Neuronal avalanches in temporal lobe epilepsy as a noninvasive diagnostic tool investigating large scale brain dynamics.

The epilepsy diagnosis still represents a complex process, with misdiagnosis reaching 40%. We aimed at building an automatable workflow, helping the clinicians in the diagnosis of temporal lobe epilepsy (TLE). We hypothesized that neuronal avalanches (NA) represent a feature better encapsulating the rich brain dynamics compared to classically used functional connectivity measures (Imaginary Coherence; ImCoh). We analyzed large-scale activation bursts (NA) from source estimation of resting-state electroencephalography. Using a support vector machine, we reached a classification accuracy of TLE versus controls of 0.86 ± 0.08 (SD) and an area under the curve of 0.93 ± 0.07. The use of NA features increase by around 16% the accuracy of diagnosis prediction compared to ImCoh. Classification accuracy increased with larger signal duration, reaching a plateau at 5 min of recording. To summarize, NA represents an interpretable feature for an automated epilepsy identification, being related with intrinsic neuronal timescales of pathology-relevant regions.

Brain network topological changes in inflammatory bowel disease: an exploratory study.

Although the aetio-pathogenesis of inflammatory bowel diseases (IBD) is not entirely clear, the interaction between genetic and adverse environmental factors may induce an intestinal dysbiosis, resulting in chronic inflammation having effects on the large-scale brain network. Here, we hypothesized inflammation-related changes in brain topology of IBD patients, regardless of the clinical form [ulcerative colitis (UC) or Crohn's disease (CD)]. To test this hypothesis, we analysed source-reconstructed magnetoencephalography (MEG) signals in 25 IBD patients (15 males, 10 females; mean age ± SD, 42.28 ± 13.15; mean education ± SD, 14.36 ± 3.58) and 28 healthy controls (HC) (16 males, 12 females; mean age ± SD, 45.18 ± 12.26; mean education ± SD, 16.25 ± 2.59), evaluating the brain topology. The betweenness centrality (BC) of the left hippocampus was higher in patients as compared with controls, in the gamma frequency band. It indicates how much a brain region is involved in the flow of information through the brain network. Furthermore, the comparison among UC, CD and HC showed statistically significant differences between UC and HC and between CD and HC, but not between the two clinical forms. Our results demonstrated that these topological changes were not dependent on the specific clinical form, but due to the inflammatory process itself. Broader future studies involving panels of inflammatory factors and metabolomic analyses on biological samples could help to monitor the brain involvement in IBD and to clarify the clinical impact.

Virtual brain twins: from basic neuroscience to clinical use.

Virtual brain twins are personalized, generative and adaptive brain models based on data from an individual's brain for scientific and clinical use. After a description of the key elements of virtual brain twins, we present the standard model for personalized whole-brain network models. The personalization is accomplished using a subject's brain imaging data by three means: (1) assemble cortical and subcortical areas in the subject-specific brain space; (2) directly map connectivity into the brain models, which can be generalized to other parameters; and (3) estimate relevant parameters through model inversion, typically using probabilistic machine learning. We present the use of personalized whole-brain network models in healthy ageing and five clinical diseases: epilepsy, Alzheimer's disease, multiple sclerosis, Parkinson's disease and psychiatric disorders. Specifically, we introduce spatial masks for relevant parameters and demonstrate their use based on the physiological and pathophysiological hypotheses. Finally, we pinpoint the key challenges and future directions.

Altered spreading of fast aperiodic brain waves relates to disease duration in Amyotrophic Lateral Sclerosis.

