can be added to food and beverages to fight malnutrition
Around the world, about 2 billion people suffer from iron deficiency, which can lead to anemia, impaired brain development in children, and increased infant mortality.

Data-Efficient Neural Training with Dynamic Connectomes
Yutong Wu, Peilin He, Tananun Songdechakraiwut
https://arxiv.org/abs/2508.06817 https://arxiv.org/p…

Data-Efficient Neural Training with Dynamic Connectomes
The study of dynamic functional connectomes has provided valuable insights into how patterns of brain activity change over time. Neural networks process information through artificial neurons, conceptually inspired by patterns of activation in the brain. However, their hierarchical structure and high-dimensional parameter space pose challenges for understanding and controlling training dynamics. In this study, we introduce a novel approach to characterize training dynamics in neural networks by…

Evaluation of Machine Learning Reconstruction Techniques for Accelerated Brain MRI Scans
Jonathan I. Mandel, Shivaprakash Hiremath, Hedyeh Keshtgar, Timothy Scholl, Sadegh Raeisi
https://arxiv.org/abs/2509.07193

Evaluation of Machine Learning Reconstruction Techniques for Accelerated Brain MRI Scans
This retrospective-prospective study evaluated whether a deep learning-based MRI reconstruction algorithm can preserve diagnostic quality in brain MRI scans accelerated up to fourfold, using both public and prospective clinical data. The study included 18 healthy volunteers (scans acquired at 3T, January 2024-March 2025), as well as selected fastMRI public datasets with diverse pathologies. Phase-encoding-undersampled 2D/3D T1, T2, and FLAIR sequences were reconstructed with DeepFoqus-Accelerat…

Leveraging Support Vector Regression for Outcome Prediction in Personalized Ultra-fractionated Stereotactic Adaptive Radiotherapy
Yajun Yu, Steve Jiang, Robert Timmerman, Hao Peng
https://arxiv.org/abs/2509.07872

Leveraging Support Vector Regression for Outcome Prediction in Personalized Ultra-fractionated Stereotactic Adaptive Radiotherapy
Personalized ultra-fractionated stereotactic adaptive radiotherapy (PULSAR) is a novel treatment that delivers radiation in pulses of protracted intervals. Accurate prediction of gross tumor volume (GTV) changes through regression models has substantial prognostic value. This study aims to develop a multi-omics based support vector regression (SVR) model for predicting GTV change. A retrospective cohort of 39 patients with 69 brain metastases was analyzed, based on radiomics (MRI images) and do…

(2020) Archived at https://archive.ph/jSwr6 A new implant for blind people jacks directly into the brain; 5 years later, no Neuralink Blindsight brain implant in sight yet

AI-Driven Radiology Report Generation for Traumatic Brain Injuries
Riadh Bouslimi, Houda Trabelsi, Wahiba Ben Abdssalem Karaa, Hana Hedhli
https://arxiv.org/abs/2510.08498 https…

AI-Driven Radiology Report Generation for Traumatic Brain Injuries
Traumatic brain injuries present significant diagnostic challenges in emergency medicine, where the timely interpretation of medical images is crucial for patient outcomes. In this paper, we propose a novel AI-based approach for automatic radiology report generation tailored to cranial trauma cases. Our model integrates an AC-BiFPN with a Transformer architecture to capture and process complex medical imaging data such as CT and MRI scans. The AC-BiFPN extracts multi-scale features, enabling th…

cintestinalis: Tadpole larva brain (C. intestinalis)
Entire connectivity matrix for the complete brain of a larva of Ciona intestinalis. Each directed edge represents a synaptic connection from pre-synaptic cell i to post-synaptic cell j (may not be a neuron). Edge weights represent the cumulative depth of presynaptic contacts in µm.
This network has 205 nodes and 2903 edges.
Tags: Biological, Connectome, Weighted

A novel cost-effective fabrication of a flexible neural probe for brain signal recording
Alireza Irandoost, Amirreza Bahramani, Roya Mohajeri, Faezeh Shahdost-Fard, Ali Ghazizadeh, Mehdi Fardmanesh
https://arxiv.org/abs/2509.09213

