Dear Editor,
The original article by Jeppsson et al. provides substantial perspectives regarding the diagnostic
significance of cerebrospinal fluid (CSF) biomarkers in discriminating patients with idiopathic normal pressure hydrocephalus (iNPH) from patients with other neurodegenerative disorders. 1 They have found that patients with iNPH had, compared with healthy individuals, lower concentrations of P-tau and APP-derived proteins in combination with elevated MCP-1 1. Moreover, compared with the non-iNPH disorders group, iNPH was characterized by the same significant change; low concentration of tau proteins and APP-derived proteins, and elevated MCP-1. I sincerely appreciate the authors for conducting such a large-scale study of a strictly interesting topic. However, I would like to make some comments hoping to provide a better understanding of some points and some perspectives to be kept in mind while planning future related studies
In my opinion, the investigation of CSF biomarkers in patients with iNPH may provide several insights in addition to discriminating the iNPH patients from other neurodegenerative diseases. Certainly, these study results may give the opportunity to understand the unknown pathophysiological aspects of iNPH, thereby, even leading to new classifications of the disease. Actually, there may be many questions to be clarified regarding diagnostic approach, evaluation of the iNPH patients and even identification of the disease. 2,3 For instance, the diagnosis of iNPH may be a considerably challenging issue knowing that the full triad of NPH is present in under 60% of patients and the individual components of this triad are nonspecific as they may be encountered in many other neurodegenerative disorders. 2 Nevertheless, the gold standard of the diagnosis has been indicated as the short-term response to CSF drainage. 3,4 On the other hand, although a substantial rate of patients with iNPH improve by shunt surgery (80%), a crucial topic of discussion may be that why some subgroup of patients do not benefit from shunt surgery? Some authors have suggested that the presence of a possible underdiagnosed neurodegenerative disease might the main cause of shunt unresponsiveness in this patient subgroup. Arrestingly, in patients with iNPH, the high rates of the neurodegenerative comorbidities have been reported, several times. 3,5 Besides, it is acknowledged that although the presence of comorbidities does not exclude the possibility of iNPH; comorbidities do influence the prognosis after shunt surgery. 5 More interestingly, Espay et al. preferred to identify some subgroup of patients as the hydrocephalic presentations of neurodegenerative disorders (rather than NPH). 3 However, I believe that many of these discussions may be elucidated in the era of CSF biochemical investigations. Considering the various unknown aspects about the pathophysiology and pathogenesis of iNPH, the sufficiency of the current diagnostic criteria basing on the clinical triad, neuroimaging and short-term response to CSF diversion may also be interrogated. Based on the rationale of that treatment response is a critical clue providing insights about the underlying pathophysiology, I think that the response to shunt surgery may potentially be a crucial criteria to be kept in mind while diagnosing and classifying the patients with the current diagnosis of iNPH. 6 Hence, the use of CSF biomarkers may give promising conclusions for a better understanding of the iNPH pathophysiology and provide possible new classification criteria of the disease. Besides, it is remarkable to state that iNPH has been basically explained via mechanisms of CSF dynamic disturbance, mechanical stretching of periventricular tissue by the enlarging ventricles, impairment of blood-brain barrier, impaired periventricular blood flow associated to interstitial edema, ependyma disruption, microvascular infarctions, gliosis. However, there is no notable clue to classify iNPH as a primary neurodegenerative disease. On the other hand, secondary mechanisms including disturbed elimination of neurotoxic substances such as β-amyloid, tau-protein, and pro-inflammatory cytokines (basically associated with disturbed CSF turnover) have been hypothesized to be involved in the deterioration of iNPH clinic. 7,8 Ergo, investigations of CFS biomarkers in patients with iNPH may also give substantial perspectives regarding the pathophysiological features of iNPH which might be significantly varying among iNPH patients according to the current diagnostic criteria. 6 Furthermore, although it was not investigated in this study, 1 I think that analyzing the differentiating features of CSF biomarkers in iNPH patients benefiting and non-benefiting by shunt surgery would give substantial perspectives about the discussions mentioned above. The authors refer to the previous two reports 9,10 and indicate that there have not been any promising investigations on the use of CSF biomarkers to separate responders from non-responders. Nevertheless, I would like to remind that the mentioned reports included a limited number of patients and, actually, in the report by Miyajima et al., sAPPa was found as a suitable biomarker for also the prognosis of iNPH. 9 Therefore, in my opinion, the investigation of the significance of CSF biochemical pattern in distinguishing the NPH patients of shunt responders and non-responders, certainly constitutes a crucial topic of interest. The CSF biochemical patterns may also be investigated in patients with the current diagnosis of secondary NPH which have been acknowledged to have higher treatment (shunt) response rates in comparison to patients with iNPH. 11,12 In addition, the results of these mentioned studies may provide clues about the critical question of that what extent of the reversibility of the disease is due to the mechanical factors or a possible recovery of an underlying neurodegenerative process, thereby providing crucial insights about the primary underlying pathophysiology.
Abbreviations: CSF; Cerebrospinal fluid, APP; amyloid precursor protein, MCP-1; monocyte chemoattractant protein 1
Acknowledgments: None.
Funding: None.
Conflict of interests: None.

