Functional Connectivity-Based Modelling Simulates Subject-Specific Network Spreading Effects of Focal Brain Stimulation

Deep brain stimulation in Parkinson's disease: A scientometric and bibliometric analysis, trends, and research hotspots

Parkinson disease (PD), a prevalent neurodegenerative ailment in the elderly, relies mainly on pharmacotherapy, yet deep brain stimulation (DBS) emerges as a vital remedy for refractory cases. This study performs a bibliometric analysis on DBS in PD, delving into research trends and study impact to offer comprehensive insights for researchers, clinicians, and policymakers, illuminating the current state and evolutionary trajectory of research in this domain. A systematic search on March 13, 2023, in the Scopus database utilized keywords like "Parkinson disease," "PD," "Parkinsonism," "Deep brain stimulation," and "DBS." The top 1000 highly cited publications on DBS in PD underwent scientometric analysis via VOS Viewer and R Studio's Bibliometrix package, covering publication characteristics, co-authorship, keyword co-occurrence, thematic clustering, and trend topics. The bibliometric analysis spanned 1984 to 2021, involving 1000 cited articles from 202 sources. The average number of citations per document were 140.9, with 31,854 references. "Movement Disorders" led in publications (n = 98), followed by "Brain" (n = 78) and "Neurology" (n = 65). The University of Oxford featured prominently. Thematic keyword clustering identified 9 core research areas, such as neuropsychological function and motor circuit electrophysiology. The shift from historical neurosurgical procedures to contemporary focuses like "beta oscillations" and "neuroethics" was evident. The bibliometric analysis emphasizes UK and US dominance, outlining 9 key research areas pivotal for reshaping Parkinson treatment. A discernible shift from invasive neurosurgery to DBS is observed. The call for personalized DBS, integration with NIBS, and exploration of innovative avenues marks the trajectory for future research.

Dissecting Psychiatric Heterogeneity and Comorbidity with Core Region-Based Machine Learning

Machine learning approaches are increasingly being applied to neuroimaging data from patients with psychiatric disorders to extract brain-based features for diagnosis and prognosis. The goal of this review is to discuss recent practices for evaluating machine learning applications to obsessive-compulsive and related disorders and to advance a novel strategy of building machine learning models based on a set of core brain regions for better performance, interpretability, and generalizability. Specifically, we argue that a core set of co-altered brain regions (namely 'core regions') comprising areas central to the underlying psychopathology enables the efficient construction of a predictive model to identify distinct symptom dimensions/clusters in individual patients. Hypothesis-driven and data-driven approaches are further introduced showing how core regions are identified from the entire brain. We demonstrate a broadly applicable roadmap for leveraging this core set-based strategy to accelerate the pursuit of neuroimaging-based markers for diagnosis and prognosis in a variety of psychiatric disorders.

Altered neural associations with cognitive and emotional functions in cannabis dependence

Negative emotional state has been found to correlate with poor cognitive performance in cannabis-dependent (CD) individuals, but not healthy controls (HCs). To examine the neural substrates underlying such unusual emotion-cognition coupling, we analyzed the behavioral and resting state fMRI data from the Human Connectome Project and found opposite brain-behavior associations in the CD and HC groups: (i) although the cognitive performance was positively correlated with the within-network functional connectivity strength and segregation (i.e. clustering coefficient and local efficiency) of the cognitive network in HCs, these correlations were inversed in CDs; (ii) although the cognitive performance was positively correlated with the within-network Granger effective connectivity strength and integration (i.e. characteristic path length) of the cognitive network in CDs, such associations were not significant in HCs. In addition, we also found that the effective connectivity strength within cognition network mediated the behavioral coupling between emotional state and cognitive performance. These results indicate a disorganization of the cognition network in CDs, and may help improve our understanding of substance use disorder.

Association of clinical outcomes and connectivity in awake versus asleep deep brain stimulation for Parkinson disease

OBJECTIVE

Deep brain stimulation (DBS) for Parkinson disease (PD) is traditionally performed with awake intraoperative testing and/or microelectrode recording. Recently, however, the procedure has been increasingly performed under general anesthesia with image-based verification. The authors sought to compare structural and functional networks engaged by awake and asleep PD-DBS of the subthalamic nucleus (STN) and correlate them with clinical outcomes.

