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Mapping Individual Neuroanatomical Alterations to Schizophrenia Psychopathology with Normative Modeling

Code ↔ Paper

6 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 6 matches
  1. [1] § Methods › Applying clinical datasets to the normative models ↔ scripts/apply_normative_models_ct.ipynb, lines 257–387 · score 0.69 · aleatoric uncertainty, epistemic uncertainty, variance components, predicted, normative, models
  2. [2] § Methods › Applying clinical datasets to the normative models ↔ scripts/apply_normative_models_ct.py, lines 300–339 · score 0.69 · aleatoric uncertainty, epistemic uncertainty, variance components, predicted, normative, models
  3. [3] § Methods › Establishing and evaluating normative models of GMV ↔ scripts/fit_normative_models_ct.ipynb, lines 87–109 · score 0.65 · Gaussian noise, spline basis expansion, cubic, knots, linear, age
  4. [4] § Methods › Establishing and evaluating normative models of GMV ↔ scripts/fit_normative_models_yeo17.ipynb, lines 77–99 · score 0.65 · Gaussian noise, spline basis expansion, cubic, knots, linear, age
  5. [5] § Methods › Establishing and evaluating normative models of GMV ↔ scripts/fit_normative_models_ct.ipynb, lines 180–243 · score 0.53 · standardized log loss, MSLL, kurtosis, skew, fit, variance
  6. [6] § Methods › Establishing and evaluating normative models of GMV ↔ scripts/fit_normative_models_yeo17.ipynb, lines 170–233 · score 0.53 · standardized log loss, MSLL, kurtosis, skew, fit, variance

