Evoked mid-frontal activity predicts cognitive dysfunction in Parkinson's disease.

BACKGROUND: Cognitive dysfunction is a major feature of Parkinson's disease (PD), but the pathophysiology remains unknown. One potential mechanism is abnormal low-frequency cortical rhythms which engage cognitive functions and are deficient in PD. We tested the hypothesis that mid-frontal delta/theta rhythms predict cognitive dysfunction in PD. METHOD: We recruited 100 patients with PD and 49 demographically similar control participants who completed a series of cognitive control tasks, including the Simon, oddball and interval-timing tasks. We focused on cue-evoked delta (1-4 Hz) and theta (4-7 Hz) rhythms from a single mid-frontal EEG electrode (cranial vertex (Cz)) in patients with PD who were either cognitively normal, with mild-cognitive impairments (Parkinson's disease with mild-cognitive impairment) or had dementia (Parkinson's disease dementia). RESULTS: We found that PD-related cognitive dysfunction was associated with increased response latencies and decreased mid-frontal delta power across all tasks. Within patients with PD, the first principal component of evoked electroencephalography features from a single electrode (Cz) strongly correlated with clinical metrics such as the Montreal Cognitive Assessment score (r=0.34) and with National Institutes of Health Toolbox Executive Function score (r=0.46). CONCLUSIONS: These data demonstrate that cue-evoked mid-frontal delta/theta rhythms directly relate to cognition in PD. Our results provide insight into the nature of low-frequency frontal rhythms and suggest that PD-related cognitive dysfunction results from decreased delta/theta activity. These findings could facilitate the development of new biomarkers and targeted therapies for cognitive symptoms of PD.

Novelty-induced frontal-STN networks in Parkinson's disease.

Novelty detection is a primitive subcomponent of cognitive control that can be deficient in Parkinson's disease (PD) patients. Here, we studied the corticostriatal mechanisms underlying novelty-response deficits. In participants with PD, we recorded from cortical circuits with scalp-based electroencephalography (EEG) and from subcortical circuits using intraoperative neurophysiology during surgeries for implantation of deep brain stimulation (DBS) electrodes. We report three major results. First, novel auditory stimuli triggered midfrontal low-frequency rhythms; of these, 1-4 Hz "delta" rhythms were linked to novelty-associated slowing, whereas 4-7 Hz "theta" rhythms were specifically attenuated in PD. Second, 32% of subthalamic nucleus (STN) neurons were response-modulated; nearly all (94%) of these were also modulated by novel stimuli. Third, response-modulated STN neurons were coherent with midfrontal 1-4 Hz activity. These findings link scalp-based measurements of neural activity with neuronal activity in the STN. Our results provide insight into midfrontal cognitive control mechanisms and how purported hyperdirect frontobasal ganglia circuits evaluate new information.

Resting-state EEG measures cognitive impairment in Parkinson's disease.

Cognitive dysfunction is common in Parkinson's disease (PD). We developed and evaluated an EEG-based biomarker to index cognitive functions in PD from a few minutes of resting-state EEG. We hypothesized that synchronous changes in EEG across the power spectrum can measure cognition. We optimized a data-driven algorithm to efficiently capture these changes and index cognitive function in 100 PD and 49 control participants. We compared our EEG-based cognitive index with the Montreal cognitive assessment (MoCA) and cognitive tests across different domains from National Institutes of Health (NIH) Toolbox using cross-validations, regression models, and randomization tests. Finally, we externally validated our approach on 32 PD participants. We observed cognition-related changes in EEG over multiple spectral rhythms. Utilizing only 8 best-performing electrodes, our proposed index strongly correlated with cognition (MoCA: rho = 0.68, p value < 0.001; NIH-Toolbox cognitive tests: rho ≥ 0.56, p value < 0.001) outperforming traditional spectral markers (rho = -0.30-0.37). The index showed a strong fit in regression models (R2 = 0.46) with MoCA, yielded 80% accuracy in detecting cognitive impairment, and was effective in both PD and control participants. Notably, our approach was equally effective (rho = 0.68, p value < 0.001; MoCA) in out-of-sample testing. In summary, we introduced a computationally efficient data-driven approach for cross-domain cognition indexing using fewer than 10 EEG electrodes, potentially compatible with dynamic therapies like closed-loop neurostimulation. These results will inform next-generation neurophysiological biomarkers for monitoring cognition in PD and other neurological diseases.

TMS Bursts Can Modulate Local and Networks Oscillations During Lower-Limb Movement.

