Biomarkers Neuro-Dementia
The co-authors who collaborated in drafting the Neuro-dementia table:
Castellano Antonella, Calloni Sonia Francesca, Georgiopoulos Charalampos, Vernooij Meike, on behalf of ESNR Diagnostic Committee and ESR EIBALL Subcommittee
EIBALL-ESNR Biomarker Inventory for Dementia
Release: April 2025
|
Biomarker (Name) |
Unit of Measurement |
Acquisition Modality |
Data acquisition requirements |
Extracting biomarker |
Pathophysiological process |
Use of biomarker: Diagnosis |
Use of biomarker: Prediction |
Accuracy/Agreement |
Evidence level |
References |
Issues/ |
|
Cortical atrophy (N) |
Semi-quantitative scale |
CT MRI (T1w or FLAIR) |
Volumetric acquisitions for MPR reconstruction |
Global cortical atrophy (a.k.a. Pasquier or GCA score, 0-3): - Extensive or simplified version. - Assessment on multiplanar images Parietal atrophy (a.k.a. Koedam score, 0-3): - Assessment of the parietal lobe in three planes |
Cortical atrophy |
- Semi-quantitative, ordinal scale - Do not require specialist software or expertise - Quick to apply - Widely adopted (especially simplified GCA) - Proven good correlation with volumetric and post-mortem information - patterns of cerebral atrophy can point towards a specific underlying diagnosis (etiologic diagnosis) |
AUC for distinguishing each pathological group from controls ranges from 0.86–0.97; from one another, 0.75–0.92 |
2a |
(Pasquier et al., 1996) Original publication (GCA) (Koedam et al., 2011) Original publication (Koedam) (Harper et al., 2015) Review (Wahlund et al., 2017) Pictorial review (Harper et al., 2016; Harper et al., 2017) Original publication |
- Sensitivity in prodromal dementia is limited - Moderate to substantial intra- and interrater agreement |
|
|
Medial temporal lobe atrophy (N) |
Semi-quantitative scale |
CT MRI (T1w) |
Acquisition in coronal plane perpendicular to long axis of hippocampus; or volumetric acquisitions for MPR reconstruction along same axis |
Visual assessment on coronal images (a.k.a. Scheltens scale or MTA scale, 0-4): - width of the choroid fissure - width of temporal horn - height of hippocampus |
Hippocampal atrophy |
- Semi-quantitative, ordinal scale - Do not require specialist software or expertise - Quick to apply - Widely adopted - Discrimination between AD and controls and between AD and non-AD dementia. |
Atrophy has been shown to correlate with likelihood of progression from mild cognitive impairment (MCI) to dementia |
Sensitivity of ~75% and specificity of ~85% to discriminate Alzheimer disease from healthy controls |
2a |
(Scheltens et al., 1992) Original publication (Ten Kate et al., 2017) Clinical validity (Hakansson et al., 2022; Håkansson et al., 2022) Variability (Wahlund et al., 2017) Pictorial review |
- Early detection ability - Adequate intra- and interrater variability - the exact cut-off has limited value above the age of 85. Also, gender and age can be confounders to the score |
|
Entorhinal cortex atrophy (N) |
Semi-quantitative scale |
MRI (T1w) |
Volumetric acquisitions for MPR reconstruction |
Assessment on coronal sections at the level of the mammillary bodies (a.k.a. ERICA score) |
Entorhinal cortical atrophy |
- Entorhinal atrophy precedes hippocampal atrophy, with potential for earlier/more accurate AD diagnosis - Promising results in discriminating MCI from AD |
Discrimination between MCI and SCD; possible prediction in converting to Alzheimer |
Higher diagnostic accuracy (91%) than the MTA score (74%), with a sensitivity of 83% versus 57% and a specificity of 98% versus 92% in discriminating dementia caused by AD from SCD MCI and AD dementia (area under the curve: 0.71) was comparable to volumetry, and superior to the medial temporal lobe atrophy (MTA) score (p = 0.006 |
2a |
(Enkirch et al., 2018) Original publication (Roberge et al., 2023) Study on disease conversion |
- Relatively recent score, so far fewer evidence than MTA, in need of more confirmation/ validation studies - Intra- and interrater agreement has not been assessed - Not validated in the broader spectrum of dementia |
