Elucidating the nature of the foreign accent syndrome (FAS) can contribute to improve its diagnosis and treatment approaches. To understand this apparently rare syndrome, McWhirter et al. 1 studied a large case series of 49 subjects self-reporting having FAS. The participants were recruited via unmoderated online FAS support groups and surveys shared with neurologists and speech-language therapists from several countries. Participants completed an online protocol including validated scales tapping somatic symptoms, anxiety and depression, social-occupational function, and illness perception. They were also requested to provide speech samples recorded via computers or smartphones during oral reading and picture description. The overall clinical presentation of FAS in each participant was classified by consensus reached by three authors (2 neuropsychiatrists and 1 neurologist) in (1) “probably functional”, (2) “possibly structural” or (3) “probably structural”, wherein (1) meant no evidence of a neurological event or injury suggestive of a functional disorder but with no spontaneous remission; (2) alluded to the presence of some features suggestive of a functional disorder but with some uncertainty about a possible structural basis; and (3) denoted the evidence of a neurological event or injury coincident with the onset of FAS. The recorded speech samples were examined by experts to diagnose FAS and their frequent associated speech-language deficits (apraxia of speech, dysar...
Elucidating the nature of the foreign accent syndrome (FAS) can contribute to improve its diagnosis and treatment approaches. To understand this apparently rare syndrome, McWhirter et al. 1 studied a large case series of 49 subjects self-reporting having FAS. The participants were recruited via unmoderated online FAS support groups and surveys shared with neurologists and speech-language therapists from several countries. Participants completed an online protocol including validated scales tapping somatic symptoms, anxiety and depression, social-occupational function, and illness perception. They were also requested to provide speech samples recorded via computers or smartphones during oral reading and picture description. The overall clinical presentation of FAS in each participant was classified by consensus reached by three authors (2 neuropsychiatrists and 1 neurologist) in (1) “probably functional”, (2) “possibly structural” or (3) “probably structural”, wherein (1) meant no evidence of a neurological event or injury suggestive of a functional disorder but with no spontaneous remission; (2) alluded to the presence of some features suggestive of a functional disorder but with some uncertainty about a possible structural basis; and (3) denoted the evidence of a neurological event or injury coincident with the onset of FAS. The recorded speech samples were examined by experts to diagnose FAS and their frequent associated speech-language deficits (apraxia of speech, dysarthria, dysprosody and aphasia) and the abnormal segmental and suprasegmental features that characterize FAS. The main finding of this study was that the authors’ consensus classified 71 % (35 subjects) of the participants as “probably functional”, 8% (4 subjects) as “possibly structural”, and 20% (10 subjects) as “probably structural”. The high prevalence of participants meeting the “probably functional” and “possibly functional” criteria for FAS (79%) appears difficult to reconcile with previous data. 2,4 This overestimation may spuriously inflate the number of functional cases, thus biasing the currently accepted relative frequency of the FAS variants.2 It is also conceivable that subjects with “functional” FAS completed the evaluation because they feel more urge to be evaluated than those with other variants. Below, we briefly examine the caveats of this study concerning the validity of the assessment results.
First, only 13 out of 49 (26%) participants provided samples of speech production and 10 of them were classified as having “probably functional” FAS, whereas the remaining 3 cases were considered to have “probably structural” FAS. This makes the diagnosis of FAS elusive in most participants (74%) who did not submit speech samples for expert evaluation, a requirement needed for establishing the precise diagnosis of FAS.3
Second, McWhirter et al. 1 identified some features of the speech (i.e., periods of remission, ability to copy other accents, lack of typical speech-language deficits accompanying neurogenic FAS) in “functional” FAS that considered helpful for identifying such cases. Nevertheless, these characteristics have also been observed in neurogenic cases. Alternation between foreign accent, loss of regional accent, and using a previously heard accent in the same individual have been reported in a variant of neurogenic FAS (see Berthier et al., 2015 in 2). Cases with no or rapidly resolving dysarthria, apraxia of speech or aphasia but persistent foreign accent have been described as “pure” neurogenic FAS (see references in 4). The fluctuating course of FAS related to psychiatric disorders (schizophrenia and bipolar disorder) could not viewed as functional; rather, it has a neurochemical correlate (e.g., withdrawal of neuroleptics) involving abnormal dopaminergic neurotransmission (see Reeves & Norton, 2001; and Poulin et al., 2007 in 4). In this regard, anxiety and depression in the McWhirter et al’s sample occurred more frequently in the “structural” group than in the “functional” FAS cases. 1 Thus, these neuropsychiatric disorders do not have a discriminative value between subtypes.
