Seminars in pediatric neurology
January 26, 2026 · via Europe PMC
Beyond the grey scale: A focus review of microstructural, metabolic, and AI-driven advances in TBI neuroimaging.
Chavez WA, Andreu-Arasa VC, Setty BN.
AI summary — for doctors, generated from the abstract only
This review summarizes emerging microstructural, metabolic, and AI-based neuroimaging techniques that may help diagnose and characterize traumatic brain injury even when conventional scans appear normal, supporting a shift toward classifying TBI by biological subtype rather than broad severity labels.
What was studied
This is a focus review synthesizing peer-reviewed literature on advanced neuroimaging techniques for traumatic brain injury (TBI), including the 2024 NINDS Clinical-Biomarker-Imaging-Modifiers (CBI-M) framework, and specific imaging modalities: Neurite Orientation Dispersion and Density Imaging (NODDI), Quantitative Susceptibility Mapping (QSM), Arterial Spin Labeling (ASL), functional MRI connectivity analysis, and AI-based prognostic tools.
Key findings
- The review highlights NODDI as useful for detecting microstructural brain reorganization not visible on standard imaging
- Quantitative Susceptibility Mapping is described as useful for quantifying chronic iron deposition and indirectly assessing inflammation
- Arterial Spin Labeling is highlighted for assessing cerebral perfusion and neurovascular health, and functional connectivity disruption has been shown via fMRI
- Artificial intelligence tools are emerging for more accurate prognosis, supporting a shift toward classifying TBI by specific biological subtypes (biotypes) rather than broad severity scores
Clinical relevance
The findings suggest that advanced imaging and AI tools may eventually help diagnose and prognosticate TBI in patients whose conventional scans appear normal despite significant symptoms, supporting a more personalized, biotype-based approach to TBI classification. As a review of emerging techniques, this does not establish that these tools are yet validated or available for routine clinical use.
Who should care
Limitations
As a narrative review, it does not report original diagnostic accuracy data, specific statistics, or a systematic search methodology in the available abstract text; most of the reviewed techniques remain research tools rather than established clinical diagnostics.
Advanced imaging techniques like NODDI, QSM, and ASL, combined with AI, are reviewed here as promising tools to detect TBI-related brain changes invisible on conventional scans, pointing toward future precision classification of TBI by biotype rather than severity alone.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Traumatic Brain Injury (TBI) presents a significant diagnostic challenge due to the frequent dissociation between severe clinical symptoms and absence of abnormal findings on conventional neuroimaging. This focus review synthesizes pivotal peer-reviewed literature to characterize the shift from anatomical to microstructural, physiological, and computational diagnostics. We examine the implementation of the 2024 NINDS CBI-M (Clinical, Biomarker, Imaging, Modifiers) framework, which recommends a multimodal approach. Key advances reviewed include Neurite Orientation Dispersion and Density Imaging (NODDI) for detecting microstructural reorganization; Quantitative Susceptibility Mapping (QSM) for quantifying chronic iron and indirectly inflammation; Arterial Spin Labeling (ASL) for assessing cerebral perfusion and neurovascular health; systematic evidence of functional connectivity disruption via fMRI; and the emergence of Artificial Intelligence (AI) for accurate prognosis. These technologies collectively support a move toward precision medicine, characterizing TBI by specific biotypes rather than broad severity scores.
DOI
10.1016/j.spen.2026.101261
PMID
41962999
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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