Predicting optimal deep brain stimulation parameters for Parkinson's disease using functional MRI and machine learning

Clinical neurophysiology in the treatment of movement disorders: IFCN handbook chapter

In this review, different aspects of the use of clinical neurophysiology techniques for the treatment of movement disorders are addressed. First of all, these techniques can be used to guide neuromodulation techniques or to perform therapeutic neuromodulation as such. Neuromodulation includes invasive techniques based on the surgical implantation of electrodes and a pulse generator, such as deep brain stimulation (DBS) or spinal cord stimulation (SCS) on the one hand, and non-invasive techniques aimed at modulating or even lesioning neural structures by transcranial application. Movement disorders are one of the main areas of indication for the various neuromodulation techniques. This review focuses on the following techniques: DBS, repetitive transcranial magnetic stimulation (rTMS), low-intensity transcranial electrical stimulation, including transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), and focused ultrasound (FUS), including high-intensity magnetic resonance-guided FUS (MRgFUS), and pulsed mode low-intensity transcranial FUS stimulation (TUS). The main clinical conditions in which neuromodulation has proven its efficacy are Parkinson's disease, dystonia, and essential tremor, mainly using DBS or MRgFUS. There is also some evidence for Tourette syndrome (DBS), Huntington's disease (DBS), cerebellar ataxia (tDCS), and axial signs (SCS) and depression (rTMS) in PD. The development of non-invasive transcranial neuromodulation techniques is limited by the short-term clinical impact of these techniques, especially rTMS, in the context of very chronic diseases. However, at-home use (tDCS) or current advances in the design of closed-loop stimulation (tACS) may open new perspectives for the application of these techniques in patients, favored by their easier use and lower rate of adverse effects compared to invasive or lesioning methods. Finally, this review summarizes the evidence for keeping the use of electromyography to optimize the identification of muscles to be treated with botulinum toxin injection, which is indicated and widely performed for the treatment of various movement disorders.

Reinforcement learning for closed-loop regulation of cardiovascular system with vagus nerve stimulation: a computational study

Objective. Vagus nerve stimulation (VNS) is being investigated as a potential therapy for cardiovascular diseases including heart failure, cardiac arrhythmia, and hypertension. The lack of a systematic approach for controlling and tuning the VNS parameters poses a significant challenge. Closed-loop VNS strategies combined with artificial intelligence (AI) approaches offer a framework for systematically learning and adapting the optimal stimulation parameters. In this study, we presented an interactive AI framework using reinforcement learning (RL) for automated data-driven design of closed-loop VNS control systems in a computational study.Approach.Multiple simulation environments with a standard application programming interface were developed to facilitate the design and evaluation of the automated data-driven closed-loop VNS control systems. These environments simulate the hemodynamic response to multi-location VNS using biophysics-based computational models of healthy and hypertensive rat cardiovascular systems in resting and exercise states. We designed and implemented the RL-based closed-loop VNS control frameworks in the context of controlling the heart rate and the mean arterial pressure for a set point tracking task. Our experimental design included two approaches; a general policy using deep RL algorithms and a sample-efficient adaptive policy using probabilistic inference for learning and control.Main results.Our simulation results demonstrated the capabilities of the closed-loop RL-based approaches to learn optimal VNS control policies and to adapt to variations in the target set points and the underlying dynamics of the cardiovascular system. Our findings highlighted the trade-off between sample-efficiency and generalizability, providing insights for proper algorithm selection. Finally, we demonstrated that transfer learning improves the sample efficiency of deep RL algorithms allowing the development of more efficient and personalized closed-loop VNS systems.Significance.We demonstrated the capability of RL-based closed-loop VNS systems. Our approach provided a systematic adaptable framework for learning control strategies without requiring prior knowledge about the underlying dynamics.

Artificial Intelligence-Guided Gut-Microenvironment-Triggered Imaging Sensor Reveals Potential Indicators of Parkinson's Disease

The gut-brain axis has recently emerged as a crucial link in the development and progression of Parkinson's disease (PD). Dysregulation of the gut microbiota has been implicated in the pathogenesis of this disease, sparking growing interest in the quest for non-invasive biomarkers derived from the gut for early PD diagnosis. Herein, an artificial intelligence-guided gut-microenvironment-triggered imaging sensor (Eu-MOF@Au-Aptmer) to achieve non-invasive, accurate screening for various stages of PD is presented. The sensor works by analyzing α-Syn in the gut using deep learning algorithms. By monitoring changes in α-Syn, the sensor can predict the onset of PD with high accuracy. This work has the potential to revolutionize the diagnosis and treatment of PD by allowing for early intervention and personalized treatment plans. Moreover, it exemplifies the promising prospects of integrating artificial intelligence (AI) and advanced sensors in the monitoring and prediction of a broad spectrum of diseases and health conditions.

Emerging Pro-neurogenic Therapeutic Strategies for Neurodegenerative Diseases: A Review of Pre-clinical and Clinical Research

The neuroscience community has largely accepted the notion that functional neurons can be generated from neural stem cells in the adult brain, especially in two brain regions: the subventricular zone of the lateral ventricles and the subgranular zone in the dentate gyrus of the hippocampus. However, impaired neurogenesis has been observed in some neurodegenerative diseases, particularly in Alzheimer's, Parkinson's, and Huntington's diseases, and also in Lewy Body dementia. Therefore, restoration of neurogenic function in neurodegenerative diseases emerges as a potential therapeutic strategy to counteract, or at least delay, disease progression. Considering this, the present study summarizes the different neuronal niches, provides a collection of the therapeutic potential of different pro-neurogenic strategies in pre-clinical and clinical research, providing details about their possible modes of action, to guide future research and clinical practice.

Progress and trends in neurological disorders research based on deep learning

In recent years, deep learning (DL) has emerged as a powerful tool in clinical imaging, offering unprecedented opportunities for the diagnosis and treatment of neurological disorders (NDs). This comprehensive review explores the multifaceted role of DL techniques in leveraging vast datasets to advance our understanding of NDs and improve clinical outcomes. Beginning with a systematic literature review, we delve into the utilization of DL, particularly focusing on multimodal neuroimaging data analysis-a domain that has witnessed rapid progress and garnered significant scientific interest. Our study categorizes and critically analyses numerous DL models, including Convolutional Neural Networks (CNNs), LSTM-CNN, GAN, and VGG, to understand their performance across different types of Neurology Diseases. Through particular analysis, we identify key benchmarks and datasets utilized in training and testing DL models, shedding light on the challenges and opportunities in clinical neuroimaging research. Moreover, we discuss the effectiveness of DL in real-world clinical scenarios, emphasizing its potential to revolutionize ND diagnosis and therapy. By synthesizing existing literature and describing future directions, this review not only provides insights into the current state of DL applications in ND analysis but also covers the way for the development of more efficient and accessible DL techniques. Finally, our findings underscore the transformative impact of DL in reshaping the landscape of clinical neuroimaging, offering hope for enhanced patient care and groundbreaking discoveries in the field of neurology. This review paper is beneficial for neuropathologists and new researchers in this field.

Identification of crucial inflammaging related risk factors in multiple sclerosis

Background

Multiple sclerosis (MS) is an immune-mediated disease characterized by inflammatory demyelinating lesions in the central nervous system. Studies have shown that the inflammation is vital to both the onset and progression of MS, where aging plays a key role in it. However, the potential mechanisms on how aging-related inflammation (inflammaging) promotes MS have not been fully understood. Therefore, there is an urgent need to integrate the underlying mechanisms between inflammaging and MS, where meaningful prediction models are needed.

Methods

First, both aging and disease models were developed using machine learning methods, respectively. Then, an integrated inflammaging model was used to identify relative risk factors, by identifying essential "aging-inflammation-disease" triples. Finally, a series of bioinformatics analyses (including network analysis, enrichment analysis, sensitivity analysis, and pan-cancer analysis) were further used to explore the potential mechanisms between inflammaging and MS.

Results

A series of risk factors were identified, such as the protein homeostasis, cellular homeostasis, neurodevelopment and energy metabolism. The inflammaging indices were further validated in different cancer types. Therefore, various risk factors were integrated, and even both the theories of inflammaging and immunosenescence were further confirmed.

