Using precision approaches to improve brain-behavior prediction.
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| Title: | Using precision approaches to improve brain-behavior prediction. |
|---|---|
| Authors: | Lee, Hyejin J.1,2 (AUTHOR) leex6248@gmail.com, Dworetsky, Ally1 (AUTHOR), Labora, Nathan1 (AUTHOR), Gratton, Caterina1,2 (AUTHOR) cgratton@illinois.edu |
| Source: | Trends in Cognitive Sciences. Feb2025, Vol. 29 Issue 2, p170-183. 14p. |
| Subjects: | Brain anatomy, Prediction models, Statistical power analysis, Statistical models, Individual differences |
| Abstract: | Recent work has highlighted that a large number of participants are needed to reproducibly predict individual behavior traits based on characteristics in brain structure or function. However, having enough data per participant is also critical for prediction. Here, we review recent evidence that the limited performance of current brain-behavior prediction models is driven by two major causes, which are noisy data and small effects. We offer a framework to tackle these challenges through 'precision' brain and behavioral approaches that collect more per-participant data and implement within-subject experimental designs. We discuss how integrating precision approaches with consortium studies may provide improved brain-behavior predictions necessary to achieve more powerful clinical applications. Predicting individual behavioral traits from brain idiosyncrasies has broad practical implications, yet predictions vary widely. This constraint may be driven by a combination of signal and noise in both brain and behavioral variables. Here, we expand on this idea, highlighting the potential of extended sampling 'precision' studies. First, we discuss their relevance to improving the reliability of individualized estimates by minimizing measurement noise. Second, we review how targeted within-subject experiments, when combined with individualized analysis or modeling frameworks, can maximize signal. These improvements in signal-to-noise facilitated by precision designs can help boost prediction studies. We close by discussing the integration of precision approaches with large-sample consortia studies to leverage the advantages of both. [ABSTRACT FROM AUTHOR] |
| Copyright of Trends in Cognitive Sciences is the property of Elsevier B.V. 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: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 182481653 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Using precision approaches to improve brain-behavior prediction. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lee%2C+Hyejin+J%2E%22">Lee, Hyejin J.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> leex6248@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Dworetsky%2C+Ally%22">Dworetsky, Ally</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Labora%2C+Nathan%22">Labora, Nathan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gratton%2C+Caterina%22">Gratton, Caterina</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> cgratton@illinois.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Trends+in+Cognitive+Sciences%22">Trends in Cognitive Sciences</searchLink>. Feb2025, Vol. 29 Issue 2, p170-183. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Brain+anatomy%22">Brain anatomy</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+power+analysis%22">Statistical power analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Individual+differences%22">Individual differences</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Recent work has highlighted that a large number of participants are needed to reproducibly predict individual behavior traits based on characteristics in brain structure or function. However, having enough data per participant is also critical for prediction. Here, we review recent evidence that the limited performance of current brain-behavior prediction models is driven by two major causes, which are noisy data and small effects. We offer a framework to tackle these challenges through 'precision' brain and behavioral approaches that collect more per-participant data and implement within-subject experimental designs. We discuss how integrating precision approaches with consortium studies may provide improved brain-behavior predictions necessary to achieve more powerful clinical applications. Predicting individual behavioral traits from brain idiosyncrasies has broad practical implications, yet predictions vary widely. This constraint may be driven by a combination of signal and noise in both brain and behavioral variables. Here, we expand on this idea, highlighting the potential of extended sampling 'precision' studies. First, we discuss their relevance to improving the reliability of individualized estimates by minimizing measurement noise. Second, we review how targeted within-subject experiments, when combined with individualized analysis or modeling frameworks, can maximize signal. These improvements in signal-to-noise facilitated by precision designs can help boost prediction studies. We close by discussing the integration of precision approaches with large-sample consortia studies to leverage the advantages of both. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Trends in Cognitive Sciences is the property of Elsevier B.V. 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.tics.2024.09.007 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 170 Subjects: – SubjectFull: Brain anatomy Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Statistical power analysis Type: general – SubjectFull: Statistical models Type: general – SubjectFull: Individual differences Type: general Titles: – TitleFull: Using precision approaches to improve brain-behavior prediction. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lee, Hyejin J. – PersonEntity: Name: NameFull: Dworetsky, Ally – PersonEntity: Name: NameFull: Labora, Nathan – PersonEntity: Name: NameFull: Gratton, Caterina IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 13646613 Numbering: – Type: volume Value: 29 – Type: issue Value: 2 Titles: – TitleFull: Trends in Cognitive Sciences Type: main |
| ResultId | 1 |