A novel cost-effective fabrication of a flexible neural probe for brain signal recording
This study introduces a novel, flexible, and implantable neural probe using a cost-effective microfabrication process based on a thin polyimide film. Polyimide film, known as Kapton, serves as a flexible substrate for microelectrodes, conductive tracks, and contact pads of the probe, which are made from a thin film of gold (Au). SU-8 is used to cover the corresponding tracks for electrical isolation and to increase the stiffness of the probe for better implantation. To evaluate the performance …

Oliver Sacks on sensory substitution in 2010, predictions about the next 30 years https://www.discovermagazine.com/we-are-learning-to-exploit-the-amazing-plasticity-of-the-brain-16718 We are learning to exploit the amazing plasticity…

We Are Learning to Exploit the Amazing Plasticity of the Brain
Neurologist Oliver Sacks on his hopes for science over the next 30 years

DLGE: Dual Local-Global Encoding for Generalizable Cross-BCI-Paradigm
Jingyuan Wang, Junhua Li
https://arxiv.org/abs/2509.07991 https://arxiv.org/pdf/2509.…

DLGE: Dual Local-Global Encoding for Generalizable Cross-BCI-Paradigm
Deep learning models have been frequently used to decode a single brain-computer interface (BCI) paradigm based on electroencephalography (EEG). It is challenging to decode multiple BCI paradigms using one model due to diverse barriers, such as different channel configurations and disparate task-related representations. In this study, we propose Dual Local-Global Encoder (DLGE), enabling the classification across different BCI paradigms. To address the heterogeneity in EEG channel configuration…

Brain Tumor Detection Through Diverse CNN Architectures in IoT Healthcare Industries: Fast R-CNN, U-Net, Transfer Learning-Based CNN, and Fully Connected CNN
Mohsen Asghari Ilani, Yaser M. Banad
https://arxiv.org/abs/2509.05821

Brain Tumor Detection Through Diverse CNN Architectures in IoT Healthcare Industries: Fast R-CNN, U-Net, Transfer Learning-Based CNN, and Fully Connected CNN
Artificial intelligence (AI)-powered deep learning has advanced brain tumor diagnosis in Internet of Things (IoT)-healthcare systems, achieving high accuracy with large datasets. Brain health is critical to human life, and accurate diagnosis is essential for effective treatment. Magnetic Resonance Imaging (MRI) provides key data for brain tumor detection, serving as a major source of big data for AI-driven image classification. In this study, we classified glioma, meningioma, and pituitary tumo…

💫 Soccer heading does most damage to brain area critical for cognition, brain imaging study finds
https://medicalxpress.com/news/2025-09-soccer-brain-area-critical-cognition.html

Soccer heading does most damage to brain area critical for cognition, brain imaging study finds
A brain imaging technique developed by Columbia researchers has identified areas in the brain's cerebral cortex—just behind the forehead—that are most damaged by the repetitive impacts from heading a soccer ball. Their study also found that the damage leads to cognitive deficits seen in soccer players who head the ball frequently.

MRI-Based Brain Tumor Detection through an Explainable EfficientNetV2 and MLP-Mixer-Attention Architecture
Mustafa Yurdakul, \c{S}akir Ta\c{s}demir
https://arxiv.org/abs/2509.06713

MRI-Based Brain Tumor Detection through an Explainable EfficientNetV2 and MLP-Mixer-Attention Architecture
Brain tumors are serious health problems that require early diagnosis due to their high mortality rates. Diagnosing tumors by examining Magnetic Resonance Imaging (MRI) images is a process that requires expertise and is prone to error. Therefore, the need for automated diagnosis systems is increasing day by day. In this context, a robust and explainable Deep Learning (DL) model for the classification of brain tumors is proposed. In this study, a publicly available Figshare dataset containing 3,…

(CNET, YouTube) Synchron vs. Neuralink: BCI brain implants for thought-control #BCI #NeuroTech

Synthetic Data Generation for Classifying Electrophysiological and Morpho-Electrophysiological Neurons from Mouse Visual Cortex
Xavier Vasques, Laura Cif
https://arxiv.org/abs/2508.06514

