TMS-evoked potentials: neurophysiological biomarkers for diagnosis and response to ventriculoperitoneal shunt in normal pressure hydrocephalus

TMS-evoked potentials: neurophysiological biomarkers for diagnosis and response to ventriculoperitoneal shunt in 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 TMS-evoked potentials: neurophysiological biomarkers for diagnosis and response to ventriculoperitoneal shunt in normal pressure hydrocephalus Tal Davidy, Saar Anis, Alexandra Suminski, Yakov Zauberman, Tsvia Fay-Karmon, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4167675/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 Current practice for normal pressure hydrocephalus (NPH) relies upon clinical presentation, imaging and invasive clinical procedures for indication of treatment with ventriculoperitoneal shunt (VPS). Here we assessed the utility of a TMS-evoked potentials (TEPs)-based evaluation, for prediction of response to VPS in NPH, as an alternative for the cerebrospinal fluid tap test (CTT). 37 "possible iNPH" patients and 16 age-matched healthy controls (HC) were included. All subjects performed Delphi (TMS-EEG and automated analysis of TEP), in response to primary motor cortex (M1) and dorsolateral prefrontal (DLPFC) stimulations. Sixteen patients underwent VPS and response was evaluated with change in modified Rankin Scale (MRS), clinical global impression of change (CGIC) regarding gait and the change on a repeated 3-meter timed up and Go (TUG) after 3 months. TEP Delphi-NPH index was most successful in discrimination of iNPH responders to VPS (ROC-AUC of 0.91, p = 0.006) compared to CSF Tap-Test (CTT) (AUC CTT =0.65, p = 0.35) and other imaging measures. The TEP M1 P60 and P180 latencies were earlier in responders compared to controls (p M1 P60 =0.016, p M1 P180 =0.009, respectively). TEPs, may be an alternative for CTT, in prediction of response to VPS in patients suspected as iNPH, exhibiting higher efficacy with reduced patient discomfort and risks. Biological sciences/Neuroscience/Cognitive ageing Biological sciences/Neuroscience/Diseases of the nervous system Biological sciences/Neuroscience/Neural ageing Biological sciences/Neuroscience/Neural circuit Biological sciences/Neuroscience/Neuronal physiology Biological sciences/Neuroscience/Sensorimotor processing Normal Pressure Hydrocephalus TMS Evoked Potentials Ventriculoperitoneal Shunt Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Primary or idiopathic NPH (iNPH) is defined as a disturbance of cerebrospinal fluid (CSF) dynamics without identifiable cause, usually in individuals at the ages of 60–70 1,2 . The exact mechanism remains unknown. Unlike most other age-related degenerative movement and cognitive disorders, NPH is a treatable disorder. The treatment for NPH is implantation of a ventriculoperitoneal shunt (VPS) to drain the CSF from the cerebral ventricles to the peritoneal cavity with good treatment response 3,4 . Current practice includes an established probable NPH diagnosis 4 based on a positive change in gait following large volume CSF drainage. Various methods exist, from CSF Tap-Test (CTT) to extended lumbar drainage (ELD) and infusion tests (measuring resistance of flow-Rout) 5,6 but require an invasive lumbar puncture (LP), which is painful and has possible complications 7 . Furthermore, the sensitivity and specificity of these clinical tests has been questioned. To that end it is crucial to have effective non-invasive diagnostic and predictive methods to use in the clinic. A cornerstone in research of neurological disorders manifesting as movement disorders, is the investigation of neurophysiological changes as potential biomarkers that could help in diagnosis, monitoring disease progression and response to interventions. The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) has been suggested as a promising tool to identify and quantify neurophysiological mechanisms 8 and could serve as a tool for identification of such biomarkers. TMS is a non-invasive brain stimulation method that allows to study human cortical function in vivo 9,10 . Using TMS with simultaneous EEG enables the measurement of TMS-evoked potential (TEP), which produces waves of activity that reverberate throughout the cortex and that are reproducible and reliable 11,12 thus providing direct information about cortical excitability and connectivity with excellent time resolution 13 . By evaluating the propagation of evoked activity in different behavioral states and in different tasks, TMS-EEG has been used to causally probe the dynamic effective connectivity of human brain networks 14 . This multimodal approach allows for the evaluation of several neurophysiological mechanisms such as cortical reactivity, excitation and inhibition in local and distal regions, effective connectivity, and neural plasticity 15 . TMS was applied in NPH in several works 16–18 , demonstrating the involvement of the motor network, but to best of our knowledge this is the first TMS-EEG study in NPH. TEPs demonstrated potential clinical utility as a diagnostic and treatment monitoring tool, in many other neurological conditions 19 including movement disorders such as Parkinson’s Disease (PD) 20,21 . Delphi is an automated analysis of TEP measures that has previously been shown to differentiate healthy age groups, mild dementia 22 and PD from age-matched healthy controls (HC) 23 . In addition, Delphi measures have proved to be correlated to white matter microstructural differences in post stroke and TBI patients and in cognitively impaired with different severities 24,25 . We predict that TEP measures (acquired and analyzed by Delphi) have the potential to identify a typical pattern or electrophysiological fingerprint for the specific brain dysfunction that is associated with NPH pathology, and might become an ancillary diagnostic tool which can help the clinician achieve a more accurate diagnosis of patients that could benefit from VPS. In this exploratory study, we aimed to elucidate whether there are typical TEP features for definite NPH (responders to VPS) that may help improve the prediction of response to treatment and which clinical features are associated with this change in TEP. Materials and Methods Study participants Male and female patients with possible iNPH 4 at age of 60–85 presenting with the triad of symptoms and meeting the NPH diagnostic criteria were recruited to this exploratory, non-interventional longitudinal study. Patients came in for diagnosis and treatment at the Movement Disorders Institute of the Department of Neurology at Sheba Medical Center (SMDI), Israel, presenting with gait impairment, preferably associated with urinary problems, cognitive impairment or both. All had a CT or MRI scan demonstrating ventriculomegaly. Patients with additional cerebral structural anomalies per imaging or central nervous system disease, and those with other major medical problems affecting gait were excluded. Age-matched HC with normal cognitive status, were also included, from a previous multisite study, with an identical TMS-EEG procedure; in addition, these subjects had normal neurological exams, cognitive tests and brain MRI scans and their medical history and medications were reviewed to rule out neurological disorders. The studies were approved by local IRBs and all subjects signed an informed consent in accordance with GCP guidelines. Study Procedures Baseline symptom evaluation: Candidate NPH patients with hydrocephalus per imaging and typical clinical symptoms, that were undergoing work-up for suspected NPH, were included on the day of appointment for assessment for response to CTT at the SMDI. Demographic and clinical information were obtained from medical records. The patients underwent a full neurological examination as well as evaluation of gait and balance, cognitive state and continence. Gait speed, pattern and quality were assessed while the patients performed the "timed up and go" test (TUG) 26 at their most comfortable speed, and the walking time recorded for a 3-meter TUG. Patients also underwent Montreal cognitive assessment (MoCA) and MMSE 27,28 , and general functionality was assessed by modified Rankin Scale (MRS) 29 Urinary assessment was rated using a five rank scale from the iNPH grading scale 30 . Next, a single baseline TMS-EEG (Delphi) evaluation was performed, specified below. Soon after, a CTT consisting of a lumbar puncture (LP) with drainage of 30–50 ml of CSF, was performed by a neurologist or neurosurgeon under local anesthesia; during the procedure, the initial opening pressure is determined, and CSF specimens are collected for analysis. Another TUG was performed 3–4 hours following the LP. The change in TUG times following LP in comparison to baseline constitutes the CTT result herein. In the SMDI, the final decision on VPS eligibility is made by a multidisciplinary team, comprising movement disorder neurologists and a neurosurgeon. Factors considered include clinical characteristics, imaging, gait improvement in CTT, comorbidities, frailty, age, cognitive function, and overall functionality. Throughout the study, the Delphi technician and automated analysis algorithm were unaware of the clinical scores of the participants and moreover the clinical team, responsible for referral to surgery and assessment of outcomes after VPS, was blinded to the Delphi results. VPS implantation and peri and post-operative shunt setting was performed according to clinical practice 6 , for details please refer to supplementary materials. MRI/CT brain scans Each patient had either a brain CT or MR imaging scan. A neuroradiologist evaluated and ranked the scans for Evans index (the ratio of the maximum width of the frontal horns of the lateral ventricles and the maximal internal diameter of the skull), Sylvian fissure dilation, focally enlarged fissures, crowding sulci at the vertex, temporal horns width, callosal angle, peri-ventricular hypodensities and Fazekas scale score (quantifies the amount of deep white matter lesions), and disproportionately enlarged subarachnoid-space (DESH) 31 , which was positive when all 3 components of enlarged ventricles, tight high convexity, and dilated Sylvian fissure, were present. Brain MRI scans of HC were previously evaluated and ruled out for significantly pathological or remarkable findings per age. TMS-EEG procedure: TMS-EEG acquisition was performed with the Delphi system version 1.0 including Delphi acquisition and analysis software (QuantalX Neuroscience), EEG compatible TMS stimulator and 65 mm Fig. 8 coil (MagPro R30 stimulator (MagVenture, Denmark) and an MCF-B65-HO figure-8 Coil (MagVenture, Denmark), TMS compatible DC coupled amplifier with sampling rate of 5Hz (Delphi Amplifier, QuantalX Neuroscience Ltd.) and 34 electrode cap with Ag\AgCl sintered electrodes (MCS