Third, 11 (22%) participants in the present study had suffered from stroke, but the authors reported structural lesions on neuroimaging in only 5 of them (10%), all belonging to the "probably structural" group (data from Table 1). No information on lesion characteristics (i.e., location, size) was provided. Recent developments on the neuroscience of accent (see 2) may explain why the scarcity of brain damage in McWhirter et al.’s study1 does not undermine the role of structural or functional lesions in those participants with normal neuroimaging. In the present study this was particularly pertinent for those cases associated with stroke, mild traumatic brain injury, Parkinson’s disease, headaches, or seizures. Overall, focal lesions responsible from FAS are very small (involving a single gyrus or portions of a nucleus) compromising of one or more components of the speech production network.3,4 and these lesions may be easily overlooked if sophisticated neuroimaging methods are not used. Note that functional and structural brain changes have been reported even in cases of psychogenic and developmental FAS when high resolution magnetic resonance imaging (MRI), diffusion tensor imaging, positron emission tomography or functional MRI were used.3-5
Fourth, the differentiation between “functional” and “structural” FAS may be deemed artificial considering the current limitations of conventional neuroimaging methods (computed tomography, low resolution MRI). 4 Such limitations prevent unveiling the neural basis of cases which until now fall under the umbrella of “functional” disorders.3 For example, the demarcation of a “possible functional FAS” (functional disorder with an uncertain structural basis)1 is uncertain. How can we interpret the hybrid identity in such FAS cases? How the attending professionals can dissect the psychogenic from the neurogenic nature of FAS to provide an integrated explanation to the affected person? We have reported that the interpretation of FAS as psychogenic in cases associated with previously undetected small stroke lesions 4 or developmental brain anomalies 3 created a social stigma in the affected persons which, in turn, heightened the negative connotation of living with a FAS.
The differentiation between FAS variants does not depends solely on the segmental and suprasegmental alterations 5 nor in the frequency of comorbid psychiatric disorders. Therefore, the implementation of other methodologies to refine the differential diagnosis between FAS variants is needed. We trust that multimodal neuroimaging and other ancillary methods will contribute to illuminate the still hidden origins of FAS subtypes.
References
1. McWhirter L, Miller N, Campbell C, et al. Understanding foreign accent syndrome. J Neurol Neurosurg Psychiatry. 2019 Mar 2. pii: jnnp-2018-319842. doi: 10.1136/jnnp-2018-319842.
2. Moreno-Torres I, Mariën P, Dávila G, et al. Editorial: Language beyond Words: The Neuroscience of Accent. Front Hum Neurosci. 2016 Dec 20;10:639. doi: 10.3389/fnhum.2016.00639.
3. Berthier ML, Roé-Vellvé N, Moreno-Torres I et al. Mild Developmental Foreign Accent Syndrome and Psychiatric Comorbidity: Altered White Matter Integrity in Speech and Emotion Regulation Networks. Front Hum Neurosci. 2016 Aug 9;10:399. doi: 10.3389/fnhum.2016.00399.
4. Moreno-Torres I, Berthier ML, Del Mar Cid M. et al. Foreign accent syndrome: a multimodal evaluation in the search of neuroscience-driven treatments. Neuropsychologia 2013; 51:520-37. doi: 10.1016/j.neuropsychologia.2012.11.010.
5. Keulen S. Foreign Accent Syndrome: A Neurolinguistic Analysis. PhD Thesis. University of Groningen (The Netherlands) and Vrije Universiteit Brussel (Belgium). May 18th, 2017.
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...
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
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...
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 appreciate the editorial by Dr. Muller-Vahl [1] about our recent article [2]. The large, international study group who co-authored our paper collectively felt that it would be useful to provide clarification of a few important points regarding the International Tourette Syndrome (TS) Deep Brain Stimulation (DBS) Database and Registry, the International Neuromodulation Registry, and our published analysis.
There is widespread agreement on the need for more randomized controlled trials (RCTs) to evaluate the efficacy of DBS for many indications, including TS, and there has been substantial discussion in the medical community about how these trials should be organized and carried out [3]. Our approach to overcome the challenges with the modest amount of data available for surgical therapies for TS has been to use symbiotic data sharing [4]. This approach encourages the broadening of investigative teams after publication of clinical studies to perform additional analyses and to develop new hypotheses. The key concept behind this approach is that new investigators work in a close, collaborative relationship with the teams that conducted the initial data collection. In addition, a recent viewpoint from the Food & Drug Administration in the United States reported that “For some devices, opportunities exist for leveraging alternative data sources, such as existing registries or modeling techniques, to allow regulators to have a good idea of the risks and benefits of...
We appreciate the editorial by Dr. Muller-Vahl [1] about our recent article [2]. The large, international study group who co-authored our paper collectively felt that it would be useful to provide clarification of a few important points regarding the International Tourette Syndrome (TS) Deep Brain Stimulation (DBS) Database and Registry, the International Neuromodulation Registry, and our published analysis.
There is widespread agreement on the need for more randomized controlled trials (RCTs) to evaluate the efficacy of DBS for many indications, including TS, and there has been substantial discussion in the medical community about how these trials should be organized and carried out [3]. Our approach to overcome the challenges with the modest amount of data available for surgical therapies for TS has been to use symbiotic data sharing [4]. This approach encourages the broadening of investigative teams after publication of clinical studies to perform additional analyses and to develop new hypotheses. The key concept behind this approach is that new investigators work in a close, collaborative relationship with the teams that conducted the initial data collection. In addition, a recent viewpoint from the Food & Drug Administration in the United States reported that “For some devices, opportunities exist for leveraging alternative data sources, such as existing registries or modeling techniques, to allow regulators to have a good idea of the risks and benefits of the device without the need for conducting detailed trials. For the majority of devices, the benefits and risks are expected to be manifest through registries and evolve as clinical techniques are refined and the technologies themselves are rapidly modified and improved. Such a continuous improvement cycle would be impossible if every device iteration required a full trial to test its safety and efficacy” [5]. We believe that the candidate population for TS DBS therapy falls squarely within this description, particularly as new capabilities are being added to commercially available DBS systems (e.g. directional electrodes, current steering, and closed loop stimulation to name a few). Hence, the type of secondary data analysis we reported was intended to support, rather than circumvent, future RCTs.
