Effect of deep brain stimulation on brain network and white matter integrity in Parkinson's disease

Presurgical structural imaging and clinical outcome in combined bed nucleus of the stria terminalis-nucleus accumbens deep brain stimulation for treatment-resistant depression

Background

Structural imaging holds great potential for precise targeting and stimulation for deep brain stimulation (DBS). The anatomical information it provides may serve as potential biomarkers for predicting the efficacy of DBS in treatment-resistant depression (TRD).

Aims

The primary aim is to identify preoperative imaging biomarkers that correlate with the efficacy of DBS in patients with TRD.

Methods

Preoperative imaging parameters were estimated and correlated with the 6-month clinical outcome of patients with TRD receiving combined bed nucleus of the stria terminalis (BNST)-nucleus accumbens (NAc) DBS. White matter (WM) properties were extracted and compared between the response/non-response and remission/non-remission groups. Structural connectome was constructed and analysed using graph theory. Distances of the volume of activated tissue (VAT) to the main modulating tracts were also estimated to evaluate the correlations.

Results

Differences in fibre bundle properties of tracts, including superior thalamic radiation and reticulospinal tract, were observed between the remission and non-remission groups. Distance of the centre of the VAT to tracts connecting the ventral tegmental area and the anterior limb of internal capsule on the left side varied between the remission and non-remission groups (p=0.010, t=3.07). The normalised clustering coefficient (γ) and the small-world property (σ) in graph analysis correlated with the symptom improvement after the correction of age.

Conclusions

Presurgical structural alterations in WM tracts connecting the frontal area with subcortical regions, as well as the distance of the VAT to the modulating tracts, may influence the clinical outcome of BNST-NAc DBS. These findings provide potential imaging biomarkers for the DBS treatment for patients with TRD.

A longitudinal electrophysiological and behavior dataset for PD rat in response to deep brain stimulation

Here we presented an electrophysiological dataset collected from layer V of the primary motor cortex (M1) and the corresponding behavior dataset from normal and hemi-parkinson rats over 5 consecutive weeks. The electrophysiological dataset was constituted by the raw wideband signal, neuronal spikes, and local field potential (LFP) signal. The open-field test was done and recorded to evaluate the behavior variation of rats among the entire experimental cycle. We conducted technical validation of this dataset through sorting the spike data to form action potential waveforms and analyzing the spectral power of LFP data, then based on these findings a closed-loop DBS protocol was developed by the oscillation activity response of M1 LFP signal. Additionally, this protocol was applied to the hemi-parkinson rat for five consecutive days while simultaneously recording the electrophysiological data. This dataset is currently the only publicly available dataset that includes longitudinal closed-loop DBS recordings, which can be utilized to investigate variations of neuronal activity within the M1 following long-term closed-loop DBS, and explore additional reliable biomarkers.

Smartphone app for lifestyle improvement improves brain health and boosts the vitality and cognitive function of healthy middle-aged adults

INTRODUCTION

The number of smartphone apps for brain training is increasing, and the number of people who are working on brain training is also increasing. However, researchers disagree about the effectiveness of brain training.

METHODS

Therefore, in this study, we conducted an intervention test with the participation of 70 healthy middle-aged men and women and measured the effect of smartphone apps on lifestyle improvement using brain healthcare quotient calculated from brain imaging data.

RESULTS

As a result, in the intervention group, significant improvements were seen in fractional anisotropy (FA) of the whole brain, corpus callosum, internal capsule, corona radiata, posterior thalamic radiation, external capsule, and superior longitudinal fasciculus. Additionally, in the intervention group, these FA increments correlated with improvements in cognitive function as measured by the trail-making test and vigor as measured by the Profile of Mood States 2nd Edition.

CONCLUSION

The results of this study suggest that improving lifestyle habits through smartphone apps can improve brain health and cognitive and emotional performance of healthy middle-aged adults. This is consistent with previous research that suggests that FA integrity in the limbic-thalamo-cortical pathway influences cognitive function and emotion regulation.

