EEG coherence in pathological conditions

Modern neurophysiological techniques indexing normal or abnormal brain aging

Brain aging is associated with a decline in cognitive performance, motor function and sensory perception, even in the absence of neurodegeneration. The underlying pathophysiological mechanisms remain incompletely understood, though alterations in neurogenesis, neuronal senescence and synaptic plasticity are implicated. Recent years have seen advancements in neurophysiological techniques such as electroencephalography (EEG), magnetoencephalography (MEG), event-related potentials (ERP) and transcranial magnetic stimulation (TMS), offering insights into physiological and pathological brain aging. These methods provide real-time information on brain activity, connectivity and network dynamics. Integration of Artificial Intelligence (AI) techniques promise as a tool enhancing the diagnosis and prognosis of age-related cognitive decline. Our review highlights recent advances in these electrophysiological techniques (focusing on EEG, ERP, TMS and TMS-EEG methodologies) and their application in physiological and pathological brain aging. Physiological aging is characterized by changes in EEG spectral power and connectivity, ERP and TMS parameters, indicating alterations in neural activity and network function. Pathological aging, such as in Alzheimer's disease, is associated with further disruptions in EEG rhythms, ERP components and TMS measures, reflecting underlying neurodegenerative processes. Machine learning approaches show promise in classifying cognitive impairment and predicting disease progression. Standardization of neurophysiological methods and integration with other modalities are crucial for a comprehensive understanding of brain aging and neurodegenerative disorders. Advanced network analysis techniques and AI methods hold potential for enhancing diagnostic accuracy and deepening insights into age-related brain changes.

Band-Specific Altered Cortical Connectivity in Early Parkinson's Disease and its Clinical Correlates

BACKGROUND

Functional connectivity (FC) has shown promising results in assessing the pathophysiology and identifying early biomarkers of neurodegenerative disorders, such as Parkinson's disease (PD).

OBJECTIVES

In this study, we aimed to assess possible resting-state FC abnormalities in early-stage PD patients using high-density electroencephalography (EEG) and to detect their clinical relationship with motor and non-motor PD symptoms.

METHODS

We enrolled 26 early-stage levodopa naïve PD patients and a group of 20 healthy controls (HC). Data were recorded with 64-channels EEG system and a source-reconstruction method was used to identify brain-region activity. FC was calculated using the weighted phase-lag index in θ, α, and β bands. Additionally, we quantified the unbalancing between β and lower frequencies through a novel index (β-functional ratio [FR]). Statistical analysis was conducted using a network-based statistical approach.

RESULTS

PD patients showed hypoconnected networks in θ and α band, involving prefrontal-limbic-temporal and frontoparietal areas, respectively, and a hyperconnected network in the β frequency band, involving sensorimotor-frontal areas. The θ FC network was negatively related to Non-Motor Symptoms Scale scores and α FC to the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III gait subscore, whereas β FC and β-FR network were positively linked to the bradykinesia subscore. Changes in θ FC and β-FR showed substantial reliability and high accuracy, precision, sensitivity, and specificity in discriminating PD and HC.

CONCLUSIONS

Frequency-specific FC changes in PD likely reflect the dysfunction of distinct cortical networks, which occur from the early stage of the disease. These abnormalities are involved in the pathophysiology of specific motor and non-motor PD symptoms, including gait, bradykinesia, mood, and cognition. © 2023 International Parkinson and Movement Disorder Society.

Identifying the distinct spectral dynamics of laminar-specific interhemispheric connectivity with bilateral line-scanning fMRI

Despite extensive efforts to identify interhemispheric functional connectivity (FC) with resting-state (rs-) fMRI, correlated low-frequency rs-fMRI signal fluctuation across homotopic cortices originates from multiple sources. It remains challenging to differentiate circuit-specific FC from global regulation. Here, we developed a bilateral line-scanning fMRI method to detect laminar-specific rs-fMRI signals from homologous forepaw somatosensory cortices with high spatial and temporal resolution in rat brains. Based on spectral coherence analysis, two distinct bilateral fluctuation spectral features were identified: ultra-slow fluctuation (<0.04 Hz) across all cortical laminae versus Layer (L) 2/3-specific evoked BOLD at 0.05 Hz based on 4 s on/16 s off block design and resting-state fluctuations at 0.08-0.1 Hz. Based on the measurements of evoked BOLD signal at corpus callosum (CC), this L2/3-specific 0.05 Hz signal is likely associated with neuronal circuit-specific activity driven by the callosal projection, which dampened ultra-slow oscillation less than 0.04 Hz. Also, the rs-fMRI power variability clustering analysis showed that the appearance of L2/3-specific 0.08-0.1 Hz signal fluctuation is independent of the ultra-slow oscillation across different trials. Thus, distinct laminar-specific bilateral FC patterns at different frequency ranges can be identified by the bilateral line-scanning fMRI method.

A systematic data-driven approach to analyze sensor-level EEG connectivity: Identifying robust phase-synchronized network components using surface Laplacian with spectral-spatial PCA

Although conventional averaging across predefined frequency bands reduces the complexity of EEG functional connectivity (FC), it obscures the identification of resting-state brain networks (RSN) and impedes accurate estimation of FC reliability. Extending prior work, we combined scalp current source density (CSD; spherical spline surface Laplacian) and spectral-spatial PCA to identify FC components. Phase-based FC was estimated via debiased-weighted phase-locking index from CSD-transformed resting EEGs (71 sensors, 8 min, eyes open/closed, 35 healthy adults, 1-week retest). Spectral PCA extracted six robust alpha and theta components (86.6% variance). Subsequent spatial PCA for each spectral component revealed seven robust regionally focused (posterior, central, and frontal) and long-range (posterior-anterior) alpha components (peaks at 8, 10, and 13 Hz) and a midfrontal theta (6 Hz) component, accounting for 37.0% of FC variance. These spatial FC components were consistent with well-known networks (e.g., default mode, visual, and sensorimotor), and four were sensitive to eyes open/closed conditions. Most FC components had good-to-excellent internal consistency (odd/even epochs, eyes open/closed) and test-retest reliability (ICCs ≥ .8). Moreover, the FC component structure was generally present in subsamples (session × odd/even epoch, or smaller subgroups [n = 7-10]), as indicated by high similarity of component loadings across PCA solutions. Apart from systematically reducing FC dimensionality, our approach avoids arbitrary thresholds and allows quantification of meaningful and reliable network components that may prove to be of high relevance for basic and clinical research applications.

