1
|
Dzialas V, Doering E, Eich H, Strafella AP, Vaillancourt DE, Simonyan K, van Eimeren T. Houston, We Have AI Problem! Quality Issues with Neuroimaging-Based Artificial Intelligence in Parkinson's Disease: A Systematic Review. Mov Disord 2024. [PMID: 39235364 DOI: 10.1002/mds.30002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2024] [Revised: 08/07/2024] [Accepted: 08/08/2024] [Indexed: 09/06/2024] Open
Abstract
In recent years, many neuroimaging studies have applied artificial intelligence (AI) to facilitate existing challenges in Parkinson's disease (PD) diagnosis, prognosis, and intervention. The aim of this systematic review was to provide an overview of neuroimaging-based AI studies and to assess their methodological quality. A PubMed search yielded 810 studies, of which 244 that investigated the utility of neuroimaging-based AI for PD diagnosis, prognosis, or intervention were included. We systematically categorized studies by outcomes and rated them with respect to five minimal quality criteria (MQC) pertaining to data splitting, data leakage, model complexity, performance reporting, and indication of biological plausibility. We found that the majority of studies aimed to distinguish PD patients from healthy controls (54%) or atypical parkinsonian syndromes (25%), whereas prognostic or interventional studies were sparse. Only 20% of evaluated studies passed all five MQC, with data leakage, non-minimal model complexity, and reporting of biological plausibility as the primary factors for quality loss. Data leakage was associated with a significant inflation of accuracies. Very few studies employed external test sets (8%), where accuracy was significantly lower, and 19% of studies did not account for data imbalance. Adherence to MQC was low across all observed years and journal impact factors. This review outlines that AI has been applied to a wide variety of research questions pertaining to PD; however, the number of studies failing to pass the MQC is alarming. Therefore, we provide recommendations to enhance the interpretability, generalizability, and clinical utility of future AI applications using neuroimaging in PD. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
Collapse
Affiliation(s)
- Verena Dzialas
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
- Faculty of Mathematics and Natural Sciences, University of Cologne, Cologne, Germany
| | - Elena Doering
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Helena Eich
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Antonio P Strafella
- Edmond J. Safra Parkinson Disease Program, Neurology Division, Krembil Brain Institute, University Health Network, Toronto, Canada
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada
- Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - David E Vaillancourt
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, Florida, USA
| | - Kristina Simonyan
- Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School and Massachusetts Eye and Ear, Boston, Massachusetts, USA
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Thilo van Eimeren
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
- Department of Neurology, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| |
Collapse
|
2
|
Iqbal MS, Belal Bin Heyat M, Parveen S, Ammar Bin Hayat M, Roshanzamir M, Alizadehsani R, Akhtar F, Sayeed E, Hussain S, Hussein HS, Sawan M. Progress and trends in neurological disorders research based on deep learning. Comput Med Imaging Graph 2024; 116:102400. [PMID: 38851079 DOI: 10.1016/j.compmedimag.2024.102400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 05/07/2024] [Accepted: 05/13/2024] [Indexed: 06/10/2024]
Abstract
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.
Collapse
Affiliation(s)
- Muhammad Shahid Iqbal
- Department of Computer Science and Information Technology, Women University of Azad Jammu & Kashmir, Bagh, Pakistan.
| | - Md Belal Bin Heyat
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, Zhejiang, China.
| | - Saba Parveen
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China.
| | | | - Mohamad Roshanzamir
- Department of Computer Engineering, Faculty of Engineering, Fasa University, Fasa, Iran.
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, VIC 3216, Australia.
| | - Faijan Akhtar
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
| | - Eram Sayeed
- Kisan Inter College, Dhaurahara, Kushinagar, India.
| | - Sadiq Hussain
- Department of Examination, Dibrugarh University, Assam 786004, India.
| | - Hany S Hussein
- Electrical Engineering Department, Faculty of Engineering, King Khalid University, Abha 61411, Saudi Arabia; Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81528, Egypt.
| | - Mohamad Sawan
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, Zhejiang, China.
| |
Collapse
|
3
|
Qiu J, Ajala A, Karigiannis J, Germann J, Santyr B, Loh A, Marinelli L, Foo T, Madhavan R, Yeo D, Boutet A, Lozano A. Deep Learning and fMRI-Based Pipeline for Optimization of Deep Brain Stimulation During Parkinson's Disease Treatment: Toward Rapid Semi-Automated Stimulation Optimization. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2024; 12:589-599. [PMID: 39247846 PMCID: PMC11379443 DOI: 10.1109/jtehm.2024.3448392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 07/23/2024] [Accepted: 08/15/2024] [Indexed: 09/10/2024]
Abstract
OBJECTIVE Optimized deep brain stimulation (DBS) is fast becoming a therapy of choice for the treatment of Parkinson's disease (PD). However, the post-operative optimization (aimed at maximizing patient clinical benefits and minimizing adverse effects) of all possible DBS parameter settings using the standard-of-care clinical protocol requires numerous clinical visits, which substantially increases the time to optimization per patient (TPP), patient cost burden and limit the number of patients who can undergo DBS treatment. The TPP is further elongated in electrodes with stimulation directionality or in diseases with latency in clinical feedback. In this work, we proposed a deep learning and fMRI-based pipeline for DBS optimization that can potentially reduce the TPP from ~1 year to a few hours during a single clinical visit. METHODS AND PROCEDURES We developed an unsupervised autoencoder (AE)-based model to extract meaningful features from 122 previously acquired blood oxygenated level dependent (BOLD) fMRI datasets from 39 a priori clinically optimized PD patients undergoing DBS therapy. The extracted features are then fed into multilayer perceptron (MLP)-based parameter classification and prediction models for rapid DBS parameter optimization. RESULTS The AE-extracted features of optimal and non-optimal DBS were disentangled. The AE-MLP classification model yielded accuracy, precision, recall, F1 score, and combined AUC of 0.96 ± 0.04, 0.95 ± 0.07, 0.92 ± 0.07, 0.93 ± 0.06, and 0.98 respectively. Accuracies of 0.79 ± 0.04, 0.85 ± 0.04, 0.82 ± 0.05, 0.83 ± 0.05, and 0.70 ± 0.07 were obtained in the prediction of voltage, frequency, and x-y-z contact locations, respectively. CONCLUSION The proposed AE-MLP models yielded promising results for fMRI-based DBS parameter classification and prediction, potentially facilitating rapid semi-automated DBS parameter optimization. Clinical and Translational Impact Statement-A deep learning-based pipeline for semi-automated DBS parameter optimization is presented, with the potential to significantly decrease the optimization duration per patient and patients' financial burden while increasing patient throughput.
