Local neurodynamics as a signature of cortical areas: new insights from sleep.

Sleep crucial for the animal survival is accompanied by huge changes in neuronal electrical activity over time, the neurodynamics. Here, drawing on intracranial stereo-electroencephalographic (sEEG) recordings from the Montreal Neurological Institute (MNI), we analyzed local neurodynamics in the waking state at rest and during the N2, N3, and rapid eye movement (REM) sleep phases. Higuchi fractal dimension (HFD)-a measure of signal complexity-was studied as a feature of the local neurodynamics of the primary motor (M1), somatosensory (S1), and auditory (A1) cortices. The key working hypothesis, that the relationships between local neurodynamics preserve in all sleep phases despite the neurodynamics complexity reduces in sleep compared with wakefulness, was supported by the results. In fact, while HFD awake > REM > N2 > N3 (P < 0.001 consistently), HFD in M1 > S1 > A1 in awake and all sleep stages (P < 0.05 consistently). Also power spectral density was studied for consistency with previous investigations. Meaningfully, we found a local specificity of neurodynamics, well quantified by the fractal dimension, expressed in wakefulness and during sleep. We reinforce the idea that neurodynamic may become a new criterion for cortical parcellation, prospectively improving the understanding and ability of compensatory interventions for behavioral disorders.

Neuronal Electrical Ongoing Activity as Cortical Areas Signature: An Insight from MNI Intracerebral Recording Atlas.

The time course of the neuronal activity in the brain network, the neurodynamics, reflects the structure and functionality of the generating neuronal pools. Here, using the intracranial stereo-electroencephalographic (sEEG) recordings of the public Montreal Neurological Institute (MNI) atlas, we investigated the neurodynamics of primary motor (M1), somatosensory (S1) and auditory (A1) cortices measuring power spectral densities (PSD) and Higuchi fractal dimension (HFD) in the same subject (M1 vs. S1 in 16 subjects, M1 vs. A1 in 9, S1 vs. A1 in 6). We observed specific spectral features in M1, which prevailed above beta band, S1 in the alpha band, and A1 in the delta band. M1 HFD was higher than S1, both higher than A1. A clear distinction of neurodynamics properties of specific primary cortices supports the efforts in cortical parceling based on this expression of the local cytoarchitecture and connectivity. In this perspective, we selected within the MNI intracortical database a first set of primary motor, somatosensory and auditory cortices' representatives to query in recognizing ongoing patterns of neuronal communication. Potential clinical impact stands primarily in exploiting such exchange patterns to enhance the efficacy of neuromodulation intervention to cure symptoms secondary to neuronal activity unbalances.

Effects on Motor Control of Personalized Neuromodulation Against Multiple Sclerosis Fatigue.

Fatigue is a hidden symptom of Multiple Sclerosis (MS) disease that nevertheless impacts severely on patients' everyday life. Evidence indicates the involvement of the sensorimotor network and its inter-nodes communication at the basis of this symptom. Two randomized controlled trials (RCTs) showed that the personalized neuromodulation called Fatigue Relief in Multiple Sclerosis (FaReMuS) efficaciously fights multiple sclerosis (MS) fatigue. By this Proof of Concept study, we tested whether FaReMuS reverts the alteration of the brain-muscular synchronization previously observed occurring with fatigue. The cortico muscular coherence (CMC) was studied in 11 patients before and after FaReMuS, a 5-day tDCS (1.5 mA, 15 min per day) anodal over the whole body's somatosensory representation (S1) via a personalized MRI-based electrode (35 cm2) against the occipital cathode (70 cm2). Before FaReMuS, the CMC was observed at a mean frequency of 31.5 ± 1.6 Hz (gamma-band) and positively correlated with the level of fatigue (p = .027). After FaReMuS, fatigue reduced in average of 28% ± 33% the baseline level, and the CMC frequency reduced to 26.6 ± 1.5 Hz (p = .022), thus forthcoming the physiological beta-band as observed in healthy people. The personalized S1 neuromodulation treatment, ameliorating the central-peripheral communication that subtends simple everyday movements, supports the appropriateness of neuromodulations aiming at increasing the parietal excitability in fighting MS fatigue. The relationship between central-peripheral features and fatigue profile strengthens a central more than peripheral origin of the symptom.

