Is the whole more than the sum of its parts? Considering global and local features of the connectome improves prediction of individuals and phenotypes.
The 1 match
- [1] § Methods › Predictive modeling with CRAM ↔ get_embedding.m, lines 10–38 · score 0.56 · classic MDS, landmark MDS
Paper
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The authors' code
MATLAB · 38 lines · 1.5 KB · no license · 1 match
- % SUMMARY: This function implements the landmark MDS (Multi-Dimensional
- % Scaling) algorithm to compute the embedding of data points based on a
- % distance matrix.
- function [L, dim] = get_embedding(distmatrix)
- [L, e] = cmdscale(distmatrix, length(distmatrix));
- dim = round(find(e > mean(e), 1, 'last'), 0);
- end
- function embedding = landmark_MDS()
- % Calculate the embedding using the landmark MDS algorithm
- % Extract the distance matrix for the landmarks
- landmark_distmatrix = distmatrix(1:n_landmarks, 1:n_landmarks);
- landmark_distmatrix_sq = landmark_distmatrix.^2;
- % Perform classical MDS on the landmark distance matrix
- [landmark_embedding, ~] = cmdscale(landmark_distmatrix, dim);
- % Calculate the pseudo-inverse of the landmark embedding
- l_sharp = pinv(landmark_embedding);
- % Calculate squared distances for non-landmark points
- landmark_to_other_distmatrix = distmatrix(1:n_landmarks, n_landmarks + 1:end);
- landmark_to_other_distmatrix_sq = landmark_to_other_distmatrix.^2;
- % Calculate delta_mu (mean squared distance) for landmarks
- delta_mu = mean(landmark_distmatrix_sq, 2);
- % Calculate embedding for non-landmark points using the landmark MDS
- % embedding
- other_embedding = 1/2 * l_sharp * ...
- (repmat(delta_mu, 1, size(landmark_to_other_distmatrix, 2)) - ...
- landmark_to_other_distmatrix_sq);
- % Concatenate landmark and non-landmark embeddings
- ret = vertcat(landmark_embedding, other_embedding');
- end
get_embedding.m at commit 2814aee, no license · at the source
Overview
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States
- Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, United States
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States
- Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Uniondale, Hempstead, NY, United States
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT, United States
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, United States
- Child Study Center, Yale School of Medicine, New Haven, CT, United States
Abstract
Popular methods for analyzing the brain’s functional connectome examine statistical associations between pairs of atlas-defined brain regions, viewing the strength of these links as independent values. However, edges within a standard connectivity matrix, that is, correlations between individual regions or nodes, are not independent. They are part of an interconnected system. Here, we propose that consideration of both independent, linear relationships (as in standard approaches such as linear kernel ridge regression and connectome-based predictive modeling) and higher order statistical associations—such as tertiary interactions between matrix components and global features of the matrix space—will enhance identification of meaningful individual differences. To test this, we adopt a geometrically grounded measure of similarity that accounts for higher-order local statistical relationships and global interactions, the Wasserstein metric. Results indicate that considering connectivity matrices as representations of their associated Gaussian distributions significantly improves identification of individuals based on their connectivity matrices (aka, “fingerprinting”). We further show that when incorporated into our novel pipeline, “connectome-regression in any metric (CRAM)” the Wasserstein and (the CRAM pipeline itself) improve prediction of individual differences in phenotypes such as fluid intelligence and openness to experience. Thus, both pairwise local and global brain connectivity properties encode for meaningful individual differences that relate to phenotypic expressions and should be considered in brain–behavior predictive models.
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 1 match between paragraphs and lines of code.
YaleYipLab/CRAM
2814aee2656884776248bfb854e9a94db7b5bbcf, 7 August 2025Availability: 1 check, the latest on 27 September 2026: the link answers
- 27 September 2026: the link answers
15 files
- CRAM.m, MATLAB, 193 lines
- cpm_check_errors.m, MATLAB, 73 lines
- cpm_cv.m, MATLAB, 52 lines
- cpm_main.m, MATLAB, 67 lines
- cpm_test.m, MATLAB, 14 lines
- cpm_train.m, MATLAB, 40 lines
- cram_data.m, MATLAB, 76 lines
- ensure_distmatrix.m, MATLAB, 142 lines
- ensure_fisher_transform.
m , MATLAB, 22 lines - get_all_preds.m, MATLAB, 55 lines
- get_embedding.m, MATLAB, 38 lines, 1 match
- metric.m, MATLAB, 16 lines
- permutation_tests.m, MATLAB, 128 lines
- rsqrtm.m, MATLAB, 14 lines
- readme.md, Text, 41 lines
The paper's code and data availability statement is in the Data section.
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Data
No dataset and no data link were found in the paper.
Data and Code Availability
The scripts for running the analyses conducted in the study are available at https://
Reproduced under the paper's license (CC BY), from the paper cited above.
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Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, pages, dates, 9 authors, 5 keywords, 4 funders, 44 references.
Cite
This paper
Riley, S., Cheng, A., Wang, Y.-W., Shen, X., Dhamala, E., Zhao, Y., Holmes, A., Constable, R. T., & Yip, S. W. (2026). Is the whole more than the sum of its parts? Considering global and local features of the connectome improves prediction of individuals and phenotypes. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1287. https://
BibTeX
@article{riley2026whole,
author = {Riley, Steve and Cheng, Annie and Wang, Yu-Wei and Shen, Xilin and Dhamala, Elvisha and Zhao, Yize and Holmes, Avram and Constable, R. Todd and Yip, Sarah W.},
title = {{Is the whole more than the sum of its parts? Considering global and local features of the connectome improves prediction of individuals and phenotypes}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = jun,
volume = {4},
pages = {IMAG.a.1287},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/
url = {https://
pmid = {42382505},
pmcid = {PMC13317015}
}
RIS
TY - JOUR
AU - Riley, Steve
AU - Cheng, Annie
AU - Wang, Yu-Wei
AU - Shen, Xilin
AU - Dhamala, Elvisha
AU - Zhao, Yize
AU - Holmes, Avram
AU - Constable, R. Todd
AU - Yip, Sarah W.
TI - Is the whole more than the sum of its parts? Considering global and local features of the connectome improves prediction of individuals and phenotypes
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/
VL - 4
SP - IMAG.a.1287
SN - 2837-6056
PB - MIT Press
DO - 10.1162/
UR - https://
LA - en
ER -
CSL-JSON
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