Alteration of functional connectivity network in population of objectively-defined subtle cognitive decline

The objectively-defined subtle cognitive decline individuals had higher progression rates of cognitive decline and pathological deposition than healthy elderly, indicating a higher risk of progressing to Alzheimer's disease. However, little is known about the brain functional alterations during this stage. Thus, we aimed to investigate the functional network patterns in objectively-defined subtle cognitive decline cohort. Forty-two cognitive normal, 29 objectively-defined subtle cognitive decline and 55 mild cognitive impairment subjects were included based on neuropsychological measures from the Alzheimer's disease Neuroimaging Initiative dataset. Thirty cognitive normal, 22 objectively-defined subtle cognitive declines and 48 mild cognitive impairment had longitudinal MRI data. The degree centrality and eigenvector centrality for each participant were calculated by using resting-state functional MRI. For cross-sectional data, analysis of covariance was performed to detect between-group differences in degree centrality and eigenvector centrality after controlling age, sex and education. For longitudinal data, repeated measurement analysis of covariance was used for comparing the alterations during follow-up period among three groups. In order to classify the clinical significance, we correlated degree centrality and eigenvector centrality values to Alzheimer's disease biomarkers and cognitive function. The results of analysis of covariance showed significant between-group differences in eigenvector centrality and degree centrality in left superior temporal gyrus and left precuneus, respectively. Across groups, the eigenvector centrality value of left superior temporal gyrus was positively related to recognition scores in auditory verbal learning test, whereas the degree centrality value of left precuneus was positively associated with mini-mental state examination total score. For longitudinal data, the results of repeated measurement analysis of covariance indicated objectively-defined subtle cognitive decline group had the highest declined rate of both eigenvector centrality and degree centrality values than other groups. Our study showed an increased brain functional connectivity in objectively-defined subtle cognitive decline individuals at both local and global level, which were associated with Alzheimer's disease pathology and neuropsychological assessment. Moreover, we also observed a faster declined rate of functional network matrix in objectively-defined subtle cognitive decline individuals during the follow-ups.

Deep brain stimulation of the subthalamic nucleus in Parkinson disease 2013-2023: where are we a further 10 years on?

Deep brain stimulation has been in clinical use for 30 years and during that time it has changed markedly from a small-scale treatment employed by only a few highly specialized centers into a widespread keystone approach to the management of disorders such as Parkinson's disease. In the intervening decades, many of the broad principles of deep brain stimulation have remained unchanged, that of electrode insertion into stereotactically targeted brain nuclei, however the underlying technology and understanding around the approach have progressed markedly. Some of the most significant advances have taken place over the last decade with the advent of artificial intelligence, directional electrodes, stimulation/recording implantable pulse generators and the potential for remote programming among many other innovations. New therapeutic targets are being assessed for their potential benefits and a surge in the number of deep brain stimulation implantations has given birth to a flourishing scientific literature surrounding the pathophysiology of brain disorders such as Parkinson's disease. Here we outline the developments of the last decade and look to the future of deep brain stimulation to attempt to discern some of the most promising lines of inquiry in this fast-paced and rapidly evolving field.

Synchronous monitoring of brain-heart electrophysiology using heart rate variability coupled with rapid quantitative electroencephalography in orthostatic hypotension patients with α-synucleinopathies: Rapid prediction of orthostatic hypotension and preliminary exploration of brain stimulation therapy

BACKGROUND

In α-synucleinopathies, the dysfunction of the autonomic nervous system which typically manifests as orthostatic hypotension (OH) often leads to severe consequences and poses therapeutic challenges. This study aims to discover the brain-cardiac electrophysiological changes in OH patients with α-synucleinopathies using the rapid quantitative electroencephalography (qEEG) coupled with heart rate variability (HRV) technique to identify rapid, noninvasive biomarkers for early warning and diagnosis, as well as shed new light on complementary treatment approaches such as brain stimulation targets.

METHODS

In this study, 26 subjects of α-synucleinopathies with OH (α-OH group), 21 subjects of α-synucleinopathies without OH (α-NOH group), and 34 healthy controls (control group) were included from September 2021 to August 2023 (NCT05527067). The heart rate-blood pressure variations in supine and standing positions were monitored, and synchronization parameters of seated resting-state HRV coupled with qEEG were collected. Time-domain and frequency-domain of HRV measures as well as peak frequency and power of the brainwaves were extracted. Differences between these three groups were compared, and correlations between brain-heart parameters were analyzed.

RESULTS

The research results showed that the time-domain parameters such as MxDMn, pNN50, RMSSD, and SDSD of seated resting-state HRV exhibited a significant decrease only in the α-OH group compared to the healthy control group (p < 0.05), while there was no significant difference between the α-NOH group and the healthy control group. Several time-domain and frequency-domain parameters of seated resting-state HRV were found to be correlated with the blood pressure changes within the first 5 min of transitioning from supine to standing position (p < 0.05). Differences were observed in the power of beta1 waves (F4 and Fp2) and beta2 waves (Fp2 and F4) in the seated resting-state qEEG between the α-OH and α-NOH groups (p < 0.05). The peak frequency of theta waves in the Cz region also showed a difference (p < 0.05). The power of beta2 waves in the Fp2 and F4 brain regions correlated with frequency-domain parameters of HRV (p < 0.05). Additionally, abnormal electrical activity in the alpha, theta, and beta1 waves was associated with changes in heart rate and blood pressure within the first 5 min of transitioning from supine to standing position (p < 0.05).

