Christine Ahrends
@cahrends.bsky.social
Neuroscientist, Junior Research Fellow at Linacre College/FMRIB, University of Oxford
Interested in human neuroimaging, brain dynamics, ML & biobanks
Interested in human neuroimaging, brain dynamics, ML & biobanks
We talked about the potential of using both spatial and temporal information like dynamic FC for prediction instead of averaging over time, and how our model can be used to compare individuals, including patients, to a reference population. Paper: elifesciences.org/articles/95125
Predicting individual traits from models of brain dynamics accurately and reliably using the Fisher kernel
The HMM-Fisher kernel approach leverages individual signatures of brain dynamics for prediction, which can be used, for example, to search for brain dynamics-informed biomarkers of neuropsychiatric di...
elifesciences.org
April 16, 2025 at 12:19 PM
We talked about the potential of using both spatial and temporal information like dynamic FC for prediction instead of averaging over time, and how our model can be used to compare individuals, including patients, to a reference population. Paper: elifesciences.org/articles/95125
The whole workflow, from fitting the HMM to constructing the kernel and predicting from it, is part of the GLHMM toolbox in Python: github.com/vidaurre/glhmm and the old HMM-MAR toolbox in Matlab: github.com/OHBA-analysi.... Find all code to replicate the paper in github.com/ahrends/Fish....
GitHub - vidaurre/glhmm
Contribute to vidaurre/glhmm development by creating an account on GitHub.
github.com
February 7, 2025 at 11:32 AM
The whole workflow, from fitting the HMM to constructing the kernel and predicting from it, is part of the GLHMM toolbox in Python: github.com/vidaurre/glhmm and the old HMM-MAR toolbox in Matlab: github.com/OHBA-analysi.... Find all code to replicate the paper in github.com/ahrends/Fish....
The HMM-Fisher kernel approach has no issues here, but we found that several other kernels were problematic in this respect. The key here is the projection: We found that, like for static FC, the right projection leads to more accurate and more reliable predictions.
February 7, 2025 at 11:32 AM
The HMM-Fisher kernel approach has no issues here, but we found that several other kernels were problematic in this respect. The key here is the projection: We found that, like for static FC, the right projection leads to more accurate and more reliable predictions.
Beyond accuracy, we thought a lot about reliability. If we run the model again, using standard CV and regularisation with new randomised folds, do the results change dramatically? And 2. Are there single cases where a prediction is so terrible that it would be useless in real-world applications?
February 7, 2025 at 11:32 AM
Beyond accuracy, we thought a lot about reliability. If we run the model again, using standard CV and regularisation with new randomised folds, do the results change dramatically? And 2. Are there single cases where a prediction is so terrible that it would be useless in real-world applications?
The HMM-Fisher kernel approach allows leveraging the entire rich description of dynamic functional connectivity and amplitude changes to predict, e.g. an individual’s cognitive test scores or demographics. It is also computationally efficient and flexible to be used with various other models.
February 7, 2025 at 11:32 AM
The HMM-Fisher kernel approach allows leveraging the entire rich description of dynamic functional connectivity and amplitude changes to predict, e.g. an individual’s cognitive test scores or demographics. It is also computationally efficient and flexible to be used with various other models.