Neural ageing, made measurable.

A T1 MRI brain-age research platform built around fine-tuned SFCN, automated image preparation and a regional assessment dashboard.

1,691Distinct people · 4 data sources
464External DLBS test · held out
3DFine-tuned SFCN
218Fixed validation set

Trusted by industry leaders.

Voices from the neurologists, imaging researchers and study teams evaluating NeuroMetric.

“Our memory-clinic patients are followed with yearly scans. What the reviews needed was a single standardized readout — the same regions, the same report structure at every visit — rather than a fresh interpretation each time.”
MK Dr. Merve KayaConsultant Neurologist · Ege University Hospital
“A single gap figure is easy to dismiss. A report that shows which structures carry the deviation — and where the model’s attention concentrates — gives our readers something concrete to examine.”
MÇ Dr. M. ÇelikNeuroradiologist · Marmara University Hospital
“In cohort work, the measure you track has to be defined the same way at every site and every visit. An age-normalized brain-age slope from one fixed pipeline is the clearest candidate we have for that role.”
SM Sofia MarchettiTrial Operations Lead · Quibim
“Ageing on a scan is something we read subjectively — two experienced readers rarely land on the same number. One calibrated pipeline replaces that quiet disagreement with a defined figure and an interval you can actually discuss.”
LH Dr. Lena HartwigConsultant Radiologist · Ruhr University Bochum
“In multi-centre imaging, most of the debate sits in everything around the model — protocols, motion, corrections. Automated preparation to one common space takes most of that off the table before the estimate is even computed.”
TL Dr. Tomas LindqvistSenior MR Physicist · DRCMR, Copenhagen
“Our scans span a decade and several scanner generations. A downstream measure that is computed the same way for every scan is really the only way those years stay comparable within one person.”
IS Prof. Ingrid SørheimSenior Researcher · HUNT Study, NTNU
“Families always ask what the scan actually tells them. A structured report — one number for the gap, named regions below it — is much easier to explain honestly than an overall impression.”
AO Dr. Amara OseiGeriatrician · Memory Clinic, University Hospital Basel

The problem

People of the same age can have very different brain ageing profiles.

Brain-predicted age difference (PAD) is predicted age minus chronological age. It is a model-derived estimate, not a measured biological age or a diagnosis.

Same birthday, different brains

People of the same chronological age can receive different MRI-derived predictions. The figures below illustrate the concept; they are not study results.

Change needs repeat scans

Repeat MRI could support future change tracking. Slope is a planned research view; we have not validated longitudinal ageing rates.

The problem: same brain, different ages.

Multiple radiologists can read different brain ages from the same MRI — each estimate carries reader subjectivity. NeuroMetric removes that spread: the same brain passes the same pipeline and returns one objective, reproducible estimate every time. Conceptual comparison; inter-reader statistics were not measured.

Method

From image to report in four stages.

T1 MRI → preprocessing → quality control → SFCN prediction → dashboard

  1. 01

    Image preparation

    Raw scan → aligned volume

  2. 02

    Quality control

    MNI alignment checks

  3. 03

    Prediction

    Brain age + PAD

  4. 04

    Explanation

    Regional dashboard + report

Illustrative regional report
Brain age gap (PAD) +6.4yr
Hippocampus (left)+1.9
Entorhinal cortex+1.1
Prefrontal cortex+0.8
Cerebellar cortex−0.3

Explainability

Report the rationale, not just the number.

Whole-brain estimates and regional views need different interpretation. The neighboring report uses illustrative values.

  1. Regional analysis — The dashboard presents brain regions separately; regional gaps are not additive parts of the whole-brain gap.

  2. Matched cohort testing — Our OASIS protocol compares available cases with age/sex-matched controls. Validated reference percentiles are not available.

  3. Model heatmap — OAS1_0030 includes real signed Grad-CAM from SFCN contrast-cont4. Other demos remain schematic; attribution and registration still require review.

View dashboard

Longitudinal research

A single scan is a snapshot; repeat scans add context.

A future view could compare predictions across repeat scans.

Illustration — 3 visits, 7 yrs Predicted brain age Identity line (1 : 1)
54 58 62 52 60 70 CHRONOLOGICAL AGE (YRS) PREDICTED BRAIN AGE (YRS) 1:1 line V0 · 58.9 V1 · 64.1 V2 · 69.6 slope k ≈ 1.53
Illustrative slope = change in predicted age / change in chronological age. This is not an established biological ageing rate or clinical threshold.

Team

Our project combines MRI preparation, machine learning experiments and a research-focused dashboard.

Asil Doğan Gümüş

Asil Doğan Gümüş

Model architecture · Imaging lead

Development of the image preparation pipeline, fine-tuned 3D SFCN experiments and research dashboard.

Deniz Ovalı

Deniz Ovalı

Project team · Research communication

Project collaboration and research communication. Clinical credentials, approved study protocols and PACS integration are not claimed.

Use cases

Three directions for research evaluation.

Alzheimer-cohort research

Explore brain-age differences in OASIS-1 cases and matched controls; not a diagnostic screening tool.

Model development

Improve age prediction while monitoring performance on a fixed validation cohort.

Population cohorts

Test generalization to unseen participants and datasets before making clinical claims.