CONCLUSION

Rapid resting-state HRV with certain time-domain parameters below normal levels may serve as a predictive indicator for the occurrence of orthostatic hypotension (OH) in patients with α-synucleinopathies. Additionally, the deterioration of HRV parameters correlates with synchronous abnormal qEEG patterns, which can provide insights into the brain stimulation target areas for OH in α-synucleinopathy patients.

Deep brain stimulation of the subthalamic nucleus in severe Parkinson's disease: relationships between dual-contact topographic setting and 1-year worsening of speech and gait

BACKGROUND

Subthalamic nucleus (STN) deep brain stimulation (DBS) alleviates severe motor fluctuations and dyskinesia in Parkinson's disease, but may result in speech and gait disorders. Among the suspected or demonstrated causes of these adverse effects, we focused on the topography of contact balance (CB; individual, right and left relative dual positions), a scantly studied topic, analyzing the relationships between symmetric or non-symmetric settings, and the worsening of these signs.

METHOD

An observational monocentric study was conducted on a series of 92 patients after ethical approval. CB was specified by longitudinal and transversal positions and relation to the STN (CB sub-aspects) and totalized at the patient level (patient CB). CB was deemed symmetric when the two contacts were at the same locations relative to the STN. CB was deemed asymmetric when at least one sub-aspect differed in the patient CB. Baseline and 1-year characteristics were routinely collected: (i) general, namely, Unified Parkinson's Disease Rating Scores (UPDRS), II, III motor and IV, daily levodopa equivalent doses, and Parkinson's Disease Questionnaire of Quality of Life (PDQ39) scores; (ii) specific, namely scores for speech (II-5 and III-18) and axial signs (II-14, III-28, III-29, and III-30). Only significant correlations were considered (p < 0.05).

RESULTS

Baseline characteristics were comparable (symmetric versus asymmetric). CB settings were related to deteriorations of speech and axial signs: communication PDQ39 and UPDRS speech and gait scores worsened exclusively with symmetric settings; the most influential CB sub-aspect was symmetric longitudinal position.

CONCLUSION

Our findings suggest that avoiding symmetric CB settings, whether by electrode positioning or shaping of electric fields, could reduce worsening of speech and gait.

Relationship between serum uric acid levels and the outcome of STN-DBS in Parkinson's disease

BACKGROUND

Uric acid is a natural antioxidant and it has been shown that low levels of uric acid may be a risk factor for the development of Parkinson's disease. We aimed to investigate the relationship between uric acid and improvement of motor symptoms in patients with Parkinson's disease after subthalamic nucleus deep brain stimulation.

METHODS

We analyzed the correlation between serum uric acid levels in 64 patients with Parkinson's disease and the rate of improvement of motor symptoms 2 years after subthalamic nucleus deep brain stimulation.

RESULTS

A non-linear correlation was observed between uric acid levels and the rate of motor symptom improvement after subthalamic nucleus deep brain stimulation, during both the drug-off and drug-on periods.

CONCLUSIONS

Uric acid is positively associated with the rate of motor symptom improvement in subthalamic nucleus deep brain stimulation within a certain range.

Premature drug reduction after subthalamic nucleus deep brain stimulation leading to worse depression in patients with Parkinson's disease

Background

Reduction of medication in Parkinson's disease (PD) following subthalamic nucleus deep brain stimulation (STN-DBS) has been recognized, but the optimal timing for medication adjustments remains unclear, posing challenges in postoperative patient management.

Objective

This study aimed to provide evidence for the timing of medication reduction post-DBS using propensity score matching (PSM).

Methods

In this study, initial programming and observation sessions were conducted over 1 week for patients 4-6 weeks postoperatively. Patients were subsequently categorized into medication reduction or non-reduction groups based on their dyskinesia evaluation using the 4.2-item score from the MDS-UPDRS-IV. PSM was employed to maintain baseline comparability. Short-term motor and neuropsychiatric symptom assessments for both groups were conducted 3-6 months postoperatively.

Results

A total of 123 PD patients were included. Baseline balance in motor and non-motor scores was achieved between the two groups based on PSM. Short-term efficacy revealed a significant reduction in depression scores within the non-reduction group compared to baseline (P < 0.001) and a significant reduction compared to the reduction group (P = 0.037). No significant differences were observed in UPDRS-III and HAMA scores between the two groups. Within-group analysis showed improvements in motor symptoms, depression, anxiety, and subdomains in the non-reduction group, while the reduction group exhibited improvements only in motor symptoms.

