No Predictive Value of Aqueduct CSF Flow Dynamics for Shunt Response in Idiopathic Normal Pressure Hydrocephalus

No Predictive Value of Aqueduct CSF Flow Dynamics for Shunt Response in Idiopathic Normal Pressure Hydrocephalus | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article No Predictive Value of Aqueduct CSF Flow Dynamics for Shunt Response in Idiopathic Normal Pressure Hydrocephalus Afroditi D Lalou, Adam Vitelli Bryngelsson, Anders Wåhlin, Pär Asplund, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7400171/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract INTRODUCTION: INPH is diagnosed based on clinical criteria and physiological measurements, including brain imaging parameters. Increased aqueductal CSF flow dynamics, assessed with Phase-Contrast MRI (PC-MRI), is one of the supportive features in iNPH diagnostic guidelines. A predictive value has been suggested but remains largely debatable. This study aimed to clarify the role of aqueductal flow in supporting diagnosis and shunt selection for iNPH patients. METHODS We retrospectively included 92 iNPH patients with preoperative PC-MRI together with pre- and post-operative gait speed measurement. Aqueductal CSF flow dynamics were calculated and correlated with gait outcomes and baseline gait speed. Additionally, we compared our cohort with 42 age-matched healthy controls. RESULTS We found no significant differences in CSF flow parameters between shunt responders and non-responders: Stroke volume was 130 ± 90 and 150 ± 100 µl, p = 0.32 respectively, with net flows of 0.06 ± 1.71 and − 0.07 ± 1.51 ml/min, p = 0.563. There were no correlations of aqueduct CSF dynamics with baseline gait performance, nor with gait change (-0.15 < R 0.5 for all parameters). Furthermore, comparisons with healthy controls revealed differences in stroke volume (140 ± 100µl iNPH vs 80 ± 41 healthy, p 0.05). CONCLUSIONS Our findings indicate no significant predictive value of aqueductal CSF dynamics for shunt efficacy in iNPH patients. The heterogeneity of iNPH and variability in CSF dynamics across its time course, may contribute to these negative results. From a clinical point of view, aqueductal flow measured by 2D PC-MRI appears to have very limited value for selecting patients for shunting. Health sciences/Diseases Health sciences/Medical research Health sciences/Neurology Biological sciences/Neuroscience Figures Figure 1 Introduction Idiopathic normal pressure hydrocephalus (iNPH) is diagnosed based on clinical criteria and physiological measurements, including brain imaging parameters 1 , 2 . While increased aqueductal CSF flow dynamics (including stroke volume and flows) assessed with Phase-Contrast MRI (PC-MRI) is one of the supportive brain imaging features in the iNPH diagnostic guidelines, the clinical usefulness of this technique and its metrics remains largely unknown 3 . Furthermore, regarding prognostic tests in iNPH, guidelines and standard practice recommend CSF withdrawal of 30–50 ml with a tap test, with a number of patients additionally requiring extended lumbar drainage of CSF over three or more days 4 – 6 . As such, the need for a non-invasive predictive test remains as current as ever, with aqueductal CSF dynamics being an easily conducted test frequently suggested over the last few decades. However, there have been limited as well as inconsistent reports in the literature regarding the diagnostic and predictive utility of PC-MRI-measured aqueductal flow dynamics. Previous studies have shown mixed results, with some suggesting a correlation of increased aqueductal stroke volume, netflow and peak velocities with positive surgical outcomes, while others have found no significant predictive value. Moreover, the current body of research is limited by small sample sizes and technical differences between MRI protocols, which hinder the ability to draw definitive conclusions 7 – 14 . A summary of prognostic studies to date using PC MRI can be found in Table 1 . To date, no large cohort study has comprehensively investigated the relationship between aqueductal CSF dynamics and outcomes after shunting in iNPH patients. This gap in the literature underscores the need for a robust, large-scale study to provide clinically useful answers. In view of the above, we aimed to clarify the role of aqueductal flow in supporting diagnosis and shunt selection for iNPH patients. The primary hypothesis was that disturbed aqueductal flow dynamics (increased velocity, flow and/or stroke volume), as measured by PC-MRI, are predictive of a positive outcome after shunt insertion. Additionally, we aimed to explore the relationship between aqueductal flow and baseline gait performance (as gait impairment forms the cardinal sign of the iNPH triad), and whether abnormally increased CSF flow dynamics have any diagnostic value when compared to a cohort of normal controls. Table 1 Summary of studies that report on the significance/insignificance of CSF flow parameters as a predictive test before surgery. Study N (responders vs non-responders Methodology SV Net flow/Peak velocity Bradley et al (1996) 10 18 (15 vs 3) No venc reported. 1.5 T. Retrospective Threshold of SV explored. PPV 100% SV > 42 µL (in 12/15 responders ) – Dixon et al (2002) 7 49 Venc 10-20cm/sec. Retrospective Comparison among different levels of gait improvement. NS Net flow > 33 ml/min could have significance Poca MA et al (2002) 11 33 (31 vs 2) 1.5 T, Venc 10cm/sec. Prospective 28/29 with peak flow > 97th percentile improved – Significant if > 97.5 percentile of controls. Sensitivity:90% Specificity:50% Kahlon et al (2007) 15 32 Venc 20cm/sec. Retrospective Ranges of SV explored in responders vs non-responders. NS in all ranges: 1–50, 51–100 and > 100 µ L. – Algin et al (2010) 16 18 (12 vs 6) Venc 20cm/sec. Prospective NS – Ringstad et al (2015) 17 21 (17 shunted vs 4 not shunted) Venc 10cm/sec. Prospective . No outcome data NS – Blitz et al (2018) 9 71 Venc 8–30 cm/sec. Retrospective NS – Lindstrom et al (2018) 18 26 (17 vs 9) Venc 10cm/sec. Prospective Non-responders include non-shunted. – 0.023 ± 0.021 vs -0.006 ± 0.02 ml/cycle* (responders vs. non-responders) Stecco et al (2020) 8 38 (26 vs 12) Venc 15-20cm/sec. Retrospective 271,85 ± 143,03 vs 72,83 ± 28,66 ml * (responders vs. non-responders) – He et al (2022) 19 46 (28 vs 18) Venc 20cm/sec. Retrospective NS NS All were measured at aqueductal level. Venc:Velocity encoding, SV:stroke volume, PPV:positive predictive value, NS: not significant, N = patients included who received a shun t * =statistically significant Material and Methods In summary, we retrospectively included 92 iNPH patients with a preoperative PC-MRI and available post-operative outcome, defined by gait speed assessment before and after shunting. Patients were included after referral to our specialty clinic, and if they met the International iNPH guidelines criteria for possible or probable INPH. A physiotherapist assessed the patients’ gait speed pre- and postoperatively. We calculated aqueductal CSF flow dynamics and evaluated whether these measurements correlated to gait outcome and baseline gait speed. Finally, we compared our iNPH cohort to a cohort of 42 age-matched healthy controls. Study population Idiopathic Normal Pressure Hydrocephalus Patients The study was approved by the Swedish Ethical Review Authority (No. 2020–04469). Informed consent was obtained from patients that had been investigated for NPH at our Neurology Clinic at the University Hospital of Umeå between 2007 and 2019. Participants were informed about the study via a letter, which included an option to opt out if they did not wish to participate. Individuals deceased at the time inclusion began (2020) were automatically incorporated in the cohort. The routine clinical protocol included assessment from a neurologist, a specialist nurse and a physiotherapist pre and post-operatively. Follow-ups were performed 3–6 months after the operation. Evaluation at baseline included medical history, neurological examination and MRI of the brain, with ventriculomegaly confirmed via Evans index > 0.3. Other radiological measurements for assessment included the callosal angle and the presence of disproportionately enlarged subarachnoid space hydrocephalus 2 , 20 . Aqueductal flow from PC-MRI flow was measured as a routine in our clinical protocol as part of neuroradiological assessment for aqueductal flow obstruction and did not contribute to shunt selection. A CSF tap test and lumbar infusion test were performed and used to select candidates for shunt placement. In uncertain cases, a three-day external lumbar drainage was conducted. The final decision for shunt operation was made by a multidisciplinary team based on these evaluations. A total of 295 patients within the inclusion period had been diagnosed with probable or possible iNPH and were shunted. We further selected those with an analysable PC-MRI sequence (see details on PC-MRI quality criteria below) and a documented pre and post-operative measurement of gait speed. Outcome assessment At postoperative follow-up, most patients had undergone a repeat lumbar CSF infusion test to confirm that the CSF outflow resistance had been lowered, confirming a functioning shunt in situ. Patients with non-functioning shunts were excluded from the analysis unless they had a renewed outcome assessment after surgical revision within 1 year of the original follow-up. We defined improvement based on postoperative change in maximal gait speed, as gait has been shown to be the most consistent to improve amongst the cardinal symptoms of iNPH 21 – 24 , and it also constitutes a parameter that can be quantified. For maximal gait speed assessment, patients walked ten meters at their maximum speed, repeating the task six times, after which the average speed was calculated. We classified as responders those with an increase of at least 0.16 m/s after shunting. This threshold was chosen based on a recent report on stroke patients that linked an improvement of 0.16 m/s to a one-point change on the modified Rankin Scale, a measure that has been reported to reflect clinically significant functional improvement 25 . All gait speed data were measured by dedicated physiotherapists, who performed a comprehensive battery of tests for gait and balance, including Tinetti scores 26 . Some patients required a second follow-up in addition to the standard 3–6 months due to shunt adjustments, complications or