Synthetic Data Generation for Classifying Electrophysiological and Morpho-Electrophysiological Neurons from Mouse Visual Cortex
The accurate classification of neuronal cell types is central to decoding brain function, yet remains hindered by data scarcity and cellular heterogeneity. Here, we benchmarked classical and deep generative synthetic data augmentation strategies -- including SMOTE, GANs, VAEs, Normalizing Flows, and DDPMs -- for supervised classification of both electrophysiological (e-type) and morpho-electrophysiological (mee-type) neuron types from the mouse visual cortex. Using a curated dataset annotated w…

budapest_connectome: Budapest Reference Connectome 3.0
A parameterizable consensus brain graph, derived from connectomes of 477 people, each computed from MRI datasets of the Human Connectome Project. Nodes are brain regions, and edges are weighted by the number of "tracks" that run between two nodes, as well as fiber length, fractional anisotropy and the number of occurrences in each of the 477 individuals.
This network has 1015 nodes and 105293 edges.
Tags: Biol…

SpikingBrain Technical Report: Spiking Brain-inspired Large Models
Yuqi Pan, Yupeng Feng, Jinghao Zhuang, Siyu Ding, Zehao Liu, Bohan Sun, Yuhong Chou, Han Xu, Xuerui Qiu, Anlin Deng, Anjie Hu, Peng Zhou, Man Yao, Jibin Wu, Jian Yang, Guoliang Sun, Bo Xu, Guoqi Li
https://arxiv.org/abs/2509.05276

SpikingBrain Technical Report: Spiking Brain-inspired Large Models
Mainstream Transformer-based large language models face major efficiency bottlenecks: training computation scales quadratically with sequence length, and inference memory grows linearly, limiting long-context processing. Building large models on non-NVIDIA platforms also poses challenges for stable and efficient training. To address this, we introduce SpikingBrain, a family of brain-inspired models designed for efficient long-context training and inference. SpikingBrain leverages the MetaX GPU …

So now we have a patient registry for a Neuralink Blindsight brain implant at https://neuralink.com/trials/visual-prosthesis/ and a patient registry for a Science Corporation retinal implant at

Visual Prosthesis | Neuralink
Learn more about our future visual prosthesis clinical trials.

SER-Diff: Synthetic Error Replay Diffusion for Incremental Brain Tumor Segmentation
Sashank Makanaboyina
https://arxiv.org/abs/2510.06283 https://arxiv.org…

SER-Diff: Synthetic Error Replay Diffusion for Incremental Brain Tumor Segmentation
Incremental brain tumor segmentation is critical for models that must adapt to evolving clinical datasets without retraining on all prior data. However, catastrophic forgetting, where models lose previously acquired knowledge, remains a major obstacle. Recent incremental learning frameworks with knowledge distillation partially mitigate forgetting but rely heavily on generative replay or auxiliary storage. Meanwhile, diffusion models have proven effective for refining tumor segmentations, but h…

There's a really thin line between fighting windmills (Elon Musk's Neuralink Blindsight brain implant hype) and annoying blind people who are serious about functional vision. Sorry to those blind people, sorry to you! It is impossible to reach everybody with the right "tone".

Validation of Various Normalization Methods for Brain Tumor Segmentation: Can Federated Learning Overcome This Heterogeneity?
Jan Fiszer, Dominika Ciupek, Maciej Malawski
https://arxiv.org/abs/2510.07126

Validation of Various Normalization Methods for Brain Tumor Segmentation: Can Federated Learning Overcome This Heterogeneity?
Deep learning (DL) has been increasingly applied in medical imaging, however, it requires large amounts of data, which raises many challenges related to data privacy, storage, and transfer. Federated learning (FL) is a training paradigm that overcomes these issues, though its effectiveness may be reduced when dealing with non-independent and identically distributed (non-IID) data. This study simulates non-IID conditions by applying different MRI intensity normalization techniques to separate da…

CWEFS: Brain volume conduction effects inspired channel-wise EEG feature selection for multi-dimensional emotion recognition
Xueyuan Xu, Wenjia Dong, Fulin Wei, Li Zhuo
https://arxiv.org/abs/2508.05228