Conclusion

In conclusion, our study systematically investigated the potential relationships between inflammaging and MS through a series of computational approaches, and could present a novel thought for other aging-related diseases.

Deep brain stimulation of the subthalamic nucleus for Parkinson's disease: A network imaging marker of the treatment response

Subthalamic nucleus deep brain stimulation (STN-DBS) alleviates motor symptoms of Parkinson's disease (PD), thereby improving quality of life. However, quantitative brain markers to evaluate DBS responses and select suitable patients for surgery are lacking. Here, we used metabolic brain imaging to identify a reproducible STN-DBS network for which individual expression levels increased with stimulation in proportion to motor benefit. Of note, measurements of network expression from metabolic and BOLD imaging obtained preoperatively predicted motor outcomes determined after DBS surgery. Based on these findings, we computed network expression in 175 PD patients, with time from diagnosis ranging from 0 to 21 years, and used the resulting data to predict the outcome of a potential STN-DBS procedure. While minimal benefit was predicted for patients with early disease, the proportion of potential responders increased after 4 years. Clinically meaningful improvement with stimulation was predicted in 18.9 - 27.3% of patients depending on disease duration.

Anatomo-physiological basis and applied techniques of electrical neuromodulation in chronic pain

Chronic pain, a complex and debilitating condition, poses a significant challenge to both patients and healthcare providers worldwide. Conventional pharmacological interventions often prove inadequate in delivering satisfactory relief while carrying the risks of addiction and adverse reactions. In recent years, electric neuromodulation emerged as a promising alternative in chronic pain management. This method entails the precise administration of electrical stimulation to specific nerves or regions within the central nervous system to regulate pain signals. Through mechanisms that include the alteration of neural activity and the release of endogenous pain-relieving substances, electric neuromodulation can effectively alleviate pain and improve patients' quality of life. Several modalities of electric neuromodulation, with a different grade of invasiveness, provide tailored strategies to tackle various forms and origins of chronic pain. Through an exploration of the anatomical and physiological pathways of chronic pain, encompassing neurotransmitter involvement, this narrative review offers insights into electrical therapies' mechanisms of action, clinical utility, and future perspectives in chronic pain management.

Effect of surgical modification of deep brain stimulation lead trajectories on radiofrequency heating during MRI at 3T: from phantom experiments to clinical implementation

OBJECTIVE

Radiofrequency (RF) tissue heating around deep brain stimulation (DBS) leads is a well-known safety risk during MRI, resulting in strict imaging guidelines and limited allowable protocols. The implanted lead's trajectory and orientation with respect to the MRI electric fields contribute to variations in the magnitude of RF heating across patients. Currently, there are no surgical requirements for implanting the extracranial portion of the DBS lead, resulting in substantial variations in clinical lead trajectories and consequently RF heating. Recent studies have shown that incorporating concentric loops in the extracranial lead trajectory can reduce RF heating. However, optimal positioning of the loops and the quantitative benefit of trajectory modification in terms of added safety margins during MRI remain unknown. In this study, the authors systematically evaluated the characteristics of DBS lead trajectories that minimize RF heating during 3T MRI to develop the best surgical practices for safe access to postoperative MRI, and they present the first surgical implementation of these modified trajectories.

METHODS

The authors performed experiments to assess the maximum temperature increase of 244 distinct lead trajectories. They investigated the effect of the position, number, and size of the concentric loops on the skull. Experiments were performed in an anthropomorphic phantom implanted with a commercial DBS system, and RF exposure was generated by applying a high specific absorption rate sequence (B1+rms = 2.7 µT). The authors conducted test-retest experiments to assess the reliability of measurements. Additionally, they evaluated the effect of imaging landmarks and perturbations to the DBS device configuration on the efficacy of low-heating trajectories. Finally, two neurosurgeons implanted the recommended modified trajectories in patients, and the authors characterized their RF heating in comparison with unmodified trajectories.

RESULTS

The maximum temperature increase ranged from 0.09°C to 7.34°C. The authors found that increasing the number of loops and positioning them closer to the surgical burr hole, particularly for the contralateral lead, substantially reduced RF heating. These trajectory modifications were easily incorporated during the surgical procedure and resulted in a threefold reduction in RF heating.

CONCLUSIONS

Surgically modifying the extracranial portion of the DBS lead trajectory can substantially reduce RF heating during 3T MRI. The authors' results indicate that simple adjustments to the lead's configuration, such as small, concentric loops near the burr hole, can be readily adopted during DBS lead implantation to improve patient safety during MRI.

Paradigm shift required for translational research on the brain

Biomedical research on the brain has led to many discoveries and developments, such as understanding human consciousness and the mind and overcoming brain diseases. However, historical biomedical research on the brain has unique characteristics that differ from those of conventional biomedical research. For example, there are different scientific interpretations due to the high complexity of the brain and insufficient intercommunication between researchers of different disciplines owing to the limited conceptual and technical overlap of distinct backgrounds. Therefore, the development of biomedical research on the brain has been slower than that in other areas. Brain biomedical research has recently undergone a paradigm shift, and conducting patient-centered, large-scale brain biomedical research has become possible using emerging high-throughput analysis tools. Neuroimaging, multiomics, and artificial intelligence technology are the main drivers of this new approach, foreshadowing dramatic advances in translational research. In addition, emerging interdisciplinary cooperative studies provide insights into how unresolved questions in biomedicine can be addressed. This review presents the in-depth aspects of conventional biomedical research and discusses the future of biomedical research on the brain.

U-shaped convolutional transformer GAN with multi-resolution consistency loss for restoring brain functional time-series and dementia diagnosis

Introduction

The blood oxygen level-dependent (BOLD) signal derived from functional neuroimaging is commonly used in brain network analysis and dementia diagnosis. Missing the BOLD signal may lead to bad performance and misinterpretation of findings when analyzing neurological disease. Few studies have focused on the restoration of brain functional time-series data.

Methods

In this paper, a novel U-shaped convolutional transformer GAN (UCT-GAN) model is proposed to restore the missing brain functional time-series data. The proposed model leverages the power of generative adversarial networks (GANs) while incorporating a U-shaped architecture to effectively capture hierarchical features in the restoration process. Besides, the multi-level temporal-correlated attention and the convolutional sampling in the transformer-based generator are devised to capture the global and local temporal features for the missing time series and associate their long-range relationship with the other brain regions. Furthermore, by introducing multi-resolution consistency loss, the proposed model can promote the learning of diverse temporal patterns and maintain consistency across different temporal resolutions, thus effectively restoring complex brain functional dynamics.

Results

We theoretically tested our model on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and our experiments demonstrate that the proposed model outperforms existing methods in terms of both quantitative metrics and qualitative assessments. The model's ability to preserve the underlying topological structure of the brain functional networks during restoration is a particularly notable achievement.

Conclusion

Overall, the proposed model offers a promising solution for restoring brain functional time-series and contributes to the advancement of neuroscience research by providing enhanced tools for disease analysis and interpretation.

Model-based closed-loop control of thalamic deep brain stimulation

Introduction: Closed-loop control of deep brain stimulation (DBS) is beneficial for effective and automatic treatment of various neurological disorders like Parkinson's disease (PD) and essential tremor (ET). Manual (open-loop) DBS programming solely based on clinical observations relies on neurologists' expertise and patients' experience. Continuous stimulation in open-loop DBS may decrease battery life and cause side effects. On the contrary, a closed-loop DBS system uses a feedback biomarker/signal to track worsening (or improving) of patients' symptoms and offers several advantages compared to the open-loop DBS system. Existing closed-loop DBS control systems do not incorporate physiological mechanisms underlying DBS or symptoms, e.g., how DBS modulates dynamics of synaptic plasticity. Methods: In this work, we propose a computational framework for development of a model-based DBS controller where a neural model can describe the relationship between DBS and neural activity and a polynomial-based approximation can estimate the relationship between neural and behavioral activities. A controller is used in our model in a quasi-real-time manner to find DBS patterns that significantly reduce the worsening of symptoms. By using the proposed computational framework, these DBS patterns can be tested clinically by predicting the effect of DBS before delivering it to the patient. We applied this framework to the problem of finding optimal DBS frequencies for essential tremor given electromyography (EMG) recordings solely. Building on our recent network model of ventral intermediate nuclei (Vim), the main surgical target of the tremor, in response to DBS, we developed neural model simulation in which physiological mechanisms underlying Vim-DBS are linked to symptomatic changes in EMG signals. By using a proportional-integral-derivative (PID) controller, we showed that a closed-loop system can track EMG signals and adjust the stimulation frequency of Vim-DBS so that the power of EMG reaches a desired control target. Results and discussion: We demonstrated that the model-based DBS frequency aligns well with that used in clinical studies. Our model-based closed-loop system is adaptable to different control targets and can potentially be used for different diseases and personalized systems.

