Search Results (67)

Background/Objectives: Poor bone quality is strongly associated with adverse surgical events. Although dual-energy X-ray absorptiometry (DXA) remains the gold standard for bone mineral density (BMD) assessment, logistical barriers may limit its preoperative application. The Endplate Bone Quality (EBQ) score is an MRI-derived metric quantifying subchondral bone quality at the vertebral endplate with demonstrated predictive value for cage subsidence following lumbar interbody fusion. However, EBQ has been measured exclusively at the operative level in surgical cohorts. This study aimed to assess level-specific EBQ scores across the entire lumbar spine and compare distributions across age, sex and osteoporosis subgroups. Methods: A single-institution retrospective review of T1-weighted lumbar MRI studies from patients evaluated for lower back pain from 2020 to 2025 was performed. EBQ was independently scored by two blinded raters at each disc space from L1–L2 to L5–S1 using 3 mm endplate ROIs normalized to a CSF ROI at L3. Interrater reliability was assessed via ICC, Pearson correlation, and RMSE. Patients were stratified by age (≤60 vs. >60 years), sex, and osteoporosis status, and subgroup comparisons were performed for overall and level-specific EBQ score. Results: A total of 96 patients with an average age of 61.0 ± 9.42 years were included in this study. The majority of patients included were female (87.5%), and 18.8% had been diagnosed with osteoporosis. EBQ scores demonstrated a progressive caudal increase across all subgroups from L2–L3 to L5–S1. Overall interrater reliability was acceptable (ICC = 0.76), with level-specific ICCs ranging from 0.70 to 0.83. No significant differences were observed between age or sex subgroups. Osteoporotic patients demonstrated significantly higher EBQ at L1–L2, L2–L3, and overall (all p < 0.05), with no significant differences at L3–L4 through L5–S1. Conclusions: This study provides normative, level-specific EBQ reference data throughout all levels of the lumbar spine. The increase in EBQ scores seen among caudal levels and reduced osteoporotic discriminatory power support the importance of level-specific context when interpreting EBQ thresholds. These findings may support future studies evaluating threshold development for EBQ. Full article

Orbital floor fractures may cause long-term functional and esthetic impairments. Diplopia due to impaired function of the inferior rectus muscle is frequently an indication for surgical repair, but some cases, such as those where the diagnosis has been delayed or a previous attempt at repair has been made, may not always be amenable to surgical correction. It is advantageous for the surgeon to know whether the proper function of the inferior rectus muscle can be restored for the purposes of surgical planning and prognostication. The authors hypothesized that real-time MRI could be used to characterize the appearance of the inferior rectus muscle in a way that would facilitate future analysis of inferior rectus function in patients with diplopia due to orbital floor fractures. Real-time MRI was performed on 10 volunteer participants with normal ophthalmic function and orbital anatomy to assess inferior rectus appearance during vertical duction testing. ImageJ software was used to measure and record characteristics of the inferior rectus muscle, viewed in a quasi-sagittal plane. The ratios evaluated included inferior rectus muscle length in upgaze versus downgaze (UDR, mean 1.58) as well as inferior rectus muscle length versus distance from inferior rectus origin to inferior rectus inflection point in upgaze (LIR, mean 1.30) and downgaze (mean 1.20). These values were found to be conserved between orbits and individuals. This data offers quantitative insight regarding inferior rectus muscle appearance across the full arc of vertical gaze in healthy individuals. We plan to use this normative baseline dataset as a comparison for future phases of this project, using real-time MRI to evaluate traumatized orbits with diplopia and derangement of the inferior rectus muscle. Full article

