Genetics of brain age suggest an overlap with common brain disorders

Kaufmann, Tobias; Meer, Dennis van der; Nt, Doan; Schwarz, Emanuel; Lund, Martina J.; Agartz, Ingrid; Alnæs, Dag; Baur‐Streubel, Ramona; Bertolino, Alessandro; Bettella, Francesco; Beyer, Mona K.; Bøen, Erlend; Borgwardt, Stefan; Cl, Brandt; Buitelaar, Jan K.; Eg, Celius; Červenka, Simon; Conzelmann, Annette; Córdova‐Palomera, Aldo; Dale, Anders M.; Quervain, Dominique J.‐F. de; P, Di Carlo; Djurovic, Srdjan; Es, Dørum; Eisenacher, Sarah; Elvsåshagen, Torbjørn; Espeseth, Thomas; Fatouros‐Bergman, Helena; Flyckt, Lena; Franke, Barbara; Frei, Oleksandr; Haatveit, Beathe; Ak, Håberg; Hf, Harbo; Ca, Hartman; Heslenfeld, Dirk J.; Hoekstra, Pieter J.; Høgestøl, Einar August; Jernigan, Terry L.; Jonassen, Rune; Eg, Jönsson; Koehler, Peter; Kłoszewska, Iwona; Kolskår, Knut K.; Ni, Landrø; Hellard, Stéphanie Le; Lesch, Klaus‐Peter; Lovestone, Simon; Lundervold, Astri J.; Aj, Lundervold; La, Maglanoc; Malt, Ulrik Fredrik; Mecocci, Patrizia; Melle, Ingrid; Meyer‐Lindenberg, Andreas; Moberget, Torgeir; Lb, Norbom; Je, Nordvik; Nyberg, Lars; Oosterlaan, Jaap; Papalino, Marco; Papassotiropoulos, Andreas; Pauli, Paul; Pergola, Giulio; Persson, Karin; Richard, Geneviève; Rokicki, Jaroslav; Sanders, Anne-Marthe; Selbæk, Geir; Aa, Shadrin; Ob, Smeland; Soininen, Hilkka; Sowa, Piotr; Vm, Steen; Tsolaki, Magda; Km, Ulrichsen; Vellas, Bruno; Wang, L; Westman, Eric; Gc, Ziegler; Zink, Mathias; Andreassen, Ole A.; Lt, Westlye

doi:10.1101/303164

Cited by 23 publications

(38 citation statements)

References 92 publications

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“…Table 6 shows that PAD is higher in individuals with schizophrenia than in controls. This is consistent with findings from other studies [56,64,41,35] that have looked at brain ageing of schizophrenia patients. Brain structure irregularities in schizophrenia, such as cortical thinning [76] and cerebral ventricular enlargement [79] also seen in healthy ageing [62,36] may be driving the prediction.…”

Section: Appendix E: Predicting Brain Age Of Neurodevelopmental and Msupporting

confidence: 93%

“…PAD has previously been shown to be heritable [11,35], however, to our knowledge no sequence variants conferring risk of or protecting against PAD have been identified. In order to look for such variants, we ran a genome wide association scan (GWAS) in the UK Biobank sample on PAD (same PAD as Section 2.4).…”

Section: Genome-wide Association Studymentioning

confidence: 99%

“…On the other hand, a negative PAD associates with higher educational attainment [72], increased physical activity [72] and meditation [50]. Moreover, PAD has been demonstrated to be heritable [11,35] and to have a polygenic overlap with brain disorders such as schizophrenia, bipolar disorder, multiple sclerosis, and Alzheimer's disease [35]. Furthermore, the high degree of genetic correlation found among psychiatric and some neurological disorders suggests that current diagnostic boundaries do not necessarily reflect underlying biology [14].…”

Section: Introductionmentioning

confidence: 99%

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Deep learning based brain age prediction uncovers associated sequence variants

Jónsson

Björnsdóttir

Thorgeirsson

et al. 2019

Preprint

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Machine learning algorithms trained to recognize age-related structural changes in magnetic resonance images (MRIs) of healthy individuals can be used to predict biological brain age in independent samples. The difference between predicted and chronological age, predicted age difference (PAD), is a phenotype holding promise for the study of normal brain ageing and brain diseases, and genetic discovery via genome-wide association studies (GWASs). Here, we present a new deep learning approach to predict brain age from a T1-weighted MRI. The method was trained on a dataset of healthy Icelanders (N = 1264) and tested on two datasets, the IXI (N = 544) and UK Biobank (N = 12395) datasets, utilizing transfer learning to improve accuracy on new sites. A GWAS of PAD in the UK Biobank data (discovery set: N=12395, replication set: N=4453) yielded two sequence variants, rs1452628-T (β=-0.08, P = 1.15 · 10 −9 ) and rs2435204-G (β=0.102, P = 9.73 · 10 −12 ). The former is near KCNK2 and correlates with reduced sulcal width, whereas the latter correlates with reduced white matter surface area and tags a wellknown inversion at 17q21.31 (H2). The genetic association analysis was also confined to variants known to associate with brain structure, yielding three additional sequence variants associating with PAD.

