[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-125320-105":59,"doc-detail-125320-en":134},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":127,"head_meta":129,"extra_data":131,"updated_unix":133},105,"en","machine-learning-driven-biomarker-discovery-for-depression-and-ptsd-in-traumatic-brain-injury","Machine Learning-Driven Biomarker Discovery for Depression and PTSD in Traumatic Brain Injury","","Machine learning enables more precise psychiatry by linking neuroimaging patterns to post-traumatic psychiatric comorbidities. Using logistic regression with Elastic Net on harmonised segmented 3D T1-weighted and diffusion MRI, the study distinguishes TBI only, TBI with depression, and TBI with PTSD (including combined PTSD/depression) from healthy controls. Covariates include age, sex, and intracranial volume. Results highlight cingulum-related microstructural differences and associated white matter changes, achieving moderate AUC values and outperforming null models, supporting development of sensitive early biomarkers.",{"@graph":69,"@context":126},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-driven-biomarker-discovery-for-depression-and-ptsd-in-traumatic-brain-injury/125320/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-driven-biomarker-discovery-for-depression-and-ptsd-in-traumatic-brain-injury/125320.png","ImageObject",300,407,{"name":92,"@type":93},"Quinn","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-18","2026-08-05",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does this study target?","Question",{"text":112,"@type":113},"It addresses the lack of standard diagnostic criteria for psychiatric complications after traumatic brain injury, aiming to identify neural biomarkers for conditions such as depression and PTSD.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which machine learning approach and imaging data were used?",{"text":117,"@type":113},"The study used logistic regression with Elastic Net regularization on segmented 3D T1-weighted and diffusion MRI data, harmonised across multiple datasets using ComBat.",{"name":119,"@type":110,"acceptedAnswer":120},"What neuroimaging regions and features were most discriminative?",{"text":121,"@type":113},"Key discriminative effects involved the cingulum section adjoining the hippocampus (CGH) and cingulum-related tracts, with changes in diffusion metrics (MD, AD, RD) and fractional anisotropy (FA).",{"name":123,"@type":110,"acceptedAnswer":124},"How well did the models perform and what does it imply?",{"text":125,"@type":113},"Models achieved AUC values around 0.58–0.66 and performed significantly better than a null model, suggesting these biomarkers may support early identification of at-risk patients and targeted interventions.","https://schema.org",{"og:url":83,"og:type":128,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":130,"canonical":83},"index,follow",{"doc_id":132,"site_id":62},125320,1785898159,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":19,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":143,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":144,"faqs":145,"seo_title":146,"seo_description":67,"update_tm":133,"read_time":81},2336475104736,"https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222","Machine Learning-Driven Biomarker Discovery for Depression and PTSD in  \nTraumatic Brain Injury  \nJoanne PM Kenney* 1 ,  \nEmily L Dennis2,3,4 , Robert Whelan5,6 , Laura M Rueda-Delgado7 , Paul M Thompson4,8 , David F Tate2,3 , Elisabeth A Wilde2,3  \n| INTRODUCTION |\n| --- |\n| Machine Learning holds significant promise in advancing precision psychiatry. Post-psychiatric complications such as PTSD and depression are common after a Traumatic Brain Injury (TBI) (Mayer & Quinn, 2022, Ahmed et al., 2017) . Yet, we still lack standard diagnostic criteria for post-TBI psychiatric complications, leaving many individuals undiagnosed and without appropriate healthcare. Through the Enhancing Neuroimaging Genetics through MetaAnalysis (ENIGMA) Brain Injury working group, we addressed this issue by applying state-of-the-art machine learning and imaging analysis techniques to identify specific and localized neural markers of psychiatric illness in TBI. The findings of this research can assist in developing sensitive, personalised biomarkers for early diagnosis of psychiatric disorders in TBI, guiding treatment strategies (Siqueira Pinto et al., 2023) . |\n\nMETHODS  \nMachine learning using logistic regression with Elastic Net regularization was applied to segmented 3D T1-weighted and diffusion MRI data of the brain to classify 1) individuals with TBI only vs healthy controls with no TBI (HC) 2) TBI with psychiatric diagnosis vs HC (see Figure 1) . Age, sex, and intracranial volume were covaried for. Participants consisted of n=73 females and n = 624 males (mean age: 47.2 ± 15. 