Analysis of Brain Functional Network Based on EEG Signals for Early-Stage Parkinson’s Disease Detection

Lack of sleep causes central fatigue in the body, which in turn affects brain function, and similarly, intense exercise causes both central and peripheral fatigue. This study aims to characterize the brain state, and in particular the functional changes in the relevant brain regions, after intense exercise in sleep-deprived conditions by detecting EEG signals. Thirty healthy adolescents were screened to participate in the trial, a sleep-deprivation model was developed, and a running exercise was performed the following morning. Meanwhile, pre-exercise and post-exercise Electroencephalogram (EEG) data were collected from the subjects using a 32-conductor electroencephalogram acquisition system (Neuroscan), and the data were analyzed using MATLAB (2013b) to process the data and analyzed Phase Lag Index (PLI) and graph theory metrics for different brain connections. Compared with the control group, the pre-exercise sleep-deprivation group showed significantly lower functional brain connectivity in the central and right temporal lobes in the Delta band (p < 0.05), significantly lower functional brain connectivity in the parietal and occipital regions in the Theta band (p < 0.05), and significantly higher functional brain connectivity in the left temporal and right parietal regions in the Beta2 band (p < 0.05). In the post-exercise sleep-deprivation group, functional brain connectivity was significantly lower in the central to right occipital and central regions in the Delta band (p < 0.05), significantly higher in the whole brain regions in the Theta, Alpha2, and Beta1 bands (p < 0.05 and 0.001), significantly higher in the right central, right parietal, and right temporal regions in the Alpha1 band (p < 0.05), and in the Beta2 band, the functional brain connections from the left frontal region to the right parietal region were significantly lower (p < 0.05). The results of the brain functional network properties showed that the clustering coefficients in the Delta band were significantly lower in the pre-exercise sleep-deprivation group compared to the control group (p < 0.05); the characteristic path length and global efficiency in the Theta band were significantly lower (p < 0.05 and 0.001). The post-exercise sleep-deprivation group showed significantly higher clustering coefficients, input lengths, and local efficiencies (p < 0.001), and significantly lower global efficiencies in the Delta and Theta bands (p < 0.001), and significantly higher clustering coefficients and local efficiencies (p < 0.001) and significantly lower input lengths and global efficiencies in the Alpha1 band compared with the control group (p < 0.001). After sleep deprivation, the pre-exercise resting state reduces the rate of information transfer in the functional networks of the adolescent brain, slowing the transfer of information between brain regions. After performing strenuous exercise, sleep deprivation leads to decreased athletic performance in adolescents. After a prolonged period of intense exercise, brain activity is gradually suppressed, resulting in even slower work efficiency and, eventually, increased information transfer in adolescents.

Neuromodulation of resting state brain network topography by heterolateral prefrontal transcranial photobiomodulation.

Electroencephalographic patterns observed during increasing depth anesthesia with thiopental sodium-ether, with ketamine hydrochloride-ether and with pentobarbital sodium were correlated with concentration of anesthetic agents in the blood. Analysis of electroencephalogram was made by histogram method.The results are as follows:1) Before anesthesia phase; Beta band of 20-25 Hz waves were shown 60-70% following alpha band was 30%.2) Induction phase; Delta band and theta band were increasing about 40% in place of beta band, but case of ketamine hydrochloride intervenous were increased beta band.3) Maintenance phase; 20-30% ether gas inhalation 5 minutes, a few beta band increased when this stage ether concentration in blood was about 60 mg/dl. Ether concentration in blood was over 70 mg/dl increasing delta band and theta band appearance ratio.4) Ether inhalation 30 minutes stage; Ether concentration in blood of 110 mg/dl when it was delta band only appearance ratio over about 50%.It was suggested that this method of analysis is adaptable to the quantitative study of contribution of each anesthetic agents when used simultaneously, provided that a significant change in EEG patterns and frequency band (delta, theta, alpha and beta waves bands), subject to classification, is produced by the agents considered.From the results of the present work, it was concluded that the changes EEG patterns were closely related to the depth of anesthesia and pharmacokinetics in the dog anesthetized with ether.

ObjectiveThis study aimed to elucidate the distributions of abnormal activities, as well as the functional connectivity and topological properties of brain networks, in patients diagnosed with anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis.MethodFrom February 2016 to February 2025, acute-phase magnetoencephalography (MEG) data were successfully acquired from 16 patients diagnosed with anti-NMDAR encephalitis at the Affiliated Brain Hospital of Nanjing Medical University. MEG was employed to evaluate the power spectral density (PSD) across multiple frequency bands. Further analysis concentrated on functional connectivity and the topological characteristics of brain networks in order to identify distinctive neurophysiological features associated with the condition.ResultsDuring the acute phase, the PSD in the delta band (1–3 Hz) showed greater power in posterior regions and lower power in anterior regions, with the highest energy concentrated bilaterally in the occipitoparietal and temporal areas. In the theta band (5–7 Hz), the PSD was predominantly localized to the bilateral occipitoparietal regions. The beta1 band (13–20 Hz) was primarily distributed in the right temporo-occipitoparietal regions, while the beta2 band (20–30 Hz) was predominantly distributed in the left temporal, occipitoparietal, and certain frontal regions. Functional connectivity analysis revealed enhanced connections between the left caudal anterior cingulate (CAC_L) and the left superior parietal lobe in the delta and theta bands. Increased connectivity was also observed between the left frontal pole and Precuneus_L, CAC_L, and the left superior temporal gyrus (STG_L)in the theta and beta2 bands. Furthermore, enhanced connectivity between STG_L and Pericalcarine_R was observed in the theta, beta2, and gamma bands. Patients with anti-NMDAR encephalitis demonstrated significantly reduced global efficiency and notable increases in average path length, local efficiency, and clustering coefficient in multiple bands, suggesting local clustering of brain networks during the acute phase.ConclusionAlterations in PSD distribution and brain networks across different frequency bands may provide valuable insights into the electrophysiological changes observed in the brains of anti-NMDAR encephalitis patients. Furthermore, these findings may offer valuable mechanistic insights that could contribute to the development of future diagnostic strategies.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00415-025-13302-x.

