Charting brain growth and aging at high spatial precision

Rutherford, Saige; Fraza, Charlotte; Dinga, Richard; Kia, Seyed Mostafa; Wolfers, Thomas; Zabihi, Mariam; Berthet, Pierre; Worker, Amanda; Verdi, Serena; Andrews, Derek Sayre; Han, Laura K.M.; Bayer, Johanna; Dazzan, Paola; McGuire, P; Mocking, Roel J. T.; Schene, Aart H.; Sripada, Chandra; Tso, Ivy F.; Duval, Elizabeth R.; Chang, Soo-Eun; Penninx, Brenda W. J. H.; Heitzeg, Mary M.; Burt, S. Alexandra; Hyde, Luke W.; Amaral, David G.; Nordahl, Christine Wu; Andreasssen, Ole A; Westlye, Lars T.; Zahn, Roland; Ruhé, Henricus G.; Beckmann, Christian F.; Marquand, André F.

doi:10.7554/elife.72904

Cited by 93 publications

(156 citation statements)

References 50 publications

Supporting

Mentioning

126

Contrasting

Order By: Relevance

“…By applying this approach to derive neuroanatomical normative models at brain regions, a map can be generated of where and to what extent an individual’s brain differs from the norm 28,29 . This technique has shown to be suitable for precise mapping of individual patterns of variation in brain structure across multiple psychiatric and neurodevelopmental disorders, thereby parsing the neuroanatomical heterogeneity present 30,31 . These include attention-deficit hyperactivity disorder 32 , autism 33,34 , bipolar disorder and schizophrenia 35 .…”

Section: Introductionmentioning

confidence: 99%

Revealing Individual Neuroanatomical Heterogeneity in Alzheimer’s Disease

Verdi

Kia

Yong

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Alzheimer's disease is clinically heterogeneous, in symptom profiles, progression rates and outcomes. This clinical heterogeneity is linked to underlying neuroanatomical heterogeneity. To explore this, we employed the emerging technique of neuroanatomical normative modelling to index regional patterns of variability in cortical thickness in individual patients from the large multi-site Alzheimer's Disease Neuroimaging Initiative. We aimed to characterise individual differences and outliers in cortical thickness in patients with Alzheimer's disease, people with mild cognitive impairment and cognitively normal controls. Furthermore, we assessed the relationships between cortical thickness heterogeneity and cognitive function, amyloid-beta, tau, ApoE genotype. Finally, we examined whether individual neuroanatomical normative maps were predictive of conversion from mild cognitive impairment to diagnosed Alzheimer's disease. Data on cortical thickness from the 148 brain regions of the Destrieux FreeSurfer atlas was obtained from T1-weighted MRI scans of 1492 participants scanned at 62 different sites. A neuroanatomical normative model was developed to index normal cortical thickness distributions using a separate healthy reference dataset (n= 33,072), employing hierarchical Bayesian regression to predict cortical thickness per region using age and sex. These regional normative models were then fine-tuned to the ADNI dataset after which cortical thickness z-scores per region were calculated, resulting in a z-score 'map' for each participant. Regions with z-scores < -1.96 were classified as outliers. Patients with Alzheimer's disease had a median of 12 outlier regions out of a possible 148. Individual patterns of outlier regions were highly variable, with the highest overlap in the parahippocampal gyrus at only 47% of patients. For 62 regions, over 90% of these patients had cortical thicknesses within the normal range. Patients with Alzheimer's disease had significantly more outlier regions than people with mild cognitive impairment or controls [F(2, 1022) = 95.39), P = 2.0x10-16]. They were also statistically more dissimilar to each other than were people with mild cognitive impairment or cognitive normal controls [F(2, 1024) = 209.42, P = 2.2x10-16]. Having a greater number of outlier regions was associated with worse cognitive function, CSF protein concentrations and an increased risk of converting from mild cognitive impairment to Alzheimer's disease within three years (HR =1.028, 95% CI[1.016,1.039], P = 1.8x10-16). Individualised normative maps of cortical thickness highlight the heterogeneity of Alzheimer's effects on the brain. Regional outlier estimates have the potential to be a marker of disease and could be used to track an individual's disease progression or treatment response in clinical trials.

show abstract

Section: Introductionmentioning

confidence: 99%

Revealing Individual Neuroanatomical Heterogeneity in Alzheimer’s Disease

Verdi

Kia

Yong

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…There are multiple end products created from running a normative model analysis. 85 . This work suggests validity, but this is an on-going evaluation and out of sample model fit must always be considered and reported.…”