References
1. Jeppsson A, Wikkelso C, Blennow K, et al. CSF biomarkers distinguish idiopathic normal pressure hydrocephalus from its mimics. J Neurol Neurosurg Psychiatry. Jun 5 2019.
2. Marmarou A, Young HF, Aygok GA, et al. Diagnosis and management of idiopathic normal-pressure hydrocephalus: a prospective study in 151 patients. J Neurosurg. Jun 2005;102(6):987-997.
3. Espay AJ, Da Prat GA, Dwivedi AK, et al. Deconstructing normal pressure hydrocephalus: Ventriculomegaly as early sign of neurodegeneration. Ann Neurol. Oct 2017;82(4):503-513.
4. Ishikawa M, Guideline Committe for Idiopathic Normal Pressure Hydrocephalus JSoNPH. Clinical guidelines for idiopathic normal pressure hydrocephalus. Neurol Med Chir (Tokyo). Apr 2004;44(4):222-223.
5. Williams MA, Malm J. Diagnosis and Treatment of Idiopathic Normal Pressure Hydrocephalus. Continuum (Minneap Minn). Apr 2016;22(2 Dementia):579-599.
6. Relkin N, Marmarou A, Klinge P, Bergsneider M, Black PM. Diagnosing idiopathic normal-pressure hydrocephalus. Neurosurgery. Sep 2005;57(3 Suppl):S4-16; discussion ii-v.
7. Kudo T, Mima T, Hashimoto R, et al. Tau protein is a potential biological marker for normal pressure hydrocephalus. Psychiatry Clin Neurosci. Apr 2000;54(2):199-202.
8. Kondziella D, Sonnewald U, Tullberg M, Wikkelso C. Brain metabolism in adult chronic hydrocephalus. J Neurochem. Aug 2008;106(4):1515-1524.
9. Miyajima M, Nakajima M, Ogino I, Miyata H, Motoi Y, Arai H. Soluble amyloid precursor protein alpha in the cerebrospinal fluid as a diagnostic and prognostic biomarker for idiopathic normal pressure hydrocephalus. Eur J Neurol. Feb 2013;20(2):236-242.
10. Jeppsson A, Holtta M, Zetterberg H, Blennow K, Wikkelso C, Tullberg M. Amyloid mis-metabolism in idiopathic normal pressure hydrocephalus. Fluids Barriers CNS. Jul 29 2016;13(1):13.
11. Borgesen SE. Conductance to outflow of CSF in normal pressure hydrocephalus. Acta Neurochir (Wien). 1984;71(1-2):1-45.
12. Daou B, Klinge P, Tjoumakaris S, Rosenwasser RH, Jabbour P. Revisiting secondary normal pressure hydrocephalus: does it exist? A review. Neurosurg Focus. Sep 2016;41(3):E6.

We, the authors, thank Berthier for his comments on our study of 49 individuals with self-reported Foreign Accent Syndrome.

In response, we would first like to clarify that we do not use Berthier’s term ‘psychogenic’, but ‘functional’ in our paper, referring to foreign accent symptoms due to changes in neural function rather than (or in addition to) the direct effects of a structural lesion. The body-mind dualism implied by the terms ‘psychological/psychogenic’ vs ‘neurogenic’ no longer holds water. Berthier himself notes that the differentiation between “functional” and “structural” may be artificial and that there has been great progress in “unveiling of the neural basis” of functional disorders. As we frequently emphasise in explaining the diagnosis to individuals with functional neurological disorders, their symptoms are definitely ‘real’; not ‘imagined’; and have a basis in changes in neural function which we are beginning to understand more clearly [1,2].