Fatigue in multiple sclerosis: Does the functional or structural damage prevail?

Fatigue in multiple sclerosis (MS) is a highly invalidating symptom, lacking efficacious drugs. This topical review aims at assessing the signs in the literature of functional versus structural damage prevalence at the origin of MS fatigue by focusing on papers that assessed the two counterparts in the same patients, paying attention that the fatigue levels do not correlate with clinical severity. We summarize and discuss evidence of increased levels of fatigue occurring together with the alterations of functional connectivity at multiple levels, in the absence of any relationship with lesion load and local atrophy of the involved structures. Specifically, neuronal communication mainly altered in the corticomuscular synchronizations, between hemispheric homologs and in the resting-state networks involved in emotion (cingulate cortex) and effort-reward balance (striatum and inferior parietal lobule). Finally, given the functional prevalence in neuronal network alterations at the origin of fatigue in MS, we highlight the relevance of developing treatments aiming at compensating the neuronal electric communication dysfunctions.

A New, High-Efficacy, Noninvasive Transcranial Electric Stimulation Tuned to Local Neurodynamics.

In this paper, we pose the following working hypothesis: in humans, transcranial electric stimulation (tES) with a time course that mimics the endogenous activity of its target is capable of altering the target's excitability. In our case, the target was the primary motor cortex (M1). We identified the endogenous neurodynamics of hand M1's subgroups of pyramidal neuronal pools in each of our subjects by applying Functional Source Separation (FSS) to their EEG recordings. We then tested whether the corticospinal excitability of the hand representation under the above described stimulation, which we named transcranial individual neurodynamics stimulation (tIDS), was higher than in the absence of stimulation (baseline). As a check, we compared tIDS with the most efficient noninvasive facilitatory corticospinal tES known so far, which is 20 Hz transcranial alternating current stimulation (tACS). The control conditions were as follows: (1) sham, (2) transcranial random noise stimulation (tRNS) in the same frequency range as tIDS (1-250 Hz), and (3) a low current tIDS (tIDSlow). Corticospinal excitability was measured with motor-evoked potentials under transcranial magnetic stimulation. The mean motor-evoked potential amplitude increase was 31% of the baseline during tIDS (p < 0.001), and it was 15% during tACS (p = 0.096). tRNS, tIDSlow, and sham induced no effects. Whereas tACS did not produce an enhancement in any subject at the individual level, tIDS was successful in producing an enhancement in 8 of the 16 subjects. The results of the present proof-of-principle study showed that proper exploitation of local neurodynamics can enhance the efficacy of personalized tES.SIGNIFICANCE STATEMENT This study demonstrated that, in humans, transcranial individual neurodynamics stimulation (tIDS), which mimics the endogenous dynamics of the target neuronal pools, effectively changes the excitability of these pools. tIDS holds promise for high-efficacy personalized neuromodulations based on individual local neurodynamics.

Neuronal dynamics enable the functional differentiation of resting state networks in the human brain.

Intrinsic brain activity is organized in spatial-temporal patterns, called resting-state networks (RSNs), exhibiting specific structural-functional architecture. These networks presumably reflect complex neurophysiological processes and have a central role in distinct perceptual and cognitive functions. In this work, we propose an innovative approach for characterizing RSNs according to their underlying neural oscillations. We investigated specific electrophysiological properties, including spectral features, fractal dimension, and entropy, associated with eight core RSNs derived from high-density electroencephalography (EEG) source-reconstructed signals. Specifically, we found higher synchronization of the gamma-band activity and higher fractal dimension values in perceptual (PNs) compared with higher cognitive (HCNs) networks. The inspection of this underlying rapid activity becomes of utmost importance for assessing possible alterations related to specific brain disorders. The disruption of the coordinated activity of RSNs may result in altered behavioral and perceptual states. Thus, this approach could potentially be used for the early detection and treatment of neurological disorders.

Acute Phase Neuronal Activity for the Prognosis of Stroke Recovery.