Coherence between the hippocampus and anterior thalamic nucleus as a tool to improve the effect of neurostimulation in temporal lobe epilepsy: An experimental study

BACKGROUND

Although the mechanisms by which deep brain stimulation (DBS) modifies the activity of the ictal network are mostly undefined, recent studies have suggested that DBS of the anterior nucleus of the thalamus (ANT) can be an effective treatment for mesial temporal lobe epilepsy (MTLE) when resective surgery cannot be performed. In a nonhuman primate (NHP) model of MTL seizures, we showed that the ANT was actively involved during interictal and ictal periods through different patterns and that the hippocampus (HPC) and ANT synchronously oscillate in the high beta-band during seizures.

OBJECTIVE

Based on those findings, we evaluated whether the frequency of stimulation is an important parameter that interferes with seizures and how to adapt stimulation protocols to it.

METHODS

We investigated the effects of low-frequency (40 Hz - determined as the ictal frequency of correlation between structures) and high-frequency (130 Hz - as commonly used in clinic) ANT stimulation in three monkeys in which MTLE seizures were initiated.

RESULTS

Low-frequency stimulation had a strong effect on the number of seizures and the total time spent in seizure, whereas high-frequency stimulation had no effect. The coherence of oscillations between the HPC and the ANT was significantly correlated with the success of low-frequency stimulation: the greater the coherence was, the greater the antiepileptic effect of ANT-DBS.

CONCLUSION

Our results suggest that low-frequency stimulation is efficient in treating seizures in a nonhuman primate model. More importantly, the study of the coherence between the ANT and HPC during seizures can help to predict the anti-epileptic effects of ANT stimulation. Furthermore, the DBS paradigm could be customized in frequency for each patient on the basis of the coherence spectral pattern.

Interhemispheric Brain Switching Correlates with Severity of Sleep-Disordered Breathing for Obstructive Sleep Apnea Patients

(1) Background: Alternating interhemispheric slow-wave activity during sleep is well-established in birds and cetaceans, but its investigation in humans has been largely neglected. (2) Methods: Fuzzy entropy was used to calculate a laterality index (LI) from C3 and C4 EEG channels. The subjects were grouped according to an apnoea-hypopnoea index (AHI) for statistical analyses: Group A AHI < 15 (mild); Group B 15 ≤ AHI < 30 (moderate); Group C AHI ≥ 30 (severe). The LI distribution was analysed to characterise the brain activity variation in both hemispheres, and the cross-zero switching rate was given statistical tests to find the correlations with the severity of obstructive sleep apnea and sleep states, i.e., wake (W), light sleep (LS), deep sleep (DS), and REM. (3) Results: EEG brain switching activity was observed in all sleep stages, and the LI distribution shows that, for obstructive sleep apnea patients, the interhemispheric asymmetry of brain activity is more obvious than healthy people. A one-way ANOVA revealed a significant difference of switching rate among three groups (F(2,95) = 7.23, p = 0.0012), with Group C shows the least, and also a significant difference among four sleep stages (F(3,94) = 5.09, p = 0.0026), with REM the highest. (4) Conclusions: The alternating interhemispheric activity is confirmed ubiquitous for humans during sleep, and sleep-disordered breathing intends to exacerbate the interhemispheric asymmetry.

The Longitudinal Effects of Electroconvulsive Therapy on Ictal Interhemispheric Coherence and Its Associations With Treatment Outcome: A Naturalistic Cohort Study

OBJECTIVES

Electroconvulsive therapy (ECT) is an effective treatment for severe depression. Electroencephalogram (EEG) measures between ECT sessions seem to be related to the antidepressant efficacy of ECT. In this naturalistic cohort study, we examine longitudinal effects of ECT on interhemispheric EEG coherence measures during seizure activity and its relation to the antidepressant efficacy.

METHODS

This study included 65 patients diagnosed with severe depressive disorder. Depressive symptoms were rated according to the Montgomery-Åsberg Depression Rating Scale before and after the course of ECT. Frequency-specific ictal interhemispheric (fp1-fp2) EEG coherence measures were established during the first and each consecutive sixth treatment session. Linear mixed-effect models were used to determine longitudinal changes in ictal coherence measures during the course of ECT and its relation to treatment efficacy.

RESULTS

Ictal interhemispheric coherence in the theta and alpha frequency bands increased over the course of treatment, whereas no significant change was found for the delta and beta frequency bands. A main effect of treatment efficacy on the interhemispheric coherence in the delta and theta band was revealed. However, the longitudinal effects of ECT were not associated with treatment efficacy.

CONCLUSION

The current study suggests that interhemispheric coherence during ECT-induced seizures increases over the course of treatment. Furthermore, these longitudinal effects seem to be unrelated to the antidepressant efficacy of ECT. These findings contribute to the understanding of the mechanism of action of ECT.

Changes of EEG Spectra and Functional Connectivity during an Object-Location Memory Task in Alzheimer's Disease

Object-location memory is particularly fragile and specifically impaired in Alzheimer’s disease (AD) patients. Electroencephalogram (EEG) was utilized to objectively measure memory impairment for memory formation correlates of EEG oscillatory activities. We aimed to construct an object-location memory paradigm and explore EEG signs of it. Two groups of 20 probable mild AD patients and 19 healthy older adults were included in a cross-sectional analysis. All subjects took an object-location memory task. EEG recordings performed during object-location memory tasks were compared between the two groups in the two EEG parameters (spectral parameters and phase synchronization). The memory performance of AD patients was worse than that of healthy elderly adults The power of object-location memory of the AD group was significantly higher than the NC group (healthy elderly adults) in the alpha band in the encoding session, and alpha and theta bands in the retrieval session. The channels-pairs the phase lag index value of object-location memory in the AD group was clearly higher than the NC group in the delta, theta, and alpha bands in encoding sessions and delta and theta bands in retrieval sessions. The results provide support for the hypothesis that the AD patients may use compensation mechanisms to remember the items and episode.