NSF DARE-Transforming modeling in neurorehabilitation: Four threads for catalyzing progress

We present an overview of the Conference on Transformative Opportunities for Modeling in Neurorehabilitation held in March 2023. It was supported by the Disability and Rehabilitation Engineering (DARE) program from the National Science Foundation's Engineering Biology and Health Cluster. The conference brought together experts and trainees from around the world to discuss critical questions, challenges, and opportunities at the intersection of computational modeling and neurorehabilitation to understand, optimize, and improve clinical translation of neurorehabilitation. We organized the conference around four key, relevant, and promising Focus Areas for modeling: Adaptation & Plasticity, Personalization, Human-Device Interactions, and Modeling 'In-the-Wild'. We identified four common threads across the Focus Areas that, if addressed, can catalyze progress in the short, medium, and long terms. These were: (i) the need to capture and curate appropriate and useful data necessary to develop, validate, and deploy useful computational models (ii) the need to create multi-scale models that span the personalization spectrum from individuals to populations, and from cellular to behavioral levels (iii) the need for algorithms that extract as much information from available data, while requiring as little data as possible from each client (iv) the insistence on leveraging readily available sensors and data systems to push model-driven treatments from the lab, and into the clinic, home, workplace, and community. The conference archive can be found at (dare2023.usc.edu). These topics are also extended by three perspective papers prepared by trainees and junior faculty, clinician researchers, and federal funding agency representatives who attended the conference.

Global research trends and hotspots of artificial intelligence research in spinal cord neural injury and restoration-a bibliometrics and visualization analysis

Background

Artificial intelligence (AI) technology has made breakthroughs in spinal cord neural injury and restoration in recent years. It has a positive impact on clinical treatment. This study explores AI research's progress and hotspots in spinal cord neural injury and restoration. It also analyzes research shortcomings related to this area and proposes potential solutions.

Methods

We used CiteSpace 6.1.R6 and VOSviewer 1.6.19 to research WOS articles on AI research in spinal cord neural injury and restoration.

Results

A total of 1,502 articles were screened, in which the United States dominated; Kadone, Hideki (13 articles, University of Tsukuba, JAPAN) was the author with the highest number of publications; ARCH PHYS MED REHAB (IF = 4.3) was the most cited journal, and topics included molecular biology, immunology, neurology, sports, among other related areas.

Conclusion

We pinpointed three research hotspots for AI research in spinal cord neural injury and restoration: (1) intelligent robots and limb exoskeletons to assist rehabilitation training; (2) brain-computer interfaces; and (3) neuromodulation and noninvasive electrical stimulation. In addition, many new hotspots were discussed: (1) starting with image segmentation models based on convolutional neural networks; (2) the use of AI to fabricate polymeric biomaterials to provide the microenvironment required for neural stem cell-derived neural network tissues; (3) AI survival prediction tools, and transcription factor regulatory networks in the field of genetics were discussed. Although AI research in spinal cord neural injury and restoration has many benefits, the technology has several limitations (data and ethical issues). The data-gathering problem should be addressed in future research, which requires a significant sample of quality clinical data to build valid AI models. At the same time, research on genomics and other mechanisms in this field is fragile. In the future, machine learning techniques, such as AI survival prediction tools and transcription factor regulatory networks, can be utilized for studies related to the up-regulation of regeneration-related genes and the production of structural proteins for axonal growth.

Does Vestibular Motion Perception Correlate with Axonal Pathways Stimulated by Subthalamic Deep Brain Stimulation in Parkinson's Disease?

Perception of our linear motion - heading - is critical for postural control, gait, and locomotion, and it is impaired in Parkinson's disease (PD). Deep brain stimulation (DBS) has variable effects on vestibular heading perception, depending on the location of the electrodes within the subthalamic nucleus (STN). Here, we aimed to find the anatomical correlates of heading perception in PD. Fourteen PD participants with bilateral STN DBS performed a two-alternative forced-choice discrimination task where a motion platform delivered translational forward movements with a heading angle varying between 0 and 30° to the left or to the right with respect to the straight-ahead direction. Using psychometric curves, we derived the heading discrimination threshold angle of each patient from the response data. We created patient-specific DBS models and calculated the percentages of stimulated axonal pathways that are anatomically adjacent to the STN and known to play a major role in vestibular information processing. We performed correlation analyses to investigate the extent of these white matter tracts' involvement in heading perception. Significant positive correlations were identified between improved heading discrimination for rightward heading and the percentage of activated streamlines of the contralateral hyperdirect, pallido-subthalamic, and subthalamo-pallidal pathways. The hyperdirect pathways are thought to provide top-down control over STN connections to the cerebellum. In addition, STN may also antidromically activate collaterals of hyperdirect pathway that projects to the precerebellar pontine nuclei. In select cases, there was strong activation of the cerebello-thalamic projections, but it was not consistently present in all participants. Large volumetric overlap between the volume of tissue activation and the STN in the left hemisphere positively impacted rightward heading perception. Altogether, the results suggest heavy involvement of basal ganglia cerebellar network in STN-induced modulation of vestibular heading perception in PD.

Deep brain stimulation for Alzheimer's disease - current status and next steps

INTRODUCTION

Alzheimer's disease (AD) requires novel therapeutic approaches due to limited efficacy of current treatments.

AREAS COVERED

This article explores AD as a manifestation of neurocircuit dysfunction and evaluates deep brain stimulation (DBS) as a potential intervention. Focusing on fornix-targeted stimulation (DBS-f), the article summarizes safety, feasibility, and outcomes observed in phase 1/2 trials, highlighting findings such as cognitive improvement, increased metabolism, and hippocampal growth. Topics for further study include optimization of electrode placement, and the role of stimulation-induced autobiographical-recall. Nucleus basalis of Meynert (DBS-NBM) DBS is also discussed and compared with DBS-f. Challenges with both DBS-f and DBS-NBM are identified, emphasizing the need for further research on optimal stimulation parameters. The article also reviews alternative DBS targets, including medial temporal lobe structures and the ventral capsule/ventral striatum.

EXPERT OPINION

Looking ahead, a phase-3 DBS-f trial, and the prospect of closed-loop stimulation using EEG-derived biomarkers or hippocampal theta activity are highlighted. Recent FDA-approved therapies and other neuromodulation techniques like temporal interference and low-intensity ultrasound are considered. The article concludes by underscoring the importance of imaging-based diagnosis and staging to allow for circuit-targeted therapies, given the heterogeneity of AD and varied stages of neurocircuit dysfunction.

Modeling Instantaneous Firing Rate of Deep Brain Stimulation Target Neuronal Ensembles in the Basal Ganglia and Thalamus

OBJECTIVE

Deep brain stimulation (DBS) is an effective treatment for movement disorders, including Parkinson disease and essential tremor. However, the underlying mechanisms of DBS remain elusive. Despite the capability of existing models in interpreting experimental data qualitatively, there are very few unified computational models that quantitatively capture the dynamics of the neuronal activity of varying stimulated nuclei-including subthalamic nucleus (STN), substantia nigra pars reticulata (SNr), and ventral intermediate nucleus (Vim)-across different DBS frequencies.

MATERIALS AND METHODS

Both synthetic and experimental data were used in the model fitting; the synthetic data were generated by an established spiking neuron model that was reported in our previous work, and the experimental data were provided using single-unit microelectrode recordings (MERs) during DBS (microelectrode stimulation). Based on these data, we developed a novel mathematical model to represent the firing rate of neurons receiving DBS, including neurons in STN, SNr, and Vim-across different DBS frequencies. In our model, the DBS pulses were filtered through a synapse model and a nonlinear transfer function to formulate the firing rate variability. For each DBS-targeted nucleus, we fitted a single set of optimal model parameters consistent across varying DBS frequencies.