Collapse
Affiliation(s)
- Jianwei Qiu
- GE HealthCare Technology & Innovation Center Niskayuna NY 12309 USA
| | - Afis Ajala
- GE HealthCare Technology & Innovation Center Niskayuna NY 12309 USA
| | | | - Jurgen Germann
- Division of NeurosurgeryDepartment of SurgeryUniversity Health Network Toronto ON M5G 2C4 Canada
- Division of NeurosurgeryDepartment of SurgeryUniversity of Toronto Toronto ON M5G 2C4 Canada
| | - Brendan Santyr
- Division of NeurosurgeryDepartment of SurgeryUniversity Health Network Toronto ON M5G 2C4 Canada
- Division of NeurosurgeryDepartment of SurgeryUniversity of Toronto Toronto ON M5G 2C4 Canada
| | - Aaron Loh
- Division of NeurosurgeryDepartment of SurgeryUniversity Health Network Toronto ON M5G 2C4 Canada
- Division of NeurosurgeryDepartment of SurgeryUniversity of Toronto Toronto ON M5G 2C4 Canada
| | - Luca Marinelli
- GE HealthCare Technology & Innovation Center Niskayuna NY 12309 USA
| | - Thomas Foo
- GE HealthCare Technology & Innovation Center Niskayuna NY 12309 USA
| | - Radhika Madhavan
- GE HealthCare Technology & Innovation Center Niskayuna NY 12309 USA
| | - Desmond Yeo
- GE HealthCare Technology & Innovation Center Niskayuna NY 12309 USA
| | - Alexandre Boutet
- Division of NeurosurgeryDepartment of SurgeryUniversity Health Network Toronto ON M5G 2C4 Canada
- Division of NeurosurgeryDepartment of SurgeryUniversity of Toronto Toronto ON M5G 2C4 Canada
- Joint Department of Medical ImagingUniversity of Toronto Toronto M5S 1A1 Canada
| | - Andres Lozano
- Division of NeurosurgeryDepartment of SurgeryUniversity Health Network Toronto ON M5G 2C4 Canada
- Division of NeurosurgeryDepartment of SurgeryUniversity of Toronto Toronto ON M5G 2C4 Canada
- Krembil Brain Institute Toronto ON M5T 1M8 Canada
- Center for Advancing Neurotechnological Innovation to Application (CRANIA) Toronto ON M5S 1A4 Canada
| |
Collapse
|
4
|
Ellis EG, Meyer GM, Kaasinen V, Corp DT, Pavese N, Reich MM, Joutsa J. Multimodal neuroimaging to characterize symptom-specific networks in movement disorders. NPJ Parkinsons Dis 2024; 10:154. [PMID: 39143114 PMCID: PMC11324766 DOI: 10.1038/s41531-024-00774-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 08/05/2024] [Indexed: 08/16/2024] Open
Abstract
Movement disorders, such as Parkinson's disease, essential tremor, and dystonia, are characterized by their predominant motor symptoms, yet diseases causing abnormal movement also encompass several other symptoms, including non-motor symptoms. Here we review recent advances from studies of brain lesions, neuroimaging, and neuromodulation that provide converging evidence on symptom-specific brain networks in movement disorders. Although movement disorders have traditionally been conceptualized as disorders of the basal ganglia, cumulative data from brain lesions causing parkinsonism, tremor and dystonia have now demonstrated that this view is incomplete. Several recent studies have shown that lesions causing a given movement disorder occur in heterogeneous brain locations, but disrupt common brain networks, which appear to be specific to each motor phenotype. In addition, findings from structural and functional neuroimaging in movement disorders have demonstrated that brain abnormalities extend far beyond the brain networks associated with the motor symptoms. In fact, neuroimaging findings in each movement disorder are strongly influenced by the constellation of patients' symptoms that also seem to map to specific networks rather than individual anatomical structures or single neurotransmitters. Finally, observations from deep brain stimulation have demonstrated that clinical changes, including both symptom improvement and side effects, are dependent on the modulation of large-scale networks instead of purely local effects of the neuromodulation. Combined, this multimodal evidence suggests that symptoms in movement disorders arise from distinct brain networks, encouraging multimodal imaging studies to better characterize the underlying symptom-specific mechanisms and individually tailor treatment approaches.
Collapse
Affiliation(s)
- Elizabeth G Ellis
- Turku Brain and Mind Center, University of Turku, Turku, Finland.
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC, Australia.
| | - Garance M Meyer
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Valtteri Kaasinen
- Clinical Neurosciences, University of Turku, Turku, Finland
- Neurocenter, Turku University Hospital, Turku, Finland
| | - Daniel T Corp
- Turku Brain and Mind Center, University of Turku, Turku, Finland
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC, Australia
| | - Nicola Pavese
- Institute of Clinical Medicine, Department of Nuclear Medicine & PET, Aarhus University, Aarhus, Denmark
- Translational and Clinical Research Institute, Newcastle University, Upon Tyn, UK
| | - Martin M Reich
- Department of Neurology, University Hospital of Würzburg, Josef-Schneider-Straße 11, 97080, Würzburg, Germany
| | - Juho Joutsa
- Turku Brain and Mind Center, University of Turku, Turku, Finland.
- Clinical Neurosciences, University of Turku, Turku, Finland.
