Biology subjects

Marquand, A. F.

Publications and source records attributed to Marquand, A. F..

At least 19 recordsLinked to original sources

bioRxiv2026

SUITPy: A Python-based toolbox for the analysis of cerebellar functional and anatomical imaging data across the human lifespan

The human cerebellum plays a central role in motor, emotional, and cognitive functions, and is implicated in many brain disorders. To improve the analysis of functional and anatomical imaging from the cerebellum, we introduce SUITPy, an improved and fully revised Python implementation of the widely used SUIT toolbox. For this new version, we developed a U-Net based model to automatically isolate the cerebellum from adjacent cortical tissue, which achieves higher fidelity than existing algorithms. The isolation works robustly without manual corrections for imaging data across the lifespan. We show that isolation and subsequent normalization to a cerebellum-only template lead to a more precise alignment of cerebellar structures across participants compared to normalization using a whole-brain template. We also show the utility of the cerebellar mask to prevent contamination of cerebellar functional data from surrounding cortical structures. The toolbox also provides functionality for visualizing cerebellar data on a flatmap, along with a range of anatomical and functional cerebellar atlases, thereby offering an essential tool that enables accurate cerebellar analysis across the lifespan.

neuroscience↗

bioRxiv2026

Dissecting heterogeneous brain development and aging using voxelwise normative models

Brain disorders are often characterized by biological heterogeneity that is poorly captured by group-average analyses. Normative modeling has emerged as a promising tool to parse out such heterogeneity, yet existing lifespan reference models rely on coarse parcellations, which may obscure individual variability. Using an aggregated reference sample (n=58,597 scans from n=51,107 participants), we provide openly available normative models of brain morphometry at the voxel level across the lifespan, and we illustrate their potential utility with two complementary applications. First, we investigated long-term brain development after preterm birth across two independent cohorts (n=284; n=304) and found individualized, replicable and persistent brain alterations. Second, we extracted high-resolution patient-level morphometric deviations in two samples with rare, genetic neurodegenerative disorders (spinocerebellar ataxia type 1 and 3; n=29, n=15), which showed marked heterogeneity. Together, our findings highlight that voxelwise normative modeling can detect clinically relevant, individualized deviations from a reference model with high spatial precision.

neuroscience↗

bioRxiv2026

Protocol Update: The Normative Modelling Paradigm for Computational Psychiatry

Normative Modelling ( brain growth charting) is now a well-established method for computational psychiatry and involves charting centiles of variation across a population in terms of mappings between biology and behavior, providing statistical inferences at the level of the individual. These models have helped the field to move away from case-control analysis toward individual-level analysis. Correspondingly, normative modelling has now been applied to chart brain development and ageing in many populations and has been used to quantify individual deviations across various neurological and psychiatric conditions. This has been supported by large-scale models that are openly accessible for diverse brain imaging modalities. As normative modelling continues to grow, several recent methodological developments, such as non-Gaussian models, longitudinal models, and federated learning, have been implemented in different software tools, including the Predictive Clinical Neuroscience toolkit (PCNtoolkit). In this protocol update, we provide: (i) a revised overview of this methodological landscape; (ii) an update to our 2022 standardised analytical protocol for normative modelling of neuroimaging data, including options for federated and longitudinal normative models; (iii) practical guidance suited to both novice and experienced practitioners supported by open-source code examples implemented in the refactored version of PCNtoolkit; and (iv) updated models for cortical thickness, surface area, volumetric data, functional connectivity and diffusion-weighted imaging for use by the community.

neuroscience↗

bioRxiv2026

Distributional and Centile Calibration of Diffusion Tensor Imaging Normative Models as Training Sample Sizes Increase to 40,000 Subjects

Normative modeling (NM) is a powerful framework for quantifying individual deviations in brain structure and function relative to a population reference. However, its clinical utility depends on well-calibrated models trained on heterogeneous datasets such as those found in neuroimaging. Here, we systematically examine the effect of training sample size on the distributional and centile calibration of hierarchical Bayesian regression (HBR)-based NMs. Using multisite 3D diffusion MRI scans of the brain from 54,583 subjects, spanning almost the entire lifespan (age: 4-91 years), we trained NMs of white matter fractional anisotropy, a key microstructural metric, on subsamples ranging from 5,000 to 40,000 subjects. HBR was modeled with a Sinh-Arcsinh likelihood. Model calibration was evaluated using Kernelized Stein Discrepancy (KSD) to assess distributional agreement of Z-scores with the standard normal distribution; we also used Mean Absolute Centile Error (MACE) to quantify centile accuracy. Both metrics showed consistent and substantial improvements as the training sample size increased, indicating reduced posterior uncertainty and improved estimation of distributional parameters, particularly at the centile extremes. These results demonstrate that large training cohorts are essential for well-calibrated NMs derived from heterogeneous neuroimaging data and highlight the importance of large-scale data aggregation for reliable individual-level inference.

