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Project Introduction

Parcellations and neuroimaging atlases are ubiquitous in neuroimaging, namely because they allow for a principled reduction of features. This project focuses in particular on the question of choice of parcellation, in particular, how does choice of parcellation influence performance within a machine learning context (See Goals / Considerations for Machine Learning Based Neuroimaging). We perform a number of different experiments in order to probe this and related questions in detail.

This website acts as both a standalone project site and as online supplementary materials for the corresponding project paper - Why a website?

Base Experiment Setup

The base experiment conducted within this project was a systematic test of performance of different pre-defined parcellations. The structure of the evaluation is shown below:


We evaluated each combination of target variable, parcellation and ML pipeline with five-fold cross validation using the full set of available participants. The CV fold structure was kept constant and therefore directly comparable across all combinations of ML pipeline, target variable and parcellation. This evaluation procedure was used to generate different metrics of performance, R2 for regression predictors and area under the receiver operator characteristic curve (ROC AUC) for binary predictors, for each of the combinations. Performance metrics were then converted in the results into a measure of Mean Rank.

Base Experiment Results

The below figure plots performance, as measured by mean relative ranking between all 220 parcellations, against the number of parcels / size in each parcellation. Results are further colored by type of parcellation and a log10-log10 inset of the same plot is provided. It may be useful to also review the Intro to Results page first, which provides a gradual introduction to the format the results are plotted with below.

Base Results

Click the figure above to open an interactive version of the plot

Multiple Parcellation Strategies

As an additional set of analyses we sought to characterize the potential gains in performance from employing strategies that can make use of information from multiple parcellations in order to inform predictions. These extensions to the base analysis can be broken up into three different types: choice of parcellation as a nested hyper-parameter - (“Grid”), ensembling over multiple parcellations using voting - (“Voted”), and ensembling using stacking - (“Stacked”). See Multiple Parcellations Setup for more detailed information on how this experiment was structured.

The figure below compares the prior single parcellation only results to the introduced multiple parcellation strategies. The plotted mean ranks are therefore computed now between 412 (220 single parcellation and 192 multiple parcellation based) configurations. The results are further broken down by if the pool of parcellations was sourced from fixed sizes or across multiple sizes (See Multiple Parcellations Evaluation).

Multiple Parcellation Results Click the figure above to open an interactive version of the plot

Discussion Points


In testing a variety of parcellation schemes and ML modeling approaches, we have identified an apparent power law scaling of increasing predictive performance by increasing parcellation resolution. The details of this relationship were found to vary according to type of parcellation as well as ML pipeline employed, though the general pattern proved stable. The large sample size, range of predictive targets, and collection of existing and random parcellations tested all serve to lend confidence to the observed results. Researchers selecting a parcellation for predictive modelling may wish to consider this size-performance trade-off in addition to other factors such as interpretability and computational resources. We also highlighted important factors that improved performance above and beyond the size-scaling, for example, finding existing parcellations performed better than randomly generated parcellations. Further, we demonstrated the benefit of ensembling over multiple parcellations, which yielded a performance boost relative to results from single parcellations.


Sage Hahn, Max M. Owens, DeKang Yuan, Anthony C Juliano, Alexandra Potter, Hugh Garavan, Nicholas Allgaier

Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401


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Site Map

This project website is surprisingly expansive when considering nested hyper-links. Listed below are links in alphabetical order to all main site pages (many of which include multiple sub-sections):