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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.
I note the clinical analysis of differential weakness in ALS in elbow flexion (biceps brachii) compared to elbow extension (triceps) reported by Khalaf et al.1 This is described as analogous to similar 'split' muscle weakness around the ankle joint and, particularly, as that found in flexor digitorum indicis (FDI) compared to abductor digit minim (ADM) in the hand in the disease. It should be remembered that, although characteristic of ALS, this differential pattern of weakness has repeatedly been found not to be unique to ALS, even from the first descriptions.2,3 As the authors, and Vucic in his editorial remark,1,4 the cause of this interesting pattern of weakness in ALS remains uncertain. The finding of an association between the pattern of weakness and increased excitability in the upper motor neuron system in ALS does not necessarily provide primary support for an upper motor neuron (UMN) causation. Nonetheless this pattern of weakness must be important in the disease. It is worth remembering that differential susceptibility to neurogenic lower motor neuron weakness is also a characteristic feature of some peripheral neuropathies, e.g., the Charcot-Marie-Tooth syndromes. Furthermore, differential muscle weakness and atrophy is a characteristic finding that is important in clinical diagnosis in the myriad different genetically determined muscular dystrophies.5 Although the causation of this differential susceptibility of certain muscles in this la...
I note the clinical analysis of differential weakness in ALS in elbow flexion (biceps brachii) compared to elbow extension (triceps) reported by Khalaf et al.1 This is described as analogous to similar 'split' muscle weakness around the ankle joint and, particularly, as that found in flexor digitorum indicis (FDI) compared to abductor digit minim (ADM) in the hand in the disease. It should be remembered that, although characteristic of ALS, this differential pattern of weakness has repeatedly been found not to be unique to ALS, even from the first descriptions.2,3 As the authors, and Vucic in his editorial remark,1,4 the cause of this interesting pattern of weakness in ALS remains uncertain. The finding of an association between the pattern of weakness and increased excitability in the upper motor neuron system in ALS does not necessarily provide primary support for an upper motor neuron (UMN) causation. Nonetheless this pattern of weakness must be important in the disease. It is worth remembering that differential susceptibility to neurogenic lower motor neuron weakness is also a characteristic feature of some peripheral neuropathies, e.g., the Charcot-Marie-Tooth syndromes. Furthermore, differential muscle weakness and atrophy is a characteristic finding that is important in clinical diagnosis in the myriad different genetically determined muscular dystrophies.5 Although the causation of this differential susceptibility of certain muscles in this latter group of disorders remains uncertain, as does the pattern of susceptibility to denervation in ALS, local factor such as structural differences or even patterns of usage in causation are implied. The causation of differential neurogenic weakness and, indeed, of UMN weakness in ALS, is likely to be complex, dependent on local factors, motor nerve dysfunction and spinal and UMN factors.
May I also point out that there is nothing new in all this? Kinnier Wilson6 gives a clear description in his textbook. He remarks that in the classic spinal type of Charcot syndrome:
“In the majority the small hand muscles are first affected, on either side indifferently. If the process begins in one arm it soon spreads to the other, either of a homologous place or another level. With loss of substance power is reduced, varying degrees of the two being found in one or other segment of the limbs. Muscles of the thenar and hypothenar groups waste as whole, that is, globally, and not in part……………..The process goes on to implicate one muscle after another proximally, wrist and finger flexors before extensors as a rule, and biceps before triceps, but a jump from the hand to the deltoid or shoulder muscle is not uncommon. When it begins in the forearm the long extensors are involved soonest, particularly those for the fingers and the ulnar side of the wrist: the supinators may hold out till the biceps weaken.”
Wilson describes various other dissociated patterns of muscle involvement in a very detailed clinical description of the spinal type of ALS, a description that attests to the precision with which he and his contemporaries examined their patients, and their subsequent careful clinical notetaking. It is always wise to remember the skill of our forebears!
1.Khalaf R, Martin S, Ellis C, et al. Relative preservation of triceps over biceps strength in upper limb-onset ALS: the ’split elbow’. J Neurol Neurosurg Psychiatry. In Press 2019. doi:10.1136/jnnp-2018-319894. [Epub ahead of print: 07 Mar 2019].