bioinformatics↗

bioRxiv2025

Linking White-Matter Development to Clinical Variation in Autism: Longitudinal Normative Modelling of FA in the EU-AIMS LEAP Cohort

BackgroundAutism is characterised by neurobiological and clinical heterogeneity, which often limits the sensitivity of traditional group comparisons. Longitudinal normative modelling offers a way to capture heterogeneity and to describe an individuals brain-behaviour relationships beyond diagnostic categories. MethodsWe analysed diffusion MRI scans from 3 waves of the EU-AIMS Longitudinal European Autism Project (LEAP). Using normative models of white matter fractional anisotropy (FA) trained on a neurotypical reference cohort, we computed individual deviations relative to age-typical norms. Our sample included 544 individuals (Wave 1: 189 autistic 162 non-autistic; Wave 2: 162 autistic136 non-autistic; Wave 3: 145 autistic 84 non-autistic), aged 7-37 years, with mean intervals of [~]1.5 years (W1-W2) and [~]6 years (W2-W3). We first tested for group level differences between autistic and non-autistic participants in FA deviations across 48 white matter tracts and then examined the associations between these deviation scores and sensory, adaptive, cognitive, and behavioural measures across development within the autism group. ResultsGroup-wise comparisons revealed no significant differences in FA deviations between autistic and non-autistic participants. Within the autism group, multivariate analysis (CCA) showed significant (p<0.05) cross-sectional brain-behaviour associations at Wave 1 (r=0.26) and Wave 2 (r=0.32), but not at Wave 3. Longitudinally, we found significant multivariate associations only for the W1-W3 interval (r=0.36). Follow-up univariate correlations supported these findings, with Wave 2 showing widespread moderate (r{approx}0.35) associations while longitudinally, the univariate associations were generally weaker (r{approx}0.2). Across analyses, higher FA was generally associated with higher Vineland Adaptive Behaviour Scales (VABS) and lower Short Sensory Profile (SSP) scores. Associations with the Autism Diagnostic Observation Schedule (ADOS) were mixed, and the Social Responsiveness Scale (SRS) and Repetitive Behaviour Scale (RBS) showed wave-dependent relationships, with specific tract involvement varying across timepoints. ConclusionsWhite-matter organisation in autism does not differ from that of non-autistic on average but rather follows a dimensional architecture within the autism phenotype. We showed that person-specific deviations from shared neurodevelopmental pathways are linked to behavioural variation among autistic individuals, which cannot be captured by group-level approaches alone. Longitudinal normative modelling provides a practical approach to understand and quantify this heterogeneity. EU-AIMS LEAP GroupZuzana Suchomelova, Mee Rim Oh, Chirag Mehra, Mei Lin Law, Sanjana Gandhi, Laura Bravo Balsa, Maria Dauvermann, Isabel Yorke, Beth Oakley, Rosemary J. Holt, Edward Bullock, Yumnah Kahn, Esme Hayes, Leona Strauss, Noel Lam, Anouk Dykstra, Kim Lamers, Marije Mars, Lucas Geelen, Lotte Beckers, Anna Praat, Feline van Aagten, Sjors Reith, Viola Hollestein, Sanne Kluin, Natalie Forde, Jill Naaijen, Anna Kaiser, Sarah Baumeister, Pascal-M. Aggensteiner, Jumana Ahmad, Sara Ambrosino, Bonnie Auyeung, Tobias Banaschewski, Simon Baron-Cohen, Sarah Baumeister, Christian F. Beckmann, Sven Bolte, Thomas Bourgeron, Carsten Bours, Michael Brammer, Daniel Brandeis, Claudia Brogna, Yvette de Bruijn, Jan K. Buitelaar, Bhismadev Chakrabarti, Tony Charman, Ineke Cornelissen, Daisy Crawley, Flavio DellAcqua, Guillaume Dumas, Sarah Durston, Christine Ecker, Jessica Faulkner, Vincent Frouin, Pilar Garces, David Goyard, Lindsay Ham, Hannah Hayward, Joerg Hipp, Rosemary J. Holt, Mark H. Johnson, Emily J. H. Jones, Prantik Kundu, Meng-Chuan Lai, Xavier Liogier Dardhuy, Michael V. Lombardo, Eva Loth, David J. Lythgoe, Rene Mandl, Andre Marquand, Luke Mason, Maarten Mennes, Andreas Meyer-Lindenberg, Carolin Moessnang, Nico Mueller, Declan G. M. Murphy, Laurence ODwyer, Marianne Oldehinkel, Bob Oranje, Gahan Pandina, Antonio M. Persico, Jack Price, Annika Rausch, Barbara Ruggeri, Amber N. V. Ruigrok, Jessica Sabet, Roberto Sacco, Antonia San Jose Caceres, Emily Simonoff, Will Spooren, Julian Tillmann, Roberto Toro, Heike Tost, Jack Waldman, Steve C. R. Williams, Caroline Wooldridge, Iva Ilioska, Ting Mei, Marcel P. Zwiers. Key PointsO_ST_ABSQuestionC_ST_ABSIn the context of heterogeneity in autism, what do individual differences in white-matter development reveal about brain-behaviour relationships over time? FindingsIn this study, we found significant relationships between individual white-matter deviations and clinical traits in autism both cross-sectionally as well as longitudinally. Increased adaptive functioning and decreased sensory processing were associated with higher FA in long-association and commissural pathways, including the cingulum bundle, superior longitudinal fasciculus, and corpus-callosum, with longitudinal effects most evident over a 7.5-year interval. MeaningDevelopmentally informed normative modelling of white matter offers a pathway toward biologically grounded stratification for precision medicine in autism.

