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Contact information

Email
fidel.alfaroalmagro@ndcn.ox.ac.uk

Colleges

Pembroke College

Teaching Experience

FMRIB Graduate Teaching Programme (internal Oxford course)
FSL Course (external course)

Websites

ResearchGate Profile
ResearchGate
@NeuroFidelity
Twitter Profile

Research groups

Useful Links

UK Biobank Imaging Study

Media Coverage on the UK Biobank Project

UK Biobank Pipeline code

Ambassadors for Science Program

SRUK VIII International Symposium committee

Fidel Alfaro Almagro

Bioinformatician

DPhil in Brain Image Analysis.

MSc in Artificial Intelligence.

BSc in Computer Sciences.

My research is focused on developing fully automated analyses tools for UK Biobank Project, the biggest brain imaging study up to date with 6 different brain imaging modalities for 100,000 subjects. In that project my aim will be to develop new methods for multimodal-imaging population modelling using supervised machine learning methods. The final goal will be to find early biomarkers of brain diseases.

Department: FMRIB Analysis Group

Research interests: Machine learning, artificial intelligence, brain imaging, quality control, big data.

Personal interests: Science policy, public engagement and science communication.

Supervisors: Mark Jenkinson and Stephen Smith.

UK Biobank

UK Biobank

Introduction to the UK Biobank Brain Imaging repository.

Key publications

SARS-CoV-2 is associated with changes in brain structure in UK Biobank.

Douaud G. et al, (2022), medRxiv

Medium-term effects of SARS-CoV-2 infection on multiple vital organs, exercise capacity, cognition, quality of life and mental health, post-hospital discharge.

Raman B. et al, (2021), EClinicalMedicine, 31

Confound modelling in UK Biobank brain imaging.

Alfaro-Almagro F. et al, (2021), NeuroImage, 224, 117002 - 117002

Handedness, language areas and neuropsychiatric diseases: insights from brain imaging and genetics.

Wiberg A. et al, (2019), Brain, 142, 2938 - 2947

Genome-wide association studies of brain imaging phenotypes in UK Biobank.

Elliott LT. et al, (2018), Nature, 562, 210 - 216

Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank.

Alfaro-Almagro F. et al, (2018), Neuroimage, 166, 400 - 424

An empirical, 21st century evaluation of phrenology

Jones OP. et al, (2018)

Hand classification of fMRI ICA noise components.

Griffanti L. et al, (2017), Neuroimage, 154, 188 - 205

BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods.

Gorgolewski KJ. et al, (2017), PLoS Comput Biol, 13

Multimodal population brain imaging in the UK Biobank prospective epidemiological study.

Miller KL. et al, (2016), Nat Neurosci, 19, 1523 - 1536

Recent publications

PANDORA: Population Archive of Neuroimaging Data Organized for Rapid Analysis

Abivardi A. et al, (2026)

Brain Perfusion Imaging of a Large Population: Arterial Spin Labelling MRI in UK Biobank

Okell TW. et al, (2025)

Brain Perfusion Imaging of a Large Population: Arterial Spin Labelling MRI in UK Biobank

Okell TW. et al, (2025)

An Improved Pipeline for Constructing UK Biobank Brain Imaging Confounds

Radosavljević L. et al, (2025)

Brain Perfusion Imaging of a Large Population: Arterial Spin Labelling MRI in UK Biobank

Okell TW. et al, (2025)

From Big Data to the Clinic: Methodological and Statistical Enhancements to Implement the UK Biobank Imaging Framework in a Memory Clinic.

Gillis G. et al, (2025), Hum Brain Mapp, 46

From Big Data to the clinic: methodological and statistical enhancements to implement the UK Biobank imaging framework in a memory clinic

Gillis G. et al, (2024)

The effects of genetic and modifiable risk factors on brain regions vulnerable to ageing and disease.

Manuello J. et al, (2024), Nat Commun, 15

A resource for development and comparison of multi-modal brain 3T MRI harmonisation approaches

Warrington S. et al, (2023)

Association of gout with brain reserve and vulnerability to neurodegenerative disease.

Topiwala A. et al, (2023), Nat Commun, 14

Automated detection of cerebral microbleeds on MR images using knowledge distillation framework.

Sundaresan V. et al, (2023), Front Neuroinform, 17

Adapting UK Biobank imaging for use in a routine memory clinic setting: the Oxford Brain Health Clinic

Griffanti L. et al, (2022)

SARS-CoV-2 is associated with changes in brain structure in UK Biobank.

Douaud G. et al, (2022), medRxiv

Phenotypic and genetic associations of quantitative magnetic susceptibility in UK Biobank brain imaging

Wang C. et al, (2021)

An expanded set of genome-wide association studies of brain imaging phenotypes in UK Biobank.

Smith SM. et al, (2021), Nat Neurosci, 24, 737 - 745

Medium-term effects of SARS-CoV-2 infection on multiple vital organs, exercise capacity, cognition, quality of life and mental health, post-hospital discharge.