2.Wilbourn AJ, Sweeney PJ. Dissociated wasting of medial and lateral hand muscles with motor neuron disease. Can J Neurol Sci 1994;21 (suppl 2):S9
3.Schelhaas HJ, van de Warrenburg BPC, Kremer HPH, Zwarts MJ. The “split hand” phenomenon: evidence of a spinal origin. Neurology 2003;61:1619-1620
4.Vucic S. Split elbow sign: more evidence for a corticospinal origin. J Neurol Neurosurg Psychiatry. doi:10.1136/jnnp-2019-320534
5.Swash M. Six issues in muscle disease. J Neurol Neurosurg Psychiatry 2017;88:603-607 doi:10.1136/jnnp-2017-315771
6.Kinnier Wilson SA. Amyotrophic lateral sclerosis: spinal types. In Neurology. Edited by A Ninian Bruce. London. Butterworth & Co. 2nd edition. 1954 p1145
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
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 thank Dr Platt and colleagues for their critical review of our work, especially of the methodology that we have used in this study. It is understandable that comparative studies of treatment effectiveness trigger constructive discussions among industry and academics. We also vehemently agree that rigorous methodology and cautious interpretation of results is mandatory, especially for analyses of observational data.1 2 Therefore, in this letter, we will provide additional clarifications in response to the concerns raised.
We appreciate that the categories that are underrepresented in multivariable logistic regression models may lead to inflation of estimates of the corresponding coefficients and their variance. Such inflation would, however, result in an overly conservative matching rather than the opposite. Due to the use of a caliper, patients with an extreme propensity score can not be matched to patients within the bulk of the distribution of the propensity scores. Such patients were excluded from the matched cohorts.
The issue of residual imbalance is important in any non-randomised comparative study. We acknowledge that the standardised mean difference in annualised relapse rates (ARR) between teriflunomide and fingolimod exceeded the nominal threshold of 20%. It is therefore reassuring that the sensitivity analyses, in which the residual imbalance fell below the accepted threshold of 20% (patients with prior on-treatment relapses, Cohen’s d 14%, and...
We thank Dr Platt and colleagues for their critical review of our work, especially of the methodology that we have used in this study. It is understandable that comparative studies of treatment effectiveness trigger constructive discussions among industry and academics. We also vehemently agree that rigorous methodology and cautious interpretation of results is mandatory, especially for analyses of observational data.1 2 Therefore, in this letter, we will provide additional clarifications in response to the concerns raised.
We appreciate that the categories that are underrepresented in multivariable logistic regression models may lead to inflation of estimates of the corresponding coefficients and their variance. Such inflation would, however, result in an overly conservative matching rather than the opposite. Due to the use of a caliper, patients with an extreme propensity score can not be matched to patients within the bulk of the distribution of the propensity scores. Such patients were excluded from the matched cohorts.
The issue of residual imbalance is important in any non-randomised comparative study. We acknowledge that the standardised mean difference in annualised relapse rates (ARR) between teriflunomide and fingolimod exceeded the nominal threshold of 20%. It is therefore reassuring that the sensitivity analyses, in which the residual imbalance fell below the accepted threshold of 20% (patients with prior on-treatment relapses, Cohen’s d 14%, and analysis with no MRI data included, Cohen’s d 16%) confirmed the results of the primary analysis. We have chosen not to explicitly report absolute differences in proportions reported in Table 1; this information is redundant as the absolute differences can easily be calculated from the proportions shown in the table.
Dr Platt and colleagues make an important point that the comparison of baseline patient characteristics should account for the weights used due to matching in a one-to-multiple variable ratio. We have recalculated the differences for the primary analysis and have observed that our original standardised mean differences overestimated the true weighted standardised mean differences present (see the Table below). Reassuringly, the compared groups are in reality more closely aligned than what the Table 1 in our article would suggest.
TABLE: Recalculated weighted standardised mean differences (Cohen’s d) for continuous baseline characteristics. (DMF, dimethyl fumarate)
DMF vs. teriflunomide fingolimod vs. DMF fingolimod vs. teriflunomide
age 0.006 0.01 0.02
disease duration 0.006 0.003 0.01
disability (EDSS) 0.04 0.002 0.026
relapses 12 months pre-baseline 0.05 0.015 0.03
Combining the results of multiple imputation is a standard procedure, inherent in multiple imputation methodology.3 We have calculated the mode in order to combine the 17 datasets into one. The resulting variable represents the combined result of the 17 imputed data sets and therefore reflects the values with the greatest support within the imputed data space.