revision. In those cases, the best postoperative results were chosen, to adequately reflect the resulting benefit from a shunt placement for each respective patient. Control group The control group consisted of 42 healthy, age-matched volunteers who had previously undergone an MRI in our centre. The controls have been described in detail in previous publications 27 , 28 . PC-MRI acquisition All acquisitions were performed as part of our local MRI protocol for NPH. Due to the long inclusion period, different MRI scanners were used, with protocols that slightly varied over time as well as between scanners. In order to homogenise our data, we selected only those with the same acquisition resolution. We also selected those with the same number of cardiac frames, which was 32 frames per cardiac cycle. Data was subsequently only analysed from one MRI scanner, a 3T Philips Achieva (Best, the Netherlands), with an 8-channel head coil. The settings for the PC-MRI scan were velocity encoding 20 cm/s (in cases of aliasing, the sequence had been repeated with 30cm/s), echo time 5.1–10 ms, flip angle 6,10 or 15 degrees. In-plane voxel size was 1.2x1.2 mm^2 and slice thickness was 5 mm. The majority of scans were acquired using retrospective cardiac gating, however, a prospective cardiac gating protocol had been in place between 2008–2012. The preoperative PC-MRI acquisitions were visually assessed for quality (placement of the plane at the correct level through the aqueduct, clearly visible aqueduct without artefacts) and checked for aliasing. Two regions of interest (ROI) were manually drawn for each patient using the open-source ImageJ software 29 : one encircling the aqueduct ( Fig. 1 a ) and a crescent-shaped ROI placed anterior to the aqueduct in order to correct for Eddy current effects (background correction) ( Fig. 1 b ). Area and velocity measurements were derived directly from the ROI analysis of ImageJ. We calculated and assessed the diagnostic and predictive performance of the following 5 total parameters, two related to aqueductal CSF flow, two aqueductal CSF velocities in different directions (caudal and cranial), and a CSF pulsatility descriptor. These were: netflow (average flow over the 32 frames) and absolute flow (calculated as the average of all 32 frames after conversion to a positive number), peak systolic velocity (maximal velocity in the caudal direction, negative values in our dataset), peak diastolic velocity (maximal velocity in the cranial direction, positive values in our dataset), and stroke volume (SV). Peak velocities were assessed as the peak average velocity in the entire aqueduct in each respective direction. SV was calculated as the amplitude of the cumulative integral of the mean-corrected flow waveform, representing the volume of CSF that moves back and forth through the aqueduct with each cardiac cycle. Radiological measurements Radiological measures were used for diagnosis as described above, and for describing our cohort in a standardised manner; in addition we also assessed ventricular volume to investigate the relationship between this and CSF flow parameters. All linear and volumetric measurements below were extracted from 3D T1-weighted images acquired during the same investigation as the PC-MRI acquisition. Evan’s Index was calculated in accordance with traditional consensus (ratio of the maximum width of the frontal horns to the maximal internal diameter). Callosal angle was measured as per the iNPH RadScale 30 , 31 . Lateral ventricular volume was extracted using the automated Freesurfer tool SynthSeg 32 .The extracted volumes were then visually reviewed. In cases where the automated segmentation was of suboptimal quality, we used instead a flood-fill function with an in-house developed Matlab script available here: https://github.com/SofiaBehndig/qDESH , published in Behndig et al (in press). Our inter-rater reliability and post-hoc analysis are shown in the Appendix. Statistical analysis All data were analysed using Rstudio version 2024.12.1 Visualisation with a histogram showed non-normal distribution with mild left skewness for all parameters. We confirmed that our data were not normally distributed using the Kolmogorov-Smirnov test (significant difference from normal distribution, p < 0.001 on all CSF flow parameters). Subsequently, non-parametric tests were applied, including the Mann-Whitney U test to compare CSF flow parameters between responders and non-responders, as well as iNPH and healthy controls. As a difference in cardiac gating type (retro- vs prospective) could affect net flow estimation, a generalized linear model with logistic regression was used to compare the net flow between responders and non-responders, with outcome as the dependent parameter and gating type as a factor. Non-parametric correlation (Spearman’s rho) was used to analyse the relationship between CSF flow parameters and preoperative maximal gait speed, as well as changes in maximal gait speed from pre- to postoperative assessments and ventricular volume. ROC analysis was performed to calculate the predictive power of CSF flow parameters (R packages pROC and ROCR). We additionally calculated the 90th percentile of all parameters for the healthy cohort. Finally, we calculated the proportion of patients with net flow in the cranial direction for the different groups and compared them with a Fisher’s exact test. We set statistical significance at 0.05 (two-tailed). Results From the initial 295 shunted iNPH patients, a total of 92 had a PC MRI sequence consistent with our criteria of acquisition protocol homogeneity and measurement quality. Prospective cardiac gating had been used in 42 examinations. Using the threshold of 0.16 m/s postoperative gait speed change, 54 patients improved and 38 did not. Patient characteristics are presented in Table 2 . Table 2 Demographics and baseline characteristics of the idiopathic Normal Pressure Hydrocephalus cohort. Total sample (N = 92) Responders 54/92 (59%) Non-responders 38/92 (41%) P-value * Female/Male 24 / 68 13:41 (32%) 11:27 (41%) 0.6357 1 Age at operation (years; median, range) 75 (57–85) 76 (57–85) 74 (64–83) 0.679 Preoperative gait speed (m/s) 0.78 ± 0.30 0.77 ± 0.32 0.80 ± 0.27 0.152 Gait speed change (m/s) 0.22 ± 0.25 0.37 ± 0.20 -0.01 ± 0.10 < 0.001 Preoperative Tinetti score (median, range) 12 (1–16) 13(9–14) 12(9–14) 0.852 Tinetti change (median, IQR) 1 (0–4) 2 (0–4) 0 (-1 – +3) 0.025 Preoperative Evans index 0.40 ± 0.04 0.40 ± 0.04 0.40 ± 0.05 0.866 Preoperative Callosal angle (degrees) 63 ± 22 64 ± 20 61 ± 22 0.552 Time MRI to shunting (days) 145 ± 76 139 ± 70 155 ± 91 0.46 Lateral Ventricular Volume (mL) 153 ± 37 153 ± 35 153 ± 40 0.883 Values are represented as mean ± SD unless otherwise specified. Tinetti POMA (Performance Oriented Mobility Assessment) balance score was only available in 87/92 patients. * p-value for responders vs non-responders. 1 Fisher’s exact test. Statistically significant p-values are in bold. CSF flow parameters and gait improvement The results of the comparison of CSF flow parameters between responders and non-responders are shown in Table 3 . No CSF flow parameters differed between the groups. Netflow was in the cranial direction (positive) in 49/92 patients (53.3%), 30/50 (60%) with retrospective gating and 19/42 (45.2%) with prospective cardiac gating. The AUC for the 5 explored parameters were all between 0.49 (lowest, peak diastolic velocity) − 0.62 (highest, ROI area). Table 3 Comparison of Aqueductal CSF flow parameters in responders versus non-responders. Responders 54/92 (59%) Non-responders 38/92 (41%) p-value Stroke volume (µL) 130 ± 90 150 ± 100 0.324 Netflow (ml/min) 0.06 ± 1.71 -0.07 ± 1.51 0.563 2 Netflow direction distribution (cranial:caudal, % cranial) 28:26 (52%) 18:20 (47%) 0.833 1 Absolute flow (ml/min) 0.30 ± 0.20 0.34 ± 0.22 0.342 Peak systolic velocity (mm/sec) -40.99 ± 21.9 -43.02 ± 16.9 0.544 Peak diastolic velocity (mm/sec) 33.4 ± 17.4 29.59 ± 19.3 0.868 ROI Area (mm2) 14.6 ± 7.7 16.5 ± 7.4 0.055 Values are presented in mean ± SD unless stated otherwise. Cut-off for improvement was 0.16 m/s maximum gait speed increase post-operatively. IQR: Interquantile range. 1 Fisher’s exact test 2 Adjusted for cardiac gating type (25 responders and 17 non-responders with prospective gating). In the post-hoc analysis with Tinetti change as the outcome parameter (cut-off of 3 points), no predictive value was found for any of the 5 explored CSF flow parameters (see Supplementary material). CSF flow parameters and maximal gait speed There was no significant correlation in the entire cohort, nor in the separate outcome groups, between baseline gait and any of the 5 CSF flow parameters. All correlation coefficients were between − 0.15 and 0.1, p > 0.3 (see Appendix for analytical numerals). Similarly, there was no correlation between these parameters and changes in maximal gait speed post-operatively (correlation of stroke volume is shown in Fig. 2 , see Appendix for analytical numerals). CSF flow parameters and Lateral Ventricular Volume There was no correlation between the CSF volume of the lateral ventricles and stroke volume, nor any of the other investigated CSF flow parameters (all R coefficients 0.5). Comparison with age-matched healthy controls The comparison of flows between the controls and iNPH patients is shown in Fig. 3 . 