BackgroundMild cognitive impairment (MCI) exhibits abnormal resting-state EEG oscillations in delta (1–4 Hz), theta (4–7 Hz), and alpha (8–13 Hz) bands, though findings remain inconsistent. Moreover, dynamic functional connectivity (FC) alterations in these bands are poorly understood. To address this, we aimed to characterize resting-state EEG oscillations and dynamic FC in these frequency bands in MCI.MethodWe recruited 21 MCI and 20 age−/education-matched normal controls (NC). Resting-state EEG was recorded for 5 min (eyes-open). We utilized power spectral density to investigate abnormalities in neural oscillations, and employed the directed transfer function (DTF) to explore dynamic functional connectivity (FC) alterations within the delta, alpha, and theta frequency bands 4among two groups.ResultsCompared to NC, for neural oscillation, MCI showed significantly increased delta oscillation (prefrontal, parietal, temporal, and central regions) mainly located in the frontal and parietal lobes, significantly decreased alpha oscillation of the entire brain region mainly located in the frontal lobe, and both significantly increased and decreased theta oscillation (prefrontal, parietal, and occipital lobes) with fewer electrodes. For dynamic brain FC, in the delta band, the MCI exhibited significantly enhanced bidirectional FC between the prefrontal and parietal lobes, as well as two bottom-up FC from the occipital lobe to the central and parietal regions; In the theta band, the MCI showed significant enhancement of two FC from the temporal lobe to the frontal lobe, two FC from the occipital lobe to the parietal lobe, and one FC from the parietal lobe to the frontal lobe; In the alpha band, the MCI had one significantly enhanced bottom-up FC from the occipital lobe to the prefrontal lobe.ConclusionDuring the eyes-open resting-state, differences of two groups in neural oscillations were primarily observed in the alpha and delta bands. The MCI exhibited significantly decreased alpha oscillations in the frontal lobe and increased delta oscillations in the frontal and parietal lobes. However, dynamic FC differences were most prominent in the delta and theta bands, including significantly increased interconnectivity of the prefrontal parietal network and significantly increased bottom-up FC. These findings emphasize the necessity of comprehensive analysis of local activity and large-scale network dynamics in MCI.

We aimed to compare network properties between focal-onset nonconvulsive status epilepticus (NCSE) and toxic/metabolic encephalopathy (TME) during periods of periodic discharge using graph theoretical analysis, and to evaluate the applicability of graph measures as markers for the differential diagnosis between focal-onset NCSE and TME, using machine learning algorithms. Electroencephalography (EEG) data from 50 focal-onset NCSE and 44 TMEs were analyzed. Epochs with nonictal periodic discharges were selected, and the coherence in each frequency band was analyzed. Graph theoretical analysis was performed to compare brain network properties between the groups. Eight different traditional machine learning methods were implemented to evaluate the utility of graph theoretical measures as input features to discriminate between the two conditions. The average degree (in delta, alpha, beta, and gamma bands), strength (in delta band), global efficiency (in delta and alpha bands), local efficiency (in delta band), clustering coefficient (in delta band), and transitivity (in delta band) were higher in TME than in NCSE. TME showed lower modularity (in delta band) and assortativity (in alpha, beta, and gamma bands) than NCSE. Machine learning algorithms based on EEG global graph measures classified NCSE and TME with high accuracy, and gradient boosting was the most accurate classification model with an area under the receiver operating characteristics curve of 0.904. Our findings on differences in network properties may provide novel insights that graph measures reflecting the network properties could be quantitative markers for the differential diagnosis between focal-onset NCSE and TME.

Excessive cortical synchrony within neural ensembles has been implicated as an important mechanism driving epileptiform activity. The current study measures and compares background electroencephalographic (EEG) phase synchronization in patients having various types of epilepsies and healthy controls during awake and sleep stages. A total of 120 patients with epilepsy (PWE) subdivided into 3 groups (juvenile myoclonic epilepsy [JME], temporal lobe epilepsy [TLE], and extra-temporal lobe epilepsy [Ex-TLE]; n = 40 in each group) and 40 healthy controls were subjected to overnight polysomnography. EEG phase synchronization (SI) between the 8 EEG channels was assessed for delta, theta, alpha, sigma, and high beta frequency bands using ensemble measure on 10-second representative time windows and compared between patients and controls and also between awake and sleep stages. Mean ± SD of SI was compared using 2-way analysis of variance followed by pairwise comparison ( P ≤ .05). In both delta and theta bands, the SI was significantly higher in patients with JME, TLE, and Ex-TLE compared with controls, whereas in alpha, sigma, and high beta bands, SI was comparable between the groups. On comparison of SI between sleep stages, delta band: progressive increase in SI from wake ⇒ N1 ⇒ N2 ⇒ N3, whereas REM (rapid eye movement) was comparable to wake; theta band: decreased SI during N2 and increase during N3; alpha band: SI was highest in wake and lower in N1, N2, N3, and REM; and sigma and high beta bands: progressive increase in SI from wake ⇒ N1 ⇒ N2 ⇒ N3; however, sigma band showed lower SI during REM. This study found an increased background cortical synchronization in PWE compared with healthy controls in delta and theta bands during wake and sleep. This background hypersynchrony may be an important property of epileptogenic brain circuitry in PWE, which enables them to effortlessly generate a paroxysmal EEG depolarization shift.