Paper

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The authors' code

Jupyter notebook · 249 lines · 10 KB · GPL-3.0 · 2 matches

  1. # %% [markdown]
  2. # ## Estimating lifespan normative models
  3. #
  4. # This notebook provides a complete walkthrough for an analysis of normative modelling in a large sample as described in the accompanying paper. Note that this script is provided principally for completeness (e.g. to assist in fitting normative models to new datasets). All pre-estimated normative models are already provided.
  5. #
  6. # First, if necessary, we install PCNtoolkit (note: this tutorial requires at least version 0.27)
  8. # %%
  9. ! pip install pcntoolkit==0.35
  11. # %% [markdown]
  12. # Then we import the required libraries
  14. # %%
  15. import os
  16. import numpy as np
  17. import pandas as pd
  18. import pickle
  19. from matplotlib import pyplot as plt
  20. import seaborn as sns
  22. from pcntoolkit.normative import estimate, predict, evaluate
  23. from pcntoolkit.util.utils import compute_MSLL, create_design_matrix
  24. from nm_utils import calibration_descriptives, remove_bad_subjects, load_2d
  26. # %% [markdown]
  27. # Now, we configure the locations in which the data are stored. You will need to configure this for your specific installation
  28. #
  29. # **Notes:**
  30. # - The data are assumed to be in CSV format and will be loaded as pandas dataframes
  31. # - Generally the raw data will be in a different location to the analysis
  32. # - The data can have arbitrary columns but some are required by the script, i.e. 'age', 'sex' and 'site', plus the phenotypes you wish to estimate (see below)
  34. # %%
  35. # where the raw data are stored
  36. data_dir = '<path-to-your>/data'
  38. # where the analysis takes place
  39. root_dir = '<path-to-your>/braincharts'
  40. out_dir = os.path.join(root_dir,'models','test')
  42. # create the output directory if it does not already exist
  43. os.makedirs(out_dir, exist_ok=True)
  45. # %% [markdown]
  46. # Now we load the data.
  47. #
  48. # We will load one pandas dataframe for the training set and one dataframe for the test set. We will also filter out low quality scans on the basis of the Freesurfer [Euler Characteristic](https://surfer.nmr.mgh.harvard.edu/fswiki/EulerNumber) (EC). This is a proxy for scan quality and is described in the publications below. Note that this requires the column 'avg_en' in the pandas dataframe, which is simply the average EC of left and right hemisphere.
  49. #
  50. # We also configrure a list of site ids
  51. #
  52. # **References**
  53. # - [Kia et al 2021](https://www.biorxiv.org/content/10.1101/2021.05.28.446120v1.abstract)
  54. # - [Rosen et al 2018](https://www.sciencedirect.com/science/article/abs/pii/S1053811917310832?via%3Dihub)
  56. # %%
  57. df_tr = pd.read_csv(os.path.join(data_dir,'lifespan_big_controls_tr_mqc.csv'), index_col=0)
  58. df_te = pd.read_csv(os.path.join(data_dir,'lifespan_big_controls_te_mqc.csv'), index_col=0)
  60. # remove some bad subjects
  61. df_tr, bad_sub = remove_bad_subjects(df_tr, df_tr)
  62. df_te, bad_sub = remove_bad_subjects(df_te, df_te)
  64. # extract a list of unique site ids from the training set
  65. site_ids = sorted(set(df_tr['site'].to_list()))
  67. # %% [markdown]
  68. # ### Configure which models to fit
  69. #
  70. # Next, we load the image derived phenotypes (IDPs) which we will process in this analysis. This is effectively just a list of columns in your dataframe. Here we estimate normative models for the left hemisphere, right hemisphere and cortical structures.
  72. # %%
  73. # load the idps to process
  74. with open(os.path.join(root_dir,'docs','phenotypes_ct_lh.txt')) as f:
  75. idp_ids_lh = f.read().splitlines()
  76. with open(os.path.join(root_dir,'docs','phenotypes_ct_rh.txt')) as f:
  77. idp_ids_rh = f.read().splitlines()
  78. with open(os.path.join(root_dir,'docs','phenotypes_sc.txt')) as f:
  79. idp_ids_sc = f.read().splitlines()
  81. # we choose here to process all idps
  82. idp_ids = idp_ids_lh + idp_ids_rh + idp_ids_sc
  84. # we could also just specify a list of IDPs
  85. #idp_ids = ['lh_MeanThickness_thickness', 'rh_MeanThickness_thickness']
  87. # %% [markdown]
  88. # ### Configure model parameters
  89. #
  90. # Now, we configure some parameters for the regression model we use to fit the normative model. Here we will use a 'warped' Bayesian linear regression model. To model non-Gaussianity, we select a sin arcsinh warp and to model non-linearity, we stick with the default value for the basis expansion (a cubic b-spline basis set with 5 knot points). Since we are sticking with the default value, we do not need to specify any parameters for this, but we do need to specify the limits. We choose to pad the input by a few years either side of the input range. We will also set a couple of options that control the estimation of the model
  91. #
  92. # For further details about the likelihood warping approach, see [Fraza et al 2021](https://www.biorxiv.org/content/10.1101/2021.04.05.438429v1).
  94. # %%
  95. # which data columns do we wish to use as covariates?
  96. cols_cov = ['age','sex']
  98. # which warping function to use? We can set this to None in order to fit a vanilla Gaussian noise model
  99. warp = 'WarpSinArcsinh'
  101. # limits for cubic B-spline basis
  102. xmin = -5
  103. xmax = 110