We accept the limitations provided by our method of data collection, including limited data about investigations and a likelihood of selection bias where those with predominantly functional FAS may be somewhat over-represented in our sample. We wish to clarify, however, that cases were classified as ‘probably functional’ on the basis of reported positive clinical features of a functional disorder (e.g. periods of return to normal accent, adoption of stereotypical behaviours) and not by the presence of psychiatric comorbidity [3].

We agree with Berthier that positive features do not exclude the presence of any structural lesion; but we reach a very different conclusion. So, where Berthier indicates that this discounts the discriminative utility of these features, we conclude that the presence of these features suggests that FAS may have a partially or entirely functional basis even in those with a structural lesion. We hope that future prospective research will test this hypothesis.

In our collective clinical experience, positive identification of functional neurological symptoms is universally helpful, including in those with comorbid structural lesions. Functional symptoms are potentially reversible, and respond to different therapeutic methods: for example, physiotherapy for functional movement disorders concentrates on distracting somatosensory attention away from the affected limb and encouraging natural ‘automatic’ movements rather than concentrated repeated strength exercises using the affected limb [4]. It seems likely that similar treatment approaches might be helpful in those with partially or predominantly functional FAS.

Berthier questions how we can “dissect the psychogenic from the neurogenic to provide an integrated explanation to the affected person?” In our experience, individuals who have both ‘structural’ and ‘functional’ symptoms (such as those with epilepsy and dissociative seizures) are generally both receptive to and interested in an integrated explanation of the ways in which some of their symptoms may have a functional basis and therefore potential for improvement.

The ‘social stigma’ which Berthier notes may be associated with a functional diagnosis, and which unfortunately can also come from health professionals, is perhaps the most important problem which Berthier identifies in his letter. We agree that multimodal neuroimaging may continue to better our understanding of the mechanisms of various FAS subtypes, and hope that this work will move forward collaboratively, embracing the possibility that a new foreign accent may sometimes arise as a result of disruptions not directly related to a structural lesion.

1 Hallett M, Stone J, Carson A. Functional Neurologic Disorders, Volume 139 of the Handbook of Clinical Neurology series. Amsterdam: Elsevier 2016.

2 Espay AJ, Aybek S, Carson A, et al. Current Concepts in Diagnosis and Treatment of Functional Neurological Disorders. JAMA Neurol

3 Lee O, Ludwig L, Davenport R, et al. Functional foreign accent syndrome. Pract Neurol 2016.

4 Nielsen G, Stone J, Edwards MJ. Physiotherapy for functional (psychogenic) motor symptoms: A systematic review. J Psychosom Res 2013

We thank Dr Platt and colleagues for their critical review of our work, especially of the methodology that we have used in this study. It is understandable that comparative studies of treatment effectiveness trigger constructive discussions among industry and academics. We also vehemently agree that rigorous methodology and cautious interpretation of results is mandatory, especially for analyses of observational data.1 2 Therefore, in this letter, we will provide additional clarifications in response to the concerns raised.

We appreciate that the categories that are underrepresented in multivariable logistic regression models may lead to inflation of estimates of the corresponding coefficients and their variance. Such inflation would, however, result in an overly conservative matching rather than the opposite. Due to the use of a caliper, patients with an extreme propensity score can not be matched to patients within the bulk of the distribution of the propensity scores. Such patients were excluded from the matched cohorts.

The issue of residual imbalance is important in any non-randomised comparative study. We acknowledge that the standardised mean difference in annualised relapse rates (ARR) between teriflunomide and fingolimod exceeded the nominal threshold of 20%. It is therefore reassuring that the sensitivity analyses, in which the residual imbalance fell below the accepted threshold of 20% (patients with prior on-treatment relapses, Cohen’s d 14%, and analysis with no MRI data included, Cohen’s d 16%) confirmed the results of the primary analysis. We have chosen not to explicitly report absolute differences in proportions reported in Table 1; this information is redundant as the absolute differences can easily be calculated from the proportions shown in the table.

