mrsitoolbox 1.0.11

Analysis toolbox for MRSI data

🧠 MRSI Toolbox Kit

This repository provides tools and preprocessing utilites to construct a within-subject Metabolic Similarity Matrix (MetSiM) based on MRSI scans, as detailed in Nature Communications 2025 and preparing files for a voxel-based analysis as detailed in biorxiv

📚 Table of Contents

• 🧩 Construct a within-subject MetSiM
• 📊 MetSiM Analysis
• 🔧 Pre-Processing Pipeline for Voxel-Based Analysis

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📜 License

The repository is distributed under the CHUV license LICENSE.

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🧑‍💻 Contributors

Name	GitHub Profile	Email
Federico Lucchetti	@fedlucchetti	[e-mail]
Edgar Céléreau	@mrspsy	[e-mail]

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📂 Dataset

A demo dataset is available at data/BIDS/Dummy-Project and constructed MetSiMs from the Geneva-Study in data/BIDS/Geneva-Study/derivatives/connectivity.

To access the full dataset, contact the authors with a detailed research proposal explaining your intended use.

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⚙️ Installation

Requirements

• Python 3.x
• Conda / Miniconda (optional, but recommended)
• CHIMERA for anatomical parcellation

Setup Instructions

1. Clone the Repository

   git clone [e-mail]:MRSI-Psychosis-UP/MRSI-Metabolic-Connectome.git
   cd MRSI-Metabolic-Connectome
   

2. Install the Environment

   bash build_env.sh
   

3. Activate the Environment

   conda activate mrsitooldemo_env
   

4. Set Environment Paths

   python set_env_paths.py
   

   Use the provided demo BIDS dataset (data/BIDS) if applicable.

Install From PyPI

pip install mrsitoolbox

Python Imports

Use package-prefixed imports:

from mrsitoolbox.tools.datautils import DataUtils
from mrsitoolbox.tools.mridata import MRIData
from mrsitoolbox.registration.registration import Registration
from mrsitoolbox.connectomics.network import NetBasedAnalysis
from mrsitoolbox.graphplot.simmatrix import SimMatrixPlot

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🗂️ Inputs

List of Participants/Subject File

• BIDS directory PROJECT_NAME/ should contain a tab-separated participants_allsessions.tsv file following the BIDS standard subject-id \t session-id.

MRSI Files

• MRSI files should be placed in:

  PROJECT_NAME/derivatives/mrsi-<space>/sub-<subject_id>/ses-<session>/
  

• File naming convention:

  sub-<subject_id>_ses-<session>_space-<space>_met-<metabolite>_desc-<description>_mrsi.nii.gz
  

BIDS Prefix	Description	Choices
subject_id	Subject/Participant ID
session	Session ID	[V1, V2, V3, ...]
space	MRI Acquisition space	orig, t1w, mni
metabolite	MRSI resolved Metabolite	B0 = 3T: Ins, CrPCr, GPCPCh, GluGln, NAANAAG, water
		B0 = 7T: NAA, NAAG, Ins, GPCPCh, Glu, Gln, CrPCr, GABA, GSH
description	MRSI Map Description	signal, crlb, fwhm, snr, filtharmonic, brainmask

Anatomical Files

• Chimera Anatomical Parcellation Files:

  • Example for subjects T1w filenames stores in found in t1s.txt with chimera atlas LFMIHISIFF using the Lausanne cortical parcellation at scale 3 :

    chimera -b data/BIDS/Dummy-Project/ \
            -d data/BIDS/Dummy-Project/derivatives/ \
            --freesurferdir data/BIDS/Dummy-Project/derivatives/freesurfer/ \
            -p LFMIHISIFF -g 2 -s 3 -ids t1s.txt --nthreads 28
    

  • Parcellations saved in PROJECT_NAME/derivatives/chimera-atlases.

