Predicting brain age for veterans with traumatic brain injuries and healthy controls: an exploratory analysis
BackgroundTraumatic brain injury (TBI) is associated with increased dementia risk. This may be driven by underlying biological changes resulting from the injury. Machine learning algorithms can use structural MRIs to give a predicted brain age (pBA). When the estimated age is greater than the chrono...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Aging Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2025.1472207/full |
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| author | John P. Coetzee John P. Coetzee Xiaojian Kang Xiaojian Kang Victoria Liou-Johnson Victoria Liou-Johnson Victoria Liou-Johnson Ines Luttenbacher Srija Seenivasan Elika Eshghi Daya Grewal Siddhi Shah Frank Hillary Emily L. Dennis Emily L. Dennis Maheen M. Adamson Maheen M. Adamson Maheen M. Adamson |
| author_facet | John P. Coetzee John P. Coetzee Xiaojian Kang Xiaojian Kang Victoria Liou-Johnson Victoria Liou-Johnson Victoria Liou-Johnson Ines Luttenbacher Srija Seenivasan Elika Eshghi Daya Grewal Siddhi Shah Frank Hillary Emily L. Dennis Emily L. Dennis Maheen M. Adamson Maheen M. Adamson Maheen M. Adamson |
| author_sort | John P. Coetzee |
| collection | DOAJ |
| description | BackgroundTraumatic brain injury (TBI) is associated with increased dementia risk. This may be driven by underlying biological changes resulting from the injury. Machine learning algorithms can use structural MRIs to give a predicted brain age (pBA). When the estimated age is greater than the chronological age (CA), this is called the brain age gap (BAg). We analyzed this outcome in men and women with and without TBI.ObjectiveTo determine whether factors that contribute to BAg, as estimated using the brainageR algorithm, differ between men and women who are US military Veterans with and without TBI.MethodsIn an exploratory, hypothesis-generating analysis, we analyzed data from 85 TBI patients and 22 healthy controls (HCs). High-resolution T1W images were processed using FreeSurfer 7.0. pBAs were calculated from T1s. Differences between the two groups were tested using the Mann-Whitney U. Associations between the BAg and other factors were tested using partial Pearson’s r within groups, controlling for CA, followed by construction of regression models.ResultsAfter correcting for multiple comparisons, TBI patients and HCs differed on PCL score (higher for TBI patients) and cortical thickness (CT) in both hemispheres (higher for HCs). Among women TBI patients, BAg was correlated with pBA and hippocampal volume (HV), and among men TBI patients, BAg was correlated with pBA and CT. Among both men and women HCs, BAg was correlated only with CA. Four hierarchical regression models were constructed to predict BAg in each group, which controlled for CA and excluded pBA for multicollinearity. These models showed that HV predicted BAg among women with TBI, while CT predicted BAg among men with TBI, while only CA predicted BAg among HCs.InterpretationThese results offer tentative support to the view the factors associated with BAg among individuals with TBI differ from factors associated with BAg among HCs, and between men and women. Specifically, BAg among individuals with TBI is predicted by neuroanatomical factors, while among HCs it is predicted only by CA. This may reflect features of the algorithm, an underlying biological process, or both. |
| format | Article |
| id | doaj-art-0e96ae5ac79d4e3d8f16c17d92041d35 |
| institution | OA Journals |
| issn | 1663-4365 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Aging Neuroscience |
| spelling | doaj-art-0e96ae5ac79d4e3d8f16c17d92041d352025-08-20T02:31:51ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652025-05-011710.3389/fnagi.2025.14722071472207Predicting brain age for veterans with traumatic brain injuries and healthy controls: an exploratory analysisJohn P. Coetzee0John P. Coetzee1Xiaojian Kang2Xiaojian Kang3Victoria Liou-Johnson4Victoria Liou-Johnson5Victoria Liou-Johnson6Ines Luttenbacher7Srija Seenivasan8Elika Eshghi9Daya Grewal10Siddhi Shah11Frank Hillary12Emily L. Dennis13Emily L. Dennis14Maheen M. Adamson15Maheen M. Adamson16Maheen M. Adamson17Rehabilitation Service, VA Palo Alto Health Care System, Palo Alto, CA, United StatesDepartment of Psychiatry and Behavioral Sciences, Stanford Medicine, Stanford, CA, United StatesRehabilitation Service, VA Palo Alto Health Care System, Palo Alto, CA, United StatesWOMEN CoE, VA Palo Alto Health Care System, Palo Alto, CA, United StatesRehabilitation Service, VA Palo Alto Health Care System, Palo Alto, CA, United StatesClinical Excellence Research Center, Stanford School of Medicine, Stanford, CA, United StatesDepartment