There is not a current consensus on how future RCTs should be designed. Enrollment can be challenging given so few cases are implanted, even at centers with extensive DBS experience. We have previously published post-hoc analyses of failed RCTs using neurostimulation devices. For example, one study strongly suggested the trial would have reached its primary endpoint if it was designed to better accommodate known sources of variability such as lead location, type of stimulation, or the length of time given to assess primary outcomes [6]. Our purpose in performing these types of analyses is not to be overly critical of past RCTs, but rather to make use of lessons learned in order to design more effective future trials. While much of the TS DBS registry data in our study was open label, and therefore we cannot rule out placebo response, only a few patients chose to be explanted for lack of effectiveness. Our 12 month data on the effectiveness and safety of DBS for TS [7] revealed that one patient was explanted and one patient underwent pulse generator removal. In addition, the therapeutic effect size in most of the responders was substantial and durable. Lastly, for a few cases in which the pulse generator battery was inadvertently depleted without the patient’s knowledge, the sudden recurrence of symptoms suggests positive and reversible effects of DBS. When combined with the positive outcomes of two successful RCTs of DBS for TS [8,9], we feel that our data provide sufficient evidence to support future studies. These studies should be designed to accommodate several known sources of variability identified through the analyses of the TS DBS registry. Hence, our goal was to report how DBS for TS has been applied across multiple international sites and to generate testable hypotheses to guide future studies.
Our study revealed that the anatomical location of the applied stimulation did not fully explain the variability in clinical outcomes across patients. In an effort to further disentangle the complexities of this treatment, we are preparing a forthcoming paper that includes additional analyses on our cohort. The results suggest that there may be a common network that is correlated with clinical improvement across surgical targets, which could provide important insights on the underlying TS network and how neuromodulation can be refined. Importantly, this type of analysis would not be possible without a TS database and registry.
In closing, we agree with the value of additional RCTs as suggested by Dr. Muller-Vahl; however we must also recognize the current logistical difficulties and be creative and pluralistic in our methodologic approaches. In addition, we suggest the greater inclusion of patient voice in driving the research agenda consistent with the disability rights adage, “nothing about us without us" [10]. Instead of the prescriptive critique of Muller-Vahl, we should strive to develop more adaptive patient-centered methodologies. Through this effort we will be better positioned to meet the needs of individuals with severe TS as they consider the risks and benefits of DBS.
References
1. Muller-Vahl KR. Deep brain stimulation in Tourette syndrome: the known and the unknown. J Neurol Neurosurg Psychiatry Published Online First: 12 July 2019. doi:10.1136/jnnp-2019-321008
2. Johnson KA, Fletcher PT, Servello D, et al. Image-based analysis and long-term clinical outcomes of deep brain stimulation for Tourette syndrome: a multisite study. J Neurol Neurosurg Psychiatry Published Online First: 25 May 2019. doi:10.1136/jnnp-2019-320379
3. Fins JJ, Kubu CS, Mayberg HS, et al. Being open minded about neuromodulation trials: Finding success in our “failures”. Brain Stimul 2017;10:181–6. doi:10.1016/j.brs.2016.12.012
4. Longo DL, Drazen JM. Data Sharing. N Engl J Med 2016;374:276–7. doi:10.1056/NEJMe1516564
5. Faris O, Shuren J. An FDA Viewpoint on Unique Considerations for Medical-Device Clinical Trials. N Engl J Med 2017;376:1350–7. doi:10.1056/NEJMra1512592
6. Pathak Y, Kopell BH, Szabo A, et al. The role of electrode location and stimulation polarity in patient response to cortical stimulation for major depressive disorder. Brain Stimul 2013;6:254–60. doi:10.1016/j.brs.2012.07.001
7. Martinez-Ramirez D, Jiminez-Shahed J, Leckman JF, et al. Efficacy and Safety of Deep Brain Stimulation in Tourette Syndrome The International Tourette Syndrome Deep Brain Stimulation Public Database and Registry. JAMA Neurol 2018;32607:1–7. doi:10.1001/jamaneurol.2017.4317
8. Kefalopoulou Z, Zrinzo L, Jahanshahi M, et al. Bilateral globus pallidus stimulation for severe Tourette’s syndrome: A double-blind, randomised crossover trial. Lancet Neurol 2015;14:595–605. doi:10.1016/S1474-4422(15)00008-3
9. Ackermans L, Duits A, van der Linden C, et al. Double-blind clinical trial of thalamic stimulation in patients with Tourette syndrome. Brain 2011;134:832–44. doi:10.1093/brain/awq380
10. Charlton JI. Nothing About Us Without Us: Disability Oppression and Empowerment. Berkley: University of California Press 1998.