According to the sensory-neural Temporal Sampling theory of developmental dyslexia, neural sampling of auditory information at slow rates (<10 Hz, related to speech rhythm) is atypical in dyslexic individuals, particularly in the delta band (0.5–4 Hz). Here we examine the underlying neural mechanisms related to atypical sampling using a simple repetitive speech paradigm. Fifty-one children (21 control children [15M, 6F] and 30 children with dyslexia [16M, 14F]) aged 9 years with or without developmental dyslexia watched and listened as a ‘talking head’ repeated the syllable “ba” every 500 ms, while EEG was recorded. Occasionally a syllable was “out of time”, with a temporal delay calibrated individually and adaptively for each child so that it was detected around 79.4% of the time by a button press. Phase consistency in the delta (rate of stimulus delivery), theta (speech-related) and alpha (control) bands was evaluated for each child and each group. Significant phase consistency was found for both groups in the delta and theta bands, demonstrating neural entrainment, but not the alpha band. However, the children with dyslexia showed a different preferred phase and significantly reduced phase consistency compared to control children, in the delta band only. Analysis of pre- and post-stimulus angular velocity of group preferred phases revealed that the children in the dyslexic group showed an atypical response in the delta band only. The delta-band pre-stimulus angular velocity (−130 ms to 0 ms) for the dyslexic group appeared to be significantly faster compared to the control group. It is concluded that neural responding to simple beat-based stimuli may provide a unique neural marker of developmental dyslexia. The automatic nature of this neural response may enable new tools for diagnosis, as well as opening new avenues for remediation.

T134. Quantifying dynamical interactions between neural assemblies

Background Prolonged disorders of consciousness (pDOC) are common in neurology and place a heavy burden on families and society. This study is aimed at investigating the characteristics of brain connectivity in patients with pDOC based on quantitative EEG (qEEG) and extending a new direction for the evaluation of pDOC. Methods Participants were divided into a control group (CG) and a DOC group by the presence or absence of pDOC. Participants underwent magnetic resonance imaging (MRI) T1 three-dimensional magnetization with a prepared rapid acquisition gradient echo (3D-T1-MPRAGE) sequence, and video EEG data were collected. After calculating the power spectrum by EEG data analysis tool, DTABR ((δ + θ)/(α + β) ratio), Pearson's correlation coefficient (Pearson r), Granger's causality, and phase transfer entropy (PTE), we performed statistical analysis between two groups. Finally, receiver operating characteristic (ROC) curves of connectivity metrics were made. Results The proportion of power in frontal, central, parietal, and temporal regions in the DOC group was lower than that in the CG. The percentage of delta power in the DOC group was significantly higher than that in the CG, the DTABR in the DOC group was higher than that in the CG, and the value was inverted. The Pearson r of the DOC group was higher than that of CG. The Pearson r of the delta band (Z = −6.71, P < 0.01), theta band (Z = −15.06, P < 0.01), and alpha band (Z = −28.45, P < 0.01) were statistically significant. Granger causality showed that the intensity of directed connections between the two hemispheres in the DOC group at the same threshold was significantly reduced (Z = −82.43, P < 0.01). The PTE of each frequency band in the DOC group was lower than that in the CG. The PTE of the delta band (Z = −42.68, P < 0.01), theta band (Z = −56.79, P < 0.01), the alpha band (Z = −35.11, P < 0.01), and beta band (Z = −63.74, P < 0.01) had statistical significance. Conclusion Brain connectivity analysis based on EEG has the advantages of being noninvasive, convenient, and bedside. The Pearson r of DTABR, delta, theta, and alpha bands, Granger's causality, and PTE of the delta, theta, alpha, and beta bands can be used as biological markers to distinguish between pDOC and healthy people, especially when behavior evaluation is difficult or ambiguous; it can supplement clinical diagnosis.

Dementia is a common neurodegenerative disease in the elderly, which affects the structural and functional connectivity of the brain. Recent studies indicate that electrophysiological measures, such as power spectral features and functional connectivity (FC), show promise for the diagnosis and classification of dementia. However, findings across studies remain inconsistent, and distinct electrophysiological patterns separating dementia subtypes, as well as Frontotemporal Dementia (FTD) and cognitively normal (CN) individuals, are not yet well established. This study focuses on spectral power and functional connectivity (FC) analyses of the Electroencephalography (EEG) frequency bands (delta, theta, alpha, beta, and gamma) in Alzheimer's Disease (AD) and FTD. A publicly available eyes-closed (EC), resting-state (RS) EEG dataset comprising 88 age-matched participants, 36 with AD, 29 CN, and 23 with FTD, was used in this study. Absolute power was computed using Welch's method, while FC within each frequency band was assessed using Inter-Site Phase Clustering (ISPC) and network-based statistics, edge and node strength. The global power analysis revealed a significantly higher alpha power in CN compared to both AD and FTD. Regional analysis revealed a significantly lower temporal and parietal alpha in AD relative to CN and a significantly lower occipital alpha and beta in both AD and FTD compared to CN. Topographical power analysis showed unique significant differences within lobes in delta, theta, alpha, and gamma bands in AD and FTD, with AD illustrating a relatively more heterogeneous power distribution than FTD. Furthermore, FC analysis indicated that compared with CN, AD exhibited significantly lower edge strength in delta, theta, beta, and gamma bands, while significantly lower node strength in delta, theta, and gamma bands. Likewise, compared with CN, FTD showed significantly lower edge and node strength in the delta and theta bands, while significantly higher in the beta band. Furthermore, when compared to FTD, AD revealed a significantly lower edge and node strength in the delta, beta, and gamma bands. In conclusion, AD was associated with widespread FC disruptions, while FTD retained partially preserved connectivity, with the temporal lobe more affected than the frontal lobe. These findings suggest that band power and FC alterations may serve as potential biomarkers for diagnosing and classifying dementia into AD and FTD.