90th percentile thresholds were 120 µL for stroke volume and 0.15 ml/min for netflow, respectively. Stroke volume (iNPH = 140 ± 100, healthy = 80 ± 41 µL; p < 0.001) and peak systolic velocity (iNPH = -41.5 ± 24.1, healthy = -35.9 ± 18.4; p = 0.032) showed significant differences between iNPH patients and healthy individuals. In the respective outcome groups, SV was still significantly higher than healthy controls (p = 0.005 for responders and p < 0.001 for non-responders), whilst peak systolic velocity was almost significant (p = 0.065 for responders and 0.059 for non-responders). Peak diastolic velocity (iNPH = 32.6 ± 13.6, healthy = 30.9 ± 12.1 mm/s; p = 0.09) and netflow (iNPH = − 0.03 ± 1.60, healthy = -0.21 ± 0.34 ml/min ;p = 0.166), on the other hand, did not differ significantly. Netflow was towards the cranial direction for 8/42 controls (19%), a smaller proportion than for iNPH (Fisher exact test p-value = 0.012). Discussion We found no prognostic power in aqueductal CSF flow measured with PC-MRI in our analysis of 92 iNPH patients, including 54 shunt-responders and 38 non-responders. Whilst previous studies have shown conflicting results, most are small or based on underpowered cohorts (50 patients or less). We have now addressed this long standing knowledge gap in a larger cohort, supporting that the negative results are truly due to an absence of predictive power. There are two main potential interpretations of our results: First, that aqueductal CSF flow is not related to the reversibility of iNPH symptoms and signs. Another potential explanation would be that any such relation is obscured by the heterogeneity of the iNPH population, which could in turn be due both to the progressive nature of the disease and a multifactorial pathophysiology. The scientific community has repeatedly hypothesised on the temporal changes of CSF flow during long-term monitoring periods. Scollato et al 33 have published a preliminary series of 9 patients demonstrating significant changes in stroke volume in NPH, particularly in the first 12 months since the onset of symptoms. To date, it has not been possible to identify and mark the exact phase when iNPH is reversible, making it difficult to extrapolate an optimal timing for the performance of CSF flow measurements before shunting. Investigating such time-related or pathophysiological differences and comorbidities could reveal any hidden prognostic value of aqueductal flow. However, the clinical utility would likely still be limited, since it is hard to control and confirm when in the disease course patients seek health care. Comparison of responders, non-responders and healthy controls We found that stroke volume and peak systolic velocities were significantly higher in iNPH patients compared to healthy controls. In respondents and non-responders, this difference was also present, albeit non-significant for peak systolic velocity(most likely due to the limited number of patients in the outcome groups). It is also of particular interest to note that, even at some of the most extreme elevations of stroke volume, improvement was not achieved - as depicted in Fig. 3 . As such, increased CSF pulsatility appears to be a characteristic feature of iNPH rather than a predictor of shunt response. Thus, our data corroborated the use of aqueduct flow measurements as a supportive diagnostic sign for iNPH 34 – 37 . Even so, we would advise against the use of any fixed numerical thresholds for stroke volume or any other variable, since they have been demonstrated to vary significantly and highly depend on several technical characteristics of each PC MRI sequence 10 , 38 , 39 . The absence of a relationship between elevated stroke volume and the degree of ventriculomegaly—quantified by CSF volume in the lateral ventricles—supports the view that, although increased pulsatility and ventricular load are both characteristic of iNPH, they do not establish a definitive pathophysiological or aetiological link to the disease. Nor do they offer a clear explanation for its potential reversibility 40 , 41 . Conversely, net flow and peak diastolic velocity did not show significant differences between iNPH patients and healthy controls, and while net flow was more often in the cranial direction in iNPH there was substantial overlap. This suggests that these two parameters, as quantified with PC-MRI, are neither a distinguishing factor for iNPH, nor a predictor of outcome after shunting. Poca et al (2002) 11 had first reported a promising predictive value of peak diastolic velocity, albeit at extremes of elevation and based on two non-responders (see Table 1 ). Pertaining to netflow, despite initial reports that increased or inversed net flow could be an accurate predictor of shunt response 7 , 18 , it appears to be the parameter most prone to controversy and calculation errors 17 , 38 , 42 . Such sensitivity to calculation error was also revealed by the fact that netflow had the lowest ICC amongst raters, despite meticulous scrutiny of ROI areas and background correction by all 3 raters. We selected a gait threshold of 0.16 m/s to define outcome after shunting in our iNPH cohort. As iNPH is primarily a gait disorder, measuring maximal gait velocity provides objective, quantifiable, and clinically relevant data that reflect improvements in the patient's functional status and quality of life 25 . We also considered whether balance could complement gait assessments by providing additional insights into the patient's functional mobility but found no significant change in the classification to responders versus non-responders in our cohort, nor in the resulting predictive value (shown in appendix). Relationship with baseline gait speed and gait changes Our study found no significant relationship between baseline gait speed and aqueductal CSF flow parameters, regardless of response to shunting. This suggests that the severity of gait impairment at baseline is not influenced by the dynamics of CSF flow through the aqueduct. Similarly, we found no significant correlation between changes in gait speed post-shunting and aqueductal CSF flow parameters. This indicates that improvements in gait following shunt surgery are not predicted by preoperative measurements of aqueductal CSF dynamics. As such, not only did aqueductal flow lack predictive value in differentiating shunt responders, it did not show any relationship to iNPH symptoms. Consequently, PC-MRI aqueductal flow sequences appear unable to aid treatment decisions in iNPH. Limitations Our data was retrospectively collected, spanning a range between 2007 and 2019. During this time, there were slight changes in clinical practice that could have influenced shunt selection. Moreover, our scanning protocol was clinically-driven and hence varied. We avoided significant technical variations by selecting only those sequences with the same spatial and time resolution, as well as the same MRI scanner. Both spatial and time resolution can affect the derived measurements 38 , 43 . In selecting our cohort, we compared time resolution (32 frames versus 12 frames, results not in the main article), and found significant differences in the calculated stroke volumes, as would be expected, supporting our choice to exclude the lower time resolution sequences from the analysis. Overall, our spatial resolution was 1.2x1.2 mm, which could lead to slight overestimation of aqueductal areas and stroke volumes 38 . However, any partial volume effects resulting from this limited resolution would affect the entire cohort similarly, since there were no differences in area between the outcome groups, and would not be sufficient to completely mask any predictive value of our measurements. The spatial resolution, and our manual segmentation of the aqueduct, may have also caused some underestimation of the systolic and diastolic peak velocities, since any surrounding tissue included in the aqueduct ROI would contribute to reducing the area-averaged velocity. This methodological aspect may have contributed to the lower ICC for the peak velocities, as well as netflow, as compared to the excellent ICC for stroke volume. Finally, we had acquired a combination of prospective and retrospective cardiac gating in our cohort. Acknowledging the potential effects of prospective gating on the netflow, we verified that there were no significant effects on netflow in our cohort 44 , 45 . Nonetheless, we corrected for this in the comparison between responders and non-responders. Conclusion As a predictive test, aqueductal flow measured by PC-MRI provided no value as a self-standing evaluation. From a clinical point of view, this measurement appears to have very limited value for selecting patients for shunting, as well as for supplementing clinical practice. Declarations Competing Interests Sara Qvarlander has received honorarium from Likvor AB (Umeå, Sweden) for education material.SQ also serves as a member of the editorial board of Fluids and Barriers of the CNS. Funding Declaration This work was supported by the Swedish Foundation for Strategic Research, grant number RMX18-0152. Author Contribution ADL participated in the design of the study, analysed and interpreted the data and drafted the manuscript. AVB rated the ROIs as the second rated and contributed to the drafting of the manuscript, as well as the revised versions. JML, AW and PA provided input on the data analysis and participated in interpretation of the data, as well as the drafting and revising of the manuscript. SQ participated in the design of the study, selection of the cohort, interpretation of the data and in all the manuscript versions. Acknowledgement We would like to thank Dr Petter Holmlund for his input and collaboration.We would also like to acknowledge Dr Sofia Behndig, who has measured Evans' Index and the callosal angle as part of the large Umeå retrospective cohort.Finally, we thank everyone involved in the data collection to our Redcap database. Data Availability Data in the formal of our extracted measuremenets (csv file) can be shared by the authors upon reasonable request.The original MRI acquisitions belong to the University and can only be shared after specific request. The researchers responsible for gathering that data and the University will need to review and agree in each case. References Marmarou, A. et al. INPH guidelines, part I: Development of guidelines for idiopathic normal-pressure hydrocephalus. Introduction Neurosurg. 57 , 2–4 (2005). Relkin, N. R. et al. Diagnosing idiopathic normal pressure hydrocephalus. Neurosurgery 40 , 959–965 (2012). Halperin, J. J. et al. 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Motor cortex disinhibition in normal-pressure hydrocephalus. J. Neurosurg. 116 , 453–459 (2012). Armand, S. et al. Interest of dual-task-related gait changes in idiopathic normal pressure hydrocephalus. Eur. J. Neurol. 18 , 1081–1084 (2011). Allali, G. et al. Impact of impaired executive function on gait stability. Dement. Geriatr. Cogn. Disord . 26 , 364–369 (2008). Tilson, J. K. et al. Meaningful gait speed improvement during the first 60 days poststroke: minimal clinically important difference. Phys. Ther. 90 , 196–208 (2010). Tinetti, M. E. Performance-oriented assessment of mobility problems in elderly patients. J. Am. Geriatr. Soc. 34 , 119–126 (1986). Malm, J. et al. Reference values for CSF outflow resistance and intracranial pressure in healthy elderly. Neurology 76 , 903–909 (2011). Qvarlander, S. et al. Cerebrospinal fluid and blood flow patterns in idiopathic normal pressure hydrocephalus. Acta Neurol. Scand. 