  105. # Do we want to force the model to be refit every time?
  106. force_refit = True
  108. # Absolute Z treshold above which a sample is considered to be an outlier (without fitting any model)
  109. outlier_thresh = 7
  111. # %% [markdown]
  112. # ### Fit the models
  113. #
  114. # Now we fit the models. This involves looping over the IDPs we have selected. We will use a module from PCNtoolkit to set up the design matrices, containing the covariates, fixed effects for site and nonlinear basis expansion.
  116. # %%
  117. for idp_num, idp in enumerate(idp_ids):
  118. print('Running IDP', idp_num, idp, ':')
  120. # set output dir
  121. idp_dir = os.path.join(out_dir, idp)
  122. os.makedirs(os.path.join(idp_dir), exist_ok=True)
  123. os.chdir(idp_dir)
  125. # extract the response variables for training and test set
  126. y_tr = df_tr[idp].to_numpy()
  127. y_te = df_te[idp].to_numpy()
  129. # remove gross outliers and implausible values
  130. yz_tr = (y_tr - np.mean(y_tr)) / np.std(y_tr)
  131. yz_te = (y_te - np.mean(y_te)) / np.std(y_te)
  132. nz_tr = np.bitwise_and(np.abs(yz_tr) < outlier_thresh, y_tr > 0)
  133. nz_te = np.bitwise_and(np.abs(yz_te) < outlier_thresh, y_te > 0)
  134. y_tr = y_tr[nz_tr]
  135. y_te = y_te[nz_te]
  137. # write out the response variables for training and test
  138. resp_file_tr = os.path.join(idp_dir, 'resp_tr.txt')
  139. resp_file_te = os.path.join(idp_dir, 'resp_te.txt')
  140. np.savetxt(resp_file_tr, y_tr)
  141. np.savetxt(resp_file_te, y_te)
  143. # configure the design matrix
  144. X_tr = create_design_matrix(df_tr[cols_cov].loc[nz_tr],
  145. site_ids = df_tr['site'].loc[nz_tr],
  146. basis = 'bspline',
  147. xmin = xmin,
  148. xmax = xmax)
  149. X_te = create_design_matrix(df_te[cols_cov].loc[nz_te],
  150. site_ids = df_te['site'].loc[nz_te],
  151. all_sites=site_ids,
  152. basis = 'bspline',
  153. xmin = xmin,
  154. xmax = xmax)
  156. # configure and save the covariates
  157. cov_file_tr = os.path.join(idp_dir, 'cov_bspline_tr.txt')
  158. cov_file_te = os.path.join(idp_dir, 'cov_bspline_te.txt')
  159. np.savetxt(cov_file_tr, X_tr)
  160. np.savetxt(cov_file_te, X_te)
  162. if not force_refit and os.path.exists(os.path.join(idp_dir, 'Models', 'NM_0_0_estimate.pkl')):
  163. print('Making predictions using a pre-existing model...')
  164. suffix = 'predict'
  166. # Make prdictsion with test data
  167. predict(cov_file_te,
  168. alg='blr',
  169. respfile=resp_file_te,
  170. model_path=os.path.join(idp_dir,'Models'),
  171. outputsuffix=suffix)
  172. else:
  173. print('Estimating the normative model...')
  174. estimate(cov_file_tr, resp_file_tr, testresp=resp_file_te,
  175. testcov=cov_file_te, alg='blr', optimizer = 'l-bfgs-b',
  176. savemodel=True, warp=warp, warp_reparam=True)
  177. suffix = 'estimate'
  180. # %% [markdown]
  181. # ### Compute error metrics
  182. #
  183. # In this section we compute the following error metrics for all IDPs (all evaluated on the test set):
  184. #
  185. # - Negative log likelihood (NLL)
  186. # - Explained variance (EV)
  187. # - Mean standardized log loss (MSLL)
  188. # - Bayesian information Criteria (BIC)
  189. # - Skew and Kurtosis of the Z-distribution
  191. # %%
  192. # initialise dataframe we will use to store quantitative metrics
  193. blr_metrics = pd.DataFrame(columns = ['eid', 'NLL', 'EV', 'MSLL', 'BIC','Skew','Kurtosis'])
  195. for idp_num, idp in enumerate(idp_ids):
  196. idp_dir = os.path.join(out_dir, idp)
  198. # load the predictions and true data. We use a custom function that ensures 2d arrays
  199. # equivalent to: y = np.loadtxt(filename); y = y[:, np.newaxis]
  200. yhat_te = load_2d(os.path.join(idp_dir, 'yhat_' + suffix + '.txt'))
  201. s2_te = load_2d(os.path.join(idp_dir, 'ys2_' + suffix + '.txt'))
  202. y_te = load_2d(os.path.join(idp_dir, 'resp_te.txt'))
  204. with open(os.path.join(idp_dir,'Models', 'NM_0_0_estimate.pkl'), 'rb') as handle:
  205. nm = pickle.load(handle)
  207. # compute error metrics
  208. if warp is None:
  209. metrics = evaluate(y_te, yhat_te)
  211. # compute MSLL manually as a sanity check
  212. y_tr_mean = np.array( [[np.mean(y_tr)]] )
  213. y_tr_var = np.array( [[np.var(y_tr)]] )
  214. MSLL = compute_MSLL(y_te, yhat_te, s2_te, y_tr_mean, y_tr_var)
  215. else:
  216. warp_param = nm.blr.hyp[1:nm.blr.warp.get_n_params()+1]
  217. W = nm.blr.warp
  219. # warp predictions
  220. med_te = W.warp_predictions(np.squeeze(yhat_te), np.squeeze(s2_te), warp_param)[0]
  221. med_te = med_te[:, np.newaxis]
  223. # evaluation metrics
  224. metrics = evaluate(y_te, med_te)
  226. # compute MSLL manually
  227. y_te_w = W.f(y_te, warp_param)
  228. y_tr_w = W.f(y_tr, warp_param)
  229. y_tr_mean = np.array( [[np.mean(y_tr_w)]] )
  230. y_tr_var = np.array( [[np.var(y_tr_w)]] )
  231. MSLL = compute_MSLL(y_te_w, yhat_te, s2_te, y_tr_mean, y_tr_var)
  233. Z = np.loadtxt(os.path.join(idp_dir, 'Z_' + suffix + '.txt'))
  234. [skew, sdskew, kurtosis, sdkurtosis, semean, sesd] = calibration_descriptives(Z)
  236. BIC = len(nm.blr.hyp) * np.log(y_tr.shape[0]) + 2 * nm.neg_log_lik
  238. blr_metrics.loc[len(blr_metrics)] = [idp, nm.neg_log_lik, metrics['EXPV'][0],
  239. MSLL[0], BIC, skew, kurtosis]
  241. display(blr_metrics)
  243. blr_metrics.to_pickle(os.path.join(out_dir,'blr_metrics.pkl'))
  245. # %%
  246. blr_metrics.to_csv(os.path.join(out_dir,'blr_metrics.csv'))
  248. # %%

