White matter micro- and macrostructure brain charts for the human lifespan.
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The authors' code
Python · 218 lines · 9.2 KB · CC-BY-NC-4.0
- from lifespan import modeling, OOS, utils
- from pathlib import Path
- import numpy as np
- from scipy.stats import norm
- from scipy.special import gammaln
- import pandas as pd
- import argparse
- TRACTS = utils.TRACTS
- def pa():
- p = argparse.ArgumentParser(description="Power analysis (z-statistic, 2-tailed) for GAMLSS curves")
- p.add_argument("tract", type=str, choices=TRACTS, help="The tract of interest (not necessary if --is_global is set)")
- p.add_argument("measure", type=str, choices=utils.MEASURES + utils.NORMALIZED_MEASURES + utils.GLOBAL_METRICS, help="The measure of interest")
- p.add_argument("--sex", type=str, choices=['M', 'F', 'agnostic'], default='agnostic', help="Specified sex for which to use in calculating model population variance. Use \"agnostic\" for sex agnostic results.")
- p.add_argument("--is_global", action="store_true", help=f"Flag to indicate if the measure is one of the global measures: {utils.GLOBAL_METRICS} (default is False)")
- p.add_argument("--ages", type=float, nargs='+', required=True, help="ages to evaluate at")
- p.add_argument("--effect_sizes", type=float, nargs='+', required=True, help="effect sizes to evaluate at")
- p.add_argument("--z_model_sd", type=float, default=0.15,
- help="Stdev of the GAMLSS model centile stability in z-units. Empirically determined to be 0.15 at the reasonable highest, and so 0.15 is suggested as the default")
- p.add_argument("--alpha", type=float, default=0.05, help="False positive rate")
- p.add_argument("--beta", type=float, default=0.2, help="False negative rate")
- p.add_argument("--q0", type=float, default=0.5, help="ratio (0 < q0 < 1) of total population being taken up by control subgroup")
- p.add_argument("--out_csv", type=str, default=None, help="Path to output CSV if you wish to save the power analysis table.")
- p.add_argument("--model_path", type=str, default=None, help="Override path to .RDS GAMLSS brain chart")
- return p.parse_args()
- def calculate_model_sd(model, ages, sex=None, return_params=False):
- """
- Returns the SD of the GAMLSS GG model fit at the specific ages provided.
- If sex is None, then returns the age-agnostic version. Otherwise, returns the M/F version depending on what is provdided
- """
- if not sex is None:
- assert sex == 'M' or sex == 'F', f"The provided sex {sex} is not acceptable. Either select M or F (or leave blank for age agnostic)"
- sex_array = np.zeros_like(ages)
- if sex == 'M':
- sex_array += 1
- else:
- sex_array = None
- mus = model._model_inference(ages, sex_array=sex_array, sex_agnostic=sex is None, param='mu')
- sigs = model._model_inference(ages, sex_array=sex_array, sex_agnostic=sex is None, param='sigma')
- nus = np.zeros(len(ages)) + model.model_params['nu']['coefficients'][0]
- vars = GG_var(mus, sigs, nus)
- sds = np.sqrt(vars)
- if not return_params:
- return sds
- else:
- return mus, sigs, sds
- def calculate_power_z(sd, d=0.1, a=0.05, b=0.2, q0=0.5):
- """
- Perform a power analysis given the SD of the population and other parameters (Z statistic)
- Assumes two-tailed
- sd: population sd
- d: effect size
- a: FPR
- b: FNR
- q0: ratio of population 1
- q1: ratio of population 2
- """
- Za = norm.ppf(1-(a/2))
- Zb = norm.ppf(1-b)
- q1 = 1 - q0
- A = (1/q1 + 1/q0)
- B = (Za + Zb)**2
- N = A*B/(d/sd)**2
- return N
- def add_model_variance(pop_sds, model_z_sd):
- """
- Calculates the new SD after adding in the model variance
- pop_sds: SD of the population distribution
- model_z_sd: standard devation of the centile scoring in the z-score units
- -IN THE ZSCORE UNITS, NOT THE ORIGINAL UNITS
- """
- z_sd_trans = model_z_sd * pop_sds
- sd_tot = np.sqrt(pop_sds**2 + z_sd_trans**2)
- return sd_tot
- def calculate_model_sample_size(model_file, ages, sex, z_model_sd=0.15, d=[0.1], a=0.05, b=0.2, q0=0.5, return_sds=False):
- """
- Given the fit GAMLSS model, calculates the sample size needed for a power analysis.