A comprehensive assessment of cardiovascular autonomic control using photoplethysmograms recorded from the earlobe and fingers

We compare the spectral indices of photoplethysmogram variability (PPGV) estimated using photoplethysmograms recorded from the earlobe and the middle fingers of the right and left hand and analyze their correlation with similar indices of heart rate variability (HRV) in 30 healthy subjects (26 men) aged 27 (25, 29) years (median with inter-quartile ranges) at rest and under the head-up tilt test. The following spectral indices of PPGV and HRV were compared: mean heart rate (HR), total spectral power (TP), high-frequency (HF) and low-frequency (LF) ranges of TP in percents (HF% and LF%), LF/HF ratio, and spectral coherence. We assess also the index S of synchronization between the LF oscillations in HRV and PPGV. The constancy of blood pressure (BP) and moderate increase of HR under the tilt test indicate the presence of fast processes of cardiovascular adaptation with the increase of the sympathetic activity in studied healthy subjects. The impact of respiration on the PPGV spectrum (accessed by HF%) is less than on the HRV spectrum. It is shown that the proportion of sympathetic vascular activity (accessed by LF%) is constant in the PPGV of three analyzed PPGs during the tilt test. The PPGV for the ear PPG was less vulnerable to breathing influence accessed by HF% (independently from body position) than for PPGs from fingers. We reveal the increase of index S under the tilt test indicating the activation of interaction between the heart and distal vessels. The PPGV spectra for finger PPGs from different hands are highly coherent, but differ substantially from the PPGV spectrum for the ear PPG. We conclude that joint analysis of frequency components of PPGV (for the earlobe and finger PPGs of both hands) and HRV and assessment of their synchronization provide additional information about cardiovascular autonomic control.

Increased resting-state EEG functional connectivity in benign childhood epilepsy with centro-temporal spikes

PURPOSE

To explore intrahemispheric, cortico-cortical EEG functional connectivity (EEGfC) in benign childhood epilepsy with rolandic spikes (BECTS).

METHODS

21-channel EEG was recorded in 17 non-medicated BECTS children and 19 healthy controls. 180s of spike- and artifact-free activity was selected for EEGfC analysis. Correlation of Low Resolution Electromagnetic Tomography- (LORETA-) defined current source density time series were computed between two cortical areas (region of interest, ROI). Analyses were based on broad-band EEGfC results. Groups were compared by statistical parametric network (SPN) method. Statistically significant differences between group EEGfC values were emphasized at p<0.05 corrected for multiple comparison by local false discovery rate (FDR).

RESULTS

(1) Bilaterally increased beta EEGfC occurred in the BECTS group as compared to the controls. Greatest beta abnormality emerged between frontal and frontal, as well as frontal and temporal ROIs. (2) Locally increased EEGfC emerged in all frequency bands in the right parietal area.

CONCLUSIONS

Areas of increased EEGfC topographically correspond to cortical areas that, based on relevant literature, are related to speech and attention deficit in BECTS children.

Altered resting-state EEG source functional connectivity in schizophrenia: the effect of illness duration

Despite the increasing body of evidence supporting the hypothesis of schizophrenia as a disconnection syndrome, studies of resting-state EEG Source Functional Connectivity (EEG-SFC) in people affected by schizophrenia are sparse. The aim of the present study was to investigate resting-state EEG-SFC in 77 stable, medicated patients with schizophrenia (SCZ) compared to 78 healthy volunteers (HV). In order to study the effect of illness duration, SCZ were divided in those with a short duration of disease (SDD; n = 25) and those with a long duration of disease (LDD; n = 52). Resting-state EEG recordings in eyes closed condition were analyzed and lagged phase synchronization (LPS) indices were calculated for each ROI pair in the source-space EEG data. In delta and theta bands, SCZ had greater EEG-SFC than HV; a higher theta band connectivity in frontal regions was observed in LDD compared with SDD. In the alpha band, SCZ showed lower frontal EEG-SFC compared with HV whereas no differences were found between LDD and SDD. In the beta1 band, SCZ had greater EEG-SFC compared with HVs and in the beta2 band, LDD presented lower frontal and parieto-temporal EEG-SFC compared with HV. In the gamma band, SDD had greater connectivity values compared with LDD and HV. This study suggests that resting state brain network connectivity is abnormally organized in schizophrenia, with different patterns for the different EEG frequency components and that EEG can be a powerful tool to further elucidate the complexity of such disordered connectivity.

Phase Synchronization and Spectral Coherence Analysis of EEG Activity During Mental Fatigue

In this article, 2 synchronization measures, phase locking value (PLV) and spectral coherence value (SCV), are used to characterize the changes of the phase synchronization and functional coupling of electroencephalogram (EEG) waves at different brain cortical areas for mental fatigue caused by a long-term cognitive task. The long-term cognitive task induces a significant decrease in the interhemispheric SCVs and PLVs of beta in central and parietal regions, a significant decrease in the intrahemispheric SCVs and PLVs of beta frequency band at the frontal-parietal and central-parietal, and frontal-central middle electrode pairs, suggesting EEG decreases in cooperative processing, strength of functional coupling, and flux of mutual information exchange in the corresponding inter- and intrahemispheres. However, when the mental fatigue level increases, the interhemispheric PLVs of theta are enhanced in the frontal region and C3-Cz electrode pair, and the intrahemispheric PLVs of theta are heightened at frontal-central middle electrode pairs. PLV and SCV measures could reflect the changes of the phase synchronization and functional coupling of EEG waves from time and frequency domains, which are sensitive to mental fatigue. Therefore, PLVs and SCVs can provide an effective and reliable way to quantify brain response to mental fatigue.

Prestimulus Interhemispheric Coupling of Brain Rhythms Predicts Cognitive–Motor Performance in Healthy Humans

Physiological and neuroimaging studies suggest that human actions are characterized by time-varying engagement of functional distributed networks within the brain. In this study, we investigated whether specific prestimulus interhemispheric connectivity, as a measure of synchronized network between the two hemispheres, could lead to a better performance (as revealed by RT) in a simple visuomotor task. Eighteen healthy adults underwent EEG recording during a visual go/no-go task. In the go/no-go task, a central fixation stimulus was followed by a green (50% of probability) or red visual stimulus. Participants had to press the mouse button after the green stimuli (go trials). Interhemispheric coupling was evaluated by the spectral coherence among all the electrodes covering one hemisphere and matched with those on the other. The frequency bands of interest were 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). The task-related results showed that interhemispheric connectivity decreased in delta and increased in alpha band. Furthermore, we observed positive delta and negative alpha correlations with the RT; namely, the faster the RT, the lower delta and the higher alpha connection between the two hemispheres. These results suggested that the best performance is anticipated by the better functional coupling of cortical circuits involved during the processing of the sensorimotor information, occurring between the two hemispheres pending cognitive go/no-go task.