RESULTS

Our model accurately reproduced the firing rates observed and calculated from both synthetic and experimental data. The optimal model parameters were consistent across different DBS frequencies.

CONCLUSIONS

The result of our model fitting was in agreement with experimental single-unit MER data during DBS. Reproducing neuronal firing rates of different nuclei of the basal ganglia and thalamus during DBS can be helpful to further understand the mechanisms of DBS and to potentially optimize stimulation parameters based on their actual effects on neuronal activity.

Diffusion and functional MRI in surgical neuromodulation

Surgical neuromodulation has witnessed significant progress in recent decades. Notably, deep brain stimulation (DBS), delivered precisely within therapeutic targets, has revolutionized the treatment of medication-refractory movement disorders and is now expanding for refractory psychiatric disorders, refractory epilepsy, and post-stroke motor recovery. In parallel, the advent of incisionless treatment with focused ultrasound ablation (FUSA) can offer patients life-changing symptomatic relief. Recent research has underscored the potential to further optimize DBS and FUSA outcomes by conceptualizing the therapeutic targets as critical nodes embedded within specific brain networks instead of strictly anatomical structures. This paradigm shift was facilitated by integrating two imaging modalities used regularly in brain connectomics research: diffusion MRI (dMRI) and functional MRI (fMRI). These advanced imaging techniques have helped optimize the targeting and programming techniques of surgical neuromodulation, all while holding immense promise for investigations into treating other neurological and psychiatric conditions. This review aims to provide a fundamental background of advanced imaging for clinicians and scientists, exploring the synergy between current and future approaches to neuromodulation as they relate to dMRI and fMRI capabilities. Focused research in this area is required to optimize existing, functional neurosurgical treatments while serving to build an investigative infrastructure to unlock novel targets to alleviate the burden of other neurological and psychiatric disorders.

Applications of Artificial Intelligence in Pain Medicine

PURPOSE OF REVIEW

This review explores the current applications of artificial intelligence (AI) in the field of pain medicine with a focus on machine learning.

RECENT FINDINGS

Utilizing a literature search conducted through the PubMed database, several current trends were identified, including the use of AI as a tool for diagnostics, predicting pain progression, predicting treatment response, and performance of therapy and pain management. Results of these studies show promise for the improvement of patient outcomes. Current gaps in the research and subsequent directions for future study involve AI in optimizing and improving nerve stimulation and more thoroughly predicting patients' responses to treatment.

Artificial intelligence of imaging and clinical neurological data for predictive, preventive and personalized (P3) medicine for Parkinson Disease: The NeuroArtP3 protocol for a multi-center research study

BACKGROUND

The burden of Parkinson Disease (PD) represents a key public health issue and it is essential to develop innovative and cost-effective approaches to promote sustainable diagnostic and therapeutic interventions. In this perspective the adoption of a P3 (predictive, preventive and personalized) medicine approach seems to be pivotal. The NeuroArtP3 (NET-2018-12366666) is a four-year multi-site project co-funded by the Italian Ministry of Health, bringing together clinical and computational centers operating in the field of neurology, including PD.

OBJECTIVE

The core objectives of the project are: i) to harmonize the collection of data across the participating centers, ii) to structure standardized disease-specific datasets and iii) to advance knowledge on disease's trajectories through machine learning analysis.

METHODS

The 4-years study combines two consecutive research components: i) a multi-center retrospective observational phase; ii) a multi-center prospective observational phase. The retrospective phase aims at collecting data of the patients admitted at the participating clinical centers. Whereas the prospective phase aims at collecting the same variables of the retrospective study in newly diagnosed patients who will be enrolled at the same centers.

RESULTS

The participating clinical centers are the Provincial Health Services (APSS) of Trento (Italy) as the center responsible for the PD study and the IRCCS San Martino Hospital of Genoa (Italy) as the promoter center of the NeuroartP3 project. The computational centers responsible for data analysis are the Bruno Kessler Foundation of Trento (Italy) with TrentinoSalute4.0 -Competence Center for Digital Health of the Province of Trento (Italy) and the LISCOMPlab University of Genoa (Italy).

CONCLUSIONS

The work behind this observational study protocol shows how it is possible and viable to systematize data collection procedures in order to feed research and to advance the implementation of a P3 approach into the clinical practice through the use of AI models.

Individualized brain mapping for navigated neuromodulation

ABSTRACT

The brain is a complex organ that requires precise mapping to understand its structure and function. Brain atlases provide a powerful tool for studying brain circuits, discovering biological markers for early diagnosis, and developing personalized treatments for neuropsychiatric disorders. Neuromodulation techniques, such as transcranial magnetic stimulation and deep brain stimulation, have revolutionized clinical therapies for neuropsychiatric disorders. However, the lack of fine-scale brain atlases limits the precision and effectiveness of these techniques. Advances in neuroimaging and machine learning techniques have led to the emergence of stereotactic-assisted neurosurgery and navigation systems. Still, the individual variability among patients and the diversity of brain diseases make it necessary to develop personalized solutions. The article provides an overview of recent advances in individualized brain mapping and navigated neuromodulation and discusses the methodological profiles, advantages, disadvantages, and future trends of these techniques. The article concludes by posing open questions about the future development of individualized brain mapping and navigated neuromodulation.

Amelioration of Parkinsonian tremor evoked by DBS: which role play cerebello-(sub)thalamic fiber tracts?

BACKGROUND

Current pathophysiological models of Parkinson's disease (PD) assume a malfunctioning network being adjusted by the DBS signal. As various authors showed a main involvement of the cerebellum within this network, cerebello-cerebral fiber tracts are gaining special interest regarding the mediation of DBS effects.

OBJECTIVES

The crossing and non-decussating fibers of the dentato-rubro-thalamic tract (c-DRTT/nd-DRTT) and the subthalamo-ponto-cerebellar tract (SPCT) are thought to build up an integrated network enabling a bidimensional communication between the cerebellum and the basal ganglia. The aim of this study was to investigate the influence of these tracts on clinical control of Parkinsonian tremor evoked by DBS.

METHODS

We analyzed 120 electrode contacts from a cohort of 14 patients with tremor-dominant or equivalence-type PD having received bilateral STN-DBS. Probabilistic tractography was performed to depict the c-DRTT, nd-DRTT, and SPCT. Distance maps were calculated for the tracts and correlated to clinical tremor control for each electrode pole.

RESULTS

A significant difference between "effective" and "less-effective" contacts was only found for the c-DRTT (p = 0.039), but not for the SPCT, nor the nd-DRTT. In logistic and linear regressions, significant results were also found for the c-DRTT only (pmodel logistic = 0.035, ptract logistic = 0,044; plinear = 0.027).

CONCLUSIONS

We found a significant correlation between the distance of the DBS electrode pole to the c-DRTT and the clinical efficacy regarding tremor reduction. The c-DRTT might therefore play a major role in the mechanisms of alleviation of Parkinsonian tremor and could eventually serve as a possible DBS target for tremor-dominant PD in future.

MRI economics: Balancing sample size and scan duration in brain wide association studies

A pervasive dilemma in neuroimaging is whether to prioritize sample size or scan duration given fixed resources. Here, we systematically investigate this trade-off in the context of brain-wide association studies (BWAS) using resting-state functional magnetic resonance imaging (fMRI). We find that total scan duration (sample size × scan duration per participant) robustly explains individual-level phenotypic prediction accuracy via a logarithmic model, suggesting that sample size and scan duration are broadly interchangeable. The returns of scan duration eventually diminish relative to sample size, which we explain with principled theoretical derivations. When accounting for fixed costs associated with each participant (e.g., recruitment, non-imaging measures), we find that prediction accuracy in small-scale BWAS might benefit from much longer scan durations (>50 min) than typically assumed. Most existing large-scale studies might also have benefited from smaller sample sizes with longer scan durations. Both logarithmic and theoretical models of the relationships among sample size, scan duration and prediction accuracy explain well-predicted phenotypes better than poorly-predicted phenotypes. The logarithmic and theoretical models are also undermined by individual differences in brain states. These results replicate across phenotypic domains (e.g., cognition and mental health) from two large-scale datasets with different algorithms and metrics. Overall, our study emphasizes the importance of scan time, which is ignored in standard power calculations. Standard power calculations inevitably maximize sample size at the expense of scan duration. The resulting prediction accuracies are likely lower than would be produced with alternate designs, thus impeding scientific discovery. Our empirically informed reference is available for future study design: WEB_APPLICATION_LINK.