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 exclusivel...
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. Dy...
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.
We thoroughly enjoyed reading the comment on our paper which analysed expert ratings of the movement disorder associated with NMDAR antibody-encephalitis.1 Thompson et al’s elegant pathophysiological explanation provides an excellent framework of the most plausible neural structures involved in NMDAR-antibody encephalitis. Further, they note these movements can occur in semi-conscious patients, and this concurs well with the previous description of anti-gravity movements in the context of ‘status dissociatus’.2 A review of our 76 videos, revealed Thompson et al’s account of “variable, complex jerky semi-rhythmic movements….in the obtunded state” in 45 (59%) of cases. Therefore, this complex description was not present in almost half of patients. Furthermore, our recent clinical experiences note some NMDAR-antibody patients with abnormal movements but without obtundation: perhaps, given the known stepwise progression of many cases, this is a function of increasingly early disease recognition.3
By contrast to Thompson et al, our published study design intentionally used conventional phenomenological terms to define the movement disorder associated with NMDAR antibody-encephalitis.1 This approach aimed to define a pragmatic method, available to all clinicians, which could identify and faithfully communicate this complex movement disorder, with the important aim of earlier disease recognition. The results identified a dominant set of recognised classifications – dyston...
We thoroughly enjoyed reading the comment on our paper which analysed expert ratings of the movement disorder associated with NMDAR antibody-encephalitis.1 Thompson et al’s elegant pathophysiological explanation provides an excellent framework of the most plausible neural structures involved in NMDAR-antibody encephalitis. Further, they note these movements can occur in semi-conscious patients, and this concurs well with the previous description of anti-gravity movements in the context of ‘status dissociatus’.2 A review of our 76 videos, revealed Thompson et al’s account of “variable, complex jerky semi-rhythmic movements….in the obtunded state” in 45 (59%) of cases. Therefore, this complex description was not present in almost half of patients. Furthermore, our recent clinical experiences note some NMDAR-antibody patients with abnormal movements but without obtundation: perhaps, given the known stepwise progression of many cases, this is a function of increasingly early disease recognition.3
By contrast to Thompson et al, our published study design intentionally used conventional phenomenological terms to define the movement disorder associated with NMDAR antibody-encephalitis.1 This approach aimed to define a pragmatic method, available to all clinicians, which could identify and faithfully communicate this complex movement disorder, with the important aim of earlier disease recognition. The results identified a dominant set of recognised classifications – dystonia, stererotypies and chorea – with a paucity of tremor. In agreement with Thompson et al’s observations, it revealed some additional under-represented features. These were within our ‘other’ classification category (Figure 1D), and included mutism, stupor, myorhythmia, myokymia, tics, opisthotonus, ataxia, dyskinesias, waxy flexibility, oculogyric crises, athetosis, agitation, startle and vocal perseveration. However, we noted in our discussion that even the expert raters found it difficult to accurately describe this movement disorder within the predictable constraints of preconceived and established categorisations, and much of their feedback reflected a dissatisfaction with the final category chosen. Quantitatively, and by comparison to the other mixed movement disorders they rated, this limitation was strikingly reflected by the appreciably poorer inter-rater variability and the significantly more descriptive terms used to capture the essence of this movement disorder (P<0.0001).
Indeed, our findings are best interpreted within the intrinsic framework of movement disorder nomenclature. By extension, and particularly to the expert eye, we agree with Thompson et al that the disorder is often even more distinctive than this rare combination would suggest. In fact, in some cases, it may be unique. Interestingly, use of the term ‘unique’ may only be inaccurate given some of the closest mimics include ketamine / phencyclidine use and GRIN1A mutations. Of course, all of these imply the targeted specific-NMDAR modulation is at the core of this network-based pathophysiology.
Overall, within inevitable contemporary nomenclature constraints, we anticipate that the constellation of dystonia, stereotypies and chorea, with limited tremor, will provide a comprehensive, clear and concise message to clinicians and alert them to the possibility of this highly-treatable disorder. Yet, in future, perhaps this multifaceted movement disorder merits its own nomenclature to better emphasise the distinctive nature of the clinical observations. However, a term such as NMDAR-antibody associated movement disorder would be self-fulfilling and perhaps divert from the aim of its accurate recognition. Therefore, a reliance on conventional terms will yet be necessary to communicate this entity.
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 Neuro Neurosurg Psychiatry 2018;
2. Stamelou M, Plazzi G, Lugaresi E, et al. The distinct movement disorder in anti‐NMDA receptor encephalitis may be related to status dissociatus: A hypothesis. Mov Disord. 2012;27(11):1360–1363.
3. Irani SR, Bera K, Waters P, et al. N-methyl-D-aspartate antibody encephalitis: temporal progression of clinical and paraclinical observations in a predominantly non-paraneoplastic disorder of both sexes. Brain 2010;133(Pt 6):1655–1667.
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.