Strokes causing similar lesions and clinical states can be followed by diverse regains of neurological functions, indicating that the clinical recovery can depend on individual modulating factors. A promising line to disclose these factors, to finally open new therapeutic strategies, is to search for individual indices of recovery prognosis. Here, we pursued on strengthening the value of acute phase electrophysiological biomarkers for poststroke functional recovery in a wide group of patients. We enrolled 120 patients affected by a monohemispheric stroke within the middle cerebral artery territory (70 left and 50 right damages) and collected the NIH stroke scale (NIHSS) score in the acute phase (T0, median 4 days) and chronic follow-up (T1, median 6 months). At T0, we executed electrophysiological noninvasive assessment (19-channel electroencephalography (EEG) or 28 channels per side magnetoencephalography (MEG)) of brain activity at rest by means of band powers in the contra- and ipsilesional hemispheres (CLH, ILH) or the homologous area symmetry (HArS). Low-band (2-6 Hz) HArS entered the regression model for predicting the stabilized clinical state (p < 0.001), with bilateral impairment correlated with a poor outcome. Present data strengthen the fact that low-band impairment of homologous ipsi- and contralesional hemispheric regions in the acute stroke indicate a negative prognosis of clinical recovery.

Personalized, bilateral whole-body somatosensory cortex stimulation to relieve fatigue in multiple sclerosis.

BACKGROUND: The patients suffering from multiple sclerosis (MS) often consider fatigue the most debilitating symptom they experience, but conventional medicine currently offers poorly efficacious therapies. OBJECTIVE: We executed a replication study of an innovative approach for relieving MS fatigue. METHODS: According to the sample size estimate, we recruited 10 fatigued MS patients who received 5-day transcranial direct current stimulation (tDCS) in a randomized, double-blind, Sham-controlled, crossover study, with modified Fatigue Impact Scale (mFIS) score reduction at the end of the treatment as primary outcome. A personalized anodal electrode, shaped on the magnetic resonance imaging (MRI)-derived individual cortical folding, targeted the bilateral whole-body primary somatosensory cortex (S1) with an occipital cathode. RESULTS: The amelioration of fatigue symptoms after Real stimulation (40% of baseline) was significantly larger than after Sham stimulation (14%, p = 0.012). Anodal whole body S1 induced a significant fatigue reduction in mildly disabled MS patients when the fatigue-related symptoms severely hampered their quality of life. CONCLUSION: This second result in an independent group of patients supports the idea that neuromodulation interventions that properly select a personalized target might be a suitable non-pharmacological treatment for MS fatigue.

fMRI characterisation of widespread brain networks relevant for behavioural variability in fine hand motor control with and without visual feedback.

A bilateral visuo-parietal-motor network is responsible for fine control of hand movements. However, the sub-regions which are devoted to maintenance of contraction stability and how these processes fluctuate with trial-quality of task execution and in the presence/absence of visual feedback remains unclear. We addressed this by integrating behavioural and fMRI measurements during right-hand isometric compression of a compliant rubber bulb, at 10% and 30% of maximum voluntary contraction, both with and without visual feedback of the applied force. We quantified single-trial behavioural performance during 1) the whole task period and 2) stable contraction maintenance, and regressed these metrics against the fMRI data to identify the brain activity most relevant to trial-by-trial fluctuations in performance during specific task phases. fMRI-behaviour correlations in a bilateral network of visual, premotor, primary motor, parietal and inferior frontal cortical regions emerged during performance of the entire feedback task, but only in premotor, parietal cortex and thalamus during the stable contraction period. The trials with the best task performance showed increased bilaterality and amplitude of fMRI responses. With feedback, stronger BOLD-behaviour coupling was found during 10% compared to 30% contractions. Only a small subset of regions in this network were weakly correlated with behaviour without feedback, despite wider network activated during this task than in the presence of feedback. These findings reflect a more focused network strongly coupled to behavioural fluctuations when providing visual feedback, whereas without it the task recruited widespread brain activity almost uncoupled from behavioural performance.

Impaired brainstem and thalamic high-frequency oscillatory EEG activity in migraine between attacks.

