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Headache is a common symptom among children and teenagers. Both bruxism and muscle and joint tenderness have been found in children with headache. Children with migraine headache report more temporomandibular disorder (TMD) symptoms than do those with tension-type headache. The aim of the present study was to investigate the association of different types of headache with TMD and sex in children. Altogether 297 randomly selected schoolchildren aged 13-14 years participated in a blind study setting. There were no statistically significant differences between the headache groups with regard to TMD signs, although the migraine and migraine-type headache groups had the highest percentage of subjects with more severe TMD signs. Nor were there any statistical differences between sexes or between the headache groups with regard to subjective symptoms of TMD. The present results with children differed from earlier results with adults. First, no association was found between tension headache and TMD, and, second, no sex difference in TMD children was observed at this age. [ABSTRACT FROM AUTHOR]

A simplified nonlinear gyrokinetic formalism has been developed for general distribution functions and general magnetic perturbations. It has been found that the nonlinear coupling strength for low frequency electromagnetic modes in plasmas with anisotropic temperature is larger than for the isotropie case for small Larmor radius. A nonlinear cascade where the pump mode is the mode with intermediate parallel phase velocity has been found. [ABSTRACT FROM AUTHOR]

We have characterized the cellular content and some antibody reactivities in synovial fluid (SF) from DA rats with CIA. Since CIA is widely used as a model for RA, in which many studies concerning immune responses are performed on SF samples, we considered it important to describe the local, disease-causing immune reactions in CIA. At the peak of disease (day 22 after immunization), the major cell population in CIA SF was granulocytes (72%), but macrophages (17.9%), plasma cells (2.6%) and lymphocytes (7.7%) were also present. The CIA synovial membrane (SM) obtained at the same time was mainly infiltrated by monocytes, with granulocytes, lymphocytes and plasma cells also present. Cell populations in blood did not differ between arthritic and normal DA rats. Equally, high anti-collagen type II (CII) and rheumatoid factor (RF) levels could be detected both in SF and in sera. Notably, RF levels were also increased in normal DA rats. Moderate levels of anti-heat shock protein 65 kD (hsp) antibodies were recorded systemically in both normal and diseased animals. In conclusion, the cellular composition in SF and in SM are similar in rat CIA and in RA. The morphological differences between SF and SM that are characteristic for RA could also be demonstrated in CIA. The antibody data indicate systemic production of anti-CII and anti-hsp antibodies as well as RF. but they give no support for local production of these antibodies in the joints, which is the case in RA. [ABSTRACT FROM AUTHOR]

Objective: Diseases affecting sensorimotor function impair physical independence. Reliable functional clinical biomarkers allowing early diagnosis or targeting treatment and rehabilitation could reduce this burden. Magnetoencephalography (MEG) non-invasively measures brain rhythms such as the somatomotor 'rolandic' rhythm which shows intermittent high-amplitude beta (14-30 Hz) 'events' that predict behavior across tasks and species and are altered by sensorimotor neurological diseases., Methods: We assessed test-retest stability, a prerequisite for biomarkers, of spontaneous sensorimotor aperiodic (1/f) signal and beta events in 50 healthy human controls across two MEG sessions using the intraclass correlation coefficient (ICC). Beta events were determined using an amplitude-thresholding approach on a narrow-band filtered amplitude envelope obtained using Morlet wavelet decomposition., Results: Resting sensorimotor characteristics showed good to excellent test-retest stability. Aperiodic component (ICC 0.77-0.88) and beta event amplitude (ICC 0.74-0.82) were very stable, whereas beta event duration was more variable (ICC 0.55-0.7). 2-3 minute recordings were sufficient to obtain stable results. Analysis automatization was successful in 86%., Conclusions: Sensorimotor beta phenotype is a stable feature of an individual's resting brain activity even for short recordings easily measured in patients., Significance: Spontaneous sensorimotor beta phenotype has potential as a clinical biomarker of sensorimotor system integrity., (Copyright © 2024 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