OBJECTIVE: To test the hypothesis that patients affected by Amyotrophic Lateral Sclerosis (ALS) show an altered spatio-temporal spreading of neuronal avalanches in the brain, and that this may related to the clinical picture. METHODS: We obtained the source-reconstructed magnetoencephalography (MEG) signals from thirty-six ALS patients and forty-two healthy controls. Then, we used the construct of the avalanche transition matrix (ATM) and the corresponding network parameter nodal strength to quantify the changes in each region, since this parameter provides key information about which brain regions are mostly involved in the spreading avalanches. RESULTS: ALS patients presented higher values of the nodal strength in both cortical and sub-cortical brain areas. This parameter correlated directly with disease duration. CONCLUSIONS: In this work, we provide a deeper characterization of neuronal avalanches propagation in ALS, describing their spatio-temporal trajectories and identifying the brain regions most likely to be involved in the process. This makes it possible to recognize the brain areas that take part in the pathogenic mechanisms of ALS. Furthermore, the nodal strength of the involved regions correlates directly with disease duration. SIGNIFICANCE: Our results corroborate the clinical relevance of aperiodic, fast large-scale brain activity as a biomarker of microscopic changes induced by neurophysiological processes.

The Effect of Sleep Deprivation on Brain Fingerprint Stability: A Magnetoencephalography Validation Study.

This study examined the stability of the functional connectome (FC) over time using fingerprint analysis in healthy subjects. Additionally, it investigated how a specific stressor, namely sleep deprivation, affects individuals' differentiation. To this aim, 23 healthy young adults underwent magnetoencephalography (MEG) recording at three equally spaced time points within 24 h: 9 a.m., 9 p.m., and 9 a.m. of the following day after a night of sleep deprivation. The findings indicate that the differentiation was stable from morning to evening in all frequency bands, except in the delta band. However, after a night of sleep deprivation, the stability of the FCs was reduced. Consistent with this observation, the reduced differentiation following sleep deprivation was found to be negatively correlated with the effort perceived by participants in completing the cognitive task during sleep deprivation. This correlation suggests that individuals with less stable connectomes following sleep deprivation experienced greater difficulty in performing cognitive tasks, reflecting increased effort.

Flexibility of brain dynamics is increased and predicts clinical impairment in relapsing-remitting but not in secondary progressive multiple sclerosis.

Large-scale brain activity has long been investigated under the erroneous assumption of stationarity. Nowadays, we know that resting-state functional connectivity is characterized by aperiodic, scale-free bursts of activity (i.e. neuronal avalanches) that intermittently recruit different brain regions. These different patterns of activity represent a measure of brain flexibility, whose reduction has been found to predict clinical impairment in multiple neurodegenerative diseases such as Parkinson's disease, amyotrophic lateral sclerosis and Alzheimer's disease. Brain flexibility has been recently found increased in multiple sclerosis, but its relationship with clinical disability remains elusive. Also, potential differences in brain dynamics according to the multiple sclerosis clinical phenotypes remain unexplored so far. We performed a brain dynamics study quantifying brain flexibility utilizing the 'functional repertoire' (i.e. the number of configurations of active brain areas) through source reconstruction of magnetoencephalography signals in a cohort of 25 multiple sclerosis patients (10 relapsing-remitting multiple sclerosis and 15 secondary progressive multiple sclerosis) and 25 healthy controls. Multiple sclerosis patients showed a greater number of unique reconfigurations at fast time scales as compared with healthy controls. This difference was mainly driven by the relapsing-remitting multiple sclerosis phenotype, whereas no significant differences in brain dynamics were found between secondary progressive multiple sclerosis and healthy controls. Brain flexibility also showed a different predictive power on clinical disability according to the multiple sclerosis type. For the first time, we investigated brain dynamics in multiple sclerosis patients through high temporal resolution techniques, unveiling differences in brain flexibility according to the multiple sclerosis phenotype and its relationship with clinical disability.

The effect of dopaminergic treatment on whole body kinematics explored through network theory.

Three-dimensional motion analysis represents a quantitative approach to assess spatio-temporal and kinematic changes in health and disease. However, these parameters provide only segmental information, discarding minor changes of complex whole body kinematics characterizing physiological and/or pathological conditions. We aimed to assess how levodopa intake affects the whole body, analyzing the kinematic interactions during gait in Parkinson's disease (PD) through network theory which assess the relationships between elements of a system. To this end, we analysed gait data of 23 people with PD applying network theory to the acceleration kinematic data of 21 markers placed on participants' body landmarks. We obtained a matrix of kinematic interactions (i.e., the kinectome) for each participant, before and after the levodopa intake, we performed a topological analysis to evaluate the large-scale interactions among body elements, and a multilinear regression analysis to verify whether the kinectome's topology could predict the clinical variations induced by levodopa. We found that, following levodopa intake, patients with PD showed less trunk and head synchronization (p-head = 0.048; p-7th cervical vertebrae = 0.032; p-10th thoracic vertebrae = 0.006) and an improved upper-lower limbs synchronization (elbows right, p = 0.002; left, p = 0.005), (wrists right, p = 0.003; left, p = 0.002; knees right, p = 0.003; left, p = 0.039) proportional to the UPDRS-III scores. These results may be attributable to the reduction of rigidity, following pharmacological treatment.

