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.

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

We read with interest the description of the movement disorder manifestations in patients with N-methyl-D-aspartate receptor antibody mediated encephalitis (NMDAR-AbE) by a panel of movement disorders experts (1). The authors conclude that the co-existence of dystonia, chorea and stereotypies within the same patient, variability in phenomenology within the course of a single day and evolution over time, are helpful pointers to the diagnosis of NMDAR-AbE and therefore early treatment. We agree with this conclusion. However, this analysis overlooks consideration of the distinctive, if not unique, phenomenology of the “classical” movement disorder of NMDAR-AbE (2).

In our earlier description of this complex movement disorder we reported the presence of variable, complex, jerky semi-rhythmic bulbar and limb movements, associated with posturing and oculogyric crises, but in summarising the overall clinical syndrome we deliberately avoided conventional movement disorder terms because none captured the entire clinical picture (2). Classification of a movement disorder, particularly when complex, is guided by the most obvious, dominant or overwhelming clinical feature. The ‘classical’ movement disorder in NMDAR-AbE is complex but as acknowledged by the expert reviewers, is not typical of any of the movement disorder categories (1). Stereotypies are purposeless repetitive motor behaviours that occur when awake and are interrupted by a shift in attention or distraction. Dystonia and chorea characteristically accompany voluntary or postural movements, subsiding with rest and disappearing during sleep. We drew attention to the occurrence of the movement disorder in the obtunded state, ‘awake unresponsiveness’ or coma, the disappearance of the movements following recovery of consciousness, and highlighted the modulation of movements with sensory stimuli. These features are not typical of stereotypies, dystonia or chorea. Mutism, waxy flexibility, catatonia, oculogyric crises, opisthotonus and other signs occur as elements of the clinical picture but are not dominant features of the syndrome.

We suggested the occurrence of these movements in obtunded states provided an important clue to the pathophysiology (2) and drew attention to similar movements in phencyclidine and ketamine intoxication associated with “dissociative anaesthesia” (ie, loss of responsiveness to external stimuli, also referred to as akinetic mutism, coma vigil or wakeful unresponsiveness in the neurological literature), stupor or coma. The initial neuropsychiatric and behavioural abnormalities suggest frontal-limbic involvement but this does not account for the altered conscious state and movement disorder. In order to explain this we postulated progression to diffuse cortical NMDA receptor blockade, interrupting glutamate transmission and suspending or “silencing” supratentorial descending tonic GABAergic inhibitory input to brainstem reticular systems, releasing patterned rhythmic movements such as chewing, bruxism, facial grimacing, frowning, lip smacking, tongue movements and rudimentary limb movements from pattern generators within pontomesencephalic locomotor regions or the pontomedullary reticular formation. These movements are analogous to primitive motor synergies such as undulatory gill and fin motion in fish, chewing and stepping. Decerebrate posturing, opisthotonus and oculogyric crises may be generated by similar mechanisms releasing the brainstem reticular systems from supratentorial control. As conscious state, awareness and responsiveness improve the movements subside, indicating return of the normal hierarchical descending hemispheric control over the brainstem centres. This model therefore explains the observed correlation between level of consciousness and movement severity.

References
1. Varley JA, Webb AJS, Balint B, et al. The movement disorder associated with NMDAR antibody encephalitis is complex and characteristic: an expert video rating study. J Neurol Neurosurg Psychiatry doi:10.1136/jnnp-2018-318584.
2. Kleinig TJ, Thompson PD, Matar W, et al. The distinctive movement disorder of ovarian teratoma associated encephalitis. Mov Disord 2008, 23: 1256-1261.

Hou et al. are to be commended for an in-depth systematic review of currently available dementia risk models that quantify the probability of developing dementia, covering both studies on community-dwelling individuals as well as clinic-based MCI studies.1 One of the key conclusions was that “the predictive ability of existing dementia risk models is acceptable, but the lack of validation limited the extensive application of the models for dementia risk prediction in general population or across subgroups in the population.” Based on recent insights, we believe that the discriminative ability of existing dementia prediction models in the general population is currently not acceptable for clinical use.

We recently validated four promising dementia risk models (CAIDE, ANU-ADRI, BDSI, and DRS).2 In addition to external validation of these models in the Dutch general population, we also sought to investigate how these models compared to predicting dementia based on the age component of these models only. We found that full models do not have better discriminative properties than age alone. As such, we would like to make three suggestions to establish a reliable dementia prediction model.

First, prediction models typically only report model performance on the basis of a full model.1-4 For dementia risk, however, age plays a pivotal role. Therefore, any new model should compare its predictive accuracy to age alone.

Second, the setting in which a prediction model is to be used will dictate which predictors are available. For instance, a ‘basic’ model would be practical in a primary care setting, by including easily and relatively low-cost obtainable information, such as non-laboratory measurements. In this setting, not only risk factors but also prodromal features of dementia may be useful, such as subjective memory complaints. Therefore, the ability to identify high-risk individuals many years before a clinical dementia diagnosis can be used in future prevention trials to intervene in the earliest phase of the disease process. In settings beyond primary care, an ‘extended’ model could be considered, which adds specialist assessments, such as cognitive testing, brain MRI, and possibly genetics.

Finally, improvements in the use of more advanced modelling techniques and reporting of the underlying model properties are needed. For example, future studies could consider more complex effects of age on dementia risk in their candidate models, by using non-linear effects or interactions with other predictors. We also encourage the exploration of developing age stratified models, but only if sample size permits in order to prevent model overfitting. As the authors briefly pointed out, most of the included models only report discriminative properties (i.e., AUCs or C-statistics), yet information on model calibration is also required to assess model performance.5 Without data on model calibration, proper validation attempts are limited, but most importantly also hamper actual model application to clinical practice.

In conclusion, updated and well-validated dementia risk models are still urgently needed. These models could be used in the near future to design (preventive) trials by reliably selecting high-risk individuals into such trials. Such a tailored approach could eventually benefit progress to delay or even prevent the onset of dementia.

References
1. Hou XH, Feng L, Zhang C, et al. Models for predicting risk of dementia: a systematic review. J Neurol Neurosurg Psychiatry 2018 doi: jnnp-2018-318212 [pii]
10.1136/jnnp-2018-318212 [published Online First: 2018/06/30]
2. Licher S, Yilmaz P, Leening MJG, et al. External validation of four dementia prediction models for use in the general community-dwelling population: a comparative analysis from the Rotterdam Study. Eur J Epidemiol 2018;33(7):645-55. doi: 10.1007/s10654-018-0403-y
10.1007/s10654-018-0403-y [pii] [published Online First: 2018/05/10]
3. Stephan BC, Kurth T, Matthews FE, et al. Dementia risk prediction in the population: are screening models accurate? Nat Rev Neurol 2010;6(6):318-26. doi: nrneurol.2010.54 [pii]
10.1038/nrneurol.2010.54 [published Online First: 2010/05/26]
4. Tang EY, Harrison SL, Errington L, et al. Current Developments in Dementia Risk Prediction Modelling: An Updated Systematic Review. PLoS One 2015;10(9):e0136181. doi: 10.1371/journal.pone.0136181
PONE-D-15-14570 [pii] [published Online First: 2015/09/04]
5. Collins GS, Reitsma JB, Altman DG, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med 2015;162(1):55-63. doi: 2088549 [pii]
10.7326/M14-0697 [published Online First: 2015/01/07]