Subthalamic nucleus phase-amplitude coupling correlates with motor impairment in Parkinson's disease.

paper Cited Public

Authors: van Wijk, Bernadette C M; Beudel, Martijn; Jha, Ashwani; Oswal, Ashwini; Foltynie, Tom; Hariz, Marwan I; Limousin, Patricia; Zrinzo, Ludvic; Aziz, Tipu Z; Green, Alexander L; Brown, Peter; Litvak, Vladimir
Year: 2016
Journal: Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
PMID: 26971483
DOI: 10.1016/j.clinph.2016.01.015
PMCID: PMC4803022

Fig. 1

Grand-average PAC and power spectral densities separated by OFF and ON dopaminergic states. (A) Higher PAC values were observed in the OFF state, which were centred around frequencies in the lower beta and HFO range. (B) Percentage of cases with significant PAC (p < .05) for all beta and HFO frequency combinations in the spectrum. (C) PAC averaged across all HFO frequencies (left) or across all beta frequencies (right) was significantly higher for lower beta band frequencies in the OFF (blue) compared to ON (purple) medication state, and showed a shift in peak frequency within the HFO range. Yellow patches indicate for which 1 Hz frequency bins significant differences were detected between OFF and ON as determined with independent samples t-tests (p < .05). (D) The grand-average power spectral densities expressed similar modulations as PAC. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

Histogram of peak frequencies within the spectral beta band. All spectral peaks and sub-peaks within the beta band were included for all subjects, conditions, and STN channels. This revealed a clear bimodal pattern. Overlaid in red is the optimal fit of a mixture of two normal distributions. The first distribution had its mean at 16.6 Hz and a standard deviation of 1.99 Hz, the second distribution at 24.79 Hz and a standard deviation of 2.81 Hz. The lowest point between distributions (20.36 Hz) was taken as the cut-off frequency between low- and high-beta ranges. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

Distribution of peak frequencies and phase angles within the low- and high-beta range. (A) Histograms show the distance between peak frequencies for PAC and corresponding power spectral densities within individual channels. Bins represent 0.5 Hz intervals. The observed number of cases for each bin was compared against 1000 surrogate histograms obtained by randomly reshuffling the PAC peak frequencies within the low- or high-beta ranges. Spectral peak frequencies were left at their observed values. Bins exceeding the .05 or .95 percentiles are indicated with an asterisk. For the low-beta range (left), a significantly large proportion of samples were found to have PAC and spectral peaks within 0.5 Hz proximity. For visualisation, a histogram is plotted for the pair-wise distance between a large number of uniformly distributed frequencies within the low-beta range (middle). The high-beta range did not show an exceedingly large proportion of samples with peaks in close proximity (right). (B) Distribution of phase angles computed for the beta–HFO frequency combinations for which PAC was maximal. Only one channel per STN site was taken into account, resulting in 101 samples considered. A significant non-uniform distribution was found only for the low-beta range: mean angle = 8.8° (p = .022).

Fig. 4

Regression analyses between hemibody bradykinesia/rigidity UPDRS scores and mean beta/HFO power and PAC separately. Samples taken from the OFF medication state are depicted in blue, samples from the ON state in purple. Note that OFF and ON states were combined in the computation of the regression coefficients. Correlation coefficients and p-values are indicated for each plot with significant relations in bold. (A) Low-beta range; (B) High-beta range; (C) HFO. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 5

Correlations between UDPRS scores and PAC/power across different frequency ranges. Pair-wise correlations were performed between hemibody bradykinesia/rigidity UPDRS scores and either PAC or spectral power using a moving frequency window with 4 Hz width and centred on frequencies from 7 to 33 Hz with 1 Hz steps. Correlation coefficients r were highest and p-values lowest for centre-frequencies in the lower-beta range. Correlations ceased to be significant towards the theta/alpha range, as well as the higher beta band.