Measuring neuronal avalanches to inform brain-computer interfaces.

Large-scale interactions among multiple brain regions manifest as bursts of activations called neuronal avalanches, which reconfigure according to the task at hand and, hence, might constitute natural candidates to design brain-computer interfaces (BCIs). To test this hypothesis, we used source-reconstructed magneto/electroencephalography during resting state and a motor imagery task performed within a BCI protocol. To track the probability that an avalanche would spread across any two regions, we built an avalanche transition matrix (ATM) and demonstrated that the edges whose transition probabilities significantly differed between conditions hinged selectively on premotor regions in all subjects. Furthermore, we showed that the topology of the ATMs allows task-decoding above the current gold standard. Hence, our results suggest that neuronal avalanches might capture interpretable differences between tasks that can be used to inform brain-computer interfaces.

Brain flexibility increases during the peri-ovulatory phase as compared to early follicular phase of the menstrual cycle.

The brain operates in a flexible dynamic regime, generating complex patterns of activity (i.e. neuronal avalanches). This study aimed at describing how brain dynamics change according to menstrual cycle (MC) phases. Brain activation patterns were estimated from resting-state magnetoencephalography (MEG) scans, acquired from women at early follicular (T1), peri-ovulatory (T2) and mid-luteal (T3) phases of the MC. We investigated the functional repertoire (number of brain configurations based on fast high-amplitude bursts of the brain signals) and the region-specific influence on large-scale dynamics across the MC. Finally, we assessed the relationship between sex hormones and changes in brain dynamics. A significantly larger number of visited configurations in T2 as compared to T1 was specifically observed in the beta frequency band. No relationship between changes in brain dynamics and sex hormones was evident. Finally, we showed that the left posterior cingulate gyrus and the right insula were recruited more often in the functional repertoire during T2 as compared to T1, while the right pallidum was more often part of the functional repertoires during T1 as compared to T2. In summary, we showed hormone-independent increased flexibility of the brain dynamics during the ovulatory phase. Moreover, we demonstrated that several specific brain regions play a key role in determining this change.

Altered spread of waves of activities at large scale is influenced by cortical thickness organization in temporal lobe epilepsy: a magnetic resonance imaging-high-density electroencephalography study.

Temporal lobe epilepsy is a brain network disorder characterized by alterations at both the structural and the functional levels. It remains unclear how structure and function are related and whether this has any clinical relevance. In the present work, we adopted a novel methodological approach investigating how network structural features influence the large-scale dynamics. The functional network was defined by the spatio-temporal spreading of aperiodic bursts of activations (neuronal avalanches), as observed utilizing high-density electroencephalography in patients with temporal lobe epilepsy. The structural network was modelled as the region-based thickness covariance. Loosely speaking, we quantified the similarity of the cortical thickness of any two brain regions, both across groups and at the individual level, the latter utilizing a novel approach to define the subject-wise structural covariance network. In order to compare the structural and functional networks (at the nodal level), we studied the correlation between the probability that a wave of activity would propagate from a source to a target region and the similarity of the source region thickness as compared with other target brain regions. Building on the recent evidence that large-waves of activities pathologically spread through the epileptogenic network in temporal lobe epilepsy, also during resting state, we hypothesize that the structural cortical organization might influence such altered spatio-temporal dynamics. We observed a stable cluster of structure-function correlation in the bilateral limbic areas across subjects, highlighting group-specific features for left, right and bilateral temporal epilepsy. The involvement of contralateral areas was observed in unilateral temporal lobe epilepsy. We showed that in temporal lobe epilepsy, alterations of structural and functional networks pair in the regions where seizures propagate and are linked to disease severity. In this study, we leveraged on a well-defined model of neurological disease and pushed forward personalization approaches potentially useful in clinical practice. Finally, the methods developed here could be exploited to investigate the relationship between structure-function networks at subject level in other neurological conditions.