135 , 576–584 (2017). Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods . 9 , 671–675 (2012). Kockum, K. et al. The idiopathic normal-pressure hydrocephalus Radscale: a radiological scale for structured evaluation. Eur. J. Neurol. 25 , 569–576 (2018). Fällmar, D. et al. Imaging features associated with idiopathic normal pressure hydrocephalus have high specificity even when comparing with vascular dementia and atypical parkinsonism. Fluids Barriers CNS . 18 , 35 (2021). Billot, B. et al. SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining. Med. Image Anal. 86 , 102789 (2023). Scollato, A. et al. Changes in aqueductal CSF stroke volume and progression of symptoms in patients with unshunted idiopathic normal pressure hydrocephalus. Am. J. Neuroradiol. 29 , 192–197 (2008). Whitley, H. et al. Volumes and velocities: Meta-analysis of PC-MRI studies in normal pressure hydrocephalus. Acta Neurochir. (Wien) . 166 , 463 (2024). Shanks, J. et al. Aqueductal CSF Stroke Volume Is Increased in Patients with Idiopathic Normal Pressure Hydrocephalus and Decreases after Shunt Surgery. AJNR Am. J. Neuroradiol. 40 , 453–459 (2019). Baroncini, M. et al. Ventriculomegaly in the Elderly: Who Needs a Shunt? A MRI Study on 90 Patients. Acta Neurochir. Suppl. 126 , 221–228 (2018). Luetmer, P. H. et al. Measurement of cerebrospinal fluid flow at the cerebral aqueduct by use of phase-contrast magnetic resonance imaging: technique validation and utility in diagnosing idiopathic normal pressure hydrocephalus. Neurosurgery 50 , 534–543 (2002). discussion 543-4. Wåhlin, A. et al. Phase contrast MRI quantification of pulsatile volumes of brain arteries, veins, and cerebrospinal fluids compartments: repeatability and physiological interactions. J. Magn. Reson. Imaging . 35 , 1055–1062 (2012). Tawfik, A. M. et al. Phase-Contrast MRI CSF Flow Measurements for the Diagnosis of Normal-Pressure Hydrocephalus: Observer Agreement of Velocity Versus Volume Parameters. AJR Am. J. Roentgenol. 208 , 838–843 (2017). Holmlund, P. et al. Can pulsatile CSF flow across the cerebral aqueduct cause ventriculomegaly? A prospective study of patients with communicating hydrocephalus. Fluids Barriers CNS . 16 , 40 (2019). Eide, P. K. & Saehle, T. Is ventriculomegaly in idiopathic normal pressure hydrocephalus associated with a transmantle gradient in pulsatile intracranial pressure? Acta Neurochir. (Wien) . 152 , 989–995 (2010). Balédent, O. et al. Brain hydrodynamics study by phase-contrast magnetic resonance imaging and transcranial color Doppler. J. Magn. Reson. IMAGING . 24 , 995–1004 (2006). Liu, P. et al. Real-time phase contrast MRI versus conventional phase contrast MRI at different spatial resolutions and velocity encodings. Clin. Imaging . 94 , 93–102 (2023). Nitz, W. R. et al. Flow dynamics of cerebrospinal fluid: assessment with phase-contrast velocity MR imaging performed with retrospective cardiac gating. Radiology 183 , 395–405 (1992). Yamada, S. et al. Current and emerging MR imaging techniques for the diagnosis and management of CSF flow disorders: a review of phase-contrast and time-spatial labeling inversion pulse. AJNR Am. J. Neuroradiol. 36 , 623–630 (2015). Additional Declarations Competing interest reported. Sara Qvarlander has received honorarium from Likvor AB (Umeå, Sweden) for education material. SQ also serves as a member of the editorial board of Fluids and Barriers of the CNS. Supplementary Files aqflowappendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7400171","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":523062561,"identity":"e0478e9f-17a0-43ea-8310-4b363e5ba2f2","order_by":0,"name":"Afroditi D Lalou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIie3PrQ7CMBDA8VuatKYEyzI+XqFklsCrjJCgUWhUMTzASHgFBGZ6y5JhyLAkCDo/UYJBTNCRLXNlEtG/OvPL3QGYTP8YCgEoAwoELDVOWhDslYRRQACKLNsSYFCR+Lfo8k4m8lXRB4RE+OTX+dH3kJQa0kuIOz58D8Ms2vP7PLh52PZ1ax4cO9UvLO5UBFGNGCW4JkQqkpYEvQoNYQ2h5ZawJODo7honGKlfXKqOWUV+unCDS8btnYYME2yJvBjOumR7EnI9HQTnRSzfujV1uBmtTRtgMplMJk0f/bxHUFouNQwAAAAASUVORK5CYII=","orcid":"","institution":"Umeå University","correspondingAuthor":true,"prefix":"","firstName":"Afroditi","middleName":"D","lastName":"Lalou","suffix":""},{"id":523062562,"identity":"54dcbbb1-e04a-4a18-9fe8-aa78d05c299c","order_by":1,"name":"Adam Vitelli Bryngelsson","email":"","orcid":"","institution":"University Hospital of Umeå","correspondingAuthor":false,"prefix":"","firstName":"Adam","middleName":"Vitelli","lastName":"Bryngelsson","suffix":""},{"id":523062563,"identity":"c1ec9d90-2153-404c-acb1-656a531627d1","order_by":2,"name":"Anders Wåhlin","email":"","orcid":"","institution":"Umeå University","correspondingAuthor":false,"prefix":"","firstName":"Anders","middleName":"","lastName":"Wåhlin","suffix":""},{"id":523062564,"identity":"b0b279aa-00d9-4cb4-89ea-c18bd3e6c97d","order_by":3,"name":"Pär Asplund","email":"","orcid":"","institution":"Umeå University","correspondingAuthor":false,"prefix":"","firstName":"Pär","middleName":"","lastName":"Asplund","suffix":""},{"id":523062565,"identity":"6dc469e3-3b60-4a4f-90af-d8d9fbc796cd","order_by":4,"name":"Jenny Ma Larsson","email":"","orcid":"","institution":"University Hospital of Umeå","correspondingAuthor":false,"prefix":"","firstName":"Jenny","middleName":"Ma","lastName":"Larsson","suffix":""},{"id":523062566,"identity":"c43b4e39-4537-4ab0-a4ff-2fd877fb7666","order_by":5,"name":"Sara Qvarlander","email":"","orcid":"","institution":"Umeå University","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Qvarlander","suffix":""}],"badges":[],"createdAt":"2025-08-18 13:23:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7400171/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7400171/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92655059,"identity":"cbaac10a-b1f9-4626-9ce6-d84756a9d841","added_by":"auto","created_at":"2025-10-02 13:16:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":307708,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegion of Interests as drawn in our axial PC-MRI images. \u003c/strong\u003eIllustration of a randomly selected ROI area from our cohort. a: Aqueductal ROI area as manually drawn with ImageJ b: Eddy current correction area.\u003c/p\u003e","description":"","filename":"Screenshot20251002at9.15.29AM.png","url":"https://assets-eu.researchsquare.com/files/rs-7400171/v1/e9dbc733b6b8815e4d9e2630.png"},{"id":92655719,"identity":"51791e13-1051-4999-af0d-08ee1c360cfb","added_by":"auto","created_at":"2025-10-02 13:24:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1616639,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7400171/v1/dbdc1aec-bf00-468f-91bc-37b55dab2cc2.pdf"},{"id":92655060,"identity":"63f3b922-a0b5-4c54-a2fa-9bbe5c668a35","added_by":"auto","created_at":"2025-10-02 13:16:43","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":296363,"visible":true,"origin":"","legend":"","description":"","filename":"aqflowappendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-7400171/v1/4f9a0f3bcb7713518ac7f490.docx"}],"financialInterests":"Competing interest reported. Sara Qvarlander has received honorarium from Likvor AB (Umeå, Sweden) for education material.\nSQ also serves as a member of the editorial board of Fluids and Barriers of the CNS.","formattedTitle":"No Predictive Value of Aqueduct CSF Flow Dynamics for Shunt Response in Idiopathic Normal Pressure Hydrocephalus","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIdiopathic normal pressure hydrocephalus (iNPH) is diagnosed based on clinical criteria and physiological measurements, including brain imaging parameters \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. While increased aqueductal CSF flow dynamics (including stroke volume and flows) assessed with Phase-Contrast MRI (PC-MRI) is one of the supportive brain imaging features in the iNPH diagnostic guidelines, the clinical usefulness of this technique and its metrics remains largely unknown \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFurthermore, regarding prognostic tests in iNPH, guidelines and standard practice recommend CSF withdrawal of 30\u0026ndash;50 ml with a tap test, with a number of patients additionally requiring extended lumbar drainage of CSF over three or more days \u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. As such, the need for a non-invasive predictive test remains as current as ever, with aqueductal CSF dynamics being an easily conducted test frequently suggested over the last few decades. However, there have been limited as well as inconsistent reports in the literature regarding the diagnostic and predictive utility of PC-MRI-measured aqueductal flow dynamics. Previous studies have shown mixed results, with some suggesting a correlation of increased aqueductal stroke volume, netflow and peak velocities with positive surgical outcomes, while others have found no significant predictive value. Moreover, the current body of research is limited by small sample sizes and technical differences between MRI protocols, which hinder the ability to draw definitive conclusions\u003csup\u003e\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11 CR12 CR13\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. A summary of prognostic studies to date using PC MRI can be found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. To date, no large cohort study has comprehensively investigated the relationship between aqueductal CSF dynamics and outcomes after shunting in iNPH patients. This gap in the literature underscores the need for a robust, large-scale study to provide clinically useful answers.\u003c/p\u003e\u003cp\u003eIn view of the above, we aimed to clarify the role of aqueductal flow in supporting diagnosis and shunt selection for iNPH patients. The primary hypothesis was that disturbed aqueductal flow dynamics (increased velocity, flow and/or stroke volume), as measured by PC-MRI, are predictive of a positive outcome after shunt insertion. Additionally, we aimed to explore the relationship between aqueductal flow and baseline gait performance (as gait impairment forms the cardinal sign of the iNPH triad), and whether abnormally increased CSF flow dynamics have any diagnostic value when compared to a cohort of normal controls.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of studies that report on the significance/insignificance of CSF flow parameters as a predictive test before surgery.