Нейроинтерфейсы

Forwarded from All about AI, Web 3.0, BCI

Scientists at Yale University have developed BrainLM, the first foundation model for analyzing functional MRI brain recordings.

Here's what makes it revolutionary:

- Trained on 6,700 hours of brain activity recordings
- Uses self-supervised masked-prediction training
- Processes data from 77,298 fMRI samples
- Analyzes 424 brain regions simultaneously

Capabilities:

- Accurately predicts clinical variables like age, anxiety, and PTSD
- Forecasts future brain states
- Identifies functional networks without supervision
- Generates interpretable representations of brain activity patterns

What Sets It Apart:

- Generalizes well to new patients and external datasets
- Outperforms baseline models in clinical predictions
- Serves as a powerful "lens" for analyzing massive fMRI repositories
- Creates meaningful insights about brain organization

Potential Applications:
- Non-invasive assessment of cognitive health
- Early detection of psychiatric disorders
- Research tool for understanding brain dynamics
- Biomarker discovery for mental health conditions

Technical Implementation:

- Based on Transformer architecture
- Trained on UK Biobank and Human Connectome Project data
- Uses advanced preprocessing and brain parcellation techniques
- Employs state-of-the-art deep learning methods

www.group-telegram.com/jp/bci_ru.com/4083

1.0K viewsSergei Shishkin, Nov 29 at 22:02

Scientists at Yale University have developed BrainLM, the first foundation model for analyzing functional MRI brain recordings.

Here's what makes it revolutionary:

- Trained on 6,700 hours of brain activity recordings
- Uses self-supervised masked-prediction training
- Processes data from 77,298 fMRI samples
- Analyzes 424 brain regions simultaneously

Capabilities:

- Accurately predicts clinical variables like age, anxiety, and PTSD
- Forecasts future brain states
- Identifies functional networks without supervision
- Generates interpretable representations of brain activity patterns

What Sets It Apart:

- Generalizes well to new patients and external datasets
- Outperforms baseline models in clinical predictions
- Serves as a powerful "lens" for analyzing massive fMRI repositories
- Creates meaningful insights about brain organization

Potential Applications:
- Non-invasive assessment of cognitive health
- Early detection of psychiatric disorders
- Research tool for understanding brain dynamics
- Biomarker discovery for mental health conditions

Technical Implementation:

- Based on Transformer architecture
- Trained on UK Biobank and Human Connectome Project data
- Uses advanced preprocessing and brain parcellation techniques
- Employs state-of-the-art deep learning methods

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