Elevating the field for applying neuroimaging to individual patients in psychiatry

Although neuroimaging has been widely applied in psychiatry, much of the exuberance in decades past has been tempered by failed replications and a lack of definitive evidence to support the utility of imaging to inform clinical decisions. There are multiple promising ways forward to demonstrate the relevance of neuroimaging for psychiatry at the individual patient level. Ultra-high field magnetic resonance imaging is developing as a sensitive measure of neurometabolic processes of particular relevance that holds promise as a new way to characterize patient abnormalities as well as variability in response to treatment. Neuroimaging may also be particularly suited to the science of brain stimulation interventions in psychiatry given that imaging can both inform brain targeting as well as measure changes in brain circuit communication as a function of how effectively interventions improve symptoms. We argue that a greater focus on individual patient imaging data will pave the way to stronger relevance to clinical care in psychiatry. We also stress the importance of using imaging in symptom-relevant experimental manipulations and how relevance will be best demonstrated by pairing imaging with differential treatment prediction and outcome measurement. The priorities for using brain imaging to inform psychiatry may be shifting, which compels the field to solidify clinical relevance for individual patients over exploratory associations and biomarkers that ultimately fail to replicate.

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.

Multiphysics simulation of magnetoelectric micro core-shells for wireless cellular stimulation therapy via magnetic temporal interference

This paper presents an innovative approach to wireless cellular stimulation therapy through the design of a magnetoelectric (ME) microdevice. Traditional electrophysiological stimulation techniques for neural and deep brain stimulation face limitations due to their reliance on electronics, electrode arrays, or the complexity of magnetic induction. In contrast, the proposed ME microdevice offers a self-contained, controllable, battery-free, and electronics-free alternative, holding promise for targeted precise stimulation of biological cells and tissues. The designed microdevice integrates core shell ME materials with remote coils which applies magnetic temporal interference (MTI) signals, leading to the generation of a bipolar local electric stimulation current operating at low frequencies which is suitable for precise stimulation. The nonlinear property of the magnetostrictive core enables the demodulation of remotely applied high-frequency electromagnetic fields, resulting in a localized, tunable, and manipulatable electric potential on the piezoelectric shell surface. This potential, triggers electrical spikes in neural cells, facilitating stimulation. Rigorous computational simulations support this concept, highlighting a significantly high ME coupling factor generation of 550 V/m·Oe. The high ME coupling is primarily attributed to the operation of the device in its mechanical resonance modes. This achievement is the result of a carefully designed core shell structure operating at the MTI resonance frequencies, coupled with an optimal magnetic bias, and predetermined piezo shell thickness. These findings underscore the potential of the engineered ME core shell as a candidate for wireless and minimally invasive cellular stimulation therapy, characterized by high resolution and precision. These results open new avenues for injectable material structures capable of delivering effective cellular stimulation therapy, carrying implications across neuroscience medical devices, and regenerative medicine.

Diagnostic performance of artificial intelligence-assisted PET imaging for Parkinson's disease: a systematic review and meta-analysis

Artificial intelligence (AI)-assisted PET imaging is emerging as a promising tool for the diagnosis of Parkinson's disease (PD). We aim to systematically review the diagnostic accuracy of AI-assisted PET in detecting PD. The Ovid MEDLINE, Ovid Embase, Web of Science, and IEEE Xplore databases were systematically searched for related studies that developed an AI algorithm in PET imaging for diagnostic performance from PD and were published by August 17, 2023. Binary diagnostic accuracy data were extracted for meta-analysis to derive outcomes of interest: area under the curve (AUC). 23 eligible studies provided sufficient data to construct contingency tables that allowed the calculation of diagnostic accuracy. Specifically, 11 studies were identified that distinguished PD from normal control, with a pooled AUC of 0.96 (95% CI: 0.94-0.97) for presynaptic dopamine (DA) and 0.90 (95% CI: 0.87-0.93) for glucose metabolism (18F-FDG). 13 studies were identified that distinguished PD from the atypical parkinsonism (AP), with a pooled AUC of 0.93 (95% CI: 0.91 - 0.95) for presynaptic DA, 0.79 (95% CI: 0.75-0.82) for postsynaptic DA, and 0.97 (95% CI: 0.96-0.99) for 18F-FDG. Acceptable diagnostic performance of PD with AI algorithms-assisted PET imaging was highlighted across the subgroups. More rigorous reporting standards that take into account the unique challenges of AI research could improve future studies.

Uncovering neuroanatomical correlates of impaired coordinated movement after pallidal deep brain stimulation

BACKGROUND

The loss of the ability to swim following deep brain stimulation (DBS), although rare, poses a worrisome risk of drowning. It is unclear what anatomic substrate and neural circuitry underlie this phenomenon. We report a case of cervical dystonia with lost ability to swim and dance during active stimulation of globus pallidus internus. We investigated the anatomical underpinning of this phenomenon using unique functional and structural imaging analysis.

METHODS

Tesla (3T) functional MRI (fMRI) of the patient was used during active DBS and compared with a cohort of four matched patients without this side effect. Structural connectivity mapping was used to identify brain network engagement by stimulation.

RESULTS

fMRI during stimulation revealed significant (Pbonferroni<0.0001) stimulation-evoked responses (DBS ON

CONCLUSIONS

These stimulation-induced impairments are likely a manifestation of a broader deficit in interlimb coordination mediated by stimulation effects on the SMA. This neuroanatomical underpinning can help inform future patient-specific stimulation and targeting.

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Key Words

• DYSTONIA
• ELECTRICAL STIMULATION
• FUNCTIONAL IMAGING
• MRI
• NEUROSURGERY

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MESH Headings

• Humans
• Globus Pallidus/diagnostic imaging
• Globus Pallidus/physiology
• Deep Brain Stimulation/adverse effects
• Deep Brain Stimulation/methods
• Treatment Outcome
• Magnetic Resonance Imaging

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Affiliation(s)

Brendan Santyr Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada
Aaron Loh Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada
Artur Vetkas Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada
Dave Gwun Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada
Wilson Kw Fung Edmond J. Safra Program in Parkinson's Disease and Morton and Gloria Shulman Movement Disorders Centre, Toronto Western Hospital, Toronto, Ontario, Canada
Shakeel Qazi Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada
Jurgen Germann Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada
Alexandre Boutet Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada Joint Department of Medical Imaging, Toronto Western Hospital, Toronto, Ontario, Canada
Can Sarica Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada
Andrew Yang Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada
Gavin Elias Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada
Suneil K Kalia Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada Center for Advancing Neurotechnological Innovation to Application (CRANIA), University Health Network, Toronto, Ontario, Canada
Alfonso Fasano Edmond J. Safra Program in Parkinson's Disease and Morton and Gloria Shulman Movement Disorders Centre, Toronto Western Hospital, Toronto, Ontario, Canada Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada Center for Advancing Neurotechnological Innovation to Application (CRANIA), University Health Network, Toronto, Ontario, Canada Division of Neurology, University of Toronto, Toronto, Ontario, Canada
Andres M Lozano Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada

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Closed-loop neurostimulation for the treatment of psychiatric disorders

Despite increasing prevalence and huge personal and societal burden, psychiatric diseases still lack treatments which can control symptoms for a large fraction of patients. Increasing insight into the neurobiology underlying these diseases has demonstrated wide-ranging aberrant activity and functioning in multiple brain circuits and networks. Together with varied presentation and symptoms, this makes one-size-fits-all treatment a challenge. There has been a resurgence of interest in the use of neurostimulation as a treatment for psychiatric diseases. Initial studies using continuous open-loop stimulation, in which clinicians adjusted stimulation parameters during patient visits, showed promise but also mixed results. Given the periodic nature and fluctuations of symptoms often observed in psychiatric illnesses, the use of device-driven closed-loop stimulation may provide more effective therapy. The use of a biomarker, which is correlated with specific symptoms, to deliver stimulation only during symptomatic periods allows for the personalized therapy needed for such heterogeneous disorders. Here, we provide the reader with background motivating the use of closed-loop neurostimulation for the treatment of psychiatric disorders. We review foundational studies of open- and closed-loop neurostimulation for neuropsychiatric indications, focusing on deep brain stimulation, and discuss key considerations when designing and implementing closed-loop neurostimulation.