- model_file: path to .rds model file
- ages: numpy array of ages to evaluate at
- sex: sex to calculate population variance of
- z_model_sd: standard deviation of centile scoring error in z-units
- d: list of effect sizes to evaluate at
- a: FPR
- b: FNR
- q0: ratio (0 < q0 < 1) of total population being taken up by control subgroup
- return_sds: Boolean for if you want to also return the new population SDs after accounting for model variability
- Returns a dataframe of the information
- """
- model = modeling.GAMLSS(family='GG')
- model.load_fit_model(model_file)
- mus, sigs, sds = calculate_model_sd(model, ages, sex=None if sex=='agnostic' else sex, return_params=True)
- #add the error in centile scoring
- new_sds = add_model_variance(sds, z_model_sd)
- #use these to run the power analysis
- dfs = []
- for eff in d:
- Ns = calculate_power_z(new_sds, d=eff, a=a, b=b, q0=q0)
- temp_df = pd.DataFrame({
- 'age': ages,
- 'effect_size': eff,
- 'sample_size': Ns
- })
- dfs.append(temp_df)
- res_df = pd.concat(dfs, ignore_index=True)
- if return_sds:
- return res_df, new_sds
- return res_df
- def GG_var(mu,sigma,nu):
- """
- Calculates the variance of the GG distribution (GAMLSS implementation) based on the distributional parameters.
- Derivation based on information from:
- Rigby RA, Stasinopoulos DM. Generalized additive models for location, scale and shape (with discussion). Appl Stat 2005; 54
- y ~ GG(mu,sigma,nu)
- z = (y/mu)^nu ~ GA(1,[mu*sigma]^2)
- where for z~GA(a,b)
- E[z] = 1
- Var[z] = (ab)^2
- Thus,
- y = mu * z**(1/nu)
- and so,
- E[y] = E[mu * z^(1/nu)]
- E[y] = mu*E[z^(1/nu)]
- But we don't know the equation for the pth moment of this implementation of the Gamma distribution (remember, z ~ GA(...))
- So we must convert to the known form, where
- X ~ Gamma(a,b)
- mean = ab
- var = a(b)^2
- E[X**p] = (b^p) * [(gamma(a+p) / gamma(a))]
- where gamma(x) is the gamma function gamma(x) = (x-1)!
- After much simplification (and taking the logarithm for numerical stability), we obtain the equations below
- """
- k = 1/(sigma*nu)**2
- #calculate E[Y], or the first moment of the distribution
- log_mean = (np.log(mu) - (1.0/nu) * np.log(k) + gammaln(k + 1.0/nu) - gammaln(k))
- mean_val = np.exp(log_mean)
- #calculate E[Y^2], or the second moment of the distribution
- log_mom2 = (2.0 * np.log(mu) - (2.0/nu) * np.log(k) + gammaln(k + 2.0/nu) - gammaln(k))
- mom2_val = np.exp(log_mom2)
- #calcualte the variance, or E[Y^2] - (E[Y])**2
- var = mom2_val - (mean_val)**2
- if any(var < 0):
- print(f"WARNING: a variance element was calculated as negative. All negative variances for GG will be set to 0.")