Introduction We investigated whether interictal thalamic dysfunction in migraine without aura (MO) patients is a primary determinant or the expression of its functional disconnection from proximal or distal areas along the somatosensory pathway. Methods Twenty MO patients and twenty healthy volunteers (HVs) underwent an electroencephalographic (EEG) recording during electrical stimulation of the median nerve at the wrist. We used the functional source separation algorithm to extract four functionally constrained nodes (brainstem, thalamus, primary sensory radial, and primary sensory motor tangential parietal sources) along the somatosensory pathway. Two digital filters (1-400 Hz and 450-750 Hz) were applied in order to extract low- (LFO) and high- frequency (HFO) oscillatory activity from the broadband signal. Results Compared to HVs, patients presented significantly lower brainstem (BS) and thalamic (Th) HFO activation bilaterally. No difference between the two cortical HFO as well as in LFO peak activations between the two groups was seen. The age of onset of the headache was positively correlated with HFO power in the right brainstem and thalamus. Conclusions This study provides evidence for complex dysfunction of brainstem and thalamocortical networks under the control of genetic factors that might act by modulating the severity of migraine phenotype.

Prognostic Value of EEG Microstates in Acute Stroke.

Given the importance of neuronal plasticity in recovery from a stroke and the huge variability of recovery abilities in patients, we investigated neuronal activity in the acute phase to enhance information about the prognosis of recovery in the stabilized phase. We investigated the microstates in 47 patients who suffered a first-ever mono-lesional ischemic stroke in the middle cerebral artery territory and in 20 healthy control volunteers. Electroencephalographic (EEG) activity at rest with eyes closed was acquired between 2 and 10 days (T0) after ischemic attack. Objective criteria allowed for the selection of an optimal number of microstates. Clinical condition was quantified by the National Institute of Health Stroke Scale (NIHSS) both in acute (T0) and stabilized (T1, 5.4 ± 1.7 months) phases and Effective Recovery (ER) was calculated as (NIHSS(T1)-NIHSS(T0))/NIHSS(T0). The microstates A, B, C and D emerged as the most stable. In patients with a left lesion inducing a language impairment, microstate C topography differed from controls. Microstate D topography was different in patients with a right lesion inducing neglect symptoms. In patients, the C vs D microstate duration differed after both a left and a right lesion with respect to controls (C lower than D in left and D lower than C in right lesion). A preserved microstate B in acute phase correlated with a better effective recovery. A regression model indicated that the microstate B duration explained the 11% of ER variance. This first ever study of EEG microstates in acute stroke opens an interesting path to identify neuronal impairments with prognostic relevance, to develop enriched compensatory treatments to drive a better individual recovery.

Biological factors and age-dependence of primary motor cortex experimental plasticity.

To evaluate whether the age-dependence of brain plasticity correlates with the levels of proteins involved in hormone and brain functions we executed a paired associative stimulation (PAS) protocol and blood tests. We measured the PAS-induced plasticity in the primary motor cortex. Blood levels of the brain-derived neurotrophic factor (BDNF), estradiol, the insulin-like growth factor (IGF)-1, the insulin-like growth factor binding protein (IGFBP)-3, progesterone, sex hormone-binding globulin (SHBG), testosterone, and the transforming growth factor beta 1 (TGF-ß1) were determined in 15 healthy men and 20 healthy women. We observed an age-related reduction of PAS-induced plasticity in females that it is not present in males. In females, PAS-induced plasticity displayed a correlation with testosterone (p = 0.006) that became a trend after the adjustment for the age effect (p = 0.078). In males, IGF-1 showed a nominally significant correlation with the PAS-induced plasticity (p = 0.043). In conclusion, we observed that hormone blood levels (testosterone in females and IGF-1 in males) may be involved in the age-dependence of brain plasticity.

The spontaneous fluctuation of the excitability of a single node modulates the internodes connectivity: a TMS-EEG study.