Topological changes of fast large-scale brain dynamics in mild cognitive impairment predict early memory impairment: a resting-state, source reconstructed, magnetoencephalography study.

Functional connectivity has been used as a framework to investigate widespread brain interactions underlying cognitive deficits in mild cognitive impairment (MCI). However, many functional connectivity metrics focus on the average of the periodic activities, disregarding the aperiodic bursts of activity (i.e., the neuronal avalanches) characterizing the large-scale dynamic activities of the brain. Here, we apply the recently described avalanche transition matrix framework to source-reconstructed magnetoencephalography signals in a cohort of 32 MCI patients and 32 healthy controls to describe the spatio-temporal features of neuronal avalanches and explore their topological properties. Our results showed that MCI patients showed a more centralized network (as assessed by higher values of the degree divergence and leaf fraction) as compared to healthy controls. Furthermore, we found that the degree divergence (in the theta band) was predictive of hippocampal memory impairment. These findings highlight the role of the changes of aperiodic bursts in clinical conditions and may contribute to a more thorough phenotypical assessment of patients.

Brain fingerprint is based on the aperiodic, scale-free, neuronal activity.

Subject differentiation bears the possibility to individualize brain analyses. However, the nature of the processes generating subject-specific features remains unknown. Most of the current literature uses techniques that assume stationarity (e.g., Pearson's correlation), which might fail to capture the non-linear nature of brain activity. We hypothesize that non-linear perturbations (defined as neuronal avalanches in the context of critical dynamics) spread across the brain and carry subject-specific information, contributing the most to differentiability. To test this hypothesis, we compute the avalanche transition matrix (ATM) from source-reconstructed magnetoencephalographic data, as to characterize subject-specific fast dynamics. We perform differentiability analysis based on the ATMs, and compare the performance to that obtained using Pearson's correlation (which assumes stationarity). We demonstrate that selecting the moments and places where neuronal avalanches spread improves differentiation (P < 0.0001, permutation testing), despite the fact that most of the data (i.e., the linear part) are discarded. Our results show that the non-linear part of the brain signals carries most of the subject-specific information, thereby clarifying the nature of the processes that underlie individual differentiation. Borrowing from statistical mechanics, we provide a principled way to link emergent large-scale personalized activations to non-observable, microscopic processes.

Reduced clinical connectome fingerprinting in multiple sclerosis predicts fatigue severity.

BACKGROUND: Brain connectome fingerprinting is progressively gaining ground in the field of brain network analysis. It represents a valid approach in assessing the subject-specific connectivity and, according to recent studies, in predicting clinical impairment in some neurodegenerative diseases. Nevertheless, its performance, and clinical utility, in the Multiple Sclerosis (MS) field has not yet been investigated. METHODS: We conducted the Clinical Connectome Fingerprint (CCF) analysis on source-reconstructed magnetoencephalography signals in a cohort of 50 subjects: twenty-five MS patients and twenty-five healthy controls. RESULTS: All the parameters of identifiability, in the alpha band, were reduced in patients as compared to controls. These results implied a lower similarity between functional connectomes (FCs) of the same patient and a reduced homogeneity among FCs in the MS group. We also demonstrated that in MS patients, reduced identifiability was able to predict, fatigue level (assessed by the Fatigue Severity Scale). CONCLUSION: These results confirm the clinical usefulness of the CCF in both identifying MS patients and predicting clinical impairment. We hope that the present study provides future prospects for treatment personalization on the basis of individual brain connectome.

The Effects of Social Processing and Role Type on Attention Networks: Insights from Team Ball Athletes.

