Performance prediction in a visuo-motor task: the contribution of EEG analysis.

Brain state in the time preceding the task affects motor performance at single trial level. Aim of the study was to investigate, through a single trial analysis of the Power Spectral Density (PSD) of the cortical sources of EEG rhythms, whether there are EEG markers, which can predict trial-by-trial the subject's performance as measured by the reaction time (RT). 20 healthy adult volunteers performed a specific visuomotor task while continuously recorded with a 64 electrodes EEG. For each single trial, the PSD of the cortical sources of EEG rhythms was obtained from EEG data to cortical current density time series in 12 regions of interest at Brodmann areas level. Results showed a statistically significant increase of posterior and limbic alpha 1 and of frontal beta 2 power, and a reduction of frontal and limbic delta and of temporal alpha 1 power, during triggering stimulus presentation for better performance, namely faster responses. At single trial level, correlation analyses between RTs and significant PSD, revealed positive correlations in frontal delta, temporal alpha 1, and limbic delta bands, and negative ones in frontal beta 2, parietal alpha 1, and occipital alpha 1 bands. Furthermore, the subject's faster responses have been found as correlated with the similarity between the PSD values in parietal and occipital alpha 1. Predicting individual's performance at single trial level, might be extremely useful in the clinical context, since it could allow to launch rehabilitative therapies in the most efficient brain state, avoiding useless interventions.

Transcranial Direct Current Stimulation Enhances Neuroplasticity and Accelerates Motor Recovery in a Stroke Mouse Model.

BACKGROUND: More effective strategies are needed to promote poststroke functional recovery. Here, we evaluated the impact of bihemispheric transcranial direct current stimulation (tDCS) on forelimb motor function recovery and the underlying mechanisms in mice subjected to focal ischemia of the motor cortex. METHODS: Photothrombotic stroke was induced in the forelimb brain motor area, and tDCS was applied once per day for 3 consecutive days, starting 72 hours after stroke. Grid-walking, single pellet reaching, and grip strength tests were conducted to assess motor function. Local field potentials were recorded to evaluate brain connectivity. Western immunoblotting, ELISA, quantitative real-time polymerase chain reaction, and Golgi-Cox staining were used to uncover tDCS-mediated stroke recovery mechanisms. RESULTS: Among our results, tDCS increased the rate of motor recovery, anticipating it at the early subacute stage. In this window, tDCS enhanced BDNF (brain-derived neurotrophic factor) expression and dendritic spine density in the peri-infarct motor cortex, along with increasing functional connectivity between motor and somatosensory cortices. Treatment with the BDNF TrkB (tropomyosin-related tyrosine kinase B) receptor inhibitor, ANA-12, prevented tDCS effects on motor recovery and connectivity as well as the increase of spine density, pERK (phosphorylated extracellular signal-regulated kinase), pCaMKII (phosphorylated calcium/calmodulin-dependent protein kinase II), pMEF (phosphorylated myocyte-enhancer factor), and PSD (postsynaptic density)-95. The tDCS-promoted rescue was paralleled by enhanced plasma BDNF level, suggesting its potential role as circulating prognostic biomarker. CONCLUSIONS: The rate of motor recovery is accelerated by tDCS applied in the subacute phase of stroke. Anticipation of motor recovery via vicariate pathways or neural reserve recruitment would potentially enhance the efficacy of standard treatments, such as physical therapy, which is often delayed to a later stage when plastic responses are progressively lower.

Human Brain Networks in Physiological and Pathological Aging: Reproducibility of Electroencephalogram Graph Theoretical Analysis in Cortical Connectivity.