- var[var < 0] = 0
- return var
- def main():
- def args_check(args):
- tract = args.tract
- metric = args.measure
- sex = args.sex
- ages = np.array(args.ages)
- ds = np.array(args.effect_sizes)
- model_path = args.model_path
- z_model_sd = args.z_model_sd
- a = args.alpha
- b = args.beta
- q0 = args.q0
- out_csv = args.out_csv
- if any(ages <= 0.1):
- print(f"WARNING: trying to evaluate on ages {ages}, some of which are less than 0.1 years and may be unstable.")
- assert all(ds >= 0), f"ERROR: effect sizes must all be greater than zero: {ds}"
- if not model_path is None:
- model_path = Path(model_path)
- assert model_path.exists(), f"ERROR: User specified an explicit model path using --model_path, but {model_path} does not exist"
- else:
- model_root = Path("/MODELS/fit_models")
- if not args.is_global:
- metric_col = f"{tract}_{metric}"
- else:
- metric_col = metric
- model_path = OOS.get_model_file(metric_col, model_root)
- assert a > 0 and a < 1, f"ERROR: the false positive rate (alpha) must be between 0 and 1 (set to {a})"
- assert b > 0 and b < 1, f"ERROR: the false negative rate (beta) must be between 0 and 1 (set to {b})"
- assert q0 > 0 and q0 < 1, f"ERROR: control population cohort fraction (q0) must be between 0 and 1 (set to {q0})"
- if not out_csv is None:
- out_csv = Path(out_csv)
- assert not out_csv.exists(), f"ERROR: {out_csv} already exists. Please either delete or specify another path."
- return ages, sex, ds, model_path, z_model_sd, a, b, q0, out_csv
- args = pa()
- ages, sex, ds, model_path, z_model_sd, a, b, q0, out_csv = args_check(args)
- res_df, sds = calculate_model_sample_size(model_path, ages, sex, return_sds=True, d=ds, z_model_sd=z_model_sd, a=a, b=b, q0=q0)
- #order the df so that the ages are grouped together
- res_df = res_df.sort_values(by=["age", "effect_size"], ascending=[True, False])
- print(f"Population stdevs (accounting for model stability error): {sds} at the ages {ages}")
- print("##############")
- print(res_df)
- if not out_csv is None:
- res_df.to_csv(out_csv, index=False)
- if __name__ == main():
- main()
- # model = [x for x in srch_dir.glob(f"{metric_column}*.rds") if re.match(f"{metric_column}_\d_\d.rds", x.name)]
model_zpower_analysis.py, under CC-BY-NC-4.0 · at the source
Overview
and 11 other authors
Yanbin Niu9, Sophia Vinci-Booher9, Carissa J. Cascio17, The HABS-HD Study Team18,19, L. Taylor Davis11, Zhiyuan Li20, Simon N. Vandekar21, Panpan Zhang4,21, John C. Gore11,12,13, Bennett A. Landman1,2,6,8,9,10,11,12,13,21, Kurt G. Schilling2,11,1221 affiliations
- Department of Computer Science, Vanderbilt University,Nashville, TN USA
- Department of Electrical and Computer Engineering, Vanderbilt University,Nashville, TN USA
- Medical Scientist Training Program, Vanderbilt University,Nashville, TN USA
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center,Nashville, TN USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center,Nashville, TN USA
- Vanderbilt Brain Institute, Vanderbilt University,Nashville, TN USA
- Department of Medicine, Vanderbilt University Medical Center,Nashville, TN USA
- Department of Neurology, Vanderbilt University Medical Center,Nashville, TN USA
- Department of Psychology and Human Development, Vanderbilt University,Nashville, TN USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center,Nashville, TN USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center,Nashville, TN USA
- Vanderbilt University Institute of Imaging Science,Nashville, TN USA
- Department of Biomedical Engineering, Vanderbilt University,Nashville, TN USA
- Department of Neurological Surgery, Vanderbilt University Medical Center,Nashville, TN USA
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health,Baltimore, MD USA
- Department of Special Education, Peabody College of Education and Human Development,Nashville, TN USA
- Life Span Institute and Department of Psychology, University of Kansas,Lawrence, KS USA
- Laboratory of Neuro Imaging, Department of Neurology, University of California School of Medicine,Los Angeles, CA USA
- Department of Neurology, Johns Hopkins University,Baltimore, MD USA
- Department of Computer Science, Park University,Parkville, MO USA
- Department of Biostatistics, Vanderbilt University Medical Center,Nashville, TN USA
Abstract