Direct subthalamic nucleus stimulation influences speech and voice quality in Parkinson's disease patients

BACKGROUND

DBS of the subthalamic nucleus (STN) considerably ameliorates cardinal motor symptoms in PD. Reported STN-DBS effects on secondary dysarthric (speech) and dysphonic symptoms (voice), as originating from vocal tract motor dysfunctions, are however inconsistent with rather deleterious outcomes based on post-surgical assessments.

OBJECTIVE

To parametrically and intra-operatively investigate the effects of deep brain stimulation (DBS) on perceptual and acoustic speech and voice quality in Parkinson's disease (PD) patients.

METHODS

We performed an assessment of instantaneous intra-operative speech and voice quality changes in PD patients (n = 38) elicited by direct STN stimulations with variations of central stimulation features (depth, laterality, and intensity), separately for each hemisphere.

RESULTS

First, perceptual assessments across several raters revealed that certain speech and voice symptoms could be improved with STN-DBS, but this seems largely restricted to right STN-DBS. Second, computer-based acoustic analyses of speech and voice features revealed that both left and right STN-DBS could improve dysarthric speech symptoms, but only right STN-DBS can considerably improve dysphonic symptoms, with left STN-DBS being restricted to only affect voice intensity features. Third, several subareas according to stimulation depth and laterality could be identified in the motoric STN proper and close to the associative STN with optimal (and partly suboptimal) stimulation outcomes. Fourth, low-to-medium stimulation intensities showed the most optimal and balanced effects compared to high intensities.

CONCLUSIONS

STN-DBS can considerably improve both speech and voice quality based on a carefully arranged stimulation regimen along central stimulation features.

Machine Learning-Based Classification of Parkinson's Disease Patients Using Speech Biomarkers

BACKGROUND

Parkinson's disease (PD) is the most prevalent neurodegenerative movement disorder and a growing health concern in demographically aging societies. The prevalence of PD among individuals over the age of 60 and 80 years has been reported to range between 1% and 4%. A timely diagnosis of PD is desirable, even though it poses challenges to medical systems.

OBJECTIVE

This study aimed to classify PD and healthy controls based on the analysis of voice records at different frequencies using machine learning (ML) algorithms.

METHODS

The voices of 252 individuals aged 33 to 87 years were recorded. Based on the voice record data, ML algorithms can distinguish PD patients and healthy controls. One binary decision variable was associated with 756 instances and 754 attributes. Voice records data were analyzed through supervised ML algorithms and pipelines. A 10-fold cross-validation method was used to validate models.

RESULTS

In the classification of PD patients, ML models were performed with 84.21 accuracy, 93 precision, 89 Sensitivity, 89 F1-scores, and 87 AUC. The pipeline performance improved to accuracy: 85.09, precision: 92, Sensitivity:91, F1-score: 89, and AUC: 90. The Pipeline methods improved the performance of classifying PD from voice record.

CONCLUSIONS

Our study demonstrated that ML classifiers and pipelines can classify PD patients based on speech biomarkers. It was found that pipelines were more effective at selecting the most relevant features from high-dimensional data and at accurately classifying PD patients and healthy controls. This approach can therefore be used for early diagnosis of initial forms of PD.

Predicting sequenced dental treatment plans from electronic dental records using deep learning

BACKGROUND

Designing appropriate clinical dental treatment plans is an urgent need because a growing number of dental patients are suffering from partial edentulism with the population getting older.

OBJECTIVES

The aim of this study is to predict sequential treatment plans from electronic dental records.

METHODS

We construct a clinical decision support model, MultiTP, explores the unique topology of teeth information and the variation of complicated treatments, integrates deep learning models (convolutional neural network and recurrent neural network) adaptively, and embeds the attention mechanism to produce optimal treatment plans.

RESULTS

MultiTP shows its promising performance with an AUC of 0.9079 and an F score of 0.8472 over five treatment plans. The interpretability analysis also indicates its capability in mining clinical knowledge from the textual data.

CONCLUSIONS

MultiTP's novel problem formulation, neural network framework, and interpretability analysis techniques allow for broad applications of deep learning in dental healthcare, providing valuable support for predicting dental treatment plans in the clinic and benefiting dental patients.

CLINICAL IMPLICATIONS

The MultiTP is an efficient tool that can be implemented in clinical practice and integrated into the existing EDR system. By predicting treatment plans for partial edentulism, the model will help dentists improve their clinical decisions.

Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting From Multimodal Data

Deep neural networks are often applied to medical images to automate the problem of medical diagnosis. However, a more clinically relevant question that practitioners usually face is how to predict the future trajectory of a disease. Current methods for prognosis or disease trajectory forecasting often require domain knowledge and are complicated to apply. In this paper, we formulate the prognosis prediction problem as a one-to-many prediction problem. Inspired by a clinical decision-making process with two agents-a radiologist and a general practitioner - we predict prognosis with two transformer-based components that share information with each other. The first transformer in this framework aims to analyze the imaging data, and the second one leverages its internal states as inputs, also fusing them with auxiliary clinical data. The temporal nature of the problem is modeled within the transformer states, allowing us to treat the forecasting problem as a multi-task classification, for which we propose a novel loss. We show the effectiveness of our approach in predicting the development of structural knee osteoarthritis changes and forecasting Alzheimer's disease clinical status directly from raw multi-modal data. The proposed method outperforms multiple state-of-the-art baselines with respect to performance and calibration, both of which are needed for real-world applications. An open-source implementation of our method is made publicly available at https://github.com/Oulu-IMEDS/CLIMATv2.

Gut microbiota landscape and potential biomarker identification in female patients with systemic lupus erythematosus using machine learning

Objectives

Systemic Lupus Erythematosus (SLE) is a complex autoimmune disease that disproportionately affects women. Early diagnosis and prevention are crucial for women's health, and the gut microbiota has been found to be strongly associated with SLE. This study aimed to identify potential biomarkers for SLE by characterizing the gut microbiota landscape using feature selection and exploring the use of machine learning (ML) algorithms with significantly dysregulated microbiotas (SDMs) for early identification of SLE patients. Additionally, we used the SHapley Additive exPlanations (SHAP) interpretability framework to visualize the impact of SDMs on the risk of developing SLE in females.

Methods

Stool samples were collected from 54 SLE patients and 55 Negative Controls (NC) for microbiota analysis using 16S rRNA sequencing. Feature selection was performed using Elastic Net and Boruta on species-level taxonomy. Subsequently, four ML algorithms, namely logistic regression (LR), Adaptive Boosting (AdaBoost), Random Forest (RF), and eXtreme gradient boosting (XGBoost), were used to achieve early identification of SLE with SDMs. Finally, the best-performing algorithm was combined with SHAP to explore how SDMs affect the risk of developing SLE in females.

Results

Both alpha and beta diversity were found to be different in SLE group. Following feature selection, 68 and 21 microbiota were retained in Elastic Net and Boruta, respectively, with 16 microbiota overlapping between the two, i.e., SDMs for SLE. The four ML algorithms with SDMs could effectively identify SLE patients, with XGBoost performing the best, achieving Accuracy, Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value, and AUC values of 0.844, 0.750, 0.938, 0.923, 0.790, and 0.930, respectively. The SHAP interpretability framework showed a complex non-linear relationship between the relative abundance of SDMs and the risk of SLE, with Escherichia_fergusonii having the largest SHAP value.

Conclusions

This study revealed dysbiosis in the gut microbiota of female SLE patients. ML classifiers combined with SDMs can facilitate early identification of female patients with SLE, particularly XGBoost. The SHAP interpretability framework provides insight into the impact of SDMs on the risk of SLE and may inform future scientific treatment for SLE.