fit_normative_models_ct.ipynb at commit 5a1a782, under GPL-3.0 · at the source

Overview

Authors: Johanna Spaeth1, Charlotte Fraza2,3, Deniz Yilmaz1,4, Lena Deller1, BrainTrain Working Group1, CDP Working Group1, Genc Hasanaj1,5,6, Marcel Kallweit1,5, Maxim Korman1,5, Emanuel Boudriot1,7, Vladislav Yakimov1,8, Joanna Moussiopoulou1,5, Florian J. Raabe1,7, Elias Wagner6,9, Andrea Schmitt1,7,10,11, Astrid Roeh9, Peter Falkai1,7,11, Daniel Keeser1,5, Isabel Maurus1,8, Lukas Roell1,5,7,12
ORCID iDs: Johanna Spaeth, Charlotte Fraza, Lena Deller, Vladislav Yakimov, Daniel Keeser, Lukas Roell
  1. Department of Psychiatry and Psychotherapy, LMU University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany
  2. Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands
  3. Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
  4. Max Planck School of Cognition, Leipzig, Germany
  5. NeuroImaging Core Unit Munich (NICUM), LMU University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany
  6. Evidence-Based Psychiatry and Psychotherapy, Faculty of Medicine, University of Augsburg, Augsburg, Germany
  7. Max Planck Institute of Psychiatry, Munich, Germany
  8. International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
  9. Department of Psychiatry, Psychotherapy, and Psychosomatics, Medical Faculty, University of Augsburg, Bezirkskrankenhaus Augsburg, Augsburg, Germany
  10. Laboratory of Neuroscience (LIM27), Institute of Psychiatry, University of Sao Paulo, São Paulo, Brazil
  11. German Center for Mental Health (DZPG), partner site Munich-Augsburg, Munich, Germany
  12. Systems Lab, Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia
Institutions: LMU Klinikum (Germany); Ludwig-Maximilians-Universität München (Germany); Radboud University Nijmegen (Netherlands); Radboud University Medical Center (Netherlands); University of Augsburg (Germany); Max Planck Institute of Psychiatry (Germany); Universidade de São Paulo (Brazil); Deutsches Zentrum für Psychische Gesundheit (Germany); The University of Melbourne (Australia)
Journal: medRxiv (preprint)
Dates: published online 1 April 2026
Type: Preprint
License: CC BY-ND
Identifiers: DOI 10.64898/2026.03.31.26349848 · OpenAlex W7147270884
Open access: green, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), schizophrenia / psychosis (population), computational (subfield)
Methods: Statistics, Machine learning, Preprocessing, Connectivity, fMRI & imaging, Physiology & signal measures
Topic: Schizophrenia research and treatment (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: Biotechnology and Biological Sciences Research Council (BB/H008217/1)
Citations: not cited yet (Europe PMC); 99 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