Similar to our previous studies, we have chosen to match the studied patients on country.4 This is a conservative decision that aims to mitigate systematic differences in patient follow-up (modelled as the surrogate ‘country’ variable). However, there are methods that are considerably more effective in accounting for inter-centre heterogeneity. We have taken precautions to minimise the heterogeneity in the studied cohort directly – through adjusting for the length and the frequency of recorded follow-up as well as its consistency across the participating centres (the requirement of Neurostatus certification at each centre, adjustment for visit frequency, pairwise censoring, and the quality control process). In fact, differences in treating conventions access to therapies among centres and regions increase the chance that patients will be matched with comparable counterparts who were offered a different therapy for reasons unrelated to their disease severity.
We have calculated ARR in individual patients in order to derive point and interval estimates of the distributions of ARR. The estimates (mean and variance) were weighted for variable one-to-multiple matching and duration of pairwise-censored follow-up. The presented mean ARRs and their 95% confidence intervals are based on this method.
Individual estimation of ARR in a cohort where 75% of patients have a recorded, pairwise-censored follow up greater than 1 year (and the minimum required follow-up of 6 months) is subject to only a negligible risk of inflation due to short follow-up. Notionally, estimation of ARR of a population based on individual observed ARRs from a sample follows standard inferential reasoning within frequentist framework. It enables direct estimation of ARR in studied sample. As in our previous studies, we have used a negative binomial model to compare the incidence of relapse events throughout the pairwise-censored follow-up between the matched patients.4 5 Here, we have used the overall number of relapses from either treatment group and cumulative follow-up, as appropriate. As stated in the Methods, all analyses used weights to account for variable matching ratio and either ‘cluster’ or ‘frailty’ terms to account for the paired data structure. We agree that it would have been more accurate to use the term ‘incidence of relapses’ rather than ‘ARR’ in the description of the negative binomial model in the Methods section. Most importantly, all three methods that we have employed to evaluate relapse outcomes (the two methods described above and the survival model) showed consistent results in the primary analysis.
Appropriately, the authors have closely examined our estimates of variance for ARRs. Unfortunately, the table presented in their communication is difficult to understand. It is unclear what method for the back-calculation of standard deviations from the reported 95% confidence intervals was applied, given that it resulted in two divergent values for both our study and the given examples of randomised clinical trials – reported as ‘SD left’ and ‘SD right’. As described above, all of our analyses used weights to adjust for variable matching ratio, and these were also used to calculate interval estimates for the weighted mean ARRs. It is reasonable to observe variability in the error recorded in each sample, especially in observational studies, which naturally encapsulate more broadly defined cohorts in exchange for greater ecological validity. Furthermore, one would expect the variability in the error to be contingent on the specific selection of study samples, as a result of matching to other treatment groups. We agree that the implications of variable standard deviations across studies are of research interest, but it is not clear that their bench marking to a particular randomised control trial is justified.
We thank the authors for pointing out the inconsistency in the reported exposure to therapies between the groups before and after matching. Sixteen patients treated with dimethyl fumarate were previously exposed to teriflunomide as their highest-efficacy treatment, and the corresponding entry in Supplementary Table 6 should be 16 (2%). We apologise for the typographical error.
A well-powered analysis is not a weakness of a study. We are aware that a statistically significant difference and clinically meaningful difference are complementary but different concepts. Studies carried out within the frequentist framework are reliant on testing of significance of null hypotheses and are destined to provide binary answers – an arbitrary outcome that reflects its origin in randomised controlled trials. Therefore, a result that rejects a null hypothesis provides its reader with more certainty than a result that fails to do so, even when the difference may be of minimal clinical significance. The role of the clinical readership is to interpret clinical meaningfulness of the results. We refrained from attempting to develop cut-offs for what represents clinically meaningful difference and chose to leave this decision to the readers. More robust and intuitive answers to this problem lie in Bayesian methods. We are strong proponents of such methods, as the interpretation of their results is more intuitive for a clinical reader.
The title of their discussion point suggests that Dr Platt and colleagues consider reports that require further clarification of some of its concepts to be methodologically flawed. In the study under discussion, we have utilised the methodology previously used in several studies of comparative effectiveness in the MSBase data set, in particular the comparison of treatment escalation to fingolimod or natalizumab.4 We have systematically addressed the relevant sources of bias, in particular indication bias.1 We take great comfort in the fact that our previous comparative analyses have been highly convergent with the results of pivotal randomised controlled trials.5 6 We have now provided clarification of additional points in response to our colleagues’ review of our article. We value their constructive criticism and believe that our thorough response further strengthens the credibility of the reported results.