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]
We read with great interest the recent paper of Sechi et al. that describes 13 patients with anti-GFAP related myelitis and compares them with 41 patients with anti-AQP4 related myelitis.1 To date, very little data is available about anti-GFAP related disorders.2-3 Sechi et al. highlight some differences between the two entities to help clinicians differentiate them.1 One of these clinical differences relates to area postrema syndrome (APS). Indeed, it is well known that APS is a classical feature of neuromyelitis optica spectrum disorders, particularly among anti-AQP4 positive patients.4 Sechi et al. report this syndrome as a prodromal event in 20% of anti-AQP4 related myelitis. Conversely, the authors do not report any case of APS preceding or accompanying myelitis related to anti-GFAP.1 Given these data, APS could be an indicator for ruling out anti-GFAP encephalomyelitis, particularly useful for centers that do not yet have access to biological testing for anti-GFAP Abs.
However, we report the case of a 41-year-old woman who in April 2016 developed intractable nausea and vomiting lasting for five weeks and leading to 35 kilograms in weight loss. An extensive search for a digestive disease was negative, and no neurological explorations were performed. One month following the resolution of digestive symptoms, she developed mental confusion, diplopia, dysarthria, dizziness, bilateral blurred vision (with optic disc edema) and paraparesis. Brain...
We read with great interest the recent paper of Sechi et al. that describes 13 patients with anti-GFAP related myelitis and compares them with 41 patients with anti-AQP4 related myelitis.1 To date, very little data is available about anti-GFAP related disorders.2-3 Sechi et al. highlight some differences between the two entities to help clinicians differentiate them.1 One of these clinical differences relates to area postrema syndrome (APS). Indeed, it is well known that APS is a classical feature of neuromyelitis optica spectrum disorders, particularly among anti-AQP4 positive patients.4 Sechi et al. report this syndrome as a prodromal event in 20% of anti-AQP4 related myelitis. Conversely, the authors do not report any case of APS preceding or accompanying myelitis related to anti-GFAP.1 Given these data, APS could be an indicator for ruling out anti-GFAP encephalomyelitis, particularly useful for centers that do not yet have access to biological testing for anti-GFAP Abs.
However, we report the case of a 41-year-old woman who in April 2016 developed intractable nausea and vomiting lasting for five weeks and leading to 35 kilograms in weight loss. An extensive search for a digestive disease was negative, and no neurological explorations were performed. One month following the resolution of digestive symptoms, she developed mental confusion, diplopia, dysarthria, dizziness, bilateral blurred vision (with optic disc edema) and paraparesis. Brain MRI was highly suggestive of anti-GFAP encephalomyelitis, notably with the presence of diffuse T2 abnormalities in periventricular white matter, with linear perivascular enhancement oriented radially to the ventricles.Spinal cord MRI revealed longitudinally extensive T2 hyperintensity (extended to the brainstem, with an upper limit involving the area postrema), with ill-defined margins, as described by Sechi et al.1. Anti-AQP4 and anti-MOG IgG were negative in the serum. Anti-GFAP IgG were detected in the serum (by cell-based assay and tissue-based assay). Lumbar puncture was performed before the patient was referred to our department, and was not repeated given the dramatic clinical improvement rapidly achieved with intravenous steroids. Among the 13 patients reported by Sechi et al., anti-GFAP IgG were detected only in the serum for two patients for which no CSF was available, like for our patient.
The patient gave written informed consent for information and images to be published
Our case underlines that the clinical spectrum of anti-GFAP encephalomyelitis is not fully described and understood yet and that clinical features of this syndrome may overlap with those of neuromyelitis optica spectrum disorders (related to anti-AQP4 IgG, to anti-MOG IgG or negative for both antibodies). As such, combining clinical data with radiological and biological data remains essential in order to properly classify patients with such rare central nervous system inflammatory disorders.
References
1 Sechi E, Morris PP, McKeon A et al. Glial fibrillary acidic protein IgG related myelitis: characterisation and comparison with aquaporin-4-IgG myelitis. J Neurol Neurosurg Neuropsychiatry 2018 (in press).
2 Fang B, McKeon A, Hinson SR et al. Autoimmune glial fibrillary acidic protein astrocytopathy: a novel meningoencephalomyelitis. JAMA Neurol 2016;73(11):1297-1307.
3 Flanagan EP, Hinson SR, Lennon VA et al. Glial fibrillary acidic protein immunoglobulin G as biomarker of autoimmune astrocytopathy: analysis of 102 patients. Ann Neurol 2017;81:298-309.
4 Wingerchuk DM, Banwell B, Bennett JL et al. International consensus diagnostic criteria for neuromyelitis optica spectrum disorders. Neurology 2015;85:177-189.
We read with interest the article by Kalincik et al. [1] comparing fingolimod, dimethyl fumarate and teriflunomide in a cohort of relapsing-remitting multiple sclerosis (MS) patients. The authors investigated several endpoints and performed various sensitivity analyses, and we commend them for reporting technical details in the online supplementary material. We, however, have some concerns about the design, analysis and reporting of the study.