This study seeks to investigate the electrophysiological mechanisms associated with mild cognitive impairment (MCI) in elderly patients with coronary artery disease (CAD) through the application of source-reconstructed EEG in conjunction with machine learning methodologies. We retrospectively analyzed clinical data and resting-state 64-channel EEG recorded during hospitalization at The First Hospital of Changsha. Participants included primary hypertension without CAD (n = 53) and CAD with primary hypertension (n = 117), with CAD stratified by Montreal Cognitive Assessment (MoCA) into MCI (n = 49) and cognitively normal (n = 68). EEG sources were reconstructed using an ICBM152-based head model and BEM forward modeling, yielding 82 Brodmann-atlas ROIs; functional connectivity was quantified using lagged phase synchronization (LPS) in delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) bands. Group comparisons applied false discovery rate correction. For MCI classification among patients with CAD, the dataset was randomly split into training and testing sets (7:3). Feature selection was performed in the training set using an independent-samples t-test followed by L1-penalized logistic regression. Subsequently, eight machine-learning classifiers were trained using the selected LPS features, with hyperparameters optimized by grid search under five-fold cross-validation. Model interpretability was assessed using SHAP. Baseline demographics and vascular comorbidities were comparable across groups; MoCA scores were lower in the MCI subgroup. Relative to hypertensive controls without CAD, cognitively normal CAD patients showed reduced frontal connectivity, including decreased alpha-band LPS (BA8L-46R) and beta-band LPS (BA44L-44R). Compared with cognitively normal CAD, CAD with MCI exhibited broader multi-band dysconnectivity across alpha, beta, theta, and delta bands, with mixed delta-band changes. In the test set, the Gradient Boosting model achieved the best performance for identifying MCI within CAD (AUC = 0.895). SHAP highlighted the most influential features, led by decreased alpha-band BA8L-46R connectivity, alongside delta- and beta-band alterations. Coronary artery disease is associated with frontal network disruption, which becomes more extensive and frequency-diverse as MCI progresses. Interpretable machine learning further highlights a small set of connectivity abnormalities-particularly within premotor-prefrontal pathways-as candidate markers for MCI classification within a CAD cohort, supporting a vascular-relevant interpretation, which warrants further validation.

Wavelet spectra of visual evoked potentials: Time course of delta, theta, alpha and beta bands

Background: Post-stroke gait rehabilitation strategies predominantly target steady-state patterns. Demanding walking environments more closely approximate real-world motor control demands than stable conditions, yet their neural correlates in stroke patients remain unclear. Methods: Sixty stroke patients completed three walking tasks: stable level-ground walking, asymmetrical walking task, and visual-deprived ambulation, with synchronized electroencephalography (EEG) recordings. Spectral power was computed across delta, theta, alpha, beta, and gamma frequency bands. Brain functional connectivity was assessed via weighted phase lag index, with graph theory metrics quantifying brain functional network features. Results: During the asymmetrical walking task, spectral power analysis exhibited reduced theta-band power and increased power in beta and gamma frequency bands. Brain functional networks showed weakened theta-band functional connectivity, and enhanced frontal-occipital connections in alpha, beta, and gamma frequency bands, accompanied by prolonged character path length in the delta frequency band and diminished clustering coefficients in alpha and gamma frequency bands. Under visual-deprivation ambulation, spectral power analysis exhibited suppressed delta and theta power and attenuated dominance in alpha, beta, and gamma frequency bands. The corresponding brain functional networks showed decoupled functional connectivity in delta and theta frequency bands, enhanced alpha-band frontal-parietal-temporal-occipital connections, and frontal-parietal-temporal interactions in the beta band, accompanied by increased delta character path length, diminished clustering coefficient, longer character path lengths and smaller clustering coefficients shown in alpha, beta, and gamma frequency bands. Conclusions: Demanding environmental challenges drive beneficial brain adaptations and could be harnessed to promote adaptive neuroplasticity in stroke rehabilitation. Trial registration:The study protocol was registered on ClinicalTrials.gov (No. NCT06395142).