Delphi cap, QuantalX Neuroscience Ltd.). The reference and ground electrodes were affixed to the ear lobes. All the subjects included in the study performed the same TMS-EEG (Delphi) evaluation prior to CTT. The left and right resting motor threshold (RMT) were obtained, according to guidelines 10 , Single-pulse (< 0.3Hz), with 85% of RMT intensity was applied to four stimulation sites: left and right primary motor cortex (M1), left and right dorsolateral prefrontal cortex) DLPFC. Each stimulation site was averaged with its contralateral counterpart, resulting in two output stimulation sites. Data acquisition, pre-processing, and cleaning of the transcranial evoked response was performed with Delphi software 1.0 22–25 , see supplementary material for detailed procedure. TEP analysis: The Delphi algorithm automatically analyses the regional and network TEP recorded in response to each stimulation site and extracts numeric output of TEP features 22–25 . These features include specific peak latencies, amplitudes and slopes of typical negative and positive peaks 11,32,332 detected automatically by the Delphi algorithm (Fig. 1 A, B). P60 latency (msec)- measures latency of local maxima between 25 msec post stimulus to 10 msec before the detected N100 peak. N100 latency (msec)- measures latency of the local minima of TEP in the time frame of 60–190 msec. P180 Latency (msec)- measures latency of the positive maxima of TEP in the time frame of 100-220msec. The Slopes are calculated (∆uV/∆msec) in between two detected peaks. Either P60_N100, typically negative slope or N100_P180 which is typically a positive slope. The difference in latencies between the P180 to P60 peaks (P180-P60 (msec)) is also calculated. Determination of clinical benefit During follow-up visits, evaluation of symptoms and adverse events is performed by a multidisciplinary team of neurologists and neurosurgeons; for this study the 3-months follow-up outcomes are reported. The clinical team was blinded to the TMS-EEG automatic analysis until the end of the study. Evaluations of symptoms were gathered through patient and caregiver interviews, in addition to three scales at the 3-months follow-up visit compared to baseline: the Modified Rankin scale (MRS) 29 , the clinical global impression of change scale (CGI-C) 34 regarding gait (1 = marked improvement; 2 = moderate improvement; 3 = minimal improvement; 4 = no change; 5 = minimal worsening; 6 = moderate worsening; 7 = marked worsening) and 3-meter TUG test. Adverse effects were collected through subjective, and caregivers reports and a neurological exam. See supplementary materials for detailed peri and post-surgical protocol. Statistical Analysis Independent two tailed t-tests or Fisher's exact tests were used to examine differences in characteristic measures between the groups. Spearman’s rank correlation and Pearson correlation were used to test associations of disease characteristics and Delphi output measures. The VPS treatment effect size calculating the Cohen’s d with Hadges correction following a paired sample t-test 35 . Multiple logistic regression and receiver operating curve (ROC) analysis were used to examine Delphi output measures in discrimination of NPH responders from non-responders. Statistical analysis was performed with GraphPad Prism version 10.1.1 Results Subjects’ characteristics Table 1 Baseline characteristics of possible iNPH patients and healthy controls All possible iNPH patients Possible iNPH that had VPS Healthy controls p-value All NPH-NPH With VPS ALL NPH-HC NPH WITH VPS-HC Number 37 17 16 Age (years), mean (± SD) 74.6 ± 4.1 74.8 ± 4.4 74 ± 0.6 p = 0.87 p = 0.56 p = 0.50 Females, number (%) 7/37 (19%) 2/17 (11.7%) 7/16 (43%) p = 0.71 p = 0.09 p = 0.14 Symptoms duration (years), mean (± SD) 3.1 ± 2.4 3.3 ± 2.5 - p = 0.68 - - MoCA score, mean (± SD) 19.7 ± 4.1 19.9 ± 4.2 26.6 ± 2.6 p = 0.87 p < 0.0001 p < 0.0001 MMSE score, mean (± SD) 25.1 ± 3.9 24.3 ± 5.05 29.4 ± 0.8 p = 0.51 p < 0.0001 p 0.99 CSF opening Pressure (mmH 2 O) 145 ± 45 153.2 ± 47 - p = 0.57 Imaging* Evan’s Index 0.37 ± 0.05 0.37 ± 0.04 p = 0.91 Temporal Horns width (mm) 8.5 ± 3.1 8.6 ± 3.04 p = 0.91 Callosal angle (degrees) 88.6 ± 22.4 89.8 ± 19.9 p = 0.85 DESH (% positive) 26/37 (70.2%) 14/17 (82.3%) P = 0.82 Fazekas % (0/1/2/3) 22/19.4/16.6/40.5% 11.7/29.4/11.7/47% p = 0.75 CTT TUG Measures Baseline TUG (sec) 24 ± 9.8 21.1 ± 6.8 - p = 0.28 CTT ∆TUG (sec) 5.2 ± 9.1 5.5 ± 3.6 - p = 0.87 Table 1 -MoCA-Montreal Cognitive Assessment; MMSE- Mini Mental State Examination; MRS- Modified Rankin Scale; CTT- Cerebrospinal fluid Tap-Test; DESH- disproportionately enlarged subarachnoid-space; TUG-Timed Up and Go *Imaging: MRI (n = 21) and CT (n = 13). CTT ∆TUG – the difference in 3-meter TUG duration post LP compared to baseline. A total of 37 possible iNPH patients (mean age = 75 ± 4.1, 7 females) and n = 16 HC (mean age = 74 ± 1.9 years, 7 females) were included. Nineteen of the 37 patients were indicated for VPS, based on the improvement in CTT and additional data, discussed by the multidisciplinary clinical team. Seventeen patients underwent a VPS operation. One patient died due to pneumonia 2 months after the operation and was therefore not included in the 3-months post-operative evaluation. The whole possible iNPH group had a 3 ± 2.4 years symptom duration, since onset of first symptom, which was a gait disturbance for 78%. Most patients presented with the full triad (64%) while 5% had gait disturbance alone, 16.2% had gait problems with urinary incontinence, 13.5% had gait problems and cognitive impairment. Most patients were cognitively impaired, displaying cognitive scores below normal for MoCA and MMSE 27,28 and majority of the patients had a slight to moderate disability (MRS level 2–3) but were able to walk without assistance (Table 1 ). The iNPH patients that underwent VPS were not different in their baseline characteristics compared to the total iNPH group. See Table 1 for complete subjects' characteristics. TEP Delphi measures: difference between NPH responders and HC A significant difference in the TEP Delphi measures between NPH responders and HC was demonstrated in response to motor network (M1) stimulation. The M1 P60 and P180 latencies were earlier, (mean 60.3 ± 9.6 msec for responders and 77.5 ± 15.4 for HC, p = 0.016) and (mean 175.5 ± 15.7 msec for responders and 194.8 ± 20.6 for HC, p = 0.009), respectively (Fig. 2 a-c). No difference in latency was observed in response to DLPFC stimulation (Fig. 2 d-f). Outcome of VPS and correlation to TEP Delphi measures Response to VPS assessed by the multidisciplinary team at 3 months, was evaluated using three different measures: I) CGIC regarding gait II) Change in TUG from baseline (3 mo ΔTUG). III) Change in MRS. According to the CGIC, 14 of the 17 patients (82%) that underwent VPS implantation, had an improvement in gait. Larger M1 P180-P60 and delayed DLPFC P180 latency correlated to better treatment outcome (according to CGIC), with (r M1 P180−P60 =-0.55, p = 0.0289, Fig. 3 a) and (r DLPFC P180 =-0.66, p = 0.0074, Fig. 3 b) respectively. CTT ΔTUG was not significantly correlated to improvement in CGI-C (r = 0.35, p = 0.19, Fig. 3 c). Eleven patients were also evaluated for their change in walking speed, measured by 3-meter TUG 3 months from VPS compared to baseline TUG (3 mo ΔTUG). M1 late slope (M1 N100-P180) was highly correlated (r = 0.798, p = 0.003) to change in TUG time following VPS. In addition, DLPFC early slope (DLPFC P60-N100) demonstrated a high negative correlation to 3 mo ΔTUG (r=-0.88. p = 0.0017) while DLPFC late slope was positively correlated (r = 0.69, p-value = 0.038) (Fig. 4 a,b). Again, we also tested CTT ΔTUG, as it is one of the main clinical measures used to evaluated indication for VPS. CTT ΔTUG was significantly correlated to the 3 mo ΔTUG (r = 0.76, p = 0.002) (Fig. 4 c). When defining the response to VPS as change in MRS 3 months after VPS compared to baseline, 10 (62%) patients had a positive change (decrease in MRS scale) and 6 had no change or worsening. The VPS had an effect size of 0.792, indicating a near-large effect size (Cohen’s d > 0.8). The average M1 and DLPFC TEP response of NPH responders and non-responders is illustrated in Fig. 5 . It is evident that the TEP peak latencies (marked in little red and blue circles and rectangles) specifically M1 P60 and M1 N100 are earlier in NPH responders and the DLPFC P180 is delayed compared to the non-responders, however this did not reach significance (p > 0.05). Delphi TEP measures in prediction of response to VPS Based on these results, we hypothesized that the baseline TEP of NPH responders is different, in that the early peak latencies move further away from late latencies; so, we employed a multiple logistic regression combining the M1 P60 and DLPFC P180 with both M1 and DLPFC P180-P60, composing the Delphi-NPH index. We also tested the ability of quantitative imaging measures and the CTT ∆TUG in discrimination of NPH responders from non-responders. The Delphi-NPH index predicted responders (patients with improvement in MRS) to VPS with ROC-AUC of 0.91, p = 0.006 (Fig. 6 f) with a negative predictive power of 83.3% and positive predictive power of 88.8%. Among typical NPH imaging measures, only the callosal angle successfully discriminated NPH responders (AUC CA =0.83, p = 0.03) while Evan’s index, temporal horns (AUC EI =0.58, p = 0.58; AUC TH =0.65, p = 0.32) and baseline TUG and CTT ∆TUG, did not (AUC TUG =0.63, p = 0.42; AUC CSF−TT =0.65, p = 0.35) (Fig. 6 a-e). TEP Delphi measures compared to baseline clinical and imaging characteristics The link between TEP Delphi measures and baseline disease characteristics was tested to elucidate the association of neurophysiological TEP changes and NPH related structural changes and disease duration. Earlier M1 P60 latency, larger M1 P180-P60 and a delayed DLPFC P180 latency were associated with longer disease duration (r P60 =-0.59, p = 0.033; r M1 P180−P60 =0.6, p = 0.036; r P180 =0.64, p = 0.018). Additionally, earlier M1 P60 and N100 latencies were associated with larger widening of temporal horns (r=-0.67, p = 0.016, r=-0.63, p = 0.014, respectively). A later DLPFC P180 latency was associated with an enlarged Sylvian fissure (r = 0.42, p = 0.0141) Discussion In this exploratory study we tested the potential usefulness of TMS-EEG, reflected as TEP measures, as a neurophysiological assessment, for indication of VPS in iNPH patients. We found that TEP M1 P60 and P180 latencies were earlier in responders to VPS compared to controls and presented significant correlations of TEP Delphi measures in comparison to the rank CGIC and magnitude of change in TUG times following VPS. We introduce the TEP Delphi-NPH index that was successful in discriminating iNPH responders to VPS from non-responders (ROC-AUC of 