Electroencephalographic lateralization, clinical correlates and pharmacological response in untreated Parkinson's disease

Testing machine learning algorithms to evaluate fluctuating and cognitive profiles in Parkinson's disease by motion sensors and EEG data

Objective

We aimed to test machine learning algorithms for classifying fluctuating and cognitive profiles in Parkinson's Disease (PD) by using multimodal instrumental data.

Methods

Data of motion transducers while performing instrumented Timed-Up-and-Go test (iTUG) (N = 30 subjects) and EEG (N = 49 subjects) from PD patients were collected. Study patients were classified based on cognitive profile ("mild cognitive impairment" by standardized criteria vs "normal cognition") and L-dopa acute motor response ("fluctuating" vs "stable") to be analyzed by machine learning algorithms and compared with historical control data from healthy subjects group-matched by age for both iTUG and EEG study (for iTUG: N = 31 subjects; for EEG: N = 27 subjects).

Results

Artificial Neural Network-based models revealed the best performances when applied to specific phases of the iTUG in differentiating PD vs controls (91 % accuracy) as well as in differentiating cognitive profile (95 % accuracy) and motor response status (96 % accuracy) among PD subjects. K-Nearest Neighbors revealed best performances when applied to EEG data in discriminating PD vs controls (85 % accuracy). Random Forest Classifier revealed best performances when applied to EEG data in differentiating cognitive profile (96 % accuracy) and motor response status (91 % accuracy) among PD subjects.

Conclusions

By processing multimodal instrumental data, specific machine learning algorithms have been identified which discriminated L-dopa responsiveness and cognitive profile in PD. Further studies are needed to validate them in independent samples using a user-friendly software interface created ad hoc.

Differentiating neurodegenerative diseases based on EEG complexity

Neurodegenerative diseases are common causes of impaired mobility and cognition in the elderly. Among them, tauopathies and α-synucleinopathies were considered. The neurodegenerative processes and relative differential diagnosis were addressed through a qEEG non-linear analytic method. Study aims were to test accuracy of the power law exponent β applied to EEG in differentiating neurodegenerative diseases and to explore differences in neuronal connectivity among different neurodegenerative processes based on β. N = 230 patients with a diagnosis of tauopathy or α-synucleinopathy and at least one artifact-free EEG recording were selected. Periodogram was applied to EEG signal epochs from continuous recordings. Power law exponent β was determined by the slope of the signal power spectrum versus frequency in logarithmic scale. A data-driven clustering based on β values was performed to identify independent subgroups. Data-driven clustering based on β differentiated tauopathies (overall lower β values) from α-synucleinopathies (higher β values) with high sensitivity and specificity. Tauopathies also presented lower values in the correlation coefficients matrix among frontal sites of recording. In conclusion, significant differences in β values were found between tauopathies and α-synucleinopathies. Hence, β is proposed as a possible biomarker of differential diagnosis and neuronal connectivity.

Quantitative EEG in Parkinson's disease: when REM sleep behavior disorder onset really matters

Parkinson's Disease (PD) body-first subtype is characterized by prodromal autonomic symptoms and REM sleep behavior disorder (RBD), symmetric dopaminergic degeneration, and increased risk of dementia. On the other hand, the PD brain-first subtype has fewer non-motor symptoms and a milder motor phenotype. The temporal relationship between RBD onset and motor symptoms onset may differentiate these two subtypes. We aimed to investigate electrocortical differences between brain-first and body-first PD patients. PD patients with an available routinely collected EEG were retrospectively selected. RBD was diagnosed using the RBD screening questionnaire (≥ 6). According to the onset of RBD patients were classified into PD-RBDpre (RBD onset before motor symptoms) and PD-RBDpost (RBD onset after motor symptoms). Patients without RBD were classified as PD-RBD-. Presence of Mild Cognitive Impairment (MCI) was diagnosed according to the MDS criteria. EEG Spectral analysis was performed in resting state by computing the Power Spectral Density (PSD) of site-specific signal epochs for the common frequency bands (delta, theta, alpha, beta). Thirty-eight PD-RBD-, 14 PD-RBDpre and 31 PD-RBDpost patients were recruited. Comparing both global and site-specific absolute values, we found a significant trend toward beta band reduction going from PD-RBD-, PD-RBDpost and PD-RBDpre. No significant differences were found between PD-RBDpost and PD-RBD- patients. PD-RBDpre patients may represent a different subset of patients as compared to patients without RBD, while patients with later onset have intermediate EEG spectral features. Quantitative EEG may provide new hints in PD subtyping.