Conclusion

This study provides evidence for the timing of medication reduction following DBS. Our findings suggest that early maintenance of medication stability is more favorable for improving neuropsychiatric symptoms.

Functional brain network features specify DBS outcome for patients with treatment resistant depression

Deep brain stimulation (DBS) has shown therapeutic benefits for treatment resistant depression (TRD). Stimulation of the subcallosal cingulate gyrus (SCG) aims to alter dysregulation between subcortical and cortex. However, the 50% response rates for SCG-DBS indicates that selection of appropriate patients is challenging. Since stimulation influences large-scale network function, we hypothesized that network features can be used as biomarkers to inform outcome. In this pilot project, we used resting-state EEG recorded longitudinally from 10 TRD patients with SCG-DBS (11 at baseline). EEGs were recorded before DBS-surgery, 1-3 months, and 6 months post surgery. We used graph theoretical analysis to calculate clustering coefficient, global efficiency, eigenvector centrality, energy, and entropy of source-localized EEG networks to determine their topological/dynamical features. Patients were classified as responders based on achieving a 50% or greater reduction in Hamilton Depression (HAM-D) scores from baseline to 12 months post surgery. In the delta band, false discovery rate analysis revealed that global brain network features (segregation, integration, synchronization, and complexity) were significantly lower and centrality of subgenual anterior cingulate cortex (ACC) was higher in responders than in non-responders. Accordingly, longitudinal analysis showed SCG-DBS increased global network features and decreased centrality of subgenual ACC. Similarly, a clustering method separated two groups by network features and significant correlations were identified longitudinally between network changes and depression symptoms. Despite recent speculation that certain subtypes of TRD are more likely to respond to DBS, in the SCG it seems that underlying brain network features are associated with ability to respond to DBS. SCG-DBS increased segregation, integration, and synchronizability of brain networks, suggesting that information processing became faster and more efficient, in those patients in whom it was lower at baseline. Centrality results suggest these changes may occur via altered connectivity in specific brain regions especially ACC. We highlight potential mechanisms of therapeutic effect for SCG-DBS.

A structural magnetic resonance imaging review of clinical motor outcomes from deep brain stimulation in movement disorders

Patients with movement disorders treated by deep brain stimulation do not always achieve successful therapeutic alleviation of motor symptoms, even in cases where surgery is without complications. Magnetic resonance imaging (MRI) offers methods to investigate structural brain-related factors that may be predictive of clinical motor outcomes. This review aimed to identify features which have been associated with variability in clinical post-operative motor outcomes in patients with Parkinson's disease, dystonia, and essential tremor from structural MRI modalities. We performed a literature search for articles published between 1 January 2000 and 1 April 2022 and identified 5197 articles. Following screening through our inclusion criteria, we identified 60 total studies (39 = Parkinson's disease, 11 = dystonia syndromes and 10 = essential tremor). The review captured a range of structural MRI methods and analysis techniques used to identify factors related to clinical post-operative motor outcomes from deep brain stimulation. Morphometric markers, including volume and cortical thickness were commonly identified in studies focused on patients with Parkinson's disease and dystonia syndromes. Reduced metrics in basal ganglia, sensorimotor and frontal regions showed frequent associations with reduced motor outcomes. Increased structural connectivity to subcortical nuclei, sensorimotor and frontal regions was also associated with greater motor outcomes. In patients with tremor, increased structural connectivity to the cerebellum and cortical motor regions showed high prevalence across studies for greater clinical motor outcomes. In addition, we highlight conceptual issues for studies assessing clinical response with structural MRI and discuss future approaches towards optimizing individualized therapeutic benefits. Although quantitative MRI markers are in their infancy for clinical purposes in movement disorder treatments, structural features obtained from MRI offer the powerful potential to identify candidates who are more likely to benefit from deep brain stimulation and provide insight into the complexity of disorder pathophysiology.

Prediction of STN-DBS for Parkinson's disease by uric acid-related brain function connectivity: A machine learning study based on resting state function MRI

Introduction

Parkinson's disease (PD) is a neurodegenerative disorder characterized by dyskinesia and is closely related to oxidative stress. Uric acid (UA) is a natural antioxidant found in the body. Previous studies have shown that UA has played an important role in the development and development of PD and is an important biomarker. Subthalamic nucleus deep brain stimulation (STN-DBS) is a common treatment for PD.

Methods

Based on resting state function MRI (rs-fMRI), the relationship between UA-related brain function connectivity (FC) and STN-DBS outcomes in PD patients was studied. We use UA and DC values from different brain regions to build the FC characteristics and then use the SVR model to predict the outcome of the operation.

