I’m currently exploring a project that requires high-granularity analysis of temporal resolution in fMRI data. Given the inherent limitations in the temporal resolution of fMRI, I’m considering an approach that utilizes frames from different subjects to construct an extended time series for each stimulus.
So I wonder if there’re any way to minimize the inter-subject variability. Specifically:
Measuring Inter-Subject Variability: What are the recommended statistical metrics or models for assessing variability between subjects in fMRI studies?
Mitigating Inter-Subject Variability: Are there effective preprocessing techniques or normalization methods that can help align and standardize brain activity data across multiple subjects in fMRI studies?
I cannot think of any application that would warrant concatenating data from different subjects into a single time series. Unless anyone else has any thoughts, I would not recommend doing this.
You might have to be more specific about what kind of analysis you have in mind. But one example is looking at the error map in a second-level general linear model, which would quantify variance in Beta coefficients for a task-contrast of interest across subjects in each voxel . You could also create probabilistic maps from your first-level (subject-level) fMRI maps to quantify where you see overlaps (assuming this is a typical task-based fMRI linear model).
Most preprocessing pipelines include spatial normalization to a standard space (e.g., MNI space). In post-processing you can z-score time series to standardize them to unit variance. For some applications, expressing a time series as a percentage signal change can, in a sense, normalize measurements across subjects.
Thank you for your response and the helpful suggestions regarding the use of GLM and probabilistic maps! I’d like to provide a bit more context on my dataset’s constraints, which pose a unique challenge:
In my project, each stimulus (arithmetic problem) corresponds to only 1-2 fMRI frames per subject. This extremely limited number of data points per stimulus is a significant hurdle for conducting traditional GLM analyses. The sparsity of the data is precisely why I am considering using frames from different subjects to construct an extended time series for each stimulus.
With this in mind, I have a couple of follow-up questions:
Measuring Inter-Subject Variability: Given the limited frames available per stimulus for each subject, is there an alternative method to GLM for measuring inter-subject variability that would be more suitable for my dataset?
Mitigating Inter-Subject Variability: Does pooling frames from different subjects (Or expressing a time series as a percentage signal change as you suggested) help address the issue of inter-subject variability for my case? Or could it potentially introduce more complexity due to increased variability between subjects?
IIUC, you are very constrained regarding the data you have, due to very tight onset asynchrony between stimuli. When data are weak, you need to rely on models. For this reason, I would recommend relying on GLM-type of approach that rely on deconvolution. You probably want to take a look at LSA/LSS appproaches.
Jeanette A Mumford, Benjamin O Turner, F Gregory Ashby, and Russell A Poldrack. Deconvolving bold activation in event-related designs for multivoxel pattern classification analyses. Neuroimage, 59(3):2636–2643, 2012.
Benjamin O Turner, Jeanette A Mumford, Russell A Poldrack, and F Gregory Ashby. Spatiotemporal activity estimation for multivoxel pattern analysis with rapid event-related designs. NeuroImage, 62(3):1429–1438, 2012.
Regarding GLM-free approaches, I guess that the most standard approach is to rely on inter-subject correlation measures. But beware that this is not very informative and won’t help you much in understanding your data.
Thank you for the insightful suggestions and references!
My main aim is to study different encoding models, and I’m concerned about the suitability of GLM-related methods due to their reliance on a pre-determined HRF, potentially leading to uniform encoding models.
I am considering pooling frames from different subjects to create an extended time series for each stimulus and am interested in the shared response model (SRM) approach you mentioned. Could this combination, possibly along with expressing time series as a percentage signal change, effectively mitigate inter-subject variability for my scenario?
Any further insights or recommendations would be greatly appreciated!