• Partial Volume Correction (PVC) files:

  Replace <N> with the appropriate tissue type index (e.g., 1 for GM, 2 for WM, 3 for CSF):

  PROJECT_NAME/derivatives/<PVCORR_DIR>/sub-<subject_id>/ses-<session>/sub-<subject_id>_ses-<session>_desc-p<N>_T1w.nii.gz
  

  • Minimum required:

    • p1: gray matter
    • p2: white matter
    • p3: CSF

  • Additional tissue files may be included (optional).

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🧩 Construct a within-subject MetSiM

Batch mode semantics

• --batch file requires --participants to point to a .tsv file listing the subject–session pairs to process.
• --batch off requires both --subject_id and --session and processes one acquisition (a single subject–session pair).
• --batch all processes all discoverable subject–session pairs in the group.

1. Create MRSI-to-T1w Transforms

   python experiments/Preprocessing/registration_mrsi_to_t1.py --group Dummy-Project --ref_met CrPCr --subject_id S001 --session V1 --nthreads 16
   

2. Map Chimera Parcel Image to MRSI Space

   python experiments/MetSiM_pipeline/map_parcel_image_to_mrsi.py --group Dummy-Project --subject_id S001 --session V1 --parc LFMIHIFIS --scale 3
   

3. Construct within-subject MetSiM

   python experiments/MetSiM_pipeline/construct_MetSiM_subject.py --group Dummy-Project --subject_id S001 --session V1 --parc LFMIHIFIS --scale 3 --npert 50 --show_plot 1 --nthreads 16 --analyze 1
   

4. Construct within-subject MetSiM (batch)

   python experiments/MetSiM_pipeline/construct_MetSiM_subject.py --group Dummy-Project --parc LFMIHIFIS --scale 3 --npert 50 --show_plot 0 --nthreads 16 --analyze 1 --batch file --participants $PATH2_PARTICIPANT-SESSION_FILE --t1mask acq-memprage_desc-brain_T1w 
   

5. Construct MetSiM Population Average

   python experiments/MetSiM_pipeline/construct_MetSiM_pop.py --group Geneva-Study --parc LFMIHIFIS --scale 3 --npert 50 --participants $PATH2_PARTICIPANT-SESSION_FILE
   

• Outputs: Transforms, coregistered parcellations, and MetSiMs are saved in the derivatives/ folder.

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Input Options Description

Arg Name	Description	Type	Default
--group	BIDS project folder name	str	Dummy-Project
--subject_id	Subject ID (e.g., S001). Required when --batch off (processes a single acquisition).	str	S001
--session	Session label (e.g., V1). Required when --batch off (processes a single acquisition).	str	V1
--parc	Chimera parcellation string	str	LFMIHIFIS
--npert	Number of metabolic profile perturbations	int	50
--leave_one_out	Leave-one-metabolite-out option	int (0 or 1)	0
--show_plot	Show plots	int (0 or 1)	0
--overwrite	Overwrite existing results	int (0 or 1)	0
--ref_met	Reference metabolite for coregistration	str	CrPCr
--nthreads	Number of parallel CPU threads	int	4
--t1	Path or pattern to T1-weighted image	str	None
--t1mask	Path or pattern to T1-weighted brain mask	str	None
--b0	MRI B0 field in Tesla	float (choices: 3, 7)	3
--batch	Batch mode. file requires --participants; off requires --subject_id and --session (processes a single acquisition); all uses all pairs.	str (choices: all,file,off)	off
--participants	Path to a .tsv listing subject–session pairs to process (required when --batch file; ignored for --batch off and --batch all).	path	None

• Note: --participants refers to a BIDS-style participants_allsessions.tsv. Defaults to $BIDSDATAPATH/group if not specified.

