Table of Contents Author Guidelines Submit a Manuscript
Behavioural Neurology
Volume 2018 (2018), Article ID 8584653, 8 pages
https://doi.org/10.1155/2018/8584653
Review Article

Repetitive Transcranial Magnetic Stimulation, Cognition, and Multiple Sclerosis: An Overview

1Department of Speech and Language Therapy, Higher Educational Institute of Epirus, Ioannina, Greece
2Department of Neurology, Neuropsychology Section, University of Patras Medical School, 26504 Patras, Greece
3Department of Neurology, University of Thessaly Medical School, Larisa, Greece
4Department of Neurology, University of Patras Medical School, 26504 Patras, Greece

Correspondence should be addressed to Grigorios Nasios

Received 16 July 2017; Accepted 7 December 2017; Published 18 January 2018

Academic Editor: Guido Rubboli

Copyright © 2018 Grigorios Nasios et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Multiple sclerosis (MS) affects cognition in the majority of patients. A major aspect of the disease is brain volume loss (BVL), present in all phases and types (relapsing and progressive) of the disease and linked to both motor and cognitive disabilities. Due to the lack of effective pharmacological treatments for cognition, cognitive rehabilitation and other nonpharmacological interventions such as repetitive transcranial magnetic stimulation (rTMS) have recently emerged and their potential role in functional connectivity is studied. With recently developed advanced neuroimaging and neurophysiological techniques, changes related to alterations of the brain’s functional connectivity can be detected. In this overview, we focus on the brain’s functional reorganization in MS, theoretical and practical aspects of rTMS utilization in humans, and its potential therapeutic role in treating cognitively impaired MS patients.

1. What Is Multiple Sclerosis (MS)?

Multiple sclerosis (MS) is an autoimmune, chronic central nervous system disease of unknown etiology, presenting as an ongoing demyelinating, inflammatory, and degenerative process, affecting both grey and white matters of the brain and the spinal cord, and resulting in the accumulation over the years of disabling motor and cognitive handicaps. Quality of life; personal, social, and professional status; and life expectancy are all significantly challenged by the disease [13].

One of its most puzzling characteristics is the subclinical phase prior to diagnosis, which could last for years and subtly affect cognitive aspects of nervous system functioning. Indeed, there is evidence suggesting that deterioration of cognitive performance could be detected years (even decades) before formal diagnosis [4, 5]. Unfortunately, there are currently no validated biomarkers to preliminary track the neuroimmunological phenomena underlying this subclinically active disease phase [6].

Additionally, patients which are initially diagnosed with a radiologically isolated or clinically isolated syndrome (RIS or CIS) which years later progress to definite MS forms have only recently been targeted with disease-modifying medications during the initial phase, resulting in an overall large number of patients worldwide in whom treatment initiation comes rather late in the disease course. This disappointing fact, resulting perhaps in the accumulation of disability in the majority of MS patients over the years (especially after the 1st or 2nd decade of the disease course), may be linked to the continuing and increasing CNS lesion load and tissue damage and has fortunately forced specialists in the field to become alert of the notion that “time is brain” and that “effective intervention during a limited period early in the course of MS is critical for maintaining neurological function and preventing subsequent disability” [7].

2. Cognition in MS

The cognitive aspect of MS was not recognized widely until the last two decades, although Charcot has described it as part of its clinical picture almost 150 years ago [8]. Now we know that 40–70% of all MS patients do have cognitive impairment which affects their lives [2, 9]. Even in the so-called “benign” form of the disease, 15 years after the diagnosis with an EDDS score remaining considerably low (up to 3), cognitive disorders can be diagnosed in half of these patients [10]. The database PubMed which was accessed on 15 May 2017, with keywords “cognition in Multiple sclerosis” revealed 2256 items, 1698 of them (75.26%) were published after 2006.

Another major aspect of the disease is brain volume loss (BVL), which is present in all phases and all types (relapsing and progressive) of the disease, and it is linked to both motor and cognitive disabilities [1113]. BVL is widespread in both white and grey matter tissues and cortical and subcortical structures. Among other sites, thalamic damage is directly related to cognitive deficits in all forms of the disease, even in clinically isolated syndromes (CIS) [14, 15]. Moreover, while white matter atrophy is 3-fold compared to that in healthy controls and remains 3-fold during the course of the disease, gray matter atrophy is initially 3-fold in CIS patients compared to healthy controls but increases to 14-fold in SPMS patients [16, 17].

The deterioration of cognitive performance, usually subtle at least during the first years of the disease course, is almost impossible to be diagnosed by routine neurological testing; therefore, special neuropsychological assessments are needed [18]. This deterioration may not be clinically evident at first and may be hidden by neuroplasticity, that is, the brain’s capacity to reorganize its networks in order to keep on functioning despite tissue damage. Using state-of-the-art functional and static neuroimaging magnetic resonance imaging techniques such as fMRI and diffusion tensor imaging (DTI), we can study the brain’s effort to overcome the ongoing structural damage and maintain sufficient functions and many new important insights are becoming apparent, as we will discuss them in the sections that follow.

We do not know a lot about disease-modifying medications’ ability to act directly on patients’ cognitive performance, or what we know is that they do not have a significant influence [19] what we suspect (and hope) is that they do so indirectly by protecting the accumulation of brain tissue damage and delaying brain volume loss. It seems that even in the small proportion of patients achieving the desired NEDA status (no evidence of disease activity) over time, cognitive deterioration was not precluded [20]. The role of cognitive rehabilitation in various central nervous system diseases and MS has recently emerged [21]. Additionally, other nonpharmacological interventions are also being discussed as having a potentially beneficial role in ameliorating physical and cognitive aspects of the disease [22]. Among these interventions, repetitive transcranial magnetic stimulation (rTMS) seems to have both the scientific and theoretical support and also evidence from experimental models of the disease and trials in patients that can play an important role in MS’s management.