- Neurocenter, Turku University Hospital, Turku, Finland.
| |
Collapse
|
5
|
Baker SK, Radcliffe EM, Kramer DR, Ojemann S, Case M, Zarns C, Holt-Becker A, Raike RS, Baumgartner AJ, Kern DS, Thompson JA. Comparison of beta peak detection algorithms for data-driven deep brain stimulation programming strategies in Parkinson's disease. NPJ Parkinsons Dis 2024; 10:150. [PMID: 39122725 PMCID: PMC11315991 DOI: 10.1038/s41531-024-00762-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 07/24/2024] [Indexed: 08/12/2024] Open
Abstract
Oscillatory activity within the beta frequency range (13-30 Hz) serves as a Parkinson's disease biomarker for tailoring deep brain stimulation (DBS) treatments. Currently, identifying clinically relevant beta signals, specifically frequencies of peak amplitudes within the beta spectral band, is a subjective process. To inform potential strategies for objective clinical decision making, we assessed algorithms for identifying beta peaks and devised a standardized approach for both research and clinical applications. Employing a novel monopolar referencing strategy, we utilized a brain sensing device to measure beta peak power across distinct contacts along each DBS electrode implanted in the subthalamic nucleus. We then evaluated the accuracy of ten beta peak detection algorithms against a benchmark established by expert consensus. The most accurate algorithms, all sharing similar underlying algebraic dynamic peak amplitude thresholding approaches, matched the expert consensus in performance and reliably predicted the clinical stimulation parameters during follow-up visits. These findings highlight the potential of algorithmic solutions to overcome the subjective bias in beta peak identification, presenting viable options for standardizing this process. Such advancements could lead to significant improvements in the efficiency and accuracy of patient-specific DBS therapy parameterization.
Collapse
Affiliation(s)
- Sunderland K Baker
- Pennsylvania State University, Department of Biobehavioral Health, University Park, PA, 16802, USA
| | - Erin M Radcliffe
- University of Colorado Anschutz Medical Campus, Department of Neurosurgery, Aurora, CO, 80045, USA
- University of Colorado Anschutz Medical Campus, Department of Bioengineering, Aurora, CO, 80045, USA
| | - Daniel R Kramer
- University of Colorado Anschutz Medical Campus, Department of Neurosurgery, Aurora, CO, 80045, USA
| | - Steven Ojemann
- University of Colorado Anschutz Medical Campus, Department of Neurosurgery, Aurora, CO, 80045, USA
- University of Colorado Anschutz Medical Campus, Department of Neurology, Aurora, CO, 80045, USA
| | - Michelle Case
- Medtronic PLC, Neuromodulation Operating Unit, Minneapolis, MN, 55432, USA
| | - Caleb Zarns
- Medtronic PLC, Neuromodulation Operating Unit, Minneapolis, MN, 55432, USA
| | - Abbey Holt-Becker
- Medtronic PLC, Neuromodulation Operating Unit, Minneapolis, MN, 55432, USA
| | - Robert S Raike
- Medtronic PLC, Neuromodulation Operating Unit, Minneapolis, MN, 55432, USA
| | - Alexander J Baumgartner
- University of Colorado Anschutz Medical Campus, Department of Neurosurgery, Aurora, CO, 80045, USA
- University of Colorado Anschutz Medical Campus, Department of Neurology, Aurora, CO, 80045, USA
| | - Drew S Kern
- University of Colorado Anschutz Medical Campus, Department of Neurosurgery, Aurora, CO, 80045, USA
- University of Colorado Anschutz Medical Campus, Department of Neurology, Aurora, CO, 80045, USA
| | - John A Thompson
- University of Colorado Anschutz Medical Campus, Department of Neurosurgery, Aurora, CO, 80045, USA.
- University of Colorado Anschutz Medical Campus, Department of Neurology, Aurora, CO, 80045, USA.
- University of Colorado Anschutz Medical Campus, Department of Psychiatry, Aurora, CO, 80045, USA.
| |
Collapse
|
6
|
Lefaucheur JP, Moro E, Shirota Y, Ugawa Y, Grippe T, Chen R, Benninger DH, Jabbari B, Attaripour S, Hallett M, Paulus W. Clinical neurophysiology in the treatment of movement disorders: IFCN handbook chapter. Clin Neurophysiol 2024; 164:57-99. [PMID: 38852434 DOI: 10.1016/j.clinph.2024.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 03/02/2024] [Accepted: 05/15/2024] [Indexed: 06/11/2024]
Abstract
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.
Collapse
Affiliation(s)
- Jean-Pascal Lefaucheur
- Clinical Neurophysiology Unit, Henri Mondor University Hospital, AP-HP, Créteil, France; EA 4391, ENT Team, Paris-Est Créteil University, Créteil, France.
| | - Elena Moro
- Grenoble Alpes University, Division of Neurology, CHU of Grenoble, Grenoble Institute of Neuroscience, Grenoble, France
| | - Yuichiro Shirota
- Department of Neurology, Division of Neuroscience, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yoshikazu Ugawa
- Department of Human Neurophysiology, School of Medicine, Fukushima Medical University, Fukushima, Japan
| | - Talyta Grippe
- Division of Neurology, University of Toronto, Toronto, Ontario, Canada; Neuroscience Graduate Program, Federal University of Minas Gerais, Belo Horizonte, Brazil; Krembil Brain Institute, Toronto, Ontario, Canada
| | - Robert Chen
- Division of Neurology, University of Toronto, Toronto, Ontario, Canada; Krembil Brain Institute, Toronto, Ontario, Canada
| | - David H Benninger
- Service of Neurology, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Bahman Jabbari
- Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
| | - Sanaz Attaripour
- Department of Neurology, University of California, Irvine, CA, USA
| | - Mark Hallett
- Human Motor Control Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Walter Paulus
- Department of Neurology, Ludwig Maximilians University, Munich, Germany
| |
Collapse
|
7
|
Slepneva N, Basich-Pease G, Reid L, Frank AC, Norbu T, Krystal AD, Sugrue LP, Motzkin JC, Larson PS, Starr PA, Morrison MA, Lee AM. Therapeutic DBS for OCD Suppresses the Default Mode Network. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.21.601827. [PMID: 39091832 PMCID: PMC11291060 DOI: 10.1101/2024.07.21.601827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Background Deep brain stimulation (DBS) of the anterior limb of the internal capsule (ALIC) is an emerging treatment for severe, refractory obsessive-compulsive disorder (OCD). The therapeutic effects of DBS are hypothesized to be mediated by direct modulation of a distributed cortico-striato-thalmo-cortical network underlying OCD symptoms. However, the exact underlying mechanism by which DBS exerts its therapeutic effects still remains unclear. Method In five participants receiving DBS for severe, refractory OCD (3 responders, 2 non-responders), we conducted a DBS On/Off cycling paradigm during the acquisition of functional MRI to determine the network effects of stimulation across a variety of bipolar configurations. We also performed tractography using diffusion-weighted imaging (DWI) to relate the functional impact of DBS to the underlying structural connectivity between active stimulation contacts and functional brain networks. Results We found that therapeutic DBS had a distributed effect, suppressing BOLD activity within regions such as the orbitofrontal cortex, dorsomedial prefrontal cortex, and subthalamic nuclei compared to non-therapeutic configurations. Many of the regions suppressed by therapeutic DBS were components of the default mode network (DMN). Moreover, the estimated stimulation field from the therapeutic configurations exhibited significant structural connectivity to core nodes of the DMN. Conclusions Therapeutic DBS for OCD suppresses BOLD activity within a distributed set of regions within the DMN relative to non-therapeutic configurations. We propose that these effects may be mediated by interruption of communication through structural white matter connections surrounding the DBS active contacts.