Background: Gaucher disease (GD) is a rare lysosomal storage disorder caused by mutations in the GBA1 gene. Traditionally, GD is classified into three subtypes based on the severity of neurological involvement; however, overlapping clinical features increasingly suggest a continuum of phenotypes rather than distinct categories. In this prospective observational cohort study, we conducted a multidisciplinary assessment of patients with GD to identify and monitor neurological, cognitive, auditory, and visual impairments. Materials and Methods: A comprehensive clinical and instrumental evaluation was performed at baseline and repeated at follow-up, with a median interval of 37 months (IQR 36–38). Neurological assessments included physical examination, clinical rating scales, video-EEG, and brain MRI. Cognitive status was assessed using a standardized battery of neuropsychological tests. Detailed audiological and ophthalmological evaluations were also conducted. Paired parametric or non-parametric tests were applied as appropriate, with Bonferroni correction for cognitive outcomes (p < 0.05). Results: Of the 22 patients assessed at baseline, 18 completed the follow-up evaluation. Neurological assessments showed a worsening of subtle parkinsonian signs, with significant increases in Movement Disorder Society–Unified Parkinson’s Disease Rating Scale Part III scores (p = 0.04) and non-motor symptom scores (p = 0.01). Two of the eighteen patients developed epilepsy during follow-up. A high prevalence of sleep disturbances was confirmed, with 27.8% exhibiting excessive daytime sleepiness and 16.7% reporting REM sleep behaviour disorder on standardized questionnaires. Compared with baseline, cognitive assessments revealed a higher proportion of patients with performance below normative population scores in at least one cognitive domain, particularly memory. Sensorineural hearing loss was confirmed in 11 of 15 patients (73.3%) who underwent audiological evaluation, with progressive worsening of audiometric thresholds observed in 7 of 11 (64%). Ophthalmological evaluations showed no changes in visual acuity or OCT findings; however, multifocal electroretinography abnormalities were detected in 12 of 13 patients. Conclusions: Through in-depth phenotyping, this study identifies measurable neurological, cognitive, and sensory progressive changes in patients with GD over time, supporting the value of tailored, multidisciplinary long-term care strategies to monitor and address emerging clinical needs in this rare disease. Full article

Objective: The aim was to evaluate cochlear implantation (CI) outcomes in children with Down syndrome (DS) with severe-to-profound sensorineural hearing loss (SNHL), addressing a literature gap and discussing challenges including anatomical abnormalities, cognitive deficits, and Eustachian tube dysfunction. Data Sources: Systematic searches were conducted in PubMed, Web of Science, Scopus, and Embase from inception through to June 2025. Review Methods: A systematic review adhering to PRISMA guidelines was performed. Included studies reported CI outcomes in DS patients receiving otolaryngologic care for SNHL. Extracted data included findings on ear anatomy, auditory performance, speech/language development, intelligibility, and duration of CI use. Results: A total of 149 abstracts were screened, yielding six studies with 26 patients that met the inclusion criteria. The review included pediatric DS patients with documented ages at implantation spanning from 11 months to 17.9 years. CI provided significant benefits for DS patients, including improved audiometric results, enhanced environmental awareness, and psychosocial gains. Optimal outcomes were associated with early implantation, thorough preoperative imaging (CT/MRI), and management of middle ear disease. Variability in outcomes often reflected cognitive limitations and anatomical challenges such as cochlear nerve hypoplasia and Eustachian tube dysfunction. Conclusions: CI can significantly improve quality of life and communication in children with DS when tailored to their unique needs. Preoperative imaging is essential to assess candidacy, and middle ear disease should be addressed prior to surgery. Clinicians should counsel families with individualized goals that emphasize functional hearing gains over normative speech benchmarks. Broader adoption of CI in this population may be supported by standardized, population-sensitive outcome measures and future prospective studies. Full article