3. TMS and rTMS

Transcranial magnetic stimulation (TMS) is a neurostimulatory and neuromodulatory technique, based on the principle of electromagnetic induction of an electric field in the brain [23]. This method has behavioral consequences and therapeutic potentials. Barker et al. in 1985 described a method of directly stimulating the human motor cortex using a pulsed magnetic field [24]. During the last 2-3 decades, TMS has become a method of choice for noninvasive stimulation of the brain in conscious human subjects to study the excitability of different cortical areas and to map the connectivity of neuronal pathways [25, 26]. When TMS pulses are applied repetitively, they can modulate cortical excitability, either decreasing or increasing it, depending on the parameters of stimulation. TMS has immediate as well as after-effects on the human cortex. rTMS has local and remote effects on neural function which can be excitatory or inhibitory [27]. The direction, magnitude, and duration of conditioning rTMS effects depend on the stimulation site, frequency, intensity, and the duration of the rTMS training. For example, after-effects last longer when the number of rTMS stimuli applied is increased [28]. Low-frequency (1 Hz) rTMS given over the primary motor cortex reduces corticospinal excitability [29], but higher-frequency rTMS increases corticospinal excitability (Pascual-Leone et al., 1994 and [30]). It has also been shown that repeated rTMS is capable of evoking long-lasting cumulative plastic changes of cortical function not only in the stimulated cortex but also in the remote functionally interconnected areas that outlast the stimulation period [31]. The way rTMS acts on molecular and neuronal level is not yet well understood. It has become clear that rTMS can change structural, functional, and molecular properties of neurons, which may depend on the simultaneous conduction of action potentials. rTMS-mediated changes interfere with the ability of neurons to express distinct forms of plasticity beyond the stimulation period [30, 32]. Evidence is growing about the rTMS-induced modification of cerebral blood flow, glucose metabolism, and neuronal excitability in the stimulated area as well as in interconnected brain regions [33]. After-effects of rTMS may represent changes in synaptic efficacy known as long-term potentiation (LTP) and long-term depression (LTD). The balance between “LTP/LTD-like” phenomena, which underlie many processes happening in the brain, that is, learning and memory, is altered by rTMS. Esser et al. exploited a new approach based on combined rTMS/high-density electroencephalography (hd-EEG) providing a direct noninvasive evidence for LTP bilaterally over the premotor cortex in humans induced by rTMS [34].

TMS could possibly have additional effects such as endocrine after-effects, histotoxicity, and effects on neurotransmitters, immune system, and autonomic function, which are not yet fully understood [23]. Potential therapeutic effects of rTMS have already been explored, and “the use of TMS has grown dramatically in the past decade, new protocols of TMS have been developed, changes in the devices have been implemented, TMS is being increasingly combined with other brain imaging and neurophysiologic techniques including fMRI and EEG, and a growing number of subjects and patients are being studied with expanding numbers of longer stimulation sessions” [23].

An increasing number of trials worldwide investigated the therapeutic role of rTMS in depression, schizophrenia, addictions, posttraumatic stress disorders, pain, migraine, stroke, autism, multiple sclerosis, and neurodegenerative disorders such as Alzheimer’s and Parkinson’s disease [35]. Accordingly, animal studies have been employed to assess the effects of rTMS on synaptic plasticity. Among them, there are studies indicating an additional therapeutic role of electromagnetic stimulation in demyelinating processes: experimental animal models of MS (experimental autoimmune encephalomyelitis) have proven that rTMS modifies astrocytosis, cell density, and lipopolysaccharide levels, suggesting that TMS could be a promising treatment for neuroinflammatory conditions such as multiple sclerosis [36].

Sherafat et al. have shown that after inducing demyelination, using local injection of lysophosphatidylcholine within the corpus callosum of adult female Sprague-Dawley rats and then applying electromagnetic fields (EMFs) postlesionally significantly reduced the extent of the demyelinated area and increased the level of myelin basic protein staining within the lesion area, suggesting that EMFs potentiate proliferation and migration of neural stem cells and enhance the repair of myelin in the context of demyelinating conditions [37].

What is very interesting—and there is accumulating evidence towards this—is that we can affect cognitive processing in healthy humans by rTMS. Guse et al. conducted a systematic overview of high-frequency rTMS (HF-rTMS) studies assessing neurocognition in order to better understand the potential of rTMS to induce long-term effects on cognition. High-frequency rTMS (10–20 Hz) is most likely to cause significant cognitive improvement when applied over the left (dorsolateral) prefrontal cortex, within a range of 10–15 successive sessions and an individual motor threshold between 80 and 110% [38].

The correct positioning of the coil is also very important for the effects of rTMS. Localization of the stimulation site by individually fMRI-guided TMS neuronavigation, instead of using the 10–20 EEG system, results in stronger and more robust TMS effects, inducing long-lasting cognitive improvement [39]. Sato et al. designed a study by using event-related potentials (ERPs) to clarify the effect of magnetic stimulation on cognitive processing. They found that a 1.00 Hz rTMS pulse train over the left dorsolateral prefrontal cortex increased P300 latencies by 8.50 ms at Fz, 12.85 ms at Cz, and 11.25 ms at Pz. In contrast, neither 0.75 nor 0.50 Hz rTMS pulse trains over the left dorsolateral prefrontal cortex nor 1.00, 0.75, and 0.50 Hz rTMS pulse trains over the right dorsolateral prefrontal cortex altered P300 latencies. These results indicate that rTMS frequency affects cognitive processing. The authors suggested that the effects of rTMS vary according to the activity of excitatory and inhibitory neurons in the cerebral cortex [40].