An overview of clinical machine learning applications in neurology

Machine learning techniques for clinical applications are evolving, and the potential impact this will have on clinical neurology is important to recognize. By providing a broad overview on this growing paradigm of clinical tools, this article aims to help healthcare professionals in neurology prepare to navigate both the opportunities and challenges brought on through continued advancements in machine learning. This narrative review first elaborates on how machine learning models are organized and implemented. Machine learning tools are then classified by clinical application, with examples of uses within neurology described in more detail. Finally, this article addresses limitations and considerations regarding clinical machine learning applications in neurology.

Prediction of Parkinson's Disease Using Machine Learning Methods

The detection of Parkinson's disease (PD) in its early stages is of great importance for its treatment and management, but consensus is lacking on what information is necessary and what models should be used to best predict PD risk. In our study, we first grouped PD-associated factors based on their cost and accessibility, and then gradually incorporated them into risk predictions, which were built using eight commonly used machine learning models to allow for comprehensive assessment. Finally, the Shapley Additive Explanations (SHAP) method was used to investigate the contributions of each factor. We found that models built with demographic variables, hospital admission examinations, clinical assessment, and polygenic risk score achieved the best prediction performance, and the inclusion of invasive biomarkers could not further enhance its accuracy. Among the eight machine learning models considered, penalized logistic regression and XGBoost were the most accurate algorithms for assessing PD risk, with penalized logistic regression achieving an area under the curve of 0.94 and a Brier score of 0.08. Olfactory function and polygenic risk scores were the most important predictors for PD risk. Our research has offered a practical framework for PD risk assessment, where necessary information and efficient machine learning tools were highlighted.

Chiral metal-organic frameworks incorporating nanozymes as neuroinflammation inhibitors for managing Parkinson's disease

Nanomedicine-based anti-neuroinflammation strategy has become a promising dawn of Parkinson's disease (PD) treatment. However, there are significant gaps in our understanding of the therapeutic mechanisms of antioxidant nanomedicines concerning the pathways traversing the blood-brain barrier (BBB) and subsequent inflammation mitigation. Here, we report nanozyme-integrated metal-organic frameworks with excellent antioxidant activity and chiral-dependent BBB transendocytosis as anti-neuroinflammatory agents for the treatment of PD. These chiral nanozymes are synthesized by embedding ultra-small platinum nanozymes (Ptzymes) into L-chiral and D-chiral imidazolate zeolite frameworks (Ptzyme@L-ZIF and Ptzyme@D-ZIF). Compared to Ptzyme@L-ZIF, Ptzyme@D-ZIF shows higher accumulation in the brains of male PD mouse models due to longer plasma residence time and more pathways to traverse BBB, including clathrin-mediated and caveolae-mediated endocytosis. These factors contribute to the superior therapeutic efficacy of Ptzyme@D-ZIF in reducing behavioral disorders and pathological changes. Bioinformatics and biochemical analyses suggest that Ptzyme@D-ZIF inhibits neuroinflammation-induced apoptosis and ferroptosis in damaged neurons. The research uncovers the biodistribution, metabolic variances, and therapeutic outcomes of nanozymes-integrated chiral ZIF platforms, providing possibilities for devising anti-PD drugs.

Motor networks, but also non-motor networks predict motor signs in Parkinson's disease

OBJECTIVE

Investigate the brain functional networks associated with motor impairment in people with Parkinson's disease (PD).

BACKGROUND

PD is primarily characterized by motor dysfunction. Resting-state functional connectivity (RsFC) offers a unique opportunity to non-invasively characterize brain function. In this study, we hypothesized that the motor dysfunction observed in people with PD involves atypical connectivity not only in motor but also in higher-level attention networks. Understanding the interaction between motor and non-motor RsFC that are related to the motor signs could provide insights into PD pathophysiology.

METHODS

We used data from 88 people with PD (mean age: 68.2(SD:10), 55 M/33F) coming from 2 cohorts. Motor severity was assessed in practical OFF-medication state, using MDS-UPDRS Part-III motor scores (mean: 49 (SD:10)). RsFC was characterized using an atlas of 384 regions that were grouped into 13 functional networks. Associations between RsFC and motor severity were assessed independently for each RsFC using predictive modeling.

RESULTS

The top 5 % models that predicted the MDS-UPDRS-III motor scores with effect size >0.5 were the connectivity between (1) the somatomotor and Subcortical-Basal-ganglia, (2) somatomotor and Visual and (3) CinguloOpercular (CiO) and language/Ventral attention (Lan/VeA) network pairs.

DISCUSSION

Our findings suggest that, along with motor networks, visual- and attention-related cortical networks are also associated with the motor symptoms of PD. Non-motor networks may be involved indirectly in motor-coordination. When people with PD have deficits in motor networks, more attention may be needed to carry out formerly automatic motor functions, consistent with compensatory mechanisms in parkinsonian movement disorders.

Advances in Deep Brain Stimulation: From Mechanisms to Applications

Deep brain stimulation (DBS) is an effective therapy for various neurologic and neuropsychiatric disorders, involving chronic implantation of electrodes into target brain regions for electrical stimulation delivery. Despite its safety and efficacy, DBS remains an underutilized therapy. Advances in the field of DBS, including in technology, mechanistic understanding, and applications have the potential to expand access and use of DBS, while also improving clinical outcomes. Developments in DBS technology, such as MRI compatibility and bidirectional DBS systems capable of sensing neural activity while providing therapeutic stimulation, have enabled advances in our understanding of DBS mechanisms and its application. In this review, we summarize recent work exploring DBS modulation of target networks. We also cover current work focusing on improved programming and the development of novel stimulation paradigms that go beyond current standards of DBS, many of which are enabled by sensing-enabled DBS systems and have the potential to expand access to DBS.

Functional neuroimaging as a catalyst for integrated neuroscience

Functional magnetic resonance imaging (fMRI) enables non-invasive access to the awake, behaving human brain. By tracking whole-brain signals across a diverse range of cognitive and behavioural states or mapping differences associated with specific traits or clinical conditions, fMRI has advanced our understanding of brain function and its links to both normal and atypical behaviour. Despite this headway, progress in human cognitive neuroscience that uses fMRI has been relatively isolated from rapid advances in other subdomains of neuroscience, which themselves are also somewhat siloed from one another. In this Perspective, we argue that fMRI is well-placed to integrate the diverse subfields of systems, cognitive, computational and clinical neuroscience. We first summarize the strengths and weaknesses of fMRI as an imaging tool, then highlight examples of studies that have successfully used fMRI in each subdomain of neuroscience. We then provide a roadmap for the future advances that will be needed to realize this integrative vision. In this way, we hope to demonstrate how fMRI can help usher in a new era of interdisciplinary coherence in neuroscience.

Functional network connectivity patterns predicting the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer's disease

BACKGROUND

Neuro-navigated repetitive transcranial magnetic stimulation (rTMS) is potentially effective in enhancing cognitive performance in the spectrum of Alzheimer's disease (AD). We explored the effect of rTMS-induced network reorganization and its predictive value for individual treatment response.

METHODS

Sixty-two amnestic mild cognitive impairment (aMCI) and AD patients were recruited. These subjects were assigned to multimodal magnetic resonance imaging scanning before and after a 4-week stimulation. Then, we investigated the neural mechanism underlying rTMS treatment based on static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) analyses. Finally, the support vector regression was used to predict the individual rTMS treatment response through these functional features at baseline.

RESULTS

We found that rTMS at the left angular gyrus significantly induced cognitive improvement in multiple cognitive domains. Participants after rTMS treatment exhibited significantly the increased sFNC between the right frontoparietal network (rFPN) and left frontoparietal network (lFPN) and decreased sFNC between posterior visual network and medial visual network. We revealed remarkable dFNC characteristics of brain connectivity, which was increased mainly in higher-order cognitive networks and decreased in primary networks or between primary networks and higher-order cognitive networks. dFNC characteristics in state 1 and state 4 could further predict individual higher memory improvement after rTMS treatment (state 1, R = 0.58; state 4, R = 0.54).

CONCLUSION

Our findings highlight that neuro-navigated rTMS could suppress primary network connections to compensate for higher-order cognitive networks. Crucially, dynamic regulation of brain networks at baseline may serve as an individualized predictor of rTMS treatment response.

RELEVANCE STATEMENT

Dynamic reorganization of brain networks could predict the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer's disease.