Cortical modulations before lower limb motor blocks are associated with freezing of gait in Parkinson's disease: an EEG source localization study

BACKGROUND

Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease (PD) characterized by paroxysmal episodes in which patients are unable to step forward. A research priority is identifying cortical changes before freezing in PD-FOG.

METHODS

We tested 19 patients with PD who had been assessed for FOG (n=14 with FOG and 5 without FOG). While seated, patients stepped bilaterally on pedals to progress forward through a virtual hallway while 64-channel EEG was recorded. We assessed cortical activities before and during lower limb motor blocks (LLMB), defined as a break in rhythmic pedaling, and stops, defined as movement cessation following an auditory stop cue. This task was selected because LLMB correlates with FOG severity in PD and allows recording of high-quality EEG. Patients were tested after overnight withdrawal from dopaminergic medications ("off" state) and in the "on" medications state. EEG source activities were evaluated using individual MRI and standardized low resolution brain electromagnetic tomography (sLORETA). Functional connectivity was evaluated by phase lag index between seeds and pre-defined cortical regions of interest.

RESULTS

EEG source activities for LLMB vs. cued stops localized to right posterior parietal area (Brodmann area 39), lateral premotor area (Brodmann area 6), and inferior frontal gyrus (Brodmann area 47). In these areas, PD-FOG (n=14) increased alpha rhythms (8-12 Hz) before LLMB vs. typical stepping, whereas PD without FOG (n=5) decreased alpha power. Alpha rhythms were linearly correlated with LLMB severity, and the relationship became an inverted U-shape when assessing alpha rhythms as a function of percent time in LLMB in the "off" medication state. Right inferior frontal gyrus and supplementary motor area connectivity was observed before LLMB in the beta band (13-30 Hz). This same pattern of connectivity was seen before stops. Dopaminergic medication improved FOG and led to less alpha synchronization and increased functional connections between frontal and parietal areas.

CONCLUSIONS

Right inferior parietofrontal structures are implicated in PD-FOG. The predominant changes were in the alpha rhythm, which increased before LLMB and with LLMB severity. Similar connectivity was observed for LLMB and stops between the right inferior frontal gyrus and supplementary motor area, suggesting that FOG may be a form of "unintended stopping." These findings may inform approaches to neurorehabilitation of PD-FOG.

Asymmetries of the subthalamic activity in Parkinson's disease: phase-amplitude coupling among local field potentials

The role of brain asymmetries of dopaminergic neurons in motor symptoms of Parkinson's disease is still undefined. Local field recordings from the subthalamic nucleus revealed some neurophysiological biomarkers of the disease: increased beta activity, increased low-frequency activity and high-frequency oscillations. Phase-amplitude coupling coordinates the timing of neuronal activity and allows determining the mechanism for communication within distinct regions of the brain. In this study, we discuss the use of phase-amplitude coupling to assess the differences between the two hemispheres in a cohort of 24 patients with Parkinson's disease before and after levodopa administration. Subthalamic low- (12-20 Hz) and high-beta (20-30 Hz) oscillations were compared with low- (30-45 Hz), medium- (70-100 Hz) and high-frequency (260-360 Hz) bands. We found a significant beta-phase-amplitude coupling asymmetry between left and right and an opposite-side-dependent effect of the pharmacological treatment, which is associated with the reduction of motor symptoms. In particular, high coupling between high frequencies and high-beta oscillations was found during the OFF condition (P < 0.01) and a low coupling during the ON state (P < 0.0001) when the right subthalamus was assessed; exactly the opposite happened when the left subthalamus was considered in the analysis, showing a lower coupling between high frequencies and high-beta oscillations during the OFF condition (P < 0.01), followed by a higher one during the ON state (P < 0.01). Interestingly, these asymmetries are independent of the motor onset side, either left or right. These findings have important implications for neural signals that may be used to trigger adaptive deep brain stimulation in Parkinson's and could provide more exhaustive insights into subthalamic dynamics.