0.91, p = 0.006), beyond imaging parameters and TUG tests. It is often difficult to differentiate iNPH from other neurodegenerative or secondary disorders, such as degenerative parkinsonian disorders or small vessel cerebrovascular disease 36 as there is great variability with the presentation and progression of the syndrome. The clinician faces a great diagnostic challenge, trying to avoid unwarranted surgery, with its associated complications. Although NPH prevalence is reported to be 0.2–2.9% for persons 65 years and older 37 , it is reasonable to believe it is actually much higher, with estimated 80% of NPH patients remain unrecognized 38 and are therefore not treated appropriately, when it is still possible to reverse the condition. Unfortunately, current standard practice relies on imaging and CSF drainage tests 4 , which are based on an invasive and painful procedure, with possible complications and depends on a specialized team and facilities in addition to hospitalization. Moreover, according to recent studies, while the positive predictive value (PPV) of a CSF-TT is quite high (92% (range from 73–100%), its negative predictive value (NPV) is very low (37% (18–50%)) 37 . Imaging scales likewise not effective in discriminating responders to VPS 40 . DESH for example had a 77% PPV and 25% NPV 41 . These reports imply that the reliance on imaging and CTT alone in detection of the full range of patients who would benefit from VPS, is suboptimal, as it suffers from low sensitivity 4,39 . The use of TEP is thought to reflect the activation of cortical excitatory (glutamatergic) and inhibitory (GABAergic) neurotransmitter systems differently and at different time scales. These excitatory/inhibitory activations create separate components or peaks that construct TEP 15, 42,43 . TEP clinical utility was also demonstrated across different neurological conditions for the past three decades 19 , among it, M1 P60 amplitude was causally related to tremors in PD patients 44 . Also, TEPs were shown to correlate to MRI white matter integrity and connectivity measures 24, 25, 46 . In our study, a composite of four M1 and DLPFC TEP parameters was very successful in prediction of a meaningful clinical improvement of symptoms (an improvement of at least 1 level in MRS). The motor network (M1) TEP showed shifts of TEP components to earlier latencies. Opposite to that, in response to frontal network (DLPFC) stimulation, the P180 peak latency seems to be delayed. TMS-EEG literature established that TEP peak latencies are generally delayed with old age 47–50 . In our study NPH responders demonstrated an earlier M1 P60 and P180 latencies in comparison to age matched controls, showing a different effect than typical healthy ageing. The TEP components, measured at different electrodes are also an indication of the propagation of the signal following the magnetic stimulation 51 . Thus, these changes to peak latencies reflect changes in the spread of the signal. It was previously suggested that early TEP peaks are related to localized network responses while the later peaks might reflect distant networks in relation to stimulation site 52 . While early latencies of TEP reflect direct connections within functional networks, later components of TEPs reveal more distant nodes and more complex interactions, suggesting bottom-up signal propagation from lower-degree nodes to brain hubs 52 . It is interesting to consider, when looking at these results, that these opposite shifts in latency, in M1 compared to DLPFC, may indicate a possible common source of deficiency. Further to that, the manifestation of changes in TEP peak latencies illustrated here might be related to the changes in the corticospinal tract, as previously demonstrated to have increased fractional anisotropy values in diffusion MRI 53,54 , which was also shown to be associated to the degree of improvement following VPS 55 . Additionally, we should consider the associations found, between the M1 earlier peak latencies (M1 P60, N100) and delayed DLPFC P180 to a longer disease duration, the associations of the earlier M1 peak latencies (M1 P60, N100) to wider temporal horns, and the delayed DLPFC P180 to wider Sylvian fissure. An imaging study on a large sample (n = 168) of iNPH patients, revealed that widening of temporal horns was independently associated with all examined iNPH symptoms (impaired gait, impaired cognition, and incontinence), while a narrow callosal angle (CA) was associate to specific motor and cognitive functions 56 . However, the CA was the only imaging measure effectively discriminating shunt responders from non-responders in another study with 109 iNPH patients that underwent shunt installation 57 . In our study this was further established, where the only baseline measure other than Delphi-NPH index, that significantly discriminated NPH responders from non-responders, was the CA. Other imaging measures such as the temporal horns and Evan’s index were not successful in discriminating NPH patients who benefited from VPS. The CTT was also unsuccessful in discrimination of responders and was very much alike the performance of baseline TUG. This poses a question on the role of CTT to predict shunt response and the obligation to perform it in all cases. Although, it was associated with improvement in walking speed measured with TUG 3 months from VPS implant. This is reasonable given that the CTT calculation itself includes subtraction of the baseline TUG time, which is also included in the calculation of the ∆ 3 m TUG test (taken 3 months following VPS). Our study has limitations, including the small iNPH sample, the fact that patients had a CTT and not extended lumbar drainage or infusion tests to compare to the TEP. Additionally, the follow up time was short (3 months). Furthermore, the outcome measures were only those relating to gait, and we did not assess outcomes regarding urinary or cognitive problems. In our study 3 clinical outcome measures were used, none of which can fully reflect the impact of the NPH gait disorder. Preferably sensor-based home monitoring of gait, balance and activity should replace the subjective measures that we used, the semi- quantitative CGIC and MRS as well as a single clinic-based TUG test. The result of this study summons well-designed prospective studies in larger samples of candidate iNPH patients using well-established and objective tools for the multidimensional symptoms of NPH, that cover longer follow-up periods, to establish the predictive value for long-term improvement with VPS. Moreover, it might be worth examining the change of TEP following VPS, to establish a more concrete relationship of the clinical improvement to the differences in TEP features and suggest a causal relationship. Further down the line iNPH patients demonstrating these typical TEP changes, that are not indicated for VPS with current tools, could be possibly referred to VPS, if they failed antiparkinsonian therapy and rehabilitative interventions, to challenge current practice guidelines. The TEP acquisition, using TMS-EEG, is a short procedure, which is non-invasive, with well-established safety that is likewise not associated with significant discomfort, and it can be repeated during follow up. The TMS-EEG can easily be placed and practiced in many clinical settings; the Delphi analysis algorithm of the TEP such as the one utilized in this study, is produced automatically within reasonable time from the test. Altogether these results present Delphi as an intriguing new modality that may aid in the management of NPH patients and improvement of screening for VPS implantation. Declarations Competing Interests QuantalX Neuroscience is the company developing Delphi software used for acquisition and analysis of the TMS-EEG data. The company loaned to the medical institute the hardware and software and supported the performance of the Delphi evaluations. NZ, HF and EA are employees of QuantalX Neuroscience.TD was reimbursed by QuantalX for her travel and registration expenses to the American Academy of Neurology meeting in 2023. Remaining authors have no disclosures or potential competing interests regarding this paper. No other funding was provided by the company for the conduct of the study. There are no other competing interest to report. Author Contribution T.D. contributed to conceptualization, methodology, writing of original draft, investigation, data curation, writing – review & editing. S.A. contributed to conceptualization, writing – review & editing. AS: contributed to the investigation. Y.Z. contributed to the investigation, writing - original draft. T.F.K. contributed to writing – review & editing. O.L.S. contributed to data curation, investigation, writing – review & editing, A.S. contributed to project administration. N.Z. contributed to writing-original draft, formal analysis. H.F. contributed to software, writing – review & editing, E.A. contributed to investigation, writing – review & editing, S.H.B. contributed to conceptualization, methodology, project administration, data Curation, supervision, writing – original draft, visualization, and writing – review & editing. Data availability The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Code availability The underlying code for this study is not publicly available for proprietary reasons. References Vanneste JAL, Vanneste JAL. Diagnosis and management of normal-pressure hydrocephalus. 2000. Skalický, Petr, et al. 