Article Figures and data Abstract Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Pathological oscillations including elevated beta activity in the subthalamic nucleus (STN) and between STN and cortical areas are a hallmark of neural activity in Parkinson's disease (PD). Oscillations also play an important role in normal physiological processes and serve distinct functional roles at different points in time. We characterised the effect of dopaminergic medication on oscillatory whole-brain networks in PD in a time-resolved manner by employing a hidden Markov model on combined STN local field potentials and magnetoencephalography (MEG) recordings from 17 PD patients. Dopaminergic medication led to coherence within the medial and orbitofrontal cortex in the delta/theta frequency range. This is in line with known side effects of dopamine treatment such as deteriorated executive functions in PD. In addition, dopamine caused the beta band activity to switch from an STN-mediated motor network to a frontoparietal-mediated one. In contrast, dopamine did not modify local STN–STN coherence in PD. STN–STN synchrony emerged both on and off medication. By providing electrophysiological evidence for the differential effects of dopaminergic medication on the discovered networks, our findings open further avenues for electrical and pharmacological interventions in PD. Introduction Oscillatory activity serves crucial cognitive roles in the brain (Akam and Kullmann, 2010; Akam and Kullmann, 2014), and alterations of oscillatory activity have been linked to neurological and psychiatric diseases (Schnitzler and Gross, 2005). Different large-scale brain networks operate with their own oscillatory fingerprint and carry out specific functions (Keitel and Gross, 2016; Mellem et al., 2017; Vidaurre et al., 2018b). Given the dynamics of cognition, different brain networks need to be recruited and deployed flexibly. Hence, the duration for which a network is active, its overall temporal presence, and even the interval between the different activations of a specific network might provide a unique window to understanding brain functions. Crucially, alterations of these temporal properties or networks might be related to neurological disorders. In Parkinson's disease (PD), beta oscillations within the subthalamic nucleus (STN) and motor cortex (13–30 Hz) correlate with the motor symptoms of PD (Marreiros et al., 2013; van Wijk et al., 2016; West et al., 2018). Beta oscillations also play a critical role in communication in a healthy brain (Engel and Fries, 2010). (For the purposes of our paper, we refer to oscillatory activity or oscillations as recurrent but transient frequency-specific patterns of network activity, even though the underlying patterns can be composed of either sustained rhythmic activity, neural bursting, or both [Quinn et al., 2019]. Disambiguating the exact nature of these patterns is, however, beyond the scope of this work.) At the cellular level, loss of nigral dopamine neurons in PD leads to widespread changes in brain networks, to varying degrees across different patients. Dopamine loss is managed in patients via dopaminergic medication. Dopamine is a widespread neuromodulator in the brain (Gershman and Uchida, 2019), raising the question of whether each medication-induced change restores physiological oscillatory networks. In particular, dopaminergic medication is known to produce cognitive side effects in PD patients (Voon et al., 2009). According to the dopamine overdose hypothesis, a reason for these effects is the presence of excess dopamine in brain regions not affected in PD (MacDonald et al., 2011; MacDonald and Monchi, 2011). Previous task-based and neuroimaging studies in PD demonstrated frontal cognitive impairment due to dopaminergic medication (Cools et al., 2002; Ray and Strafella, 2010; MacDonald et al., 2011). Using resting-state whole-brain MEG analysis, network changes related to both motor and non-motor symptoms of PD have been described (Olde Dubbelink et al., 2013a; Olde Dubbelink et al., 2013b). However, these studies could not account for simultaneous STN–STN or cortico–STN activity affecting these networks, which would require combined MEG/electroencephalogram (EEG)–LFP recordings (Litvak et al., 2021). Such recordings are possible during the implantation of deep brain stimulation (DBS) electrodes, an accepted treatment in the later stages of PD (Volkmann et al., 2004; Deuschl et al., 2006; Kleiner-Fisman et al., 2006). Combined MEG–LFP studies in PD involving dopaminergic intervention report changes in beta and alpha band connectivity between specific cortical regions and the STN (Litvak et al., 2011; Hirschmann et al., 2013; Oswal et al., 2016). Decreased cortico–STN coherence under dopaminergic medication (ON) correlates with improved motor functions in PD (George et al., 2013). STN–STN intra-hemispheric oscillations positively correlate to motor symptom severity in PD without dopaminergic medication (OFF), whereas dopamine-dependent nonlinear phase relationships exist between inter-hemispheric STN–STN activity (West et al., 2016). Crucially, previous studies could not rule out the influence of cortico–STN connectivity on these inter-hemispheric STN–STN interactions. To further characterise the differential effects of dopaminergic medication and delineate pathological versus physiological-relevant spectral connectivity in PD, we study PD brain activity via a hidden Markov model (HMM), a data-driven learning algorithm (Vidaurre et al., 2016; Vidaurre et al., 2018b). Due to the importance of cortico–subcortical interactions in PD, we investigated these interactions with combined spontaneous whole-brain magnetoencephalography (MEG) and STN local field potentials (LFPs) recordings from PD patients. We study whole-brain connectivity including the STN using spectral coherence as a proxy for communication based on the communication through coherence hypothesis (Fries, 2005; Fries, 2015). This will allow us to delineate differences in communication OFF and ON medication. Furthermore, we extended previous work that was limited to investigating communication between specific pairs of brain areas (Litvak et al., 2011; George et al., 2013; Hirschmann et al., 2013). Moreover, we identified the temporal properties of the networks both ON and OFF medication. The temporal properties provide an encompassing view of network alterations in PD and the effect of dopamine on these networks. We found that cortico–cortical, cortico–STN, and STN–STN networks were differentially modulated by dopaminergic medication. For the cortico–cortical network, medication led to additional connections that can be linked to the side effects of dopamine. At the same time, dopamine changed the cortico–STN network towards a pattern