(1) Background: Several findings have shown how social stimuli can influence attentional processes. Social attention is crucial in team ball sports, in which players have to react to dynamically changing, unpredictable, and externally paced environments. Our study aimed at demonstrating the influence of social processing on team ball sports athletes' attentional abilities. (2) Methods: A total of 103 male players divided by sport (soccer, handball, and basketball) and by role (striker, midfielder, or defender) were tested through a modified version of the Attention Network Test (ANT) in which they were exposed to both social and non-social stimuli. (3) Results: Social stimuli positively impacted the athletes' abilities to focus on target stimuli and ignore conflicting environmental requests (t = -2.600, p = 0.011 *). We also found that the athletes' roles impacted their performance accuracy. Specifically, differences were found in the ability to maintain a general state of reactivity between athletes (strikers vs. midfielders: t = 3.303, p = 0.004 **; striker vs. defenders: t = -2.820, p = 0.017 *; midfielders vs. defenders: t = -5.876, p < 001 ***). (4) Conclusion: These findings revealed that social stimuli are crucial for performance enhancement in team ball sports athletes. Further, we suggest that it is possible to draw specific attentional profiles for athletes in different roles.

Altered spreading of neuronal avalanches in temporal lobe epilepsy relates to cognitive performance: A resting-state hdEEG study.

OBJECTIVE: Large aperiodic bursts of activations named neuronal avalanches have been used to characterize whole-brain activity, as their presence typically relates to optimal dynamics. Epilepsy is characterized by alterations in large-scale brain network dynamics. Here we exploited neuronal avalanches to characterize differences in electroencephalography (EEG) basal activity, free from seizures and/or interictal spikes, between patients with temporal lobe epilepsy (TLE) and matched controls. METHOD: We defined neuronal avalanches as starting when the z-scored source-reconstructed EEG signals crossed a specific threshold in any region and ending when all regions returned to baseline. This technique avoids data manipulation or assumptions of signal stationarity, focusing on the aperiodic, scale-free components of the signals. We computed individual avalanche transition matrices to track the probability of avalanche spreading across any two regions, compared them between patients and controls, and related them to memory performance in patients. RESULTS: We observed a robust topography of significant edges clustering in regions functionally and structurally relevant for the TLE, such as the entorhinal cortex, the inferior parietal and fusiform area, the inferior temporal gyrus, and the anterior cingulate cortex. We detected a significant correlation between the centrality of the entorhinal cortex in the transition matrix and the long-term memory performance (delay recall Rey-Osterrieth Complex Figure Test). SIGNIFICANCE: Our results show that the propagation patterns of large-scale neuronal avalanches are altered in TLE during the resting state, suggesting a potential diagnostic application in epilepsy. Furthermore, the relationship between specific patterns of propagation and memory performance support the neurophysiological relevance of neuronal avalanches.

The Effects of Vibratory and Acoustic Stimulations on Postural Control in Healthy People: A Systematic Review.

Research on human posture and balance control has grown in recent years, leading to continued advances in their understanding. The ability to maintain balance is attributed to the interplay of the visual, vestibular, and somatosensory systems, although an important role is also played by the auditory system. The lack or deficit in any of these systems leads to a reduced stability that may be counterbalanced by the integration of all the remaining sensory information. Auditory and vibratory stimulation have been found to be useful to enhance balance alongside daily activities either in healthy or pathological subjects; nevertheless, while widely investigated, the literature relating to these approaches is still fragmented. This review aims at addressing this by collecting, organising, and discussing all the literature to date on the effects of the various acoustic and vibratory stimulation techniques available on static upright posture in healthy subjects. In addition, this review intends to provide a solid and comprehensive starting point for all the researchers interested in these research areas. A systematic search of the literature was performed and a total of 33 articles (24 on vibratory stimulation and 9 on acoustic stimulation) were included in our analysis. For all articles, several elements were highlighted including: the study sample, the characteristics of the stimulations, the recording instruments, the experimental protocols, and outcomes. Overall, both stimulations analysed were found to have a positive effect on balance but more research is needed to align those alternative approaches to the traditional ones.

Fading of brain network fingerprint in Parkinson's disease predicts motor clinical impairment.