The human brain relies on a complex network of connections to function, with white matter acting as the primary communication highway between different brain regions1,2. Disruptions in these critical communication pathways are linked to several neurological, psychiatric and developmental disorders3,4. Although clinicians have long used standard growth charts to track physical development5, with more recent work translating these to whole-brain and grey matter measurements6–9, there has been no equivalent reference standard for white matter. Establishing a readily available normative reference is an imperative first step if we hope to utilize these white matter structural biomarkers clinically. Here we present lifespan reference charts for human brain white matter. By processing and standardizing 35,120 brain scans from diverse global studies, we mapped the typical growth, maturation and age-related decline of specific brain pathways from birth to 100 years of age. These reference charts establish a fundamental benchmark for healthy brain development and ageing, allowing researchers and clinicians to quantify how an individual’s brain deviates from typical patterns and highlighting disorder-related alterations. Furthermore, the accompanying open access charts enable the scientific and clinical communities to evaluate new patient and research data against these normative baselines, facilitating future clinical and neuroscience studies.
Reproduced under the paper's license (CC BY), from the paper cited above.
Repositories
Its files are read in the Code ↔ Paper reader above.
Zenodo 15367425
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
- 28 September 2026: the link answers (HTTP 200)
1 file
- model_zpower_analysis.py
, Python, 218 lines
Zenodo 14058394
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
- 28 September 2026: the link answers (HTTP 200)
Zenodo 17144460
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
- 28 September 2026: the link answers (HTTP 200)
hub.docker.com/r/kimm58
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
- 28 September 2026: the link answers (HTTP 200)
Zenodo 18435695
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
- 28 September 2026: the link answers (HTTP 200)
1 file
- model_zpower_analysis.py
, Python, 218 lines
Code availability
Code for OOS data alignment, obtaining centile curves, and fitting the GAMLSS models is available in a containerized Docker image that is downloadable from https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Tracing map
Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.
What the map holds:
- 5 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
- 2 scripts, each with its path and the digest of its content;
- no match between paragraphs and code yet;
- neither the text of the paper nor the code itself.
Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.
Data
Datasets cited
- humanconnectome.org/
study/ , at Human Connectome Project; found in “Data availability”connectomes-related-anxi ety-depression - humanconnectome.org/
study/ , at Human Connectome Project; found in “Data availability”hcp-lifespan-aging - humanconnectome.org/
study/ , at Human Connectome Project; found in “Data availability”hcp-lifespan-development - humanconnectome.org/
study/ , at Human Connectome Project; found in “Data availability”hcp-young-adult - humanconnectome.org/
study/ , at Human Connectome Project; found in “Data availability”lifespan-baby-connectome -project - openneuro:ds000030, at OpenNeuro; found in “Data availability”
- openneuro:ds001894, at OpenNeuro; found in “Data availability”
- openneuro:ds002785, at OpenNeuro; found in “Data availability”
- openneuro:ds002790, at OpenNeuro; found in “Data availability”
- openneuro:ds002843, at OpenNeuro; found in “Data availability”
- openneuro:ds003097, at OpenNeuro; found in “Data availability”
- openneuro:ds003416, at OpenNeuro; found in “Data availability”
- openneuro:ds003604, at OpenNeuro; found in “Data availability”
- openneuro:ds004146, at OpenNeuro; found in “Data availability”
- openneuro:ds004856, at OpenNeuro; found in “Data availability”
- openneuro:ds005123, at OpenNeuro; found in “Data availability”
- osf:axz5r, at OSF; found in “Data availability”
- zenodo:17144461, at Zenodo; found in “Code availability”
- zenodo:18891847, at Zenodo; found in “Data availability”
- zenodo:18891848, at Zenodo; found in the references
Data availability
All derived data and associated demographic information from publicly available datasets, as well as from datasets for which the Data Use Agreement (DUA) permits sharing, have been made accessible at https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.
