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Objective: Idiopathic normal pressure hydrocephalus (INPH) is a treatable neurological condition, yet predicting which patients will benefit from a cerebrospinal fluid shunt remains challenging. Structural brain MRI is a core part of the diagnostic workup, but traditional radiological measures show limited predictive accuracy. This study aimed to assess whether deep learning and radiomics-based machine learning approaches can provide clinically useful predictions of shunt outcome based on preoperative MRI.

Methods: We investigated 149 shunted INPH patients with available preoperative T1-weighted, T2-weighted, and FLAIR images. Patients were classified as responders (n = 113) or non-responders (n = 36) based on postoperative gait speed improvement. For INPH, this is a large sample with typical outcome distribution. Three artificial intelligence approaches were tested: a late-fusion ensemble of multiple 3D convolutional neural networks; a multimodal intermediate fusion model; and radiomics-based machine learning models trained on features extracted from whole-brain masks. Models were assessed using 10-fold cross-validation. The best performing model on the validation set was selected from each approach. Performance metrics included the area under the receiving operating characteristic curve (AUROC), sensitivity, and specificity.

Results: Performance was considered poor in all models, and none reached an area under the receiving operating characteristic curve above 70%. Of the three methodologies, the best performance was achieved with a radiomics-based model (Linear Discriminant Analysis classifier on T1-weighted images) which achieved an AUROC of 63.7%. In a reduced subset of clearly separated responders and non-responders (n = 72), the best model (late fusion ensemble of 5 convolutional neural networks) reached an AUROC of 69.2%.

Conclusions: Despite the use of advanced artificial intelligence techniques, structural MRI alone were insufficient for reliably predicting gait outcome after surgery in idiopathic normal pressure hydrocephalus. To capture the complexity of the condition and enable clinically meaningful predictions, our findings indicate the need for research investigating multimodal input and using large multi-center datasets.

Background: Aquaporin-4 (AQP4) is crucial for brain fluid regulation and glymphatic system function. Idiopathic normal pressure hydrocephalus (INPH) is characterized by impaired CSF flow and is treated with shunt surgery. This study investigated AQP4 levels in INPH patients to explore its role in pathophysiology and as a potential biomarker for shunt response.

Methods: CSF samples from 233 INPH patients and 29 controls were analysed. AQP4 levels were compared between preoperative patients and controls, before and after shunt surgery (110 patients), and between shunt responders and non-responders (204 patients). A bead-based assay was used to measure AQP4, and outcomes were assessed by postoperative changes in maximum gait velocity.

Results: In unadjusted analyses, preoperative AQP4 levels were lower in INPH patients than in controls; however, this difference did not remain after adjustment for pre-analytical and demographic confounders (p = 0.87). Postoperative AQP4 levels were higher (median 1646 AU IQR 1347–1976) than preoperative levels (1166 AU IQR: 976–1345; p < 0.001) and the magnitude of increase showed a modest correlation with gait improvement (rₛ = 0.22, p = 0.022). Shunt responders had lower preoperative AQP4 levels median 1089 AU, IQR 971–1277) than non-responders (median 1213 AU, IQR 1074–1361; p = 0.008). Pre-analytical factors, including storage duration and sample processing, were strong determinants of measured AQP4 levels.

Conclusions: CSF AQP4 levels in INPH did not differ from those in controls and were highly sensitive to pre-analytical sample handling. CSF AQP4 levels increased following shunt surgery. A potential prognostic value of CSF AQP4 is suggested but requires further investigation.

According to glymphatic system theory, cerebrospinal fluid (CSF) perfuses the brain’s interstitial space to support waste clearance, but the magnitude of this flow and the outflow pathway of interstitial fluid (ISF) in humans remain uncertain. To achieve flow quantification, we applied a compartment-model approach applied in conjunction with serial quantitative MRI data acquired after intrathecal gadolinium administration. Using the method, we estimated CSF-to-ISF inflow to 45 ± 20 mL/h, in patients with suspected idiopathic normal pressure hydrocephalus. Tissue-specific contributions were 34 ± 14 mL/h in cortical gray matter, 11±6 mL/h in white matter, and 0.4 ± 0.3 mL/h in subcortical gray matter, suggesting that CSF perfusion occurs primarily in superficial regions near the subarachnoid space. A lack of correlation between inflow and total craniospinal system outflow (r = 0.03, P = 0.91) suggested that ISF recirculates back into CSF rather than exiting the craniospinal system via a separate route. Independent experiments in healthy older individuals using intravenous gadolinium administration supported ISF-to-CSF recirculation, where contrast material that presumably crossed the blood–brain barrier subsequently appeared in the subarachnoid space, allowing ISF-to-CSF flow quantification. These findings provide a quantitative framework for studying brain clearance in humans and support subarachnoid space recirculation as an important efflux route.