Brain effective connectivity can be tracked by cerebral recruitments evoked by transcranial magnetic stimulation (TMS), as measured by simultaneous electroencephalography (TMS-EEG). When TMS is targeting the primary motor area, motor evoked potentials (MEPs) can be collected from the "target" muscles. The aim of this study was to measure whether or not effective brain connectivity changes with the excitability level of the corticospinal motor pathway (CSMP) as parameterized by MEP amplitude. After averaging two subgroups of EEG-evoked responses corresponding to high and low MEP amplitudes, we calculated the individual differences between them and submitted the grand average to sLORETA algorithm obtaining localized regions of interest (RoIs). Statistical differences of RoI recruitment strength between low and high CSMP excitation was assessed in single subjects. Preceding the feedback arrival, neural recruitment for stronger CSMP activation were weaker at 6-10 ms of homotopic sensorimotor areas BA3/4/5 of the right nonstimulated hemisphere (trend), weaker at 18-25 ms of left parietal BA2/3/40, and stronger at 26-32 ms of bilateral frontal motor areas BA6/8. The proposed method enables the tracking of brain network connectivity during stimulation of one node by measuring the strength of the connected recruited node activations. Spontaneous increases of the excitation of the node originating the transmission within the hand control network gave rise to dynamic recruitment patterns with opposite behaviors, weaker in homotopic and parietal circuits, stronger in frontal ones. The effective connectivity within bilateral circuits orchestrating hand control appeared dynamically modulated in time even in resting state as probed by TMS.

Fractal Neurodynamics.

The neuronal ongoing electrical activity in the brain network, the neurodynamics, reflects the structure and functionality of generating neuronal pools. The activity of neurons due to their excitatory and inhibitory projections is associated with specific brain functions. Here, the purpose was to investigate if the local ongoing electrical activity exhibits its characteristic spectral and fractal features in wakefulness and sleep across and within subjects. Moreover, we aimed to show that measures typical of complex systems catch physiological features missed by linear spectral analyses. For this study, we concentrated on the evaluation of the power spectral density (PSD) and Higuchi fractal dimension (HFD) measures. Relevant clinical impact of the specific features of neurodynamics identification stands primarily in the potential of classifying cortical parcels according to their neurodynamics as well as enhancing the effectiveness of neuromodulation interventions to cure symptoms secondary to neuronal activity unbalances.

Clinical Sensitivity of Fractal Neurodynamics.

Among the significant advances in the understanding of the organization of the neuronal networks that coordinate the body and brain, their complex nature is increasingly important, resulting from the interaction between the very large number of constituents strongly organized hierarchically and at the same time with "self-emerging." This awareness drives us to identify the measures that best quantify the "complexity" that accompanies the continuous evolutionary dynamics of the brain. In this chapter, after an introductory section (Sect. 15.1), we examine how the Higuchi fractal dimension is able to perceive physiological processes (15.2), neurological (15.3) and psychiatric (15.4) disorders, and neuromodulation effects (15.5), giving a mention of other methods of measuring neuronal electrical activity in addition to electroencephalography, such as magnetoencephalography and functional magnetic resonance. Conscious that further progress will support a deeper understanding of the temporal course of neuronal activity because of continuous interaction with the environment, we conclude confident that the fractal dimension has begun to uncover important features of the physiology of brain activity and its alterations.

Fractal Dimension as a discriminative feature for high accuracy classification in motor imagery EEG-based brain-computer interface.

BACKGROUND AND OBJECTIVE: The brain-computer interface (BCI) technology acquires human brain electrical signals, which can be effectively and successfully used to control external devices, potentially supporting subjects suffering from motor impairments in the interaction with the environment. To this aim, BCI systems must correctly decode and interpret neurophysiological signals reflecting the intention of the subjects to move. Therefore, the accurate classification of single events in motor tasks represents a fundamental challenge in ensuring efficient communication and control between users and BCIs. Movement-associated changes in electroencephalographic (EEG) sensorimotor rhythms, such as event-related desynchronization (ERD), are well-known features of discriminating motor tasks. Fractal dimension (FD) can be used to evaluate the complexity and self-similarity in brain signals, potentially providing complementary information to frequency-based signal features. METHODS: In the present work, we introduce FD as a novel feature for subject-independent event classification, and we test several machine learning (ML) models in behavioural tasks of motor imagery (MI) and motor execution (ME). RESULTS: Our results show that FD improves the classification accuracy of ML compared to ERD. Furthermore, unilateral hand movements have higher classification accuracy than bilateral movements in both MI and ME tasks. CONCLUSIONS: These results provide further insights into subject-independent event classification in BCI systems and demonstrate the potential of FD as a discriminative feature for EEG signals.