Background: Physiological and pathological brain aging plays a central role in brain network modulation. The aim of the present article was to assess the stability of a proposed method for evaluation of small-world (SW) characteristics for the study of the human connectome. Subjects and Methods: Eighty subjects were recruited: 36 young healthy controls, 32 elderly healthy controls, and 12 patients affected by Alzheimer's disease (AD). Electroencephalograms (EEGs) were recorded during six separate sessions (480 recordings) at an average intersession interval of 3.8 ± 0.2 days. We applied graph theory functions to the weighted and undirected networks obtained by the lagged linear coherence estimated by exact low-resolution electromagnetic tomography (eLORETA). We explored the following frequency bands: delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), beta 2 (20-30 Hz), and gamma (30-40 Hz). Results: The proposed method for evaluation of SW characteristics exhibited good reproducibility and stability. Furthermore, results showed the pattern, Young>Elderly>AD, in low-frequency delta and theta bands and vice versa in the higher alpha band. Finally, a correlation with age was confirmed in healthy subjects, showing that the older the age, the higher the SW values for alpha 2. Discussion: Evidences from the present study confirm the stability of the SW index and suggest that the analysis of connectivity patterns evaluated from EEGs can be supported by the graph theory. The proposed method for evaluation of SW characteristics has shown good reproducibility and stability. This technique, applied to patient data, could provide more information on the pathophysiological processes underlying age-related brain disconnection, as well as on administration of rehabilitation treatments at the right time, which could allow patients to avoid unnecessary interventions. Impact statement The graph analysis tools described in this study represent an interesting approach to study the distinctive characteristics of physiological aging by focusing on functional connectivity networks. The proposed method for evaluation of small-world characteristics has shown good reproducibility and stability. This technique, applied to patient data, could provide more information on the pathophysiological processes underlying age-related brain disconnection, as well as on delivery of rehabilitation treatments at the right time, which could allow patients to avoid unnecessary interventions.

Cognitive Decline Risk Stratification in People with Late-Onset Epilepsy of Unknown Etiology: An Electroencephalographic Connectivity and Graph Theory Pilot Study.

BACKGROUND: Although people with late onset epilepsy of unknown etiology (LOEU) are at higher risk of cognitive decline compared to the general population, we still lack affordable tools to predict and stratify their risk of dementia. OBJECTIVE: This pilot-study investigates the potential application of electroencephalography (EEG) network small-world (SW) properties in predicting cognitive decline among patients with LOEU. METHODS: People diagnosed with LOEU and normal cognitive examination at the time of epilepsy diagnosis were included. Cerebrospinal fluid biomarkers, brain imaging, and neuropsychological assessment were performed at the time of epilepsy diagnosis. Baseline EEG was analyzed for SW properties. Patients were followed-up over time with neuropsychological testing to define the trajectory of cognitive decline. RESULTS: Over 5.1 years of follow-up, among 24 patients diagnosed with LOEU, 62.5% were female, mean age was 65.3 years, thirteen developed mild cognitive impairment (MCI), and four developed dementia. Patients with LOEU developing MCI had lower values of SW coefficients in the delta (p = 0.03) band and higher SW values in the alpha frequency bands (p = 0.02) compared to patients having normal cognition at last follow-up. The two separate ANOVAs, for low and alpha bands, confirmed an interaction between SW and cognitive decline at follow-up. A similar gradient was confirmed for patients developing dementia compared to those with normal cognitive function as well as to those developing MCI. CONCLUSION: Baseline EEG analysis through SW is worth investigating as an affordable, widely available tool to stratify LOEU patients for their risk of cognitive decline.

Graph Theory on Brain Cortical Sources in Parkinson's Disease: The Analysis of 'Small World' Organization from EEG.

Parkinson's disease (PD) is the second most common neurodegenerative disease in the elderly population. Similarly to other neurodegenerative diseases, the early diagnosis of PD is quite difficult. The current pilot study aimed to explore the differences in brain connectivity between PD and NOrmal eLDerly (Nold) subjects to evaluate whether connectivity analysis may speed up and support early diagnosis. A total of 26 resting state EEGs were analyzed from 13 PD patients and 13 age-matched Nold subjects, applying to cortical reconstructions the graph theory analyses, a mathematical representation of brain architecture. Results showed that PD patients presented a more ordered structure at slow-frequency EEG rhythms (lower value of SW) than Nold subjects, particularly in the theta band, whereas in the high-frequency alpha, PD patients presented more random organization (higher SW) than Nold subjects. The current results suggest that PD could globally modulate the cortical connectivity of the brain, modifying the functional network organization and resulting in motor and non-motor signs. Future studies could validate whether such an approach, based on a low-cost and non-invasive technique, could be useful for early diagnosis, for the follow-up of PD progression, as well as for evaluating pharmacological and neurorehabilitation treatments.

Brain network modulation in transradial amputee with finger perception restored through biomimetic intraneural stimulation.