Fig. 6

Distinct beta frequency ranges for STN activity and STN-motor cortex interactions. (A) Grand-average PAC between HFO in the STN and beta band activity in the motor cortex. Spectra are averaged across OFF and ON medication states and left/right STN channels in 11 subjects, comprising a total of 128 recordings. Unlike beta–HFO PAC in the STN, PAC between STN and motor cortex (STN-M) was found to be weaker and large clusters of significant beta/HFO frequency combinations were rarely detected. (B) Differential effects in PAC and coherence within the low- and high-beta range. Beta–HFO PAC within STN was mainly strong for low beta band frequencies, whereas beta band coherence between STN and motor cortex centred around high beta frequencies. This might explain why no distinct PAC was found between the phase of beta band cortical activity and HFO amplitude in the STN. The strongest modulations in spectral power with dopaminergic treatment were also found in the lower beta frequency range within the STN.

#	Section	Preview
0	Introduction	Bradykinesia in Parkinson’s disease is typically associated with high-amplitude beta band (13–30…
1	Introduction	The beta band is not the only frequency band associated with Parkinson’s disease. Modulations…
2	Introduction	Recently, de Hemptinne et al. (2013) revealed a pathological coupling between the instantaneous…
3	Introduction	Local field potential (LFP) recordings obtained from DBS electrodes in the STN often contain yet…
4	Introduction	To investigate the relation between beta/HFO PAC and severity of motor impairment in more detail, we…
5	Material and methods — Patients and surgery	All patients underwent STN electrode implantation for DBS treatment of Parkinson’s disease at the…
6	Material and methods — Patients and surgery	The surgical procedure involved the bilateral implantation of deep brain stimulation electrodes each…
7	Material and methods — Data recordings and preprocessing	Simultaneous recordings were obtained from the deep brain stimulation electrodes and 275-channel MEG…
8	Material and methods — Data recordings and preprocessing	and to focus their attention on a fixation cross without performing any explicit task. Separate…
9	Material and methods — Data recordings and preprocessing	We first focus our account on the analyses conducted for the LFP recordings obtained from the DBS…
10	Material and methods — Measures of spectral power and phase–amplitude coupling	We computed power spectral densities up to 400 Hz to see if we could replicate earlier findings of…
11	Material and methods — Measures of spectral power and phase–amplitude coupling	For the computation of PAC we followed the parametric approach described in Penny et al. (2008) and…
12	Material and methods — Measures of spectral power and phase–amplitude coupling	channel: (1) with time series concatenated across all epochs to assess the overall PAC; (2) for each…
13	Material and methods — Measures of spectral power and phase–amplitude coupling	We were interested in the association between the severity of motor impairment and activity within…
14	Material and methods — Measures of spectral power and phase–amplitude coupling	Due to the small spatial volume of the STN, we expect some variability in the precise location of…
15	Material and methods — Measures of spectral power and phase–amplitude coupling	Finally, we obtained the mean phase angle for the phase and amplitude frequency combination at which…
16	Material and methods — Correlation with clinical scores	Our measures of spectral power and PAC hence included: mean low-beta power, mean high-beta power,…
17	Material and methods — Correlation with clinical scores	Pairwise correlations were computed between all variables. In addition, given the large covariation…
18	Material and methods — Coupling between subthalamic nucleus and motor cortex	In a subset of 13 patients we have previously reported beta band coherence between STN and…
19	Material and methods — Coupling between subthalamic nucleus and motor cortex	the subject’s preoperative MRI image that was normalised to a canonical template MRI scan. Raw…