Esslinger et al., using a multimodal fMRI-rTMS approach, demonstrated changes in cortical plasticity in humans during executive cognition [41]. They examined 12 healthy control subjects in a crossover study with fMRI while performing an n-back working memory (WM) task and a flanker task engaging cognitive control, after real and sham 5 Hz rTMS to the right dorsolateral prefrontal cortex (DLPFC). Reaction times during the n-back task were significantly shorter after rTMS than after sham stimulation, supporting an excitatory effect of high-frequency rTMS. Interestingly, rTMS compared with sham stimulation caused no activation changes at the stimulation site (right DLPFC) itself but significantly increased connectivity within the WM network during n-back and reduced activation in the anterior cingulate cortex during the flanker task. These findings show the plastic changes in prefrontal connected networks downstream of the stimulation site as the substrate of the behavioral effect [31]. Li et al. investigated the effects of high-frequency (10 Hz) rTMS applied over the left DLPFC on cognitive control of young healthy participants and explored the time course changes of cognitive processing after rTMS using event-related potentials (ERPs). A Stroop task was performed, and an electroencephalogram (EEG) was recorded. The results revealed that multiple sessions of rTMS can decrease reaction time (RT) under both congruent and incongruent conditions and also increased the amplitudes of both N2 and N450 compared with sham rTMS. This observation supports the view that high-frequency rTMS over the left DLPFC not only recruits more neural resources from the prefrontal cortex by inducing an electrophysiologic excitatory effect but also enhances efficiency of resources to deploy for conflict resolution during multiple stages of cognitive control processing in healthy young people [42]. Hsu and colleagues conducted a systematic review and meta-analysis of the literature (1990–2014) to evaluate the effects of noninvasive brain stimulation (rTMS and tDCS) on cognitive function in healthy older adults and patients with Alzheimer’s disease (AD). They concluded that noninvasive brain stimulation has a positive effect on cognitive function in physiological and pathological aging [43].

4. Brain’s Functional Reorganization in MS

As sophisticated techniques have been introduced in the near past, we are facing a new era in which neuroplasticity can be studied not only as a unique brain ability to reorganize its functional networks in order to overcome aging and diseases but also as a new therapeutic target. In fact, neuropsychological rehabilitation (neurorehabilitation), accompanied by new noninvasive neurostimulation–neuromodulation methods, is becoming popular, partially due to the lack of effective pharmacological treatments. As Maggio and Vlachos state, “understanding the role of neural plasticity under pathological conditions, novel therapeutic approaches could be designed to promote, block, or shift the balance between distinct forms of plasticity in specific brain regions and at diverse stages of pathological brain conditions” [44].

Neuroplasticity is increasingly studied as altered brain functional connectivity both at rest (resting-state functional connectivity (rs-FC)) and during tasks. Hyperconnectivity or hypoconnectivity can be detected, depending on the severity and extension of structural brain damage, the nature of disease process, and its time course. These alterations could be adaptive or maladaptive.

Particularly in multiple sclerosis, studies have shown that patients in early stages activate additional brain areas adjacent to those primarily involved during task performance, allowing patients to perform normally prior to cognitive deficits being detectable on neuropsychological assessment [45]. This additional activation serves as a compensatory mechanism allowing the individual to maintain intact cognitive functioning for a period of time, functionally compensating for injury associated with progression of the disease and thus masking defects [46, 47]. Mainero and colleagues scanned matched healthy subjects and patients with relapsing-remitting MS (RRMS) with no or only mild cognitive deficits while performing neuropsychological testing (the Paced Auditory Serial Addition Test (PASAT) and a recall task), and the relation between fMRI changes during both tasks and T2 lesion load was investigated. Patients with RRMS exhibit altered patterns of activation during tasks exploring sustained attention, information processing, and memory. During these tasks, fMRI activity was greater in patients with better cognitive function than in those with lower cognitive function. Authors concluded that functional changes in specific brain areas increase with increasing tissue damage suggesting that they may also represent adaptive mechanisms that reflect underlying neural disorganization or disinhibition, possibly associated with MS [48].

Staffen and colleagues performed a functional MRI study during PVSAT (Paced Visual Serial Addition Task), a visual analogue to PASAT (Paced Auditory Serial Addition Task), in 21 recently diagnosed RRMS patients and matched healthy controls. A group analysis of the functional imaging data during the PVSAT revealed different activation patterns for patients compared with control subjects. In healthy volunteers, the main activation was detected at the right hemispheric frontal cortex (Brodmann area 32). In patients, the main activation was detected at the right hemispheric frontal cortex (Brodmann areas 6, 8, and 9). In addition, the left hemispheric Brodmann area 39 was activated. The different patterns of activation, accompanied with intact performance in a sustained attention task of this multiple sclerosis sample compared with healthy controls, were interpreted as the consequence of compensatory mechanisms, in other words as an expression of neuronal plasticity during early stages of a chronic disease [49].

In contrast to task-based fMRI, resting-state functional connectivity (rs-FC) examines the communication between different brain regions within neural networks at “rest.” Resting-state functional connectivity (rs-FC) studies have noted that increased activation could be interpreted as either adaptive or maladaptive, depending on the progression of the disease. Increased connectivity during rs-FC is thought to serve as a compensatory mechanism for cognitive deficits early in the MS disease process [21, 50, 51], but later in the disease, these extra connections are associated with worse performance [21, 52]. Cader et al. concluded that both forms of adaptive functional change, that is, the enhancement of the coherence of interactions between brain regions normally recruited (functional enhancement) and the recruitment of alternative areas or the use of complementary cognitive strategies, could limit clinical expression of the disease and particularly of cognitive impairments [51].

MS patients, trying to compensate the ongoing structural damage, do not only activate additional cerebral areas but also change strategies, and indeed, this is partially effective. An excellent proof of this is provided in the article of Bonnet et al.: while performing a go/no-go task of increasing complexity, patients could follow the performance of healthy control subjects to a point. For the most complex condition, patients presented both collapse of additional cerebral recruitment and significant lower cognitive performance compared to controls. Authors questioned the cerebral mechanisms allowing the maintenance of normal performances in patients with RRMS according to the level of cognitive demand. They found that, “contrary to healthy subjects, patients with MS did not exhibit a correlation between cerebellar activation and better performances.” Patients’ retained performance was correlated with higher activation in medial prefrontal regions (IG and CG), areas known to be involved in decision-making; in other words, they exhibit a transfer of function to cerebral areas skilled to manage controlled processes. This new medial frontal recruitment could support a functional strategy of compensation in patients with MS. In a multicenter study, significant correlations were found between abnormal fMRI patterns of activations and deactivations and behavioral measures, cognitive performance, and brain T2 and T1 lesion volumes. These results support the theory that a preserved fMRI activity of the frontal lobe is associated with a better cognitive profile in MS patients [53].