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State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 2 files
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Found in: “Code availability and Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
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At the source: osf.io/etdvu/overview?view_only=3a61e761f0bf4fb9b38baa15d7e15c61

predictive-clinical-neuroscience/braincharts

License: GPL-3.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 5a1a7822e6f54405b0a826cf137d584240af307c, 30 June 2025
Languages: Jupyter (6), Python (3)
Size: 1,193 files, 9 scripts
Software Heritage: archived
Found in: “Code availability and Data availability”
Holds: README, license file, documentation, 6 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration
Tools: NumPy (8 files), pandas (8 files), Matplotlib (7 files), seaborn (7 files), FreeSurfer (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
11 files
At the source: github.com/predictive-clinical-neuroscience/braincharts

The paper's code and data availability statement is in the Data section.

Tracing map

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Read it in the paper: doi.org/10.64898/2026.03.31.26349848.

Versions

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Version 2, 28 September 2026

  • Authors: added Johanna Spaeth (0009-0007-6550-0638); Vladislav Yakimov (0000-0001-9559-7492); Lukas Roell (0000-0002-0284-2290); removed Johanna Spaeth; Vladislav Yakimov; Lukas Roell

Version 1, 28 September 2026: the first record

Recorded: type, journal, dates, 20 authors, 1 funder, 88 references.

Cite

This paper

Spaeth, J., Fraza, C., Yilmaz, D., Deller, L., BrainTrain Working Group, CDP Working Group, Hasanaj, G., Kallweit, M., Korman, M., Boudriot, E., Yakimov, V., Moussiopoulou, J., Raabe, F. J., Wagner, E., Schmitt, A., Roeh, A., Falkai, P., Keeser, D., Maurus, I., & Roell, L. (2026). Mapping Individual Neuroanatomical Alterations to Schizophrenia Psychopathology with Normative Modeling. medRxiv (preprint). https://doi.org/10.64898/2026.03.31.26349848

BibTeX

@article{spaeth2026mapping,
author = {Spaeth, Johanna and Fraza, Charlotte and Yilmaz, Deniz and Deller, Lena and {BrainTrain Working Group} and {CDP Working Group} and Hasanaj, Genc and Kallweit, Marcel and Korman, Maxim and Boudriot, Emanuel and Yakimov, Vladislav and Moussiopoulou, Joanna and Raabe, Florian J. and Wagner, Elias and Schmitt, Andrea and Roeh, Astrid and Falkai, Peter and Keeser, Daniel and Maurus, Isabel and Roell, Lukas},
title = {{Mapping Individual Neuroanatomical Alterations to Schizophrenia Psychopathology with Normative Modeling}},
journal = {medRxiv (preprint)},
year = {2026},
month = apr,
publisher = {medRxiv},
doi = {10.64898/2026.03.31.26349848},
url = {https://doi.org/10.64898/2026.03.31.26349848}
}