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Subthalamic dynamic neural states correlate with motor symptoms in Parkinson's Disease.	Nie Y et al.	—	2021	→
Towards adaptive deep brain stimulation: clinical and technical notes on a novel commercial device for chronic brain sensing.	Thenaisie Y et al.	—	2021	→
Altered Pallidocortical Low-Beta Oscillations During Self-Initiated Movements in Parkinson Disease.	Choi JW et al.	—	2020	→
Debugging Adaptive Deep Brain Stimulation for Parkinson's Disease.	Little S et al.	—	2020	→
Distinct subthalamic coupling in the ON state describes motor performance in Parkinson's disease.	Ozturk M et al.	—	2020	→
Eight cylindrical contact lead recordings in the subthalamic region localize beta oscillations source to the dorsal STN.	Tamir I et al.	—	2020	→
Evoked potentials reveal neural circuits engaged by human deep brain stimulation.	Schmidt SL et al.	—	2020	→
High density microelectrode recording predicts span of therapeutic tissue activation volumes in subthalamic deep brain stimulation for Parkinson disease.	Lu CW et al.	—	2020	→
Identification of nonlinear features in cortical and subcortical signals of Parkinson's Disease patients via a novel efficient measure.	Özkurt TE et al.	—	2020	→
Improved detection of Parkinsonian resting tremor with feature engineering and Kalman filtering.	Yao L et al.	—	2020	→
Mapping of subthalamic nucleus using microelectrode recordings during deep brain stimulation.	Koirala N et al.	—	2020	→
Neuroimaging and electrophysiology meet invasive neurostimulation for causal interrogations and modulations of brain states.	Gonzalez-Escamilla G et al.	—	2020	→
Rapid motor fluctuations reveal short-timescale neurophysiological biomarkers of Parkinson's disease.	Ahn M et al.	—	2020	→
Subthalamic nucleus activity dynamics and limb movement prediction in Parkinson's disease.	Khawaldeh S et al.	—	2020	→
Subthalamic Single Cell and Oscillatory Neural Dynamics of a Dyskinetic Medicated Patient With Parkinson's Disease.	Ozturk M et al.	—	2020	→
Tensorpac: An open-source Python toolbox for tensor-based phase-amplitude coupling measurement in electrophysiological brain signals.	Combrisson E et al.	—	2020	→
Waveform changes with the evolution of beta bursts in the human subthalamic nucleus.	Yeh CH et al.	—	2020	→
Anodal tDCS modulates cortical activity and synchronization in Parkinson's disease depending on motor processing.	Schoellmann A et al.	—	2019	→
An update on best practice of deep brain stimulation in Parkinson's disease.	Hartmann CJ et al.	—	2019	→
A systematic review of MEG-based studies in Parkinson's disease: The motor system and beyond.	Boon LI et al.	—	2019	→
Bifurcation structure determines different phase-amplitude coupling patterns in the activity of biologically plausible neural networks.	Velarde OM et al.	—	2019	→
Changing views of the pathophysiology of Parkinsonism.	Wichmann T	—	2019	→
Characterization of pallidocortical motor network in Parkinson's disease through complex network analysis.	Xiao R et al.	—	2019	→
Computer Aided Diagnosis System for multiple sclerosis disease based on phase to amplitude coupling in covert visual attention.	Ahmadi A et al.	—	2019	→
Deep brain stimulation for Parkinson's disease modulates high-frequency evoked and spontaneous neural activity.	Sinclair NC et al.	—	2019	→
Deep Brain Stimulation Programming 2.0: Future Perspectives for Target Identification and Adaptive Closed Loop Stimulation.	Hell F et al.	—	2019	→
Dopamine substitution alters effective connectivity of cortical prefrontal, premotor, and motor regions during complex bimanual finger movements in Parkinson's disease.	Nettersheim FS et al.	—	2019	→
Effect of levodopa on electroencephalographic biomarkers of the parkinsonian state.	Miller AM et al.	—	2019	→
Electrophysiological differences between upper and lower limb movements in the human subthalamic nucleus.	Tinkhauser G et al.	—	2019	→
Empirical analysis of phase-amplitude coupling approaches.	Caiola M et al.	—	2019	→
Neural Circuit and Clinical Insights from Intraoperative Recordings During Deep Brain Stimulation Surgery.	Tekriwal A et al.	—	2019	→
Resting Tremor Detection in Parkinson's Disease with Machine Learning and Kalman Filtering.	Yao L et al.	—	2019	→
Synchronised spiking activity underlies phase amplitude coupling in the subthalamic nucleus of Parkinson's disease patients.	Meidahl AC et al.	—	2019	→
An update on adaptive deep brain stimulation in Parkinson's disease.	Habets JGV et al.	—	2018	→