In an elegant recent study, Rocca and colleagues [54] investigated rs-FC abnormalities within the principal brain networks in a large cohort of MS patients, with various forms and stages of the disease. Connectivity abnormalities and correlations with clinical/neuropsychological/imaging measures were evaluated. MS patients showed reduced network average rs-FC versus controls in the default-mode network. At regional level, a complex pattern of decreased and increased rs-FC was found. Reduced rs-FC correlated with T2 lesions. Reduced thalamic rs-FC correlated with better neuropsychological performance, whereas for all the remaining networks, reduced FC correlated with more severe clinical/cognitive impairment. Similar findings have been reported for Alzheimer’s disease, in which subjects in an early preclinical phase show relatively increased prefrontal cortical activation with memory deficits [55].

Sumowski and colleagues explored the cognitive reserve hypothesis by testing how could lifetime intellectual enrichment (estimated with vocabulary knowledge) lessen the negative impact of brain disease on cognition; in other words, patients with greater enrichment are able to withstand more severe neuropathology before suffering cognitive impairment or dementia. Multiple sclerosis patients’ cerebral activity (functional magnetic resonance imaging blood oxygen level-dependent signal) and behavioral performance were recorded during the visual n-back working memory task. Results revealed strong positive correlations between intellectual enrichment and cerebral activity within the brain’s default network, indicating that patients with greater enrichment were able to maintain resting-state activity during cognitive processing better. Furthermore, intellectual enrichment was negatively associated with prefrontal recruitment, suggesting that patients with lesser enrichment required more cerebral resources to perform the same cognitive task as patients with greater enrichment [56].

However, it is important to appreciate the complexities of interpreting differences in patterns of activation across the brains of subjects with pathology relative to healthy controls. First, fMRI identifies brain regions in which activity is associated with task performance, not those that are necessary [57]. Secondly, alternative strategies for performance of a task can be associated with differences in patterns of activation without being able to be interpreted in a simple way as adaptive [58]. Schoonheim et al. reviewed the recent functional connectivity literature in MS and the potential effects on cognition that functional connectivity changes may have [59]. A “compensatory” change is seen in the brains of MS patients in the form of both increased activation and increased connectivity. Studies investigating the “default mode network” (DMN) found increased DMN connectivity in clinically isolated syndrome (CIS) patients [60] and decreased DMN connectivity in progressive MS, which was related to cognitive impairment [61]. Which reported connectivity changes can be said to be “compensatory”? Which are “maladaptive”? Authors conclude on the requirement of “a more holistic approach, encompassing both activation and connectivity data into a frame of network dynamics in a longitudinal fashion.”

5. rTMS in MS

Palm et al. reviewed the application of noninvasive brain stimulation techniques for the improvement of several neurologic and psychiatric disorders in MS patients. Specifically, the efficacy of tDCS and TMS for the treatment of depressive symptoms, fatigue, tactile sensory deficit, pain, motor performance, and spasticity was assessed in several studies and showed mixed results [22].

Due to the lack of effective pharmacological treatments alone, rTMS in combination with medication has been used with significant efficacy mainly for the improvement of spasticity [6265], fatigue and depression [22], lower urinary tract dysfunction [66], gait [67], and hand dexterity [61, 68]. Most studies however have certain methodological limitations, such as small number of participants and low-to-moderate level of efficacy, indicating the emerging need for more studies in the future. Symptoms, such as fatigue, are better targeted with tDCS [69].

6. rTMS for Cognition in MS

Considering the previously presented literature, we have several reasons why one should consider using rTMS to treat cognitively impaired MS patients: firstly, we do not have effective pharmacological treatments for the nearly two-thirds of all MS patients who become cognitively impaired through the disease course, and their lives are negatively influenced; secondly, there is an accumulating body of evidence that patients’ brains undergo functional reorganization even from the initial disease phases, by altering functional connectivity in various regions, and this acts as a compensatory mechanism; thirdly, a growing number of MS patients are exposed to rTMS training protocols for other symptoms, without any major safety or adverse event considerations; and fourthly and more importantly, noninvasive neurostimulation techniques such as rTMS have shown beneficial effects on cognitive performance in healthy persons and in patients with various neurological diseases, by evoking neuroplasticity changes, in other words enhancing the brain’s functional capacity.

Additionally, higher cognitive reserve [56] and cognitive rehabilitation interventions [70, 71] have proved effective in ameliorating cognitive performance in MS patients, and the underlying mechanism seems to be the induced neuroplasticity changes [21, 56, 72]. One could, therefore, consider using, and even combining, these available nonpharmacological, noninvasive interventions.

Despite the theoretical support of such clinical use, there is, to our knowledge, only one, recently published, study for the therapeutic use of rTMS on cognition in MS patients [73]. In this study, Hulst et al. investigated the effects of high-frequency rTMS of the right dorsolateral prefrontal cortex (DLPFC) on working memory performance, while measuring task-related brain activation and task-related brain connectivity in patients with MS. The authors reported that n-back task accuracy improved after applying real rTMS (and not after sham rTMS) only in patients. At baseline, MS patients, compared to healthy controls, showed higher task-related frontal activation, which disappeared after real rTMS. Task-related functional connectivity between the right DLPFC and the right caudate nucleus and bilateral (para) cingulate gyrus increased in patients after real rTMS when compared to sham stimulation. The authors interpret these results as an rTMS-induced change in network efficiency in MS patients, implicating a potential role for rTMS in cognitive rehabilitation in MS. With the limitation of the small sample of participants (17 MS patients and 11 HCs), the results of this study are very promising and of course call for more trials in order to provide more robust evidence of rTMS therapeutic effects on cognitively impaired MS patients.