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eStudy\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eN (responders vs\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003enon-responders\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eMethodology\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eSV\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eNet flow/Peak velocity\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBradley et al\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(1996)\u003c/b\u003e\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (15 vs 3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo venc reported. 1.5 T. Retrospective \u003c/p\u003e\u003cp\u003eThreshold of SV explored.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePPV 100%\u003c/p\u003e\u003cp\u003eSV\u0026thinsp;\u0026gt;\u0026thinsp;42 \u0026micro;L (in 12/15 responders\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDixon et al\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(2002)\u003c/b\u003e\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVenc 10-20cm/sec. \u003c/p\u003e\u003cp\u003eRetrospective\u003c/p\u003e\u003cp\u003eComparison among different levels of gait improvement.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNet flow\u0026thinsp;\u0026gt;\u0026thinsp;33 ml/min could have significance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePoca MA et al\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(2002)\u003c/b\u003e \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (31 vs 2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.5 T, Venc 10cm/sec. \u003c/p\u003e\u003cp\u003eProspective\u003c/p\u003e\u003cp\u003e28/29 with peak flow\u0026thinsp;\u0026gt;\u0026thinsp;97th percentile improved\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSignificant if\u0026thinsp;\u0026gt;\u0026thinsp;97.5 percentile of controls.\u003c/p\u003e\u003cp\u003eSensitivity:90%\u003c/p\u003e\u003cp\u003eSpecificity:50%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eKahlon et al\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(2007)\u003c/b\u003e\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVenc 20cm/sec. \u003c/p\u003e\u003cp\u003eRetrospective\u003c/p\u003e\u003cp\u003eRanges of SV explored in responders vs non-responders.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNS in all ranges: 1\u0026ndash;50,\u003c/p\u003e\u003cp\u003e51\u0026ndash;100 and \u0026gt;\u0026thinsp;100 \u0026micro;\u003cb\u003eL.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAlgin et al\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(2010)\u003c/b\u003e \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (12 vs 6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVenc 20cm/sec.\u003c/p\u003e\u003cp\u003eProspective\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRingstad et al\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(2015)\u003c/b\u003e \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (17 shunted vs \u003c/p\u003e\u003cp\u003e4 not shunted)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVenc 10cm/sec. \u003c/p\u003e\u003cp\u003eProspective .\u003c/p\u003e\u003cp\u003eNo outcome data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBlitz et al\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(2018)\u003c/b\u003e\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVenc 8\u0026ndash;30 cm/sec. \u003c/p\u003e\u003cp\u003eRetrospective\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLindstrom et al\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(2018)\u003c/b\u003e \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (17 vs 9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVenc 10cm/sec. \u003c/p\u003e\u003cp\u003eProspective\u003c/p\u003e\u003cp\u003eNon-responders include non-shunted.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.023\u0026thinsp;\u0026plusmn;\u0026thinsp;0.021 vs -0.006\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 ml/cycle* (responders vs. non-responders)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStecco et al\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(2020)\u003c/b\u003e\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38 (26 vs 12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVenc 15-20cm/sec. \u003c/p\u003e\u003cp\u003eRetrospective\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e271,85\u0026thinsp;\u0026plusmn;\u0026thinsp;143,03 vs 72,83\u0026thinsp;\u0026plusmn;\u0026thinsp;28,66 ml * (responders vs. non-responders)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHe et al\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(2022)\u003c/b\u003e \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46 (28 vs 18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVenc 20cm/sec. \u003c/p\u003e\u003cp\u003eRetrospective\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eAll were measured at aqueductal level. Venc:Velocity encoding, SV:stroke volume, PPV:positive predictive value, NS: not significant, N\u0026thinsp;=\u0026thinsp;patients included who received a shun\u003c/em\u003et * =statistically significant\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003eIn summary, we retrospectively included 92 iNPH patients with a preoperative PC-MRI and available post-operative outcome, defined by gait speed assessment before and after shunting. Patients were included after referral to our specialty clinic, and if they met the International iNPH guidelines criteria for possible or probable INPH. A physiotherapist assessed the patients\u0026rsquo; gait speed pre- and postoperatively. We calculated aqueductal CSF flow dynamics and evaluated whether these measurements correlated to gait outcome and baseline gait speed. Finally, we compared our iNPH cohort to a cohort of 42 age-matched healthy controls.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy population\u003c/h2\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003ch2\u003eIdiopathic Normal Pressure Hydrocephalus Patients\u003c/h2\u003e\u003cp\u003eThe study was approved by the Swedish Ethical Review Authority (No. 2020\u0026ndash;04469). Informed consent was obtained from patients that had been investigated for NPH at our Neurology Clinic at the University Hospital of Ume\u0026aring; between 2007 and 2019. Participants were informed about the study via a letter, which included an option to opt out if they did not wish to participate. Individuals deceased at the time inclusion began (2020) were automatically incorporated in the cohort.\u003c/p\u003e\u003cp\u003eThe routine clinical protocol included assessment from a neurologist, a specialist nurse and a physiotherapist pre and post-operatively. Follow-ups were performed 3\u0026ndash;6 months after the operation. Evaluation at baseline included medical history, neurological examination and MRI of the brain, with ventriculomegaly confirmed via Evans index\u0026thinsp;\u0026gt;\u0026thinsp;0.3. Other radiological measurements for assessment included the callosal angle and the presence of disproportionately enlarged subarachnoid space hydrocephalus\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Aqueductal flow from PC-MRI flow was measured as a routine in our clinical protocol as part of neuroradiological assessment for aqueductal flow obstruction and did not contribute to shunt selection.\u003c/p\u003e\u003cp\u003eA CSF tap test and lumbar infusion test were performed and used to select candidates for shunt placement. In uncertain cases, a three-day external lumbar drainage was conducted. The final decision for shunt operation was made by a multidisciplinary team based on these evaluations.\u003c/p\u003e\u003cp\u003eA total of 295 patients within the inclusion period had been diagnosed with probable or possible iNPH and were shunted. We further selected those with an analysable PC-MRI sequence (see details on PC-MRI quality criteria below) and a documented pre and post-operative measurement of gait speed.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eOutcome assessment\u003c/h3\u003e\n\u003cp\u003eAt postoperative follow-up, most patients had undergone a repeat lumbar CSF infusion test to confirm that the CSF outflow resistance had been lowered, confirming a functioning shunt in situ. Patients with non-functioning shunts were excluded from the analysis unless they had a renewed outcome assessment after surgical revision within 1 year of the original follow-up.\u003c/p\u003e\u003cp\u003eWe defined improvement based on postoperative change in maximal gait speed, as gait has been shown to be the most consistent to improve amongst the cardinal symptoms of iNPH\u003csup\u003e\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, and it also constitutes a parameter that can be quantified. For maximal gait speed assessment, patients walked ten meters at their maximum speed, repeating the task six times, after which the average speed was calculated. We classified as responders those with an increase of at least 0.16 m/s after shunting. This threshold was chosen based on a recent report on stroke patients that linked an improvement of 0.16 m/s to a one-point change on the modified Rankin Scale, a measure that has been reported to reflect clinically significant functional improvement \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. All gait speed data were measured by dedicated physiotherapists, who performed a comprehensive battery of tests for gait and balance, including Tinetti scores\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSome patients required a second follow-up in addition to the standard 3\u0026ndash;6 months due to shunt adjustments, complications or revision. In those cases, the best postoperative results were chosen, to adequately reflect the resulting benefit from a shunt placement for each respective patient.