PURPOSE: Lower-limb motor functions involve processing information via both motor and cognitive control networks. Measuring oscillations is a key element in communication within and between cortical networks during high-order motor functions. Increased midfrontal theta oscillations are related to improved lower-limb motor performances in patients with movement disorders. Noninvasive neuromodulation approaches have not been explored extensively to understand the oscillatory mechanism of lower-limb motor functions. This study aims to examine the effects of repetitive transcranial magnetic stimulation on local and network EEG oscillations in healthy elderly subjects. METHODS: Eleven healthy elderly subjects (67-73 years) were recruited via advertisements, and they underwent both active and sham stimulation procedures in a random, counterbalanced design. Transcranial magnetic stimulation bursts (θ-transcranial magnetic stimulation; 4 pulses/second) were applied over the midfrontal lead (vertex) before a GO-Cue pedaling task, and signals were analyzed using time-frequency methods. RESULTS: Transcranial magnetic stimulation bursts increase the theta activity in the local ( p = 0.02) and the associated network during the lower-limb pedaling task ( p = 0.02). Furthermore, after task-related transcranial magnetic stimulation burst sessions, increased resting-state alpha activity was observed in the midfrontal region ( p = 0.01). CONCLUSIONS: This study suggests the ability of midfrontal transcranial magnetic stimulation bursts to directly modulate local and network oscillations in a frequency manner during lower-limb motor task. Transcranial magnetic stimulation burst-induced modulation may provide insights into the functional roles of oscillatory activity during lower-limb movement in normal and disease conditions.

Cerebellar oscillatory dysfunction during lower-limb movement in Parkinson's disease with freezing of gait.

Studies have demonstrated dysfunctional connectivity between the cortico-basal ganglia and cerebellar networks in Parkinson's disease (PD). These networks are critical for appropriate motor and cognitive functions, specifically to control gait and postural tasks in PD. Our recent reports have shown abnormal cerebellar oscillations during rest, motor, and cognitive tasks in people with PD compared to healthy individuals, however, the role of cerebellar oscillations in people with PD and freezing of gait (PDFOG+) during lower-limb movements has not been examined. Here, we evaluated cerebellar oscillations using electroencephalography (EEG) electrodes during cue-triggered lower-limb pedaling movement in 13 PDFOG+, 13 PDFOG-, and 13 age-matched healthy subjects. We focused analyses on the mid-cerebellar Cbz as well as lateral cerebellar Cb1 and Cb2 electrodes. PDFOG+ performed the pedaling movement with reduced linear speed and higher variation compared to healthy subjects. PDFOG+ exhibited attenuated theta power during pedaling motor tasks in the mid-cerebellar location compared to PDFOG- or healthy subjects. Cbz theta power was also associated with FOG severity. No significant differences between groups were seen in Cbz beta power. In the lateral cerebellar electrodes, lower theta power was seen between PDFOG+ and healthy subjects. Our cerebellar EEG data demonstrate the occurrence of reduced theta oscillations in PDFOG+ during lower-limb movement and suggest a potential cerebellar biosignature for neurostimulation therapy to improve gait dysfunctions.

Functional Connectivity in Patients With Parkinson's Disease and Freezing of Gait Using Resting-State EEG and Graph Theory.

BACKGROUND: Although many studies have shown abnormalities in brain structure and function in people with Parkinson's disease (PD), we still have a poor understanding of how brain structure and function relates to freezing of gait (FOG). Graph theory analysis of electroencephalography (EEG) can explore the relationship between brain network structure and gait function in PD. METHODS: Scalp EEG signals of 83 PD (42 PDFOG+ and 41 PDFOG-) and 42 healthy controls were recorded in an eyes-opened resting-state. The phase lag index was calculated for each electrode pair in different frequency bands, but we focused our analysis on the theta-band and performed global analyses along with nodal analyses over a midfrontal channel. The resulting connectivity matrices were converted to weighted graphs, whose structure was characterized using strength and clustering coefficient measurements, our main outcomes. RESULTS: We observed increased global strength and increased global clustering coefficient in people with PD compared to healthy controls in the theta-band, though no differences were observed in midfrontal nodal strength and midfrontal clustering coefficient. Furthermore, no differences in global nor midfrontal nodal strength nor global clustering coefficients were observed between PDFOG+ and PDFOG- in the theta-band. However, PDFOG+ exhibited a significantly diminished midfrontal nodal clustering coefficient in the theta-band compared to PDFOG-. Furthermore, FOG scores were negatively correlated with midfrontal nodal clustering coefficient in the theta-band. CONCLUSION: The present findings support the involvement of midfrontal theta oscillations in FOG symptoms in PD and the sensitivity of graph metrics to characterize functional networks in PDFOG+.

A pilot study of machine learning of resting-state EEG and depression in Parkinson's disease.

Introduction: Depression is a non-motor symptom of Parkinson's disease (PD). PD-related depression is difficult to diagnose, and the neurophysiological basis is poorly understood. Depression can markedly affect cortical function, which suggests that scalp electroencephalography (EEG) may be able to distinguish depression in PD. We conducted a pilot study of depression and resting-state EEG in PD. Methods: We recruited 18 PD patients without depression, 18 PD patients with depression, and 12 demographically similar non-PD patients with clinical depression. All patients were on their usual medications. We collected resting-state EEG in all patients and compared cortical brain signal features between patients with and without depression. We used a machine learning algorithm that harnesses the entire power spectrum (linear predictive coding of EEG Algorithm for PD: LEAPD) to distinguish between groups. Results: We found differences between PD patients with and without depression in the alpha band (8-13 Hz) globally and in the beta (13-30 Hz) and gamma (30-50 Hz) bands in the central electrodes. From two minutes of resting-state EEG, we found that LEAPD-based machine learning could robustly distinguish between PD patients with and without depression with 97 % accuracy and between PD patients with depression and non-PD patients with depression with 100 % accuracy. We verified the robustness of our finding by confirming that the classification accuracy gracefully declines as data are randomly truncated. Conclusions: Our results suggest that resting-state EEG power spectral analysis has the potential to distinguish depression in PD accurately. We demonstrated the efficacy of the LEAPD algorithm in identifying PD patients with depression from PD patients without depression and controls with depression. Our data provide insight into cortical mechanisms of depression and could lead to novel neurophysiological markers for non-motor symptoms of PD.