Collapse
Affiliation(s)
- Natalya Slepneva
- Weill Institute for Neurosciences, University of California, San Francisco
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco
| | - Genevieve Basich-Pease
- Weill Institute for Neurosciences, University of California, San Francisco
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco
| | - Lee Reid
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco
- Department of Radiology and Biomedical Imaging, University of California, San Francisco
| | - Adam C. Frank
- Department of Psychiatry and Behavioral Sciences, Keck School of Medicine of USC
| | - Tenzin Norbu
- Weill Institute for Neurosciences, University of California, San Francisco
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco
| | - Andrew D Krystal
- Weill Institute for Neurosciences, University of California, San Francisco
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco
| | - Leo P Sugrue
- Weill Institute for Neurosciences, University of California, San Francisco
- Department of Radiology and Biomedical Imaging, University of California, San Francisco
| | - Julian C Motzkin
- Weill Institute for Neurosciences, University of California, San Francisco
- Departments of Neurology and Anesthesia and Perioperative Care, University of California, San Francisco
| | | | - Philip A. Starr
- Weill Institute for Neurosciences, University of California, San Francisco
- Department of Neurological Surgery, University of California, San Francisco
| | - Melanie A. Morrison
- Weill Institute for Neurosciences, University of California, San Francisco
- Department of Radiology and Biomedical Imaging, University of California, San Francisco
| | - A Moses Lee
- Weill Institute for Neurosciences, University of California, San Francisco
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco
| |
Collapse
|
8
|
Chen L, Xu H, Gong T, Jin J, Lin L, Zhou Y, Huang J, Chen Z. Accelerating multipool CEST MRI of Parkinson's disease using deep learning-based Z-spectral compressed sensing. Magn Reson Med 2024. [PMID: 39044635 DOI: 10.1002/mrm.30233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 06/23/2024] [Accepted: 07/09/2024] [Indexed: 07/25/2024]
Abstract
PURPOSE To develop a deep learning-based approach to reduce the scan time of multipool CEST MRI for Parkinson's disease (PD) while maintaining sufficient prediction accuracy. METHOD A deep learning approach based on a modified one-dimensional U-Net, termed Z-spectral compressed sensing (CS), was proposed to recover dense Z-spectra from sparse ones. The neural network was trained using simulated Z-spectra generated by the Bloch equation with various parameter settings. Its feasibility and effectiveness were validated through numerical simulations and in vivo rat brain experiments, compared with commonly used linear, pchip, and Lorentzian interpolation methods. The proposed method was applied to detect metabolism-related changes in the 6-hydroxydopamine PD model with multipool CEST MRI, including APT, CEST@2 ppm, nuclear Overhauser enhancement, direct saturation, and magnetization transfer, and the prediction performance was evaluated by area under the curve. RESULTS The numerical simulations and in vivo rat-brain experiments demonstrated that the proposed method could yield superior fidelity in retrieving dense Z-spectra compared with existing methods. Significant differences were observed in APT, CEST@2 ppm, nuclear Overhauser enhancement, and direct saturation between the striatum regions of wild-type and PD models, whereas magnetization transfer exhibited no significant difference. Receiver operating characteristic analysis demonstrated that multipool CEST achieved better predictive performance compared with individual pools. Combined with Z-spectral CS, the scan time of multipool CEST MRI can be reduced to 33% without distinctly compromising prediction accuracy. CONCLUSION The integration of Z-spectral CS with multipool CEST MRI can enhance the prediction accuracy of PD and maintain the scan time within a reasonable range.
Collapse
Affiliation(s)
- Lin Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China
- Institute of Artificial Intelligence, Xiamen University, Xiamen, China
| | - Haipeng Xu
- Institute of Artificial Intelligence, Xiamen University, Xiamen, China
| | - Tao Gong
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Junxian Jin
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China
| | - Liangjie Lin
- Clinical & Technical Supports, Philips Healthcare, Beijing, China
| | - Yang Zhou
- Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Jianpan Huang
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
| | - Zhong Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China
- Institute of Artificial Intelligence, Xiamen University, Xiamen, China
| |
Collapse
|
9
|
Sarikhani P, Hsu HL, Zeydabadinezhad M, Yao Y, Kothare M, Mahmoudi B. Reinforcement learning for closed-loop regulation of cardiovascular system with vagus nerve stimulation: a computational study. J Neural Eng 2024; 21:036027. [PMID: 38718787 PMCID: PMC11145940 DOI: 10.1088/1741-2552/ad48bb] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 04/24/2024] [Accepted: 05/08/2024] [Indexed: 06/04/2024]
Abstract
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.