more closely resembling physiological connectivity as reported in the PD literature. Within the third network, dopamine only had an influence on local STN–STN coherence. These results provide novel information on the oscillatory network connectivity occurring in PD and the differential changes caused by dopaminergic intervention. These whole-brain networks, along with their electrophysiological signatures, open up new potential targets for both electric and pharmacological interventions in PD. Results Under resting-state conditions in PD patients, we simultaneously recorded whole-brain MEG activity with LFPs from the STN using directional electrodes implanted for DBS. Using an HMM, we identified recurrent patterns of transient network connectivity between the cortex and the STN, which we henceforth refer to as an 'HMM state'. In comparison to classic sliding window analysis, an HMM solution can be thought of as a data-driven estimation of time windows of variable length (within which a particular HMM state was active): once we know the time windows when a particular state is active, we compute coherence between different pairs of regions for each of these recurrent states. Each HMM state itself is a multidimensional, time-delay embedded (TDE) covariance matrix across the whole brain, containing information about cross-regional coherence and power in the frequency domain. Additionally, the temporal evolution of the HMM states was determined. The PD data were acquired under medication (L-DOPA) OFF and ON conditions, which allowed us to delineate the physiological versus pathological spatio-spectral and temporal changes observed in PD. To allow the system to dynamically evolve, we use time delay embedding. Theoretically, delay embedding can reveal the state space of the underlying dynamical system (Packard et al., 1980). Thus, by delay-embedding PD time series OFF and ON medication, we uncover the differential effects of a neurotransmitter such as dopamine on underlying whole-brain connectivity. OFF medication, patients had on average a Unified Parkinson's Disease Rating Scale (UPDRS) part III of 29.24 ± 10.74. This was reduced by L-DOPA (176.5 ± 56.2 mg) to 19.47 ± 8.52, indicating an improvement in motor symptoms. Spontaneous brain activity in PD can be resolved into distinct states Using an HMM, we delineated cortico–subthalamic spectral changes from both global source-level cortical interactions as well as local STN–STN interactions. Three of the six HMM states could be attributed to physiologically interpretable connectivity patterns. We could not interpret the other three states within the current physiological frameworks both OFF and ON medication and they are therefore not considered in the following (see Figure 2—figure supplement 1). The connectivity between different brain regions for each state was visualised for the frequency modes shown in Figure 1. Figures 2–4 show the connectivity patterns for the three physiologically meaningful states in both the OFF (top row) and ON medication condition (bottom row). We refer to the state obtained in Figure 2 as the cortico–cortical state (Ctx–Ctx). This state was characterised mostly by local coherence within segregated networks OFF medication in the alpha and beta band. In contrast, there was a widespread increase in coherence across the brain from OFF to ON medication. Therefore, ON medication, the connectivity strength in the alpha and beta band was not significantly different from the mean noise level. Figure 3 displays the second state. A large proportion of spectral connections in this state enable cortico–STN communication via spectral coherence (Lalo et al., 2008; Litvak et al., 2011; Hirschmann et al., 2013; Oswal et al., 2013; van Wijk et al., 2016) and thus we labelled this as the cortico–STN state (Ctx–STN). This state was characterised by connectivity between multiple cortical regions and the STN OFF medication, but increased specificity of cortical–STN connectivity ON medication. Finally, Figure 4 shows the third state. Within this state, highly synchronous STN–STN spectral connectivity emerged, both OFF and ON medication and therefore we named it the STN–STN state (STN–STN). The spectral characteristics of this state largely remain unaffected under the influence of dopaminergic medication. In the following sections, we describe these three states in detail. Figure 1 Download asset Open asset Data-driven frequency modes. Each plotted curve shows a different spectral band. The x-axis represents frequency in Hz and the y-axis represents the weights obtained from the non-negative matrix factorisation (NNMF) in arbitrary units. The NNMF weights are like regression coefficients. The frequency resolution of the modes is 0.5 Hz. Panels A and B show the OFF and ON medication frequency modes, respectively. Source data are provided as Figure 1—source data 1–2. Figure 1—source data 1 Source data of Figure 1a. https://cdn.elifesciences.org/articles/66057/elife-66057-fig1-data1-v1.mat Download elife-66057-fig1-data1-v1.mat Figure 1—source data 2 Source data of Figure 1b. https://cdn.elifesciences.org/articles/66057/elife-66057-fig1-data2-v1.mat Download elife-66057-fig1-data2-v1.mat Figure 2 with 1 supplement see all Download asset Open asset Cortico–cortical state. The cortico–cortical state was characterised by a significant increase in coherence ON compared to OFF medication (see panel B). Due to this, no connections within the alpha and beta band ON medication were significantly higher than the mean (panel C). However, in the delta band, ON medication medial prefrontal–orbitofrontal connectivity emerged. (A and C) Each node in the circular graph represents a brain region based on the Mindboggle atlas. The regions from the atlas are listed in Table 1 along with their corresponding numbers that are used in the circular graph. The colour code in the circular graph represents a group of regions clustered according to the atlas (starting from node number 1) STN contacts (contacts 1, 2, 3 = right STN and contacts 4, 5, 6 = left STN), frontal, medial frontal, temporal, sensorimotor, parietal, and visual cortices. In the circular graph, only the significant connections (p<0.05; corrected for multiple comparisons, IntraMed analysis) are displayed as black curves connecting the nodes. The circles from left to right represent the delta/theta, alpha, and beta bands. Panel A shows results for OFF medication data and panel C for the ON medication condition. For every circular graph, we also show a corresponding top view of the brain with the connectivity represented by yellow lines and the red dot represents the anatomical seed vertex of the brain region. Only the cortical connections are shown. Panel B shows the result for inter-medication analysis (InterMed) for the