Iron accumulation in multiple sclerosis and implications for cognitive and clinical progression: a systematic review of advanced neuroimaging studies
Clinical Snapshot
PICO Framework
| P — Population | Patients with multiple sclerosis |
| I — Intervention | Iron-sensitive susceptibility-based magnetic resonance imaging (MRI) metrics |
| C — Comparator | Not specified (observational studies examining associations) |
| O — Outcomes | Cognitive performance, clinical progression, particularly information processing speed and executive control |
Bottom Line
This systematic review identifies iron-sensitive MRI metrics as promising biomarkers for cognitive decline in multiple sclerosis, with basal ganglia measures showing the most consistent associations with processing speed and executive function deficits. However, the evidence base remains heterogeneous, particularly for thalamic measurements, and lacks the methodological rigour needed for clinical implementation. The absence of quantitative meta-analysis and limited quality assessment reduces confidence in the findings. While the research direction is promising for understanding MS-related cognitive impairment, clinicians should await more robust longitudinal studies with standardised methodology before incorporating these measures into routine practice. Current evidence supports continued research investment but not immediate clinical application of iron-sensitive MRI as a cognitive biomarker in MS management.
Key Findings
P Value: Not reported
Effect Size: Not quantified - qualitative synthesis only
Primary Outcome: Association between iron-sensitive MRI metrics and cognitive performance in MS
Nnt Or Sensitivity: Basal ganglia susceptibility measures show consistent association with information processing speed and executive control deficits
Confidence Interval: Not reported
Clinical Application
Limited by availability of susceptibility-weighted MRI sequences and specialised post-processing software Relevant to Australian MS centres with advanced MRI capabilities; may inform PBS-funded MRI protocols and RACGP referral guidelines for cognitive assessment in MS Multiple sclerosis patients undergoing cognitive assessment and advanced MRI
Abstract
BACKGROUND AND AIMS: Iron-related alterations are frequently implicated in multiple sclerosis (MS) neurodegeneration, but its relationship with clinical symptoms and cognitive impairment remains unclear. We systematically reviewed evidence linking iron-sensitive susceptibility-based magnetic resonance imaging (MRI) metrics to cognitive outcomes, in order to evaluate their potential role as diagnostic and prognostic biomarkers of cognitive decline in MS. MATERIALS AND METHODS: A systematic review was conducted using the PRISMA guidelines. 600 studies were identified by searching PubMed, Scopus, Web of Science, and Embase in October 2025. After screening, 12 studies fulfilled the eligibility criteria and were included. RESULTS: Two main patterns emerged: (i) higher basal ganglia susceptibility/iron-related metrics are consistently linked to worse cognitive performance, particularly information processing speed and executive control; and (ii) thalamic findings are more variable in direction and clinical meaning, with susceptibility often capturing a mixed signal that overlaps with thalamic atrophy. CONCLUSIONS: Iron-sensitive quantitative MRI captures clinically relevant deep gray matter (DGM) pathology associated with cognitive impairment in MS, with basal ganglia measures showing the strongest linkage with cognitive and clinical outcomes. Future studies should include harmonized methodology and a longitudinal component.
References
- 1.Susinna, C., Restuccia, G., Triolo, G., Marafioti, G., Cammaroto, S., Sessa, E., D'Aleo, G., Quartarone, A., & Lo Buono, V. (2026). Iron accumulation in multiple sclerosis and implications for cognitive and clinical progression: a systematic review of advanced neuroimaging studies. Journal of Neurology. https://doi.org/10.3174/ajnr.A3030
Related Research
Sleep medicine reviews
Machine and deep learning in REM sleep behavior disorder: a scoping review and analysis of reporting quality
3 Aug 2026
Neurological research
Federated deep learning model for epilepsy seizure detection using electroencephalogram (EEG) signal
2 Aug 2026
Journal of psychiatric research
Systematic review of machine learning and deep learning models for EEG-based detection of depression.
2 Aug 2026
This content is for educational purposes for healthcare professionals only and does not constitute clinical advice. Clinical decisions should be based on individual patient assessment, current guidelines, and appropriate specialist consultation. Editorial Standards · Privacy Policy · Terms of Service