Background: Alzheimer’s disease is the most common type of dementia and a progressive neurodegenerative disease that begins with neuronal damage and leads to a reduction in brain tissue. Currently, there is no cure for this disease, and existing approaches focus on alleviating symptoms. Methods: This study proposes NeuroFusion-ViT, a highly accurate and computationally efficient hybrid deep learning model for early-stage detection of Alzheimer’s disease. The model combines an EVA-02-based Vision Transformer (ViT) with the ConvNeXt-Small CNN architecture, providing powerful representation learning that can process both global context and local details. The proposed Gated Cross-Attention Fusion (G-CAF) mechanism dynamically combines two different features, offering high discriminative power and model stability. Results: In experiments conducted on the OASIS MRI dataset, the model achieved 99.86% accuracy, 0.9989 Macro F1, and 0.999 ROC-AUC values, demonstrating clear superiority over single-modal and hybrid models described in the literature. Furthermore, 5-fold cross-validation results also support the model’s high generalizability. Ablation studies showed that each of the components—cross-attention, gate mechanism, Dual LayerNorm, and FFN-Dropout—made a meaningful contribution to performance. Conclusions: The results demonstrate that the NeuroFusion-ViT architecture offers a reliable, stable, and clinically applicable solution for Alzheimer’s stage classification. Full article

The National Institutes of Health (NIH) Toolbox cognition battery and Trail Making Tests (TMT) are widely used to quantify cognitive aging and to detect early cognitive vulnerability in Alzheimer’s disease and related conditions. However, these tests are often treated as interchangeable markers of global cognition, despite likely differences in their dependence on specific brain systems, limiting interpretability across studies and clinical contexts. To address this gap, we examined associations between four commonly used cognitive measures—fluid cognition, crystallized cognition, TMT-A, and TMT-B—and multimodal MRI metrics in 725 healthy volunteers aged 36 to 100 years from the Human Connectome Project–Aging. Voxel-wise diffusion MRI and vertex-wise cortical thickness and volume analyses were adjusted for age, sex, and years of education. Higher crystallized and fluid cognition scores and faster TMT-A/B completion times were generally associated with greater white matter integrity. TMT-B showed the most extensive diffusion and cortical associations, involving major projection, commissural, and association pathways and frontoparietal and temporo-occipital cortices. TMT-A and crystallized cognition demonstrated intermediate, overlapping patterns, whereas fluid cognition showed only focal brainstem and limited cortical correlates. These findings demonstrate systematic differences in the neuroanatomical substrates underlying commonly used cognitive tests and provide normative structure–cognition reference maps that can improve test selection, mechanistic interpretation, and sensitivity to brain health in studies of aging, vascular risk, and preclinical neurodegenerative disease. Full article

Background/Objectives: SARS-CoV-2 infection has been linked to long-term neurological sequelae and structural brain alterations. Previous analyses, including baseline results from the COVIMMUNE-Clin study, showed brain volume reductions in COVID-19 patients. Longitudinal data on progression are scarce. This study examined brain volume changes 12 months after baseline MRI in individuals who have recovered from mild or severe COVID-19 compared with controls. Methods: In this IRB-approved cohort study, 112 out of 172 recruited age- and sex-matched participants (38 controls, 36 mild/asymptomatic 38 severe COVID-19) underwent standardized brain MRI 12 months after baseline. Volumetric analysis was performed using AI-based software (mdbrain). Regional volumes were compared between groups with respect to absolute and normalized values. Multivariate regression controlled for demographics. Results: After 12 months, a significant decline in right hippocampal volume was observed across all groups, most pronounced in severe COVID-19 (SEV: Δ = −0.32 mL, p = 0.001). Normalized to intracranial volume, the reduction remained significant (SEV: Δ = −0.0003, p = 0.001; ASY: Δ = −0.0001, p = 0.001; CTL: minimal reduction, Δ ≈ 0, p = 0.005). Minor reductions in frontal and parietal lobes (e.g., right frontal SEV: Δ = −1.35 mL, p = 0.001), largely fell within physiological norms. These mild regional changes are consistent with expected ageing-related variability and do not suggest pathological progression. No widespread progressive atrophy was detected. Conclusions: This study demonstrates delayed, severity-dependent right hippocampal atrophy in recovered COVID-19 patients, suggesting long-term vulnerability of this memory-related region. In contrast, no progression of atrophy in other areas was observed. These findings highlight the need for extended post-COVID neurological monitoring, particularly of hippocampal integrity and its cognitive relevance. Full article

