Academic Journal
BrainInsights: a comprehensive framework for pre-processing, analysis, and interpretation of neuroimaging data using traditional statistics and machine learning.
| Title: | BrainInsights: a comprehensive framework for pre-processing, analysis, and interpretation of neuroimaging data using traditional statistics and machine learning. |
|---|---|
| Authors: | Selvakumar, Mageshwar, Mendez Torrijos, Andrea, Konerth, Laura Cristina, Horndasch, Stefanie, Atreya, Raja, Doerfler, Arnd, Schett, Georg, Rech, Juergen, Hess, Andreas |
| Source: | Frontiers in Neuroinformatics; 2026, p1-22, 22p |
| Subject Terms: | Magnetic resonance imaging, Machine learning, Therapeutics, Data scrubbing, Brain imaging, Statistics, Computer-assisted image analysis (Medicine), Electronic data processing |
| Abstract: | Neuroimaging presents us with an in-depth understanding about brain structure and function, yet the data complexity poses significant analytical challenges. Current frameworks suffer from issues such as scalability, poor integration with traditional statistics and a need for a programing background, which hinder researchers from focusing on neuroscience questions. To address these limitations, we present BrainInsights, an integrated and automated GUI-based pipeline ecosystem designed to facilitate the analysis of multi-modal or multi-parametric neuroimaging data in a flexible way. The framework comprises three core tools: MARIA (MAgnetic Resonance Imaging data Analysis and inspection tool) for data inspection and hypotheses testing, ML Pipeline for automated feature selection and model construction, and ML DaViz for model evaluation and bio-signature generation. Deployed as a singularity container, the system ensures reproducibility and scalability across computing environments. We validated BrainInsights using diverse datasets, including multi-parametric MRI studies of Anorexia Nervosa, Crohn's disease, and Rheumatoid Arthritis. Specifically, the framework distinguished young Anorexia Nervosa patients from controls with a balanced accuracy of 65%, while in the PreCePRA trial, it predicted Rheumatoid Arthritis treatment response with a balanced accuracy of up to 95.4% using functional pain markers. The results demonstrate the ability of the framework to achieve high separation of subgroups and treatment success and additionally bridge hypotheses-driven statistical analysis with data-driven machine learning analysis. By enabling interpretability tools like SHAP, BrainInsights empowers researchers to move beyond "black-box" modeling to uncover stable, biologically plausible bio-signatures. Ultimately, this framework aids in accelerating the translation of complex neuroimaging data into meaningful clinical insights. [ABSTRACT FROM AUTHOR] |
| Copyright of Frontiers in Neuroinformatics is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Biomedical Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edm&genre=article&issn=16625196&ISBN=&volume=&issue=&date=20260429&spage=1&pages=1-22&title=Frontiers in Neuroinformatics&atitle=BrainInsights%3A%20a%20comprehensive%20framework%20for%20pre-processing%2C%20analysis%2C%20and%20interpretation%20of%20neuroimaging%20data%20using%20traditional%20statistics%20and%20machine%20learning.&aulast=Selvakumar%2C%20Mageshwar&id=DOI:10.3389/fninf.2026.1760583 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
|---|---|
| Header | DbId: edm DbLabel: Biomedical Index An: 193361832 RelevancyScore: 1061 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1060.76049804688 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: BrainInsights: a comprehensive framework for pre-processing, analysis, and interpretation of neuroimaging data using traditional statistics and machine learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Selvakumar%2C+Mageshwar%22">Selvakumar, Mageshwar</searchLink><br /><searchLink fieldCode="AR" term="%22Mendez+Torrijos%2C+Andrea%22">Mendez Torrijos, Andrea</searchLink><br /><searchLink fieldCode="AR" term="%22Konerth%2C+Laura+Cristina%22">Konerth, Laura Cristina</searchLink><br /><searchLink fieldCode="AR" term="%22Horndasch%2C+Stefanie%22">Horndasch, Stefanie</searchLink><br /><searchLink fieldCode="AR" term="%22Atreya%2C+Raja%22">Atreya, Raja</searchLink><br /><searchLink fieldCode="AR" term="%22Doerfler%2C+Arnd%22">Doerfler, Arnd</searchLink><br /><searchLink fieldCode="AR" term="%22Schett%2C+Georg%22">Schett, Georg</searchLink><br /><searchLink fieldCode="AR" term="%22Rech%2C+Juergen%22">Rech, Juergen</searchLink><br /><searchLink fieldCode="AR" term="%22Hess%2C+Andreas%22">Hess, Andreas</searchLink> – Name: TitleSource Label: Source Group: Src Data: Frontiers in Neuroinformatics; 2026, p1-22, 22p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging%22">Magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Therapeutics%22">Therapeutics</searchLink><br /><searchLink fieldCode="DE" term="%22Data+scrubbing%22">Data scrubbing</searchLink><br /><searchLink fieldCode="DE" term="%22Brain+imaging%22">Brain imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Computer-assisted+image+analysis+%28Medicine%29%22">Computer-assisted image analysis (Medicine)</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Neuroimaging presents us with an in-depth understanding about brain structure and function, yet the data complexity poses significant analytical challenges. Current frameworks suffer from issues such as scalability, poor integration with traditional statistics and a need for a programing background, which hinder researchers from focusing on neuroscience questions. To address these limitations, we present BrainInsights, an integrated and automated GUI-based pipeline ecosystem designed to facilitate the analysis of multi-modal or multi-parametric neuroimaging data in a flexible way. The framework comprises three core tools: MARIA (MAgnetic Resonance Imaging data Analysis and inspection tool) for data inspection and hypotheses testing, ML Pipeline for automated feature selection and model construction, and ML DaViz for model evaluation and bio-signature generation. Deployed as a singularity container, the system ensures reproducibility and scalability across computing environments. We validated BrainInsights using diverse datasets, including multi-parametric MRI studies of Anorexia Nervosa, Crohn's disease, and Rheumatoid Arthritis. Specifically, the framework distinguished young Anorexia Nervosa patients from controls with a balanced accuracy of 65%, while in the PreCePRA trial, it predicted Rheumatoid Arthritis treatment response with a balanced accuracy of up to 95.4% using functional pain markers. The results demonstrate the ability of the framework to achieve high separation of subgroups and treatment success and additionally bridge hypotheses-driven statistical analysis with data-driven machine learning analysis. By enabling interpretability tools like SHAP, BrainInsights empowers researchers to move beyond "black-box" modeling to uncover stable, biologically plausible bio-signatures. Ultimately, this framework aids in accelerating the translation of complex neuroimaging data into meaningful clinical insights. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Frontiers in Neuroinformatics is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edm&AN=193361832 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3389/fninf.2026.1760583 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 1 Subjects: – SubjectFull: Magnetic resonance imaging Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Therapeutics Type: general – SubjectFull: Data scrubbing Type: general – SubjectFull: Brain imaging Type: general – SubjectFull: Statistics Type: general – SubjectFull: Computer-assisted image analysis (Medicine) Type: general – SubjectFull: Electronic data processing Type: general Titles: – TitleFull: BrainInsights: a comprehensive framework for pre-processing, analysis, and interpretation of neuroimaging data using traditional statistics and machine learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Selvakumar, Mageshwar – PersonEntity: Name: NameFull: Mendez Torrijos, Andrea – PersonEntity: Name: NameFull: Konerth, Laura Cristina – PersonEntity: Name: NameFull: Horndasch, Stefanie – PersonEntity: Name: NameFull: Atreya, Raja – PersonEntity: Name: NameFull: Doerfler, Arnd – PersonEntity: Name: NameFull: Schett, Georg – PersonEntity: Name: NameFull: Rech, Juergen – PersonEntity: Name: NameFull: Hess, Andreas IsPartOfRelationships: – BibEntity: Dates: – D: 29 M: 04 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 16625196 Titles: – TitleFull: Frontiers in Neuroinformatics Type: main |
| ResultId | 1 |