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

Rokicki, Jaroslav ; Wolfers, Thomas ; Nordhøy, Wibeke ; Tesli, Natalia ; Quintana, Daniel S. ; Alnæs, Dag ; Richard, Genevieve ; Lange, Ann‐Marie G. ; Lund, Martina J. ; Norbom, Linn ; Agartz, Ingrid ; Melle, Ingrid ; Nærland, Terje ; Selbæk, Geir ; Persson, Karin ; Nordvik, Jan Egil ; Schwarz, Emanuel ; Andreassen, Ole A. ; Kaufmann, Tobias ; Westlye, Lars T.

Human brain mapping, 2021-04, Vol.42 (6), p.1714-1726 [Periódico revisado por pares]

Hoboken, USA: John Wiley & Sons, Inc

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Título:
Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders
Autor: Rokicki, Jaroslav ; Wolfers, Thomas ; Nordhøy, Wibeke ; Tesli, Natalia ; Quintana, Daniel S. ; Alnæs, Dag ; Richard, Genevieve ; Lange, Ann‐Marie G. ; Lund, Martina J. ; Norbom, Linn ; Agartz, Ingrid ; Melle, Ingrid ; Nærland, Terje ; Selbæk, Geir ; Persson, Karin ; Nordvik, Jan Egil ; Schwarz, Emanuel ; Andreassen, Ole A. ; Kaufmann, Tobias ; Westlye, Lars T.
Assuntos: Abnormalities ; Accuracy ; Adolescent ; Adult ; Age ; Age Factors ; Aged ; Aged, 80 and over ; Aging ; Alzheimer Disease - diagnostic imaging ; Alzheimer Disease - pathology ; Alzheimer's disease ; arterial spin labeling ; Biological activity ; Bipolar disorder ; Bipolar Disorder - diagnostic imaging ; Bipolar Disorder - pathology ; Blood flow ; Brain ; Brain - blood supply ; Brain - diagnostic imaging ; Brain - pathology ; brain age ; brain disorders ; Brain mapping ; Care and treatment ; Case-Control Studies ; Cerebral blood flow ; Cerebrovascular Circulation - physiology ; Cognitive ability ; Cognitive Dysfunction - diagnostic imaging ; Cognitive Dysfunction - pathology ; Deviation ; Diagnostic imaging ; Female ; Humans ; Machine learning ; Magnetic resonance imaging ; Magnetic Resonance Imaging - methods ; Male ; Medical imaging ; Medical research ; Medicin och hälsovetenskap ; Medicine, Experimental ; Mental disorders ; Middle Aged ; MRI ; Multimodal Imaging ; Nervous system diseases ; Neurodegenerative diseases ; Neuroimaging ; Neuroimaging - methods ; Neurological diseases ; Neurological disorders ; Predictions ; Schizophrenia ; Schizophrenia - diagnostic imaging ; Schizophrenia - pathology ; Spin Labels ; T1w/T2w ratio ; Young Adult
É parte de: Human brain mapping, 2021-04, Vol.42 (6), p.1714-1726
Notas: Funding information
ERA PerMed, Grant/Award Number: IMPLEMENT; Horizon 2020 Framework Programme, Grant/Award Number: ERC StG, Grant 802998; Norges Forskningsråd, Grant/Award Numbers: 223273, 248238, 249795, 276082, 286838, 298646, 300767; the Norwegian ExtraFoundation for Health and Rehabilitation, Grant/Award Number: 2015/FO5146; the Novo Nordisk Foundation, Grant/Award Number: NNF16OC0019856; the South‐Eastern Norway Regional Health Authority, Grant/Award Numbers: 2014097, 2015044, 2015073, 2016083, 2018037, 2018076, 2019101
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Funding information ERA PerMed, Grant/Award Number: IMPLEMENT; Horizon 2020 Framework Programme, Grant/Award Number: ERC StG, Grant 802998; Norges Forskningsråd, Grant/Award Numbers: 223273, 248238, 249795, 276082, 286838, 298646, 300767; the Norwegian ExtraFoundation for Health and Rehabilitation, Grant/Award Number: 2015/FO5146; the Novo Nordisk Foundation, Grant/Award Number: NNF16OC0019856; the South‐Eastern Norway Regional Health Authority, Grant/Award Numbers: 2014097, 2015044, 2015073, 2016083, 2018037, 2018076, 2019101
Descrição: 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. The deviation between chronological age and age predicted using brain MRI is a marker of overall brain health. 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). We found that, combining multiple MRI modalities improves brain age prediction and reveals distinct deviations in patients with psychiatric and neurological disorders.
Editor: Hoboken, USA: John Wiley & Sons, Inc
Idioma: Inglês;Norueguês

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

Hoboken, USA: John Wiley & Sons, Inc

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