6 years) . The dataset consisted of LIMBIC-CENC, ADNI-DoD and Duke University datasets. Data was harmonised across consortia using the ComBat algorithm and consisted mostly of deployment-related TBI. White matter features were segmented using the JHU White Matter atlas in a TBSS approach; grey matter cortical and subcortical features were segmented using FreeSurfer. The total number of grey and white matter features included in each model was 239.  \nRESULTS  \nNeuroimaging data classified individuals with TBI and depression vs HCs returning an area under the curve (AUC) of 0.66. The cingulum section adjoining the hippocampus (CGH) was a top discriminant feature-revealing reductions in mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD) in right and left CGH and increases in fractional anisotropy (FA) in the cingulum in the cingulated cortex (CGC) predominantly in the left hemisphere. The TBI/Depression/PTSD vs HC model returned an AUC of 0.64 again showing reductions in MD, AD, RD in CGH. The TBI/PTSD vs HC model returned an AUCof 0.58 with reductions in MD, AD and RD in tracts such as ALIC, CST and CGH. The TBI-only vs HC model returned an AUC of 0.59. There were increases in FA across a range of limbic and association tracts, including pathways involved in emotion regulation and cognitive processing, while reductions in MD, AD, and RD were observed in projection and cingulum-related tracts. All models performed significantly better than a null model.  \nTBI vs HC TBI with Depression vs HC TBI with PTSD vs HC TBI with PTSD/Depression vs HC  \nAUC: 0.59  \nAUC: 0.66  \nAUC: 0.58  \nAUC: 0.64  \nFigure 2: Receiver operating characteristic curve. Red = original model; Blue = null model. All original models performed significantly better than a null model.  \n\n| CONCLUSION |\n| --- |\n| The results from four machine learning models identify distinct neuroimaging biomarkers associated with traumatic brain injury (TBI) and psychiatric comorbidities. In TBI and depression, disruptions in the microstructural organization of the CGH may contribute to both cognitive and emotional symptoms commonly seen in post-TBI depression. The cingulum is critical for emotional processing, mood regulation, and linking the hippocampus to other emotion-related areas. Its involvement in depression is well established. In this group, increased FA in the CGC may reflect compensatory struct","cbCainU2d4qkw5cs","https://ap.wps.com/l/cbCainU2d4qkw5cs","pdf",383681,"English","# Introduction\n# Methods\n# Results\n# Conclusion","[{\"question\":\"What problem does this study target?\",\"answer\":\"It addresses the lack of standard diagnostic criteria for psychiatric complications after traumatic brain injury, aiming to identify neural biomarkers for conditions such as depression and PTSD.\"},{\"question\":\"Which machine learning approach and imaging data were used?\",\"answer\":\"The study used logistic regression with Elastic Net regularization on segmented 3D T1-weighted and diffusion MRI data, harmonised across multiple datasets using ComBat.\"},{\"question\":\"What neuroimaging regions and features were most discriminative?\",\"answer\":\"Key discriminative effects involved the cingulum section adjoining the hippocampus (CGH) and cingulum-related tracts, with changes in diffusion metrics (MD, AD, RD) and fractional anisotropy (FA).\"},{\"question\":\"How well did the models perform and what does it imply?\",\"answer\":\"Models achieved AUC values around 0.58–0.66 and performed significantly better than a null model, suggesting these biomarkers may support early identification of at-risk patients and targeted interventions.\"}]","Machine Learning-Driven Biomarker Discovery for Depression and PTSD in Traumatic Brain Injury | PDF"]