1. In the primary analyses, three separate propensity score models were developed to construct a matched cohort for each of the three pairwise comparisons. Supplementary Table 6 clearly indicates the existence of zero or low frequencies in some variables (e.g., most active previous therapy and magnetic resonance imaging [MRI] T2 lesions). Yet, those variables were used as covariates in the propensity score models, unsurprisingly resulting in extremely high point estimates and standard errors (SE; as reported in Supplementary Table 7). For example, teriflunomide was not the most active therapy for any patient in the dimethyl fumarate cohort (n=0 from Supplementary Table 6), but that category was nevertheless included in the propensity score model, leading to an unrealistic point estimate of 18.65 with SE of 434.5 (Supplementary Table 7). Even higher SEs (greater than 1000) are observed in the other propensity score models. Propensity scores estimated from these poorly constructed models were then used to cr...
We read with interest the article by Kalincik et al. [1] comparing fingolimod, dimethyl fumarate and teriflunomide in a cohort of relapsing-remitting multiple sclerosis (MS) patients. The authors investigated several endpoints and performed various sensitivity analyses, and we commend them for reporting technical details in the online supplementary material. We, however, have some concerns about the design, analysis and reporting of the study.
1. In the primary analyses, three separate propensity score models were developed to construct a matched cohort for each of the three pairwise comparisons. Supplementary Table 6 clearly indicates the existence of zero or low frequencies in some variables (e.g., most active previous therapy and magnetic resonance imaging [MRI] T2 lesions). Yet, those variables were used as covariates in the propensity score models, unsurprisingly resulting in extremely high point estimates and standard errors (SE; as reported in Supplementary Table 7). For example, teriflunomide was not the most active therapy for any patient in the dimethyl fumarate cohort (n=0 from Supplementary Table 6), but that category was nevertheless included in the propensity score model, leading to an unrealistic point estimate of 18.65 with SE of 434.5 (Supplementary Table 7). Even higher SEs (greater than 1000) are observed in the other propensity score models. Propensity scores estimated from these poorly constructed models were then used to create three matched cohorts, which are the basis for the primary analyses in this work. Readers need to be skeptical about any inference (estimated SE of the treatment effect, and consequently the confidence intervals/p-values) made from these cohorts, because of the instability in the propensity score models. Further, while teriflunomide was not the ‘most active previous therapy’ for any patient in the original (unmatched) dimethyl fumarate cohort (n=0 and 0% from Supplementary Table 6), Table 1 reported n=14 (2%) patients with this therapy after matching. Naturally the matched cohort should not produce more patients than originally present in the unmatched cohort for any category.
2. A threshold lower than 10% or 20% in absolute value for standardized mean differences (i.e. Cohen’s d) is normally considered to assess imbalances in baseline covariates. However, in this study, a standardized mean difference was reported to be equal to 26% for relapse activity prior to the baseline for the comparison of fingolimod vs. teriflunomide matched sample (Table 1). Neither the standardized or raw difference in proportions was reported for any of the categorical variables in Table 1, even though some of the percentages in matched cohorts were substantially different (e.g., relapse rate). Large residual differences observed in the distribution of the covariates (likely due to poorly built propensity score models) will contribute to bias in the resulting estimates. Furthermore, matching by country, a crucial variable which would allow minimizing outcome assessment bias [2] was not reported in Table 1, but as Supplementary Table 4 which clearly shows that matching by country is far from being obtained. Even more important, since the matching process was conducted in a variable ratio manner for the primary analyses, standardized differences in Table 1 should be replaced with weighted standardized means or proportion differences to obtain a correct check of residual baseline imbalances after matching [3].
3. In the primary analysis, missing baseline MRI values were imputed to generate 17 imputed datasets (MRI information was available for only 20-27% of the population as reported in Supplementary Table 6). In a propensity score analysis, multiple imputation (instead of single imputation) would substantially complicate the analysis due to the pooling of estimates from the 17 imputed datasets (using Rubin’s rules). Both Supplementary Tables 7 and 11 included one set of estimates from each stage of the analysis (propensity score analysis and primary analysis of the matched cohorts respectively), making it unclear how the results were pooled. If the results were not pooled and a single imputed dataset was used for the analysis (as suggested by Supplementary Tables 7 and 11), then such a process would fail to account for the uncertainty in the missing values, leading to SEs and p-values that are smaller than expected.
4. We applaud the authors for conducting a series of sensitivity analyses to evaluate the robustness of their findings. However, readers would have more confidence in the findings if the supplementary materials included more details of how those sensitivity analyses were done. For example, when 1:1 matching was done, it is not clear whether and how the authors have accounted for the matched-pairs designs. In particular, despite having almost identical sample sizes in some matched cohorts (e.g., comparing analyses of ‘no MRI data included’ vs. ‘matching on 2-year relapse rate’ for fingolimod vs. dimethyl fumarate in Supplementary Table 11), high variability in p-values in most cases deserve further explanation.