References
1. Kalincik T, Butzkueven H. Observational data: Understanding the real MS world. Mult Scler 2016;22(13):1642-48. doi: 10.1177/1352458516653667 [published Online First: 2016/06/09]
2. Trojano M, Tintore M, Montalban X, et al. Treatment decisions in multiple sclerosis - insights from real-world observational studies. Nat Rev Neurol 2017;13(2):105-18. doi: 10.1038/nrneurol.2016.188 [published Online First: 2017/01/14]
3. Heraud-Bousquet V, Larsen C, Carpenter J, et al. Practical considerations for sensitivity analysis after multiple imputation applied to epidemiological studies with incomplete data. BMC medical research methodology 2012;12:73. doi: 10.1186/1471-2288-12-73 [published Online First: 2012/06/12]
4. Kalincik T, Horakova D, Spelman T, et al. Switch to natalizumab vs fingolimod in active relapsing-remitting multiple sclerosis. Ann Neurol 2015;77:425-35. [published Online First: 2014/12/27]
5. Kalincik T, Brown JWL, Robertson N, et al. Comparison of alemtuzumab with natalizumab, fingolimod, and interferon beta for multiple sclerosis: a longitudinal study. Lancet Neurol 2017;16(4):271-81.
6. He A, Spelman T, Jokubaitis V, et al. Comparison of switch to fingolimod or interferon beta/glatiramer acetate in active multiple sclerosis. JAMA Neurol 2015;72(4):405-13. doi: 10.1001/jamaneurol.2014.4147 [published Online First: 2015/02/11]
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
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
Komatsu et al. presented an interesting clinicopathological case of anti-myelin oligodendrocyte glycoprotein (MOG) demyelinating disease of the CNS. (1) Their patient had a rather unusual subacute encephalopathic presentation with extensive supratentorial fluid-attenuation inversion recovery white matter hyperintensities. The authors focused mainly on the conspicuous MRI punctuate and curvilinear enhancement pattern within the hemispheric lesions.
It is well established that intraparenchymal punctuate and curvilinear gadolinium enhancement may arise in the context of Moyamoya syndrome, various endotheliopathies and most commonly, in disorders causing small vessels blood-brain barrier disruption. (2)These entities are associated histologically with perivascular cellular infiltrates and include inflammatory autoimmune diseases (i.e. primary or secondary angiitis of the CNS, neurosarcoidosis, histiocytosis and demyelinating diseases of the CNS), pre-lymphoma states (i.e. sentinel lesions of primary CNS lymphoma), non-Hodgkin lymphoma (i.e. intravascular lymphoma) and CLIPPERS syndrome. (2) Notably, among demyelinating disorders, multiple sclerosis and aquaporin-4 antibody (AQP4-Ab) neuromyelitis optica spectrum disorders (NMOSD) manifest this specific neuroimaging pattern in rare cases. (2,3)
We agree with Komatsu et al. that their case is the first report of the perivascular enhancement in anti-MOG antibody disease. Indeed, gadolinium enhancement was observed in...
Komatsu et al. presented an interesting clinicopathological case of anti-myelin oligodendrocyte glycoprotein (MOG) demyelinating disease of the CNS. (1) Their patient had a rather unusual subacute encephalopathic presentation with extensive supratentorial fluid-attenuation inversion recovery white matter hyperintensities. The authors focused mainly on the conspicuous MRI punctuate and curvilinear enhancement pattern within the hemispheric lesions.
It is well established that intraparenchymal punctuate and curvilinear gadolinium enhancement may arise in the context of Moyamoya syndrome, various endotheliopathies and most commonly, in disorders causing small vessels blood-brain barrier disruption. (2)These entities are associated histologically with perivascular cellular infiltrates and include inflammatory autoimmune diseases (i.e. primary or secondary angiitis of the CNS, neurosarcoidosis, histiocytosis and demyelinating diseases of the CNS), pre-lymphoma states (i.e. sentinel lesions of primary CNS lymphoma), non-Hodgkin lymphoma (i.e. intravascular lymphoma) and CLIPPERS syndrome. (2) Notably, among demyelinating disorders, multiple sclerosis and aquaporin-4 antibody (AQP4-Ab) neuromyelitis optica spectrum disorders (NMOSD) manifest this specific neuroimaging pattern in rare cases. (2,3)
We agree with Komatsu et al. that their case is the first report of the perivascular enhancement in anti-MOG antibody disease. Indeed, gadolinium enhancement was observed in 6 out of 108 MRIs among adult patients with CNS MOG autoimmunity in a large French natiowide cohort. (4) Leptomeningeal enhancement, thought to indicate cortical encephalitis, was detected in only 3 scans. Most importantly, none of the patients in this study or other related publications, demonstrated punctuate or fine, linear-shaped enhancing lesions.