Collapse
Affiliation(s)
- Parisa Sarikhani
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States of America
| | - Hao-Lun Hsu
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America
| | - Mahmoud Zeydabadinezhad
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States of America
| | - Yuyu Yao
- Department of Chemical & Biomolecular Engineering, Lehigh University, Bethlehem, PA, United States of America
| | - Mayuresh Kothare
- Department of Chemical & Biomolecular Engineering, Lehigh University, Bethlehem, PA, United States of America
| | - Babak Mahmoudi
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States of America
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America
| |
Collapse
|
10
|
Li Y, Ren H, Chi C, Miao Y. Artificial Intelligence-Guided Gut-Microenvironment-Triggered Imaging Sensor Reveals Potential Indicators of Parkinson's Disease. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307819. [PMID: 38569219 PMCID: PMC11187919 DOI: 10.1002/advs.202307819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 03/16/2024] [Indexed: 04/05/2024]
Abstract
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.
Collapse
Affiliation(s)
- Yiwei Li
- Department of Haematology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's HospitalSchool of Medicine of University of Electronic Science and Technology of ChinaNo. 32, West Section 2, First Ring Road, Qingyang DistrictChengdu610000China
- Institute of Communications Engineering & Department of Electrical EngineeringNational Tsing Hua UniversityHsinchu30013Taiwan
| | - Hong‐Xia Ren
- Sichuan Technology & Business CollegeChengdu611800China
| | - Chong‐Yung Chi
- Institute of Communications Engineering & Department of Electrical EngineeringNational Tsing Hua UniversityHsinchu30013Taiwan
| | - Yang‐Bao Miao
- Department of Haematology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's HospitalSchool of Medicine of University of Electronic Science and Technology of ChinaNo. 32, West Section 2, First Ring Road, Qingyang DistrictChengdu610000China
| |
Collapse
|
11
|
Vassal M, Martins F, Monteiro B, Tambaro S, Martinez-Murillo R, Rebelo S. Emerging Pro-neurogenic Therapeutic Strategies for Neurodegenerative Diseases: A Review of Pre-clinical and Clinical Research. Mol Neurobiol 2024:10.1007/s12035-024-04246-w. [PMID: 38816676 DOI: 10.1007/s12035-024-04246-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 05/14/2024] [Indexed: 06/01/2024]
Abstract
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.
Collapse
Affiliation(s)
- Mariana Vassal
- Department of Medical Sciences, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal
| | - Filipa Martins
- Department of Medical Sciences, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal
| | - Bruno Monteiro
- Department of Medical Sciences, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal
| | - Simone Tambaro
- Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Karolinska Institutet, Huddinge, Sweden
| | - Ricardo Martinez-Murillo
- Neurovascular Research Group, Department of Translational Neurobiology, Cajal Institute (CSIC), Madrid, Spain
| | - Sandra Rebelo
- Department of Medical Sciences, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal.
| |
Collapse
|
12
|
Xu M, Wang H, Ren S, Wang B, Yang W, Lv L, Sha X, Li W, Wang Y. Identification of crucial inflammaging related risk factors in multiple sclerosis. Front Mol Neurosci 2024; 17:1398665. [PMID: 38836117 PMCID: PMC11148336 DOI: 10.3389/fnmol.2024.1398665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Accepted: 04/30/2024] [Indexed: 06/06/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Mengchu Xu
- Department of Biomedical Engineering, School of Intelligent Sciences, China Medical University, Shenyang, Liaoning, China
| | - Huize Wang
- Department of Nursing, First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
| | - Siwei Ren
- Department of Biomedical Engineering, School of Intelligent Sciences, China Medical University, Shenyang, Liaoning, China
| | - Bing Wang
- Department of Biomedical Engineering, School of Intelligent Sciences, China Medical University, Shenyang, Liaoning, China
| | - Wenyan Yang
- Department of Biomedical Engineering, School of Intelligent Sciences, China Medical University, Shenyang, Liaoning, China
| | - Ling Lv
- Department of Thorax, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Xianzheng Sha
- Department of Biomedical Engineering, School of Intelligent Sciences, China Medical University, Shenyang, Liaoning, China
| | - Wenya Li
- Department of Thorax, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Yin Wang
- Department of Biomedical Engineering, School of Intelligent Sciences, China Medical University, Shenyang, Liaoning, China
- Tumor Etiology and Screening Department of Cancer Institute and General Surgery, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
| |
Collapse
|
13
|
Unadkat P, Vo A, Ma Y, Peng S, Nguyen N, Niethammer M, Tang CC, Dhawan V, Ramdhani R, Fenoy A, Caminiti SP, Perani D, Eidelberg D. Deep brain stimulation of the subthalamic nucleus for Parkinson's disease: A network imaging marker of the treatment response. RESEARCH SQUARE 2024:rs.3.rs-4178280. [PMID: 38766007 PMCID: PMC11100869 DOI: 10.21203/rs.3.rs-4178280/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
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.
Collapse
Affiliation(s)
| | - An Vo
- The Feinstein Institutes for Medical Research
| | - Yilong Ma
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Manhasset, New York, USA
| | - Shichun Peng
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Manhasset, New York, USA
| | | | | | | | | | - Ritesh Ramdhani
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell
| | | | | | | | | |
Collapse
|
14
|
Guzzi G, Della Torre A, Bruni A, Lavano A, Bosco V, Garofalo E, La Torre D, Longhini F. Anatomo-physiological basis and applied techniques of electrical neuromodulation in chronic pain. JOURNAL OF ANESTHESIA, ANALGESIA AND CRITICAL CARE 2024; 4:29. [PMID: 38698460 PMCID: PMC11064427 DOI: 10.1186/s44158-024-00167-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 04/24/2024] [Indexed: 05/05/2024]
Abstract
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.