neuroscience↗

bioRxiv2025

Cerebellar growth is associated with domain-specific cerebral maturation and socio-linguistic behavioral outcomes

The cerebellums involvement in cognitive functions is increasingly recognized, yet its developmental contribution to cognition remains poorly understood. The cerebellum undergoes rapid development in early life, paralleling major cognitive and behavioral changes. Although clinical studies have linked early cerebellar disruptions to profound developmental deficits, it remains largely unclear how typical cerebellar maturation supports the development of cognitive functions and how it interacts with broader brain development. Here, we apply a normative modeling framework to map cerebellar volumetric growth from infancy to young adulthood (N = 751; ages 1-21 years). Using lobular and functional cerebellar parcellations, we comprehensively characterize typical cerebellar development and examine how it aligns with cerebral development and behavioral outcomes. Across parcellations, posterior higher association areas consistently show steeper growth trajectories than anterior sensorimotor areas. Cerebellar and cerebral areas with similar functional roles demonstrate coordinated maturation, and volumetric growth in the posterior cerebellum relates to individual differences in socio-linguistic behaviors. These findings establish a comprehensive reference for typical cerebellar development, highlight cerebellar co-maturation with the cerebral cortex, and underscore the cerebellums role in supporting emerging higher cognitive functions.

neuroscience↗

bioRxiv2025

MEGaNorm: Normative Modeling of MEG Brain Oscillations Across the Human Lifespan

Normative modeling provides a principled framework for quantifying individual deviations from typical brain development and is increasingly used to study heterogeneity in neuropsychiatric conditions. While widely applied to structural phenotypes, functional normative models remain underdeveloped. Here, we introduce MEGaNorm, the first normative modeling framework for charting lifespan trajectories of resting-state magnetoencephalography (MEG) brain oscillations. Using a large, multi-site dataset comprising 1,846 individuals aged 6-88 and spanning three MEG systems, we model relative oscillatory power in canonical frequency bands using hierarchical Bayesian regression, accounting for age, sex, and site effects. To support interpretation at multiple scales, we introduce Neuro-Oscillo Charts, visual tools that summarize normative trajectories at the population level and quantify individual-level deviations, enabling personalized assessment of functional brain dynamics. Applying this framework to a Parkinsons disease cohort (n = 160), we show that normative deviation scores reveal disease-related abnormalities and uncover a continuum of patients in theta-beta deviation space. This work provides the first lifespan-encompassing normative reference for MEG oscillations, enabling population-level characterization and individualized benchmarking. All models and tools are openly available and designed for federated, continual adaptation as new data become available, offering a scalable resource for precision neuropsychiatry.