The clinical connectome fingerprint (CCF) was recently introduced as a way to assess brain dynamics. It is an approach able to recognize individuals, based on the brain network. It showed its applicability providing network features used to predict the cognitive decline in preclinical Alzheimer's disease. In this article, we explore the performance of CCF in 47 Parkinson's disease (PD) patients and 47 healthy controls, under the hypothesis that patients would show reduced identifiability as compared to controls, and that such reduction could be used to predict motor impairment. We used source-reconstructed magnetoencephalography signals to build two functional connectomes for 47 patients with PD and 47 healthy controls. Then, exploiting the two connectomes per individual, we investigated the identifiability characteristics of each subject in each group. We observed reduced identifiability in patients compared to healthy individuals in the beta band. Furthermore, we found that the reduction in identifiability was proportional to the motor impairment, assessed through the Unified Parkinson's Disease Rating Scale, and, interestingly, able to predict it (at the subject level), through a cross-validated regression model. Along with previous evidence, this article shows that CCF captures disrupted dynamics in neurodegenerative diseases and is particularly effective in predicting motor clinical impairment in PD.

Spontaneous neuronal avalanches as a correlate of access consciousness.

Decades of research have advanced our understanding of the biophysical mechanisms underlying consciousness. However, an overarching framework bridging between models of consciousness and the large-scale organization of spontaneous brain activity is still missing. Based on the observation that spontaneous brain activity dynamically switches between epochs of segregation and large-scale integration of information, we hypothesize a brain-state dependence of conscious access, whereby the presence of either segregated or integrated states marks distinct modes of information processing. We first review influential works on the neuronal correlates of consciousness, spontaneous resting-state brain activity and dynamical system theory. Then, we propose a test experiment to validate our hypothesis that conscious access occurs in aperiodic cycles, alternating windows where new incoming information is collected but not experienced, to punctuated short-lived integration events, where conscious access to previously collected content occurs. In particular, we suggest that the integration events correspond to neuronal avalanches, which are collective bursts of neuronal activity ubiquitously observed in electrophysiological recordings. If confirmed, the proposed framework would link the physics of spontaneous cortical dynamics, to the concept of ignition within the global neuronal workspace theory, whereby conscious access manifest itself as a burst of neuronal activity.

Sensory Processing Disorders in Children and Adolescents: Taking Stock of Assessment and Novel Therapeutic Tools.

Sensory processing disorders (SPDs) can be described as difficulty detecting, modulating, interpreting, and/or responding to sensory experiences. Because SPDs occur in many individuals with autism spectrum disorder and in other populations with neurodevelopmental disorders, it is important to distinguish between typical and atypical functioning in sensory processes and to identify early phenotypic markers for developing SPDs. This review considers different methods for diagnosing SPDs to outline a multidisciplinary approach useful for developing valid diagnostic measures. In particular, the advantages and limitations of the most commonly used tools in assessment of SPDs, such as caregiver reports, clinical observation, and psychophysical and neuroimaging studies, will be reviewed. Innovative treatment methods such as neuromodulation techniques and virtual reality will also be suggested.

Motor Imagery as a Key Factor for Healthy Ageing: A Review of New Insights and Techniques.

Motor imagery (MI) describes a dynamic cognitive process where a movement is mentally simulated without taking place and holds potential as a means of stimulating motor learning and regaining motor skills. There is growing evidence that imagined and executed actions have common neural circuitry. Since MI counteracts cognitive and motor decline, a growing interest in MI-based mental exercise for older individuals has emerged. Here we review the last decade's scientific literature on age-related changes in MI skills. Heterogeneity in the experimental protocols, as well as the use of populations with unrepresentative age, is making it challenging to draw unambiguous conclusions about MI skills preservation. Self-report and behavioural tasks have shown that some MI components are preserved, while others are impaired. Evidence from neuroimaging studies revealed that, during MI tasks, older individuals hyperactivate their sensorimotor and attentional networks. Some studies have argued that this represents a compensatory mechanism, others claim that this is a sign of cognitive decline. However, further studies are needed to establish whether MI could be used as a promotion factor to improve cognitive functioning and well-being in older people.