Abnormally low sensorimotor α band nonlinearity serves as an effective EEG biomarker of Parkinson's disease

Biomarkers obtained from the neurophysiological signals of patients with Parkinson's disease (PD) have objective value in assessing their motor condition for effective diagnosis, monitoring, and clinical intervention. Prominent cortical biomarkers of PD have typically been derived from various β band wave features. This study approached the topic from an alternative perspective and attempted to estimate a recently suggested measure representing α band nonlinear autocorrelative memory from a publicly available EEG dataset that involves 15 patients with earlier-stage PD (dopaminergic medication OFF and ON states) and 16 age-matched healthy controls. The cortical nonlinearity was elevated for the PD ON state compared with the OFF state for bilateral sensorimotor channels C3 and C4 (n = 26; P = 0.003). A similar statistical difference was also identified between PD OFF state and healthy subjects (n = 26; P = 0.049). Analysis over all channels revealed that the α band nonlinearity induced upon medication was constrained to sensorimotor regions. The α nonlinearity measure was compared with a well-accepted cortical biomarker of β-γ phase-amplitude coupling (PAC). They were in moderate negative correlation (r = -0.412; P = 0.036) for only healthy subjects, but not for the patients. The nonlinearity measure was found to be insusceptible to the nonstationary variations within the particular data. Our study provides further evidence that the α band nonlinearity measure can serve as a promising cortical biomarker of PD. The suggested measure can be estimated from a noninvasive low-resolution single scalp EEG channel of patients with relatively early-stage PD, who did not yet need to undergo deep brain stimulation operation.NEW & NOTEWORTHY This study suggests a nonlinearity measure that differentiates Parkinson's disease (PD) dopamine OFF-state scalp EEG data from those of dopamine ON-state patients and healthy subjects. Unlike typical PD cortical biomarkers based on β band activity, this metric shows elevation upon dopaminergic medication in the α band. We provide evidence supporting its potential as an early-stage promising PD biomarker that can be estimated from noninvasive EEG recordings with low resolution and SNR.

Characterizing neurocognitive impairments in Parkinson's disease with mobile EEG when walking and stepping over obstacles