KEY POINTS

• rTMS at the left angular gyrus could induce cognitive improvement. • rTMS could suppress primary network connections to compensate for higher-order networks. • Dynamic reorganization of brain networks could predict individual treatment response to rTMS.

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.

Altered functional activity of the precuneus and superior temporal gyrus in patients with residual dizziness caused by benign paroxysmal positional vertigo

Objective

Benign paroxysmal positional vertigo (BPPV) is a common clinical vertigo disease, and the most effective treatment for this disease is canal repositioning procedures (CRP). Most patients return to normal after a single treatment. However, some patients still experience residual dizziness (RD) after treatment, and this disease's pathogenesis is currently unclear. The purpose of this study is to explore whether there are abnormal brain functional activities in patients with RD by using resting-state functional magnetic resonance imaging (rs-fMRI) and to provide imaging evidence for the study of the pathogenesis of RD.

Materials and methods

The BPPV patients in the Second Affiliated Hospital of Xuzhou Medical University had been included from December 2021 to November 2022. All patients had been received the collection of demographic and clinical characteristics (age, gender, involved semicircular canal, affected side, CRP times, BPPV course, duration of RD symptoms, and whether they had hypertension, diabetes, coronary heart disease.), scale assessment, including Dizziness Handicap Inventory (DHI), Hamilton Anxiety Inventory (HAMA), Hamilton Depression Inventory (HAMD), rs-fMRI data collection, CRP treatment, and then a one-month follow-up. According to the follow-up results, 18 patients with RD were included. At the same time, we selected 19 healthy individuals from our hospital's physical examination center who matched their age, gender as health controls (HC). First, the amplitude of low-frequency fluctuations (ALFF) analysis method was used to compare the local functional activities of the two groups of subjects. Then, the brain regions with different ALFF results were extracted as seed points. Functional connectivity (FC) analysis method based on seed points was used to explore the whole brain FC of patients with RD. Finally, a correlation analysis between clinical features and rs-fMRI data was performed.

Results

Compared to the HC, patients with RD showed lower ALFF value in the right precuneus and higher ALFF value in the right superior temporal gyrus (STG). When using the right STG as a seed point, it was found that the FC between the right STG, the right supramarginal gyrus (SMG), and the left precuneus was decreased in RD patients. However, no significant abnormalities in the FC were observed when using the right precuneus as a seed point.

Conclusion

In patients with RD, the local functional activity of the right precuneus is weakened, and the local functional activity of the right STG is enhanced. Furthermore, the FC between the right STG, the right SMG, and the left precuneus is weakened. These changes may explain the symptoms of dizziness, floating sensation, walking instability, neck tightness, and other symptoms in patients with RD to a certain extent.

Diagnostic Efficacy and Clinical Relevance of Artificial Intelligence in Detecting Cognitive Decline

Cognitive impairment is an age-associated disorder of increasing prevalence as the aging population continues to grow. Classified based on the level of cognitive decline, memory, function, and capacity to conduct activities of daily living, cognitive impairment ranges from mild cognitive impairment to dementia. When considering the insidious nature of the etiologies responsible for varying degrees of cognitive impairment, early diagnosis may provide a clinical benefit through the facilitation of early treatment. Typical diagnosis relies heavily on evaluation in a primary care setting. However, there is evidence that other diagnostic tools may aid in an earlier diagnosis of the different underlying pathologies responsible for cognitive impairment. Artificial intelligence represents a new intersecting field with healthcare that may aid in the early detection of neurodegenerative disorders. When assessing the role of AI in detecting cognitive decline, it is important to consider both the diagnostic efficacy of AI algorithms and the clinical relevance and impact of early interventions as a result of early detection. Thus, this review highlights promising investigations and developments in the space of artificial intelligence and healthcare and their potential to impact patient outcomes.

Variations in Determining Actual Orientations of Segmented Deep Brain Stimulation Leads Using the DiODe Algorithm: A Retrospective Study Across Different Lead Designs and Medical Institutions

INTRODUCTION

Directional deep brain stimulation (DBS) leads have become widely used in the past decade. Understanding the asymmetric stimulation provided by directional leads requires precise knowledge of the exact orientation of the lead in respect to its anatomical target. Recently, the DiODe algorithm was developed to automatically determine the orientation angle of leads from the artifact on postoperative computed tomography (CT) images. However, manual DiODe results are user-dependent. This study analyzed the extent of lead rotation as well as the user agreement of DiODe calculations across the two most common DBS systems, namely, Boston Scientific's Vercise and Abbott's Infinity, and two independent medical institutions.

METHODS

Data from 104 patients who underwent an anterior-facing unilateral/bilateral directional DBS implantation at either Northwestern Memorial Hospital (NMH) or Albany Medical Center (AMC) were retrospectively analyzed. Actual orientations of the implanted leads were independently calculated by three individual users using the DiODe algorithm in Lead-DBS and patients' postoperative CT images. The deviation from the intended orientation and user agreement were assessed.

RESULTS

All leads significantly deviated from the intended 0° orientation (p < 0.001), regardless of DBS lead design (p < 0.05) or institution (p < 0.05). However, the Boston Scientific leads showed an implantation bias toward the left at both institutions (p = 0.014 at NMH, p = 0.029 at AMC). A difference of 10° between at least two users occurred in 28% (NMH) and 39% (AMC) of all Boston Scientific and 76% (NMH) and 53% (AMC) of all Abbott leads.

CONCLUSION

Our results show that there is a significant lead rotation from the intended surgical orientation across both DBS systems and both medical institutions; however, a bias toward a single direction was only seen in the Boston Scientific leads. Additionally, these results raise questions into the user error that occurs when manually refining the orientation angles calculated with DiODe.

Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions

Parkinson's disease (PD) is a progressive and complex neurodegenerative disorder associated with age that affects motor and cognitive functions. As there is currently no cure, early diagnosis and accurate prognosis are essential to increase the effectiveness of treatment and control its symptoms. Medical imaging, specifically magnetic resonance imaging (MRI), has emerged as a valuable tool for developing support systems to assist in diagnosis and prognosis. The current literature aims to improve understanding of the disease's structural and functional manifestations in the brain. By applying artificial intelligence to neuroimaging, such as deep learning (DL) and other machine learning (ML) techniques, previously unknown relationships and patterns can be revealed in this high-dimensional data. However, several issues must be addressed before these solutions can be safely integrated into clinical practice. This review provides a comprehensive overview of recent ML techniques analyzed for the automatic diagnosis and prognosis of PD in brain MRI. The main challenges in applying ML to medical diagnosis and its implications for PD are also addressed, including current limitations for safe translation into hospitals. These challenges are analyzed at three levels: disease-specific, task-specific, and technology-specific. Finally, potential future directions for each challenge and future perspectives are discussed.

Machine versus physician-based programming of deep brain stimulation in isolated dystonia: A feasibility study

BACKGROUND

Deep brain stimulation of the internal globus pallidus effectively alleviates dystonia motor symptoms. However, delayed symptom control and a lack of therapeutic biomarkers and a single pallidal sweetspot region complicates optimal programming. Postoperative management is complex, typically requiring multiple, lengthy follow-ups with an experienced physician - an important barrier to widespread adoption in medication-refractory dystonia patients.

OBJECTIVE

Here we prospectively tested the best machine-predicted programming settings in a dystonia cohort treated with GPi-DBS against the settings derived from clinical long-term care in a specialised DBS centre.

METHODS

Previously, we reconstructed an anatomical map of motor improvement probability across the pallidal region using individual stimulation volumes and clinical outcomes in dystonia patients. We used this to develop an algorithm that tests in silico thousands of putative stimulation settings in de novo patients after reconstructing an individual, image-based anatomical model of electrode positions, and suggests stimulation parameters with the highest likelihood of optimal symptom control. To test real-life application, our prospective study compared results in 10 patients against programming settings derived from long-term care.

RESULTS

In this cohort, dystonia symptom reduction was observed at 74.9 ± 15.3% with C-SURF programming as compared to 66.3 ± 16.3% with clinical programming (p < 0.012). The average total electrical energy delivered (TEED) was similar for both the clinical and C-SURF programming (262.0 μJ/s vs. 306.1 μJ/s respectively).

CONCLUSION

Our findings highlight the clinical potential of machine-based programming in dystonia, which could markedly reduce the programming burden in postoperative management.