Cover Image

Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders

The deviation between chronological age and age predicted using brain MRI is a putative marker of overall brain health. Age prediction based on structural MRI data shows high accuracy in common brain disorders. However, brain aging is complex and heterogenous, both in terms of individual differences...

Full description

Saved in:
Bibliographic Details
Main Authors: Rokicki, Jaroslav (Author) , Wolfers, Thomas (Author) , Nordhøy, Wibeke (Author) , Tesli, Natalia (Author) , Quintana, Daniel S. (Author) , Alnæs, Dag (Author) , Richard, Genevieve (Author) , Lange, Ann-Marie G. de (Author) , Lund, Martina J. (Author) , Norbom, Linn (Author) , Agartz, Ingrid (Author) , Melle, Ingrid (Author) , Nærland, Terje (Author) , Selbæk, Geir (Author) , Persson, Karin (Author) , Nordvik, Jan Egil (Author) , Schwarz, Emanuel (Author) , Andreassen, Ole A. (Author) , Kaufmann, Tobias (Author) , Westlye, Lars T. (Author)
Format: Article (Journal)
Language:English
Published: April 15, 2021
In: Human brain mapping
Year: 2021, Volume: 42, Issue: 6, Pages: 1714-1726
ISSN:1097-0193
DOI:10.1002/hbm.25323
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1002/hbm.25323
Verlag, lizenzpflichtig, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.25323
Get full text
Author Notes:Jaroslav Rokicki, Thomas Wolfers, Wibeke Nordhøy, Natalia Tesli, Daniel S. Quintana, Dag Alnæs, Genevieve Richard, Ann-Marie G. de Lange, Martina J. Lund, Linn Norbom, Ingrid Agartz, Ingrid Melle, Terje Nærland, Geir Selbæk, Karin Persson, Jan Egil Nordvik, Emanuel Schwarz, Ole A. Andreassen, Tobias Kaufmann, Lars T. Westlye
Description
Summary:The deviation between chronological age and age predicted using brain MRI is a putative marker of overall brain health. Age prediction based on structural MRI data shows high accuracy in common brain disorders. However, brain aging is complex and heterogenous, both in terms of individual differences and the underlying biological processes. Here, we implemented a multimodal model to estimate brain age using different combinations of cortical area, thickness and sub-cortical volumes, cortical and subcortical T1/T2-weighted ratios, and cerebral blood flow (CBF) based on arterial spin labeling. For each of the 11 models we assessed the age prediction accuracy in healthy controls (HC, n = 750) and compared the obtained brain age gaps (BAGs) between age-matched subsets of HC and patients with Alzheimer's disease (AD, n = 54), mild (MCI, n = 90) and subjective (SCI, n = 56) cognitive impairment, schizophrenia spectrum (SZ, n = 159) and bipolar disorder (BD, n = 135). We found highest age prediction accuracy in HC when integrating all modalities. Furthermore, two-group case-control classifications revealed highest accuracy for AD using global T1-weighted BAG, while MCI, SCI, BD and SZ showed strongest effects in CBF-based BAGs. Combining multiple MRI modalities improves brain age prediction and reveals distinct deviations in patients with psychiatric and neurological disorders. The multimodal BAG was most accurate in predicting age in HC, while group differences between patients and HC were often larger for BAGs based on single modalities. These findings indicate that multidimensional neuroimaging of patients may provide a brain-based mapping of overlapping and distinct pathophysiology in common disorders.
Item Description:Gesehen am 02.08.2021
Physical Description:Online Resource
ISSN:1097-0193
DOI:10.1002/hbm.25323

Similar Items