Intriguingly, the enhancement pattern in the case of Komatsu et al. shares many similarities with the imaging abnormalities seen in a recently described disorder, namely autoimmune glial fibrillary acidic protein (GFAP) astrocytopathy. This is a novel form of a steroid-responsive meningoencephalomyelitis associated with IgG binding to GFAP. A characteristic pattern on neuroimaging of brain linear, perivascular enhancement, oriented radially to the ventricles was identified in the seminal paper by Fang et al. (5) This striking abnormality was also observed in a substantial proportion of patients with the disorder in subsequent cohorts. (6)
Therefore, it would be useful to know the CSF status of GFAP-Ab of the forementioned patient. Besides, about 40% of individuals with GFAP antibody-positive meningoencephalomyelitis had coexisting neural autoantibodies. (5, 6)
Many questions regarding GFAP autoimmunity and, to a lesser extent, MOG-Ab–associated diseases remain to be answered in future studies. Overall, it seems that in the right clinical context, when perivascular enhancement on MRI is observed, one should be alert in making a differential diagnosis of GFAP astrocytopathy rather than anti-MOG antibody demyelinating disease.
1. Komatsu T, Matsushima S, Kaneko K, et al. Perivascular enhancement in anti-MOG antibody demyelinating disease of the CNS. J Neurol Neurosurg Psychiatry 2019;90:111-112.
2. Taieb, G., Duran-Peña, A., de Chamfleur, N.M. et al. Neuroradiology 2016; 58: 221-235 https://doi.org/10.1007/s00234-015-1629-y
3. Pekcevik Y, Izbudak I. Perivascular enhancement in a patient with neuromyelitis optica spectrum disease during an optic neuritis attack. J Neuroimaging 2015; 25:686–687
4. Cobo-Calvo I, Ruiz A, Maillart E, et al. Clinical spectrum and prognostic value of CNS MOG autoimmunity in adults. The MOGADOR study. Neurology 2018; 90 (21) e1858-e1869; DOI: 10.1212/WNL.0000000000005560
5. Fang B, McKeon A, Hinson SR, et al. Autoimmune Glial Fibrillary Acidic Protein Astrocytopathy: A Novel Meningoencephalomyelitis. JAMA Neurol. 2016;73(11):1297–1307. doi:10.1001/jamaneurol.2016.2549
6. Zarkali A, Cousins O, Athauda D, et al. Glial fibrillary acidic protein antibody-positive meningoencephalomyelitis. Practical Neurology 2018;18:315-319
Re: A unifying theory for cognitive abnormalities in functional neurological disorders, fibromyalgia and chronic fatigue syndrome
Viraj Bharambe Specialist registrar in neurology
John C Williamson Specialist registrar in neurology
Andrew J Larner Consultant Neurologist
Cognitive Function Clinic
Walton Centre for Neurology and Neurosurgery
Lower Lane
Fazakerley
Liverpool
L9 7LJ
UK
e-mail: a.larner@thewaltoncentre.nhs.uk
Teodoro et al. present evidence for shared cognitive symptoms in fibromyalgia, chronic fatigue syndrome, and functional neurological disorders, and hypothesize that functional cognitive disorders (FCD) may share similar symptoms.1 We present data which speak to this issue.
We have previously reported preliminary data examining performance on the mini-Addenbrooke’s Cognitive Examination (MACE) by patients diagnosed with fibromyalgia2 as part of a larger study of MACE.3 Here, we update these data for fibromyalgia patients (n = 17; F:M = 17:0; age range 33-56 years, median 49) and compare them to MACE performance by patients diagnosed with FCD (n = 43; F:M = 18:25; age range 28-82 years, median 58).4
There was no statistical difference (p > 0.1) in the proportions of patients scoring below the two cut-off scores (≤21/30, ≤25/30) defined in the index MACE report.5 Looking at MACE subscores (Attention, Registration,...