7. Conclusions

The road that lies ahead is long, but the first steps have been made: the neurological community now recognizes that cognitive impairment is an important component of MS (with the recently introduced concept of cognitive impairment associated with multiple sclerosis (CIAMS)) [74], stipulating that cognition must be included in diagnostic, follow-up, and therapeutic evaluations. Methods to neuropsychologically assess patients with MS and suitable imaging techniques to monitor cognitive function are now more widely accessible. Functional connectivity changes in the healthy and diseased brain can be detected and modified by interventions. We must go one step further and target cognitive functions therapeutically through well-designed clinical trials, with carefully selected large numbers of suitable patients, combining neuropsychological methods and noninvasive neurostimulation–neuromodulation and neuroimaging techniques, in order to offer widely effective treatments to our patients living with MS.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Authors’ Contributions

All authors contributed to the conception, drafting, revising, and finalizing of the manuscript and agreed to be accountable for all aspects of the work.

References

  1. V. Janardhan and R. Bakshi, “Quality of life and its relationship to brain lesions and atrophy on magnetic resonance images in 60 patients with multiple sclerosis,” Archives of Neurology, vol. 57, no. 10, pp. 1485–1491, 2000. View at Publisher · View at Google Scholar
  2. N. D. Chiaravalloti and J. DeLuca, “Cognitive impairment in multiple sclerosis,” Lancet Neurology, vol. 7, no. 12, pp. 1139–1151, 2008. View at Publisher · View at Google Scholar · View at Scopus
  3. H. M. B. Lunde, J. Assmus, K. M. Myhr, L. Bø, and N. Grytten, “Survival and cause of death in multiple sclerosis: a 60-year longitudinal population study,” Journal of Neurology, Neurosurgery, and Psychiatry, vol. 88, pp. 621–625, 2017. View at Publisher · View at Google Scholar
  4. V. Sinay, M. Perez Akly, G. Zanga, C. Ciardi, and J. M. Racosta, “School performance as a marker of cognitive decline prior to diagnosis of multiple sclerosis,” Multiple Sclerosis, vol. 21, no. 7, pp. 945–952, 2014. View at Publisher · View at Google Scholar · View at Scopus
  5. M. Cortese, T. Riise, K. Bjørnevik et al., “Preclinical disease activity in multiple sclerosis: a prospective study of cognitive performance prior to first symptom,” Annals of Neurology, vol. 80, no. 4, pp. 616–624, 2016. View at Publisher · View at Google Scholar · View at Scopus
  6. M. Comabella and X. Montalban, “Body fluid biomarkers in multiple sclerosis,” Lancet Neurology, vol. 13, no. 1, pp. 113–126, 2014. View at Publisher · View at Google Scholar · View at Scopus
  7. T. Ziemssen, T. Derfuss, N. de Stefano et al., “Optimizing treatment success in multiple sclerosis,” Journal of Neurology, vol. 263, no. 6, pp. 1053–1065, 2016. View at Publisher · View at Google Scholar · View at Scopus
  8. J. M. Charcot, Lectures on the Diseases of the Nervous System, New Sydenham Society, London, 1877.
  9. A. Winkelmann, C. Engel, A. Apel, and U. K. Zettl, “Cognitive impairment in multiple sclerosis,” Journal of Neurology, vol. 254, Supplementary 2, pp. II35–II42, 2007. View at Publisher · View at Google Scholar · View at Scopus
  10. J. Correale, M. C. Ysrraelit, and M. P. Fiol, “Benign multiple sclerosis: does it exist?” Current Neurology and Neuroscience Reports, vol. 12, no. 5, pp. 601–609, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. T. Vollmer, L. Huynh, C. Kelley et al., “Relationship between brain volume loss and cognitive outcomes among patients with multiple sclerosis: a systematic literature review,” Neurological Sciences, vol. 37, no. 2, pp. 165–179, 2016. View at Publisher · View at Google Scholar · View at Scopus
  12. N. De Stefano, A. Giorgio, M. Battaglini et al., “Assessing brain atrophy rates in a large population of untreated multiple sclerosis subtypes,” Neurology, vol. 74, no. 23, pp. 1868–1876, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. E. Fisher, R. A. Rudick, J. H. Simon et al., “Eight-year follow-up study of brain atrophy in patients with MS,” Neurology, vol. 59, no. 9, pp. 1412–1420, 2002. View at Publisher · View at Google Scholar
  14. T. Štecková, P. Hluštík, V. Sládková, F. Odstrčil, J. Mareš, and P. Kaňovský, “Thalamic atrophy and cognitive impairment in clinically isolated syndrome and multiple sclerosis,” Journal of the Neurological Sciences, vol. 342, no. 1-2, pp. 62–68, 2014. View at Publisher · View at Google Scholar · View at Scopus
  15. A. Papathanasiou, L. Messinis, P. Zampakis et al., “Thalamic atrophy predicts cognitive impairment in relapsing remitting multiple sclerosis. Effect on instrumental activities of daily living and employment status,” Journal of the Neurological Sciences, vol. 358, no. 1-2, pp. 236–242, 2015. View at Publisher · View at Google Scholar · View at Scopus
  16. E. Fisher, J. C. Lee, K. Nakamura, and R. A. Rudick, “Gray matter atrophy in multiple sclerosis: a longitudinal study,” Annals of Neurology, vol. 64, no. 3, pp. 255–265, 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. R. A. Rudick, J. C. Lee, K. Nakamura, and E. Fisher, “Gray matter atrophy correlates with MS disability progression measured with MSFC but not EDSS,” Journal of the Neurological Sciences, vol. 282, no. 1-2, pp. 106–111, 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. K. Romero, P. Shammi, and A. Feinstein, “Neurologists’ accuracy in predicting cognitive impairment in multiple sclerosis,” Multiple Sclerosis and Related Disorders, vol. 4, no. 4, pp. 291–295, 2015. View at Publisher · View at Google Scholar · View at Scopus