METHODS

Levodopa equivalent daily dose (LEDD), pre- and postoperative motor scores on the Movement Disorders Society-Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III), and total electrical energy delivered (TEED) at 6 months were retroactively assessed in patients with PD who received implants of bilateral DBS leads. In subset analysis, implanted electrodes were reconstructed using the Lead-DBS toolbox. Volumes of tissue activated (VTAs) were used as seed points in group volumetric and connectivity analysis.

RESULTS

The clinical courses of 122 patients (52 asleep, 70 awake) were reviewed. Operating room and procedure times were significantly shorter in asleep cases. LEDD reduction, MDS-UPDRS III score improvement, and TEED at the 6-month follow-up did not differ between groups. In subset analysis (n = 40), proximity of active contact, VTA overlap, and desired network fiber counts with motor STN correlated with lower DBS energy requirement and improved motor scores. Discriminative structural fiber tracts involving supplementary motor area, thalamus, and brainstem were associated with optimal clinical improvement. Areas of highest structural and functional connectivity with VTAs did not significantly differ between the two groups.

CONCLUSIONS

Compared to awake STN DBS, asleep procedures can achieve similarly optimal targeting-based on clinical outcomes, electrode placement, and connectivity estimates-in more efficient procedures and shorter operating room times.

Differences in electric field strength between clinical and non-clinical populations induced by prefrontal tDCS: A cross-diagnostic, individual MRI-based modeling study

•

MDD and SCZ showed lower prefrontal tDCS-induced e-field strengths compared to HC.

•

Average e-field strengths did not significantly differ between MDD and SCZ patients.

•

Inter-individual variability of e-field intensities and distribution was prominent.

•

Inter-rater variability emphasizes the importance of standardized positioning.

Introduction

Prefrontal cortex (PFC) regions are promising targets for therapeutic applications of non-invasive brain stimulation, e.g. transcranial direct current stimulation (tDCS), which has been proposed as a novel intervention for major depressive disorder (MDD) and negative symptoms of schizophrenia (SCZ). However, the effects of tDCS vary inter-individually, and dose–response relationships have not been established. Stimulation parameters are often tested in healthy subjects and transferred to clinical populations. The current study investigates the variability of individual MRI-based electric fields (e-fields) of standard bifrontal tDCS across individual subjects and diagnoses.

Method

The study included 74 subjects, i.e. 25 patients with MDD, 24 patients with SCZ, and 25 healthy controls (HC). Individual e-fields of a common tDCS protocol (i.e. 2 mA stimulation intensity, bifrontal anode-F3/cathode-F4 montage) were modeled by two investigators using SimNIBS (2.0.1) based on structural MRI scans.

Result

On a whole-brain level, the average e-field strength was significantly reduced in MDD and SCZ compared to HC, but MDD and SCZ did not differ significantly. Regions of interest (ROI) analysis for PFC subregions showed reduced e-fields in Sallet areas 8B and 9 for MDD and SCZ compared to HC, whereas there was again no difference between MDD and SCZ. Within groups, we generally observed high inter-individual variability of e-field intensities at a higher percentile of voxels.

Conclusion

MRI-based e-field modeling revealed significant differences in e-field strengths between clinical and non-clinical populations in addition to a general inter-individual variability. These findings support the notion that dose–response relationships for tDCS cannot be simply transferred from healthy to clinical cohorts and need to be individually established for clinical groups. In this respect, MRI-based e-field modeling may serve as a proxy for individualized dosing.

Common and differential connectivity profiles of deep brain stimulation and capsulotomy in refractory obsessive-compulsive disorder