CWEFS: Brain volume conduction effects inspired channel-wise EEG feature selection for multi-dimensional emotion recognition
Due to the intracranial volume conduction effects, high-dimensional multi-channel electroencephalography (EEG) features often contain substantial redundant and irrelevant information. This issue not only hinders the extraction of discriminative emotional representations but also compromises the real-time performance. Feature selection has been established as an effective approach to address the challenges while enhancing the transparency and interpretability of emotion recognition models. Howev…

Stacked Regression using Off-the-shelf, Stimulus-tuned and Fine-tuned Neural Networks for Predicting fMRI Brain Responses to Movies (Algonauts 2025 Report)
Robert Scholz, Kunal Bagga, Christine Ahrends, Carlo Alberto Barbano
https://arxiv.org/abs/2510.06235

Stacked Regression using Off-the-shelf, Stimulus-tuned and Fine-tuned Neural Networks for Predicting fMRI Brain Responses to Movies (Algonauts 2025 Report)
We present our submission to the Algonauts 2025 Challenge, where the goal is to predict fMRI brain responses to movie stimuli. Our approach integrates multimodal representations from large language models, video encoders, audio models, and vision-language models, combining both off-the-shelf and fine-tuned variants. To improve performance, we enhanced textual inputs with detailed transcripts and summaries, and we explored stimulus-tuning and fine-tuning strategies for language and vision models…

Beyond Grid-Locked Voxels: Neural Response Functions for Continuous Brain Encoding
Haomiao Chen, Keith W Jamison, Mert R. Sabuncu, Amy Kuceyeski
https://arxiv.org/abs/2510.07342

Beyond Grid-Locked Voxels: Neural Response Functions for Continuous Brain Encoding
Neural encoding models aim to predict fMRI-measured brain responses to natural images. fMRI data is acquired as a 3D volume of voxels, where each voxel has a defined spatial location in the brain. However, conventional encoding models often flatten this volume into a 1D vector and treat voxel responses as independent outputs. This removes spatial context, discards anatomical information, and ties each model to a subject-specific voxel grid. We introduce the Neural Response Function (NRF), a fra…

🔊 New technique uses focused sound waves and holograms to control brain circuits
https://medicalxpress.com/news/2025-08-technique-focused-holograms-brain-circuits.html

New technique uses focused sound waves and holograms to control brain circuits
A new study provides the first visual evidence showing that brain circuits in living animals can be activated by ultrasound waves projected into specific patterns (holograms).

To ChatGPT 5: Will The vOICe visual-to-auditory sensory substitution for the blind disrupt the market for invasive visual prostheses such as the Neuralink Blindsight brain implant? https://chatgpt.com/share/68c33028-95a4-8004-80f4-d83ecd843546 "Short answer: yes - if…

EEG-Based Acute Pain Classification: Machine Learning Model Comparison and Real-Time Clinical Feasibility
Aavid Mathrawala, Dhruv Kurup, Josie Lau
https://arxiv.org/abs/2510.05511

EEG-Based Acute Pain Classification: Machine Learning Model Comparison and Real-Time Clinical Feasibility
Current pain assessment within hospitals often relies on self-reporting or non-specific EKG vital signs. This system leaves critically ill, sedated, and cognitively impaired patients vulnerable to undertreated pain and opioid overuse. Electroencephalography (EEG) offers a noninvasive method of measuring brain activity. This technology could potentially be applied as an assistive tool to highlight nociceptive processing in order to mitigate this issue. In this study, we compared machine learning…

Association of Timing and Duration of Moderate-to-Vigorous Physical Activity with Cognitive Function and Brain Aging: A Population-Based Study Using the UK Biobank
Wasif Khan, Lin Gu, Noah Hammarlund, Lei Xing, Joshua K. Wong, Ruogu Fang
https://arxiv.org/abs/2509.06969

Association of Timing and Duration of Moderate-to-Vigorous Physical Activity with Cognitive Function and Brain Aging: A Population-Based Study Using the UK Biobank
Physical activity is a modifiable lifestyle factor with potential to support cognitive resilience. However, the association of moderate-to-vigorous physical activity (MVPA) intensity, and timing, with cognitive function and region-specific brain structure remain poorly understood. We analyzed data from 45,892 UK Biobank participants aged 60 years and older with valid wrist-worn accelerometer data, cognitive testing, and structural brain MRI. MVPA was measured both continuously (mins per week) a…

Space travel is bad for human eyesight - blind people traveling to Mars would be less affected https://www.youtube.com/shorts/WbQcFgfz__Y Neuralink Blindsight brain implant for already-blind astronauts?