More than 10 million Europeans show signs of mild cognitive impairment (MCI), a transitional stage between normal brain aging and dementia stage memory disorder. The path MCI takes can be divergent; while some maintain stability or even revert to cognitive norms, alarmingly, up to half of the cases progress to dementia within 5 years. Current diagnostic practice lacks the necessary screening tools to identify those at risk of progression. The European patient experience often involves a long journey from the initial signs of MCI to the eventual diagnosis of dementia. The trajectory is far from ideal. Here, we introduce the AI-Mind project, a pioneering initiative with an innovative approach to early risk assessment through the implementation of advanced artificial intelligence (AI) on multimodal data. The cutting-edge AI-based tools developed in the project aim not only to accelerate the diagnostic process but also to deliver highly accurate predictions regarding an individual's risk of developing dementia when prevention and intervention may still be possible. AI-Mind is a European Research and Innovation Action (RIA H2020-SC1-BHC-06-2020, No. 964220) financed between 2021 and 2026. First, the AI-Mind Connector identifies dysfunctional brain networks based on high-density magneto- and electroencephalography (M/EEG) recordings. Second, the AI-Mind Predictor predicts dementia risk using data from the Connector , enriched with computerized cognitive tests, genetic and protein biomarkers, as well as sociodemographic and clinical variables. AI-Mind is integrated within a network of major European initiatives, including The Virtual Brain, The Virtual Epileptic Patient, and EBRAINS AISBL service for sensitive data, HealthDataCloud, where big patient data are generated for advancing digital and virtual twin technology development. AI-Mind's innovation lies not only in its early prediction of dementia risk, but it also enables a virtual laboratory scenario for hypothesis-driven personalized intervention research. This article introduces the background of the AI-Mind project and its clinical study protocol, setting the stage for future scientific contributions., Competing Interests: CH-H was employed by BrainSymph AS. VAy, JP, and LP were employed by Lurtis Rules S.L. EC was employed by Pre Diagnostics AS. The remaining 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., (Copyright © 2024 Haraldsen, Hatlestad-Hall, Marra, Renvall, Maestú, Acosta-Hernández, Alfonsin, Andersson, Anand, Ayllón, Babic, Belhadi, Birck, Bruña, Caraglia, Carrarini, Christensen, Cicchetti, Daugbjerg, Di Bidino, Diaz-Ponce, Drews, Giuffrè, Georges, Gil-Gregorio, Gove, Govers, Hallock, Hietanen, Holmen, Hotta, Kaski, Khadka, Kinnunen, Koivisto, Kulashekhar, Larsen, Liljeström, Lind, Marcos Dolado, Marshall, Merz, Miraglia, Montonen, Mäntynen, Øksengård, Olazarán, Paajanen, Peña, Peña, Peniche, Perez, Radwan, Ramírez-Toraño, Rodríguez-Pedrero, Saarinen, Salas-Carrillo, Salmelin, Sousa, Suyuthi, Toft, Toharia, Tveitstøl, Tveter, Upreti, Vermeulen, Vecchio, Yazidi and Rossini.)