|
Volumetry (N) |
mm3 |
MRI (T1w) |
Volumetric acquisitions |
Quantification of brain volume (and lobar or other subregional volumes) based on 3D T1-weighted images, often adjusted for head size and converted to percentile scores compared to age- and sex-specific reference population |
Cortical atrophy |
- Quantitative - Several products are commercially available - Allows the detection of distinct patterns of volume loss, against background of normal ageing - There are initial reports demonstrating supportive role in diagnostic interpretation - Earlier detection of atrophy and more sensitive to longitudinal change |
Several most recent studies show that volumetry software may increase diagnostic accuracy when compared to visual assessment of cortical atrophy evaluation |
2b |
(Harper et al., 2017) Patterns of atrophy (Pemberton et al., 2021b) Review on validation (Pemberton et al., 2021a) Interpretation (Vernooij et al., 2019) ESNR survey
|
- Significant evidence gap regarding clinical validation, workflow integration and in-use evaluation - Not widely used - Inhomogeneity of results among different software (Zaki 2022) |
|
|
Vascular burden (N) |
Semi-quantitative scale |
MRI (FLAIR) |
Volumetric acquisitions |
Assessment of FLAIR-weighted images for white matter hyperintensities with the Fazekas (or ARWMC) score |
Microvascular lesions |
- Assessment of vascular pathology as potential explanation of cognitive impairment (etiologic diagnosis) - Semi-quantitative, ordinal scale - Do not require specialist software or expertise - Quick to apply - Widely adopted - Very good intra- and interrater agreement |
Prediction of cognitive decline, especially for highest grade |
N/A |
2a |
(Fazekas et al., 1987) Original publication (Fazekas) (Wahlund et al., 2001) ARWMC (Boutet et al., 2016) Agreement (Jokinen et al., 2011) LADIS study (Wardlaw et al., 2013) Neuroimaging standards on small vessel disease |
- Subjective - Not sensitive to small change - No clear link to symptoms/patient outcomes - No normative reference data available |
|
Microbleeds (X) |
Semi-quantitative scale |
MRI |
Comparable sequence available from all major vendors, ~5 mins |
Visual assessment of Susceptibility Weighted Imaging (SWI) or T2*GRE |
Detection of superficial siderosis & microbleeds |
- Detection of superficial siderosis & microbleeds - Boston 2.0 criteria - Do not require specialist software - Quick to apply - Widely adopted |
- Predict stroke-related mortality in AD patients - predict higher cognitive decline |
N/A |
2b |
(Wheeler et al., 2024) Review (Charidimou et al., 2022) Boston 2.0 (Switzer et al., 2024) Accuracy |
Sensitivity of detection dependent on techniques (field strength, echo time SWI versus T2*GRE) |
|
Iron deposition (N) |
Dimensionless ratio; parts per million (ppm) relative to the external magnetic field strength (B0) |
MRI (SWI) |
Comparable sequence available from all major vendors, ~5 mins |
Quantitative Susceptibility Mapping (QSM) |
Detection of early pathological changes and iron deposition |
- Quantitative - Detects early pathological changes and iron deposition, as early differentiation between AD and LBD - Emerging promising tool |
- effective performance of the subject group classification and prediction for aMCI stage - can predict cognitive decline in patients with early stage AD - monitoring the progression of neurodegenerative diseases |
N/A |
2b |
(Suresh Paul et al., 2024) Review focused on dementia (Haacke et al., 2015) Overview of QSM |
- No standardized processing techniques - Limited reproducibility & reliability for the time being |
|
WM microstructure (N) |
mm2/s (MD), value range 0-1 (FA) |
MRI Diffusion Tensor Imaging (DTI) |
Comparable sequence available from all major vendors, 5-15 mins, minimum 6 directions |
ROI or VOI or |
Detection of white matter integrity, structural connectivity |
- Quantitative - Detects early regional microstructural changes as early differentiation between MCI and AD |
