Individualized phenotyping of functional amyotrophic lateral sclerosis pathology in sensorimotor cortex.
The 11 matches
- [1] § Methods › Statistical analysis › Control analysis: QSM- versus ECM-based PLSR ↔ PLSR_QSM.py, lines 130–196 · score 0.80 · X_train, y_train, latent scores, squared error, QSM, fold
- [2] § Methods › Statistical analysis › Control analysis: QSM- versus ECM-based PLSR ↔ PLSR_ECM.py, lines 157–209 · score 0.71 · X_train, y_train, squared error, fold, ECM, MSE
- [3] § Methods › Statistical analysis › Generating functional localizer masks within the sensorimotor cortex ↔ connected_cluster_masks_tasks.py, lines 59–85 · score 0.68 · connected cluster, localizer masks, combined mask, largest, body part, tongue
- [4] § Methods › Statistical analysis › Robust shared response modelling for amyotrophic lateral sclerosis versus control classification ↔ ALS_vs_control_rSRM.py, lines 230–323 · score 0.58 · rSRM, cross validation, shared space, trained, ALS
- [5] § Methods › Statistical analysis › Control analysis: QSM- versus ECM-based PLSR ↔ PLSR_QSM.py, lines 634–711 · score 0.58 · LV2 weight, PLS models, QSM, ECM, LV1, tongue
- [6] § Methods › Statistical analysis › Robust shared response modelling for amyotrophic lateral sclerosis versus control classification ↔ ALS_vs_control_rSRM.py, lines 230–323 · score 0.57 · cross validation, shared spaces, transforming, concatenated, training, predictions
- [7] § Methods › Statistical analysis › Partial least squares regression analysis ↔ PLSR_ECM.py, lines 157–209 · score 0.54 · cross validation, latent variable, LOO, MSE, split, model
- [8] § Methods › Statistical analysis › Partial least squares regression analysis ↔ PLSR_BOLD.py, lines 189–243 · score 0.53 · cross validation, latent variable, LOO, MSE, model, weights
- [9] § Methods › Statistical analysis › Per cent signal change analyses over time ↔ filtered_connectivity.py, lines 32–93 · score 0.53 · band pass filtering, signal, 0.01 Hz, voxel
- [10] § Methods › Pre-processing of fMRI data ↔ NL_AA_mni.py, lines 15–52 · score 0.52 · ANTs, affine, smoothing, anatomical, template, MNI
- [11] § Methods › Statistical analysis › Functional activation ↔ filtered_connectivity.py, lines 32–93 · score 0.51 · band pass filtering, fMRI, 0.01 Hz, maps, body
Paper
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The authors' code
Python · 711 lines · 30 KB · no license · 2 matches
PLSR_QSM.py at commit 4543945, no license · at the source
Overview
- Institute for Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University Magdeburg, Magdeburg 39120, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg 39120, Germany
- Hertie Institute for Clinical Brain Research (HIH), Tübingen 72076, Germany
- German Center for Neurodegenerative Diseases (DZNE), Tübingen 72076, Germany
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, United Kingdom
- Clinic for Neurology, Otto-von-Guericke University Magdeburg, Magdeburg 39120, Germany
- Department Biomedical Magnetic Resonance (BMMR), Otto-von-Guericke University Magdeburg, Magdeburg 39120, Germany
- Center for Behavioral Brain Sciences (CBBS) Magdeburg, Magdeburg 39120, Germany
- Leibniz Institute for Neurobiology (LIN), Otto-von-Guericke University Magdeburg, Magdeburg 39120, Germany
- Research Campus STIMULATE, Otto von Guericke University Magdeburg, Magdeburg 39106, Germany
Abstract
Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease characterized by the loss of motor neurons in primary motor cortex, leading to muscle weakness, atrophy and death within a median of 3 years. Even though ALS is characterized by different disease subtypes affecting different body parts, individualized phenotyping of functional ALS pathology has so far not been achieved. We recorded 7 Tesla functional MRI data while ALS patients and matched controls moved affected and non-affected body parts in the MR scanner. We applied robust Shared Response Modelling for capturing ALS-specific shared responses for group classification, and Partial Least Squares regression for relating the latent variables to clinical subtypes and the degree of disease progression. We show that disease onset and severity can be best modelled by functional connectivity rather than local activation changes. We also show that functional disease-defining information in primary motor cortex is not the strongest in the area that is behaviourally first-affected, deviating from the behavioural phenotype of the patients. When computing the model’s weight distribution of the King stage classification and projecting them back into voxel space, the highest mean weights are present in the foot and tongue/
Reproduced under the paper's license (CC BY), from the paper cited above.