Dr Platt and colleagues make an important point that the comparison of baseline patient characteristics should account for the weights used due to matching in a one-to-multiple variable ratio. We have recalculated the differences for the primary analysis and have observed that our original standardised mean differences overestimated the true weighted standardised mean differences present (see the Table below). Reassuringly, the compared groups are in reality more closely aligned than what the Table 1 in our article would suggest.

TABLE: Recalculated weighted standardised mean differences (Cohen’s d) for continuous baseline characteristics. (DMF, dimethyl fumarate)

DMF vs. teriflunomide fingolimod vs. DMF fingolimod vs. teriflunomide
age 0.006 0.01 0.02
disease duration 0.006 0.003 0.01
disability (EDSS) 0.04 0.002 0.026
relapses 12 months pre-baseline 0.05 0.015 0.03

Combining the results of multiple imputation is a standard procedure, inherent in multiple imputation methodology.3 We have calculated the mode in order to combine the 17 datasets into one. The resulting variable represents the combined result of the 17 imputed data sets and therefore reflects the values with the greatest support within the imputed data space.

Similar to our previous studies, we have chosen to match the studied patients on country.4 This is a conservative decision that aims to mitigate systematic differences in patient follow-up (modelled as the surrogate ‘country’ variable). However, there are methods that are considerably more effective in accounting for inter-centre heterogeneity. We have taken precautions to minimise the heterogeneity in the studied cohort directly – through adjusting for the length and the frequency of recorded follow-up as well as its consistency across the participating centres (the requirement of Neurostatus certification at each centre, adjustment for visit frequency, pairwise censoring, and the quality control process). In fact, differences in treating conventions access to therapies among centres and regions increase the chance that patients will be matched with comparable counterparts who were offered a different therapy for reasons unrelated to their disease severity.

We have calculated ARR in individual patients in order to derive point and interval estimates of the distributions of ARR. The estimates (mean and variance) were weighted for variable one-to-multiple matching and duration of pairwise-censored follow-up. The presented mean ARRs and their 95% confidence intervals are based on this method.

Individual estimation of ARR in a cohort where 75% of patients have a recorded, pairwise-censored follow up greater than 1 year (and the minimum required follow-up of 6 months) is subject to only a negligible risk of inflation due to short follow-up. Notionally, estimation of ARR of a population based on individual observed ARRs from a sample follows standard inferential reasoning within frequentist framework. It enables direct estimation of ARR in studied sample. As in our previous studies, we have used a negative binomial model to compare the incidence of relapse events throughout the pairwise-censored follow-up between the matched patients.4 5 Here, we have used the overall number of relapses from either treatment group and cumulative follow-up, as appropriate. As stated in the Methods, all analyses used weights to account for variable matching ratio and either ‘cluster’ or ‘frailty’ terms to account for the paired data structure. We agree that it would have been more accurate to use the term ‘incidence of relapses’ rather than ‘ARR’ in the description of the negative binomial model in the Methods section. Most importantly, all three methods that we have employed to evaluate relapse outcomes (the two methods described above and the survival model) showed consistent results in the primary analysis.

Appropriately, the authors have closely examined our estimates of variance for ARRs. Unfortunately, the table presented in their communication is difficult to understand. It is unclear what method for the back-calculation of standard deviations from the reported 95% confidence intervals was applied, given that it resulted in two divergent values for both our study and the given examples of randomised clinical trials – reported as ‘SD left’ and ‘SD right’. As described above, all of our analyses used weights to adjust for variable matching ratio, and these were also used to calculate interval estimates for the weighted mean ARRs. It is reasonable to observe variability in the error recorded in each sample, especially in observational studies, which naturally encapsulate more broadly defined cohorts in exchange for greater ecological validity. Furthermore, one would expect the variability in the error to be contingent on the specific selection of study samples, as a result of matching to other treatment groups. We agree that the implications of variable standard deviations across studies are of research interest, but it is not clear that their bench marking to a particular randomised control trial is justified.

We thank the authors for pointing out the inconsistency in the reported exposure to therapies between the groups before and after matching. Sixteen patients treated with dimethyl fumarate were previously exposed to teriflunomide as their highest-efficacy treatment, and the corresponding entry in Supplementary Table 6 should be 16 (2%). We apologise for the typographical error.