Re: A unifying theory for cognitive abnormalities in functional neurological disorders, fibromyalgia and chronic fatigue syndrome
Viraj Bharambe Specialist registrar in neurology
John C Williamson Specialist registrar in neurology
Andrew J Larner Consultant Neurologist
Cognitive Function Clinic
Walton Centre for Neurology and Neurosurgery
Lower Lane
Fazakerley
Liverpool
L9 7LJ
UK
e-mail: a.larner@thewaltoncentre.nhs.uk
Teodoro et al. present evidence for shared cognitive symptoms in fibromyalgia, chronic fatigue syndrome, and functional neurological disorders, and hypothesize that functional cognitive disorders (FCD) may share similar symptoms.1 We present data which speak to this issue.
We have previously reported preliminary data examining performance on the mini-Addenbrooke’s Cognitive Examination (MACE) by patients diagnosed with fibromyalgia2 as part of a larger study of MACE.3 Here, we update these data for fibromyalgia patients (n = 17; F:M = 17:0; age range 33-56 years, median 49) and compare them to MACE performance by patients diagnosed with FCD (n = 43; F:M = 18:25; age range 28-82 years, median 58).4
There was no statistical difference (p > 0.1) in the proportions of patients scoring below the two cut-off scores (≤21/30, ≤25/30) defined in the index MACE report.5 Looking at MACE subscores (Attention, Registration, Verbal fluency, Clock Drawing, Memory recall), the proportions scoring at ceiling were not statistically different (p > 0.1) in the fibromyalgia and FCD groups. Ranking performance in cognitive domains from worst to best showed the same pattern in both patient groups, namely Verbal fluency, Memory recall, Registration, Attention, and Clock drawing.
Although numbers are small and the cognitive testing relatively simple, and the group demographics differ (both gender and age p < 0.05), nevertheless we suggest this evidence supports the hypothesis of Teodoro et al. of shared cognitive symptoms in fibromyalgia and FCD.
References
1. Teodoro T, Edwards MJ, Isaacs JD. A unifying theory for cognitive abnormalities in functional neurological disorders, fibromyalgia and chronic fatigue syndrome: systematic review. J Neurol Neurosurg Psychiatry 2018; 89: 1308-19. doi: 10.1136/jnnp-2017-317823.
2. Williamson J, Larner AJ. Cognitive dysfunction in patients with fibromyalgia. Br J Hosp Med 2016; 77: 116. doi: 10.12968/hmed.2016.77.2.116.
3. Williamson J, Larner AJ. MACE for the diagnosis of dementia and MCI: 3-year pragmatic diagnostic test accuracy study. Dement Geriatr Cogn Disord 2018; 45: 300-7. doi: 10.1159/000484438.
4. Bharambe V, Larner AJ. Functional cognitive disorders: demographic and clinical features contribute to a positive diagnosis. Neurodegener Dis Manag 2018; 8: 377-83. doi: 10.2217/nmt-2018-0025.
5. Hsieh S, McGrory S, Leslie F, et al. The Mini-Addenbrooke’s Cognitive Examination: a new assessment tool for dementia. Dement Geriatr Cogn Disord 2015; 39: 1-11. doi: 10.1159/000366040.
Conflicts of interest
The authors declare no conflict of interest
Dear Editor,
We have read with great interest the work by Scarpazza et al that provided a longitudinal MRI evaluation of natalizumab-related Progressive Multifocal Leukoencephalopathy (NTZ-PML) lesions in Multiple Sclerosis (MS) patients (1).
Their central finding was the high percentage (78.1%) of patients, who eventually developed NTZ-PML, in whom highly suggestive lesions were already retrospectively detectable on pre-diagnostic MRI exams. Furthermore, the pre-diagnostic phase proved to be relatively long (150.8±74.9 days), with an estimated percentage increase of the lesions’ volume of 62.8% per month (1).
Given the widely recognized crucial role of a timely NTZ-PML identification in reducing mortality and residual disability (1), these results present the neurological and neuroradiological communities with an important clinical challenge, prompting a major effort to ensure an early diagnosis of this condition.
Although redefining the timing of MRI surveillance, with up to one brain MRI exam every 3-4 months for high-risk patients, appears as a justified strategy, we think that improving the accuracy of early identification of NTZ-PML is also mandatory.
In our opinion, such achievement should be pursued using two complementary approaches: (i) a specific training addressed to neuroradiologists working in the field of MS, who should be aware of the relevance of even very small asymptomatic PML lesions and how to differentiate them from new M...
Dear Editor,
We have read with great interest the work by Scarpazza et al that provided a longitudinal MRI evaluation of natalizumab-related Progressive Multifocal Leukoencephalopathy (NTZ-PML) lesions in Multiple Sclerosis (MS) patients (1).
