BackgroundMultiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system, for which no definitive treatment is available. Most patients start with a relapsing-remitting course (RRMS). Disease-modifying drugs (DMDs) reduce relapses and disability progression. First line DMDs include glatiramer acetate (GA), interferon-beta (INFb)-1a and INFb-1b, which are all administered via injections. Effectiveness of DMD treatment depends on adequate adherence, meaning year-long continuation of injections with a minimum of missed doses. In real-life practice DMD-treated patients miss 30% of doses. The 6-month discontinuation rate is up to 27% and most patients who discontinue do so in the first 12 months.Treatment adherence is influenced by the socio-economic situation, health care and caregivers, disease, treatment and patient characteristics. Only a few studies have dealt with adherence-related factors in DMD-treated patients. Self-efficacy expectations were found to be related to GA adherence. Patient education and optimal support improve adherence in general. Knowledge of the aspects of care that significantly relate to adherence could lead to adherence-improving measures. Moreover, identification of patients at risk of inadequate adherence could lead to more efficient care.In the near future new drugs will become available for RRMS. Detailed knowledge on factors prognostic of adherence and on care aspects that are associated with adequate adherence will improve the chances of these drugs becoming effective treatments. We investigate in RRMS patients the relationship between drug adherence and multidisciplinary care, as well as factors associated with adherence. Given the differences in the frequency of administration and in the side effects between the DMDs we decided to study patients treated with the same DMD, GA.Methods/designThe Correlative analyses of Adherence In Relapsing remitting MS (CAIR) study is an investigator-initiated, prospective, web-based, patient-centred, nation-wide cohort study in the Netherlands.The primary objective is to investigate whether GA adherence is associated with specific disciplines of care or quantities of specific care. The secondary objective is to investigate whether GA adherence is associated with specific aspects of the socio-economic situation, health care and caregivers, disease, treatment or patient characteristics.All data are acquired on-line via a study website. All RRMS patients in the Netherlands starting GA treatment are eligible. Patients are informed by neurologists, nurses, and websites from national MS patient organisations. All data, except on disability, are obtained by patient self-reports on pre-defined and random time points. The number of missed doses and the number of patients having discontinued GA treatment at 6 and 12 months are measures of adherence. Per care discipline the number of sessions and the total duration of care are measures of received care. The full spectrum of non-experimental care that is available in the ...

To study age-dependent changes in coupling between cortical neural networks we applied a new method (omega complexity) to determine overall coherence of EEGs of 34 subjects ranging in age from 3 months to 16 years. We found that the functional coupling between different brain regions is low at birth and increases significantly in the first two decades of life. We suggest that this coupling depends critically upon the system of associational and callosal fibers which is unmyelinated at birth, and which only finishes myelinization in the second or third decade. Thus age-dependant changes in omega complexity may reflect maturation of brain structures underlying higher cerebral functions. If these results can be replicated, preferably in prospective, cohort rather than transectional type studies, omega complexity might prove to be clinically useful as an objective, quantitative measure of brain maturation.

In the present study we investigated whether multiple sclerosis (MS) can be detected via exhaled breath analysis using an electronic nose (eNose). The AeonoseTM (an eNose, The eNose Company, Zutphen, the Netherlands) is a diagnostic test device to detect patterns of volatile organic compounds in exhaled breath. We evaluated whether the AeonoseTM can make a distinction between the breath patterns of patients with MS and healthy control subjects. In this mono-center, prospective, non-invasive study, 124 subjects with a confirmed diagnosis of MS and 129 control subjects each breathed into the AeonoseTM for 5 min. Exhaled breath data was used to train an artificial neural network (ANN) predictive model. To investigate the influence of medication intake we created a second predictive model with a subgroup of MS patients without medication prescribed for MS. The ANN model based on the entire dataset was able to distinguish MS patients from healthy controls with a sensitivity of 0.75 (95% CI: 0.66–0.82) and specificity of 0.60 (0.51–0.69). The model created with the subgroup of MS patients not using medication and the healthy control subjects had a sensitivity of 0.93 (0.82–0.98) and a specificity of 0.74 (0.65–0.81). The study showed that the AeonoseTM is able to make a distinction between MS patients and healthy control subjects, and could potentially provide a quick screening test to assist in diagnosing MS. Further research is needed to determine whether the AeonoseTM is able to differentiate new MS patients from subjects who will not get the diagnosis.

Periodic complexes (PC), occurring lateralised or diffuse, are relatively rare EEG phenomena which reflect acute severe brain disease. The pathophysiology is still incompletely understood. One hypothesis suggested by the alpha rhythm model of Lopes da Silva is that periodic complexes reflect limit cycle dynamics of cortical networks caused by excessive excitatory feedback. We examined this hypothesis by applying a recently developed technique to EEGs displaying periodic complexes and to periodic complexes generated by the model. The technique, non-linear cross prediction, characterises how well a time series can be predicted, and how much amplitude and time asymmetry is present. Amplitude and time asymmetry are indications of non-linearity. In accordance with the model, most EEG channels with PC showed clear evidence of amplitude and time asymmetry, pointing to non-linear dynamics. However, the non-linear predictability of true PC was substantially lower than that of PC generated by the model. Furthermore, no finite value for the correlation dimension could be obtained for the real EEG data, whereas the model time series had a dimension slighter higher than one, consistent with a limit cycle attractor. Thus we can conclude that PC reflect non-linear dynamics, but a limit cycle attractor is too simple an explanation. The possibility of more complex (high dimensional and spatio-temporal) non-linear dynamics should be investigated.