\u003c/p\u003e\n\u003ch3\u003eControl group\u003c/h3\u003e\n\u003cp\u003eThe control group consisted of 42 healthy, age-matched volunteers who had previously undergone an MRI in our centre. The controls have been described in detail in previous publications\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003ePC-MRI acquisition\u003c/h3\u003e\n\u003cp\u003e All acquisitions were performed as part of our local MRI protocol for NPH. Due to the long inclusion period, different MRI scanners were used, with protocols that slightly varied over time as well as between scanners. In order to homogenise our data, we selected only those with the same acquisition resolution. We also selected those with the same number of cardiac frames, which was 32 frames per cardiac cycle. Data was subsequently only analysed from one MRI scanner, a 3T Philips Achieva (Best, the Netherlands), with an 8-channel head coil. The settings for the PC-MRI scan were velocity encoding 20 cm/s (in cases of aliasing, the sequence had been repeated with 30cm/s), echo time 5.1\u0026ndash;10 ms, flip angle 6,10 or 15 degrees. In-plane voxel size was 1.2x1.2 mm^2 and slice thickness was 5 mm. The majority of scans were acquired using retrospective cardiac gating, however, a prospective cardiac gating protocol had been in place between 2008\u0026ndash;2012.\u003c/p\u003e\u003cp\u003eThe preoperative PC-MRI acquisitions were visually assessed for quality (placement of the plane at the correct level through the aqueduct, clearly visible aqueduct without artefacts) and checked for aliasing. Two regions of interest (ROI) were manually drawn for each patient using the open-source ImageJ software\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e : one encircling the aqueduct \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e and a crescent-shaped ROI placed anterior to the aqueduct in order to correct for Eddy current effects (background correction) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eArea and velocity measurements were derived directly from the ROI analysis of ImageJ. We calculated and assessed the diagnostic and predictive performance of the following 5 total parameters, two related to aqueductal CSF flow, two aqueductal CSF velocities in different directions (caudal and cranial), and a CSF pulsatility descriptor. These were: netflow (average flow over the 32 frames) and absolute flow (calculated as the average of all 32 frames after conversion to a positive number), peak systolic velocity (maximal velocity in the caudal direction, negative values in our dataset), peak diastolic velocity (maximal velocity in the cranial direction, positive values in our dataset), and stroke volume (SV). Peak velocities were assessed as the peak average velocity in the entire aqueduct in each respective direction. SV was calculated as the amplitude of the cumulative integral of the mean-corrected flow waveform, representing the volume of CSF that moves back and forth through the aqueduct with each cardiac cycle.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eRadiological measurements\u003c/h2\u003e\u003cp\u003eRadiological measures were used for diagnosis as described above, and for describing our cohort in a standardised manner; in addition we also assessed ventricular volume to investigate the relationship between this and CSF flow parameters. All linear and volumetric measurements below were extracted from 3D T1-weighted images acquired during the same investigation as the PC-MRI acquisition. Evan\u0026rsquo;s Index was calculated in accordance with traditional consensus (ratio of the maximum width of the frontal horns to the maximal internal diameter). Callosal angle was measured as per the iNPH RadScale\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Lateral ventricular volume was extracted using the automated Freesurfer tool SynthSeg\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.The extracted volumes were then visually reviewed. In cases where the automated segmentation was of suboptimal quality, we used instead a flood-fill function with an in-house developed Matlab script available here: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/SofiaBehndig/qDESH\u003c/span\u003e\u003cspan address=\"https://github.com/SofiaBehndig/qDESH\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, published in Behndig et al (in press).\u003c/p\u003e\u003cp\u003eOur inter-rater reliability and post-hoc analysis are shown in the Appendix.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll data were analysed using Rstudio version 2024.12.1\u003c/p\u003e\u003cp\u003eVisualisation with a histogram showed non-normal distribution with mild left skewness for all parameters. We confirmed that our data were not normally distributed using the Kolmogorov-Smirnov test (significant difference from normal distribution, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 on all CSF flow parameters). Subsequently, non-parametric tests were applied, including the Mann-Whitney U test to compare CSF flow parameters between responders and non-responders, as well as iNPH and healthy controls. As a difference in cardiac gating type (retro- vs prospective) could affect net flow estimation, a generalized linear model with logistic regression was used to compare the net flow between responders and non-responders, with outcome as the dependent parameter and gating type as a factor. Non-parametric correlation (Spearman\u0026rsquo;s rho) was used to analyse the relationship between CSF flow parameters and preoperative maximal gait speed, as well as changes in maximal gait speed from pre- to postoperative assessments and ventricular volume. ROC analysis was performed to calculate the predictive power of CSF flow parameters (R packages pROC and ROCR). We additionally calculated the 90th percentile of all parameters for the healthy cohort. Finally, we calculated the proportion of patients with net flow in the cranial direction for the different groups and compared them with a Fisher\u0026rsquo;s exact test. We set statistical significance at 0.05 (two-tailed).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eFrom the initial 295 shunted iNPH patients, a total of 92 had a PC MRI sequence consistent with our criteria of acquisition protocol homogeneity and measurement quality. Prospective cardiac gating had been used in 42 examinations.\u003c/p\u003e\u003cp\u003eUsing the threshold of 0.16 m/s postoperative gait speed change, 54 patients improved and 38 did not. Patient characteristics are presented in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographics and baseline characteristics of the idiopathic Normal Pressure Hydrocephalus cohort.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eTotal sample (N\u0026thinsp;=\u0026thinsp;92)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eResponders\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e54/92 (59%)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eNon-responders\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e38/92 (41%)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP-value *\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFemale/Male\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 / 68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13:41 (32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11:27 (41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.6357\u003c/em\u003e \u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge at operation (years; median, range)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75 (57\u0026ndash;85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76 (57\u0026ndash;85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74 (64\u0026ndash;83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.679\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePreoperative gait speed (m/s)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.152\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGait speed change (m/s)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePreoperative Tinetti score (median, range)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (1\u0026ndash;16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13(9\u0026ndash;14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12(9\u0026ndash;14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.852\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTinetti change (median, IQR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0\u0026ndash;4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (0\u0026ndash;4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (-1 \u0026ndash; +3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePreoperative Evans index\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.866\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePreoperative Callosal angle (degrees)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63\u0026thinsp;\u0026plusmn;\u0026thinsp;22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e61\u0026thinsp;\u0026plusmn;\u0026thinsp;22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.552\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTime MRI to shunting (days)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e145\u0026thinsp;\u0026plusmn;\u0026thinsp;76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e139 \u0026plusmn; 70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e155\u0026thinsp;\u0026plusmn;\u0026thinsp;91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLateral Ventricular Volume (mL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e153\u0026thinsp;\u0026plusmn;\u0026thinsp;37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e153\u0026thinsp;\u0026plusmn;\u0026thinsp;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e153\u0026thinsp;\u0026plusmn;\u0026thinsp;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.883\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eValues are represented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD unless otherwise specified. Tinetti POMA (Performance Oriented Mobility Assessment) balance score was only available in 87/92 patients. * p-value for responders vs non-responders.\u003c/em\u003e \u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eFisher\u0026rsquo;s exact test. Statistically significant p-values are in bold.\u003c/em\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCSF flow parameters and gait improvement\u003c/h2\u003e\u003cp\u003eThe results of the comparison of CSF flow parameters between responders and non-responders are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. No CSF flow parameters differed between the groups. Netflow was in the cranial direction (positive) in 49/92 patients (53.3%), 30/50 (60%) with retrospective gating and 19/42 (45.2%) with prospective cardiac gating. The AUC for the 5 explored parameters were all between 0.49 (lowest, peak diastolic velocity) \u0026minus;\u0026thinsp;0.62 (highest, ROI area).