OBS-Diff: Accurate Pruning For Diffusion Models in One-Shot
Junhan Zhu, Hesong Wang, Mingluo Su, Zefang Wang, Huan Wang
https://arxiv.org/abs/2510.06751 https://

OBS-Diff: Accurate Pruning For Diffusion Models in One-Shot
Large-scale text-to-image diffusion models, while powerful, suffer from prohibitive computational cost. Existing one-shot network pruning methods can hardly be directly applied to them due to the iterative denoising nature of diffusion models. To bridge the gap, this paper presents OBS-Diff, a novel one-shot pruning framework that enables accurate and training-free compression of large-scale text-to-image diffusion models. Specifically, (i) OBS-Diff revitalizes the classic Optimal Brain Surgeon…

A Total Variation Regularized Framework for Epilepsy-Related MRI Image Segmentation
Mehdi Rabiee, Sergio Greco, Reza Shahbazian, Irina Trubitsyna
https://arxiv.org/abs/2510.06276

A Total Variation Regularized Framework for Epilepsy-Related MRI Image Segmentation
Focal Cortical Dysplasia (FCD) is a primary cause of drug-resistant epilepsy and is difficult to detect in brain {magnetic resonance imaging} (MRI) due to the subtle and small-scale nature of its lesions. Accurate segmentation of FCD regions in 3D multimodal brain MRI images is essential for effective surgical planning and treatment. However, this task remains highly challenging due to the limited availability of annotated FCD datasets, the extremely small size and weak contrast of FCD lesions,…

State-dependent brain responsiveness, from local circuits to the whole brain
A. Destexhe, J Goldman, N. Tort-Colet, A. Roques, J. Fousek, S. Petkoski, V. Jirsa, O. David, M. Jedynak, C. Capone, C. De Luca, G. De Bonis, P. S. Paolucci, E. Mikulan, Pigorini, M Massimini, A. Galluzzi, A. Pazienti, M. Mattia, A. Arena, B. E. Juel, E. Hagen, J. F. Storm, E. Montagni, F. Resta, F. S. Pavone, A. L. Allegra Mascaro, A. Dwarakanath, T. I. Panagiotaropoulos, J. Senk, M. Diesmann, A. Camassa, L.…

State-dependent brain responsiveness, from local circuits to the whole brain
The objective of this paper is to review physiological and computational aspects of the responsiveness of the cerebral cortex to stimulation, and how responsiveness depends on the state of the system. This correspondence between brain state and brain responsiveness (state-dependent responses) is outlined at different scales from the cellular and circuit level, to the mesoscale and macroscale level. At each scale, we review how quantitative methods can be used to characterize network states base…

Conditional Fetal Brain Atlas Learning for Automatic Tissue Segmentation
Johannes Tischer, Patric Kienast, Marlene St\"umpflen, Gregor Kasprian, Georg Langs, Roxane Licandro
https://arxiv.org/abs/2508.04522

Conditional Fetal Brain Atlas Learning for Automatic Tissue Segmentation
Magnetic Resonance Imaging (MRI) of the fetal brain has become a key tool for studying brain development in vivo. Yet, its assessment remains challenging due to variability in brain maturation, imaging protocols, and uncertain estimates of Gestational Age (GA). To overcome these, brain atlases provide a standardized reference framework that facilitates objective evaluation and comparison across subjects by aligning the atlas and subjects in a common coordinate system. In this work, we introduce…

Space impacts temporal processing via a visual-dependent spatially organized neural architecture https://doi.org/10.1523/jneurosci.1444-24.2025 "spatial features affected the temporal processing of sighted but not blind people, regardless of age."