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📊 MetSiM Analysis

1. Construct Metabolic Similarity Map (Single Subject)

   python experiments/MetSiM_analysis/construct_MSI-map_subj.py --group Geneva-Study --parc LFMIHIFIS --scale 3 --npert 50 --dimalg pca_tsne
   

2. Construct Metabolic Similarity Map (Population)

   python experiments/MetSiM_analysis/construct_MSI-map_pop.py --group Geneva-Study --parc LFMIHIFIS --scale 3 --npert 50 --dimalg pca_tsne --msiscale -255.0
   

3. Inverse Map MSI to MRSI Signal (Population)

   python experiments/MetSiM_analysis/construct_MSI-map_pop.py --group Geneva-Study --parc LFMIHIFIS --scale 3 --npert 50 --dimalg pca_tsne
   

4. Derive all GM constrained networtk paths

   python experiments/MetSiM_analysis/find_all_network_paths.py --group Geneva-Study --parc LFMIHIFIS --scale 3
   --lobe LOBE --hemi HEMI --lpath 13
   

5. Construct Metabolic Principal Curve (Population)

   python experiments/MetSiM_analysis/construct_metabolic_principal_path.py --group Geneva-Study --parc LFMIHIFIS --scale 3 --diag group --lpath 13 --lobe ctx --nperm 100 --lobe ctx
   

• Note:
  • Run first find_all_network_paths.py for both hemispheres lh and rh to construct all possible network paths then select the one which maximizes metabolic entropy and minimizes local metabolic heterogeneity with construct_metabolic_principal_path.py followed by comparison with a random geometric network and results figure generation.
  • --dimalg specifies the manifold-discovery algorithm used to construct the metabolic fibre.
  • --hemi chooses the hemisphere in which the fibre is built (lh or rh).
  • --lpath sets the maximum path length.
  • --nperm defines the size of the null distribution, generated from random-geometric networks.
  • --start and --stop indicate the start and stop nodes.
    • If not provided, they default to the occipital region (start) and frontal/anterior cingulate regions (stop), which maximise the inter-node MS-mode difference.
    • The script then runs find_all_network_paths.py from scratch with these adjusted end-node labels.
  • --lobe restricts the path search to either the neocortex (ctx) or the subcortex (subc).

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🔧 Pre-Processing Pipeline for Voxel-Based Analysis

experiments/Preprocessing/preprocess.py now runs the full voxel-wise chain end-to-end: preflight checks, optional orientation correction, spike filtering, optional partial volume correction, and MRSI exports to MNI or T1w space. Missing transforms are generated where supported; overwrite flags force regeneration.

What it does

• Checks required inputs (T1w, MRSI signals/CRLB/SNR/FWHM, CAT12 p1–p3 where available) and batches subject–session pairs from participants_allsessions.tsv or a custom TSV/CSV.
• Prints a preflight availability table per subject/session: existing files are marked with a green check, missing ones with a red X, and items marked PROC (orange) are auto-generated during preprocessing (e.g., transforms or masks).
• Optional oblique FOV correction (--corr_orient).
• Filters MRSI spikes (--filtoption, --spikepc) and builds brain masks if present.
• Runs/refreshes T1w→MNI registration when needed (--overwrite_mni_reg) and exports MRSI to T1w or MNI space at native-MRSI or T1w resolution via --transform.
• Partial volume correction runs when --overwrite_pve is set and p1/p2/p3 maps are present; otherwise PVC is skipped (or explicitly bypassed with --no_pvc).

Batch mode semantics

• --batch file requires --participants to point to a .tsv/.csv listing the subject–session pairs to process.
• --batch off requires both --sub and --ses and processes one acquisition.
• --batch all processes all discoverable subject–session pairs in the group.