Collapse
Affiliation(s)
- Giusy Guzzi
- Neurosurgery Department, "R. Dulbecco" Hospital, Department of Medical and Surgical Sciences, "Magna Graecia" University of Catanzaro, Catanzaro, Italy
| | - Attilio Della Torre
- Neurosurgery Department, "R. Dulbecco" Hospital, Department of Medical and Surgical Sciences, "Magna Graecia" University of Catanzaro, Catanzaro, Italy
| | - Andrea Bruni
- Anesthesia and Intensive Care Unit, "R. Dulbecco" Univesity Hospital, Department of Medical and Surgical Sciences, Magna Graecia University, Viale Europa, Catanzaro, 88100, Italy
| | - Angelo Lavano
- Neurosurgery Department, "R. Dulbecco" Hospital, Department of Medical and Surgical Sciences, "Magna Graecia" University of Catanzaro, Catanzaro, Italy
| | - Vincenzo Bosco
- Anesthesia and Intensive Care Unit, "R. Dulbecco" Univesity Hospital, Department of Medical and Surgical Sciences, Magna Graecia University, Viale Europa, Catanzaro, 88100, Italy
| | - Eugenio Garofalo
- Anesthesia and Intensive Care Unit, "R. Dulbecco" Univesity Hospital, Department of Medical and Surgical Sciences, Magna Graecia University, Viale Europa, Catanzaro, 88100, Italy
| | - Domenico La Torre
- Neurosurgery Department, "R. Dulbecco" Hospital, Department of Medical and Surgical Sciences, "Magna Graecia" University of Catanzaro, Catanzaro, Italy
| | - Federico Longhini
- Anesthesia and Intensive Care Unit, "R. Dulbecco" Univesity Hospital, Department of Medical and Surgical Sciences, Magna Graecia University, Viale Europa, Catanzaro, 88100, Italy.
| |
Collapse
|
15
|
Vu J, Bhusal B, Rosenow JM, Pilitsis J, Golestanirad L. Effect of surgical modification of deep brain stimulation lead trajectories on radiofrequency heating during MRI at 3T: from phantom experiments to clinical implementation. J Neurosurg 2024; 140:1459-1470. [PMID: 37948679 PMCID: PMC11065613 DOI: 10.3171/2023.8.jns23580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 08/22/2023] [Indexed: 11/12/2023]
Abstract
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.
Collapse
Affiliation(s)
- Jasmine Vu
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, Illinois
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Bhumi Bhusal
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Joshua M. Rosenow
- Department of Neurosurgery, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Julie Pilitsis
- Department of Neurosciences and Experimental Therapeutics, Albany Medical College, Albany, New York
| | - Laleh Golestanirad
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, Illinois
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| |
Collapse
|
16
|
Yoon JH, Lee D, Lee C, Cho E, Lee S, Cazenave-Gassiot A, Kim K, Chae S, Dennis EA, Suh PG. Paradigm shift required for translational research on the brain. Exp Mol Med 2024; 56:1043-1054. [PMID: 38689090 PMCID: PMC11148129 DOI: 10.1038/s12276-024-01218-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 02/07/2024] [Accepted: 02/20/2024] [Indexed: 05/02/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Jong Hyuk Yoon
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea.
| | - Dongha Lee
- Cognitive Science Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Chany Lee
- Cognitive Science Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Eunji Cho
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Seulah Lee
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Amaury Cazenave-Gassiot
- Department of Biochemistry and Precision Medicine Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119077, Singapore
- Singapore Lipidomics Incubator (SLING), Life Sciences Institute, National University of Singapore, Singapore, 117456, Singapore
| | - Kipom Kim
- Research Strategy Office, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Sehyun Chae
- Neurovascular Unit Research Group, Korean Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Edward A Dennis
- Department of Pharmacology and Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, 92093-0601, USA
| | - Pann-Ghill Suh
- Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| |
Collapse
|
17
|
Zuo Q, Li R, Shi B, Hong J, Zhu Y, Chen X, Wu Y, Guo J. U-shaped convolutional transformer GAN with multi-resolution consistency loss for restoring brain functional time-series and dementia diagnosis. Front Comput Neurosci 2024; 18:1387004. [PMID: 38694950 PMCID: PMC11061376 DOI: 10.3389/fncom.2024.1387004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 04/02/2024] [Indexed: 05/04/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Qiankun Zuo
- Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, Wuhan, Hubei, China
- School of Information Engineering, Hubei University of Economics, Wuhan, Hubei, China
- Hubei Internet Finance Information Engineering Technology Research Center, Hubei University of Economics, Wuhan, Hubei, China
| | - Ruiheng Li
- Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, Wuhan, Hubei, China
- School of Information Engineering, Hubei University of Economics, Wuhan, Hubei, China
| | - Binghua Shi
- Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, Wuhan, Hubei, China
- School of Information Engineering, Hubei University of Economics, Wuhan, Hubei, China
| | - Jin Hong
- Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Yanfei Zhu
- School of Foreign Languages, Sun Yat-sen University, Guangzhou, China
| | - Xuhang Chen
- Faculty of Science and Technology, University of Macau, Taipa, Macao SAR, China
| | - Yixian Wu
- School of Mechanical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Jia Guo
- Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, Wuhan, Hubei, China
- School of Information Engineering, Hubei University of Economics, Wuhan, Hubei, China
- Hubei Internet Finance Information Engineering Technology Research Center, Hubei University of Economics, Wuhan, Hubei, China
| |
Collapse
|
18
|
Tian Y, Saradhi S, Bello E, Johnson MD, D’Eleuterio G, Popovic MR, Lankarany M. Model-based closed-loop control of thalamic deep brain stimulation. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 4:1356653. [PMID: 38650608 PMCID: PMC11033853 DOI: 10.3389/fnetp.2024.1356653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 03/18/2024] [Indexed: 04/25/2024]
Abstract
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.
Collapse
Affiliation(s)
- Yupeng Tian
- Krembil Brain Institute—University Health Network, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, Canada
| | - Srikar Saradhi
- Krembil Brain Institute—University Health Network, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Edward Bello
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Matthew D. Johnson
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | | | - Milos R. Popovic
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, Canada
- Center for Advancing Neurotechnological Innovation to Application, University Health Network and University of Toronto, Toronto, ON, Canada
| | - Milad Lankarany
- Krembil Brain Institute—University Health Network, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, Canada
- Center for Advancing Neurotechnological Innovation to Application, University Health Network and University of Toronto, Toronto, ON, Canada
- Department of Physiology, University of Toronto, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| |
Collapse
|
19
|
Valero-Cuevas FJ, Finley J, Orsborn A, Fung N, Hicks JL, Huang HH, Reinkensmeyer D, Schweighofer N, Weber D, Steele KM. NSF DARE-Transforming modeling in neurorehabilitation: Four threads for catalyzing progress. J Neuroeng Rehabil 2024; 21:46. [PMID: 38570842 PMCID: PMC10988973 DOI: 10.1186/s12984-024-01324-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 02/09/2024] [Indexed: 04/05/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Francisco J Valero-Cuevas
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, 1042 Downey Way, Los Angeles, 90089, CA, USA.