Space impacts temporal processing via a visual-dependent spatially organized neural architecture
Establishing the temporal relationship between stimuli challenges the brain, requiring some tolerance for asynchronies to form coherent representations. Based on the theory of implicit causal inference, we hypothesized temporal processing of events is influenced by spatial features as stimuli coming from the same spatial location are most likely to derive from a common source and, consequently, implicitly merged in time. As visual experience guides the formation of neural sensory maps, we expec…

Normative Modelling in Neuroimaging: A Practical Guide for Researchers
Nida Alyas, Jonathan Horsley, Peter N. Taylor, Yujiang Wang, Karoline Leiberg
https://arxiv.org/abs/2509.07237

Normative Modelling in Neuroimaging: A Practical Guide for Researchers
Normative modelling is an increasingly common statistical technique in neuroimaging that estimates population-level benchmarks in brain structure. It enables the quantification of individual deviations from expected distributions whilst accounting for biological and technical covariates without requiring large, matched control groups. This makes it a powerful alternative to traditional case-control studies for identifying brain structural alterations associated with pathology. Despite the avail…

Optimizing BCI Rehabilitation Protocols for Stroke: Exploring Task Design and Training Duration
Aniana Cruz, Marko Kuzmanoski, Gabriel Pires
https://arxiv.org/abs/2510.08082 htt…

Optimizing BCI Rehabilitation Protocols for Stroke: Exploring Task Design and Training Duration
Stroke is a leading cause of long-term disability and the second most common cause of death worldwide. Although acute treatments have advanced, recovery remains challenging and limited. Brain-computer interfaces (BCIs) have emerged as a promising tool for post-stroke rehabilitation by promoting neuroplasticity. However, clinical outcomes remain variable, and optimal protocols have yet to be established. This study explores strategies to optimize BCI-based rehabilitation by comparing motor image…

Predicting Brain Morphogenesis via Physics-Transfer Learning
Yingjie Zhao, Yicheng Song, Fan Xu, Zhiping Xu
https://arxiv.org/abs/2509.05305 https://arxiv.…

Predicting Brain Morphogenesis via Physics-Transfer Learning
Brain morphology is shaped by genetic and mechanical factors and is linked to biological development and diseases. Its fractal-like features, regional anisotropy, and complex curvature distributions hinder quantitative insights in medical inspections. Recognizing that the underlying elastic instability and bifurcation share the same physics as simple geometries such as spheres and ellipses, we developed a physics-transfer learning framework to address the geometrical complexity. To overcome the…

Monkey Perceptogram: Reconstructing Visual Representation and Presumptive Neural Preference from Monkey Multi-electrode Arrays
Teng Fei, Srinivas Ravishankar, Hoko Nakada, Abhinav Uppal, Ian Jackson, Garrison W. Cottrell, Ryusuke Hayashi, Virginia R. de Sa
https://arxiv.org/abs/2510.07576

Monkey Perceptogram: Reconstructing Visual Representation and Presumptive Neural Preference from Monkey Multi-electrode Arrays
Understanding how the primate brain transforms complex visual scenes into coherent perceptual experiences remains a central challenge in neuroscience. Here, we present a comprehensive framework for interpreting monkey visual processing by integrating encoding and decoding approaches applied to two large-scale spiking datasets recorded from macaque using THINGS images (THINGS macaque IT Dataset (TITD) and THINGS Ventral Stream Spiking Dataset (TVSD)). We leverage multi-electrode array recordings…

Dynamic Functional Connectivity Features for Brain State Classification: Insights from the Human Connectome Project
Valeriya Kirova, Dzerassa Kadieva, Daniil Vlasenko, Isak B. Blank, Fedor Ratnikov
https://arxiv.org/abs/2510.05325

Dynamic Functional Connectivity Features for Brain State Classification: Insights from the Human Connectome Project
We analyze functional magnetic resonance imaging (fMRI) data from the Human Connectome Project (HCP) to match brain activities during a range of cognitive tasks. Our findings demonstrate that even basic linear machine learning models can effectively classify brain states and achieve state-of-the-art accuracy, particularly for tasks related to motor functions and language processing. Feature importance ranking allows to identify distinct sets of brain regions whose activation patterns are unique…