neuroscience↗

bioRxiv2024

Lifespan Normative Modeling of Brain Microstructure

Normative models of brain metrics based on large populations could be extremely valuable for detecting brain abnormalities in patients with a variety of disorders, including degenerative, psychiatric and neurodevelopmental conditions, but no such models exist for the brains white matter (WM) microstructure. Here we present a large-scale normative model of brain WM microstructure - based on 19 international diffusion MRI datasets covering almost the entire lifespan (totaling N=54,583 individuals; age: 4-91 years). We extracted regional diffusion tensor imaging (DTI) metrics using a standardized analysis and quality control protocol and used hierarchical Bayesian regression (HBR) to model the statistical distribution of derived WM metrics as a function of age and sex. We extracted the average lifespan trajectories and corresponding centile curves for each WM region. We illustrate the utility of the method by applying it to detect and visualize profiles of WM microstructural deviations in a variety of contexts: in mild cognitive impairment, Alzheimers disease, and 22q11.2 deletion syndrome - a neurogenetic condition that markedly increases risk for schizophrenia. The resulting large-scale model provides a common reference to identify disease effects on the brains microstructure in individuals or groups, and to compare disorders, and discover factors affecting WM abnormalities. The derived normative models are a valuable resource publicly available to the community, adaptable and extendable to future datasets as the available data expands.

neuroscience↗

bioRxiv2024

Lifespan Normative Models of White Matter Fractional Anisotropy: Applications to Early Psychosis

This study presents large-scale normative models of white matter (WM) organization across the lifespan, using diffusion MRI data from over 25,000 healthy individuals aged 0-100 years. These models capture lifespan trajectories and inter-individual variation in fractional anisotropy (FA), a marker of white matter integrity. By addressing non-Gaussian data distributions, race, and site effects, the models offer reference baselines across diverse ages, ethnicities, and scanning conditions. We applied these FA models to the HCP Early Psychosis cohort and performed a multivariate analysis to map symptoms onto deviations from multimodal normative models using multi-view sparse canonical correlation analysis (msCCA). Our results reveal extensive white matter heterogeneity in psychosis, which is not captured by group-level analyses, with key regions identified, including the right uncinate fasciculus and thalami. These normative models offer valuable tools for individualized WM deviation identification, improving precision in psychiatric assessments. All models are publicly available for community use. TeaserLifespan models of white matter offer insights into brain health, providing tools for tracking individual deviations across ages.

neuroscience↗

bioRxiv2024

Multiscale heterogeneity of white matter morphometry in psychiatric disorders

BackgroundInter-individual variability in neurobiological and clinical characteristics in mental illness is often overlooked by classical group-mean case-control studies. Studies using normative modelling to infer person-specific deviations of grey matter volume have indicated that group means are not representative of most individuals. The extent to which this variability is present in white matter morphometry, which is integral to brain function, remains unclear. MethodsWe applied Warped Bayesian Linear Regression normative models to T1- weighted magnetic resonance imaging data and mapped inter-individual variability in person-specific white matter volume deviations in 1,294 cases (58% male) diagnosed with one of six disorders (attention-deficit/hyperactivity, autism, bipolar, major depressive, obsessive-compulsive and schizophrenia) and 1,465 matched controls (54% male) recruited across 25 scan sites. We developed a framework to characterize deviation heterogeneity at multiple spatial scales, from individual voxels, through inter- regional connections, specific brain regions, and spatially extended brain networks. ResultsThe specific locations of white matter volume deviations were highly heterogeneous across participants, affecting the same voxel in fewer than 8% of individuals with the same diagnosis. For autism and schizophrenia, negative deviations (i.e., areas where volume is lower than normative expectations) aggregated into common tracts, regions and large-scale networks in up to 35% of individuals. ConclusionsThe prevalence of white matter volume deviations was lower than previously observed in grey matter, and the specific location of these deviations was highly heterogeneous when considering voxel-wise spatial resolution. Evidence of aggregation within common pathways and networks was apparent in schizophrenia and autism but not other disorders.