- Division of Biokinesiology and Physical Therapy, University of Southern California, 1540 Alcazar St 155, Los Angeles, 90033, CA, USA.
- Thomas Lord Department of Computer Science, University of Southern California, 941 Bloom Walk, Los Angeles, 90089, CA, USA.
| | - James Finley
- Division of Biokinesiology and Physical Therapy, University of Southern California, 1540 Alcazar St 155, Los Angeles, 90033, CA, USA
| | - Amy Orsborn
- Department of Electrical and Computer Engineering, University of Washington, 185 W Stevens Way NE, Box 352500, Seattle, 98195, WA, USA
- Department of Bioengineering, University of Washington, 3720 15th Ave NE, Box 355061, Seattle, 98195, WA, USA
- Washington National Primate Research Center, University of Washington, 3018 Western Ave, Seattle, 98121, WA, USA
| | - Natalie Fung
- Thomas Lord Department of Computer Science, University of Southern California, 941 Bloom Walk, Los Angeles, 90089, CA, USA
| | - Jennifer L Hicks
- Department of Bioengineering, Stanford University, 443 Via Ortega, Stanford, 94305, CA, USA
| | - He Helen Huang
- Joint Department of Biomedical Engineering, North Carolina State University, 1840 Entrepreneur Dr Suite 4130, Raleigh, 27606, NC, USA
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, 333 S Columbia St, Chapel Hill, 27514, NC, USA
| | - David Reinkensmeyer
- Department of Mechanical and Aerospace Engineering, UCI Samueli School of Engineering, 3225 Engineering Gateway, Irvine, 92697, CA, USA
| | - Nicolas Schweighofer
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, 1042 Downey Way, Los Angeles, 90089, CA, USA
- Division of Biokinesiology and Physical Therapy, University of Southern California, 1540 Alcazar St 155, Los Angeles, 90033, CA, USA
| | - Douglas Weber
- Department of Mechanical Engineering and the Neuroscience Institute, Carnegie Mellon University, 5000 Forbes Avenue, B12 Scaife Hall, Pittsburgh, 15213, PA, USA
| | - Katherine M Steele
- Department of Mechanical Engineering, University of Washington, 3900 E Stevens Way NE, Box 352600, Seattle, 98195, WA, USA
| |
Collapse
|
20
|
Tao G, Yang S, Xu J, Wang L, Yang B. Global research trends and hotspots of artificial intelligence research in spinal cord neural injury and restoration-a bibliometrics and visualization analysis. Front Neurol 2024; 15:1361235. [PMID: 38628700 PMCID: PMC11018935 DOI: 10.3389/fneur.2024.1361235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Accepted: 03/19/2024] [Indexed: 04/19/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Guangyi Tao
- College of Orthopedics and Traumatology, Henan University of Traditional Chinese Medicine, Zhengzhou, China
| | - Shun Yang
- Department of Pain, Henan Provincial Hospital of Traditional Chinese Medicine/The Second Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, China
| | - Junjie Xu
- College of Orthopedics and Traumatology, Henan University of Traditional Chinese Medicine, Zhengzhou, China
| | - Linzi Wang
- College of Orthopedics and Traumatology, Henan University of Traditional Chinese Medicine, Zhengzhou, China
| | - Bin Yang
- Department of Pain, Henan Provincial Hospital of Traditional Chinese Medicine/The Second Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, China
| |
Collapse
|
21
|
Beylergil SB, Noecker AM, Kilbane C, McIntyre CC, Shaikh AG. Does Vestibular Motion Perception Correlate with Axonal Pathways Stimulated by Subthalamic Deep Brain Stimulation in Parkinson's Disease? CEREBELLUM (LONDON, ENGLAND) 2024; 23:554-569. [PMID: 37308757 DOI: 10.1007/s12311-023-01576-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/01/2023] [Indexed: 06/14/2023]
Abstract
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.
Collapse
Affiliation(s)
- Sinem Balta Beylergil
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- National VA Parkinson Consortium Center, Neurology Service, Daroff-Dell'Osso Ocular Motility and Vestibular Laboratory, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA
| | - Angela M Noecker
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Camilla Kilbane
- Department of Neurology, Case Western Reserve University, 11100 Euclid Avenue, Cleveland, OH, 44110, USA
- Movement Disorders Center, Neurological Institute, University Hospitals, Cleveland, OH, USA
| | - Cameron C McIntyre
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Aasef G Shaikh
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
- National VA Parkinson Consortium Center, Neurology Service, Daroff-Dell'Osso Ocular Motility and Vestibular Laboratory, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA.
- Department of Neurology, Case Western Reserve University, 11100 Euclid Avenue, Cleveland, OH, 44110, USA.
- Movement Disorders Center, Neurological Institute, University Hospitals, Cleveland, OH, USA.
| |
Collapse
|
22
|
Davidson B, Vetkas A, Germann J, Tang-Wai D, Lozano AM. Deep brain stimulation for Alzheimer's disease - current status and next steps. Expert Rev Med Devices 2024; 21:285-292. [PMID: 38573133 DOI: 10.1080/17434440.2024.2337298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 03/26/2024] [Indexed: 04/05/2024]
Abstract
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.
Collapse
Affiliation(s)
- Benjamin Davidson
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Canada
| | - Artur Vetkas
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Canada
| | - Jürgen Germann
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Canada
- Krembil Research Institute, Toronto, ON, Canada
| | - David Tang-Wai
- Krembil Research Institute, Toronto, ON, Canada
- Department of Neurology, Toronto Western Hospital, University Health Network, Toronto, University of Toronto, ON, Canada
| | - Andres M Lozano
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Canada
- Krembil Research Institute, Toronto, ON, Canada
| |
Collapse
|
23
|
Tian Y, Murphy MJH, Steiner LA, Kalia SK, Hodaie M, Lozano AM, Hutchison WD, Popovic MR, Milosevic L, Lankarany M. Modeling Instantaneous Firing Rate of Deep Brain Stimulation Target Neuronal Ensembles in the Basal Ganglia and Thalamus. Neuromodulation 2024; 27:464-475. [PMID: 37140523 DOI: 10.1016/j.neurom.2023.03.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 01/27/2023] [Accepted: 03/02/2023] [Indexed: 05/05/2023]
Abstract
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.