The neural correlates that help us understand the challenges that Parkinson's patients face when negotiating their environment remain under-researched. This deficit in knowledge reflects the methodological constraints of traditional neuroimaging techniques, which include the need to remain still. As a result, much of our understanding of motor disorders is still based on animal models. Daily life challenges such as tripping and falling over obstacles represent one of the main causes of hospitalization for individuals with Parkinson's disease. Here, we report the neural correlates of naturalistic ambulatory obstacle avoidance in Parkinson's disease patients using mobile EEG. We examined 14 medicated patients with Parkinson's disease and 17 neurotypical control participants. Brain activity was recorded while participants walked freely, and while they walked and adjusted their gait to step over expected obstacles (preset adjustment) or unexpected obstacles (online adjustment) displayed on the floor. EEG analysis revealed attenuated cortical activity in Parkinson's patients compared to neurotypical participants in theta (4-7 Hz) and beta (13-35 Hz) frequency bands. The theta power increase when planning an online adjustment to step over unexpected obstacles was reduced in Parkinson's patients compared to neurotypical participants, indicating impaired proactive cognitive control of walking that updates the online action plan when unexpected changes occur in the environment. Impaired action planning processes were further evident in Parkinson's disease patients' diminished beta power suppression when preparing motor adaptation to step over obstacles, regardless of the expectation manipulation, compared to when walking freely. In addition, deficits in reactive control mechanisms in Parkinson's disease compared to neurotypical participants were evident from an attenuated beta rebound signal after crossing an obstacle. Reduced modulation in the theta frequency band in the resetting phase across conditions also suggests a deficit in the evaluation of action outcomes in Parkinson's disease. Taken together, the neural markers of cognitive control of walking observed in Parkinson's disease reveal a pervasive deficit of motor-cognitive control, involving impairments in the proactive and reactive strategies used to avoid obstacles while walking. As such, this study identified neural markers of the motor deficits in Parkinson's disease and revealed patients' difficulties in adapting movements both before and after avoiding obstacles in their path.

(A)symmetry during gait initiation in people with Parkinson's disease: A motor and cortical activity exploratory study

Background

Gait asymmetry and deficits in gait initiation (GI) are among the most disabling symptoms in people with Parkinson's disease (PwPD). Understanding if PwPD with reduced asymmetry during GI have higher asymmetry in cortical activity may provide support for an adaptive mechanism to improve GI, particularly in the presence of an obstacle.

Objective

This study quantified the asymmetry of anticipatory postural adjustments (APAs), stepping parameters and cortical activity during GI, and tested if the presence of an obstacle regulates asymmetry in PwPD.

Methods

Sixteen PwPD and 16 control group (CG) performed 20-trials in two conditions: unobstructed and obstructed GI with right and left limbs. We measured, through symmetry index, (i) motor parameters: APAs and stepping, and (ii) cortical activity: the PSD of the frontal, sensorimotor and occipital areas during APA, STEP-I (moment of heel-off of the leading foot in the GI until the heel contact of the same foot); and STEP-II (moment of the heel-off of the trailing foot in the GI until the heel contact of the same foot) phases.

Results

Parkinson's disease showed higher asymmetry in cortical activity during APA, STEP-I and STEP-II phases and step velocity (STEP-II phase) during unobstructed GI than CG. However, unexpectedly, PwPD reduced the level of asymmetry of anterior-posterior displacement (p < 0.01) and medial-lateral velocity (p < 0.05) of the APAs. Also, when an obstacle was in place, PwPD showed higher APAs asymmetry (medial-lateral velocity: p < 0.002), with reduced and increased asymmetry of the cortical activity during APA and STEP-I phases, respectively.

Conclusion

Parkinson's disease were not motor asymmetric during GI, indicating that higher cortical activity asymmetry can be interpreted as an adaptive behavior to reduce motor asymmetry. In addition, the presence of obstacle did not regulate motor asymmetry during GI in PwPD.

Peri-lead edema and local field potential correlation in post-surgery subthalamic nucleus deep brain stimulation patients

Implanting deep brain stimulation (DBS) electrodes in patients with Parkinson’s disease often results in the appearance of a non-infectious, delayed-onset edema that disappears over time. However, the time window between the DBS electrode and DBS stimulating device implant is often used to record local field potentials (LFPs) which are used both to better understand basal ganglia pathophysiology and to improve DBS therapy. In this work, we investigated whether the presence of post-surgery edema correlates with the quality of LFP recordings in eight patients with advanced Parkinson’s disease implanted with subthalamic DBS electrodes. The magnetic resonance scans of the brain after 8.5 ± 1.5 days from the implantation surgery were segmented and the peri-electrode edema volume was calculated for both brain hemispheres. We found a correlation (ρ = −0.81, p < 0.0218, Spearman’s correlation coefficient) between left side local field potentials of the low beta band (11–20 Hz) and the edema volume of the same side. No other significant differences between the hemispheres were found. Despite the limited sample size, our results suggest that the effect on LFPs may be related to the edema localization, thus indicating a mechanism involving brain networks instead of a simple change in the electrode-tissue interface.