Section: Anticipated Resultsmentioning

confidence: 99%

The Normative Modeling Framework for Computational Psychiatry

Rutherford

Kia

Wolfers

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Normative modeling is an emerging and innovative framework for mapping individual differences at the level of a single subject or observation in relation to a reference model. It involves charting centiles of variation across a population in terms of mappings between biology and behavior which can then be used to make statistical inferences at the level of the individual. The fields of computational psychiatry and clinical neuroscience have been slow to transition away from patient versus healthy control analytic approaches, likely due to a lack of tools designed to properly model biological heterogeneity of mental disorders. Normative modeling provides a solution to address this issue and moves analysis away from case-control comparisons that rely on potentially noisy clinical labels. In this article, we define a standardized protocol to guide users through, from start to finish, normative modeling analysis using the Predictive Clinical Neuroscience toolkit (PCNtoolkit). We describe the input data selection process, provide intuition behind the various modeling choices, and conclude by demonstrating several examples of downstream analyses the normative model results may facilitate, such as stratification of high-risk individuals, subtyping, and behavioral predictive modeling.

show abstract

“…While Jacobian determinants can be derived relatively rapidly from T 1 -w scans, the primary constraint being the speed of non-linear registration, they are both less interpretable and less widely used than estimates of tissue volume. For example, recent work employing the increasingly popular approach of normative modeling 30,31 leveraged a large sample of scans to construct normative charts of variation in brain tissue volume across the lifespan 32,33 . Our results suggest that even volume estimates derived from rapid sequences such as EPImix could be used to anchor individual measures of brain morphology relative to such reference datasets.…”

Section: Tissue Volumementioning

confidence: 99%

“…While the aim of active acquisition is to ultimately obtain a personalised diagnosis, any information regarding potential abnormality -including global deviation from expected brain-age -could help inform clinical predictions. In future, this should additionally be supplemented by more specific markers of brain health, including local estimates of brain-age [45][46][47][48] as well as local deviations of brain anatomy from the norm [31][32][33] . Accordingly, further work should compare both of these local candidate biomarkers between EPImix and standard T 1 -w scans.…”

Section: Tissue Volumementioning

confidence: 99%

Tissue volume estimation and age prediction using rapid structural brain scans

Hobday

Cole

Stanyard

et al. 2022

Sci Rep

View full text Add to dashboard Cite

The multicontrast EPImix sequence generates six contrasts, including a T1-weighted scan, in ~1 min. EPImix shows comparable diagnostic performance to conventional scans under qualitative clinical evaluation, and similarities in simple quantitative measures including contrast intensity. However, EPImix scans have not yet been compared to standard MRI scans using established quantitative measures. In this study, we compared conventional and EPImix-derived T1-weighted scans of 64 healthy participants using tissue volume estimates and predicted brain-age. All scans were pre-processed using the SPM12 DARTEL pipeline, generating measures of grey matter, white matter and cerebrospinal fluid volume. Brain-age was predicted using brainageR, a Gaussian Processes Regression model previously trained on a large sample of standard T1-weighted scans. Estimates of both global and voxel-wise tissue volume showed significantly similar results between standard and EPImix-derived T1-weighted scans. Brain-age estimates from both sequences were significantly correlated, although EPImix T1-weighted scans showed a systematic offset in predictions of chronological age. Supplementary analyses suggest that this is likely caused by the reduced field of view of EPImix scans, and the use of a brain-age model trained using conventional T1-weighted scans. However, this systematic error can be corrected using additional regression of T1-predicted brain-age onto EPImix-predicted brain-age. Finally, retest EPImix scans acquired for 10 participants demonstrated high test-retest reliability in all evaluated quantitative measurements. Quantitative analysis of EPImix scans has potential to reduce scanning time, increasing participant comfort and reducing cost, as well as to support automation of scanning, utilising active learning for faster and individually-tailored (neuro)imaging.

show abstract

Charting brain growth and aging at high spatial precision

Cited by 93 publications

References 50 publications

Revealing Individual Neuroanatomical Heterogeneity in Alzheimer’s Disease

Revealing Individual Neuroanatomical Heterogeneity in Alzheimer’s Disease

The Normative Modeling Framework for Computational Psychiatry

Tissue volume estimation and age prediction using rapid structural brain scans

Contact Info

Product

Resources

About