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"Characterization of glutamatergic and GABAA-mediated neurotransmission in motor and dorsolateral prefrontal cortex using paired-pulse TMS–EEG." Neuropsychopharmacology 42.2 (2017): 502-511. ‏ Ni, Zhen, et al. "Measuring latency distribution of transcallosal fibers using transcranial magnetic stimulation." Brain stimulation 13.5 (2020): 1453-1460. ‏ Leodori, Giorgio, et al. "Re‐emergent tremor in Parkinson's disease: the role of the motor cortex." Movement Disorders 35.6 (2020): 1002-1011. ‏ Voineskos, Aristotle N., et al. "The role of the corpus callosum in transcranial magnetic stimulation induced interhemispheric signal propagation." Biological psychiatry 68.9 (2010): 825-831. ‏ Määttä, Sara, et al. "Development of cortical motor circuits between childhood and adulthood: A navigated TMS‐HdEEG study." Human Brain Mapping 38.5 (2017): 2599-2615. ‏ Määttä, Sara, et al. "Maturation changes the excitability and effective connectivity of the frontal lobe: a developmental TMS–EEG study." Human brain mapping 40.8 (2019): 2320-2335. ‏ Noda, Yoshihiro, et al. "Single-pulse transcranial magnetic stimulation-evoked potential amplitudes and latencies in the motor and dorsolateral prefrontal cortex among young, older healthy participants, and schizophrenia patients." Journal of Personalized Medicine 11.1 (2021): 54. ‏ Kallioniemi, Elisa, et al. "TMS-EEG responses across the lifespan: measurement, methods for characterisation and identified responses." Journal of Neuroscience Methods 366 (2022): 109430. ‏ Hui, Jeanette, et al. "Pharmacological mechanisms of interhemispheric signal propagation: a TMS-EEG study." Neuropsychopharmacology 45.6 (2020): 932-939. ‏ Bortoletto M, Veniero D, Thut G, Miniussi C. The contribution of TMS-EEG coregistration in the exploration of the human cortical connectome. Neurosci Biobehav Rev. Feb 2015;49:114-24. doi:10.1016/j.neubiorev.2014.12.014 Siasios I, Kapsalaki EZ, Fountas KN, et al.: The role of diffusion tensor imaging and fractional anisotropy in the evaluation of patients with idiopathic normal pressure hydrocephalus: a literature review. Neurosurg Focus 41: E12, 2016 Grazzini, Irene, et al. "Diffusion tensor imaging in idiopathic normal pressure hydrocephalus: clinical and CSF flowmetry correlations." The Neuroradiology Journal 33.1 (2020): 66-74. ‏ Scheel M, Diekhoff T, Sprung C, Hoffmann KT: Diffusion tensor imaging in hydrocephalus–findings before and after shunt surgery. Acta Neurochir (Wien) 154: 1699–1706, 2012 Lilja-Lund, Otto, et al. "Wide temporal horns are associated with cognitive dysfunction, as well as impaired gait and incontinence." Scientific Reports 10.1 (2020): 18203. ‏ Virhammar J, Laurell K, Cesarini KG, Larsson EM. The callosal angle measured on MRI as a predictor of outcome in idiopathic normal-pressure hydrocephalus. J Neurosurg. 2014 Jan;120(1):178-84. doi: 10.3171/2013.8.JNS13575. Epub 2013 Sep 27. PMID: 24074491. Additional Declarations Competing interest reported. QuantalX Neuroscience is the company developing Delphi software used for acquisition and analysis of the TMS-EEG data. The company loaned to the medical institute the hardware and software and supported the performance of the Delphi evaluations. NZ, HF and EA are employees of QuantalX Neuroscience.TD was reimbursed by QuantalX for her travel and registration expenses to the American Academy of Neurology meeting in 2023. Remaining authors have no disclosures or potential competing interests regarding this paper. No other funding was provided by the company for the conduct of the study. There are no other competing interest to report. 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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-4167675","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":286950323,"identity":"e25956d9-4424-4b4e-a732-81581a328aa8","order_by":0,"name":"Tal Davidy","email":"","orcid":"","institution":"Sheba Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Tal","middleName":"","lastName":"Davidy","suffix":""},{"id":286950324,"identity":"b74a9df2-b6e7-4ebc-898e-24a77e287dd4","order_by":1,"name":"Saar Anis","email":"","orcid":"","institution":"Sheba Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Saar","middleName":"","lastName":"Anis","suffix":""},{"id":286950326,"identity":"71220a11-a1c8-4055-a9c1-1af9661cd3e9","order_by":2,"name":"Alexandra Suminski","email":"","orcid":"","institution":"Sheba Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Alexandra","middleName":"","lastName":"Suminski","suffix":""},{"id":286950327,"identity":"6162daab-2a1f-41ad-9939-955cd70462d6","order_by":3,"name":"Yakov Zauberman","email":"","orcid":"","institution":"Sheba Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Yakov","middleName":"","lastName":"Zauberman","suffix":""},{"id":286950328,"identity":"78e03494-1275-4bf4-bce8-02c6aa493190","order_by":4,"name":"Tsvia Fay-Karmon","email":"","orcid":"","institution":"Sheba Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Tsvia","middleName":"","lastName":"Fay-Karmon","suffix":""},{"id":286950329,"identity":"ba5ccfc4-8747-4bd0-9a2f-32e11b3f8c8c","order_by":5,"name":"Adi Saar","email":"","orcid":"","institution":"Sheba Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Adi","middleName":"","lastName":"Saar","suffix":""},{"id":286950330,"identity":"284bb932-27e4-4620-b40a-8d767d3db193","order_by":6,"name":"Noa Zifman","email":"","orcid":"","institution":"QuantalX Neuroscience","correspondingAuthor":false,"prefix":"","firstName":"Noa","middleName":"","lastName":"Zifman","suffix":""},{"id":286950331,"identity":"6c1979cb-1e83-46f0-bd6d-c5df71152492","order_by":7,"name":"Hilla Fogel","email":"","orcid":"","institution":"QuantalX Neuroscience","correspondingAuthor":false,"prefix":"","firstName":"Hilla","middleName":"","lastName":"Fogel","suffix":""},{"id":286950332,"identity":"8f79d68d-bf8a-4c74-b418-f26bcd140f22","order_by":8,"name":"Eden Abulher","email":"","orcid":"","institution":"QuantalX Neuroscience","correspondingAuthor":false,"prefix":"","firstName":"Eden","middleName":"","lastName":"Abulher","suffix":""},{"id":286950333,"identity":"40631350-9d3e-4ec1-ba72-df6dbac6ad9b","order_by":9,"name":"Orit Lesman-Segev","email":"","orcid":"","institution":"Sheba Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Orit","middleName":"","lastName":"Lesman-Segev","suffix":""},{"id":286950334,"identity":"71653968-d7c8-45bc-8a50-756700a15f72","order_by":10,"name":"Sharon Hassin-Baer","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYDACCRBhwCCHJmxgQVCLMQ9UKUyLBAEtDAyJPahaGHBrkZ/d/Pjjj4I76fvZzxg+5mH4I29wgPnhB4YC3FoM7hwzk+YxeJbbw5NjbDiDwcBwwwE2Ywl8DjOQSDBjZjA4nNvDkGMm8YHBgHHDAQYzvH6Rn5H++eMPg8PpPPxvzH8kMBjYbzjA/g2/92/kGEjwGBxO4JHIMWMA2pK44QAPflsMbuSUAf1y2LDnxrNiyRkGxskzD/MUSyTgd9jmjz/+HJZn70/e+JmnQs6273j7xg8f/tjgdhiapUDMDMQJxGoYBaNgFIyCUYAVAADHWEosXmZXzwAAAABJRU5ErkJggg==","orcid":"","institution":"Sheba Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Sharon","middleName":"","lastName":"Hassin-Baer","suffix":""}],"badges":[],"createdAt":"2024-03-26 07:09:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4167675/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4167675/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54179315,"identity":"b629efa6-426a-4db8-9517-cc8302ed8b9b","added_by":"auto","created_at":"2024-04-05 16:17:20","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":240975,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTEP Waveform\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ea) Illustrative TEP waveform with typical peak latencies, specifically, positive peak (maxima) at around 60 msec, negative peak (minima) at around 100 msec and positive peak (maxima) at around 180 msec. Delphi algorithm automatically detects these peaks (red asterisks) and outputs their latencies. b) Illustration of two TEPs with detection of differences in P180 peak latency (shift demonstrated by the red arrow on the x-axis).\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4167675/v1/ff58f7443159c8fe43ce4d5e.jpg"},{"id":54179314,"identity":"cddb7e1c-7a65-4c5e-8fa3-f18726caa49e","added_by":"auto","created_at":"2024-04-05 16:17:20","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":386383,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDifferences between NPH responders (positive change in MRS) to HC: M1 and DLPFC TEP Delphi measures\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBox and whiskers (mean ±5-95 percentiles) of M1 peak latencies (upper) and DLPFC peak latencies (lower) in NPH responders (green) versus HC (grey). A) M1 P60 latency of NPH responders and HC. B) M1 N100 latency of NPH responders and HC. C) M1 P180 latency of NPH responders and HC. D) DLPFC P60 latency of NPH responders and HC. E) DLPFC N100 latency of NPH responders and HC. F) DLPFC P180 latency of NPH responders and HC. Normal pressure Hydrocephalus (NPH); Healthy Controls (HC); Primary motor cortex (M1); Dorsolateral prefrontal cortex (DLPFC); milliseconds (msec); p\u0026gt;0.05\u003csup\u003en.s\u003c/sup\u003e, p\u0026lt;0.05*, p\u0026lt;0.01**,p\u0026lt;0.001***,p\u0026lt;0.0001****\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4167675/v1/ba29bd8fa1d23d5859dc14e6.jpg"},{"id":54179318,"identity":"1ffdd1dd-fcef-4314-a3a3-0a29dcb17c14","added_by":"auto","created_at":"2024-04-05 16:17:21","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":279073,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eResponders and non-responders according to CGIC of gait: TEP Delphi measures\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eScatter plots and means (bars) presenting the correlation of either Delphi measures or CTT ΔTUG (difference in TUG times at baseline compared to post LP) to CGIC. In the right upper corner of each graph, are the spearman correlation coefficients (r) and p-values. X-axes are the CGIC scores: 0 = Not assessed; 1 = Very much improved; 2 = Much improved; 3 = Minimally improved; 4 = No change; 5 = Minimally worse; 6 = Much worse; 7 = Very much worse. The CGI-C≥4 constitutes the non-responders and the CGIC-C scores of 1 to 3 constitute the responders. A) Correlation of differences in M1 P180-P60 to CGIC. B) Correlation of DLPFC P180 to CGIC. C) Correlation of CTT ΔTUG to CGIC. Cerebrospinal Fluid Tap-Test (CTT); Timed Up and Go (TUG); Clinical Global Improvement of Change (CGI-C); Ventriculoperitoneal Shunt (VPS); Primary motor cortex (M1); Dorsolateral prefrontal cortex (DLPFC); milliseconds (msec).\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4167675/v1/876f9ced04653a0f38741fb8.jpg"},{"id":54179317,"identity":"07ce07bb-74f9-4b9b-9af4-627f06ae37e2","added_by":"auto","created_at":"2024-04-05 16:17:21","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":283365,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eChange TUG time three months following VPS compared to baseline: Correlations to TEP Delphi measures or CTT ΔTUG\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eScatter plots presenting the correlation of TEP Delphi measures or CTT ΔTUG (difference in TUG times post LP compared to baseline) to the 3 mo ΔTUG. Pearson correlation coefficient (r) and p-value are reported at the upper right corner of each graph. A) Correlation of slope between the peaks at M1 N100_P180 msec (late slope) to 3 mo ∆TUG. B) Correlation of DLPFC slope between the peaks at P60_N100 msec (early slope) to 3 mo ∆TUG. C) Correlation of CTT ∆TUG to 3 mo ∆TUG. Cerebrospinal Fluid Tap-Test (CTT); Timed Up and Go (TUG); ∆TUG – Delta Timed Up and Go (TUG) Ventriculoperitoneal shunt (VPS); Primary motor cortex (M1); Dorsolateral prefrontal cortex (DLPFC); milliseconds (msec).\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4167675/v1/caa714bcb968ee8d1afe91e8.jpg"},{"id":54179319,"identity":"021762fa-efd5-44a0-9de4-a9982687b9e7","added_by":"auto","created_at":"2024-04-05 16:17:21","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":289552,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eNPH responders and non-responders (positive change in MRS): M1 and DLPFC TEP waveforms\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTEP averaged waveform of the NPH groups. Peak detected by the Delphi algorithm marked in red and blue asterisks. A) Mean ±SEM M1 TEP in responders (positive change in MRS) (red) and in non-responders (negative or no change in MRS) (blue). B) Mean ±SEM DLPFC TEP in responders (positive change in MRS) (red) and in non-responders (negative or no change in MRS) (blue).