Sleep disturbances, affecting nearly half of middle-aged adults, have emerged as modifiable determinants of brain health and dementia risk. Recent advances in machine learning applied to MRI enable the estimation of “brain age,” a biomarker that quantifies deviation from normative neural aging. This review synthesizes and critically evaluates converging evidence that poor sleep accelerates biological brain aging, identifies mechanistic pathways, and delineates translational barriers to clinical application. Across large-scale cohorts comprising more than 25,000 participants, suboptimal sleep independently predicts 1–3 years of MRI-derived brain age acceleration, even after adjusting for vascular and metabolic confounders. Objective sleep fragmentation and altered sleep-stage architecture exhibit sleep-specific neuroanatomical signatures, independent of amyloid and tau pathology, while inflammatory, vascular, and glymphatic mechanisms mediate a small fraction of the effect. Experimental sleep deprivation studies demonstrate reversibility of accelerated brain aging, highlighting opportunities for early intervention. Translation to clinical practice is constrained by methodological heterogeneity, reliance on self-reported sleep metrics, limited population diversity, and the absence of randomized intervention trials demonstrating causal reversibility. Addressing these gaps through standardized MRI-based biomarkers, longitudinal mechanistic studies, and interventional trials could establish sleep optimization as a viable neuroprotective strategy for dementia prevention. Full article

Early detection of breast cancer commonly relies on imaging technologies such as ultrasound, mammography and MRI. Among these, breast ultrasound is widely used by radiologists to identify and assess lesions. In this study, we developed image segmentation techniques and multiclass classification artificial intelligence (AI) tools based on pretrained models to segment lesions and detect breast cancer. The proposed workflow includes both the development of segmentation models and development of a series of classification models to classify ultrasound images as normal, benign or malignant. The pretrained models were trained and evaluated on the Breast Ultrasound Images (BUSI) dataset, a publicly available collection of grayscale breast ultrasound images with corresponding expert-annotated masks. For segmentation, images and ground-truth masks were used to pretrained encoder (ResNet18, EfficientNet-B0 and MobileNetV2)–decoder (U-Net, U-Net++ and DeepLabV3) models, including the DeepLabV3 architecture integrated with a Frequency-Domain Feature Enhancement Module (FEM). The proposed FEM improves spatial and spectral feature representations using Discrete Fourier Transform (DFT), GroupNorm, dropout regularization and adaptive fusion. For classification, each image was assigned a label (normal, benign or malignant). Optuna, an open-source software framework, was used for hyperparameter optimization and for the testing of various pretrained models to determine the best encoder–decoder segmentation architecture. Five different pretrained models (ResNet18, DenseNet121, InceptionV3, MobielNetV3 and GoogleNet) were optimized for multiclass classification. DeepLabV3 outperformed other segmentation architectures, with consistent performance across training, validation and test images, with Dice Similarity Coefficient (DSC, a metric describing the overlap between predicted and true lesion regions) values of 0.87, 0.80 and 0.83 on training, validation and test sets, respectively. ResNet18:DeepLabV3 achieved an Intersection over Union (IoU) score of 0.78 during training, while ResNet18:U-Net++ achieved the best Dice coefficient (0.83) and IoU (0.71) and area under the curve (AUC, 0.91) scores on the test (unseen) dataset when compared to other models. However, the proposed Resnet18: FrequencyAwareDeepLabV3 (FADeepLabV3) achieved a DSC of 0.85 and an IoU of 0.72 on the test dataset, demonstrating improvements over standard DeepLabV3. Notably, the frequency-domain enhancement substantially improved the AUC from 0.90 to 0.98, indicating enhanced prediction confidence and clinical reliability. For classification, ResNet18 produced an F1 score—a measure combining precision and recall—of 0.95 and an accuracy of 0.90 on the training dataset, while InceptionV3 performed best on the test dataset, with an F1 score of 0.75 and accuracy of 0.83. We demonstrate a comprehensive approach to automate the segmentation and multiclass classification of breast cancer ultrasound images into benign, malignant or normal transfer learning models on an imbalanced ultrasound image dataset. Full article