of Psychology, Palo Alto University, Palo Alto, CA, United StatesDepartment of Psychology, University of Amsterdam, Amsterdam, NetherlandsUniformed Services University of the Health Sciences, Bethesda, MA, United StatesIcahn School of Medicine at Mount Sinai, New York, NY, United StatesDepartment of Psychology, Palo Alto University, Palo Alto, CA, United StatesRehabilitation Service, VA Palo Alto Health Care System, Palo Alto, CA, United StatesDepartment of Psychology, Pennsylvania State University, University Park, PA, United States0Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, United States1George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, United StatesRehabilitation Service, VA Palo Alto Health Care System, Palo Alto, CA, United StatesWOMEN CoE, VA Palo Alto Health Care System, Palo Alto, CA, United States2Department of Neurosurgery, Stanford School of Medicine, Stanford, CA, United StatesBackgroundTraumatic brain injury (TBI) is associated with increased dementia risk. This may be driven by underlying biological changes resulting from the injury. Machine learning algorithms can use structural MRIs to give a predicted brain age (pBA). When the estimated age is greater than the chronological age (CA), this is called the brain age gap (BAg). We analyzed this outcome in men and women with and without TBI.ObjectiveTo determine whether factors that contribute to BAg, as estimated using the brainageR algorithm, differ between men and women who are US military Veterans with and without TBI.MethodsIn an exploratory, hypothesis-generating analysis, we analyzed data from 85 TBI patients and 22 healthy controls (HCs). High-resolution T1W images were processed using FreeSurfer 7.0. pBAs were calculated from T1s. Differences between the two groups were tested using the Mann-Whitney U. Associations between the BAg and other factors were tested using partial Pearson’s r within groups, controlling for CA, followed by construction of regression models.ResultsAfter correcting for multiple comparisons, TBI patients and HCs differed on PCL score (higher for TBI patients) and cortical thickness (CT) in both hemispheres (higher for HCs). Among women TBI patients, BAg was correlated with pBA and hippocampal volume (HV), and among men TBI patients, BAg was correlated with pBA and CT. Among both men and women HCs, BAg was correlated only with CA. Four hierarchical regression models were constructed to predict BAg in each group, which controlled for CA and excluded pBA for multicollinearity. These models showed that HV predicted BAg among women with TBI, while CT predicted BAg among men with TBI, while only CA predicted BAg among HCs.InterpretationThese results offer tentative support to the view the factors associated with BAg among individuals with TBI differ from factors associated with BAg among HCs, and between men and women. Specifically, BAg among individuals with TBI is predicted by neuroanatomical factors, while among HCs it is predicted only by CA. This may reflect features of the algorithm, an underlying biological process, or both.https://www.frontiersin.org/articles/10.3389/fnagi.2025.1472207/fulltraumatic brain injurychronic health symptomsagingstructural MRIbrain age |
| spellingShingle | John P. Coetzee John P. Coetzee Xiaojian Kang Xiaojian Kang Victoria Liou-Johnson Victoria Liou-Johnson Victoria Liou-Johnson Ines Luttenbacher Srija Seenivasan Elika Eshghi Daya Grewal Siddhi Shah Frank Hillary Emily L. Dennis Emily L. Dennis Maheen M. Adamson Maheen M. Adamson Maheen M. Adamson Predicting brain age for veterans with traumatic brain injuries and healthy controls: an exploratory analysis Frontiers in Aging Neuroscience traumatic brain injury chronic health symptoms aging structural MRI brain age |
| title | Predicting brain age for veterans with traumatic brain injuries and healthy controls: an exploratory analysis |
| title_full | Predicting brain age for veterans with traumatic brain injuries and healthy controls: an exploratory analysis |
| title_fullStr | Predicting brain age for veterans with traumatic brain injuries and healthy controls: an exploratory analysis |
| title_full_unstemmed | Predicting brain age for veterans with traumatic brain injuries and healthy controls: an exploratory analysis |
| title_short | Predicting brain age for veterans with traumatic brain injuries and healthy controls: an exploratory analysis |
| title_sort | predicting brain age for veterans with traumatic brain injuries and healthy controls an exploratory analysis |
| topic | traumatic brain injury chronic health symptoms aging structural MRI brain age |
| url | https://www.frontiersin.org/articles/10.3389/fnagi.2025.1472207/full |
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