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Aqueductal CSF flow parameters in responders versus non-responders.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eResponders\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e54/92 (59%)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eNon-responders\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e38/92 (41%)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStroke volume (\u0026micro;L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130\u0026thinsp;\u0026plusmn;\u0026thinsp;90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e150\u0026thinsp;\u0026plusmn;\u0026thinsp;100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e0.324\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNetflow (ml/min)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e0.563\u003c/em\u003e \u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNetflow direction distribution (cranial:caudal, % cranial)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28:26 (52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18:20 (47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e0.833\u003c/em\u003e \u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAbsolute flow (ml/min)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e0.342\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePeak systolic velocity (mm/sec)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-40.99\u0026thinsp;\u0026plusmn;\u0026thinsp;21.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-43.02\u0026thinsp;\u0026plusmn;\u0026thinsp;16.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e0.544\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePeak diastolic velocity (mm/sec)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.4\u0026thinsp;\u0026plusmn;\u0026thinsp;17.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.59\u0026thinsp;\u0026plusmn;\u0026thinsp;19.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e0.868\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eROI Area (mm2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e0.055\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eValues are presented in mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD unless stated otherwise. Cut-off for improvement was 0.16 m/s maximum gait speed increase post-operatively. IQR: Interquantile range.\u003c/em\u003e \u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eFisher\u0026rsquo;s exact test\u003c/em\u003e \u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eAdjusted for cardiac gating type (25 responders and 17 non-responders with prospective gating).\u003c/em\u003e\u003c/p\u003e\u003cp\u003eIn the post-hoc analysis with Tinetti change as the outcome parameter (cut-off of 3 points), no predictive value was found for any of the 5 explored CSF flow parameters (see Supplementary material).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eCSF flow parameters and maximal gait speed\u003c/h2\u003e\u003cp\u003eThere was no significant correlation in the entire cohort, nor in the separate outcome groups, between baseline gait and any of the 5 CSF flow parameters. All correlation coefficients were between \u0026minus;\u0026thinsp;0.15 and 0.1, p\u0026thinsp;\u0026gt;\u0026thinsp;0.3 (see Appendix for analytical numerals). Similarly, there was no correlation between these parameters and changes in maximal gait speed post-operatively (correlation of stroke volume is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, see Appendix for analytical numerals).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eCSF flow parameters and Lateral Ventricular Volume\u003c/h2\u003e\u003cp\u003eThere was no correlation between the CSF volume of the lateral ventricles and stroke volume, nor any of the other investigated CSF flow parameters (all R coefficients\u0026thinsp;\u0026lt;\u0026thinsp;0.1, p-values\u0026thinsp;\u0026gt;\u0026thinsp;0.5).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eComparison with age-matched healthy controls\u003c/h2\u003e\u003cp\u003eThe comparison of flows between the controls and iNPH patients is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. 90th percentile thresholds were 120 \u0026micro;L for stroke volume and 0.15 ml/min for netflow, respectively. Stroke volume (iNPH\u0026thinsp;=\u0026thinsp;140\u0026thinsp;\u0026plusmn;\u0026thinsp;100, healthy\u0026thinsp;=\u0026thinsp;80\u0026thinsp;\u0026plusmn;\u0026thinsp;41 \u0026micro;L; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and peak systolic velocity (iNPH = -41.5\u0026thinsp;\u0026plusmn;\u0026thinsp;24.1, healthy = -35.9\u0026thinsp;\u0026plusmn;\u0026thinsp;18.4; p\u0026thinsp;=\u0026thinsp;0.032) showed significant differences between iNPH patients and healthy individuals. In the respective outcome groups, SV was still significantly higher than healthy controls (p\u0026thinsp;=\u0026thinsp;0.005 for responders and p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for non-responders), whilst peak systolic velocity was almost significant (p\u0026thinsp;=\u0026thinsp;0.065 for responders and 0.059 for non-responders). Peak diastolic velocity (iNPH\u0026thinsp;=\u0026thinsp;32.6\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6, healthy\u0026thinsp;=\u0026thinsp;30.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1 mm/s; p\u0026thinsp;=\u0026thinsp;0.09) and netflow (iNPH\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.60, healthy = -0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34 ml/min ;p\u0026thinsp;=\u0026thinsp;0.166), on\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ethe other hand, did not differ significantly. Netflow was towards the cranial direction for 8/42 controls (19%), a smaller proportion than for iNPH (Fisher exact test p-value\u0026thinsp;=\u0026thinsp;0.012).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe found no prognostic power in aqueductal CSF flow measured with PC-MRI in our analysis of 92 iNPH patients, including 54 shunt-responders and 38 non-responders. Whilst previous studies have shown conflicting results, most are small or based on underpowered cohorts (50 patients or less). We have now addressed this long standing knowledge gap in a larger cohort, supporting that the negative results are truly due to an absence of predictive power.\u003c/p\u003e\u003cp\u003eThere are two main potential interpretations of our results: First, that aqueductal CSF flow is not related to the reversibility of iNPH symptoms and signs. Another potential explanation would be that any such relation is obscured by the heterogeneity of the iNPH population, which could in turn be due both to the progressive nature of the disease and a multifactorial pathophysiology.\u003c/p\u003e\u003cp\u003eThe scientific community has repeatedly hypothesised on the temporal changes of CSF flow during long-term monitoring periods. Scollato et al\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e have published a preliminary series of 9 patients demonstrating significant changes in stroke volume in NPH, particularly in the first 12 months since the onset of symptoms. To date, it has not been possible to identify and mark the exact phase when iNPH is reversible, making it difficult to extrapolate an optimal timing for the performance of CSF flow measurements before shunting. Investigating such time-related or pathophysiological differences and comorbidities could reveal any hidden prognostic value of aqueductal flow. However, the clinical utility would likely still be limited, since it is hard to control and confirm when in the disease course patients seek health care.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eComparison of responders, non-responders and healthy controls\u003c/h2\u003e\u003cp\u003eWe found that stroke volume and peak systolic velocities were significantly higher in iNPH patients compared to healthy controls. In respondents and non-responders, this difference was also present, albeit non-significant for peak systolic velocity(most likely due to the limited number of patients in the outcome groups). It is also of particular interest to note that, even at some of the most extreme elevations of stroke volume, improvement was not achieved - as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. As such, increased CSF pulsatility appears to be a characteristic feature of iNPH rather than a predictor of shunt response. Thus, our data corroborated the use of aqueduct flow measurements as a supportive diagnostic sign for iNPH\u003csup\u003e\u003cspan additionalcitationids=\"CR35 CR36\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e–\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Even so, we would advise against the use of any fixed numerical thresholds for stroke volume or any other variable, since they have been demonstrated to vary significantly and highly depend on several technical characteristics of each PC MRI sequence\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe absence of a relationship between elevated stroke volume and the degree of ventriculomegaly—quantified by CSF volume in the lateral ventricles—supports the view that, although increased pulsatility and ventricular load are both characteristic of iNPH, they do not establish a definitive pathophysiological or aetiological link to the disease. Nor do they offer a clear explanation for its potential reversibility\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eConversely, net flow and peak diastolic velocity did not show significant differences between iNPH patients and healthy controls, and while net flow was more often in the cranial direction in iNPH there was substantial overlap. This suggests that these two parameters, as quantified with PC-MRI, are neither a distinguishing factor for iNPH, nor a predictor of outcome after shunting. Poca et al (2002)\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e had first reported a promising predictive value of peak diastolic velocity, albeit at extremes of elevation and based on two non-responders (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Pertaining to netflow, despite initial reports that increased or inversed net flow could be an accurate predictor of shunt response\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, it appears to be the parameter most prone to controversy and calculation errors \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Such sensitivity to calculation error was also revealed by the fact that netflow had the lowest ICC amongst raters, despite meticulous scrutiny of ROI areas and background correction by all 3 raters.