neuroscience↗

bioRxiv2023

Mapping Cerebellar Anatomical Heterogeneity in Mental and Neurological Illnesses

The cerebellum has been linked to motor coordination, cognitive and affective processing, in addition to a wide range of clinical illnesses. To enable robust quantification of individual cerebellar anatomy relative to population norms, we mapped the normative development and aging of the cerebellum across the lifespan using brain scans of > 54k participants. We estimated normative models at voxel-wise spatial precision, enabling integration with cerebellar atlases. Applying the normative models in independent samples revealed substantial heterogeneity within five clinical illnesses: autism spectrum disorder, mild cognitive impairment, Alzheimers disease, bipolar disorder, and schizophrenia. Notably, individuals with autism spectrum disorder and mild cognitive impairment exhibited increased numbers of both positive and negative extreme deviations in cerebellar anatomy, while schizophrenia and Alzheimers disease predominantly showed negative deviations. Finally, extreme deviations were associated with cognitive scores. Our results provide a voxel-wise mapping of cerebellar anatomy across the human lifespan and clinical illnesses, demonstrating cerebellums nuanced role in shaping human neurodiversity across the lifespan and in different clinical illnesses.

neuroscience↗

bioRxiv2023

Large Data on the Small Brain: Population-wide Cerebellar Growth Models of Children and Adolescents

In the past, the cerebellum has been best known for its crucial role in motor function. However, increasingly more findings highlight the importance of cerebellar contributions in cognitive functions and neurodevelopment. Using large scale, population-wide neuroimaging data, we describe and provide detailed, openly available models of cerebellar development in childhood and adolescence, an important time period for brain development and onset of neuropsychiatric disorders. Next to a traditionally used anatomical parcellation of the cerebellum, we generated growth models based on a recently proposed functional parcellation. In both, we find an anterior-posterior growth gradient mirroring the age-related improvements of underlying behavior and function, which is analogous to cerebral maturation patterns and offers new evidence for directly related cerebello-cortical developmental trajectories. Finally, we illustrate how the current approach can be used to detect cerebellar abnormalities in clinical samples.

neuroscience↗

bioRxiv2023

Unpacking the functional heterogeneity of the Emotional Face Matching Task: a normative modelling approach

Functional neuroimaging has contributed substantially to understanding brain function but is dominated by group analyses that index only a fraction of the variation in these data. It is increasingly clear that parsing the underlying heterogeneity is crucial to understand individual differences and the impact of different task manipulations. We estimate large-scale (N=7728) normative models of task-evoked activation during the Emotional Face Matching Task, which enables us to bind heterogeneous datasets to a common reference and dissect heterogeneity underlying group-level analyses. We apply this model to a heterogenous patient cohort, to map individual differences between patients with one or more mental health diagnoses relative to the reference cohort and determine multivariate associations with transdiagnostic symptom domains. For the face>shapes contrast, patients have a higher frequency of extreme deviations which are spatially heterogeneous. In contrast, normative models for faces>baseline have greater predictive value for individuals transdiagnostic functioning.

neuroscience↗

bioRxiv2023

Estimating cortical thickness trajectories in children across different scanners using transfer learning from normative models

This work illustrates the use of normative models in a longitudinal neuroimaging study of children aged 6-17 years and demonstrates how such models can be used to make meaningful comparisons in longitudinal studies, even when individuals are scanned with different scanners across successive study waves. More specifically, we first estimated a large-scale reference normative model using hierarchical Bayesian regression from N=40,435 individuals across the lifespan and from dozens of sites. We then transfer these models to a longitudinal developmental cohort (N=5,985) with three measurement waves acquired on two different scanners that were unseen during estimation of the reference models. We show that the use of normative models provides individual deviation scores that are independent of scanner effects and efficiently accommodate inter-site variations. Moreover, we provide empirical evidence to guide the optimization of sample size for the transfer of prior knowledge about the distribution of regional cortical thicknesses. We show that a transfer set containing as few as 25 samples per site can lead to good performance metrics on the test set. Finally, we demonstrate the clinical utility of this approach by showing that deviation scores obtained from the transferred normative models are able to detect and chart morphological heterogeneity in individuals born pre-term.

neuroscience↗

bioRxiv2022

Evidence for Embracing Normative Modeling

In this work, we expand the normative model repository introduced in Rutherford et al. (2022a) to include normative models charting lifespan trajectories of structural surface area and brain functional connectivity, measured using two unique resting-state network atlases (Yeo-17 and Smith-10), and an updated online platform for transferring these models to new data sources. We showcase the value of these models with a head-to-head comparison between the features output by normative modeling and raw data features in several benchmarking tasks: mass univariate group difference testing (schizophrenia versus control), classification (schizophrenia versus control), and regression (predicting general cognitive ability). Across all benchmarks, we confirm the advantage (i.e., stronger effect sizes, more accurate classification and prediction) of using normative modeling features. We intend for these accessible resources to facilitate wider adoption of normative modeling across the neuroimaging community.