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Section: Appendix E: Predicting Brain Age Of Neurodevelopmental and Msupporting

confidence: 93%

Section: Genome-wide Association Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep learning based brain age prediction uncovers associated sequence variants

Jónsson

Björnsdóttir

Thorgeirsson

et al. 2019

Preprint

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“…Brain age estimation has emerged as a robust MRI marker, combining sensitive measures of MRI-based brain morphometry using machine learning models, to estimate an individual brain age when correlating with a large MRI data set of healthy controls 20,21 .…”

Section: Introductionmentioning

confidence: 99%

Brain age estimation is a sensitive marker of processing speed in the early course of multiple sclerosis

Høgestøl

Nygaard

Emhjellen

et al. 2019

Preprint

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Background and objectives: Cognitive deficits in MS are common, also early in the disease course. We aimed to identify if estimated brain age from MRI could serve as an imaging marker for early cognitive symptoms in a longitudinal MS study.Methods: A group of 76 MS patients (mean age 34 years, 71% females, 96% relapsingremitting) was examined 1, 2 and 5 years after diagnosis. A machine-learning model using Freesurfer-processed T1-weighted brain MRI data from 3208 healthy controls, was applied to develop a prediction model for brain age. The difference between estimated and chronological brain age was calculated (brain age gap). Tests of memory, attention and executive functions were performed. Associations between brain age gap and cognitive performance were assessed using linear mixed effects (LME) models and corrected for multiple testing.Results: LME models revealed a significant association between the Color Naming condition of Color-Word Interference Test and brain age gap (t=2.84, p=0.005). Conclusions:In this study, decreased information processing speed correlated with increased brain age gap. Our findings suggest that brain age estimation using MRI provides a useful semi-automated approach applying machine learning for individual level brain phenotyping and correlates with information processing speed in the early course of MS. and Oslo University hospital, especially Marte Wendel-Haga for her contribution at time point 2. We also thank the research assistants Kristin Liltved Grønsberg, May-Britt Gjengstø Utheim, Julia Timofeeva, Hedda Maurud and Siren Tønnessen who all contributed in the cognitive testing of the patients.

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“…Essentially, brain age estimation uses machine learning on a large training set of MRI data from healthy controls (HC) to develop a model that can accurately predict the individual age from brain imaging data [14][15][16] . Utilizing sensitive measures of MRI-based brain morphometry, brain age estimation provides a robust imaging-based biomarker with potential to yield novel insights into similarities and differences of disease pathophysiology across brain disorders 15 17 .…”

Section: Introductionmentioning

confidence: 99%

Cross-Sectional and Longitudinal Brain Scans Reveal Accelerated Brain Aging in Multiple Sclerosis

Høgestøl

Kaufmann

Nygaard

et al. 2018

Preprint

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Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. By combining longitudinal MRI-based brain morphometry and brain age estimation using machine learning, we tested the hypothesis that MS patients have higher brain age relative to chronological age than healthy controls (HC) and that longitudinal rate of brain aging in MS patients is associated with clinical course.Seventy-six MS patients, 71 % females and mean age 34.8 years (range 21-49) at inclusion, were examined with brain MRI at three time points with a mean total follow up period of 4.4 years. A machine learning model was applied on an independent training set of 3208 HC, estimating individual brain age and calculating the difference between estimated brain age and chronological age, termed brain age gap (BAG). We also assessed the longitudinal change rate in BAG in MS individuals. We used additional cross-sectional MRI data from 235 HC for case-control comparison.MS patients showed increased BAG (4.4 ±6.6 years) compared to HC (Cohen's D = 0.69, p = 4.0 x 10 -6 ). Longitudinal estimates of BAG in MS patients suggested an accelerated rate of brain aging corresponding to an annual increase of 0.41 (±1.23) years compared to chronological aging for the MS patients (p = 0.008).On average, patients with MS have significantly higher BAG compared to HC and accelerated rate of brain aging compared to chronological aging. Brain age estimation represents a promising method for evaluation of brain changes in MS, with potential for predicting future outcome and guide treatment.

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Genetics of brain age suggest an overlap with common brain disorders

Cited by 23 publications

References 92 publications

Deep learning based brain age prediction uncovers associated sequence variants

Deep learning based brain age prediction uncovers associated sequence variants

Brain age estimation is a sensitive marker of processing speed in the early course of multiple sclerosis

Cross-Sectional and Longitudinal Brain Scans Reveal Accelerated Brain Aging in Multiple Sclerosis

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