\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4167675/v1/bca63df1a7b3142f72faae0a.jpg"},{"id":54179320,"identity":"a491914d-cbb9-4b59-8b2b-e2b51c8a23c9","added_by":"auto","created_at":"2024-04-05 16:17:21","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":403012,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eReceiver Operating Curve (ROC) describing the discrimination of NPH responders (positive change in MRS): Imaging Measures, Baseline TUG, CTT and Delphi-NPH index\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eReceiver Operating Curve (ROC) of imaging measures (CT/MRI), baseline TUG, CTT ∆TUG and Delphi-NPH index in discrimination of NPH responders (based on change in MRS). At the bottom right corner of each graph are the AUC of the ROC curve ±SD and p-value. A) ROC curve of the Evan’s Index value in discrimination of responders from non-responders. B) ROC curve of the temporal horns in discrimination of responders from non-responders. C) ROC curve of callosal angle in discrimination of responders from non-responders D) ROC curve of the Baseline TUG in discrimination of responders from non-responders E) ROC curve of the CTT ∆TUG in discrimination of responders from non-responders F) ROC curve of the Delph-NPH index, which is a result of multiple logistic regression model, combining M1 P60, DLPFC P180 and the M1 and DLPFC differences of P180 and P60 latencies, in discrimination of responders from non-responders.\u003c/p\u003e\n\u003cp\u003eArea under the curve (AUC); Cerebrospinal Fluid Tap-Test (CTT); ∆TUG – Delta Timed Up and Go (TUG); Ventriculoperitoneal Shunt (VPS).\u003c/p\u003e","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4167675/v1/49c66387e659e3e62c3f45d3.jpg"},{"id":58474244,"identity":"c225bdd3-4105-4a5a-9628-3c0543a3875e","added_by":"auto","created_at":"2024-06-17 06:33:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2535226,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4167675/v1/a67d26c9-9820-48f5-a581-db1d0a910492.pdf"},{"id":54179316,"identity":"951be61c-50b7-4a1c-b7ed-19be8d25478b","added_by":"auto","created_at":"2024-04-05 16:17:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16345,"visible":true,"origin":"","legend":"","description":"","filename":"Suppelmentarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4167675/v1/d8fe668d0abd1cf814b97469.docx"}],"financialInterests":"Competing interest reported. QuantalX Neuroscience is the company developing Delphi software used for acquisition and analysis of the TMS-EEG data. The company loaned to the medical institute the hardware and software and supported the performance of the Delphi evaluations. NZ, HF and EA are employees of QuantalX Neuroscience.TD was reimbursed by QuantalX for her travel and registration expenses to the American Academy of Neurology meeting in 2023. Remaining authors have no disclosures or potential competing interests regarding this paper. No other funding was provided by the company for the conduct of the study. There are no other competing interest to report.","formattedTitle":"TMS-evoked potentials: neurophysiological biomarkers for diagnosis and response to ventriculoperitoneal shunt in normal pressure hydrocephalus","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePrimary or idiopathic NPH (iNPH) is defined as a disturbance of cerebrospinal fluid (CSF) dynamics without identifiable cause, usually in individuals at the ages of 60\u0026ndash;70\u003csup\u003e1,2\u003c/sup\u003e. The exact mechanism remains unknown.\u003c/p\u003e \u003cp\u003eUnlike most other age-related degenerative movement and cognitive disorders, NPH is a treatable disorder. The treatment for NPH is implantation of a ventriculoperitoneal shunt (VPS) to drain the CSF from the cerebral ventricles to the peritoneal cavity with good treatment response\u003csup\u003e3,4\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCurrent practice includes an established probable NPH diagnosis\u003csup\u003e4\u003c/sup\u003e based on a positive change in gait following large volume CSF drainage. Various methods exist, from CSF Tap-Test (CTT) to extended lumbar drainage (ELD) and infusion tests (measuring resistance of flow-Rout)\u003csup\u003e5,6\u003c/sup\u003e but require an invasive lumbar puncture (LP), which is painful and has possible complications\u003csup\u003e7\u003c/sup\u003e. Furthermore, the sensitivity and specificity of these clinical tests has been questioned. To that end it is crucial to have effective non-invasive diagnostic and predictive methods to use in the clinic.\u003c/p\u003e \u003cp\u003eA cornerstone in research of neurological disorders manifesting as movement disorders, is the investigation of neurophysiological changes as potential biomarkers that could help in diagnosis, monitoring disease progression and response to interventions. The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) has been suggested as a promising tool to identify and quantify neurophysiological mechanisms\u003csup\u003e8\u003c/sup\u003e and could serve as a tool for identification of such biomarkers. TMS is a non-invasive brain stimulation method that allows to study human cortical function in vivo\u003csup\u003e9,10\u003c/sup\u003e. Using TMS with simultaneous EEG enables the measurement of TMS-evoked potential (TEP), which produces waves of activity that reverberate throughout the cortex and that are reproducible and reliable\u003csup\u003e11,12\u003c/sup\u003e thus providing direct information about cortical excitability and connectivity with excellent time resolution\u003csup\u003e13\u003c/sup\u003e. By evaluating the propagation of evoked activity in different behavioral states and in different tasks, TMS-EEG has been used to causally probe the dynamic effective connectivity of human brain networks\u003csup\u003e14\u003c/sup\u003e. This multimodal approach allows for the evaluation of several neurophysiological mechanisms such as cortical reactivity, excitation and inhibition in local and distal regions, effective connectivity, and neural plasticity\u003csup\u003e15\u003c/sup\u003e. TMS was applied in NPH in several works\u003csup\u003e16\u0026ndash;18\u003c/sup\u003e, demonstrating the involvement of the motor network, but to best of our knowledge this is the first TMS-EEG study in NPH.\u003c/p\u003e \u003cp\u003eTEPs demonstrated potential clinical utility as a diagnostic and treatment monitoring tool, in many other neurological conditions\u003csup\u003e19\u003c/sup\u003e including movement disorders such as Parkinson\u0026rsquo;s Disease (PD)\u003csup\u003e20,21\u003c/sup\u003e .\u003c/p\u003e \u003cp\u003eDelphi is an automated analysis of TEP measures that has previously been shown to differentiate healthy age groups, mild dementia\u003csup\u003e22\u003c/sup\u003e and PD from age-matched healthy controls (HC)\u003csup\u003e23\u003c/sup\u003e. In addition, Delphi measures have proved to be correlated to white matter microstructural differences in post stroke and TBI patients and in cognitively impaired with different severities\u003csup\u003e24,25\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe predict that TEP measures (acquired and analyzed by Delphi) have the potential to identify a typical pattern or electrophysiological fingerprint for the specific brain dysfunction that is associated with NPH pathology, and might become an ancillary diagnostic tool which can help the clinician achieve a more accurate diagnosis of patients that could benefit from VPS.\u003c/p\u003e \u003cp\u003eIn this exploratory study, we aimed to elucidate whether there are typical TEP features for definite NPH (responders to VPS) that may help improve the prediction of response to treatment and which clinical features are associated with this change in TEP.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants\u003c/h2\u003e \u003cp\u003eMale and female patients with possible iNPH\u003csup\u003e4\u003c/sup\u003e at age of 60\u0026ndash;85 presenting with the triad of symptoms and meeting the NPH diagnostic criteria were recruited to this exploratory, non-interventional longitudinal study. Patients came in for diagnosis and treatment at the Movement Disorders Institute of the Department of Neurology at Sheba Medical Center (SMDI), Israel, presenting with gait impairment, preferably associated with urinary problems, cognitive impairment or both. All had a CT or MRI scan demonstrating ventriculomegaly.\u003c/p\u003e \u003cp\u003ePatients with additional cerebral structural anomalies per imaging or central nervous system disease, and those with other major medical problems affecting gait were excluded.\u003c/p\u003e \u003cp\u003eAge-matched HC with normal cognitive status, were also included, from a previous multisite study, with an identical TMS-EEG procedure; in addition, these subjects had normal neurological exams, cognitive tests and brain MRI scans and their medical history and medications were reviewed to rule out neurological disorders.\u003c/p\u003e \u003cp\u003e The studies were approved by local IRBs and all subjects signed an informed consent in accordance with GCP guidelines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy Procedures\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eBaseline symptom evaluation:\u003c/h2\u003e \u003cp\u003eCandidate NPH patients with hydrocephalus per imaging and typical clinical symptoms, that were undergoing work-up for suspected NPH, were included on the day of appointment for assessment for response to CTT at the SMDI. Demographic and clinical information were obtained from medical records. The patients underwent a full neurological examination as well as evaluation of gait and balance, cognitive state and continence.\u003c/p\u003e \u003cp\u003eGait speed, pattern and quality were assessed while the patients performed the \"timed up and go\" test (TUG)\u003csup\u003e26\u003c/sup\u003e at their most comfortable speed, and the walking time recorded for a 3-meter TUG. Patients also underwent Montreal cognitive assessment (MoCA) and MMSE\u003csup\u003e27,28\u003c/sup\u003e, and general functionality was assessed by modified Rankin Scale (MRS)\u003csup\u003e29\u003c/sup\u003e Urinary assessment was rated using a five rank scale from the iNPH grading scale\u003csup\u003e30\u003c/sup\u003e. Next, a single baseline TMS-EEG (Delphi) evaluation was performed, specified below. Soon after, a CTT consisting of a lumbar puncture (LP) with drainage of 30\u0026ndash;50 ml of CSF, was performed by a neurologist or neurosurgeon under local anesthesia; during the procedure, the initial opening pressure is determined, and CSF specimens are collected for analysis. Another TUG was performed 3\u0026ndash;4 hours following the LP. The change in TUG times following LP in comparison to baseline constitutes the CTT result herein.