Effective Connectivity During Rest and Music Listening: An EEG Study on Parkinson's Disease

Music-based interventions seem to enhance motor, sensory and cognitive functions in Parkinson’s disease (PD), but the underlying action mechanisms are still largely unknown. This electroencephalography (EEG) study aimed to investigate the effective connectivity patterns characterizing PD in the resting state and during music listening. EEG recordings were obtained from fourteen non-demented PD patients and 12 healthy controls, at rest and while listening to three music tracks. Theta- and alpha-band power spectral density and multivariate partial directed coherence were computed. Power and connectivity measures were compared between patients and controls in the four conditions and in music vs. rest. Compared to controls, patients showed enhanced theta-band power and slightly enhanced alpha-band power, but markedly reduced theta- and alpha-band interactions among EEG channels, especially concerning the information received by the right central channel. EEG power differences were partially reduced by music listening, which induced power increases in controls but not in patients. Connectivity differences were slightly compensated by music, whose effects largely depended on the track. In PD, music enhanced the frontotemporal inter-hemispheric communication. Our findings suggest that PD is characterized by enhanced activity but reduced information flow within the EEG network, being only partially normalized by music. Nevertheless, music capability to facilitate inter-hemispheric communication might underlie its beneficial effects on PD pathophysiology and should be further investigated.

Electrocortical networks in Parkinson's disease patients with Mild Cognitive Impairment. The PaCoS study

INTRODUCTION

Parkinson's Disease (PD) is frequently associated with cognitive dysfunction ranging from Mild Cognitive Impairment (PD-MCI) to dementia. Few electrophysiological studies are available evaluating potential pathogenetic mechanisms linked to cognitive impairment in PD since its initial phases. The objective of the study is to analyze electrocortical networks related with cognitive decline in PD-MCI for identifying possible early electrophysiological markers of cognitive impairment in PD.

METHODS

From the PaCoS (Parkinson's disease Cognitive impairment Study) cohort, a sample of 102 subjects including 46 PD-MCI and 56 PD with normal cognition (PD-NC) was selected based on the presence of a neuropsychological assessment and at least one EEG recording. EEG signal epochs were analysed using Independent Component Analysis LORETA and spectral analysis by computing the Power Spectral Density (PSD) of site-specific signal epochs.

RESULTS

LORETA analysis revealed significant differences in PD-MCI patients compared to PD-NC, with a decreased network involving alpha activity over the occipital lobe, an increased network involving beta activity over the frontal lobe associated with a reduction over the parietal lobe, an increased network involving theta and delta activity over the frontal lobe and a reduction of networks involving theta and delta activity in the parietal lobe. Quantitative EEG analysis showed a significant decrease of alpha PSD over the occipital regions and an increase of delta PSD over the left temporal region in PD-MCI as compared to PD-NC.

CONCLUSION

Electrocortical abnormalities detected in PD-MCI patients may represent the instrumental counterpart of early cognitive decline in PD.

Personalized transcranial alternating current stimulation (tACS) and physical therapy to treat motor and cognitive symptoms in Parkinson's disease: A randomized cross-over trial