A well-powered analysis is not a weakness of a study. We are aware that a statistically significant difference and clinically meaningful difference are complementary but different concepts. Studies carried out within the frequentist framework are reliant on testing of significance of null hypotheses and are destined to provide binary answers – an arbitrary outcome that reflects its origin in randomised controlled trials. Therefore, a result that rejects a null hypothesis provides its reader with more certainty than a result that fails to do so, even when the difference may be of minimal clinical significance. The role of the clinical readership is to interpret clinical meaningfulness of the results. We refrained from attempting to develop cut-offs for what represents clinically meaningful difference and chose to leave this decision to the readers. More robust and intuitive answers to this problem lie in Bayesian methods. We are strong proponents of such methods, as the interpretation of their results is more intuitive for a clinical reader.

The title of their discussion point suggests that Dr Platt and colleagues consider reports that require further clarification of some of its concepts to be methodologically flawed. In the study under discussion, we have utilised the methodology previously used in several studies of comparative effectiveness in the MSBase data set, in particular the comparison of treatment escalation to fingolimod or natalizumab.4 We have systematically addressed the relevant sources of bias, in particular indication bias.1 We take great comfort in the fact that our previous comparative analyses have been highly convergent with the results of pivotal randomised controlled trials.5 6 We have now provided clarification of additional points in response to our colleagues’ review of our article. We value their constructive criticism and believe that our thorough response further strengthens the credibility of the reported results.

References
1. Kalincik T, Butzkueven H. Observational data: Understanding the real MS world. Mult Scler 2016;22(13):1642-48. doi: 10.1177/1352458516653667 [published Online First: 2016/06/09]
2. Trojano M, Tintore M, Montalban X, et al. Treatment decisions in multiple sclerosis - insights from real-world observational studies. Nat Rev Neurol 2017;13(2):105-18. doi: 10.1038/nrneurol.2016.188 [published Online First: 2017/01/14]
3. Heraud-Bousquet V, Larsen C, Carpenter J, et al. Practical considerations for sensitivity analysis after multiple imputation applied to epidemiological studies with incomplete data. BMC medical research methodology 2012;12:73. doi: 10.1186/1471-2288-12-73 [published Online First: 2012/06/12]
4. Kalincik T, Horakova D, Spelman T, et al. Switch to natalizumab vs fingolimod in active relapsing-remitting multiple sclerosis. Ann Neurol 2015;77:425-35. [published Online First: 2014/12/27]
5. Kalincik T, Brown JWL, Robertson N, et al. Comparison of alemtuzumab with natalizumab, fingolimod, and interferon beta for multiple sclerosis: a longitudinal study. Lancet Neurol 2017;16(4):271-81.
6. He A, Spelman T, Jokubaitis V, et al. Comparison of switch to fingolimod or interferon beta/glatiramer acetate in active multiple sclerosis. JAMA Neurol 2015;72(4):405-13. doi: 10.1001/jamaneurol.2014.4147 [published Online First: 2015/02/11]

Seizures and movement disorders : cortico-subcortical networks

Dr Aileen McGonigal

Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
APHM, Timone Hospital, Clinical Neurophysiology, Marseille, France

Corresponding author: Dr Aileen McGonigal, Service de Neurophysiologie Clinique, CHU Timone, AP-HM, Marseille, France
Email : aileen.mcgonigal@univ-amu.fr
Tel: 00 33 491384995
Fax:00 33 491385826