- DTI revealed subtle WM alterations in subjective cognitive decline that were intermediate between those in MCI and healthy controls - May be useful to detect individuals with an increased risk for AD |
N/A |
3b |
(Clerx et al., 2012; Teipel et al., 2012; Brueggen et al., 2017; Brueggen et al., 2019; Qin et al., 2021; Li et al., 2023) |
- No clinical validation - Mostly used in the context of research, group-wise comparisons - Often studies with relatively small, single-center sample |
|
Connectivity (N) |
a.u |
MRI Resting-state fMRI (rs-fMRI) |
3T preferred for optimal signal-to-noise ratio (SNR) and spatial resolution TR of 0.5 to 3 seconds is typical, with TR < 1 second being ideal for capturing faster fluctuations in BOLD signals. 5-10 mins acquisition is usually the minimum recommended for reliable connectivity analyses (10-15 mins preferred to improve robustness/ Patients are typically instructed to keep their eyes closed and to maintain a relaxed state without engaging in specific tasks |
ROI or VOI |
Detection of functional connectivity of hippocampus and other limbic regions |
- In AD, several resting-state fMRI studies revealed altered connectivity in the default mode network |
- highly connected hub regions in the brain might be an early target of AD |
N/A |
3b |
(Teipel et al., 2015) Review (Young et al., 2020) Overview on current & future directions |
- No clinical validation - Mostly used in the context of research, group-wise comparisons - Often studies with relatively small, single-center sample |
|
Perfusion (X) |
ml/min/100ml (CBF units) |
MRI |
Comparable sequence available from all major vendors (~4 min). Additional calibration scan is required (<30sec) |
Quantification of cerebral blood flow (CBF) using a kinetic model derived from arterial spin labeling (ASL) and calibration data |
Regional brain perfusion differences |
- Pattern of (hypo)perfusion may indicate early degenerative change and point towards etiologic diagnosis - Thought to provide comparable information to FDG-PET - Enables absolute quantification of CBF - No use of exogenous tracer |
Regional hyperperfusion reported in MCI, AD,frontotemporal dementia |
Lower accuracy compared to FDG-PET -sensitivity: 77–84%;specificity: 69–81%; AUC: 0.83–0.89 in differentiating patients with AD from HC subjects or SCD -sensitivity = 79%; specificity = 76%; AUC = 0.78 in differentiating patients with FTLD from HC subjects |
3b |
(Alsop et al., 2015) Review (Ferreira and Bastos-Leite, 2024) Consensus paper (Haidar et al., 2023) Meta-analysis |
- Literature is still discordant, but ASL seems to have lower temporal resolution compared to FDG-PET |
|
FDG PET (N) |
SUVr |
18F deoxy-glucose PET |
PET 10-20 min static imaging, 45 min p.i. Continuous glucose monitor (CGM) should be removed i.v. placement for radiotracer administration |
VOI-based analysis Automated quantification |
Measure of regional glucose metabolism (combination of neuronal/synaptic activity and astrocytic activity and neuroinflammation) |
Largest availability compared to other quantitative PET Patterns of reduction in metabolism are presumed to reflect early changes due to neurodegeneration and can point to etiologic diagnosis. In core clinical criteria for bvFTD: Required to take a bvFTD diagnosis from “possible” to “probable Specific pattern for MAPT or P301L mutations |
Diminished FDG uptake precedes symptoms Differences in metabolism patterns may predict AD conversion FTD: Diminished FDG uptake during follow-ups |
High accuracy: >90% sensitivity and 63–99% specificity compared with histopathological diagnosis |
3a |
General: (Frisoni et al., 2024; Boccardi et al., 2018
|
AD: - Expensive - Invasive due to the injection of radiolabeled tracer - False positives caused by other diseases and medication use - Radiation Burden FTD: - Misclassification due to psychiatric illness |
|
Amyloid deposition (A) |
SUV Centiloid (CL) scale reference-based z-scores Aβ load Aβ index AMYQ) |
PET (11C: Pittsburg compound B; 18F: flutemetamol, florbetapir and florbetaben) |
PET Continuous glucose monitor (CGM) should be removed i.v. placement for radiotracer administration |