Abnormal cortical oscillations are markers of Parkinson's Disease (PD). Transcranial alternating current stimulation (tACS) can modulate brain oscillations and possibly impact on behaviour. Mapping of cortical activity (prevalent oscillatory frequency and topographic scalp distribution) may provide a personalized neurotherapeutic target and guide non-invasive brain stimulation. This is a cross-over, double blinded, randomized trial. Electroencephalogram (EEG) from participants with PD referred to Specialist Clinic, University Hospital, were recorded. TACS frequency and electrode position were individually defined based on statistical comparison of EEG power spectra maps with normative data from our laboratory. Stimulation frequency was set according to the EEG band displaying higher power spectra (with beta excess on EEG map, tACS was set at 4 Hz; with theta excess, tACS was set at 30 Hz). Participants were randomized to tACS or random noise stimulation (RNS), 5 days/week for 2-weeks followed by ad hoc physical therapy. EEG, motor (Unified Parkinson's Disease Rating Scale-motor: UPDRS III), neuropsychological (frontal, executive and memory tests) performance and mood were measured before (T0), after (T1) and 4-weeks after treatment (T2). A linear model with random effects and Wilcoxon test were used to detect differences. Main results include a reduction of beta rhythm in theta-tACS vs. RNS group at T1 over right sensorimotor area (p = .014) and left parietal area (p = .010) and at T2 over right sensorimotor area (p = .004) and left frontal area (p = .039). Bradykinesia items improved at T1 (p = .002) and T2 (p = .047) compared to T0 in the tACS group. In the tACS group the Montréal Cognitive Assessment (MoCA) improved at T2 compared with T1 (p = .049). Individualized tACS in PD improves motor and cognitive performance. These changes are associated with a reduction of excessive fast EEG oscillations.

A systematic review of MEG-based studies in Parkinson's disease: The motor system and beyond

Parkinson's disease (PD) is accompanied by functional changes throughout the brain, including changes in the electromagnetic activity recorded with magnetoencephalography (MEG). An integrated overview of these changes, its relationship with clinical symptoms, and the influence of treatment is currently missing. Therefore, we systematically reviewed the MEG studies that have examined oscillatory activity and functional connectivity in the PD‐affected brain. The available articles could be separated into motor network‐focused and whole‐brain focused studies. Motor network studies revealed PD‐related changes in beta band (13–30 Hz) neurophysiological activity within and between several of its components, although it remains elusive to what extent these changes underlie clinical motor symptoms. In whole‐brain studies PD‐related oscillatory slowing and decrease in functional connectivity correlated with cognitive decline and less strongly with other markers of disease progression. Both approaches offer a different perspective on PD‐specific disease mechanisms and could therefore complement each other. Combining the merits of both approaches will improve the setup and interpretation of future studies, which is essential for a better understanding of the disease process itself and the pathophysiological mechanisms underlying specific PD symptoms, as well as for the potential to use MEG in clinical care.

Complexity of electrocortical activity as potential biomarker in untreated Parkinson's disease

In Parkinson's disease (PD), the identification of instrumental biomarkers is crucial to evaluate disease susceptibility and motor stage. We evaluated self-similarity of electrocortical activity as expression of brain signal complexity in untreated PD, to investigate its possible role as a neurophysiological biomarker. We analyzed the data of 34 untreated PD subjects and 18 group-matched controls who underwent standardized electroencephalography. A Welch's periodogram was applied to site-specific electroencephalographic signal epochs. To investigate self-similarity of electrocortical activity, the power law exponent β was computed for each selected coordinate. In both PD subjects and controls, β values at each coordinate increased with an antero-posterior gradient, changing from values around one in fronto-temporal sites to values around two among parieto-occipital sites. PD subjects presented overall lower β values among different sites compared to controls, with significant differences for the left fronto-temporal sites. Our findings suggest an increased level of fronto-temporal neuronal organization in untreated PD. We hypothesize a possible role of β as a neurophysiological biomarker for early untreated PD.

Clinical correlates of quantitative EEG in Parkinson disease: A systematic review

OBJECTIVE

To assess the relevance of quantitative EEG (qEEG) measures as outcomes of disease severity and progression in Parkinson disease (PD).

METHODS

Main databases were systematically searched (January 2018) for studies of sufficient methodologic quality that examined correlations between clinical symptoms of idiopathic PD and cortical (surface) qEEG metrics.