INTRODUCTION/AIMS: The hand is a sophisticated tool which allows humans to interact with the external world mainly via the sense of touch. Previous evidences demonstrated that electrical stimulations of the nerve trunks governing the hand are able to restore touch perception in transradial amputees. The aim of this study was to evaluate the neurological correlates of restored perception by the evaluation of network characteristics of the brain connectome via EEG recordings in amputees utilizing a fully sensorized bionic hand. METHODS: A 48-year-old female with a left wrist traumatic disarticulation incurred 23 years prior to the study was enrolled for 6 months, during which experimental sessions were randomized. The control group included 12 healthy subjects performing a similar protocol. RESULTS: Results showed that in both amputee and control group, a trend of SW in delta and beta 1 is recognizable (Delta and Beta 1 opposite spread) selectively in the hemisphere contralateral to the stimulus. DISCUSSION: Delta increases (less orderly network) while Beta 1 decreases (more ordered network). It could be seen as a sign of higher attention and concentration of subjects to understand the perception and this monolateral modulation is similar to the bilateral attention given to the mathematical task difficulty's increment.

Contribution of Graph Theory Applied to EEG Data Analysis for Alzheimer's Disease Versus Vascular Dementia Diagnosis.

BACKGROUND: Most common progressive brain diseases in the elderly are Alzheimer's disease (AD) and vascular dementia (VaD). They present with relatively similar clinical symptoms of cognitive decline, but the underlying pathophysiological mechanisms are different. OBJECTIVE: The aim is to explore the brain connectivity differences between AD and VaD patients compared to mild cognitive impairment (MCI) and normal elderly (Nold) subjects applying graph theory, in particular the Small World (SW) analysis. METHODS: 274 resting state EEGs were analyzed in 100 AD, 80 MCI, 40 VaD, and 54 Nold subjects. Graph theory analyses were applied to undirected and weighted networks obtained by lagged linear coherence evaluated by eLORETA tool. RESULTS: VaD and AD patients presented more ordered low frequency structure (lower value of SW) than Nold and MCI subjects, and more random organization (higher value of SW) in low and high frequency alpha rhythms. Differences between patients have been found in high frequency alpha rhythms in VaD (higher value of SW) with respect to AD, and in theta band with a trend which is more similar to MCI and Nold than to AD. MCI subjects presented a network organization which is intermediate, in low frequency bands, between Nold and patients. CONCLUSION: Graph theory applied to EEG data has proved very useful in identifying differences in brain network patterns in subjects with dementia, proving to be a valid tool for differential diagnosis. Future studies will aim to validate this method to diagnose especially in the early stages of the disease and at single subject level.

Entropy modulation of electroencephalographic signals in physiological aging.

Aging is a multifactorial physiological process characterized by the accumulation of degenerative processes impacting on different brain functions, including the cognitive one. A tool largely employed in the investigation of brain networks is the electroencephalogram (EEG). Given the cerebral complexity and dynamism, many non-linear approaches have been applied to explore age-related brain electrical activity modulation detected by the EEG: one of them is the entropy, which measures the disorder of a system. The present study had the aim to investigate aging influence on brain dynamics applying Approximate Entropy (ApEn) parameter to resting state EEG data of 68 healthy adult participants, divided with respect to their age in two groups, focusing on several specialized brain regions. Results showed that elderly participants present higher ApEn values than younger participants in the central, parietal and occipital areas, confirming the hypothesis that aging is characterized by an evolution of brain dynamics. Such findings may reflect a reduced synchronization of the neural networks cyclic activity, due to the reduction of cerebral connections typically found in aging process. Understanding the dynamics of brain networks by applying the entropy parameter could be useful for developing appropriate and personalized rehabilitation programs and for future studies on neurodegenerative diseases.

Brain sources' activity in resting state before a visuo-motor task.

Objective.In modern neuroscience, the underlying mechanisms of the elaboration and reaction to different kinds of stimuli of the brain hemispheres remain still very challenging to understand, together with the possibility to anticipate certain behaviors to improve the performance.Approach.The purpose of the present study was to investigate the brain rhythms characteristics of electroencephalographic (EEG) recordings and in particular, their interhemispheric differences in resting state condition before a visuo-motor task in a population of healthy adults. During the task, subjects were asked to react to a sequence of visual cues as quick as possible. The reaction times (RTs) to the task were measured, collected and correlated with the EEG signals recorded in a resting state condition immediately preceding the task. The EEG data were analyzed in the space of cortical sources of EEG rhythms by the computation of the global spectra power density (GSPD) in the left and in the right hemisphere, and of an index of brain laterality (L).Main results.The results showed a negative correlation between the RTs and the GSPD in the central areas in the left and in the right hemisphere in both eyes open (EO) and eyes closed (EC) conditions. A close to significant and negative correlation was found in the parietal areas. Furthermore, RTs negatively correlated withLin the central areas in EC condition. The results showed a negative correlation between the RTs and the GSPD in the central areas in the left and in the right hemisphere in both EO and EC conditions.Significance.The correlations between the brain activity before a task and the RTs to the task can represent an interesting tool for exploring the brain state characterization for the upcoming tasks performance.

tDCS effects on brain network properties during physiological aging.