  19. S. Roy, R. H. Benedict, A. S. Drake, and B. Weinstock-Guttman, “Impact of pharmacotherapy on cognitive dysfunction in patients with multiple sclerosis,” CNS Drugs, vol. 30, no. 3, pp. 209–225, 2016. View at Publisher · View at Google Scholar · View at Scopus
  20. A. Damasceno, B. P. Damasceno, and F. Cendes, “No evidence of disease activity in multiple sclerosis: implications on cognition and brain atrophy,” Multiple Sclerosis, vol. 22, no. 1, pp. 64–72, 2016. View at Publisher · View at Google Scholar · View at Scopus
  21. N. D. Chiaravalloti, H. M. Genova, and J. DeLuca, “Cognitive rehabilitation in multiple sclerosis: the role of plasticity,” Frontiers in Neurology, vol. 6, p. 67, 2015. View at Publisher · View at Google Scholar · View at Scopus
  22. U. Palm, S. S. Ayache, F. Padberg, and J. P. Lefaucheur, “Non-invasive brain stimulation therapy in multiple sclerosis: a review of tDCS, rTMS and ECT results,” Brain Stimulation, vol. 7, no. 6, pp. 849–854, 2014. View at Publisher · View at Google Scholar · View at Scopus
  23. S. Rossi, M. Hallett, P. M. Rossini, A. Pascual-Leone, and The Safety of TMS Consensus Group, “Safety, ethical considerations, and application guidelines for the use of transcranial magnetic stimulation in clinical practice and research,” Clinical Neurophysiology, vol. 120, no. 12, pp. 2008–2039, 2009. View at Google Scholar
  24. A. T. Barker, R. Jalinous, and I. L. Freeston, “Non-invasive magnetic stimulation of human motor cortex,” Lancet, vol. 1, no. 8437, pp. 1106-1107, 1985. View at Google Scholar
  25. J. C. Rothwell, “Physiological studies of electric and magnetic stimulation of the human brain,” Electroencephalography and Clinical Neurophysiology Supplement, vol. 43, pp. 29–35, 1991. View at Google Scholar
  26. C. Civardi, R. Cantello, P. Asselman, and J. C. Rothwell, “Transcranial magnetic stimulation can be used to test connections to primary motor areas from frontal and medial cortex in humans,” NeuroImage, vol. 14, no. 6, pp. 1444–1453, 2001. View at Publisher · View at Google Scholar · View at Scopus
  27. P. Fox, R. Ingham, M. S. George et al., “Imaging human intra-cerebral connectivity by PET during TMS,” Neuroreport, vol. 8, no. 12, pp. 2787–2791, 1997. View at Publisher · View at Google Scholar
  28. T. Touge, W. Gerschlager, P. Brown, and J. C. Rothwell, “Are the after-effects of low-frequency rTMS on motor cortex excitability due to changes in the efficacy of cortical synapses?” Clinical Neurophysiology, vol. 112, no. 11, pp. 2138–2145, 2001. View at Publisher · View at Google Scholar · View at Scopus
  29. R. Chen, J. Classen, C. Gerloff et al., “Depression of motor cortex excitability by low-frequency transcranial magnetic stimulation,” Neurology, vol. 48, no. 5, pp. 1398–1403, 1997. View at Publisher · View at Google Scholar
  30. V. Kozyrev, U. T. Eysel, and D. Jancke, “Voltage-sensitive dye imaging of transcranial magnetic stimulation-induced intracortical dynamics,” Proceedings of the National Academy of Sciences of the United States of America, vol. 111, no. 37, pp. 13553–13558, 2014. View at Publisher · View at Google Scholar · View at Scopus
  31. T. Bäumer, R. Lange, J. Liepert et al., “Repeated premotor rTMS leads to cumulative plastic changes of motor cortex excitability in humans,” NeuroImage, vol. 20, no. 1, pp. 550–560, 2003. View at Publisher · View at Google Scholar · View at Scopus
  32. M. Lenz, S. Platschek, V. Priesemann et al., “Repetitive magnetic stimulation induces plasticity of excitatory postsynapses on proximal dendrites of cultured mouse CA1 pyramidal neurons,” Brain Structure & Function, vol. 220, no. 6, pp. 3323–3337, 2015. View at Publisher · View at Google Scholar · View at Scopus
  33. A. Conca, W. Peschina, P. König, H. Fritzsche, and A. Hausmann, “Effect of chronic repetitive transcranial magnetic stimulation on regional cerebral blood flow and regional cerebral glucose uptake in drug treatment-resistant depressives. A brief report,” Neuropsychobiology, vol. 45, no. 1, pp. 27–31, 2002. View at Publisher · View at Google Scholar · View at Scopus
  34. S. K. Esser, R. Huber, M. Massimini, M. J. Peterson, F. Ferrarelli, and G. Tononi, “A direct demonstration of cortical LTP in humans: a combined TMS/EEG study,” Brain Research Bulletin, vol. 69, no. 1, pp. 86–94, 2006. View at Publisher · View at Google Scholar · View at Scopus
  35. J. P. Lefaucheur, N. André-Obadia, A. Antal et al., “Evidence-based guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS),” Clinical Neurophysiology, vol. 125, no. 11, pp. 2150–2206, 2014. View at Publisher · View at Google Scholar · View at Scopus
  36. F. J. Medina-Fernández, E. Luque, M. Aguilar-Luque et al., “Transcranial magnetic stimulation modifies astrocytosis, cell density and lipopolysaccharide levels in experimental autoimmune encephalomyelitis,” Life Sciences, vol. 169, pp. 20–26, 2017. View at Publisher · View at Google Scholar