Relationships between Freezing of Gait Severity and Cognitive Deficits in Parkinson's Disease.

Freezing of gait (FOG) is one of the most debilitating motor symptoms experienced by patients with Parkinson's disease (PD), as it can lead to falls and a reduced quality of life. Evidence supports an association between FOG severity and cognitive functioning; however, results remain debatable. PD patients with (PDFOG+, n = 41) and without FOG (PDFOG-, n = 39) and control healthy subjects (n = 41) participated in this study. The NIH toolbox cognition battery, the Montreal Cognitive Assessment (MoCA), and the interval timing task were used to test cognitive domains. Measurements were compared between groups using multivariable models and adjusting for covariates. Correlation analyses, linear regression, and mediation models were applied to examine relationships among disease duration and severity, FOG severity, and cognitive functioning. Significant differences were observed between controls and PD patients for all cognitive domains. PDFOG+ and PDFOG- exhibited differences in Dimensional Change Card Sort (DCCS) test, interval timing task, and MoCA scores. After adjusting for covariates in two different models, PDFOG+ and PDFOG- differed in both MoCA and DCCS scores. In addition, significant relationships between FOG severity and cognitive function (MoCA, DCCS, and interval timing) were also found. Regression models suggest that FOG severity may be a predictor of cognitive impairment, and mediation models show the effects of cognitive impairment on the relationship between disease severity and FOG severity. Overall, this study provides insight into the relationship between cognitive and FOG severity in patients with PD, which could aid in the development of therapeutic interventions to manage both.

Timing variability and midfrontal ~4 Hz rhythms correlate with cognition in Parkinson's disease.

Patients with Parkinson's disease (PD) can have significant cognitive dysfunction; however, the mechanisms for these cognitive symptoms are unknown. Here, we used scalp electroencephalography (EEG) to investigate the cortical basis for PD-related cognitive impairments during interval timing, which requires participants to estimate temporal intervals of several seconds. Time estimation is an ideal task demand for investigating cognition in PD because it is simple, requires medial frontal cortical areas, and recruits basic executive processes such as working memory and attention. However, interval timing has never been systematically studied in PD patients with cognitive impairments. We report three main findings. First, 71 PD patients had increased temporal variability compared to 37 demographically matched controls, and this variability correlated with cognitive dysfunction as measured by the Montreal Cognitive Assessment (MOCA). Second, PD patients had attenuated ~4 Hz EEG oscillatory activity at midfrontal electrodes in response to the interval-onset cue, which was also predictive of MOCA. Finally, trial-by-trial linear mixed-effects modeling demonstrated that cue-triggered ~4 Hz power predicted subsequent temporal estimates as a function of PD and MOCA. Our data suggest that impaired cue-evoked midfrontal ~4 Hz activity predicts increased timing variability that is indicative of cognitive dysfunction in PD. These findings link PD-related cognitive dysfunction with cortical mechanisms of cognitive control, which could advance novel biomarkers and neuromodulation for PD.

Frontal theta and beta oscillations during lower-limb movement in Parkinson's disease.

OBJECTIVES: Patients with Parkinson's disease (PD) have deficits in lower-limb functions such as gait, which involves both cognitive and motor dysfunction. In PD, theta and beta brain rhythms are associated with cognitive and motor functions, respectively. We tested the hypothesis that PD patients with lower-limb abnormalities would exhibit abnormal theta and beta rhythms in the mid-frontal cortical region during lower-limb action. METHODS: This study included thirty-nine participants; 13 PD patients with FOG (PDFOG+), 13 without FOG (PDFOG-), and 13 demographically-matched controls. We recorded scalp electroencephalograms (EEG) during a lower-limb pedaling motor task, which required intentional initiation and stopping of a motor movement. RESULTS: FOG scores were correlated with disease severity and cognition. PDFOG+ patients pedaled with reduced speed and decreased acceleration compared to PDFOG- patients and controls. PDFOG+ patients exhibited attenuated theta-band (4-8 Hz) power and increased beta-band (13-30 Hz) power at mid-frontal electrode Cz during pedaling. Frontal theta- and beta-band oscillations also correlated with motor and cognitive deficits. CONCLUSION: Frontal theta and beta oscillations are predictors of lower-limb motor symptoms in PD and could be used to design neuromodulation for PD-related lower-limb abnormalities. SIGNIFICANCE: These data provide insight into mechanisms of lower-limb dysfunction in PD with FOG.