RESULTS

Thirty-six out of 605 identified studied were included. Results were classified into 4 domains: cognition (23 studies), motor function (13 studies), responsiveness to interventions (7 studies), and other (10 studies). In cross-sectional studies, EEG slowing correlated with global cognitive impairment and with diffuse deterioration in other domains. In longitudinal studies, decreased dominant frequency and increased θ power, reflecting EEG slowing, were biomarkers of cognitive deterioration at an individual level. Results on motor dysfunction and treatment yielded contrasting findings. Studies on functional connectivity at an individual level and longitudinal studies on other domains or on connectivity measures were lacking.

CONCLUSION

qEEG measures reflecting EEG slowing, particularly decreased dominant frequency and increased θ power, correlate with cognitive impairment and predict future cognitive deterioration. qEEG could provide reliable and widely available biomarkers for nonmotor disease severity and progression in PD, potentially promoting early diagnosis of nonmotor symptoms and an objective monitoring of progression. More studies are needed to clarify the role of functional connectivity and network analyses.

Alterations in Functional Cortical Hierarchy in Hemiparkinsonian Rats

Parkinson's disease and experimentally induced hemiparkinsonism are characterized by increased beta synchronization between cortical and subcortical areas. This change in beta connectivity might reflect either a symmetric increase in interareal influences or asymmetric changes in directed influences among brain areas. We assessed patterns of functional and directed connectivity within and between striatum and six cortical sites in each hemisphere of the hemiparkinsonian rat model. LFPs were recorded in resting and walking states, before and after unilateral 6-hydroxydopamine lesion. The hemiparkinsonian state was characterized by increased oscillatory activity in the 20-40 Hz range in resting and walking states, and increased interhemispheric coupling (phase lag index) that was more widespread at rest than during walking. Spectral Granger-causality analysis revealed that the change in symmetric functional connectivity comprised profound reorganization of hierarchical organization and directed influence patterns. First, in the lesioned hemisphere, the more anterior, nonprimary motor areas located at the top of the cortical hierarchy (i.e., receiving many directed influences) tended to increase their directed influence onto the posterior primary motor and somatosensory areas. This enhanced influence of "higher" areas may be related to the loss of motor control due to the 6-OHDA lesion. Second, the drive from the nonlesioned toward the lesioned hemisphere (in particular to striatum) increased, most prominently during walking. The nature of these adaptations (disturbed signaling or compensation) is discussed. The present study demonstrates that hemiparkinsonism is associated with a profound reorganization of the hierarchical organization of directed influence patterns among brain areas, perhaps reflecting compensatory processes.SIGNIFICANCE STATEMENT Parkinson's disease classically first becomes manifest in one hemibody before affecting both sides, suggesting that degeneration is asymmetrical. Our results suggest that asymmetrical degeneration of the dopaminergic system induces an increased drive from the nonlesioned toward the lesioned hemisphere and a profound reorganization of functional cortical hierarchical organization, leading to a stronger directed influence of hierarchically higher placed cortical areas over primary motor and somatosensory cortices. These changes may represent a compensatory mechanism for loss of motor control as a consequence of dopamine depletion.

Startle Habituation and Midfrontal Theta Activity in Parkinson Disease

The ability to adapt to aversive stimuli is critical for mental health. Here, we investigate the relationship between habituation to startling stimuli and startle-related activity in medial frontal cortex as measured by EEG in both healthy control participants and patients with Parkinson disease (PD). We report three findings. First, patients with PD exhibited normal initial startle responses but reduced startle habituation relative to demographically matched controls. Second, control participants had midfrontal EEG theta activity in response to startling stimuli, and this activity was attenuated in patients with PD. Finally, startle-related midfrontal theta activity was correlated with the rate of startle habituation. These data indicate that impaired startle habituation in PD is a result of attenuated midfrontal cognitive control signals. Our findings could provide insight into the frontal regulation of startle habituation.