Time-varying coupling of EEG oscillations predicts excitability fluctuations in the primary motor cortex as reflected by motor evoked potentials amplitude: an EEG-TMS study

PURPOSE

Motor evoked potentials (MEPs) elicited by a train of consecutive, individual transcranial magnetic stimuli demonstrate fluctuations in amplitude with respect to time when recorded from a relaxed muscle. The influence of time-varying, instantaneous modifications of the electroencephalography (EEG) properties immediately preceding the transcranial magnetic stimulation (TMS) has rarely been explored. The aim of this study was to investigate the influence of the pre-TMS motor cortex and related areas EEG profile on time variants of the MEPs amplitude.

METHOD

MRI-navigated TMS and multichannel TMS-compatible EEG devices were used. For each experimental subject, post-hoc analysis of the MEPs amplitude that was based on the 50th percentile of the MEPs amplitude distribution provided two subgroups corresponding to "high" (large amplitude) and "low" (small amplitude). The pre-stimulus EEG characteristics (coherence and spectral profile) from the motor cortex and related areas were analyzed separately for the "high" and "low" MEPs and were then compared.

RESULTS

On the stimulated hemisphere, EEG coupling was observed more often in the high compared to the low MEP trials. Moreover, a paradigmatic pattern in which TMS was able to lead to significantly larger MEPs was found when the EEG of the stimulated motor cortex was coupled in the beta 2 band with the ipsilateral prefrontal cortex and in the delta band with the bilateral centro-parietal-occipital cortices.

CONCLUSION

This data provide evidence for a statistically significant influence of time-varying and spatially patterned synchronization of EEG rhythms in determining cortical excitability, namely motor cortex excitability in response to TMS.

How does a surgeon's brain buzz? An EEG coherence study on the interaction between humans and robot

Introduction

In humans, both primary and non-primary motor areas are involved in the control of voluntary movements. However, the dynamics of functional coupling among different motor areas have not been fully clarified yet. There is to date no research looking to the functional dynamics in the brain of surgeons working in laparoscopy compared with those trained and working in robotic surgery.

Experimental procedures

We enrolled 16 right-handed trained surgeons and assessed changes in intra- and inter-hemispheric EEG coherence with a 32-channels device during the same motor task with either a robotic or a laparoscopic approach. Estimates of auto and coherence spectra were calculated by a fast Fourier transform algorithm implemented on Matlab 5.3.

Results

We found increase of coherence in surgeons performing laparoscopy, especially in theta and lower alpha activity, in all experimental conditions (M1 vs. SMA, S1 vs. SMA, S1 vs. pre-SMA and M1 vs. S1; p < 0.001). Conversely, an increase in inter-hemispheric coherence in upper alpha and beta band was found in surgeons using the robotic procedure (right vs. left M1, right vs. left S1, right pre-SMA vs. left M1, left pre-SMA vs. right M1; p < 0.001).

Discussion

Our data provide a semi-quantitative evaluation of dynamics in functional coupling among different cortical areas in skilled surgeons performing laparoscopy or robotic surgery. These results suggest that motor and non-motor areas are differently activated and coordinated in surgeons performing the same task with different approaches. To the best of our knowledge, this is the first study that tried to assess semi-quantitative differences during the interaction between normal human brain and robotic devices.

Influence of nerve supply on hand electromyography coherence during a three-digit task

Intermuscular coupling has been investigated to understand neural inputs to coordinate muscles in a motor performance. However, little is known on the role of nerve innervation on intermuscular coupling. The purpose of this study was to investigate how the anatomy of nerve distribution affected intermuscular coupling in the hand during static grip. Electromyographic (EMG) signals were recorded from intrinsic and extrinsic muscles while subjects performed a static grip. Coherence was computed for muscle pairs innervated by either the same or different nerves. The results did not support the hypothesis that muscles sharing the same nerve exhibit greater coupling than muscles innervated by different nerves. In general, extrinsic muscle pairs displayed higher coherence than intrinsic pairs. The results suggest that intermuscular coupling in a voluntary motor task is likely modulated in a functional manner and that different nerves might transport common neural inputs to functionally coupled muscles.

Severe motor disability affects functional cortical integration in the context of brain-computer interface (BCI) use

The purpose of this study was to investigate cortical interaction between brain regions in people with and without severe motor disability during brain-computer interface (BCI) operation through coherence analysis. Eighteen subjects, including six patients with cerebral palsy (CP) and three patients with amyotrophic lateral sclerosis (ALS), participated. The results showed (1) the existence of BCI performance difference caused by severe motor disability; (2) different coherence patterns between participants with and without severe motor disability during BCI operation and (3) effects of motor disability on cortical connections varying in the brain regions for the different frequency bands, indicating reduced cortical differentiation and specialisation. Participants with severe neuromuscular impairments, as compared with the able-bodied group, recruited more cortical regions to compensate for the difficulties caused by their motor disability, reflecting a less efficient operating strategy for the BCI task. This study demonstrated that coherence analysis can be applied to examine the ways cortical networks cooperate with each other during BCI tasks.

PRACTITIONER SUMMARY

Few studies have investigated the electrophysiological underpinnings of differences in BCI performance. This study contributes by assessing neuronal synchrony among brain regions. Our findings revealed that severe motor disability causes more cortical areas to be recruited to perform the BCI task, indicating reduced cortical differentiation and specialisation.

Clinical symptoms and alpha band resting-state functional connectivity imaging in patients with schizophrenia: implications for novel approaches to treatment

BACKGROUND

Schizophrenia (SZ) is associated with functional decoupling between cortical regions, but we do not know whether and where this occurs in low-frequency electromagnetic oscillations. The goal of this study was to use magnetoencephalography (MEG) to identify brain regions that exhibit abnormal resting-state connectivity in the alpha frequency range in patients with schizophrenia and investigate associations between functional connectivity and clinical symptoms in stable outpatient participants.

METHODS

Thirty patients with SZ and 15 healthy comparison participants were scanned in resting-state MEG (eyes closed). Functional connectivity MEG source data were reconstructed globally in the alpha range, quantified by the mean imaginary coherence between a voxel and the rest of the brain.