Brain neural networks undergo relevant changes during physiological aging, which affect cognitive and behavioral functions. Currently, non-invasive brain stimulation techniques, such as transcranial direct current stimulation (tDCS), are proposed as tools able to modulate cognitive functions in brain aging, acting on networks properties and connectivity. Segregation and integration measures are used and evaluated by means of local clustering (segregation) and path length (integration). Moreover, to assess the balancing between them, the Small World (SW) parameter is employed, evaluating functional coupling in normal brain aging and in pathological conditions including neurodegeneration. The aim of this study was to systematically investigate the tDCS-induced effects on brain network proprieties in physiological aging. In order to reach this aim, cortical activity was acquired from healthy young and elderly subjects by means of EEG recorded before, during, and after anodal, cathodal, and sham tDCS sessions. Specifically, the aim to exploring tDCS polarity-dependent changes in the age-dependent network dynamics was based on a network graph theory application on two groups divided in young and elderly subjects. Eighteen healthy young (9 females; mean age = 24.7, SD = 3.2) and fifteen elderly subjects (9 females; mean = 70.1, SD = 5.1) were enrolled. Each participant received anodal, cathodal, or sham tDCS over the left prefrontal cortex (PFC) in three separate experimental sessions performed 1 week apart. SW was computed to evaluate brain network organization. The present study demonstrates that tDCS delivered in PFC can change brain network dynamics, and tDCS-EEG coregistration data can be analyzed using graph theory to understand the induced effects of different tDCS polarities in physiological and pathological brain aging.

Assessing the dependence of the number of EEG channels in the brain networks' modulations.

Aim of the study was to evaluate the influence of the EEG channels number on the brain networks' analysis, to establish whether and how much higher density EEG actually contributes to add supplementary information to brain networks analyses. 59 electrodes EEGs were recorded in 20 healthy subjects in eyes open and closed condition. For each condition, we analyzed the recording dataset of 59 channels, and three sub-datasets obtained by the selection of 44, 30, 19 channels from the 59 ones. Then we computed the EEG sources of current density and evaluated the SW index in the four EEGs data montages. Results showed that in the eyes open condition the number of recording channels influences more the SW index modulation respect that in the eyes closed condition. Conversely, in the eyes closed condition the brain activity is less affected by specific brain regions' activations and the signal's generators produced not significant variations on EEG data and consequently the small world network measure is not affected by the recording channels number. We can conclude that in the eyes closed condition, the 19 EEG channels is an acceptable montage to study brain networks' modulations, to both detect the higher and the lower brain waves' frequencies.

Approximate Entropy of Brain Network in the Study of Hemispheric Differences.

Human brain, a dynamic complex system, can be studied with different approaches, including linear and nonlinear ones. One of the nonlinear approaches widely used in electroencephalographic (EEG) analyses is the entropy, the measurement of disorder in a system. The present study investigates brain networks applying approximate entropy (ApEn) measure for assessing the hemispheric EEG differences; reproducibility and stability of ApEn data across separate recording sessions were evaluated. Twenty healthy adult volunteers were submitted to eyes-closed resting EEG recordings, for 80 recordings. Significant differences in the occipital region, with higher values of entropy in the left hemisphere than in the right one, show that the hemispheres become active with different intensities according to the performed function. Besides, the present methodology proved to be reproducible and stable, when carried out on relatively brief EEG epochs but also at a 1-week distance in a group of 36 subjects. Nonlinear approaches represent an interesting probe to study the dynamics of brain networks. ApEn technique might provide more insight into the pathophysiological processes underlying age-related brain disconnection as well as for monitoring the impact of pharmacological and rehabilitation treatments.

Classification of Alzheimer's Disease with Respect to Physiological Aging with Innovative EEG Biomarkers in a Machine Learning Implementation.

