Deviations from normative brain white and gray matter structure are associated with psychopathology in youth

Kjelkenes, Rikka; Wolfers, Thomas; Alnæs, Dag; Norbom, Linn B.; Voldsbekk, Irene; Holm, Madelene; Dahl, Andreas; Berthet, Pierre; Tamnes, Christian K.; Marquand, André F.

doi:10.1016/j.dcn.2022.101173

Cited by 10 publications

(8 citation statements)

References 91 publications

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“…However, others (23,24,27) have reported that an older looking brain, evidenced by a positive BAG, was associated with an increased depression risk in adolescence. Notably, these studies comprised clinical or at-risk test-samples and had wider age ranges (~ 8 to 21 years), while our study and (25) used a used a younger sample (9 to 13 years) and one that was not enriched for clinical symptoms.…”

Section: Discussionmentioning

confidence: 96%

“…However, one study found that while functional BAG was not associated with general mental health difficulties, a subsample of youth with higher symptom severity were more likely to have a younger looking brain, indicated by a negative BAG (26). Moreover, studies using analytic frameworks related to brain age prediction, such as normative modelling, have found that deviations from normative white and grey matter morphometry were associated with youth psychopathology (27). Importantly, existing work has been cross-sectional, precluding the examination of changes in brain age and how this relates to the development of youth depression.…”

Section: Introductionmentioning

confidence: 99%

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Multimodal brain age indicators of internalising problems in adolescence: A longitudinal investigation

MacSweeney,

Beck,

Whitmore

et al. 2024

Preprint

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Background: Adolescence is a time of increased risk for the onset of internalising problems, particularly in females. However, how individual differences in brain maturation relate to the increased vulnerability for internalising problems in adolescence remains poorly understood due to a scarcity of longitudinal studies. Methods: Using Adolescent Brain Cognitive Development (ABCD) Study data, we examined longitudinal associations between multimodal brain age and youth internalising problems. Brain age models were trained, validated, and tested independently on T1-weighted (T1; N=9523), diffusion tensor (DTI; N=8834), and resting-state functional (rs-fMRI; N=8233) MRI data at baseline (Mage= 9.9 years) and 2-year follow-up (Mage= 11.9 years). Self-reported internalising problems were measured at 3-year follow-up (Mage= 12.9 years) using the Brief Problem Monitor.Results: Latent change score models demonstrated that although brain age gap (BAG) at baseline was not related to later internalising problems, an increase in BAG between timepoints was positively associated with internalising problems at 3-year follow-up in females but not males. This association between an increasing BAG and higher internalising problems was observed in the T1 (β=0.071, SE=0.050, p=0.009) and rs-fMRI (β=0.091, SE=0.025, p=0.005) models but not DTI (β=-0.024, SE=0.053, p=0.648), and remained significant when accounting for earlier internalising problems. Conclusions: A greater increase in BAG over time may reflect the heightened vulnerability shown by female youth to internalising problems. Longitudinal research is necessary to understand if this increasing BAG signifies accelerated brain development and its relationship to the trajectory of internalising problems throughout adolescence.

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Section: Discussionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Multimodal brain age indicators of internalising problems in adolescence: A longitudinal investigation

MacSweeney,

Beck,

Whitmore

et al. 2024

Preprint

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“…In addition, we offer the applicability of such models with longitudinal data with satisfactory results. Furthermore, as deviations from normality have been widely used to assess disease severity and cognitive variability in youth [ 28 ] and in psychiatric disorders [ 29 ], we study if AD and FTD patients could be described with the previous HC model. As expected, individual AD and FTD CTh data diverged from the model defined with HC subjects.…”

Section: Discussionmentioning

confidence: 99%

Cortical thickness modeling and variability in Alzheimer’s disease and frontotemporal dementia