Quick start

• Single subject (filtering → PVC → export to MNI @ native MRSI res)

  python experiments/Preprocessing/preprocess.py \
    --group Dummy-Project --sub S001 --ses V1 \
    --t1 acq-memprage_desc-brain_T1w --b0 3 --nthreads 16 \
    --transform mni-origres --overwrite_pve
  

• Batch (participants TSV/CSV)

  python experiments/Preprocessing/preprocess.py \
    --group Dummy-Project --b0 3 --nthreads 16 \
    --batch file --participants $PATH2_PARTICIPANT-SESSION_FILE \
    --t1 acq-memprage_desc-brain_T1w \
    --transform mni-t1wres --overwrite_pve
  

• Optional add-ons: --corr_orient to fix oblique FOV; --no_pvc to skip partial volume correction; --overwrite_filt/--overwrite_transform/--overwrite_mni_reg to recompute stages; --proc_mnilong (+ --overwrite_mnilong) for MNI152-long outputs; --checksum to print a pre-run output validity summary; --v 1 for verbose logs.

Key preprocess.py options

Argument	Default	Purpose
--group	Mindfulness-Project	BIDS project under $BIDSDATAPATH.
--sub	S002	Subject ID used when --batch off.
--ses	V3	Session ID used when --batch off.
--batch	off (all/file/off)	Process all pairs, a TSV/CSV list (--participants), or a single pair.
--participants	None	TSV/CSV path used when --batch file (columns like participant_id/sub and ses/session_id).
--t1	desc-brain_T1w	T1w path or pattern (resolved per subject/session).
--b0	3 (3/7)	Sets metabolite list (3T vs 7T).
--nthreads	4	CPU threads for filtering, PVC, and transforms.
--filtoption	filtbiharmonic	Spike filtering strategy.
--spikepc	99	Percentile for spike removal.
--transform	mni-origres (mni-t1wres/t1w-origres/t1w-t1wres)	Export MRSI to MNI or T1w space at native MRSI vs T1w resolution.
--overwrite_transform	off	Recompute transforms/exports for the selected space+resolution (flag).
--no_pvc	off	Skip PVC exports (transform filtered signals only; flag).
--overwrite_pve	off	Run/refresh partial volume correction before transforms (flag).
--overwrite_filt	off	Recompute spike filtering outputs (flag).
--overwrite_t1_reg	off	Force regeneration of MRSI→T1w transforms (flag).
--overwrite_mni_reg	off	Force regeneration of T1w→MNI transforms (flag).
--proc_mnilong	off	Generate MNI152-longitudinal outputs (flag).
--overwrite_mnilong	off	Rerun MNI152-longitudinal exports (flag).
--corr_orient	off	Correct oblique FOV orientation for MRSI and masks (flag).
--checksum	off	Display the output validity summary before processing (flag).
--v	0	Verbose flag (1 to print more detail).

Input notes: --participants can be TSV/CSV (subject column such as participant_id/sub/id and session column such as ses/session_id); default is $BIDSDATAPATH/<group>/participants_allsessions.tsv (V2BIS rows are skipped). --t1 is required (defaults to the desc-brain_T1w pattern). Boolean options are flags (no 0/1 values); include them to enable. PVC needs CAT12 p1/p2/p3 maps; if absent or --no_pvc is set, PVC is skipped and logged.

Population quality mask

python experiments/Preprocessing/compute_pop_qmask.py \
  --group Dummy-Project --participants $PATH2_PARTICIPANT-SESSION_FILE \
  --snr 4 --crlb 20 --fwhm 0.1 --alpha 0.68 --b0 3

Registration helpers (optional)
All registration is triggered automatically by preprocess.py. Run these directly only for debugging or bespoke registration:

• MRSI→T1w (batch capable)

  python experiments/Preprocessing/registration_mrsi_to_t1.py \
    --group Dummy-Project --ref_met CrPCr --nthreads 16 \
    --batch file --participants $PATH2_PARTICIPANT-SESSION_FILE \
    --t1 acq-memprage_desc-brain_T1w
  

• T1w→MNI (batch capable)

  python experiments/Preprocessing/registration_t1_to_MNI.py \
    --group Dummy-Project --nthreads 16 \
    --batch file --participants $PATH2_PARTICIPANT-SESSION_FILE