While magnetic resonance imaging (MRI) is effective for detecting early-stage osteonecrosis of the femoral head (ONFH), it is often expensive and less accessible; conversely, radiography is more widely accessible but has limited sensitivity for early-stage diagnosis. We developed a deep learning approach using radiographic images to effectively classify ONFH stages, providing a more accessible method for early diagnosis and disease stage differentiation. The dataset consisted of 909 hip radiographs, yielding 1818 femoral head images (grade 0:1495; grade 1:80; grade 2:114; grade 3:93; grade 4:36). A U-Net model was used to segment the femoral heads, achieving a Dice similarity coefficient (DSC) of 0.977 on the test set, allowing precise localization of the region of interest. A variational autoencoder (VAE) was then trained using 1270 grade-0 images for training and 112 for validation to construct a normative latent distribution representing healthy femoral heads. When ONFH data from all grades were projected into the latent space, significant differences in Mahalanobis distance distributions were observed across most ONFH stages. No significant difference was found between grades 0 and 1 (p = 0.06), consistent with known radiographic subtlety. However, grades 2–4 showed significant deviation from grade 0, and significant differences were also observed among mid- and late-stage grades. These findings demonstrate that the proposed method effectively separates healthy and diseased femoral heads and captures gradewise structural progression within the latent space. This radiograph-based normative modeling approach offers an accessible alternative to MRI for ONFH stage differentiation, particularly in resource-limited clinical environments. Although early-stage differentiation remains challenging, the results highlight the potential of radiograph-based deep learning systems to improve diagnostic efficiency and support future automated ONFH staging workflows. Full article

Background/Objectives: Deep brain stimulation (DBS) of the globus pallidus interna (GPi) is a safe and established therapy for management of refractory motor fluctuations and dyskinesia in Parkinson’s disease (PD). However, the relationship between stimulation site connectivity and improvement of axial gait symptoms remains poorly understood, particularly when stimulating in the GPi. This study investigated functional and structural connectivity patterns specifically associated with axial symptom outcomes following bilateral GPi-DBS, and, as a secondary exploratory analysis, examined whether Volumes of tissue activated (VTAs)-based connectivity related to overall UPDRS-III change. Methods: We retrospectively analyzed 19 PD patients who underwent bilateral GPi-DBS at the University of Florida (2002–2017). Unified Parkinson’s Disease Rating Scale (UPDRS-III) axial gait subscores were assessed at baseline and 36-month follow-up. VTAs were reconstructed using Lead-DBS and coregistered to Montreal Neurological Institute (MNI) space. Structural connectivity was evaluated with diffusion tractography, and functional connectivity was estimated using normative resting-state fMRI datasets. Correlations between VTA connectivity and clinical improvement were examined using Spearman correlation and voxelwise analyses. Results: Patients with axial improvement in motor scales demonstrated specific VTA connectivity to sensorimotor and supplementary motor networks, particularly lobule V and lobules I–IV of the cerebellum. These associations were specific to axial gait subscores. In contrast, worsening axial gait symptoms correlated with connectivity to cerebellar Crus II, cerebellum VIII, calcarine cortex, and thalamus (p < 0.05). Total UPDRS-III scores did not show a significant positive correlation with supplementary motor area or primary motor cortex connectivity; a non-significant trend was observed for VTA–M1 connectivity (R = 0.41, p = 0.078). Worsening total motor scores were associated with cerebellar Crus II and frontal–parietal networks. These findings suggest that distinct connectivity patterns underlie differential trajectories in axial and global motor outcomes following GPi-DBS. Conclusions: Distinct connectivity profiles might underlie axial gait symptom outcomes following GPi-DBS. Connectivity to motor and sensorimotor pathways supports improvement, whereas involvement of Crus II and occipital networks predicts worsening. Additional studies to confirm and expand on these findings are needed, but our results highlight the value of connectomic mapping for refining patient-specific targeting and developing future programming strategies. Full article