RESULTS

In patients, decreased connectivity was observed in left prefrontal cortex (PFC) and right superior temporal cortex, whereas increased connectivity was observed in left extrastriate cortex and the right inferior PFC. Functional connectivity of left inferior parietal cortex was negatively related to positive symptoms. Low left PFC connectivity was associated with negative symptoms. Functional connectivity of midline PFC was negatively correlated with depressed symptoms. Functional connectivity of right PFC was associated with other (cognitive) symptoms.

CONCLUSIONS

This study demonstrates direct functional disconnection in SZ between specific cortical fields within low-frequency resting-state oscillations. Impaired alpha coupling in frontal, parietal, and temporal regions is associated with clinical symptoms in these stable outpatients. Our findings indicate that this level of functional disconnection between cortical regions is an important treatment target in SZ.

Thalamocortical sensorimotor circuit in multiple sclerosis: an integrated structural and electrophysiological assessment

Demyelination and axonal damage are pathologic hallmarks of multiple sclerosis (MS), leading to loss of neuronal synchronization, functional disconnection amongst brain relays, and clinical sequelae. To investigate these properties, the primary component of the sensorimotor network was analyzed in mildly disabled Relapsing-Remitting MS patients without sensory symptoms at the time of the investigation. By magnetoencephalography (MEG), the recruitment pattern within the primary sensory (S1) and motor (M1) areas was estimated through the morphology of the early components of somatosensory evoked magnetic fields (SEFs), after evaluating the S1 responsiveness to sensory inputs from the contralateral arm. In each hemisphere, network recruitment properties were correlated with ispilateral thalamus volume, estimated by morphometric techniques upon high-resolution 3D structural magnetic resonance images (MRI). S1 activation was preserved, whereas SEF morphology was strikingly distorted in MS patients, marking a disruption of primary somatosensory network patterning. An unbalance of S1-M1 dynamic recruitment was documented and correlated with the thalamic volume reduction in the left hemisphere. These findings support the model of MS as a disconnection syndrome, with major susceptibility to damage experienced by nodes belonging to more frequently recruited and highly specialized networks.

Brain functional network in Alzheimer's disease: diagnostic markers for diagnosis and monitoring

Alzheimer's disease (AD) is the most common type of dementia that is clinically characterized by the presence of memory impairment and later by impairment in other cognitive domains. The clinical diagnosis is based on interviews with the patient and his/her relatives and on neuropsychological assessment, which are also used to monitor cognitive decline over time. Several biomarkers have been proposed for detecting AD in its earliest stages, that is, in the predementia stage. In an attempt to find noninvasive biomarkers, researchers have investigated the feasibility of neuroimaging tools, such as MR, SPECT, and FDG-PET imaging, as well as neurophysiological measurements using EEG. In this paper, we investigate the brain functional networks in AD, focusing on main neurophysiological techniques, integrating with most relevant functional brain imaging findings.

Resting functional connectivity in patients with brain tumors in eloquent areas

OBJECTIVE

Resection of brain tumors adjacent to eloquent areas represents a challenge in neurosurgery. If maximal resection is desired without inducing postoperative neurological deficits, a detailed knowledge of the functional topography in and around the tumor is crucial. The aim of the present work is to evaluate the value of preoperative magnetoencephalography (MEG) imaging of functional connectivity to predict the results of intraoperative electrical stimulation (IES) mapping, the clinical gold standard for neurosurgical localization of functional areas.

METHODS

Resting-state whole-cortex MEG recordings were obtained from 57 consecutive subjects with focal brain tumors near or within motor, sensory, or language areas. Neural activity was estimated using adaptive spatial filtering algorithms, and the mean imaginary coherence between the rest of the brain and voxels in and around brain tumors were compared to the mean imaginary coherence between the rest of the brain and contralesional voxels as an index of functional connectivity. IES mapping was performed in all subjects. The cortical connectivity pattern near the tumor was compared to the IES results.

RESULTS

Maps with decreased resting-state functional connectivity in the entire tumor area had a negative predictive value of 100% for absence of eloquent cortex during IES. Maps showing increased resting-state functional connectivity within the tumor area had a positive predictive value of 64% for finding language, motor, or sensory cortical sites during IES mapping.

INTERPRETATION

Preoperative resting state MEG connectivity analysis is a useful noninvasive tool to evaluate the functionality of the tissue surrounding tumors within eloquent areas, and could potentially contribute to surgical planning and patient counseling.

Electroconvulsive therapy and corpus callosum aplasia: a case report

The use of electroconvulsive therapy (ECT) in mental retardation has been discussed in several case reports and case series. In this case, a 35-year-old patient with corpus callosum aplasia and severe therapy-resistant catatonia was treated with a series of unilateral ECT and improved considerably. Electroencephalographic recordings during ECT showed a complete interhemispheric synchronicity due to regular anterior and posterior commissural fibers. After ECT, the patient received long-term medication with quetiapine and lorazepam. Electroconvulsive therapy turned out to be a powerful tool for treating catatonic syndromes in patients with mental retardation and should be considered as a potent treatment option in otherwise therapy-resistant cases.

Cognitive Impairments in Schizophrenia as Assessed Through Activation and Connectivity Measures of Magnetoencephalography (MEG) Data

The cognitive dysfunction present in patients with schizophrenia is thought to be driven in part by disorganized connections between higher-order cortical fields. Although studies utilizing electroencephalography (EEG), PET and fMRI have contributed significantly to our understanding of these mechanisms, magnetoencephalography (MEG) possesses great potential to answer long-standing questions linking brain interactions to cognitive operations in the disorder. Many experimental paradigms employed in EEG and fMRI are readily extendible to MEG and have expanded our understanding of the neurophysiological architecture present in schizophrenia. Source reconstruction techniques, such as adaptive spatial filtering, take advantage of the spatial localization abilities of MEG, allowing us to evaluate which specific structures contribute to atypical cognition in schizophrenia. Finally, both bivariate and multivariate functional connectivity metrics of MEG data are useful for understanding how these interactions in the brain are impaired in schizophrenia, and how cognitive and clinical outcomes are affected as a result. We also present here data from our own laboratory that illustrates how some of these novel functional connectivity measures, specifically imaginary coherence (IC), are quite powerful in relating disconnectivity in the brain to characteristic behavioral findings in the disorder.

