There are many tools out there for processing and analyzing neuroimaging data. In the PennLINC team, we typically acquire magnetic resonance imaging (MRI) data with a handful of modalities, such as functional MRI (fMRI), diffusion-weighted imaging (DWI), structural MRI (sMRI) (especially T1- and/or T2-weighted imaging), and arterial spin labeling (ASL). This page summarizes the acquisitions we tend to use, as well as the processing tools we use for each.

We almost exclusively acquire our data on a 3T Siemens Prisma MRI scanner.

Table of Contents

1. Structural Magnetic Resonance Imaging (sMRI)
   1. sMRIPrep
   2. Freesurfer
2. Functional Magnetic Resonance Imaging
   1. PennLINC-preferred fMRI Protocols
      1. Multi-echo fMRI
      2. Complex-valued fMRI
      3. No-excitation Noise Volumes
      4. Concurrent Physiological Recording
   2. Processing fMRI Data
3. Diffusion-Weighted Imaging
   1. PennLINC-preferred DWI Protocols
   2. Processing DWI Data
4. Arterial Spin Labeling
   1. PennLINC-preferred ASL Protocols
   2. Processing ASL Data

Structural Magnetic Resonance Imaging (sMRI)

The two main types of structural images we tend to acquire are high-resolution T1-weighted and T2-weighted images.

A high-resolution T1-weighted image is required for most of the processing workflows for other modalities, such as fMRI, DWI, and ASL.

We generally try to acquire a high-resolution T2-weighted image as well, in order to (1) improve Freesurfer segmentation and (2) to calculate a myelin-weighted map.

sMRIPrep

We recommend using sMRIPrep to process T1w and T2w images. sMRIPrep is a BIDS App that minimally preprocesses structural MRI data, runs Freesurfer to segment the data and project it to the surface, and warps the data to requested template spaces.

Tip

A number of processing pipelines for other MRI modalities, including fMRIPrep and ASLPrep, will automatically run sMRIPrep. We generally rely on the structural processing from these pipelines instead of running sMRIPrep directly.

Freesurfer

Freesurfer is a very popular tool for segmenting sMRI data and projecting it to the surface. We generally run Freesurfer within one of the BIDS Apps (sMRIPrep, fMRIPrep, ASLPrep, etc.) rather than running it separately.

Functional Magnetic Resonance Imaging

PennLINC-preferred fMRI Protocols

Starting in 2023, we began using a specific fMRI protocol in our studies. This is a multi-echo fMRI protocol with complex reconstruction and no-excitation noise volumes acquired at the end of each run.

Fun fact

This is the same basic protocol used in Siegel et al. (2024) and Moser et al. (preprint).

We have some openly-available data with this protocol on OpenNeuro.

Multi-echo fMRI

We use multi-echo fMRI instead of the more common single-echo approach. While acquiring multiple echoes means that we end up with a longer TR than common highly-accelerated protocols, such as the HCP protocol, multi-echo data have improved SNR over single-echo data, and the echoes can be leveraged to distinguish BOLD and non-BOLD noise with tedana.

For more information on the costs and benefits of multi-echo fMRI please see the ME-ICA team’s multi-echo data analysis book or the tedana documentation.

Complex-valued fMRI

We enable complex reconstruction for our fMRI sequences, which produces both magnitude and phase data rather than the more common magnitude-only reconstruction. While there are many methods to leverage phase information in fMRI data out there in the literature, in practice we mostly use the phase data for thermal noise removal with NORDIC.

In the future, we will be able to use the phase data for the following:

• Dynamic distortion correction with the MEDIC algorithm.
• Phase regression (see nipreps/fMRIPost-phase)
• Complex-valued ICA (see nipreps/fMRIPost-phase)
• Phase jolt and phase jump time series calculation (see nipreps/fMRIPost-phase)

No-excitation Noise Volumes

At the end of the protocol, we acquire 3 volumes without a radiofrequency pulse. These no-excitation volumes are then used to characterize the thermal noise levels in the data, which improves thermal noise removal by NORDIC.

Concurrent Physiological Recording

We try to acquire cardiac data with plethysmography and respiration data with a chest belt during our fMRI acquisitions. In practice, we’ve had trouble with the chest belt.

These physiological data can be used to remove non-neural noise from the fMRI data, as well as for direct analysis.

Processing fMRI Data

In order to process these unique data, we use the following basic workflow:

1. [complex-valued data only] Thermal noise removal with NORDIC.
2. Minimal preprocessing with fMRIPrep.
3. [multi-echo data only] Multi-echo denoising with tedana.
4. Post-processing and connectivity extraction with XCP-D.
5. Statistical analysis with (usually) custom R code.

Diffusion-Weighted Imaging

PennLINC-preferred DWI Protocols

We generally use compressed-sensing diffusion spectrum imaging (CS-DSI) as our preferred DWI protocol.

Fun fact

We have some openly-available data with our preferred CS-DSI protocol on OpenNeuro. One important modification is that we enable complex reconstruction, which will allow for improved thermal noise removal with MP-PCA.

Processing DWI Data

We use the following workflow for DWI data:

1. Minimal preprocessing with QSIPrep.
2. Reconstruction with QSIRecon.
3. Statistical analysis with (usually) custom R code.

Arterial Spin Labeling

Arterial spin labeling (ASL) is a sequence that tags blood in the neck and tracks the flow of that blood into the brain. The primary output of an ASL sequence is cerebral blood flow (CBF), although some protocols, such as multi-delay ASL, can be used to estimate other meaningful blood flow measures, such as arterial transit time (ATT), arterial bolus arrival time (aBAT), and arterial blood volume (ABV).

PennLINC-preferred ASL Protocols

Generally, for developmental samples (PennLINC’s bread and butter), we use a single-delay PCASL protocol developed by Manuel Taso. This protocol is extremely quick (~4:30) and provides both ASL data and a low-resolution quantitative T1 map.

For aging populations, we might use a multi-delay PCASL protocol, but that hasn’t come up in our internal studies yet.

Processing ASL Data

We use the following workflow for ASL data:

1. Preprocessing, CBF estimation, and connectivity extraction with ASLPrep.
2. Statistical analysis with (usually) custom R code.