\u003c/p\u003e \u003cp\u003eIn the SMDI, the final decision on VPS eligibility is made by a multidisciplinary team, comprising movement disorder neurologists and a neurosurgeon. Factors considered include clinical characteristics, imaging, gait improvement in CTT, comorbidities, frailty, age, cognitive function, and overall functionality. Throughout the study, the Delphi technician and automated analysis algorithm were unaware of the clinical scores of the participants and moreover the clinical team, responsible for referral to surgery and assessment of outcomes after VPS, was blinded to the Delphi results.\u003c/p\u003e \u003cp\u003eVPS implantation and peri and post-operative shunt setting was performed according to clinical practice\u003csup\u003e6\u003c/sup\u003e, for details please refer to supplementary materials.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMRI/CT brain scans\u003c/h2\u003e \u003cp\u003eEach patient had either a brain CT or MR imaging scan. A neuroradiologist evaluated and ranked the scans for Evans index (the ratio of the maximum width of the frontal horns of the lateral ventricles and the maximal internal diameter of the skull), Sylvian fissure dilation, focally enlarged fissures, crowding sulci at the vertex, temporal horns width, callosal angle, peri-ventricular hypodensities and Fazekas scale score (quantifies the amount of deep white matter lesions), and disproportionately enlarged subarachnoid-space (DESH)\u003csup\u003e31\u003c/sup\u003e, which was positive when all 3 components of enlarged ventricles, tight high convexity, and dilated Sylvian fissure, were present. Brain MRI scans of HC were previously evaluated and ruled out for significantly pathological or remarkable findings per age.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eTMS-EEG procedure:\u003c/h2\u003e \u003cp\u003eTMS-EEG acquisition was performed with the Delphi system version 1.0 including Delphi acquisition and analysis software (QuantalX Neuroscience), EEG compatible TMS stimulator and 65 mm Fig.\u0026nbsp;8 coil (MagPro R30 stimulator (MagVenture, Denmark) and an MCF-B65-HO figure-8 Coil (MagVenture, Denmark), TMS compatible DC coupled amplifier with sampling rate of 5Hz (Delphi Amplifier, QuantalX Neuroscience Ltd.) and 34 electrode cap with Ag\\AgCl sintered electrodes (MCS Delphi cap, QuantalX Neuroscience Ltd.). The reference and ground electrodes were affixed to the ear lobes.\u003c/p\u003e \u003cp\u003eAll the subjects included in the study performed the same TMS-EEG (Delphi) evaluation prior to CTT. The left and right resting motor threshold (RMT) were obtained, according to guidelines\u003csup\u003e10\u003c/sup\u003e, Single-pulse (\u0026lt;\u0026thinsp;0.3Hz), with 85% of RMT intensity was applied to four stimulation sites: left and right primary motor cortex (M1), left and right dorsolateral prefrontal cortex) DLPFC. Each stimulation site was averaged with its contralateral counterpart, resulting in two output stimulation sites. Data acquisition, pre-processing, and cleaning of the transcranial evoked response was performed with Delphi software 1.0\u003csup\u003e22\u0026ndash;25\u003c/sup\u003e, see supplementary material for detailed procedure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTEP analysis:\u003c/h2\u003e \u003cp\u003eThe Delphi algorithm automatically analyses the regional and network TEP recorded in response to each stimulation site and extracts numeric output of TEP features\u003csup\u003e22\u0026ndash;25\u003c/sup\u003e. These features include specific peak latencies, amplitudes and slopes of typical negative and positive peaks\u003csup\u003e11,32,332\u003c/sup\u003edetected automatically by the Delphi algorithm (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B).\u003c/p\u003e \u003cp\u003eP60 latency (msec)- measures latency of local maxima between 25 msec post stimulus to 10 msec before the detected N100 peak.\u003c/p\u003e \u003cp\u003eN100 latency (msec)- measures latency of the local minima of TEP in the time frame of 60\u0026ndash;190 msec.\u003c/p\u003e \u003cp\u003eP180 Latency (msec)- measures latency of the positive maxima of TEP in the time frame of 100-220msec.\u003c/p\u003e \u003cp\u003eThe Slopes are calculated (∆uV/∆msec) in between two detected peaks. Either P60_N100, typically negative slope or N100_P180 which is typically a positive slope.\u003c/p\u003e \u003cp\u003eThe difference in latencies between the P180 to P60 peaks (P180-P60 (msec)) is also calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of clinical benefit\u003c/h2\u003e \u003cp\u003eDuring follow-up visits, evaluation of symptoms and adverse events is performed by a multidisciplinary team of neurologists and neurosurgeons; for this study the 3-months follow-up outcomes are reported. The clinical team was blinded to the TMS-EEG automatic analysis until the end of the study.\u003c/p\u003e \u003cp\u003eEvaluations of symptoms were gathered through patient and caregiver interviews, in addition to three scales at the 3-months follow-up visit compared to baseline: the Modified Rankin scale (MRS)\u003csup\u003e29\u003c/sup\u003e, the clinical global impression of change scale (CGI-C)\u003csup\u003e34\u003c/sup\u003e regarding gait (1\u0026thinsp;=\u0026thinsp;marked improvement; 2\u0026thinsp;=\u0026thinsp;moderate improvement; 3\u0026thinsp;=\u0026thinsp;minimal improvement; 4\u0026thinsp;=\u0026thinsp;no change; 5\u0026thinsp;=\u0026thinsp;minimal worsening; 6\u0026thinsp;=\u0026thinsp;moderate worsening; 7\u0026thinsp;=\u0026thinsp;marked worsening) and 3-meter TUG test.\u003c/p\u003e \u003cp\u003eAdverse effects were collected through subjective, and caregivers reports and a neurological exam. See supplementary materials for detailed peri and post-surgical protocol.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eIndependent two tailed t-tests or Fisher's exact tests were used to examine differences in characteristic measures between the groups. Spearman\u0026rsquo;s rank correlation and Pearson correlation were used to test associations of disease characteristics and Delphi output measures. The VPS treatment effect size calculating the Cohen\u0026rsquo;s d with Hadges correction following a paired sample t-test\u003csup\u003e35\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMultiple logistic regression and receiver operating curve (ROC) analysis were used to examine Delphi output measures in discrimination of NPH responders from non-responders. Statistical analysis was performed with GraphPad Prism version 10.1.1\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSubjects\u0026rsquo; characteristics\u003c/h2\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\u003eBaseline characteristics of possible iNPH patients and healthy controls\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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\u003eAll possible iNPH patients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePossible iNPH\u003c/p\u003e \u003cp\u003ethat had VPS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHealthy controls\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAll NPH-NPH With VPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eALL NPH-HC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNPH WITH VPS-HC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years), mean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemales, number (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7/37 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2/17 (11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7/16 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptoms duration (years), mean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoCA score, mean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSE score, mean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\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\u003eBaseline MRS % level (1/2/3/4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.8/48.6/29.7/10.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.2/50/31.2/12.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSF opening Pressure (mmH\u003csub\u003e2\u003c/sub\u003eO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e145\u0026thinsp;\u0026plusmn;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153.2\u0026thinsp;\u0026plusmn;\u0026thinsp;47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eImaging*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvan\u0026rsquo;s Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemporal Horns width (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.6\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCallosal angle (degrees)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.6\u0026thinsp;\u0026plusmn;\u0026thinsp;22.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.8\u0026thinsp;\u0026plusmn;\u0026thinsp;19.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDESH (% positive)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26/37 (70.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14/17 (82.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFazekas % (0/1/2/3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22/19.4/16.6/40.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.7/29.4/11.7/47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eCTT TUG Measures\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline TUG (sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTT ∆TUG (sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-MoCA-Montreal Cognitive Assessment; MMSE- Mini Mental State Examination; MRS- Modified Rankin Scale; CTT- Cerebrospinal fluid Tap-Test; DESH- disproportionately enlarged subarachnoid-space; TUG-Timed Up and Go *Imaging: MRI (n\u0026thinsp;=\u0026thinsp;21) and CT (n\u0026thinsp;=\u0026thinsp;13). CTT ∆TUG \u0026ndash; the difference in 3-meter TUG duration post LP compared to baseline.\u003c/p\u003e \u003cp\u003eA total of 37 possible iNPH patients (mean age\u0026thinsp;=\u0026thinsp;75\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1, 7 females) and n\u0026thinsp;=\u0026thinsp;16 HC (mean age\u0026thinsp;=\u0026thinsp;74\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9 years, 7 females) were included. Nineteen of the 37 patients were indicated for VPS, based on the improvement in CTT and additional data, discussed by the multidisciplinary clinical team. Seventeen patients underwent a VPS operation. One patient died due to pneumonia 2 months after the operation and was therefore not included in the 3-months post-operative evaluation.