New biomarkers are urgently needed for many brain disorders; for example, the diagnosis of mild traumatic brain injury (mTBI) is challenging as the clinical symptoms are diverse and nonspecific. EEG and MEG studies have demonstrated several population-level indicators of mTBI that could serve as objective markers of brain injury. However, deriving clinically useful biomarkers for mTBI and other brain disorders from EEG/MEG signals is hampered by the large inter-individual variability even across healthy people. Here, we used a multivariate machine-learning approach to detect mTBI from resting-state MEG measurements. To address the heterogeneity of the condition, we employed a normative modeling approach and modeled MEG signal features of individual mTBI patients as deviations with respect to the normal variation. To this end, a normative dataset comprising 621 healthy participants was used to determine the variation in power spectra across the cortex. In addition, we constructed normative datasets based on age-matched subsets of the full normative data. To discriminate patients from healthy control subjects, we trained support-vector-machine classifiers on the quantitative deviation maps for 25 mTBI patients and 20 controls not included in the normative dataset. The best performing classifier made use of the full normative data across the entire age and frequency ranges. This classifier was able to distinguish patients from controls with an accuracy of 79%. Inspection of the trained model revealed that low-frequency activity in the theta frequency band (4-8 Hz) is a significant indicator of mTBI, consistent with earlier studies. The results demonstrate the feasibility of using normative modeling of MEG data combined with machine learning to advance diagnosis of mTBI and identify patients that would benefit from treatment and rehabilitation. The current approach could be applied to a wide range of brain disorders, thus providing a basis for deriving MEG/EEG-based biomarkers., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Itälinna et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: Chronic pain associates with various sleep problems. Patients with complex regional pain syndrome (CRPS) often report impaired sleep, but objective measurements of sleep in CRPS patients are scarce. Neuromodulation with repetitive transcranial magnetic stimulation (rTMS) can alleviate pain and improve sleep. Secondary somatosensory cortex (S2) is a possible rTMS target for the treatment of chronic pain, but the effect of S2-targeted rTMS on sleep is unknown., Methods: This randomized, sham-controlled trial assessed the effect of S2-targeted rTMS on sleep in patients with CRPS. Patients (n = 31) received either S2-targeted rTMS (10 Hz) or sham stimulation for 3 weeks. The effect of treatment on sleep was assessed with validated questionnaires, with a sleep and pain diary, and with a consumer-grade sleep tracker, the Oura ring. In addition to an ordinary univariate analysis of the results, we conducted multivariate testing of the Oura data using linear discriminant analysis (LDA)., Results: S2-targeted rTMS decreased sleep restlessness that significantly differed between the rTMS and sham stimulation patient groups (p = .028). In the multivariate analysis of the Oura data, LDA classification accuracy to separate the rTMS and sham groups exceeded 95% confidence level in four out of the seven tested models. In the subjective evaluation of sleep, the effect of rTMS and sham did not differ., Conclusion: S2-targeted rTMS influenced sleep in patients with CRPS. Improved sleep may enhance CRPS symptom alleviation and be of clinical importance. A univariate analysis could separate the rTMS and sham treatments. The multivariate analysis revealed that including multiple sleep-related parameters can be beneficial when analyzing rTMS effects on sleep. As sleep is related both to pain and quality of life, and sleep rTMS can be directly affected by rTMS, objective monitoring of sleep in various future rTMS trials could be fruitful., (© 2023 The Authors. Brain and Behavior published by Wiley Periodicals LLC.)

Objective: Using EEG to characterise functional brain networks through graph theory has gained significant interest in clinical and basic research. However, the minimal requirements for reliable measures remain largely unaddressed. Here, we examined functional connectivity estimates and graph theory metrics obtained from EEG with varying electrode densities., Methods: EEG was recorded with 128 electrodes in 33 participants. The high-density EEG data were subsequently subsampled into three sparser montages (64, 32, and 19 electrodes). Four inverse solutions, four measures of functional connectivity, and five graph theory metrics were tested., Results: The correlation between the results obtained with 128-electrode and the subsampled montages decreased as a function of the number of electrodes. As a result of decreased electrode density, the network metrics became skewed: mean network strength and clustering coefficient were overestimated, while characteristic path length was underestimated., Conclusions: Several graph theory metrics were altered when electrode density was reduced. Our results suggest that, for optimal balance between resource demand and result precision, a minimum of 64 electrodes should be utilised when graph theory metrics are used to characterise functional brain networks in source-reconstructed EEG data., Significance: Characterisation of functional brain networks derived from low-density EEG warrants careful consideration., (Copyright © 2023 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