  37. M. A. Sherafat, M. Heibatollahi, S. Mongabadi, F. Moradi, M. Javan, and A. Ahmadiani, “Electromagnetic field stimulation potentiates endogenous myelin repair by recruiting subventricular neural stem cells in an experimental model of white matter demyelination,” Journal of Molecular Neuroscience, vol. 48, no. 1, pp. 144–153, 2012. View at Publisher · View at Google Scholar · View at Scopus
  38. B. Guse, P. Falkai, and T. Wobrock, “Cognitive effects of high-frequency repetitive transcranial magnetic stimulation: a systematic review,” Journal of Neural Transmission, vol. 117, no. 1, pp. 105–122, 2010. View at Publisher · View at Google Scholar · View at Scopus
  39. A. T. Sack and D. E. Linden, “Combining transcranial magnetic stimulation and functional imaging in cognitive brain research: possibilities and limitations,” Brain Research. Brain Research Reviews, vol. 43, no. 1, pp. 41–56, 2003. View at Publisher · View at Google Scholar · View at Scopus
  40. A. Sato, T. Torii, Y. Nakahara, M. Iwahashi, Y. Itoh, and K. Iramina, “The impact of rTMS over the dorsolateral prefrontal cortex on cognitive processing,” in 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, July 2013. View at Publisher · View at Google Scholar · View at Scopus
  41. C. Esslinger, N. Schüler, C. Sauer et al., “Induction and quantification of prefrontal cortical network plasticity using 5 Hz rTMS and fMRI,” Human Brain Mapping, vol. 35, no. 1, pp. 140–151, 2014. View at Publisher · View at Google Scholar · View at Scopus
  42. Y. Li, L. Wang, M. Jia, J. Guo, H. Wang, and M. Wang, “The effects of high-frequency rTMS over the left DLPFC on cognitive control in young healthy participants,” PLoS One, vol. 12, no. 6, article e0179430, 2017. View at Publisher · View at Google Scholar
  43. W. Y. Hsu, Y. Ku, T. P. Zanto, and A. Gazzaley, “Effects of noninvasive brain stimulation on cognitive function in healthy aging and Alzheimer’s disease: a systematic review and meta-analysis,” Neurobiology of Aging, vol. 36, no. 8, pp. 2348–2359, 2015. View at Publisher · View at Google Scholar · View at Scopus
  44. N. Maggio and A. Vlachos, “Synaptic plasticity at the interface of health and disease: new insights on the role of endoplasmic reticulum intracellular calcium stores,” Neuroscience, vol. 281C, pp. 135–146, 2014. View at Publisher · View at Google Scholar · View at Scopus
  45. B. Audoin, D. Ibarrola, J. P. Ranjeva et al., “Compensatory cortical activation observed by fMRI during a cognitive task at the earliest stage of MS,” Human Brain Mapping, vol. 20, no. 2, pp. 51–58, 2003. View at Publisher · View at Google Scholar · View at Scopus
  46. C. Forn, A. Barros-Loscertales, J. Escudero et al., “Cortical reorganization during PASAT task in MS patients with preserved working memory functions,” NeuroImage, vol. 31, no. 2, pp. 686–691, 2006. View at Publisher · View at Google Scholar · View at Scopus
  47. C. Forn, A. Barros-Loscertales, J. Escudero et al., “Compensatory activations in patients with multiple sclerosis during preserved performance on the auditory N-back task,” Human Brain Mapping, vol. 28, no. 5, pp. 424–430, 2007. View at Publisher · View at Google Scholar · View at Scopus
  48. C. Mainero, F. Caramia, C. Pozzilli et al., “fMRI evidence of brain reorganization during attention and memory tasks in multiple sclerosis,” NeuroImage, vol. 21, no. 3, pp. 858–867, 2004. View at Publisher · View at Google Scholar · View at Scopus
  49. W. Staffen, A. Mair, H. Zauner et al., “Cognitive function and fMRI in patients with multiple sclerosis: evidence for compensatory cortical activation during an attention task,” Brain, vol. 125, no. 6, pp. 1275–1282, 2002. View at Publisher · View at Google Scholar
  50. M. C. Bonnet, M. Allard, B. Dilharreguy, M. Deloire, K. G. Petry, and B. Brochet, “Cognitive compensation failure in multiple sclerosis,” Neurology, vol. 75, no. 14, pp. 1241–1248, 2010. View at Publisher · View at Google Scholar · View at Scopus
  51. S. Cader, A. Cifelli, Y. Abu-Omar, J. Palace, and P. M. Matthews, “Reduced brain functional reserve and altered functional connectivity in patients with multiple sclerosis,” Brain, vol. 129, no. 2, pp. 527–537, 2006. View at Publisher · View at Google Scholar · View at Scopus
  52. V. M. Leavitt, G. Wylie, H. M. Genova, N. D. Chiaravalloti, and J. DeLuca, “Altered effective connectivity during performance of an information processing speed task in multiple sclerosis,” Multiple Sclerosis, vol. 18, no. 4, pp. 409–417, 2012. View at Publisher · View at Google Scholar · View at Scopus
  53. M. A. Rocca, P. Valsasina, H. E. Hulst et al., “Functional correlates of cognitive dysfunction in multiple sclerosis: a multicenter fMRI study,” Human Brain Mapping, vol. 35, no. 12, pp. 5799–5814, 2014. View at Publisher · View at Google Scholar · View at Scopus
  54. M. A. Rocca, P. Valsasina, V. M. Leavitt et al., “Functional network connectivity abnormalities in multiple sclerosis: correlations with disability and cognitive impairment,” Multiple Sclerosis Journal, vol. 1, 2017. View at Publisher · View at Google Scholar
  55. S. Y. Bookheimer, M. H. Strojwas, M. S. Cohen et al., “Patterns of brain activation in people at risk for Alzheimer’s disease,” The New England Journal of Medicine, vol. 343, no. 7, pp. 450–456, 2000. View at Publisher · View at Google Scholar · View at Scopus