cortico–cortical state. In each symmetric matrix, every row and column corresponds to a specific atlas cluster denoted by the dot colour on the side of the matrix. Each matrix entry is the result of the InterMed analysis where OFF medication connectivity between ith row and jth column was compared to the ON medication connectivity between the same connections. A cell is white if the comparison mentioned on top of the matrix (either ON >OFF or OFF >ON) was significant at a threshold of p<0.05. The connectivity maps of states 4–6 are provided in Figure 2—figure supplement 1. Source data are provided as Figure 2—source data 1–3. Figure 2—source data 1 Source data of Figure 2a. https://cdn.elifesciences.org/articles/66057/elife-66057-fig2-data1-v1.mat Download elife-66057-fig2-data1-v1.mat Figure 2—source data 2 Source data of Figure 2b. https://cdn.elifesciences.org/articles/66057/elife-66057-fig2-data2-v1.mat Download elife-66057-fig2-data2-v1.mat Figure 2—source data 3 Source data of Figure 2c. https://cdn.elifesciences.org/articles/66057/elife-66057-fig2-data3-v1.mat Download elife-66057-fig2-data3-v1.mat Figure 3 Download asset Open asset Cortico–STN state. For the general description, see the note to Figure 2. The cortico–STN state was characterised by preservation of spectrally selective cortico–STN connectivity ON medication. Also, ON medication, a sensorimotor–frontoparietal network emerged. Source data are provided as Figure 3—source data 1–3. Figure 3—source data 1 Source data of Figure 3a. https://cdn.elifesciences.org/articles/66057/elife-66057-fig3-data1-v1.mat Download elife-66057-fig3-data1-v1.mat Figure 3—source data 2 Source data of Figure 3b. https://cdn.elifesciences.org/articles/66057/elife-66057-fig3-data2-v1.mat Download elife-66057-fig3-data2-v1.mat Figure 3—source data 3 Source data of Figure 3c. https://cdn.elifesciences.org/articles/66057/elife-66057-fig3-data3-v1.mat Download elife-66057-fig3-data3-v1.mat Figure 4 Download asset Open asset STN–STN state. For the general description, see the note to Figure 2. The STN–STN state was characterised by preservation of STN–STN coherence in the alpha and beta band OFF versus ON medication. STN–STN theta/delta coherence was no longer significant ON medication. Source data are provided as Figure 4—source data 1–3. Figure 4—source data 1 Source data of Figure 4a. https://cdn.elifesciences.org/articles/66057/elife-66057-fig4-data1-v1.mat Download elife-66057-fig4-data1-v1.mat Figure 4—source data 2 Source data of Figure 4b. https://cdn.elifesciences.org/articles/66057/elife-66057-fig4-data2-v1.mat Download elife-66057-fig4-data2-v1.mat Figure 4—source data 3 Source data of Figure 4c. https://cdn.elifesciences.org/articles/66057/elife-66057-fig4-data3-v1.mat Download elife-66057-fig4-data3-v1.mat Table 1 Regions of the Mindboggle atlas used. STN, subthalamic nucleus; Vis, visual; Par, parietal; Smtr, sensory motor; Tmp, temporal; Mpf, medial prefrontal; Frnt, frontal; Ctx, cortex. The colour code is for the ring figures presented as part of the results. STN1Contact one rightSmtr-Ctx12Postcentral2Contact two right13Precentral3Contact three rightTmp-Ctx14Middle temporal1Contact four left15Superior temporal2Contact five leftMpf-Ctx16Caudal middle frontal3Contact six left17Medial orbitofrontalVis-Ctx4CuneusFrnt-Ctx18Insula5Lateral occipital19Lateral orbitofrontal6Lingual20Pars opercularisPar-Ctx7Inferior parietal21Pars orbitalis8Para central22Pars triangularis9Precuneus23Rostral middlefrontal10Superior parietal24Superior frontal11Supramarginal Ctx–Ctx state is characterised by increased frontal coherence due to elevated dopamine levels Supporting the dopamine overdose hypothesis in PD (Kelly et al., 2009; MacDonald and Monchi, 2011), we identified a delta/theta oscillatory network involving intra-hemispheric connections between the lateral and medial orbitofrontal cortex as well as the pars orbitalis. The delta/theta network emerged between the lateral and medial orbitofrontal as well as left and right pars orbitalis cortex ON medication (p<0.05, Figure 2C delta). On the contrary, OFF medication no significant connectivity was detected in the delta/theta band. In the alpha and beta band OFF medication there was significant connectivity within the frontal regions, STN, and to a limited extent in the posterior parietal regions (p<0.05, Figure 2A). Another effect of excess dopamine was significantly increased connectivity of frontal cortex and temporal cortex both with the STN and multiple cortical regions across all frequency modes (p<0.01, Figure 2 delta, alpha, and beta). The change in sensorimotor–STN connectivity primarily took place in the alpha band with an increased ON medication. Sensorimotor–cortical connectivity was increased ON medication across multiple cortical regions in both the alpha and beta band (p<0.01, Figure 2 alpha and beta). However, STN–STN coherence remained unchanged OFF versus ON medication across all frequency modes. Viewed together, the Ctx–Ctx state captured increased coherence across the cortex ON medication within the alpha and beta band. This, however, implies that ON medication, no connectivity strength was significantly higher than the mean noise level within the alpha or beta band. ON medication, significant coherence emerged in the delta/theta band primarily between different regions of the orbitofrontal cortex. Dopaminergic medication selectively reduced connectivity in the Ctx–STN state Our analysis revealed that the Ctx–STN state ON medication was characterised by selective cortico–STN spectral connectivity and an overall shift in cortex-wide activity towards physiologically relevant network connectivity. In particular, ON medication, connectivity between STN and cortex became more selective in the alpha and beta band. OFF medication, STN–pre-motor (sensory), STN–frontal, and STN–parietal connectivity was present (p<0.05, Figure 3A alpha and beta). Importantly, coherence OFF medication was significantly larger than ON medication between STN and sensorimotor, STN and temporal, and STN and frontal cortices (p<0.05 for all connections, Figure 3B alpha and beta). Furthermore, ON medication, in the alpha band only the connectivity between temporal, parietal, and medial orbitofrontal cortical regions and the STN was preserved (p<0.05, Figure 3C alpha). Finally, ON medication, a sensorimotor–frontoparietal network emerged (p<0.05, Figure 3C beta), where sensorimotor, medial prefrontal, frontal, and parietal regions were no longer connected to the STN, but instead directly communicated with each other in the beta band. Hence, there was a transition from STN-mediated sensorimotor connectivity to the cortex OFF medication to a more direct cortico–cortical connectivity ON medication. Simultaneously to STN–cortico and cortico–cortical, STN–STN