The cortical areas involved in human speech should be characterized reliably prior to surgery for brain tumors or drug-resistant epilepsy. The functional mapping of language areas for surgical decision-making is usually done invasively by electrical direct cortical stimulation (DCS), which is used to identify the organization of the crucial cortical and subcortical structures within each patient. Accurate preoperative non-invasive mapping aids surgical planning, reduces time, costs, and risks in the operating room, and provides an alternative for patients not suitable for awake craniotomy. Non-invasive imaging methods like MRI, fMRI, MEG, and PET are currently applied in presurgical design and planning. Although anatomical and functional imaging can identify the brain regions involved in speech, they cannot determine whether these regions are critical for speech. Transcranial magnetic stimulation (TMS) non-invasively excites the cortical neuronal populations by means of electric field induction in the brain. When applied in its repetitive mode (rTMS) to stimulate a speech-related cortical site, it can produce speech-related errors analogous to those induced by intraoperative DCS. rTMS combined with neuronavigation (nrTMS) enables neurosurgeons to preoperatively assess where these errors occur and to plan the DCS and the operation to preserve the language function. A detailed protocol is provided here for non-invasive speech cortical mapping (SCM) using nrTMS. The proposed protocol can be modified to best fit the patient- and site-specific demands. It can also be applied to language cortical network studies in healthy subjects or in patients with diseases that are not amenable to surgery.

Different neuroimaging methods can yield different views of task-dependent neural engagement. Studies examining the relationship between electromagnetic and hemodynamic measures have revealed correlated patterns across brain regions but the role of the applied stimulation or experimental tasks in these correlation patterns is still poorly understood. Here, we evaluated the across-tasks variability of MEG-fMRI relationship using data recorded during three distinct naming tasks (naming objects and actions from action images, and objects from object images), from the same set of participants. Our results demonstrate that the MEG-fMRI correlation pattern varies according to the performed task, and that this variability shows distinct spectral profiles across brain regions. Notably, analysis of the MEG data alone did not reveal modulations across the examined tasks in the time-frequency windows emerging from the MEG-fMRI correlation analysis. Our results suggest that the electromagnetic-hemodynamic correlation could serve as a more sensitive proxy for task-dependent neural engagement in cognitive tasks than isolated within-modality measures., Competing Interests: 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., (Copyright © 2022 Mononen, Kujala, Liljeström, Leppäaho, Kaski and Salmelin.)

Exaggerated subthalamic beta oscillatory activity and increased beta range cortico-subthalamic synchrony have crystallized as the electrophysiological hallmarks of Parkinson's disease. Beta oscillatory activity is not tonic but occurs in 'bursts' of transient amplitude increases. In Parkinson's disease, the characteristics of these bursts are altered especially in the basal ganglia. However, beta oscillatory dynamics at the cortical level and how they compare with healthy brain activity is less well studied. We used magnetoencephalography (MEG) to study sensorimotor cortical beta bursting and its modulation by subthalamic deep brain stimulation in Parkinson's disease patients and age-matched healthy controls. We show that the changes in beta bursting amplitude and duration typical of Parkinson's disease can also be observed in the sensorimotor cortex, and that they are modulated by chronic subthalamic deep brain stimulation, which, in turn, is reflected in improved motor function at the behavioural level. In addition to the changes in individual beta bursts, their timing relative to each other was altered in patients compared to controls: bursts were more clustered in untreated Parkinson's disease, occurring in 'bursts of bursts', and re-burst probability was higher for longer compared to shorter bursts. During active deep brain stimulation, the beta bursting in patients resembled healthy controls' data. In summary, both individual bursts' characteristics and burst patterning are affected in Parkinson's disease, and subthalamic deep brain stimulation normalizes some of these changes to resemble healthy controls' beta bursting activity, suggesting a non-invasive biomarker for patient and treatment follow-up., Competing Interests: Declaration of Competing Interest Jukka Nenonen is employed by MEGIN Oy, the producer of the MEG device used for measurements. Eero Pekkonen has received lecturing fees from Abbott, the producer of the DBS stimulation device, and is the Finnish PI for the ‘Abbott DBS Registry of Outcomes for Indications over Time’. Apart from that, the authors declare no other potential competing interests., (Copyright © 2022. Published by Elsevier Inc.)