Their central finding was the high percentage (78.1%) of patients, who eventually developed NTZ-PML, in whom highly suggestive lesions were already retrospectively detectable on pre-diagnostic MRI exams. Furthermore, the pre-diagnostic phase proved to be relatively long (150.8±74.9 days), with an estimated percentage increase of the lesions’ volume of 62.8% per month (1).
Given the widely recognized crucial role of a timely NTZ-PML identification in reducing mortality and residual disability (1), these results present the neurological and neuroradiological communities with an important clinical challenge, prompting a major effort to ensure an early diagnosis of this condition.
Although redefining the timing of MRI surveillance, with up to one brain MRI exam every 3-4 months for high-risk patients, appears as a justified strategy, we think that improving the accuracy of early identification of NTZ-PML is also mandatory.
In our opinion, such achievement should be pursued using two complementary approaches: (i) a specific training addressed to neuroradiologists working in the field of MS, who should be aware of the relevance of even very small asymptomatic PML lesions and how to differentiate them from new MS lesions on conventional brain MRI scans (2), and (ii) the identification of new MRI markers that could help in an early neuroradiological identification of PML.
It is noteworthy to mention that recent works described the presence of significant magnetic susceptibility changes, revealing a paramagnetic dipole, at the level of the subcortical U-fibers adjacent to PML lesions (3, 4). This paramagnetic effect, which has been speculatively attributed to iron accumulation inside macrophages and microglial cells following myelin degeneration, has been reported as an early and constant finding in this condition (3, 4).
Given the known absence of significant paramagnetic effects within new MS plaques (5), this sign, which still needs validation studies on larger patient cohorts, could potentially improve the accuracy of an early neuroradiological differential diagnosis between these two conditions, eventually leading to the implementation of susceptibility-weighted sequences in routine brain MRI exams of high-risk patients.
References
1. Scarpazza C, Signori A, Prosperini L, et al. Early diagnosis of progressive multifocal leucoencephalopathy: longitudinal lesion evolution. Journal of neurology, neurosurgery, and psychiatry 2018.
2. Wijburg MT, Witte BI, Vennegoor A, et al. MRI criteria differentiating asymptomatic PML from new MS lesions during natalizumab pharmacovigilance. Journal of neurology, neurosurgery, and psychiatry 2016;87(10):1138-45.
3. Hodel J, Outteryck O, Verclytte S, et al. Brain Magnetic Susceptibility Changes in Patients with Natalizumab-Associated Progressive Multifocal Leukoencephalopathy. AJNR American journal of neuroradiology 2015;36(12):2296-302.
4. Pontillo G, Cocozza S, Lanzillo R, et al. Brain Susceptibility Changes in a Patient with Natalizumab-Related Progressive Multifocal Leukoencephalopathy: A Longitudinal Quantitative Susceptibility Mapping and Relaxometry Study. Frontiers in neurology 2017;8:294.
5. Zhang Y, Gauthier SA, Gupta A, et al. Quantitative Susceptibility Mapping and R2* Measured Changes during White Matter Lesion Development in Multiple Sclerosis: Myelin Breakdown, Myelin Debris Degradation and Removal, and Iron Accumulation. AJNR American journal of neuroradiology 2016;37(9):1629-35.
Corresponding Author:
Giuseppe Pontillo, MD
Department of Advanced Biomedical Sciences
University “Federico II”, Via Pansini, 5, 80131 - Naples - Italy
E-mail: giuseppe.pon@gmail.com
Dear 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, 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 MoreElucidating 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 thank Dr Platt and colleagues for their critical review of our work, especially of the methodology that we have used in this study. It is understandable that comparative studies of treatment effectiveness trigger constructive discussions among industry and academics. We also vehemently agree that rigorous methodology and cautious interpretation of results is mandatory, especially for analyses of observational data.1 2 Therefore, in this letter, we will provide additional clarifications in response to the concerns raised.
We appreciate that the categories that are underrepresented in multivariable logistic regression models may lead to inflation of estimates of the corresponding coefficients and their variance. Such inflation would, however, result in an overly conservative matching rather than the opposite. Due to the use of a caliper, patients with an extreme propensity score can not be matched to patients within the bulk of the distribution of the propensity scores. Such patients were excluded from the matched cohorts.