\u003c/p\u003e \u003cp\u003eThe whole possible iNPH group had a 3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4 years symptom duration, since onset of first symptom, which was a gait disturbance for 78%. Most patients presented with the full triad (64%) while 5% had gait disturbance alone, 16.2% had gait problems with urinary incontinence, 13.5% had gait problems and cognitive impairment.\u003c/p\u003e \u003cp\u003eMost patients were cognitively impaired, displaying cognitive scores below normal for MoCA and MMSE\u003csup\u003e27,28\u003c/sup\u003e and majority of the patients had a slight to moderate disability (MRS level 2\u0026ndash;3) but were able to walk without assistance (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe iNPH patients that underwent VPS were not different in their baseline characteristics compared to the total iNPH group. See Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for complete subjects' characteristics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTEP Delphi measures: difference between NPH responders and HC\u003c/h2\u003e \u003cp\u003eA significant difference in the TEP Delphi measures between NPH responders and HC was demonstrated in response to motor network (M1) stimulation. The M1 P60 and P180 latencies were earlier, (mean 60.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6 msec for responders and 77.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.4 for HC, p\u0026thinsp;=\u0026thinsp;0.016) and (mean 175.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.7 msec for responders and 194.8\u0026thinsp;\u0026plusmn;\u0026thinsp;20.6 for HC, p\u0026thinsp;=\u0026thinsp;0.009), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-c). No difference in latency was observed in response to DLPFC stimulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003ed-f).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eOutcome of VPS and correlation to TEP Delphi measures\u003c/h2\u003e \u003cp\u003eResponse to VPS assessed by the multidisciplinary team at 3 months, was evaluated using three different measures: I) CGIC regarding gait II) Change in TUG from baseline (3 mo ΔTUG). III) Change in MRS.\u003c/p\u003e \u003cp\u003eAccording to the CGIC, 14 of the 17 patients (82%) that underwent VPS implantation, had an improvement in gait. Larger M1 P180-P60 and delayed DLPFC P180 latency correlated to better treatment outcome (according to CGIC), with (r\u003csub\u003eM1 P180\u0026minus;P60\u003c/sub\u003e=-0.55, p\u0026thinsp;=\u0026thinsp;0.0289, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) and (r\u003csub\u003eDLPFC P180\u003c/sub\u003e=-0.66, p\u0026thinsp;=\u0026thinsp;0.0074, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) respectively. CTT ΔTUG was not significantly correlated to improvement in CGI-C (r\u0026thinsp;=\u0026thinsp;0.35, p\u0026thinsp;=\u0026thinsp;0.19, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eEleven patients were also evaluated for their change in walking speed, measured by 3-meter TUG 3 months from VPS compared to baseline TUG (3 mo ΔTUG). M1 late slope (M1 N100-P180) was highly correlated (r\u0026thinsp;=\u0026thinsp;0.798, p\u0026thinsp;=\u0026thinsp;0.003) to change in TUG time following VPS. In addition, DLPFC early slope (DLPFC P60-N100) demonstrated a high negative correlation to 3 mo ΔTUG (r=-0.88. p\u0026thinsp;=\u0026thinsp;0.0017) while DLPFC late slope was positively correlated (r\u0026thinsp;=\u0026thinsp;0.69, p-value\u0026thinsp;=\u0026thinsp;0.038) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003ea,b). Again, we also tested CTT ΔTUG, as it is one of the main clinical measures used to evaluated indication for VPS. CTT ΔTUG was significantly correlated to the 3 mo ΔTUG (r\u0026thinsp;=\u0026thinsp;0.76, p\u0026thinsp;=\u0026thinsp;0.002) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eWhen defining the response to VPS as change in MRS 3 months after VPS compared to baseline, 10 (62%) patients had a positive change (decrease in MRS scale) and 6 had no change or worsening.\u003c/p\u003e \u003cp\u003eThe VPS had an effect size of 0.792, indicating a near-large effect size (Cohen\u0026rsquo;s d\u0026thinsp;\u0026gt;\u0026thinsp;0.8).\u003c/p\u003e \u003cp\u003eThe average M1 and DLPFC TEP response of NPH responders and non-responders is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003e. It is evident that the TEP peak latencies (marked in little red and blue circles and rectangles) specifically M1 P60 and M1 N100 are earlier in NPH responders and the DLPFC P180 is delayed compared to the non-responders, however this did not reach significance (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDelphi TEP measures in prediction of response to VPS\u003c/h2\u003e \u003cp\u003eBased on these results, we hypothesized that the baseline TEP of NPH responders is different, in that the early peak latencies move further away from late latencies; so, we employed a multiple logistic regression combining the M1 P60 and DLPFC P180 with both M1 and DLPFC P180-P60, composing the Delphi-NPH index. We also tested the ability of quantitative imaging measures and the CTT ∆TUG in discrimination of NPH responders from non-responders.\u003c/p\u003e \u003cp\u003eThe Delphi-NPH index predicted responders (patients with improvement in MRS) to VPS with ROC-AUC of 0.91, p\u0026thinsp;=\u0026thinsp;0.006 (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003ef) with a negative predictive power of 83.3% and positive predictive power of 88.8%. Among typical NPH imaging measures, only the callosal angle successfully discriminated NPH responders (AUC\u003csub\u003eCA\u003c/sub\u003e=0.83, p\u0026thinsp;=\u0026thinsp;0.03) while Evan\u0026rsquo;s index, temporal horns (AUC\u003csub\u003eEI\u003c/sub\u003e=0.58, p\u0026thinsp;=\u0026thinsp;0.58; AUC\u003csub\u003eTH\u003c/sub\u003e=0.65, p\u0026thinsp;=\u0026thinsp;0.32) and baseline TUG and CTT ∆TUG, did not (AUC\u003csub\u003eTUG\u003c/sub\u003e=0.63, p\u0026thinsp;=\u0026thinsp;0.42; AUC\u003csub\u003eCSF\u0026minus;TT\u003c/sub\u003e=0.65, p\u0026thinsp;=\u0026thinsp;0.35) (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003ea-e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eTEP Delphi measures compared to baseline clinical and imaging characteristics\u003c/h2\u003e \u003cp\u003eThe link between TEP Delphi measures and baseline disease characteristics was tested to elucidate the association of neurophysiological TEP changes and NPH related structural changes and disease duration. Earlier M1 P60 latency, larger M1 P180-P60 and a delayed DLPFC P180 latency were associated with longer disease duration (r\u003csub\u003eP60\u003c/sub\u003e=-0.59, p\u0026thinsp;=\u0026thinsp;0.033; r\u003csub\u003eM1 P180\u0026minus;P60\u003c/sub\u003e=0.6, p\u0026thinsp;=\u0026thinsp;0.036; r\u003csub\u003eP180\u003c/sub\u003e=0.64, p\u0026thinsp;=\u0026thinsp;0.018).\u003c/p\u003e \u003cp\u003eAdditionally, earlier M1 P60 and N100 latencies were associated with larger widening of temporal horns (r=-0.67, p\u0026thinsp;=\u0026thinsp;0.016, r=-0.63, p\u0026thinsp;=\u0026thinsp;0.014, respectively). A later DLPFC P180 latency was associated with an enlarged Sylvian fissure (r\u0026thinsp;=\u0026thinsp;0.42, p\u0026thinsp;=\u0026thinsp;0.0141)\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this exploratory study we tested the potential usefulness of TMS-EEG, reflected as TEP measures, as a neurophysiological assessment, for indication of VPS in iNPH patients. We found that TEP M1 P60 and P180 latencies were earlier in responders to VPS compared to controls and presented significant correlations of TEP Delphi measures in comparison to the rank CGIC and magnitude of change in TUG times following VPS. We introduce the TEP Delphi-NPH index that was successful in discriminating iNPH responders to VPS from non-responders (ROC-AUC of 0.91, p\u0026thinsp;=\u0026thinsp;0.006), beyond imaging parameters and TUG tests.\u003c/p\u003e \u003cp\u003eIt is often difficult to differentiate iNPH from other neurodegenerative or secondary disorders, such as degenerative parkinsonian disorders or small vessel cerebrovascular disease\u003csup\u003e36\u003c/sup\u003e as there is great variability with the presentation and progression of the syndrome. The clinician faces a great diagnostic challenge, trying to avoid unwarranted surgery, with its associated complications. Although NPH prevalence is reported to be 0.2\u0026ndash;2.9% for persons 65 years and older\u003csup\u003e37\u003c/sup\u003e, it is reasonable to believe it is actually much higher, with estimated 80% of NPH patients remain unrecognized\u003csup\u003e38\u003c/sup\u003e and are therefore not treated appropriately, when it is still possible to reverse the condition.\u003c/p\u003e \u003cp\u003eUnfortunately, current standard practice relies on imaging and CSF drainage tests\u003csup\u003e4\u003c/sup\u003e, which are based on an invasive and painful procedure, with possible complications and depends on a specialized team and facilities in addition to hospitalization. Moreover, according to recent studies, while the positive predictive value (PPV) of a CSF-TT is quite high (92% (range from 73\u0026ndash;100%), its negative predictive value (NPV) is very low (37% (18\u0026ndash;50%))\u003csup\u003e37\u003c/sup\u003e. Imaging scales likewise not effective in discriminating responders to VPS\u003csup\u003e40\u003c/sup\u003e. DESH for example had a 77% PPV and 25% NPV\u003csup\u003e41\u003c/sup\u003e. These reports imply that the reliance on imaging and CTT alone in detection of the full range of patients who would benefit from VPS, is suboptimal, as it suffers from low sensitivity\u003csup\u003e4,39\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe use of TEP is thought to reflect the activation of cortical excitatory (glutamatergic) and inhibitory (GABAergic) neurotransmitter systems differently and at different time scales. These excitatory/inhibitory activations create separate components or peaks that construct TEP\u003csup\u003e15, 42,43\u003c/sup\u003e. TEP clinical utility was also demonstrated across different neurological conditions for the past three decades\u003csup\u003e19\u003c/sup\u003e, among it, M1 P60 amplitude was causally related to tremors in PD patients\u003csup\u003e44\u003c/sup\u003e. Also, TEPs were shown to correlate to MRI white matter integrity and connectivity measures\u003csup\u003e24, 25, 46\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn our study, a composite of four M1 and DLPFC TEP parameters was very successful in prediction of a meaningful clinical improvement of symptoms (an improvement of at least 1 level in MRS). The motor network (M1) TEP showed shifts of TEP components to earlier latencies. Opposite to that, in response