  56. J. F. Sumowski, G. R. Wylie, J. Deluca, and N. Chiaravalloti, “Intellectual enrichment is linked to cerebral efficiency in multiple sclerosis: functional magnetic resonance imaging evidence for cognitive reserve,” Brain, vol. 133, no. 2, pp. 362–374, 2010. View at Publisher · View at Google Scholar · View at Scopus
  57. H. Johansen-Berg, M. F. Rushworth, M. D. Bogdanovic, U. Kischka, S. Wimalaratna, and P. M. Matthews, “The role of ipsilateral premotor cortex in hand movement after stroke,” Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. 22, pp. 14518–14523, 2002. View at Publisher · View at Google Scholar · View at Scopus
  58. A. Cifelli and P. M. Matthews, “Cerebral plasticity in multiple sclerosis: insights from fMRI,” Multiple Sclerosis, vol. 8, no. 3, pp. 193–199, 2002. View at Publisher · View at Google Scholar · View at Scopus
  59. M. M. Schoonheim, K. A. Meijer, and J. J. Geurts, “Network collapse and cognitive impairment in multiple sclerosis,” Frontiers in Neurology, vol. 6, p. 82, 2015. View at Publisher · View at Google Scholar · View at Scopus
  60. C. Louapre, V. Perlbarg, D. García-Lorenzo et al., “Brain networks disconnection in early multiple sclerosis cognitive deficits: an anatomofunctional study,” Human Brain Mapping, vol. 35, no. 9, pp. 4706–4717, 2014. View at Publisher · View at Google Scholar · View at Scopus
  61. E. Elzamarany, L. Afifi, N. M. El-Fayoumy, H. Salah, and M. Nada, “Motor cortex rTMS improves dexterity in relapsing-remitting and secondary progressive multiple sclerosis,” Acta Neurologica Belgica, vol. 116, no. 2, pp. 145–150, 2016. View at Publisher · View at Google Scholar · View at Scopus
  62. D. Centonze, G. Koch, V. Versace et al., “Repetitive transcranial magnetic stimulation of the motor cortex ameliorates spasticity in multiple sclerosis,” Neurology, vol. 68, no. 13, pp. 1045–1050, 2007. View at Publisher · View at Google Scholar · View at Scopus
  63. F. Mori, G. Koch, C. Foti, G. Bernardi, and D. Centonze, “The use of repetitive transcranial magnetic stimulation (rTMS) for the treatment of spasticity,” Progress in Brain Research, vol. 175, pp. 429–439, 2009. View at Publisher · View at Google Scholar · View at Scopus
  64. F. Mori, C. Codecà, H. Kusayanagi et al., “Effects of intermittent theta burst stimulation on spasticity in patients with multiple sclerosis,” European Journal of Neurology, vol. 17, no. 2, pp. 295–300, 2010. View at Publisher · View at Google Scholar · View at Scopus
  65. B. Amatya, F. Khan, L. La Mantia, M. Demetrios, and D. T. Wade, “Non-pharmacological interventions for spasticity in multiple sclerosis,” Cochrane Database of Systematic Reviews, no. 2, article CD009974, 2013. View at Publisher · View at Google Scholar
  66. D. Centonze, F. Petta, V. Versace et al., “Effects of motor cortex rTMS on lower urinary tract dysfunction in multiple sclerosis,” Multiple Sclerosis, vol. 13, no. 2, pp. 269–271, 2007. View at Publisher · View at Google Scholar · View at Scopus
  67. A. M. Burhan, P. Subramanian, L. Pallaveshi, B. Barnes, and M. Montero-Odasso, “Modulation of the left prefrontal cortex with high frequency repetitive transcranial magnetic stimulation facilitates gait in multiple sclerosis,” Case Reports in Neurological Medicine, vol. 2015, Article ID 251829, 6 pages, 2015. View at Publisher · View at Google Scholar
  68. G. Koch, S. Rossi, C. Prosperetti et al., “Improvement of hand dexterity following motor cortex rTMS in multiple sclerosis patients with cerebellar impairment,” Multiple Sclerosis Journal, vol. 14, no. 7, pp. 995–998, 2008. View at Publisher · View at Google Scholar · View at Scopus
  69. J. P. Lefaucher, M. A. Chalah, A. Mhalla, U. Palm, S. S. Ayache, and V. Mylius, “The treatment of fatigue by non-invasive brain stimulation,” Neurophysiologie Clinique, vol. 47, no. 2, pp. 173–184, 2017. View at Publisher · View at Google Scholar
  70. S. Bonavita, R. Sacco, M. Della Corte et al., “Computer-aided cognitive rehabilitation improves cognitive performances and induces brain functional connectivity changes in relapsing remitting multiple sclerosis patients: an exploratory study,” Journal of Neurology, vol. 262, no. 1, pp. 91–100, 2015. View at Publisher · View at Google Scholar · View at Scopus
  71. M. Mitolo, A. Venneri, I. D. Wilkinson, and B. Sharrack, “Cognitive rehabilitation in multiple sclerosis: a systematic review,” Journal of the Neurological Sciences, vol. 354, no. 1-2, pp. 1–9, 2015. View at Publisher · View at Google Scholar · View at Scopus
  72. L. De Giglio, F. Tona, F. De Luca et al., “Multiple sclerosis: changes in thalamic resting-state functional connectivity induced by a home-based cognitive rehabilitation program,” Radiology, vol. 280, no. 1, pp. 202–211, 2016. View at Publisher · View at Google Scholar · View at Scopus
  73. H. E. Hulst, T. Goldschmidt, M. A. Nitsche et al., “rTMS affects working memory performance, brain activation and functional connectivity in patients with multiple sclerosis,” Journal of Neurology, Neurosurgery, and Psychiatry, vol. 88, no. 5, pp. 386–394, 2017. View at Publisher · View at Google Scholar · View at Scopus
  74. B. Brochet, “Functional training is a senseless strategy in MS cognitive rehabilitation: strategy training is the only useful approach – commentary,” Multiple Sclerosis Journal, vol. 23, no. 7, pp. 932-933, 2017. View at Publisher · View at Google Scholar