Article in Human brain mapping, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Zhen ZhouAthinoula A. Martinos Center for Biomedical Imaging, Radiology Department, Massachusetts General Hospital, Harvard Medical School, Boston, USA.ORCID https://orcid.org/0000-0002-9169-5885
Bruce FischlAthinoula A. Martinos Center for Biomedical Imaging, Radiology Department, Massachusetts General Hospital, Harvard Medical School, Boston, USA.
Iman AganjAthinoula A. Martinos Center for Biomedical Imaging, Radiology Department, Massachusetts General Hospital, Harvard Medical School, Boston, USA.ORCID https://orcid.org/0000-0002-4673-1293
Alzheimer's Disease Neuroimaging InitiativeAthinoula A. Martinos Center for Biomedical Imaging, Radiology Department, Massachusetts General Hospital, Harvard Medical School, Boston, USA.
Funding
BRAIN CONNECTS: The center for Large-scale Imaging of Neural Circuits (LINC)UM1NS132358 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI Suzanne N Haber, Elizabeth M. C. Hillman · 2023 to 2026
$17.5M
EPIDEMIOLOGY OF DEMENTIA IN THE FRAMINGHAM STUDYR01AG008122 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI AU, RHODA, SESHADRI, SUDHA · 1989 to 2015
$14.4M
MRI, Genetics &Cognitive Precursors of AD &DementiaR01AG016495 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI AU, RHODA · 1999 to 2016
$12.5M
The Human Connectome Project (HCP)U01MH093765 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI ROSEN, BRUCE R · 2010 to 2014
$12.1M
Training and Dissemination CoreP41EB030006 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI Susie Yi Huang, BRUCE R ROSEN · 2020 to 2026
$10.9M
Imaging and Analysis Techniques to Construct a Cell Census Atlas of the Human Brain Admin SupplementU01MH117023 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI BOAS, DAVID A, FISCHL, BRUCE · 2018 to 2022
$7.6M
Neuropathology of CTE and Delayed Effects of TBI: Toward In-Vivo DiagnosticsU01NS086625 · NINDS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI DAMS-O'CONNOR, KRISTEN · 2014 to 2017
$6.1M
FreeSurfer Development, Maintenance, and HardeningR01EB023281 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI Bruce Fischl, Douglas Nowlin Greve · 2016 to 2026
$5.6M
A Longitudinal Analysis Stream for FreeSurferR01NS083534 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI Bruce Fischl · 2014 to 2026
$4.6M
BRAIN CONNECTS: Mapping Connectivity of the Human Brainstem in a Nuclear Coordinate SystemU01NS132181 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI FISCHL, BRUCE, HOF, PATRICK R · 2023 to 2025
$4.2M
Deep Learning Algorithms for FreeSurferR01AG064027 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI FISCHL, BRUCE · 2020 to 2024
$3.4M
Segmenting Brain Structures for Neurological DisordersR01NS105820 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI Bruce Fischl · 2019 to 2026
$3.2M
Michael J. Fox Foundation for Parkinson's Research MJFF-021226National Institute for Biomedical Imaging and Bioengineering P41EB030006National Institute for Biomedical Imaging and Bioengineering R01EB006758National Institute for Biomedical Imaging and Bioengineering R01EB019956National Institute for Biomedical Imaging and Bioengineering R01EB023281National Institute for Biomedical Imaging and Bioengineering R21EB018907NIA NIH HHS R01 AG008122NIA NIH HHS R01AG008122NIA NIH HHS R01 AG016495NIA NIH HHS R01AG016495NIA NIH HHS R01 AG064027NIA NIH HHS R01AG064027NIA NIH HHS R01 AG068261NIA NIH HHS R01AG068261NIA NIH HHS R01 AG070988NIA NIH HHS R01AG070988NIA NIH HHS R21 AG082082NIA NIH HHS R21AG082082NIA NIH HHS RF1 AG068261NIA NIH HHS RF1AG068261NIBIB NIH HHS P41 EB030006NIBIB NIH HHS R01 EB006758NIBIB NIH HHS R01 EB019956NIBIB NIH HHS R01 EB023281NIBIB NIH HHS R21 EB018907NIH Blueprint for Neuroscience Research U01MH093765NIMH NIH HHS R01MH121885NIMH NIH HHS R01MH123195NIMH NIH HHS RF1 MH121885NIMH NIH HHS RF1 MH123195NIMH NIH HHS RF1MH123195NIMH NIH HHS U01 MH093765NIMH NIH HHS U01 MH117023NIMH NIH HHS U01MH117023NINDS NIH HHS R01NS0525851NINDS NIH HHS R01 NS070963NINDS NIH HHS R01NS070963NINDS NIH HHS R01 NS083534NINDS NIH HHS R01NS083534NINDS NIH HHS R01 NS105820NINDS NIH HHS R01NS105820NINDS NIH HHS R21 NS072652NINDS NIH HHS R21NS072652NINDS NIH HHS U01 NS086625NINDS NIH HHS U01NS086625NINDS NIH HHS U01 NS132181NINDS NIH HHS U24NS10059103NINDS NIH HHS UM1 NS132358the Brain Initiative Brain Connects consortium U01NS132181the Brain Initiative Brain Connects consortium UM1NS132358
6 · The paper itself
Abstract
The increasing prevalence of multi-site diffusion-weighted magnetic resonance imaging (dMRI) studies potentially offers enhanced statistical power to investigate brain structure. However, these studies face challenges due to variations in scanner hardware and acquisition protocols. While several methods for dMRI data harmonization exist, few specifically address structural brain connectivity. We introduce a new distribution-matching approach to harmonizing structural brain connectivity across different sites and scanners. We evaluate our method using structural brain connectivity data from three distinct datasets (OASIS-3, ADNI-2, and PREVENT-AD), comparing its performance to the widely used ComBat method and the more recent CovBat approach. We examine the impact of harmonization on the correlation of brain connectivity with the Mini-Mental State Examination score and age. Our results demonstrate that our distribution-matching technique effectively harmonizes structural brain connectivity while maintaining non-negativity of the connectivity values and produces correlation strengths and significance levels competitive with alternative approaches. Qualitative assessments illustrate the desired distributional alignment across datasets, while quantitative evaluations confirm competitive performance. This work contributes to the growing field of dMRI harmonization, potentially improving the reliability and comparability of structural connectivity studies that combine data from different sources in neuroscientific and clinical research.
Indexed as
BrainConnectomeDiffusion Magnetic Resonance ImagingImage Processing, Computer-AssistedNerve NetNeuroimagingAgedAged, 80 and overFemaleHumansMaleMiddle Agedconnectomediffusion MRIdistribution matchingharmonizationstructural brain connectivity
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.
Harmonization of Structural Brain Connectivity Through Distribution Matching. · full record | Socratic