Background/Objectives: The neonatal and infant periods represent a critical window for brain development, characterized by rapid and heterogeneous processes such as myelination and cortical maturation. Accurate assessment of these changes is essential for understanding normative trajectories and detecting early abnormalities. While conventional MRI provides valuable insights, automated classification remains challenging due to overlapping developmental stages and sex-specific variability. Methods: We propose SEPoolConvNeXt, a novel deep learning framework designed for fine-grained classification of neonatal brain development using T1- and T2-weighted MRI sequences. The dataset comprised 29,516 images organized into four subgroups (T1 Male, T1 Female, T2 Male, T2 Female), each stratified into 14 age-based classes (0–10 days to 12 months). The architecture integrates residual connections, grouped convolutions, and channel attention mechanisms, balancing computational efficiency with discriminative power. Model performance was compared with 19 widely used pre-trained CNNs under identical experimental settings. Results: SEPoolConvNeXt consistently achieved test accuracies above 95%, substantially outperforming pre-trained CNN baselines (average ~70.7%). On the T1 Female dataset, early stages achieved near-perfect recognition, with slight declines at 11–12 months due to intra-class variability. The T1 Male dataset reached >98% overall accuracy, with challenges in intermediate months (2–3 and 8–9). The T2 Female dataset yielded accuracies between 99.47% and 100%, including categories with perfect F1-scores, whereas the T2 Male dataset maintained strong but slightly lower performance (>93%), especially in later infancy. Combined evaluations across T1 + T2 Female and T1 Male + Female datasets confirmed robust generalization, with most subgroups exceeding 98–99% accuracy. The results demonstrate that domain-specific architectural design enables superior sensitivity to subtle developmental transitions compared with generic transfer learning approaches. The lightweight nature of SEPoolConvNeXt (~9.4 M parameters) further supports reproducibility and clinical applicability. Conclusions: SEPoolConvNeXt provides a robust, efficient, and biologically aligned framework for neonatal brain maturation assessment. By integrating sex- and age-specific developmental trajectories, the model establishes a strong foundation for AI-assisted neurodevelopmental evaluation and holds promise for clinical translation, particularly in monitoring high-risk groups such as preterm infants. Full article

Background/Objectives: Age-related structural changes in the human brain provide essential insights into cognitive aging and the onset of neurodegenerative diseases. This study aimed to comprehensively characterize age-related volumetric changes across multiple brain regions in a large, diverse, cognitively healthy cohort spanning adulthood (ages 21–90), integrating Korean, Information eXtraction from Images (IXI), and Alzheimer’s Disease Neuroimaging Initiative (ADNI) MRI datasets of cognitively healthy participants to characterize normative volumetric changes across adulthood using demographically diverse datasets. Methods: High resolution 3T T1-weighted MRI images from three distinct cohorts (totaling 1833 subjects) were processed through an optimized neuroimaging pipeline, combining advanced preprocessing with neural network-based segmentation. Volumetric data for 95 brain structures were segmented and analyzed across seven age bins (21–30 through 81–90). Pipeline reliability was validated against FreeSurfer using intraclass correlation coefficients (ICC) and coefficients of variation (CoV). Regression-based correction was used to correct for sex and cohort effects on brain region volume. Then, percentage change in each mean bilateral volumes of regions across the lifespan were computed to describe volumetric changes across life spans. Results: The segmentation pipeline demonstrated excellent agreement with FreeSurfer (mean ICC: 0.9965). Drastic volumetric expansions were observed in white matter hypointensities (122.6%), lateral ventricles (115.9%), and inferior lateral ventricles (116.8%). Moderate-to-notable shrinkage was found predominantly in the frontal lobe (pars triangularis: 21.5%), parietal lobe (inferior parietal: 20.4%), temporal lobe (transverse temporal: 21.6%), and cingulate cortex (caudal anterior cingulate: 16.1%). Minimal volume changes occurred in regions such as the insula (3.7%) and pallidum (2.6%). Conclusions: This study presents a comprehensive reference of normative regional brain volume changes across adulthood, highlighting substantial inter-regional variability. The findings can provide an essential foundation for differentiating normal aging patterns from early pathological alterations. Full article

