Update README.md

README.md

@@ -16,8 +16,6 @@ The pretrained model of Brain Language Model (BrainLM) aims to achieve a general
 
 We introduce the Brain Language Model (BrainLM), a foundation model for brain activity dynamics trained on 6,700 hours of fMRI recordings. Utilizing self-supervised masked-prediction training, BrainLM demonstrates proficiency in both fine-tuning and zero-shot inference tasks. Fine-tuning allows for the prediction of clinical variables and future brain states. In zero-shot inference, the model identifies functional networks and generates interpretable latent representations of neural activity. Furthermore, we introduce a novel prompting technique, allowing BrainLM to function as an in silico simulator of brain activity responses to perturbations. BrainLM offers a novel framework for the analysis and understanding of large-scale brain activity data, serving as a “lens” through which new data can be more effectively interpreted.
 
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 - **Developed by:** [van Dijk Lab](https://www.vandijklab.org/) at Yale University
 - **Shared by [optional]:** [More Information Needed]
 - **Model type:** [More Information Needed]
@@ -35,92 +33,82 @@ We introduce the Brain Language Model (BrainLM), a foundation model for brain ac
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-### Direct Use
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+BrainLM is a versatile foundation model for fMRI analysis. It can be used for:
 
-[More Information Needed]
+- Decoding cognitive variables and mental health biomarkers from brain activity patterns
+- Predicting future brain states by learning spatiotemporal fMRI dynamics
+- Discovering intrinsic functional networks in the brain without supervision
+- Perturbation analysis to simulate the effect of interventions on brain activity
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+Currently, this model has been trained and tested only on fMRI data. There are no guarantees regarding its performance on different modalities of brain recordings.  
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-[More Information Needed]
+- The model was trained only on healthy adults, so may not generalize to other populations
+- The fMRI data has limited spatial-temporal resolution and BOLD signals are an indirect measure of neural activity
+- The model has only been evaluated on reconstruction and simple regression/classification tasks so far
+- Attention weights provide one method of interpretation but have known limitations
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+- Downstream applications of the model should undergo careful testing and validation before clinical deployment.
+- Like any AI system, model predictions should be carefully reviewed by domain experts before informing decision-making.
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
 
 ## Training Details
 
-### Training Data
-
-<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-[More Information Needed]
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-### Training Procedure 
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-[More Information Needed]
+### Data
 
+Data stats: 
+- UK Biobank (UKB): 76,296 recordings (~6450 hours)
+- Human Connectome Project (HCP): 1002 recordings (~250 hours)
 
-#### Training Hyperparameters
+Preprocessing Steps: 
+- Motion Correction
+- Normalization
+- Temporal Filtering
+- ICA Denoising
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+Feature Extraction: 
+- Brain Parcellation: AAL-424 atlas is used to divide the brain into 424 regions.
+- Temporal Resolution: ~1 Hz with 0.735s for UKB and 0.72s for HCP.
+- Dimensionality: 424-dimensional time series per scan.
 
-#### Speeds, Sizes, Times [optional]
+Data Scaling
+- Robust scaling was applied, involving the subtraction of the median and division by the interquartile range across subjects for each parcel.
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+Data split: 
+- Training data: 80% of the UKB dataset 
+- Validation data: 10% of the UKB dataset
+- Test data: 10% of the UKB dataset and HCP dataset
 
-[More Information Needed]
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
+### Training Procedure 
 
-<!-- This should link to a Data Card if possible. -->
+BrainLM was pretrained on fMRI recordings from the UK Biobank and HCP datasets. Recordings were parcellated, embedded, masked, and reconstructed via a Transformer autoencoder. The model was evaluated on held-out test partitions of both datasets.
 
-[More Information Needed]
+Objective: Mean squared error loss between original and predicted parcels
 
-#### Factors
+Pretraining:
+- 100 epochs
+- Batch size 512
+- Adam optimizer
+- Masking ratios: 20%, 75% and 90%
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+Downstream training: Fine-tuning on future state prediction and regression/classification clinical variables
 
-[More Information Needed]
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+In this work, we use the following metrics to evaluate the model's performance:
+- Reconstruction error (MSE between predicted and original parcel timeseries)
+- Clinical variable regression error (e.g. age, neuroticism scores)
+- Functional network classification accuracy
 
 [More Information Needed]