The issue of residual imbalance is important in any non-randomised comparative study. We acknowledge that the standardised mean difference in annualised relapse rates (ARR) between teriflunomide and fingolimod exceeded the nominal threshold of 20%. It is therefore reassuring that the sensitivity analyses, in which the residual imbalance fell below the accepted threshold of 20% (patients with prior on-treatment relapses, Cohen’s d 14%, and...
Show MoreDear 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...
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...
Komatsu et al. presented an interesting clinicopathological case of anti-myelin oligodendrocyte glycoprotein (MOG) demyelinating disease of the CNS. (1) Their patient had a rather unusual subacute encephalopathic presentation with extensive supratentorial fluid-attenuation inversion recovery white matter hyperintensities. The authors focused mainly on the conspicuous MRI punctuate and curvilinear enhancement pattern within the hemispheric lesions.
Show MoreIt is well established that intraparenchymal punctuate and curvilinear gadolinium enhancement may arise in the context of Moyamoya syndrome, various endotheliopathies and most commonly, in disorders causing small vessels blood-brain barrier disruption. (2)These entities are associated histologically with perivascular cellular infiltrates and include inflammatory autoimmune diseases (i.e. primary or secondary angiitis of the CNS, neurosarcoidosis, histiocytosis and demyelinating diseases of the CNS), pre-lymphoma states (i.e. sentinel lesions of primary CNS lymphoma), non-Hodgkin lymphoma (i.e. intravascular lymphoma) and CLIPPERS syndrome. (2) Notably, among demyelinating disorders, multiple sclerosis and aquaporin-4 antibody (AQP4-Ab) neuromyelitis optica spectrum disorders (NMOSD) manifest this specific neuroimaging pattern in rare cases. (2,3)
We agree with Komatsu et al. that their case is the first report of the perivascular enhancement in anti-MOG antibody disease. Indeed, gadolinium enhancement was observed in...
Re: A unifying theory for cognitive abnormalities in functional neurological disorders, fibromyalgia and chronic fatigue syndrome
Viraj Bharambe Specialist registrar in neurology
John C Williamson Specialist registrar in neurology
Andrew J Larner Consultant Neurologist
Cognitive Function Clinic
Walton Centre for Neurology and Neurosurgery
Lower Lane
Fazakerley
Liverpool
L9 7LJ
UK
e-mail: a.larner@thewaltoncentre.nhs.uk
Teodoro et al. present evidence for shared cognitive symptoms in fibromyalgia, chronic fatigue syndrome, and functional neurological disorders, and hypothesize that functional cognitive disorders (FCD) may share similar symptoms.1 We present data which speak to this issue.
We have previously reported preliminary data examining performance on the mini-Addenbrooke’s Cognitive Examination (MACE) by patients diagnosed with fibromyalgia2 as part of a larger study of MACE.3 Here, we update these data for fibromyalgia patients (n = 17; F:M = 17:0; age range 33-56 years, median 49) and compare them to MACE performance by patients diagnosed with FCD (n = 43; F:M = 18:25; age range 28-82 years, median 58).4
There was no statistical difference (p > 0.1) in the proportions of patients scoring below the two cut-off scores (≤21/30, ≤25/30) defined in the index MACE report.5 Looking at MACE subscores (Attention, Registration,...
Show MoreDear Editor,
Show MoreWe have read with great interest the work by Scarpazza et al that provided a longitudinal MRI evaluation of natalizumab-related Progressive Multifocal Leukoencephalopathy (NTZ-PML) lesions in Multiple Sclerosis (MS) patients (1).
Their central finding was the high percentage (78.1%) of patients, who eventually developed NTZ-PML, in whom highly suggestive lesions were already retrospectively detectable on pre-diagnostic MRI exams. Furthermore, the pre-diagnostic phase proved to be relatively long (150.8±74.9 days), with an estimated percentage increase of the lesions’ volume of 62.8% per month (1).
Given the widely recognized crucial role of a timely NTZ-PML identification in reducing mortality and residual disability (1), these results present the neurological and neuroradiological communities with an important clinical challenge, prompting a major effort to ensure an early diagnosis of this condition.
Although redefining the timing of MRI surveillance, with up to one brain MRI exam every 3-4 months for high-risk patients, appears as a justified strategy, we think that improving the accuracy of early identification of NTZ-PML is also mandatory.
In our opinion, such achievement should be pursued using two complementary approaches: (i) a specific training addressed to neuroradiologists working in the field of MS, who should be aware of the relevance of even very small asymptomatic PML lesions and how to differentiate them from new M...
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