To the Editors

I was interested to read the recent review by Dr Freitas and colleagues1. This interesting article highlights diagnostic challenges, clinical overlap and possible shared pathophysiological processes in epileptic seizures and movement disorders. I would like to add a couple of points that seem important to acknowledge.
Firstly, in terms of clinical expression, the authors rightly mention that automatic movements occurring during focal epileptic seizures can sometimes resemble those seen in certain movement disorders, and they give the examples of orofacial automatisms (most often seen in temporal lobe seizures), as well as hyperkinetic behaviors. While the authors highlight sleep-related epilepsy as the main cause of hyperkinetic behavior, in fact hyperkinetic behavior may be seen in seizures from various cortical origins both in wakefulness and in sleep. It should be recognized that especially (though not exclusively) in prefrontal seizures2, more elaborate automatic behaviors may include complex and often repetitive movements that tend to occur in a context of altered consciousness, including naturalistic movements such as hand tapping, rocking or leg movements. These may be associated with vocalization including verbal stereotypies, laughing or singing, and sometimes emotional signs. The seizure semiological pattern is typically similar for a given patient from one seizure to the next. These excessively repetitive movements occurring within a limited behavioral repertoire could indeed be characterised as stereotypies, according to the definition of this term3.
In cases of focal pharmacoresistant epilepsy, presurgical evaluation may require intracerebral electroencephalography (EEG). This offers a rich source of data for correlating clinical seizure expression with intracerebral electrical activity measured with millisecond resolution. The depth electrode methodology of stereoelectroencephalography (SEEG) allows sampling of widely distributed structures, through which signal analysis studies have helped to establish the network basis of epilepsy4.
In a series of frontal seizures recorded with SEEG, the clinical expression of ictal stereotyped movements was shown to correlate with a rostrocaudal gradient of seizure organisation within frontal cortex, notably whether movements involved predominantly proximal or distal body segments: more distal stereotypies were associated with more anterior prefrontal regions 5. Thus, while both cortical and subcortical structures seem likely to be involved in such complex seizure-related behavior4 6, it can be suspected that cortico-subcortical circuits are topographically organised in a way that directly influences clinical expression.
While most SEEG recording is from cortical structures, aimed at identifying a pathological zone for surgical resection, deeper subcortical structures including thalamus and caudate nucleus are sometimes also explored. In the context of Freitas and colleagues’ discussion of pathophysiology, it is of interest to note that in SEEG exploration of frontal lobe epilepsy, the caudate nucleus has been shown to be involved in generation of both spontaneous and stimulation-triggered seizures7. An SEEG study of temporal lobe seizures demonstrated an association between greater impairment of awareness and increased long-distance connectivity between thalamus and associative cortical structures 8. Lastly, in a separate and recent study of patients with temporal lobe epilepsy recorded with SEEG, direct stimulation of pulvinar during hippocampal seizures produced electroclinical change, with clinically less severe seizures9.
Thus, an exciting role of SEEG exploration of epilepsy is not only in defining a potential surgical excision for each patient, but also using the recorded data to pursue better understanding of organisation of pathophysiological networks, including in terms of interactions between their cortical and subcortical components. As well as the strong neuroscientific interest of such data, this could potentially advance therapeutic approaches, for example facilitating development of tailored deep brain stimulation methods according to anatomical and electrophysiological specificities of different cases. This is of direct clinical relevance to management of epilepsy but could eventually also open up therapeutic possibilities for some movement disorders.

1. Freitas ME, Ruiz-Lopez M, Dalmau J, et al. Seizures and movement disorders: phenomenology, diagnostic challenges and therapeutic approaches. 2019:jnnp-2018-320039.
2. Bonini F, McGonigal A, Trébuchon A, et al. Frontal lobe seizures: From clinical semiology to localization. Epilepsia 2014;55.2 264-77.
3. Edwards MJ, Lang AE, Bhatia KP. Stereotypies: a critical appraisal and suggestion of a clinically useful definition. Mov Disord 2012;27(2):179-85. doi: 10.1002/mds.23994
4. Bartolomei F, Lagarde S, Wendling F, et al. Defining epileptogenic networks: Contribution of SEEG and signal analysis. Epilepsia 2017
5. McGonigal A, Chauvel P. Prefrontal seizures manifesting as motor stereotypies. Movement Disorders 2013 doi: doi: 10.1002/mds.25718
6. Chauvel P, McGonigal A. Emergence of semiology in epileptic seizures. Epilepsy & Behavior 2014
7. Aupy J, Kheder A, Bulacio J, et al. Is the caudate nucleus capable of generating seizures? Evidence from direct intracerebral recordings. Clinical Neurophysiology 2018
8. Arthuis M, Valton L, Régis J, et al. Impaired consciousness during temporal lobe seizures is related to increased long-distance cortical-subcortical synchronization. Brain 2009;132(Pt 8):2091-101. doi: 10.1093/brain/awp086
9. Filipescu C, Lagarde S, Lambert I, et al. The effect of medial pulvinar stimulation on temporal lobe seizures. Epilepsia 2019