Neurosurgical interventions including deep brain stimulation (DBS) and capsulotomy have been demonstrated effective for refractory obsessive-compulsive disorder (OCD), although treatment-shared/-specific network mechanisms remain largely unclear. We retrospectively analyzed resting-state fMRI data from three cohorts: a cross-sectional dataset of 186 subjects (104 OCD and 82 healthy controls), and two longitudinal datasets of refractory patients receiving ventral capsule/ventral striatum DBS (14 OCD) and anterior capsulotomy (27 OCD). We developed a machine learning model predictive of OCD symptoms (indexed by the Yale-Brown Obsessive Compulsive Scale, Y-BOCS) based on functional connectivity profiles and used graphic measures of network communication to characterize treatment-induced profile changes. We applied a linear model on 2 levels treatments (DBS or capsulotomy) and outcome to identify whether pre-surgical network communication was associated with differential treatment outcomes. We identified 54 functional connectivities within fronto-subcortical networks significantly predictive of Y-BOCS score in patients across 3 independent cohorts, and observed a coexisting pattern of downregulated cortico-subcortical and upregulated cortico-cortical network communication commonly shared by DBS and capsulotomy. Furthermore, increased cortico-cortical communication at ventrolateral and centrolateral prefrontal cortices induced by DBS and capsulotomy contributed to improvement of mood and anxiety symptoms, respectively (p < 0.05). Importantly, pretreatment communication of ventrolateral and centrolateral prefrontal cortices were differentially predictive of mood and anxiety improvements by DBS and capsulotomy (effect sizes = 0.45 and 0.41, respectively). These findings unravel treatment-shared and treatment-specific network characteristics induced by DBS and capsulotomy, which may facilitate the search of potential evidence-based markers for optimally selecting among treatment options for a patient.

Internal States Influence the Representation and Modulation of Food Intake by Subthalamic Neurons

Deep brain stimulation of the subthalamic nucleus (STN) is an effective therapy for motor deficits in Parkinson's disease (PD), but commonly causes weight gain in late-phase PD patients probably by increasing feeding motivation. It is unclear how STN neurons represent and modulate feeding behavior in different internal states. In the present study, we found that feeding caused a robust activation of STN neurons in mice (GCaMP6 signal increased by 48.4% ± 7.2%, n = 9, P = 0.0003), and the extent varied with the size, valence, and palatability of food, but not with the repetition of feeding. Interestingly, energy deprivation increased the spontaneous firing rate (8.5 ± 1.5 Hz, n = 17, versus 4.7 ± 0.7 Hz, n = 18, P = 0.03) and the depolarization-induced spikes in STN neurons, as well as enhanced the STN responses to feeding. Optogenetic experiments revealed that stimulation and inhibition of STN neurons respectively reduced (by 11% ± 6%, n = 6, P = 0.02) and enhanced (by 36% ± 15%, n = 7, P = 0.03) food intake only in the dark phase. In conclusion, our results support the hypothesis that STN neurons are activated by feeding behavior, depending on energy homeostatic status and the palatability of food, and modulation of these neurons is sufficient to regulate food intake.

A Comprehensive Review of Brain Connectomics and Imaging to Improve Deep Brain Stimulation Outcomes

DBS is an effective neuromodulatory therapy that has been applied in various conditions, including PD, essential tremor, dystonia, Tourette syndrome, and other movement disorders. There have also been recent examples of applications in epilepsy, chronic pain, and neuropsychiatric conditions. Innovations in neuroimaging technology have been driving connectomics, an emerging whole-brain network approach to neuroscience. Two rising techniques are functional connectivity profiling and structural connectivity profiling. Functional connectivity profiling explores the operational relationships between multiple regions of the brain with respect to time and stimuli. Structural connectivity profiling approximates physical connections between different brain regions through reconstruction of axonal fibers. Through these techniques, complex relationships can be described in various disease states, such as PD, as well as in response to therapy, such as DBS. These advances have expanded our understanding of human brain function and have provided a partial in vivo glimpse into the underlying brain circuits underpinning movement and other disorders. This comprehensive review will highlight the contemporary concepts in brain connectivity as applied to DBS, as well as introduce emerging considerations in movement disorders. © 2020 International Parkinson and Movement Disorder Society.

Computational modelling of the long-term effects of brain stimulation on the local and global structural connectivity of epileptic patients

Computational studies of the influence of different network parameters on the dynamic and topological network effects of brain stimulation can enhance our understanding of different outcomes between individuals. In this study, a brain stimulation session along with the subsequent post-stimulation brain activity is simulated for a period of one day using a network of modified Wilson-Cowan oscillators coupled according to diffusion imaging based structural connectivity. We use this computational model to examine how differences in the inter-region connectivity and the excitability of stimulated regions at the time of stimulation can affect post-stimulation behaviours. Our findings indicate that the initial inter-region connectivity can heavily affect the changes that stimulation induces in the connectivity of the network. Moreover, differences in the excitability of the stimulated regions seem to lead to different post-stimulation connectivity changes across the model network, including on the internal connectivity of non-stimulated regions.