Brain connectivity in positive and negative syndrome schizophrenia

If, as is widely believed, schizophrenia is characterized by abnormalities of brain functional connectivity, then it seems reasonable to expect that different subtypes of schizophrenia could be discriminated in the same way. However, evidence for differences in functional connectivity between the subtypes of schizophrenia is largely lacking and, where it exists, it could be accounted for by clinical differences between the patients (e.g. medication) or by the limitations of the measures used. In this study, we measured EEG functional connectivity in unmedicated male patients diagnosed with either positive or negative syndrome schizophrenia and compared them with age and sex matched healthy controls. Using new methodology (Medkour et al., 2009) based on partial coherence, brain connectivity plots were constructed for positive and negative syndrome patients and controls. Reliable differences in the pattern of functional connectivity were found with both syndromes showing not only an absence of some of the connections that were seen in controls but also the presence of connections that the controls did not show. Comparing connectivity graphs using the Hamming distance, the negative-syndrome patients were found to be more distant from the controls than were the positive syndrome patients. Bootstrap distributions of these distances were created which showed a significant difference in the mean distances that was consistent with the observation that negative-syndrome diagnosis is associated with a more severe form of schizophrenia. We conclude that schizophrenia is characterized by widespread changes in functional connectivity with negative syndrome patients showing a more extreme pattern of abnormality than positive syndrome patients.

Estimation of time-varying connectivity patterns through the use of an adaptive directed transfer function

Frequency-derived identification of the propagation of information between brain regions has quickly become a popular area in the neurosciences. Of the various techniques used to study the propagation of activation within the central nervous system, the directed transfer function (DTF) has been well used to explore the functional connectivity during a variety of brain states and pathological conditions. However, the DTF method assumes the stationarity of the neural electrical signals and the time invariance of the connectivity among different channels over the investigated time window. Such assumptions may not be valid in the abnormal brain signals such as seizures and interictal spikes in epilepsy patients. In the present study, we have developed an adaptive DTF (ADTF) method through the use of a multivariate adaptive autoregressive model to study the time-variant propagation of seizures and interictal spikes in simulated electrocorticogram (ECoG) networks. The time-variant connectivity reconstruction is achieved by the Kalman filter algorithm, which can incorporate time-varying state equations. We study the performance of the proposed method through simulations with various propagation models using either sample seizures or interictal spikes as the source waveform. The present results suggest that the new ADTF method correctly captures the temporal dynamics of the propagation models, while the DTF method cannot, and even returns erroneous results in some cases. The present ADTF method was tested in real epileptiform ECoG data from an epilepsy patient, and the ADTF results are consistent with the clinical assessments performed by neurologists.

EEG functional connectivity in term age extremely low birth weight infants

OBJECTIVE

The hypothesis is tested that electrocortical functional connectivity (quantified by coherence) of extremely low birth weight (ELBW) infants, measured at term post-menstrual age, has regional differences from that of full term infants.

METHODS

128 lead EEG data were collected during sleep from 8 ELBW infants with normal head ultrasound exams and 8 typically developing full term infants. Regional spectral power and coherence were calculated.

RESULTS

No significant regional differences in EEG power were found between infant groups. However, compared to term infants, ELBW infants had significantly reduced interhemispheric coherence (in frontal polar and parietal regions) and intrahemispheric coherence (between frontal polar and parieto-occipital regions) in the 1-12Hz band but increased interhemispheric coherence between occipital regions in the 24-50Hz band.

CONCLUSIONS

ELBW infants at term post-menstrual age manifest regional differences in EEG functional connectivity as compared to term infants.

SIGNIFICANCE

Distinctive spatial patterns of electrocortical synchrony are found in ELBW infants. These regional patterns may presage regional alterations in the structure of the cortex.

Residual functional connectivity in the split-brain revealed with resting-state functional MRI

Split-brain patients present a unique opportunity to address controversies regarding subcortical contributions to interhemispheric coordination. We characterized residual functional connectivity in a complete commissurotomy patient by examining patterns of low-frequency BOLD functional MRI signal. Using independent components analysis and region-of-interest-based functional connectivity analyses, we demonstrate bilateral resting state networks in a patient lacking all major cerebral commissures. Compared with a control group, the patient's interhemispheric correlation scores fell within the normal range for two out of three regions examined. Thus, we provide evidence for bilateral resting state networks in a patient with complete commissurotomy. Such continued interhemispheric interaction suggests that, at least in part, cortical networks in the brain can be coordinated by subcortical mechanisms.

Mutual-information-based approach for neural connectivity during self-paced finger lifting task

Frequency-dependent modulation between neuronal assemblies may provide insightful mechanisms of functional organization in the context of neural connectivity. We present a conjoined time-frequency cross mutual information (TFCMI) method to explore the subtle brain neural connectivity by magnetoencephalography (MEG) during a self-paced finger lifting task. Surface electromyogram (sEMG) was obtained from the extensor digitorum communis. Both within-modality (MEG-MEG) and between-modality studies (sEMG-MEG) were conducted. The TFCMI method measures both the linear and nonlinear dependencies of the temporal dynamics of signal power within a pre-specified frequency band. Each single trial of MEG across channels and sEMG signals was transformed into time-frequency domain with use of the Morlet wavelet to obtain better temporal spectral (power) information. As compared to coherence approach (linear dependency only) in broadband analysis, the TFCMI method demonstrated advantages in encompassing detection for the mesial frontocentral cortex and bilateral primary sensorimotor areas, clear demarcation of event- and non-event-related regions, and robustness for sEMG - MEG between-modality study, i.e., corticomuscular communication. We conclude that this novel TFCMI method promises a possibility to better unravel the intricate functional organizations of brain in the context of oscillation-coded communication.