Beta rhythm modulation has been used as a biomarker to reflect the functional state of the sensorimotor cortex in both healthy subjects and patients. Here, the effect of reduced alertness and active attention to the stimulus on beta rhythm modulation was investigated. Beta rhythm modulation to tactile stimulation of the index finger was recorded simultaneously with MEG and EEG in 23 healthy subjects (mean 23, range 19-35 years). The temporal spectral evolution method was used to obtain the peak amplitudes of beta suppression and rebound in three different conditions (neutral, snooze, and attention). Neither snooze nor attention to the stimulus affected significantly the strength of beta suppression nor rebound, although a decrease in suppression and rebound strength was observed in some subjects with a more pronounced decrease of alertness. The reduction of alertness correlated with the decrease of suppression strength both in MEG (left hemisphere r = 0.49; right hemisphere r = 0.49, *p < 0.05) and EEG (left hemisphere r = 0.43; right hemisphere r = 0.72, **p < 0.01). The results indicate that primary sensorimotor cortex beta suppression and rebound are not sensitive to slightly reduced alertness nor active attention to the stimulus at a group level. Hence, tactile stimulus-induced beta modulation is a suitable tool for assessing the sensorimotor cortex function at a group level. However, subjects' alertness should be maintained high during recordings to minimize individual variability., (© 2021 The Authors. Physiological Reports published by Wiley Periodicals LLC on behalf of The Physiological Society and the American Physiological Society.)

Reliable paradigms and imaging measures of individual-level brain activity are paramount when reaching from group-level research studies to clinical assessment of individual patients. Magnetoencephalography (MEG) provides a direct, non-invasive measure of cortical processing with high spatiotemporal accuracy, and is thus well suited for assessment of functional brain damage in patients with language difficulties. This MEG study aimed to identify, in a delayed picture naming paradigm, source-localized evoked activity and modulations of cortical oscillations that show high test-retest reliability across measurement days in healthy individuals, demonstrating their applicability in clinical settings. For patients with a language disorder picture naming can be a challenging task. Therefore, we also determined whether a semantic judgment task ('Is this item living?') with a spoken response ("yes"/"no") would suffice to induce comparably consistent activity within brain regions related to language production. The MEG data was collected from 19 healthy participants on two separate days. In picture naming, evoked activity was consistent across measurement days (intraclass correlation coefficient (ICC)>0.4) in the left frontal (400-800 ms after image onset), sensorimotor (200-800 ms), parietal (200-600 ms), temporal (200-800 ms), occipital (400-800 ms) and cingulate (600-800 ms) regions, as well as the right temporal (600-800 ms) region. In the semantic judgment task, consistent evoked activity was spatially more limited, occurring in the left temporal (200-800 ms), sensorimotor (400-800 ms), occipital (400-600 ms) and subparietal (600-800 ms) regions, and the right supramarginal cortex (600-800 ms). The delayed naming task showed typical beta oscillatory suppression in premotor and sensorimotor regions (800-1200 ms) but other consistent modulations of oscillatory activity were mostly observed in posterior cortical regions that have not typically been associated with language processing. The high test-retest consistency of MEG evoked activity in the picture naming task testifies to its applicability in clinical evaluations of language function, as well as in longitudinal MEG studies of language production in clinical and healthy populations., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020. Published by Elsevier Inc.)