VOI-based analysis Automated quantification |
18F-labeled radiotracers have a longer physical half-life and match the spreading patterns of Ab plaques |
In vivo AD pathology marker Negative amyloid PET rules out AD |
11C retention able to predict MCI to AD conversion |
High accuracy 88%–98% sensitivity and 80%–95% specificity in distinguishing older adults with moderate to frequent neuritic plaques (according to the CERAD scale) from those with absent to sparse plaques |
3b |
AD: (Wolk et al., 2012; Rowe et al., 2013; Vandenberghe et al., 2013; Klunk et al., 2015; Grothe et al., 2017; Altomare et al., 2018; Suppiah et al., 2019; Young et al., 2020; Chapleau et al., 2022) FTD: (Tan et al., 2017; De Leon et al., 2019; Shi et al., 2020) |
- Lower specificity: Amyloid can coexist also in ageing and in other neurodegenerative pathologies, e.g. in LBD or FTD - Elevated 11C also found in healthy controls - No overlap with (N) brain regions - Variance (centiloid scale) - Off target binding for 18F - Cost and availability - Radiation burden - No correlation with disease severity |
|
Tau deposition (T) |
SUV |
18F-fortaucipir (FDA approved) 4 main categories of tracers 18F-FDDNP Quinoline Derivates Pyrido-indole derivates PBB3 |
PET Continuous glucose monitor (CGM) should be removed i.v. placement for radiotracer administration |
VOI-based analysis Automated quantification |
Relationship between neurodegeneration and neurofibrillary pathology |
Potentially capable of providing the “T” biomarker information High specificity: able to “rule in” AD based on positive tau PET flortaucipir-PET is elevated in some patients with specific MAPT mutations AD pathology marker Semi-quantitative Overlaps with (N) regions of atrophy and reduced glucose metabolism |
Tau accumulation believed to be related to cognitive impairment Able to separate FTD from CBD and PSP? |
High accuracy to predict the subset of Aβ(+) individuals that will show AD-relevant cognitive decline Superior to amyloid PET in explaining cognitive changes |
3b |
AD: (Braak and Braak, 1991; Okamura et al., 2013; Cho et al., 2016; Ossenkoppele et al., 2016; Saint-Aubert et al., 2017; Klunk, 2018; Mattsson et al., 2018; Ossenkoppele et al., 2018; Villemagne et al., 2018; Pontecorvo et al., 2019; Young et al., 2020; Ioannou et al., 2024)
FTD: (Jones et al., 2018; Ghirelli et al., 2020; Lowe et al., 2020; Soleimani-Meigooni et al., 2020; Tian et al., 2022) |
AD: - Low affinity for straight ligaments - Cost and availability - Radiation burden - tracer binding in most non-AD tauopathies is weaker and overlaps to a large extent with known off-target binding regions, limiting the quantification and visualization of non-AD tau pathology in vivo - Need for validation FTD: - Sensitivity is limited to early stages - Non-specific binding - Results of its utility in different subtypes are discordant |
|
Synaptic density (N) |
a.u. |
11C UCB-J (SV2A) 18F-UCB-H 18F-flortaucipir |
PET Continuous glucose monitor (CGM) should be removed i.v. placement for radiotracer administration |
Voxel-wise and region of interest (ROI) analysis |
Radioligands with high affinity and selectivity for synaptic vesicle protein 2A (SV2A), which is the only of three isoforms that is ubiquitously expressed in synapses across the brain. |
Hippocampal uptake correlates with memory Decrease cortical and amygdala/hippocampal uptake in AD patients. Early indicator of brain dysfunction in AD compared to volume loss. 11C-UCB-J uptake was detected in the thalamus of three presymptomatic C9orf72 carriers: early diagnosis? |
Potential correlation with therapy |
N/A |
3b |
AD: (Zheng et al., 2014; Finnema et al., 2018; Carson et al., 2022; Mecca et al., 2022; Zhang et al., 2023)
|
- 11C short half life - Cost and availability - Radiation burden - Contradictory results - AD: 11C-UCB-J PET binding in neocortical areas less pronounced than the changes detected by 18F-FDG e tau-PET |
* Class: A for amyloid, T for tau, N for neurodegeneration, X for other
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