Changes of EEG spectra and coherence following performance in a cognitive task in Alzheimer's disease

Electroencephalographic measures combined with cognitive tasks are widely used for the assessment of cognitive and pathophysiological changes in Alzheimer's disease (AD). Instead of the analysis of EEG data obtained during the performance of the task, in this study data recorded in the immediate after-task period were analyzed. It was expected that this period would correspond to the electrophysiological consequences of the cognitive effort. Data of 14 patients with AD (MMS score: 16-24) were compared to that of 10 healthy control subjects. Reverse counting of a fix duration was used as a cognitive task. Changes of relative frequency spectra, and those of inter-and intrahemispheric coherence were analyzed. Relative theta power was significantly higher in AD patients compared to the controls both before and after the task. The performance of the task resulted in an increase of the relative alpha2 band in the AD group, whereas it slightly decreased in the control group. The most prominent coherence differences between AD and controls were found in the alpha1 band, especially for long-range coherence values. Coherence in this frequency band increased in the control group following the task, not seen in the AD group. We conclude that EEG parameters calculated from epochs following the completion of a cognitive task clearly differentiates patients with AD from normal controls. The electrophysiological changes found in AD may correspond to the decrease of functional connectivity of cortical areas and to the malfunctioning of the networks engaged in the cognitive task investigated.

Mobile phone emission modulates interhemispheric functional coupling of EEG alpha rhythms

We tested the working hypothesis that electromagnetic fields from mobile phones (EMFs) affect interhemispheric synchronization of cerebral rhythms, an important physiological feature of information transfer into the brain. Ten subjects underwent two electroencephalographic (EEG) recordings, separated by 1 week, following a crossover double-blind paradigm in which they were exposed to a mobile phone signal (global system for mobile communications; GSM). The mobile phone was held on the left side of the subject head by a modified helmet, and orientated in the normal position for use over the ear. The microphone was orientated towards the corner of the mouth, and the antenna was near the head in the parietotemporal area. In addition, we positioned another similar phone (but without battery) on the right side of the helmet, to balance the weight and to prevent the subject localizing the side of GSM stimulation (and consequently lateralizing attention). In one session the exposure was real (GSM) while in the other it was Sham; both sessions lasted 45 min. Functional interhemispheric connectivity was modelled using the analysis of EEG spectral coherence between frontal, central and parietal electrode pairs. Individual EEG rhythms of interest were delta (about 2-4 Hz), theta (about 4-6 Hz), alpha 1 (about 6-8 Hz), alpha 2 (about 8-10 Hz) and alpha 3 (about 10-12 Hz). Results showed that, compared to Sham stimulation, GSM stimulation modulated the interhemispheric frontal and temporal coherence at alpha 2 and alpha 3 bands. The present results suggest that prolonged mobile phone emission affects not only the cortical activity but also the spread of neural synchronization conveyed by interhemispherical functional coupling of EEG rhythms.

Increased omega complexity and decreased microstate duration in nonmedicated schizophrenic patients

To explore brain functions in schizophrenic patients, the global analytic strategy of multichannel EEG was performed that combines measures of global complexity (Omega), total power (Sigma) and generalized frequency (Phi), and EEG microstate analysis was applied to multichannel EEG data for 24 nonmedicated patients and 24 healthy subjects. The patients had higher Omega and Sigma values, and lower Phi values compared with healthy subjects. Three topographical classes were obtained from all EEG data by EEG microstate analysis. The mean duration of one topographical class in the patients was shortened compared to healthy subjects. These results indicated looser cooperativity, or decreased connectivity of the active brain process and deviant brain information processing in schizophrenic patients.

Lamotrigine decreases EEG synchronization in a use-dependent manner in patients with idiopathic generalized epilepsy

OBJECTIVE

To investigate the quantitative EEG effects of lamotrigine (LTG) monotherapy.

HYPOTHESIS

LTG was predicted to decrease thalamo-cortical neuronal synchronization in idiopathic generalized epilepsy (IGE).

METHODS

Waking EEG background activity of 19 IGE patients was investigated before treatment and in the course of LTG monotherapy. Raw absolute power (RAP), raw percent power (RRP), and raw mean frequency (RMF) were computed for 19 electrodes and four frequency bands (delta=1.5-3.5Hz, theta=3.5-7.5Hz, alpha=7.5-12.5Hz, and beta=12.5-25.0Hz). Inter- and intrahemispheric coherence was computed for eight electrode pairs and the four frequency bands. In addition, scalp-averages were calculated for each variable. Group differences were computed by means of nonparametric statistics including correction for multiple comparisons.

RESULTS

Main results were decreased delta and theta RAP (p<0.05 for scalp-averages). LTG compressed the delta, theta, and alpha RAP datasets, reducing the upper limit of the scatter in particular. Spearman r-values indicated marked correlation between the starting values (RAPuntreated) and the LTG-related decrease (RAPtreated-RAPuntreated) in three bands: delta (r=-0.72; p=0.0005), theta (r=-0.59; p=0.007), and alpha (r=-0.61; p=0.006). Thus, the greater the baseline neuronal synchronization, the marked the dampening effect of LTG on it. The remaining findings were decreased theta RRP, theta RMF, and increased alpha RMF (p<0.05 for scalp-averages). The electrode-related changes were small but topographically consistent across the 19 electrode sites. LTG did not affect coherence.

CONCLUSIONS

1. LTG partially normalized the spectral composition of EEG background activity. LTG decreased pathological thalamo-cortical synchronization in use-dependent manner. 2. LTG did not cause quantitative EEG alterations suggesting worsening of the physiological brain functions. Instead, its profile suggested a mild psychostimulant effect.

SIGNIFICANCE

The results contribute to the understanding of the effect of LTG at the network level.

Extraction of qEEG variables to diagnose early dementia

Although it is difficult to definitely diagnose dementia with noninvasive methods such as EEG and MEG, early detection of subjects with probable dementia is of importance to apply currently available treatment strategies. In the present study, we have extracted featured qEEG variables, which can be potentially used to diagnose early dementia, from resting eyes-closed continuous EEGs of 22 early dementia patients and 27 age-matched healthy controls. Among 1680 candidate qEEG variables, about 160 variables showed statistically significant difference (p < 0.01) between early dementia group and control subject group, when mean phase coherence as well as EEG coherence was evaluated for all possible combinations of electrode pairs. Some preliminary trials to discriminate the two groups with the extracted qEEG variables demonstrated that the use of mean phase coherence as a supplementary or alternative measure for the EEG coherence may enhance the accuracy of diagnosis of early dementia.