Results

The results show that PD patients with UA-related FCs are closely related to STN-DBS efficacy and can be used to predict prognosis. A machine learning model based on UA-related FC was successfully developed for PD patients.

Discussion

The two biomarkers, UA and rs-fMRI, were combined to predict the prognosis of STN-DBS in treating PD. Neurosurgeons are provided with effective tools to screen the best candidate and predict the prognosis of the patient.

Acupuncture Regulates Symptoms of Parkinson’s Disease via Brain Neural Activity and Functional Connectivity in Mice

Parkinson’s disease (PD) is a multilayered progressive brain disease characterized by motor dysfunction and a variety of other symptoms. Although acupuncture has been used to ameliorate various symptoms of neurodegenerative disorders, including PD, the underlying mechanisms are unclear. Here, we investigated the mechanism of acupuncture by revealing the effects of acupuncture treatment on brain neural responses and its functional connectivity in an animal model of PD. We observed that destruction of neuronal network between many brain regions in PD mice were reversed by acupuncture. Using machine learning analysis, we found that the key region associated with the improvement of abnormal behaviors might be related to the neural activity of M1, suggesting that the changes of c-Fos in M1 could predict the improvement of motor function induced by acupuncture treatment. In addition, acupuncture treatment was shown to significantly normalize the brain neural activity not only in M1 but also in other brain regions related to motor behavior (striatum, substantia nigra pars compacta, and globus pallidus) and non-motor symptoms (hippocampus, lateral hypothalamus, and solitary tract) of PD. Taken together, our results demonstrate that acupuncture treatment might improve the PD symptoms by normalizing the brain functional connectivity in PD mice model and provide new insights that enhance our current understanding of acupuncture mechanisms for non-motor symptoms.

Recent advances in using diffusion tensor imaging to study white matter alterations in Parkinson's disease: A mini review

Parkinson's disease (PD) is the second most common age-related neurodegenerative disease with cardinal motor symptoms. In addition to motor symptoms, PD is a heterogeneous disease accompanied by many non-motor symptoms that dominate the clinical manifestations in different stages or subtypes of PD, such as cognitive impairments. The heterogeneity of PD suggests widespread brain structural changes, and axonal involvement appears to be critical to the pathophysiology of PD. As α-synuclein pathology has been suggested to cause axonal changes followed by neuronal degeneration, diffusion tensor imaging (DTI) as an in vivo imaging technique emerges to characterize early detectable white matter changes due to PD. Here, we reviewed the past 5-year literature to show how DTI has helped identify axonal abnormalities at different PD stages or in different PD subtypes and atypical parkinsonism. We also showed the recent clinical utilities of DTI tractography in interventional treatments such as deep brain stimulation (DBS). Mounting evidence supported by multisite DTI data suggests that DTI along with the advanced analytic methods, can delineate dynamic pathophysiological processes from the early to late PD stages and differentiate distinct structural networks affected in PD and other parkinsonism syndromes. It indicates that DTI, along with recent advanced analytic methods, can assist future interventional studies in optimizing treatments for PD patients with different clinical conditions and risk profiles.

High-resolution Virtual Brain Modeling Personalizes Deep Brain Stimulation for Treatment-Resistant Depression: Spatiotemporal Response Characteristics Following Stimulation of Neural Fiber Pathways

Over the past 15 years, deep brain stimulation (DBS) has been actively investigated as a groundbreaking therapy for patients with treatment-resistant depression (TRD); nevertheless, outcomes have varied from patient to patient, with an average response rate of ∼50%. The engagement of specific fiber tracts at the stimulation site has been hypothesized to be an important factor in determining outcomes, however, the resulting individual network effects at the whole-brain scale remain largely unknown. Here we provide a computational framework that can explore each individual's brain response characteristics elicited by selective stimulation of fiber tracts. We use a novel personalized in-silico approach, the Virtual Big Brain, which makes use of high-resolution virtual brain models at a mm-scale and explicitly reconstructs more than 100 000 fiber tracts for each individual. Each fiber tract is active and can be selectively stimulated. Simulation results demonstrate distinct stimulus-induced event-related potentials as a function of stimulation location, parametrized by the contact positions of the electrodes implanted in each patient, even though validation against empirical patient data reveals some limitations (i.e., the need for individual parameter adjustment, and differential accuracy across stimulation locations). This study provides evidence for the capacity of personalized high-resolution virtual brain models to investigate individual network effects in DBS for patients with TRD and opens up novel avenues in the personalized optimization of brain stimulation.