Repository
Its files are read in the Code ↔ Paper reader above, with 11 matches between paragraphs and lines of code.
avinashkalyani/ALS_PLSr
4543945228adb31aaf0d69ec57b493dffaca05c7, 24 July 2025Availability: 1 check, the latest on 29 September 2026: the link answers
- 29 September 2026: the link answers
15 files, not copied: shown from their source
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- ALS_vs_control_rSRM.py — Python, 643 lines, 2 matches, shown from its source
- Anatomical_alignment.py — Python, 52 lines, shown from its source
- ECM_HFC.sh — Shell, 35 lines, shown from its source
- GLM1_level.m — MATLAB, 321 lines, shown from its source
- Group_2nd_2sample_test.m
— MATLAB, 170 lines, shown from its source - NL_AA_mni.py — Python, 62 lines, 1 match, shown from its source
- PLSR_BOLD.py — Python, 300 lines, 1 match, shown from its source
- PLSR_ECM.py — Python, 274 lines, 2 matches, shown from its source
- PLSR_QSM.py — Python, 711 lines, 2 matches, shown from its source
- cleaned_threshold_statma
ps.py — Python, 62 lines, shown from its source - connected_cluster_masks_
tasks.py — Python, 85 lines, 1 match, shown from its source - filtered_connectivity.py
— Python, 213 lines, 2 matches, shown from its source - group_als.py — Python, 632 lines, shown from its source
- thresholding_stats_local
izer.py — Python, 246 lines, shown from its source - README.md — Text, 204 lines, shown from its source
Code availability
All code used for PLSR-based functional data analysis is available at: https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Tracing map
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Data
Datasets cited
- github.com/
alicianorthall/ — at github.com; found in “Data availability”in-vivo-pathology-als
Data availability
fMRI/
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 1, 29 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 5 keywords, 2 funders, 41 references.
Cite
This paper
Kalyani, A., Northall, A., Schreiber, S., Brüggemann, J., Vielhaber, S., Al Dubai, M., Bin Ramadan, A., Mattern, H., Speck, O., Reichert, C., & Kuehn, E. (2026). Individualized phenotyping of functional amyotrophic lateral sclerosis pathology in sensorimotor cortex. Brain communications, 8(2), fcag127. https://
BibTeX
@article{kalyani2026indi
author = {Kalyani, Avinash and Northall, Alicia and Schreiber, Stefanie and Brüggemann, Jascha and Vielhaber, Stefan and Al Dubai, Marwa and Bin Ramadan, Abrar and Mattern, Hendrik and Speck, Oliver and Reichert, Christoph and Kuehn, Esther},
title = {{Individualized phenotyping of functional amyotrophic lateral sclerosis pathology in sensorimotor cortex}},
journal = {Brain communications},
year = {2026},
month = apr,
volume = {8},
number = {2},
pages = {fcag127},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/
url = {https://
pmid = {42058282},
pmcid = {PMC13122844}
}
RIS
TY - JOUR
AU - Kalyani, Avinash
AU - Northall, Alicia
AU - Schreiber, Stefanie
AU - Brüggemann, Jascha
AU - Vielhaber, Stefan
AU - Al Dubai, Marwa
AU - Bin Ramadan, Abrar
AU - Mattern, Hendrik
AU - Speck, Oliver
AU - Reichert, Christoph
AU - Kuehn, Esther
TI - Individualized phenotyping of functional amyotrophic lateral sclerosis pathology in sensorimotor cortex
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/
VL - 8
IS - 2
SP - fcag127
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/
UR - https://
LA - en
ER -
CSL-JSON
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