Beta burst coupling across the motor circuit in Parkinson's disease.	Tinkhauser G et al.	—	2018	→
Characteristics of Globus Pallidus Internus Local Field Potentials in Hyperkinetic Disease.	Zhu G et al.	—	2018	→
Characterizing the effects of deep brain stimulation with magnetoencephalography: A review.	Harmsen IE et al.	—	2018	→
Dorsal and ventral cortices are coupled by cross-frequency interactions during working memory.	Popov T et al.	—	2018	→
Generic dynamic causal modelling: An illustrative application to Parkinson's disease.	van Wijk BCM et al.	—	2018	→
Identifying Phase-Amplitude Coupling in Cyclic Alternating Pattern using Masking Signals.	Yeh CH et al.	—	2018	→
Improving the Standard for Deep Brain Stimulation Therapy: Target Structures and Feedback Signals for Adaptive Stimulation. Current Perspectives and Future Directions.	Hell F et al.	—	2018	→
Incongruent visual feedback during a postural task enhances cortical alpha and beta modulation in patients with Parkinson's disease.	van den Heuvel MRC et al.	—	2018	→
Local field potentials of subthalamic nucleus contain electrophysiological footprints of motor subtypes of Parkinson's disease.	Telkes I et al.	—	2018	→
Movement-Modulation of Local Power and Phase Amplitude Coupling in Bilateral Globus Pallidus Interna in Parkinson Disease.	AuYong N et al.	—	2018	→
Occurrence of thalamic high frequency oscillations in patients with different tremor syndromes.	Schnitzler S et al.	—	2018	→
Oophorectomy Reduces Estradiol Levels and Long-Term Spontaneous Neurovascular Recovery in a Female Rat Model of Focal Ischemic Stroke.	Bazzigaluppi P et al.	—	2018	→
Pallidal deep brain stimulation modulates excessive cortical high β phase amplitude coupling in Parkinson disease.	Malekmohammadi M et al.	—	2018	→
Pallidal stimulation in Parkinson disease differentially modulates local and network β activity.	Malekmohammadi M et al.	—	2018	→
Phase-amplitude coupling and epileptogenesis in an animal model of mesial temporal lobe epilepsy.	Samiee S et al.	—	2018	→
Realtime phase-amplitude coupling analysis of micro electrode recorded brain signals.	Lu DC et al.	—	2018	→
Unilateral deep brain stimulation suppresses alpha and beta oscillations in sensorimotor cortices.	Abbasi O et al.	—	2018	→
Adaptive Deep Brain Stimulation for Movement Disorders: The Long Road to Clinical Therapy.	Meidahl AC et al.	—	2017	→
Brain Oscillations and the Importance of Waveform Shape.	Cole SR et al.	—	2017	→
Communication between Brain Areas Based on Nested Oscillations.	Bonnefond M et al.	—	2017	→
Comparison of oscillatory activity in subthalamic nucleus in Parkinson's disease and dystonia.	Geng X et al.	—	2017	→
Evaluation of the safety and immunomodulatory effects of sargramostim in a randomized, double-blind phase 1 clinical Parkinson's disease trial.	Gendelman HE et al.	—	2017	→
Innovations in deep brain stimulation methodology.	Kühn AA et al.	—	2017	→
Is Broadband Gamma Activity Pathologically Synchronized to the Beta Rhythm in Parkinson's Disease?	van Wijk BCM	—	2017	→
Localization of beta and high-frequency oscillations within the subthalamic nucleus region.	van Wijk BCM et al.	—	2017	→
Low-beta cortico-pallidal coherence decreases during movement and correlates with overall reaction time.	van Wijk BCM et al.	—	2017	→
Movement-related beta oscillations show high intra-individual reliability.	Espenhahn S et al.	—	2017	→
Pallidal low β-low γ phase-amplitude coupling inversely correlates with Parkinson disease symptoms.	Tsiokos C et al.	—	2017	→
Physiological Markers of Motor Inhibition during Human Behavior.	Duque J et al.	—	2017	→
Pulsatile desynchronizing delayed feedback for closed-loop deep brain stimulation.	Popovych OV et al.	—	2017	→
Subthalamic oscillations and phase amplitude coupling are greater in the more affected hemisphere in Parkinson's disease.	Shreve LA et al.	—	2017	→
Therapeutic Applications of BCI Technologies.	McFarland DJ et al.	—	2017	→
Toward an electrophysiological "sweet spot" for deep brain stimulation in the subthalamic nucleus.	Horn A et al.	—	2017	→
What Have We Learned About Movement Disorders from Functional Neurosurgery?	Lozano AM et al.	—	2017	→
Evaluation of Phase-Amplitude Coupling in Resting State Magnetoencephalographic Signals: Effect of Surrogates and Evaluation Approach.	Gohel B et al.	—	2016	→
GPi Oscillatory Activity Differentiates Tics from the Resting State, Voluntary Movements, and the Unmedicated Parkinsonian State.	Jimenez-Shahed J et al.	—	2016	→