5. As for the PS adjusted treatment effect analyses, this work claims that individual ARRs were calculated and used in the assessment of primary endpoint analysis. This approach is controversial [4]. Furthermore, the use of individual ARRs is contradicted in the statistical analysis section in which the authors state that a weighted negative binomial accounting for matching has been used. It is unclear whether individual ARRs were fed into a negative binomial and it is important to note that, if they were, results may be biased. The authors do not make clear whether standard errors and p-values properly accounted both for matching and weighting in all assessed endpoints (they include a cluster term in the negative binomial model which only accounts for matching). Table 1 presented below reports a back-calculation of the standard deviation (SD) for ARRs, which should correspond to a stable population parameter, in particular the column ‘SD right’ which is less prone to rounding effects present the original ARRs confidence interval values. This standard deviation is benchmarked against a recent work on a new drug for MS [5]. For the studies OPERA I and II consistent and stable SD values are obtained (around 1), while highly inconsistent and underestimated SDs are obtained for the MSBASE study, especially when the weighting scheme should have been attributing within the matched group the weight of 1 to the treatment arm represented by one single patient. Our Table 1 shows that the reported estimated standard errors are incorrect (i.e. generally smaller) due to the use of a wrong weighting scheme or lack of accounting for weighting properly and, consequently, p-values significance has been inflated dramatically.
6. The large number of patients included made statistically significant a very small and perhaps not clinically meaningful difference, increasing the risk of overinterpretation of the results. From a clinical standpoint an ARR difference between 0.20 and 0.26, that is an ARR ratio of 0.80 or, more intuitively, 1 relapse over 5 years vs. 1 relapse over 4 years is close to negligible overall. This represents an effect size that no future trial would likely be powered or interested to detect, especially as it comes from an ARR threshold (0.20) quite prone to the presence of noise in detecting a relapse.
To recapitulate, we wish to highlight the need for caution while interpreting the findings of this paper. Real world evidence (RWE) is an important and necessary component of research to assess the effectiveness and safety of various therapies outside the context of randomized clinical trials. However, because RWE is prone to various sources of bias, rigorous and careful analysis, interpretation and reporting are needed to ensure that results are reliable, reproducible and useful to inform clinical decision making.
REFERENCES
[1] Kalincik T et al. Comparison of fingolimod, dimethyl fumarate and teriflunomide for multiple sclerosis. Journal of Neurology, Neurosurgery & Psychiatry. 2019: jnnp-2018-319831. doi:10.1136/jnnp-2018-31983.
[2] Bovis F et al. Expanded disability status scale progression assessment heterogeneity in multiple sclerosis according to geographical areas. Ann Neurol. 2018 Oct;84(4):621-625.
[3] Austin, Peter C. Assessing balance in measured baseline covariates when using many‐to‐one matching on the propensity‐score. Pharmacoepidemiology and drug safety 17.12 (2008): 1218-1225.
[4] Suissa S et al. Statistical Treatment of Exacerbations in Therapeutic Trials of Chronic Obstructive Pulmonary Disease. American Journal of Respiratory and Critical Care Medicine. 2006;173(8):842-846.
[5] Hauser SL, Bar-Or A, Comi G, Giovannoni G, Hartung HP, Hemmer B, Lublin F, Montalban X, Rammohan KW, Selmaj K, Traboulsee A, Wolinsky JS, Arnold DL, Klingelschmitt G, Masterman D, Fontoura P, Belachew S, Chin P, Mairon N, Garren H, Kappos L; OPERA I and OPERA II Clinical Investigators. Ocrelizumab versus Interferon Beta-1a in Relapsing Multiple Sclerosis. N Engl J Med. 2017 Jan 19;376(3):221-234.
Table 1 – Back-calculation of ARR standard deviation to benchmark flaws in the reported standard errors and p-values
Study Drug n ARR Lower ARR Upper ARR SD left SD right
OPERA I [5] Ocrelizumab 410 0.16 0.12 0.2 1.29 1.00
Interferon 411 0.29 0.24 0.36 0.85 0.97
OPERA II [5] Ocrelizumab 417 0.16 0.12 0.2 1.30 1.01
Interferon 418 0.29 0.23 0.36 1.05 0.98
ARR: Annualized relapse rate; SD: standard deviation; DMF: dimethyl fumarate
Correspondence to:
Robert W. Platt, Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, 1020 Pine Ave W, Montreal, Quebec H3A 1A2, Canada.
Email: robert.platt@mcgill.ca
Elucidating the nature of the foreign accent syndrome (FAS) can contribute to improve its diagnosis and treatment approaches. To understand this apparently rare syndrome, McWhirter et al. 1 studied a large case series of 49 subjects self-reporting having FAS. The participants were recruited via unmoderated online FAS support groups and surveys shared with neurologists and speech-language therapists from several countries. Participants completed an online protocol including validated scales tapping somatic symptoms, anxiety and depression, social-occupational function, and illness perception. They were also requested to provide speech samples recorded via computers or smartphones during oral reading and picture description. The overall clinical presentation of FAS in each participant was classified by consensus reached by three authors (2 neuropsychiatrists and 1 neurologist) in (1) “probably functional”, (2) “possibly structural” or (3) “probably structural”, wherein (1) meant no evidence of a neurological event or injury suggestive of a functional disorder but with no spontaneous remission; (2) alluded to the presence of some features suggestive of a functional disorder but with some uncertainty about a possible structural basis; and (3) denoted the evidence of a neurological event or injury coincident with the onset of FAS. The recorded speech samples were examined by experts to diagnose FAS and their frequent associated speech-language deficits (apraxia of speech, dysar...