RIS

TY - JOUR
AU - Spaeth, Johanna
AU - Fraza, Charlotte
AU - Yilmaz, Deniz
AU - Deller, Lena
AU - BrainTrain Working Group
AU - CDP Working Group
AU - Hasanaj, Genc
AU - Kallweit, Marcel
AU - Korman, Maxim
AU - Boudriot, Emanuel
AU - Yakimov, Vladislav
AU - Moussiopoulou, Joanna
AU - Raabe, Florian J.
AU - Wagner, Elias
AU - Schmitt, Andrea
AU - Roeh, Astrid
AU - Falkai, Peter
AU - Keeser, Daniel
AU - Maurus, Isabel
AU - Roell, Lukas
TI - Mapping Individual Neuroanatomical Alterations to Schizophrenia Psychopathology with Normative Modeling
T2 - medRxiv (preprint)
J2 - medRxiv
PY - 2026
DA - 2026/04/01
PB - medRxiv
DO - 10.64898/2026.03.31.26349848
UR - https://doi.org/10.64898/2026.03.31.26349848
ER -

CSL-JSON

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Empirical validation of race-neutral normative brain morphometry models across ethnoracially diverse populations.
Journal: Proceedings of the National Academy of Sciences of the United States of America
In common: FreeSurfer, pandas, NumPy, computational, structural MRI / diffusion, 6 references, author Daniel Keeser
[3] doi:10.1038/s41467-026-72875-x [code]
Lifespan normative modeling of brain microstructure.
Journal: Nature communications
In common: seaborn, pandas, Matplotlib, 1 other tool, schizophrenia / psychosis, structural MRI / diffusion, 9 references
[4] doi:10.1038/s41593-026-02359-0 [code]
The cross-site reproducibility of MRI morphometric phenotypes in psychiatric disorders.
Journal: Nature neuroscience
In common: FreeSurfer, pandas, Matplotlib, 1 other tool, schizophrenia / psychosis, structural MRI / diffusion, 7 references
[5] doi:10.64898/2026.08.13.26360304 [code]
Lifespan brain structural variation reveals shared organization across mental health conditions
Journal: medRxiv (preprint)
In common: seaborn, pandas, Matplotlib, 1 other tool, 7 references
[6] doi:10.1371/journal.pbio.3003856 [code]
Aging and metabolism contribute separately to brain-body health.
Journal: PLoS biology
In common: FreeSurfer, seaborn, pandas, 2 other tools, structural MRI / diffusion, 4 references
[7] doi:10.1038/s41398-026-03902-0 [code]
Mapping heterogeneous brain structural subtypes in alzheimer's disease and mild cognitive impairment using normative models.
Journal: Translational psychiatry
In common: seaborn, pandas, Matplotlib, 1 other tool, structural MRI / diffusion, 5 references
[8] doi:10.1371/journal.pmed.1004809 [code]
Brain morphology in Anorexia Nervosa and its subtypes: A multi-cohort study of individual participant data.
Journal: PLoS medicine
In common: seaborn, pandas, Matplotlib, 1 other tool, structural MRI / diffusion, 4 references
[9] doi:10.1038/s41467-026-75585-6 [code]
Brain network dynamics reflect psychiatric illness status and transdiagnostic symptom profiles across health and disease.
Journal: Nature communications
In common: seaborn, pandas, Matplotlib, 1 other tool, schizophrenia / psychosis, 4 references
[10] doi:10.1038/s41467-026-71555-0 [code]
A deep representation learning model to predict response to vagus nerve stimulation.
Journal: Nature communications
In common: FreeSurfer, seaborn, pandas, 2 other tools, structural MRI / diffusion, 4 references

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