connectivity changed. In the ON condition, STN–STN connectivity was significantly different from the mean noise level across all three frequency modes (p<0.05, Figure 3C). But on the other hand, there was no significant change in the STN–STN connectivity OFF versus ON medication (p=0.21 delta/theta; p=0.25 alpha; p=0.10 beta; Figure 3B). To summarise, coherence decreased ON medication across a wide range of cortical regions both at the cortico–cortical and cortico–STN level. Still, significant connectivity was selectively preserved in a spectrally specific manner ON medication both at the cortico–cortical (sensorimotor–frontoparietal network) and the cortico–STN levels. The most surprising aspect of this state was the emergence of bilateral STN–STN coherence ON medication across all frequency modes. Dopamine selectively modifies delta/theta oscillations within the STN–STN state In this STN–STN state, dopaminergic intervention had only a limited effect on STN–STN connectivity. OFF medication, STN–STN coherence was present across all three frequency modes (p<0.05, Figure 4A), while ON medication, significant STN–STN coherence emerged only in the alpha and beta band (p<0.05, Figure 4C alpha and beta). ON medication, STN–STN delta/theta connectivity strength was not significantly different from the mean noise level (p<0.05, Figure 4C delta). OFF compared to ON medication, coherence was reduced across the entire cortex both at the inter-cortical and the STN–cortex level across all frequency modes. The most affected areas were similar to the ones in the Ctx–STN state, in other words, the sensorimotor, frontal, and temporal regions. Their coherence with the STN was also significantly reduced, ON compared to OFF medication (STN–sensorimotor, p<0.01 delta/theta, beta; p<0.05 alpha; STN–temporal, p<0.01 delta/theta, alpha, beta; and STN–frontal, p<0.01 delta/theta, alpha and beta; Figure 4B). In summary, STN–STN connectivity was not significantly altered OFF to ON medication. At the same time, coherence decreased from OFF to ON medication at both the cortico–cortical and the cortico–STN level. Therefore, only significant STN–STN connectivity existed both OFF and ON medication, while cortico–STN or cortico–cortical connectivity changes remained at the mean noise level. States with a generic coherence decrease have longer lifetimes Using the temporal properties of the identified networks, we investigated whether states showing a shift towards physiological connectivity patterns lasted longer ON medication. A state that is physiological should exhibit increased lifetime and/or should occur more often ON medication. An example of the state time courses is shown in Figure 5. Figure 5 Download asset Open asset Example of a probability time course for the six hidden Markov model (HMM) states OFF medication. Note that within the main text of the paper, we are only discussing the first three states. The connectivity maps of states 4–6 are provided in Figure 2—figure supplement 1. Source data are provided as Figure 5—source data 1–2. Figure 5—source data 1 Probability time course first half in relation to Figure 5. https://cdn.elifesciences.org/articles/66057/elife-66057-fig5-data1-v1.mat Download elife-66057-fig5-data1-v1.mat Figure 5—source data 2 Probability time course second half in relation to Figure 5. Download Figure shows the temporal properties for the three states for both the OFF and ON medication on the temporal properties of the HMM states revealed an effect of HMM states on the interval of and lifetime was no effect of medication (L-DOPA) on and lifetime had a significant effect on the interval of Finally, we found an between the HMM states and medication on the interval of and lifetime But there was no between HMM states and medication on Figure 6 Download asset Open asset properties of states. Panel A shows the for the three states for the cortico–cortical cortico–STN and the STN–STN (STN–STN). Each represents the mean for a state and the represents ON medication data and OFF medication Panel B shows the mean interval of of the three states ON and OFF medication. Panel C shows the lifetime for the three states. Figure are used for in are not The y-axis of each the same as the main Source data are provided as Figure data Figure data 1 Source data of Figure OFF medication. Download Figure data 2 Source data of Figure ON medication. Download Figure data 3 Source data of Figure OFF medication. Download Figure data 4 Source data of Figure ON medication. Download Figure data 5 Source data of Figure OFF medication. Download Figure data 6 Source data of Figure ON medication. Download We on the results. OFF medication, the STN–STN state was the one with the lifetime Ctx, STN–STN The Ctx–STN state OFF medication had the lifetime all three states Ctx–STN and the interval between of Ctx–STN Ctx–STN The interval between was for the Ctx–Ctx state OFF medication Ctx–Ctx The for the STN–STN and Ctx–STN states was but significantly higher than for the Ctx–Ctx state STN–STN Ctx–STN ON medication, the comparison between temporal properties of all three states the same levels as OFF medication, for the lifetime of the Ctx–STN state, which was no longer significantly different from that of the Ctx–Ctx state Within each medication condition, the states their temporal characteristics to each medication conditions, significant changes were present in the temporal properties of the states. The lifetimes for both the STN–STN and Ctx–STN state were significantly increased by medication but the lifetime for the Ctx–Ctx state was not significantly by medication. The Ctx–Ctx state was even often ON medication ON >OFF The interval between remained unchanged for the STN–STN and Ctx–STN states. The for all three states was not significantly changed from OFF to ON medication. In summary, the cortico–cortical state was often compared to the other two states both OFF and ON medication. The cortico–STN and STN–STN states showing physiologically relevant spectral connectivity lasted significantly longer ON medication. Discussion In this we simultaneously recorded into time-resolved states to reveal distinct spectral communication patterns. We identified three states distinct coherence patterns ON and OFF a cortico–cortical, a cortico–STN, and a STN–STN state. Our results a of neural activity to in connectivity patterns in which coherence under the effect of dopaminergic medication and which selective cortico–STN connectivity and STN–STN Only within the Ctx–Ctx state did coherence increase under dopaminergic medication. These results are in line with the multiple effects of dopaminergic medication reported in and task-based PD studies et al., 2009; West et al., 2016; et al., The differential effect of dopamine allowed us to delineate pathological and