Quantitative magnetic resonance imaging (qMRI) denotes MRI methods that estimate physical tissue parameters in units, rather than relative signal. Typical readouts include T1/T2 relaxation (ms; or R1/R2 in s⁻¹), proton density (%), diffusion metrics (e.g., ADC in mm²/s, FA), magnetic susceptibility (χ, ppm), perfusion (e.g., CBF in mL/100 g/min; rCBV; K^trans), and regional brain volumes (cm³; cortical thickness). This review synthesizes brain qMRI across T1/T2 relaxometry, myelin/MT (MWF, MTR/MTsat/qMT), diffusion (DWI/DTI/DKI/IVIM), susceptibility imaging (SWI/QSM), perfusion (DSC/DCE/ASL), and volumetry using a unified framework: physics and signal model, acquisition and key parameters, outputs and units, validation/repeatability, clinical applications, limitations, and future directions. Our scope is the adult brain in neurodegenerative, neuro-inflammatory, neuro-oncologic, and cerebrovascular disease. Representative utilities include tracking demyelination and repair (T1, MWF/MTsat), grading and therapy monitoring in gliomas (rCBV, K^trans), penumbra and tissue-at-risk assessment (DWI/DKI/ASL), iron-related pathology (QSM), and early dementia diagnosis with normative volumetry. Persistent barriers to routine adoption are protocol standardization, vendor-neutral post-processing/QA, phantom-based and multicenter repeatability, and clinically validated cut-offs. We highlight consensus efforts and AI-assisted pipelines, and outline opportunities for multiparametric integration of complementary qMRI biomarkers. As methodological convergence and clinical validation mature, qMRI is poised to complement conventional MRI as a cornerstone of precision neuroimaging. Full article

Neuroaxonal damage underlies permanent disability in various neurological conditions, both neuroautoimmune and neurodegenerative. It is crucial to accurately quantify and monitor axonal injury using biomarkers to evaluate disease progression and treatment effectiveness and offer prognostic insights. Neurofilaments (NFs), and especially neurofilament light chain (NfL), show promise for this purpose, as their levels increase with neuroaxonal damage in both cerebrospinal fluid and blood, independent of specific causal pathways. Recent advances in ultrasensitive immunoassays enable the reliable detection of NFs in blood, transforming them from research tools into clinically applicable measures. In multiple sclerosis (MS), serum NfL correlates with disease activity, treatment response, and long-term disability, and may complement MRI in monitoring subclinical progression. In MS, NfL is primarily emerging as a marker of disease activity and treatment response; in amyotrophic lateral sclerosis (ALS), it has progressed further, being integrated into clinical trials as a pharmacodynamic endpoint and considered by regulatory agencies as a drug development tool. Additionally, NFs are increasingly being investigated in Alzheimer’s disease, frontotemporal dementia, and other neurodegenerative disorders, though their disease specificity is limited. Ongoing challenges include older and novel assay harmonization, normative range interpretation, biological and analytical variability, and integration with other molecular and imaging biomarkers. This critical narrative review synthesizes the existing literature on NFs as diagnostic, prognostic, predictive, and pharmacodynamic biomarkers and discusses their role in therapeutic development and precision medicine in neuroautoimmune and neurodegenerative diseases. Full article