Pérez-Millan,

Borrego-Écija,

Falgàs

et al. 2023

J Neurol

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Background and objective Alzheimer’s disease (AD) and frontotemporal dementia (FTD) show different patterns of cortical thickness (CTh) loss compared with healthy controls (HC), even though there is relevant heterogeneity between individuals suffering from each of these diseases. Thus, we developed CTh models to study individual variability in AD, FTD, and HC. Methods We used the baseline CTh measures of 379 participants obtained from the structural MRI processed with FreeSurfer. A total of 169 AD patients (63 ± 9 years, 65 men), 88 FTD patients (64 ± 9 years, 43 men), and 122 HC (62 ± 10 years, 47 men) were studied. We fitted region-wise temporal models of CTh using Support Vector Regression. Then, we studied associations of individual deviations from the model with cerebrospinal fluid levels of neurofilament light chain (NfL) and 14–3-3 protein and Mini-Mental State Examination (MMSE). Furthermore, we used real longitudinal data from 144 participants to test model predictivity. Results We defined CTh spatiotemporal models for each group with a reliable fit. Individual deviation correlated with MMSE for AD and with NfL for FTD. AD patients with higher deviations from the trend presented higher MMSE values. In FTD, lower NfL levels were associated with higher deviations from the CTh prediction. For AD and HC, we could predict longitudinal visits with the presented model trained with baseline data. For FTD, the longitudinal visits had more variability. Conclusion We highlight the value of CTh models for studying AD and FTD longitudinal changes and variability and their relationships with cognitive features and biomarkers.

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“…Previous findings demonstrated general trends of tissue anisotropy increases and water diffusivity decreases throughout childhood (8–10), and, as reversal dynamics of these trends, throughout adulthood (3, 11). Importantly, abnormal white matter (WM) development has been associated with the development of neurocognitive skills and mental health symptoms in childhood and adolescence (12) and brain disorders later in life (13, 14). A person’s genetically determined propensity to develop a certain disorder can also be summarised by polygenic risk scores (PGRS) (15–23).…”

Section: Introductionmentioning

confidence: 99%

Distinct longitudinal brain white matter microstructure changes and associated polygenic risk of common psychiatric disorders and Alzheimer’s disease in the UK Biobank

Korbmacher,

van der Meer,

Beck

et al. 2023

Preprint

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During the course of adulthood and ageing, white matter (WM) structure and organisation are characterised by slow degradation processes such as demyelination and shrinkage. An acceleration of such ageing process has been linked to the development of a range of diseases. Thus, an accurate description of healthy brain maturation, in particular, in terms of WM features, provides a cornerstone in the understanding of ageing. We use longitudinal diffusion magnetic resonance imaging to provide an overview of WM changes at different spatial and temporal scales in the UK Biobank (UKB) (N=2,678; age[scan 1]=62.38(7.23) years; age[scan 2]=64.81(7.1) years). To examine the genetic overlap between WM structure and common clinical conditions, we tested the associations between WM structure and polygenic risk scores (PGRS) for the most common neurodegenerative disorder, Alzheimer's disease, and common psychiatric disorders (uni- and bipolar depression, anxiety, obsessive-compulsive, autism, schizophrenia, attention-deficit-hyperactivity) in longitudinal (N=2,329) and cross-sectional UKB validation data (N=31,056). Global and regional single and multi-compartment fractional anisotropy, intra-axonal water fraction, and kurtosis metrics decreased (mean beta=-0.04),whereas diffusivity metrics, and free water increased with age (mean beta=0.05), with the annual rate of WM change (ARoC) accelerating at higher ages for both global (mean beta=0.01) and regional WM metrics (mean beta=0.01). Voxel-level trends indicated decreasing anisotropy, and variable spatial patterns for other diffusion metrics, suggesting differential changes in frontal compared to other brain regions. Although effect sizes were small (mean absolute beta[all]=0.01), ARoC in middle cerebral peduncle WM had the strongest association with PGRS, especially for Alzheimer's: mean absolute beta=0.01. PGRS were more strongly related to ARoC than cross-sectional measures (d[scan 1]=0.03, d[scan 2]=0.03, d[validation]=0.03). Our findings indicate spatially distributed WM changes across the brain, as well as distributed associations of PGRS with WM. Importantly, brain longitudinal changes reflected the genetic risk for disorder development better than the utilised cross-sectional measures, with regional differences giving more specific insights into gene-brain change associations than global averages.

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Deviations from normative brain white and gray matter structure are associated with psychopathology in youth

Cited by 10 publications

References 91 publications

Multimodal brain age indicators of internalising problems in adolescence: A longitudinal investigation

Multimodal brain age indicators of internalising problems in adolescence: A longitudinal investigation

Cortical thickness modeling and variability in Alzheimer’s disease and frontotemporal dementia

Distinct longitudinal brain white matter microstructure changes and associated polygenic risk of common psychiatric disorders and Alzheimer’s disease in the UK Biobank

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