Version 2, 28 September 2026
- Publisher: n/a → Nature Portfolio
Version 1, 28 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 31 authors, 2 keywords, 18 MeSH terms, 13 funders, 55 references, 1 integrity notice.
Cite
This paper
Kim, M. E., Gao, C., Ramadass, K., Newlin, N. R., Kanakaraj, P., Bogdanov, S., Rudravaram, G., Archer, D., Hohman, T. J., Jefferson, A. L., Morgan, V. L., Roche, A., Englot, D. J., Resnick, S. M., Beason-Held, L. L., Cutting, L. E., Barquero, L. A., D’archangel, M. A., Nguyen, T. Q., . . . Schilling, K. G. (2026). White matter micro- and macrostructure brain charts for the human lifespan. Nature, 655(8124), 979-989. https://
BibTeX
@article{kim2026white,
author = {Kim, Michael E. and Gao, Chenyu and Ramadass, Karthik and Newlin, Nancy R. and Kanakaraj, Praitayini and Bogdanov, Sam and Rudravaram, Gaurav and Archer, Derek and Hohman, Timothy J. and Jefferson, Angela L. and Morgan, Victoria L. and Roche, Alexandra and Englot, Dario J. and Resnick, Susan M. and Beason-Held, Lori L. and Cutting, Laurie E. and Barquero, Laura A. and D’archangel, Micah A. and Nguyen, Tin Q. and Humphreys, Kathryn L. and Niu, Yanbin and Vinci-Booher, Sophia and Cascio, Carissa J. and {The HABS-HD Study Team} and Davis, L. Taylor and Li, Zhiyuan and Vandekar, Simon N. and Zhang, Panpan and Gore, John C. and Landman, Bennett A. and Schilling, Kurt G.},
title = {{White matter micro- and macrostructure brain charts for the human lifespan}},
journal = {Nature},
year = {2026},
month = may,
volume = {655},
number = {8124},
pages = {979--989},
publisher = {Nature Portfolio},
issn = {0028-0836},
doi = {10.1038/
url = {https://
pmid = {42129567},
pmcid = {PMC13391363}
}
RIS
TY - JOUR
AU - Kim, Michael E.
AU - Gao, Chenyu
AU - Ramadass, Karthik
AU - Newlin, Nancy R.
AU - Kanakaraj, Praitayini
AU - Bogdanov, Sam
AU - Rudravaram, Gaurav
AU - Archer, Derek
AU - Hohman, Timothy J.
AU - Jefferson, Angela L.
AU - Morgan, Victoria L.
AU - Roche, Alexandra
AU - Englot, Dario J.
AU - Resnick, Susan M.
AU - Beason-Held, Lori L.
AU - Cutting, Laurie E.
AU - Barquero, Laura A.
AU - D’archangel, Micah A.
AU - Nguyen, Tin Q.
AU - Humphreys, Kathryn L.
AU - Niu, Yanbin
AU - Vinci-Booher, Sophia
AU - Cascio, Carissa J.
AU - The HABS-HD Study Team
AU - Davis, L. Taylor
AU - Li, Zhiyuan
AU - Vandekar, Simon N.
AU - Zhang, Panpan
AU - Gore, John C.
AU - Landman, Bennett A.
AU - Schilling, Kurt G.
TI - White matter micro- and macrostructure brain charts for the human lifespan
T2 - Nature
J2 - Nature
PY - 2026
DA - 2026/
VL - 655
IS - 8124
SP - 979
EP - 989
SN - 0028-0836
PB - Nature Portfolio
DO - 10.1038/
UR - https://
LA - en
ER -
CSL-JSON
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The tracing map gets a citation of its own once an author has validated it and it has a DOI.
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