to frontal network (DLPFC) stimulation, the P180 peak latency seems to be delayed. TMS-EEG literature established that TEP peak latencies are generally delayed with old age\u003csup\u003e47\u0026ndash;50\u003c/sup\u003e. In our study NPH responders demonstrated an earlier M1 P60 and P180 latencies in comparison to age matched controls, showing a different effect than typical healthy ageing. The TEP components, measured at different electrodes are also an indication of the propagation of the signal following the magnetic stimulation\u003csup\u003e51\u003c/sup\u003e. Thus, these changes to peak latencies reflect changes in the spread of the signal. It was previously suggested that early TEP peaks are related to localized network responses while the later peaks might reflect distant networks in relation to stimulation site\u003csup\u003e52\u003c/sup\u003e. While early latencies of TEP reflect direct connections within functional networks, later components of TEPs reveal more distant nodes and more complex interactions, suggesting bottom-up signal propagation from lower-degree nodes to brain hubs\u003csup\u003e52\u003c/sup\u003e. It is interesting to consider, when looking at these results, that these opposite shifts in latency, in M1 compared to DLPFC, may indicate a possible common source of deficiency. Further to that, the manifestation of changes in TEP peak latencies illustrated here might be related to the changes in the corticospinal tract, as previously demonstrated to have increased fractional anisotropy values in diffusion MRI\u003csup\u003e53,54\u003c/sup\u003e, which was also shown to be associated to the degree of improvement following VPS\u003csup\u003e55\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAdditionally, we should consider the associations found, between the M1 earlier peak latencies (M1 P60, N100) and delayed DLPFC P180 to a longer disease duration, the associations of the earlier M1 peak latencies (M1 P60, N100) to wider temporal horns, and the delayed DLPFC P180 to wider Sylvian fissure. An imaging study on a large sample (n\u0026thinsp;=\u0026thinsp;168) of iNPH patients, revealed that widening of temporal horns was independently associated with all examined iNPH symptoms (impaired gait, impaired cognition, and incontinence), while a narrow callosal angle (CA) was associate to specific motor and cognitive functions\u003csup\u003e56\u003c/sup\u003e. However, the CA was the only imaging measure effectively discriminating shunt responders from non-responders in another study with 109 iNPH patients that underwent shunt installation\u003csup\u003e57\u003c/sup\u003e. In our study this was further established, where the only baseline measure other than Delphi-NPH index, that significantly discriminated NPH responders from non-responders, was the CA. Other imaging measures such as the temporal horns and Evan\u0026rsquo;s index were not successful in discriminating NPH patients who benefited from VPS.\u003c/p\u003e \u003cp\u003eThe CTT was also unsuccessful in discrimination of responders and was very much alike the performance of baseline TUG. This poses a question on the role of CTT to predict shunt response and the obligation to perform it in all cases. Although, it was associated with improvement in walking speed measured with TUG 3 months from VPS implant. This is reasonable given that the CTT calculation itself includes subtraction of the baseline TUG time, which is also included in the calculation of the ∆ 3 m TUG test (taken 3 months following VPS).\u003c/p\u003e \u003cp\u003eOur study has limitations, including the small iNPH sample, the fact that patients had a CTT and not extended lumbar drainage or infusion tests to compare to the TEP. Additionally, the follow up time was short (3 months). Furthermore, the outcome measures were only those relating to gait, and we did not assess outcomes regarding urinary or cognitive problems. In our study 3 clinical outcome measures were used, none of which can fully reflect the impact of the NPH gait disorder. Preferably sensor-based home monitoring of gait, balance and activity should replace the subjective measures that we used, the semi- quantitative CGIC and MRS as well as a single clinic-based TUG test.\u003c/p\u003e \u003cp\u003eThe result of this study summons well-designed prospective studies in larger samples of candidate iNPH patients using well-established and objective tools for the multidimensional symptoms of NPH, that cover longer follow-up periods, to establish the predictive value for long-term improvement with VPS. Moreover, it might be worth examining the change of TEP following VPS, to establish a more concrete relationship of the clinical improvement to the differences in TEP features and suggest a causal relationship. Further down the line iNPH patients demonstrating these typical TEP changes, that are not indicated for VPS with current tools, could be possibly referred to VPS, if they failed antiparkinsonian therapy and rehabilitative interventions, to challenge current practice guidelines.\u003c/p\u003e \u003cp\u003eThe TEP acquisition, using TMS-EEG, is a short procedure, which is non-invasive, with well-established safety that is likewise not associated with significant discomfort, and it can be repeated during follow up. The TMS-EEG can easily be placed and practiced in many clinical settings; the Delphi analysis algorithm of the TEP such as the one utilized in this study, is produced automatically within reasonable time from the test.\u003c/p\u003e \u003cp\u003eAltogether these results present Delphi as an intriguing new modality that may aid in the management of NPH patients and improvement of screening for VPS implantation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eQuantalX Neuroscience is the company developing Delphi software used for acquisition and analysis of the TMS-EEG data. The company loaned to the medical institute the hardware and software and supported the performance of the Delphi evaluations. NZ, HF and EA are employees of QuantalX Neuroscience.TD was reimbursed by QuantalX for her travel and registration expenses to the American Academy of Neurology meeting in 2023. Remaining authors have no disclosures or potential competing interests regarding this paper. No other funding was provided by the company for the conduct of the study. There are no other competing interest to report.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eT.D. contributed to conceptualization, methodology, writing of original draft, investigation, data curation, writing \u0026ndash; review \u0026amp; editing. S.A. contributed to conceptualization, writing \u0026ndash; review \u0026amp; editing. AS: contributed to the investigation. Y.Z. contributed to the investigation, writing - original draft. T.F.K. contributed to writing \u0026ndash; review \u0026amp; editing. O.L.S. contributed to data curation, investigation, writing \u0026ndash; review \u0026amp; editing, A.S. contributed to project administration. N.Z. contributed to writing-original draft, formal analysis. H.F. contributed to software, writing \u0026ndash; review \u0026amp; editing, E.A. contributed to investigation, writing \u0026ndash; review \u0026amp; editing, S.H.B. contributed to conceptualization, methodology, project administration, data Curation, supervision, writing \u0026ndash; original draft, visualization, and writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\u003ch2\u003eCode availability\u003c/h2\u003e \u003cp\u003eThe underlying code for this study is not publicly available for proprietary reasons.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVanneste JAL, Vanneste JAL. 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PMID: 24074491.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Normal Pressure Hydrocephalus, TMS Evoked Potentials, Ventriculoperitoneal Shunt","lastPublishedDoi":"10.21203/rs.3.rs-4167675/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4167675/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCurrent practice for normal pressure hydrocephalus (NPH) relies upon clinical presentation, imaging and invasive clinical procedures for indication of treatment with ventriculoperitoneal shunt (VPS).\u003c/p\u003e \u003cp\u003eHere we assessed the utility of a TMS-evoked potentials (TEPs)-based evaluation, for prediction of response to VPS in NPH, as an alternative for the cerebrospinal fluid tap test (CTT).\u003c/p\u003e \u003cp\u003e37 \"possible iNPH\" patients and 16 age-matched healthy controls (HC) were included. All subjects performed Delphi (TMS-EEG and automated analysis of TEP), in response to primary motor cortex (M1) and dorsolateral prefrontal (DLPFC) stimulations. Sixteen patients underwent VPS and response was evaluated with change in modified Rankin Scale (MRS), clinical global impression of change (CGIC) regarding gait and the change on a repeated 3-meter timed up and Go (TUG) after 3 months.\u003c/p\u003e \u003cp\u003eTEP Delphi-NPH index was most successful in discrimination of iNPH responders to VPS (ROC-AUC of 0.91, p\u0026thinsp;=\u0026thinsp;0.006) compared to CSF Tap-Test (CTT) (AUC\u003csub\u003eCTT\u003c/sub\u003e=0.65, p\u0026thinsp;=\u0026thinsp;0.35) and other imaging measures. The TEP M1 P60 and P180 latencies were earlier in responders compared to controls (p\u003csub\u003eM1 P60\u003c/sub\u003e=0.016, p\u003csub\u003eM1 P180\u003c/sub\u003e=0.009, respectively).\u003c/p\u003e \u003cp\u003eTEPs, may be an alternative for CTT, in prediction of response to VPS in patients suspected as iNPH, exhibiting higher efficacy with reduced patient discomfort and risks.\u003c/p\u003e","manuscriptTitle":"TMS-evoked potentials: neurophysiological biomarkers for diagnosis and response to ventriculoperitoneal shunt in normal pressure hydrocephalus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-05 16:17:16","doi":"10.21203/rs.3.rs-4167675/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":"d26353fb-1eb3-43ad-a429-22b5db1274f2","owner":[],"postedDate":"April 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30203865,"name":"Biological sciences/Neuroscience/Cognitive ageing"},{"id":30203866,"name":"Biological sciences/Neuroscience/Diseases of the nervous system"},{"id":30203867,"name":"Biological sciences/Neuroscience/Neural ageing"},{"id":30203868,"name":"Biological sciences/Neuroscience/Neural circuit"},{"id":30203869,"name":"Biological sciences/Neuroscience/Neuronal physiology"},{"id":30203870,"name":"Biological sciences/Neuroscience/Sensorimotor processing"}],"tags":[],"updatedAt":"2024-06-17T06:25:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-05 16:17:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4167675","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4167675","identity":"rs-4167675","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}