Show MoreWe, 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...
Show MoreDear Editor,
Show MoreThe 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...
We appreciate the editorial by Dr. Muller-Vahl [1] about our recent article [2]. The large, international study group who co-authored our paper collectively felt that it would be useful to provide clarification of a few important points regarding the International Tourette Syndrome (TS) Deep Brain Stimulation (DBS) Database and Registry, the International Neuromodulation Registry, and our published analysis.
There is widespread agreement on the need for more randomized controlled trials (RCTs) to evaluate the efficacy of DBS for many indications, including TS, and there has been substantial discussion in the medical community about how these trials should be organized and carried out [3]. Our approach to overcome the challenges with the modest amount of data available for surgical therapies for TS has been to use symbiotic data sharing [4]. This approach encourages the broadening of investigative teams after publication of clinical studies to perform additional analyses and to develop new hypotheses. The key concept behind this approach is that new investigators work in a close, collaborative relationship with the teams that conducted the initial data collection. In addition, a recent viewpoint from the Food & Drug Administration in the United States reported that “For some devices, opportunities exist for leveraging alternative data sources, such as existing registries or modeling techniques, to allow regulators to have a good idea of the risks and benefits of...
Show MoreSeizures 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.
Show MoreFirstly, 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 exclusivel...
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. Dy...
Show MoreWe thoroughly enjoyed reading the comment on our paper which analysed expert ratings of the movement disorder associated with NMDAR antibody-encephalitis.1 Thompson et al’s elegant pathophysiological explanation provides an excellent framework of the most plausible neural structures involved in NMDAR-antibody encephalitis. Further, they note these movements can occur in semi-conscious patients, and this concurs well with the previous description of anti-gravity movements in the context of ‘status dissociatus’.2 A review of our 76 videos, revealed Thompson et al’s account of “variable, complex jerky semi-rhythmic movements….in the obtunded state” in 45 (59%) of cases. Therefore, this complex description was not present in almost half of patients. Furthermore, our recent clinical experiences note some NMDAR-antibody patients with abnormal movements but without obtundation: perhaps, given the known stepwise progression of many cases, this is a function of increasingly early disease recognition.3
By contrast to Thompson et al, our published study design intentionally used conventional phenomenological terms to define the movement disorder associated with NMDAR antibody-encephalitis.1 This approach aimed to define a pragmatic method, available to all clinicians, which could identify and faithfully communicate this complex movement disorder, with the important aim of earlier disease recognition. The results identified a dominant set of recognised classifications – dyston...
Show MoreHou 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...
Show MoreDear Editor,
We read with great interest the recent paper of Sechi et al. that describes 13 patients with anti-GFAP related myelitis and compares them with 41 patients with anti-AQP4 related myelitis.1 To date, very little data is available about anti-GFAP related disorders.2-3 Sechi et al. highlight some differences between the two entities to help clinicians differentiate them.1 One of these clinical differences relates to area postrema syndrome (APS). Indeed, it is well known that APS is a classical feature of neuromyelitis optica spectrum disorders, particularly among anti-AQP4 positive patients.4 Sechi et al. report this syndrome as a prodromal event in 20% of anti-AQP4 related myelitis. Conversely, the authors do not report any case of APS preceding or accompanying myelitis related to anti-GFAP.1 Given these data, APS could be an indicator for ruling out anti-GFAP encephalomyelitis, particularly useful for centers that do not yet have access to biological testing for anti-GFAP Abs.
Show MoreHowever, we report the case of a 41-year-old woman who in April 2016 developed intractable nausea and vomiting lasting for five weeks and leading to 35 kilograms in weight loss. An extensive search for a digestive disease was negative, and no neurological explorations were performed. One month following the resolution of digestive symptoms, she developed mental confusion, diplopia, dysarthria, dizziness, bilateral blurred vision (with optic disc edema) and paraparesis. Brain...
Dear Editor,
We read with interest the article by Kalincik et al. [1] comparing fingolimod, dimethyl fumarate and teriflunomide in a cohort of relapsing-remitting multiple sclerosis (MS) patients. The authors investigated several endpoints and performed various sensitivity analyses, and we commend them for reporting technical details in the online supplementary material. We, however, have some concerns about the design, analysis and reporting of the study.
1. In the primary analyses, three separate propensity score models were developed to construct a matched cohort for each of the three pairwise comparisons. Supplementary Table 6 clearly indicates the existence of zero or low frequencies in some variables (e.g., most active previous therapy and magnetic resonance imaging [MRI] T2 lesions). Yet, those variables were used as covariates in the propensity score models, unsurprisingly resulting in extremely high point estimates and standard errors (SE; as reported in Supplementary Table 7). For example, teriflunomide was not the most active therapy for any patient in the dimethyl fumarate cohort (n=0 from Supplementary Table 6), but that category was nevertheless included in the propensity score model, leading to an unrealistic point estimate of 18.65 with SE of 434.5 (Supplementary Table 7). Even higher SEs (greater than 1000) are observed in the other propensity score models. Propensity scores estimated from these poorly constructed models were then used to cr...
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