BACKGROUND: Several studies investigated clinical and instrumental differences to make diagnosis of dementia in general and in Alzheimer's disease (AD) in particular with the aim to classify, at the individual level, AD patients and healthy controls cooperating with neuropsychological tests for an early diagnosis. Advanced network analysis of electroencephalographic (EEG) rhythms provides information on dynamic brain connectivity and could be used in classification processes. If successfully reached, this goal would add a low-cost, easily accessible, and non-invasive technique with neuropsychological tests. OBJECTIVE: To investigate the possibility to automatically classify physiological versus pathological aging from cortical sources' connectivity based on a support vector machine (SVM) applied to EEG small-world parameter. METHODS: A total of 295 subjects were recruited: 120 healthy volunteers and 175 AD. Graph theory functions were applied to undirected and weighted networks obtained by lagged linear coherence evaluated by eLORETA. A machine-learning classifier (SVM) was applied. EEG frequency bands were: delta (2-4 Hz), theta (4-8 Hz), alpha1 (8-10.5 Hz), alpha2 (10.5-13 Hz), beta1 (13-20 Hz), beta2 (20-30 Hz), and gamma (30-40 Hz). RESULTS: The receiver operating characteristic curve showed AUC of 0.97±0.03 (indicating very high classification accuracy). The classifier showed 95% ±5% sensitivity, 96% ±3% specificity, and 95% ±3% accuracy for the classification. CONCLUSION: EEG connectivity analysis via a combination of source/connectivity biomarkers, highly correlating with neuropsychological AD diagnosis, could represent a promising tool in identification of AD patients. This approach represents a low-cost and non-invasive method, one that utilizes widely available techniques which, when combined, reach high sensitivity/specificity and optimal classification accuracy on an individual basis (0.97 of AUC).

Neurophysiological Hallmarks of Neurodegenerative Cognitive Decline: The Study of Brain Connectivity as A Biomarker of Early Dementia.

Neurodegenerative processes of various types of dementia start years before symptoms, but the presence of a "neural reserve", which continuously feeds and supports neuroplastic mechanisms, helps the aging brain to preserve most of its functions within the "normality" frame. Mild cognitive impairment (MCI) is an intermediate stage between dementia and normal brain aging. About 50% of MCI subjects are already in a stage that is prodromal-to-dementia and during the following 3 to 5 years will develop clinically evident symptoms, while the other 50% remains at MCI or returns to normal. If the risk factors favoring degenerative mechanisms are modified during early stages (i.e., in the prodromal), the degenerative process and the loss of abilities in daily living activities will be delayed. It is therefore extremely important to have biomarkers able to identify-in association with neuropsychological tests-prodromal-to-dementia MCI subjects as early as possible. MCI is a large (i.e., several million in EU) and substantially healthy population; therefore, biomarkers should be financially affordable, largely available and non-invasive, but still accurate in their diagnostic prediction. Neurodegeneration initially affects synaptic transmission and brain connectivity; methods exploring them would represent a 1st line screening. Neurophysiological techniques able to evaluate mechanisms of synaptic function and brain connectivity are attracting general interest and are described here. Results are quite encouraging and suggest that by the application of artificial intelligence (i.e., learning-machine), neurophysiological techniques represent valid biomarkers for screening campaigns of the MCI population.

Human brain networks: a graph theoretical analysis of cortical connectivity normative database from EEG data in healthy elderly subjects.

Moving from the hypothesis that aging processes modulate brain connectivity networks, 170 healthy elderly volunteers were submitted to EEG recordings in order to define age-related normative limits. Graph theory functions were applied to exact low-resolution electromagnetic tomography on cortical sources in order to evaluate the small-world parameter as a representative model of network architecture. The analyses were carried out in the whole brain-as well as for the left and the right hemispheres separately-and in three specific resting state subnetworks defined as follows: attentional network (AN), frontal network (FN), and default mode network (DMN) in the EEG frequency bands (delta, theta, alpha 1, alpha 2, beta 1, beta 2, gamma). To evaluate the stability of the investigated parameters, a subgroup of 32 subjects underwent three separate EEG recording sessions in identical environmental conditions after a few days interval. Results showed that the whole right/left hemispheric evaluation did not present side differences, but when individual subnetworks were considered, AN and DMN presented in general higher SW in low (delta and/or theta) and high (gamma) frequency bands in the left hemisphere, while for FN, the alpha 1 band was lower in the left with respect to the right hemisphere. It was also evident the test-retest reliability and reproducibility of the present methodology when carried out in clinically stable subjects.Evidences from the present study suggest that graph theory represents a reliable method to address brain connectivity patterns from EEG data and is particularly suitable to study the physiological impact of aging on brain functional connectivity networks.