Modulation of the ~20-Hz brain rhythm has been used to evaluate the functional state of the sensorimotor cortex both in healthy subjects and patients, such as stroke patients. The ~20-Hz brain rhythm can be detected by both magnetoencephalography (MEG) and electroencephalography (EEG), but the comparability of these methods has not been evaluated. Here, we compare these two methods in the evaluating of ~20-Hz activity modulation to somatosensory stimuli. Rhythmic ~20-Hz activity during separate tactile and proprioceptive stimulation of the right and left index finger was recorded simultaneously with MEG and EEG in twenty-four healthy participants. Both tactile and proprioceptive stimulus produced a clear suppression at 300-350 ​ms followed by a subsequent rebound at 700-900 ​ms after stimulus onset, detected at similar latencies both with MEG and EEG. The relative amplitudes of suppression and rebound correlated strongly between MEG and EEG recordings. However, the relative strength of suppression and rebound in the contralateral hemisphere (with respect to the stimulated hand) was significantly stronger in MEG than in EEG recordings. Our results indicate that MEG recordings produced signals with higher signal-to-noise ratio than EEG, favoring MEG as an optimal tool for studies evaluating sensorimotor cortical functions. However, the strong correlation between MEG and EEG results encourages the use of EEG when translating studies to clinical practice. The clear advantage of EEG is the availability of the method in hospitals and bed-side measurements at the acute phase., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Despite the high prevalence of mild traumatic brain injury (mTBI), current diagnostic tools to objectively assess cognitive complaints after mTBI continue to be inadequate. Our aim was to identify neuronal correlates for cognitive difficulties in mTBI patients by evaluating the possible alterations in oscillatory brain activity during a behavioral task known to be sensitive to cognitive impairment after mTBI. We compared oscillatory brain activity during rest and cognitive tasks (Paced Auditory Serial Addition Test [PASAT] and a vigilance test [VT]) with magnetoencephalography between 25 mTBI patients and 20 healthy controls. Whereas VT induced no significant differences compared with resting state in either group, patients exhibited stronger attenuation of 8- to 14-Hz oscillatory activity during PASAT than healthy controls in the left parietotemporal cortex ( p  ≤ 0.05). Further, significant task-related modulation in the left superior frontal gyrus and right prefrontal cortex was detected only in patients. The ∼10-Hz (alpha) peak frequency declined in frontal, temporal, and parietal regions during PASAT compared with rest ( p  < 0.016) in patients, whereas in controls it remained the same or showed a tendency to increase. In patients, the ∼10-Hz peak amplitude was negatively correlated with behavioral performance in the Trail Making Test. The observed alterations in the cortical oscillatory activity during cognitive load may provide measurable neurophysiological correlates of cognitive difficulties in mTBI patients, even at the individual level.

Mild traumatic brain injury (mTBI) patients continue to pose a diagnostic challenge due to their diverse symptoms without trauma-specific changes in structural imaging. We addressed here the possible early changes in spontaneous oscillatory brain activity after mTBI, and their feasibility as an indicator of injury in clinical evaluation. We recorded resting-state magnetoencephalography (MEG) data in both eyes-open and eyes-closed conditions from 26 patients (11 females and 15 males, aged 20-59) with mTBI 6 days-6 months after the injury, and compared their spontaneous oscillatory activity to corresponding data from 139 healthy controls. Twelve of the patients underwent a follow-up measurement at 6 months. Ten of all patients were without structural lesions in MRI. At single-subject level, aberrant 4-7 Hz (theta) band activity exceeding the + 2 SD limit of the healthy subjects was visible in 7 out of 26 patients; three out of the seven patients with abnormal theta activity were without any detectable lesions in MRI. Of the patients that participated in the follow-up measurements, five showed abnormal theta activity in the first recording, but only two in the second measurement. Our results suggest that aberrant theta-band oscillatory activity can provide an early objective sign of brain dysfunction after mTBI. In 3/7 patients, the slow-wave activity was transient and visible only in the first recording, urging prompt timing for the measurements in clinical settings.