Raman B. et al, (2021), EClinicalMedicine, 31

Adapting the UK Biobank Brain Imaging Protocol and Analysis Pipeline for the C-MORE Multi-Organ Study of COVID-19 Survivors.

Griffanti L. et al, (2021), Front Neurol, 12

Confound modelling in UK Biobank brain imaging.

Alfaro-Almagro F. et al, (2021), Neuroimage, 224

Confound modelling in UK Biobank brain imaging.

Alfaro-Almagro F. et al, (2021), NeuroImage, 224, 117002 - 117002

Adapting the UK Biobank brain imaging protocol and analysis pipeline for the C-MORE multi-organ study of COVID-19 survivors

Griffanti L. et al, (2021)

Medium-term effects of SARS-CoV-2 infection on multiple vital organs, exercise capacity, cognition, quality of life and mental health, post-hospital discharge

Raman B. et al, (2020)

Discovering correlates of age-related decline in a healthy late-midlife male birth cohort.

Zarnani K. et al, (2020), Aging (Albany NY), 12, 16709 - 16743

Enhanced Brain Imaging Genetics in UK Biobank

Smith SM. et al, (2020)

Common Genetic Variation Indicates Separate Causes for Periventricular and Deep White Matter Hyperintensities.

Armstrong NJ. et al, (2020), Stroke, 51, 2111 - 2121

The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions.

Littlejohns TJ. et al, (2020), Nat Commun, 11

Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations.

Smith SM. et al, (2020), Elife, 9

Estimation of brain age delta from brain imaging.

Smith SM. et al, (2019), Neuroimage, 200, 528 - 539

Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations

Smith SM. et al, (2019)

Handedness, language areas and neuropsychiatric diseases: insights from brain imaging and genetics.

Wiberg A. et al, (2019), Brain, 142, 2938 - 2947

Discovering markers of healthy aging: a prospective study in a Danish male birth cohort.

Zarnani K. et al, (2019), Aging (Albany NY), 11, 5943 - 5974

Common genetic variation indicates separate etiologies for periventricular and deep white matter hyperintensities

Armstrong NJ. et al, (2019)

The spatial correspondence and genetic influence of interhemispheric connectivity with white matter microstructure.

Mollink J. et al, (2019), Nat Neurosci, 22, 809 - 819

Hippocampal volume across age: Nomograms derived from over 19,700 people in UK Biobank

Nobis L. et al, (2019)

Estimation of Brain Age Delta from Brain Imaging

Smith SM. et al, (2019)

Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference.

Sundaresan V. et al, (2019), Neuroimage, 185, 434 - 445

Hippocampal volume across age: Nomograms derived from over 19,700 people in UK Biobank.

Nobis L. et al, (2019), Neuroimage Clin, 23

Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction.

Bastiani M. et al, (2019), Neuroimage, 184, 801 - 812

Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference.

Sundaresan V. et al, (2019), NeuroImage, 185, 434 - 445

Human Handedness: Genetics, Microtubules, Neuropsychiatric Diseases and Brain Language Areas

Wiberg A. et al, (2018)

Discovering dynamic brain networks from big data in rest and task.

Vidaurre D. et al, (2018), Neuroimage, 180, 646 - 656

Genome-wide association studies of brain imaging phenotypes in UK Biobank.

Elliott LT. et al, (2018), Nature, 562, 210 - 216

An empirical, 21st century evaluation of phrenology.

Parker Jones O. et al, (2018), Cortex, 106, 26 - 35

The spatial correspondence and genetic influence of inter-hemispheric connectivity with white matter microstructure

Mollink J. et al, (2018)

Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank.

Alfaro-Almagro F. et al, (2018), Neuroimage, 166, 400 - 424

An empirical, 21st century evaluation of phrenology

Jones OP. et al, (2018)

Investigations into within- and between-subject resting-state amplitude variations.

Bijsterbosch J. et al, (2017), Neuroimage, 159, 57 - 69

Genome-wide association studies of brain structure and function in the UK Biobank

Elliott LT. et al, (2017)

Hand classification of fMRI ICA noise components.

Griffanti L. et al, (2017), Neuroimage, 154, 188 - 205

Image Processing and Quality Control for the first 10,000 Brain Imaging Datasets from UK Biobank

Alfaro-Almagro F. et al, (2017)

BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods.

Gorgolewski KJ. et al, (2017), PLoS Comput Biol, 13

Multimodal population brain imaging in the UK Biobank prospective epidemiological study.

Miller KL. et al, (2016), Nat Neurosci, 19, 1523 - 1536

BIDS Apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods

Gorgolewski KJ. et al, (2016)

Cortical morphology of adolescents with bipolar disorder and with schizophrenia.

Janssen J. et al, (2014), Schizophr Res, 158, 91 - 99

The human cerebral cortex flattens during adolescence.

Alemán-Gómez Y. et al, (2013), J Neurosci, 33, 15004 - 15010