neuroscience↗

bioRxiv2022

Non-Gaussian Normative Modelling With Hierarchical Bayesian Regression

Normative modelling is an emerging technique for parsing heterogeneity in clinical cohorts. This can be implemented in practice using hierarchical Bayesian regression, which provides an elegant probabilistic solution to handle site variation in a federated learning framework. However, applications of this method to date have employed a Gaussian assumption, which may be restrictive in some applications. We have extended the hierarchical Bayesian regression framework to flexibly model non-Gaussian data with heteroskdastic skewness and kurtosis. To this end, we employ a flexible distribution from the sinh-arcsinh (SHASH) family, and introduce a novel reparameterisation and a Markov chain Monte Carlo sampling approach to perform inference in this model. Using a large neuroimaging dataset collected at 82 different sites, we show that the results achieved with this extension are equivalent or better than a warped Bayesian linear regression baseline model on most datasets, whilst providing better control over the parameters governing the shape of distributions that approach is able to model. We also demonstrate that the attained flexibility is useful for accurately modelling highly nonlinear relationships between aging and imaging derived phenotypes, which shows that the extension is important for pushing the field of normative modelling forward. All methods described here are available in the open-source pcntoolkit. HighlightsO_LIWe extended the Hierarchical Bayesian Regression framework for normative modelling C_LIO_LIOur extension allows modelling data with heteroskedastic skewness and kurtosis C_LIO_LIWe developed a reparameterization of the SHASH distribution, suitable for sampling C_LIO_LIWe provide the first implementation of the SHASH distribution in a fully Bayesian framework C_LIO_LIResults show that the extension outperforms current methods on various measures C_LI

neuroscience↗

bioRxiv2022

The Extremes of Normative Modelling

For many problems in neuroimaging, the most informative features occur in the tail of the distribution. For example, when considering psychiatric disorders as deviations from a norm, the tails of the distribution are considerably more informative than the bulk of the distribution for understanding risk, for stratifying and predicting such disorders and for anomaly detection. Yet, most statistical methods used in neuroimaging focus on modelling the bulk and fail to adequately capture extreme values occurring in the tails. To address this, we propose a framework that combines normative models with multivariate extreme value statistics to accurately model extreme deviations of a reference cohort for individual participants. Normative models are now widely used in clinical neuroscience and similar to the employment of normative growth charts in pediatric medicine to track a childs weight in relation to their age, normative models can be used with neuroimaging measurements to quantify individual neurophenotypic deviations from a reference cohort. However, formal statistical treatment of how to model the extreme deviations from these models has been lacking until now. In this article we provide such an approach, inspired by applications of extreme value statistics in meteorology. Since the presentation of extreme value statistics is usually quite technical, we begin with a non-technical introduction to the fundamental principles of extreme value statistics, to accurately map the tails of the normative distribution for biological markers, including mapping multivariate tail dependence across multiple markers. Next, we give a demonstration of this approach to the UK Biobank dataset and demonstrate how extreme values can be used to accurately estimate risk and detect atypicality. This framework provides a valuable tool for the statistical modelling of extreme deviations in neurobiological data, which could provide us with more accurate and effective diagnostic tools for neurological and psychiatric disorders.

neuroscience↗

bioRxiv2022

Evidence for absence of links between striatal dopamine synthesis capacity and working memory capacity, spontaneous eye-blink rate, and trait impulsivity

Individual differences in striatal dopamine synthesis capacity have been associated with working memory capacity, trait impulsivity and spontaneous eye-blink rate (sEBR), as measured with readily available and easily administered, off-the-shelf tests. Such findings have raised the suggestion that individual variation in dopamine synthesis capacity, estimated with expensive and invasive brain positron emission tomography (PET) scans, can be approximated with simple, more pragmatic tests. However, direct evidence for the relationship between these simple trait measures and striatal dopamine synthesis capacity has been limited and inconclusive. We measured striatal dopamine synthesis capacity using [18F]-FDOPA PET in a large sample of healthy volunteers (N=94) and assessed the correlation with simple, short tests of working memory capacity, trait impulsivity, and sEBR. We additionally explored the relationship with an index of subjective reward sensitivity. None of these trait measures correlated significantly with striatal dopamine synthesis capacity, nor did they have out-of-sample predictive power. Bayes Factor analyses indicated the evidence was in favour of absence of correlations for all but subjective reward sensitivity. These results warrant caution for using these off-the-shelf trait measures as proxies of striatal dopamine synthesis capacity.

neuroscience↗