\u003c/p\u003e\u003cp\u003eWe selected a gait threshold of 0.16 m/s to define outcome after shunting in our iNPH cohort. As iNPH is primarily a gait disorder, measuring maximal gait velocity provides objective, quantifiable, and clinically relevant data that reflect improvements in the patient's functional status and quality of life\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. We also considered whether balance could complement gait assessments by providing additional insights into the patient's functional mobility but found no significant change in the classification to responders versus non-responders in our cohort, nor in the resulting predictive value (shown in appendix).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eRelationship with baseline gait speed and gait changes\u003c/h2\u003e\u003cp\u003eOur study found no significant relationship between baseline gait speed and aqueductal CSF flow parameters, regardless of response to shunting. This suggests that the severity of gait impairment at baseline is not influenced by the dynamics of CSF flow through the aqueduct. Similarly, we found no significant correlation between changes in gait speed post-shunting and aqueductal CSF flow parameters. This indicates that improvements in gait following shunt surgery are not predicted by preoperative measurements of aqueductal CSF dynamics. As such, not only did aqueductal flow lack predictive value in differentiating shunt responders, it did not show any relationship to iNPH symptoms. Consequently, PC-MRI aqueductal flow sequences appear unable to aid treatment decisions in iNPH.\u003c/p\u003e\u003c/div\u003e"},{"header":"Limitations","content":"\u003cp\u003eOur data was retrospectively collected, spanning a range between 2007 and 2019. During this time, there were slight changes in clinical practice that could have influenced shunt selection. Moreover, our scanning protocol was clinically-driven and hence varied. We avoided significant technical variations by selecting only those sequences with the same spatial and time resolution, as well as the same MRI scanner. Both spatial and time resolution can affect the derived measurements \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. In selecting our cohort, we compared time resolution (32 frames versus 12 frames, results not in the main article), and found significant differences in the calculated stroke volumes, as would be expected, supporting our choice to exclude the lower time resolution sequences from the analysis.\u003c/p\u003e\u003cp\u003eOverall, our spatial resolution was 1.2x1.2 mm, which could lead to slight overestimation of aqueductal areas and stroke volumes\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. However, any partial volume effects resulting from this limited resolution would affect the entire cohort similarly, since there were no differences in area between the outcome groups, and would not be sufficient to completely mask any predictive value of our measurements. The spatial resolution, and our manual segmentation of the aqueduct, may have also caused some underestimation of the systolic and diastolic peak velocities, since any surrounding tissue included in the aqueduct ROI would contribute to reducing the area-averaged velocity. This methodological aspect may have contributed to the lower ICC for the peak velocities, as well as netflow, as compared to the excellent ICC for stroke volume.\u003c/p\u003e\u003cp\u003eFinally, we had acquired a combination of prospective and retrospective cardiac gating in our cohort. Acknowledging the potential effects of prospective gating on the netflow, we verified that there were no significant effects on netflow in our cohort\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Nonetheless, we corrected for this in the comparison between responders and non-responders.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAs a predictive test, aqueductal flow measured by PC-MRI provided no value as a self-standing evaluation. From a clinical point of view, this measurement appears to have very limited value for selecting patients for shunting, as well as for supplementing clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eSara Qvarlander has received honorarium from Likvor AB (Ume\u0026aring;, Sweden) for education material.SQ also serves as a member of the editorial board of Fluids and Barriers of the CNS.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding Declaration\u003c/h2\u003e\u003cp\u003e\u003cb\u003e\u003c/b\u003eThis work was supported by the Swedish Foundation for Strategic Research, grant number RMX18-0152.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eADL participated in the design of the study, analysed and interpreted the data and drafted the manuscript. AVB rated the ROIs as the second rated and contributed to the drafting of the manuscript, as well as the revised versions. JML, AW and PA provided input on the data analysis and participated in interpretation of the data, as well as the drafting and revising of the manuscript. SQ participated in the design of the study, selection of the cohort, interpretation of the data and in all the manuscript versions.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to thank Dr Petter Holmlund for his input and collaboration.We would also like to acknowledge Dr Sofia Behndig, who has measured Evans' Index and the callosal angle as part of the large Ume\u0026aring; retrospective cohort.Finally, we thank everyone involved in the data collection to our Redcap database.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData in the formal of our extracted measuremenets (csv file) can be shared by the authors upon reasonable request.The original MRI acquisitions belong to the University and can only be shared after specific request. The researchers responsible for gathering that data and the University will need to review and agree in each case.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMarmarou, A. et al. INPH guidelines, part I: Development of guidelines for idiopathic normal-pressure hydrocephalus. \u003cem\u003eIntroduction Neurosurg.\u003c/em\u003e \u003cb\u003e57\u003c/b\u003e, 2\u0026ndash;4 (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRelkin, N. R. et al. Diagnosing idiopathic normal pressure hydrocephalus. \u003cem\u003eNeurosurgery\u003c/em\u003e \u003cb\u003e40\u003c/b\u003e, 959\u0026ndash;965 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHalperin, J. J. et al. Practice guideline: Idiopathic normal pressure hydrocephalus: Response to shunting and predictors of response. \u003cem\u003eNeurology\u003c/em\u003e ; \u003cb\u003e85\u003c/b\u003e. 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Neuroradiol.\u003c/em\u003e \u003cb\u003e36\u003c/b\u003e, 623\u0026ndash;630 (2015).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7400171/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7400171/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eINTRODUCTION:\u003c/h2\u003e\u003cp\u003eINPH is diagnosed based on clinical criteria and physiological measurements, including brain imaging parameters. Increased aqueductal CSF flow dynamics, assessed with Phase-Contrast MRI (PC-MRI), is one of the supportive features in iNPH diagnostic guidelines. A predictive value has been suggested but remains largely debatable. This study aimed to clarify the role of aqueductal flow in supporting diagnosis and shunt selection for iNPH patients.\u003c/p\u003e\u003ch2\u003eMETHODS\u003c/h2\u003e\u003cp\u003eWe retrospectively included 92 iNPH patients with preoperative PC-MRI together with pre- and post-operative gait speed measurement. Aqueductal CSF flow dynamics were calculated and correlated with gait outcomes and baseline gait speed. Additionally, we compared our cohort with 42 age-matched healthy controls.\u003c/p\u003e\u003ch2\u003eRESULTS\u003c/h2\u003e\u003cp\u003eWe found no significant differences in CSF flow parameters between shunt responders and non-responders: Stroke volume was 130\u0026thinsp;\u0026plusmn;\u0026thinsp;90 and 150\u0026thinsp;\u0026plusmn;\u0026thinsp;100 \u0026micro;l, p\u0026thinsp;=\u0026thinsp;0.32 respectively, with net flows of 0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71 and \u0026minus;\u0026thinsp;0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51 ml/min, p\u0026thinsp;=\u0026thinsp;0.563. There were no correlations of aqueduct CSF dynamics with baseline gait performance, nor with gait change (-0.15\u0026thinsp;\u0026lt;\u0026thinsp;R\u0026thinsp;\u0026lt;\u0026thinsp;0.1, p\u0026thinsp;\u0026gt;\u0026thinsp;0.5 for all parameters). Furthermore, comparisons with healthy controls revealed differences in stroke volume (140\u0026thinsp;\u0026plusmn;\u0026thinsp;100\u0026micro;l iNPH vs 80\u0026thinsp;\u0026plusmn;\u0026thinsp;41 healthy, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but not in net flow (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eCONCLUSIONS\u003c/h2\u003e\u003cp\u003eOur findings indicate no significant predictive value of aqueductal CSF dynamics for shunt efficacy in iNPH patients. The heterogeneity of iNPH and variability in CSF dynamics across its time course, may contribute to these negative results. From a clinical point of view, aqueductal flow measured by 2D PC-MRI appears to have very limited value for selecting patients for shunting.\u003c/p\u003e","manuscriptTitle":"No Predictive Value of Aqueduct CSF Flow Dynamics for Shunt Response in Idiopathic Normal Pressure Hydrocephalus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-02 13:16:38","doi":"10.21203/rs.3.rs-7400171/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"05d5a18d-2228-4f1a-890f-759287d0c213","owner":[],"postedDate":"October 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55600971,"name":"Health sciences/Diseases"},{"id":55600972,"name":"Health sciences/Medical research"},{"id":55600973,"name":"Health sciences/Neurology"},{"id":55600974,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2025-10-02T13:16:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-02 13:16:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7400171","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7400171","identity":"rs-7400171","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}