Conversion from mild cognitive impairment to Alzheimer's disease is predicted by sources and coherence of brain electroencephalography rhythms

Objective. Can quantitative electroencephalography (EEG) predict the conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD)? Methods. Sixty-nine subjects fulfilling criteria for MCI were enrolled; cortical connectivity (spectral coherence) and (low resolution brain electromagnetic tomography) sources of EEG rhythms (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) were evaluated at baseline (time of MCI diagnosis) and follow up (about 14 months later). At follow-up, 45 subjects were still MCI (MCI Stable) and 24 subjects were converted to AD (MCI Converted). Results. At baseline, fronto-parietal midline coherence as well as delta (temporal), theta (parietal, occipital and temporal), and alpha 1 (central, parietal, occipital, temporal, limbic) sources were stronger in MCI Converted than stable subjects (P<0.05). Cox regression modeling showed low midline coherence and weak temporal source associated with 10% annual rate AD conversion, while this rate increased up to 40% and 60% when strong temporal delta source and high midline gamma coherence were observed respectively. Interpretation. Low-cost and diffuse computerized EEG techniques are able to statistically predict MCI to AD conversion.

Neurophysiologic and neuroimaging studies of brain plasticity in children with spastic cerebral palsy

Patients with cerebral palsy (CP) may have some problems other than this motor impairment: mental retardation, epilepsy and sensory disturbance. Healthy children and children with CP have an enhanced capacity for learning and memory compared to adults. There are few tools for brain plasticity investigations. The utility of the neurophysiologic and MRI techniques in the determination of brain reorganization and repair in patients with cerebral palsy is described. The authors discuss their results of quantitative EEG and spectroscopy MRI studies in children with CP. Quantitative EEG and spectroscopy MRI can be useful tools in the determination of these processes in children with CP.

Neurophysiological correlates of persistent vegetative and minimally conscious states

The evaluation of patients after severe brain injury is a complex process for the clinician, even with the information provided by a detailed neurological examination. The clinical examination often does not provide sufficient information to fully evaluate these patients due to several factors. Limited and inconsistent motor responses may obscure expression of greater cognitive capacities. More importantly, evaluation of the functional integrity of the cerebral cortical, thalamic and basal ganglia system is poorly indicated by the clinical examination in many patients. Neurophysiological studies provide a complementary set of objective data for evaluating brain-injured patients, as well as predicting and following the course of their recovery. This additional information can be of great importance since vegetative patients may be difficult to distinguish clinically from those in the minimally conscious state. This is important because the latter category of patients may have a significantly better prognosis for recovery in the initial phase of injury. Electrodiagnostic and imaging studies can help the practitioner to determine the degree of preserved and recovering neurological function. In this review we will assess the various neurophysiological studies currently at our disposal to evaluate and follow the clinical course of patients who have suffered severe brain injuries.

Functional frontoparietal connectivity during encoding and retrieval processes follows HERA model

Recent neuroimaging studies of long-term episodic memory have suggested that left prefrontal cortex predominates in encoding condition, whereas right prefrontal cortex predominates in retrieval condition (hemispheric encoding and retrieval asymmetry, HERA model). The present electroencephalographic (EEG) study investigated the functional coupling of fronto-parietal regions during long-term memorization of visuo-spatial contents (i.e. landscapes, interiors of apartments), to test the predictions of the HERA model. Global fronto-parietal coupling was estimated by spectral coherence, whereas the "direction" of the fronto-parietal information flow was estimated by directed transfer function (DTF). The EEG rhythms of interest were theta (4-7 Hz), alpha (8-13 Hz), beta (14-30 Hz), and gamma (30-45 Hz). Statistically significant coherence in line with the HERA model was obtained at the gamma band. Namely, the fronto-parietal gamma coherence prevailed in the left hemisphere during the encoding condition and in the right hemisphere during the retrieval condition. The DTF estimates of the gamma band showed a dominant parietal-to-frontal directional flow in the right hemisphere during the encoding condition and in the left hemisphere during the retrieval condition (i.e. hemisphere-condition combination not involved by the HERA model). In contrast, a balanced bidirectional flow of the fronto-parietal coupling was observed in the left hemisphere during the encoding condition and in the right hemisphere during the retrieval condition (i.e. hemisphere-condition combination involved by the HERA model). In conclusion, the present encoding-retrieval conditions induced maximal fronto-parietal gamma coupling with bidirectional information flow in the hemisphere-condition combination predicted by the HERA model.

The utility of EEG in dementia: a clinical perspective

BACKGROUND

Despite being simple and cheap, the EEG is not often used in clinical practice.

METHODOLOGY

Literature search using PUBMED and Medline.

RESULTS

Quantitative EEG can help to identify mild dementia and mild cognitive impairment and can increase diagnostic accuracy when used with other imaging techniques. EEG helps differentiate organic from functional brain disease and predict response to cholinesterase inhibitors and is central in the diagnosis of Creutzfeldt Jacob disease. The accuracy of EEG may be greater than that of CT or MRI scans alone.

DISCUSSION

Quantitative EEG may save on specialist interpretation time and enable more routine use of EEG in diagnosis and care. More widespread use of EEG's is indicated. Agreement on the parameters that are best measured on qEEG is still awaited.

Magnetic resonance imaging of cerebellar-prefrontal and cerebellar-parietal functional connectivity

Recent studies of the cerebellum indicated its involvement in a diverse array of functions, and analyses of non-human primate neuroanatomy have revealed connections between cerebellum and cerebral cortex that might support cerebellar contributions to a wider range of functions than traditionally thought. These include cortico-ponto-cerebellar projections originating throughout cerebral cortex, in addition to projections from the dentate nucleus of the cerebellum to prefrontal and posterior parietal cortices via the thalamus. Such projections likely serve as important substrates for cerebellar involvement in human cognition, assuming their analogues are prominent in the human brain. These connections can be examined from a functional perspective through the use of functional connectivity MRI (FCMRI), a technique that allows the in vivo examination of coherence in MR signal among functionally related brain regions. Using this approach, low-frequency fluctuations in MR signal in the dentate nucleus correlated with signal fluctuations in cerebellar, thalamic, limbic, striatal, and cerebrocortical regions including parietal and frontal sites, with prominent coherence in dorsolateral prefrontal cortex. These findings indicate that FCMRI is a useful tool for examining functional relationships between the cerebellum and other brain regions, and they support the findings from non-human primate studies showing anatomic projections from cerebellum to regions of cerebral cortex with known involvement in higher cognitive functions. To our knowledge, this represents the first demonstration of functional coherence between the dentate nucleus and parietal and prefrontal cortices in the human brain, suggesting the presence of cerebellar-parietal and cerebellar-prefrontal functional connectivity.