Small World Index in Default Mode Network Predicts Progression from Mild Cognitive Impairment to Dementia.

Aim of this study was to explore the EEG functional connectivity in amnesic mild cognitive impairments (MCI) subjects with multidomain impairment in order to characterize the Default Mode Network (DMN) in converted MCI (cMCI), which converted to Alzheimer's disease (AD), compared to stable MCI (sMCI) subjects. A total of 59 MCI subjects were recruited and divided -after appropriate follow-up- into cMCI or sMCI. They were further divided in MCI with linguistic domain (LD) impairment and in MCI with executive domain (ED) impairment. Small World (SW) index was measured as index of balance between integration and segregation brain processes. SW, computed restricting to nodes of DMN regions for all frequency bands, evaluated how they differ between MCI subgroups assessed through clinical and neuropsychological four-years follow-up. In addition, SW evaluated how this pattern differs between MCI with LD and MCI with ED. Results showed that SW index significantly decreased in gamma band in cMCI compared to sMCI. In cMCI with LD impairment, the SW index significantly decreased in delta band, while in cMCI with ED impairment the SW index decreased in delta and gamma bands and increased in alpha1 band. We propose that the DMN functional alterations in cognitive impairment could reflect an abnormal flow of brain information processing during resting state possibly associated to a status of pre-dementia.

Cortical connectivity from EEG data in acute stroke: A study via graph theory as a potential biomarker for functional recovery.

Cerebral post-stroke plasticity has been repeatedly investigated via functional neuroimaging techniques mainly based on blood flow/metabolism. However, little is known on predictive value of topological properties of widely distributed neural networks immediately following stroke on rehabilitation outcome and post-stroke recovery measured by early functional outcome. The utility of EEG network parameters (i.e. small world organization) analysis as a potential rough and simple biomarker for stroke outcome has been little explored and needs more validation. A total of 139 consecutive patients within a post-stroke acute stage underwent EEG recording. A group of 110 age paired healthy subjects constituted the control group. All patients were clinically evaluated with 3 scales for stroke: NIHSS, Barthel and ARAT. As a first result, NIHSS, Barthel and ARAT correlated with Small World index as provided by the proportional increment/decrement of low (delta) and viceversa of high (beta2 and gamma) EEG frequency bands. Furthermore, in line with the aim of the present study, we found a strong correlation between NIHSS at follow up and gamma Small World index in the acute post-stroke period, giving SW index a significant weight of recovery prediction. This study aimed to investigate possible correlations between functional abnormalities of brain networks, measured by small world characteristics detected in resting state EEG source investigation, and early post-stroke clinical outcome in order to find a possible predictive index of functional recovery to address and/or correct the rehabilitation program.

Acute cerebellar stroke and middle cerebral artery stroke exert distinctive modifications on functional cortical connectivity: A comparative study via EEG graph theory.

OBJECTIVE: We tested whether acute cerebellar stroke may determine changes in brain network architecture as defined by cortical sources of EEG rhythms. METHODS: Graph parameters of 41 consecutive stroke patients (<5 days from the event) were studied using eLORETA EEG sources. Network rearrangements of stroke patients were investigated in delta, alpha 2, beta 2 and gamma bands in comparison with healthy subjects. RESULTS: The delta network remodeling was similar in cerebellar and middle cerebral artery strokes, with a reduction of small-worldness. Beta 2 and gamma small-worldness, in the right hemisphere of patients with cerebellar stroke, increase respect to healthy subjects, while alpha 2 small-worldness increases only among patients with a middle cerebral artery stroke. CONCLUSIONS: The network remodeling characteristics are independent on the size of the ischemic lesion. In the early post-acute stages cerebellar stroke differs from the middle cerebral artery one because it does not cause alpha 2 network remodeling while it determines a high frequency network reorganization in beta 2 and gamma bands with an increase of small-worldness characteristics. SIGNIFICANCE: These findings demonstrate changes in the balance of local segregation and global integration induced by cerebellar acute stroke in high EEG frequency bands. They need to be integrated with appropriate follow-up to explore whether further network changes are attained during post-stroke outcome stabilization.