Collapse
Affiliation(s)
- Yupeng Tian
- Krembil Research Institute - University Health Network, Toronto, ON, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada; KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, Canada; CRANIA, University Health Network and University of Toronto, Toronto, ON, Canada
| | | | - Leon A Steiner
- Krembil Research Institute - University Health Network, Toronto, ON, Canada; Berlin Institute of Health, Berlin, Germany; Department of Surgery, University of Toronto, Toronto, ON, Canada; Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Suneil K Kalia
- Krembil Research Institute - University Health Network, Toronto, ON, Canada; KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, Canada; CRANIA, University Health Network and University of Toronto, Toronto, ON, Canada; Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Mojgan Hodaie
- Krembil Research Institute - University Health Network, Toronto, ON, Canada; CRANIA, University Health Network and University of Toronto, Toronto, ON, Canada; Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Andres M Lozano
- Krembil Research Institute - University Health Network, Toronto, ON, Canada; CRANIA, University Health Network and University of Toronto, Toronto, ON, Canada; Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - William D Hutchison
- CRANIA, University Health Network and University of Toronto, Toronto, ON, Canada; Department of Surgery, University of Toronto, Toronto, ON, Canada; Department of Physiology, University of Toronto, Toronto, ON, Canada
| | - Milos R Popovic
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada; KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, Canada; CRANIA, University Health Network and University of Toronto, Toronto, ON, Canada
| | - Luka Milosevic
- Krembil Research Institute - University Health Network, Toronto, ON, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada; KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, Canada; CRANIA, University Health Network and University of Toronto, Toronto, ON, Canada
| | - Milad Lankarany
- Krembil Research Institute - University Health Network, Toronto, ON, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada; KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, Canada; CRANIA, University Health Network and University of Toronto, Toronto, ON, Canada; Department of Physiology, University of Toronto, Toronto, ON, Canada.
| |
Collapse
|
24
|
Silva NA, Barrios-Martinez J, Yeh FC, Hodaie M, Roque D, Boerwinkle VL, Krishna V. Diffusion and functional MRI in surgical neuromodulation. Neurotherapeutics 2024; 21:e00364. [PMID: 38669936 PMCID: PMC11064589 DOI: 10.1016/j.neurot.2024.e00364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 04/13/2024] [Accepted: 04/15/2024] [Indexed: 04/28/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Nicole A Silva
- Department of Neurological Surgery, University of North Carolina - Chapel Hill, Chapel Hill, NC, USA
| | | | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mojgan Hodaie
- Division of Neurosurgery, University of Toronto, Toronto, Canada
| | - Daniel Roque
- Department of Neurology, University of North Carolina in Chapel Hill, NC, USA
| | - Varina L Boerwinkle
- Department of Neurology, University of North Carolina in Chapel Hill, NC, USA
| | - Vibhor Krishna
- Department of Neurological Surgery, University of North Carolina - Chapel Hill, Chapel Hill, NC, USA.
| |
Collapse
|
25
|
Abd-Elsayed A, Robinson CL, Marshall Z, Diwan S, Peters T. Applications of Artificial Intelligence in Pain Medicine. Curr Pain Headache Rep 2024; 28:229-238. [PMID: 38345695 DOI: 10.1007/s11916-024-01224-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/30/2024] [Indexed: 03/03/2024]
Abstract
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.
Collapse
Affiliation(s)
- Alaa Abd-Elsayed
- Department of Anesthesiology, School of Medicine and Public Health, University of Wisconsin, 750 Highland Ave, Madison, WI, 53726, USA.
| | - Christopher L Robinson
- Department of Anesthesiology, Critical Care, and Pain Medicine Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | | | - Sudhir Diwan
- Albert Einstein College of Medicine, Lenox Hill Hospital, New York City, NY, USA
| | - Theodore Peters
- Department of Anesthesiology, School of Medicine and Public Health, University of Wisconsin, 750 Highland Ave, Madison, WI, 53726, USA
| |
Collapse
|
26
|
Malaguti MC, Gios L, Giometto B, Longo C, Riello M, Ottaviani D, Pellegrini M, Di Giacopo R, Donner D, Rozzanigo U, Chierici M, Moroni M, Jurman G, Bincoletto G, Pardini M, Bacchin R, Nobili F, Di Biasio F, Avanzino L, Marchese R, Mandich P, Garbarino S, Pagano M, Campi C, Piana M, Marenco M, Uccelli A, Osmani V. 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. PLoS One 2024; 19:e0300127. [PMID: 38483951 PMCID: PMC10939244 DOI: 10.1371/journal.pone.0300127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 02/15/2024] [Indexed: 03/17/2024] Open
Abstract
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.
Collapse
Affiliation(s)
| | - Lorenzo Gios
- TrentinoSalute4.0 –Competence Center for Digital Health of the Province of Trento, Trento, Italy
| | - Bruno Giometto
- Centro Interdipartimentale di Scienze Mediche (CISMed), Facoltà di Medicina e Chirurgia, Università di Trento, Trento, Italy
| | - Chiara Longo
- Azienda Provinciale per i Servizi Sanitari (APSS) di Trento, Trento, Italy
| | - Marianna Riello
- Azienda Provinciale per i Servizi Sanitari (APSS) di Trento, Trento, Italy
| | | | | | | | - Davide Donner
- Azienda Provinciale per i Servizi Sanitari (APSS) di Trento, Trento, Italy
- Department of Medical and Surgical Sciences, Alma Mater Studiorum Università di Bologna, Bologna, Italy
| | - Umberto Rozzanigo
- Azienda Provinciale per i Servizi Sanitari (APSS) di Trento, Trento, Italy
| | | | - Monica Moroni
- Fondazione Bruno Kessler Research Center, Trento, Italy
| | | | | | - Matteo Pardini
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Neuroscience, Rehabilitation, Maternal and Child Health, University of Genoa, Genoa, Italy
| | - Ruggero Bacchin
- Azienda Provinciale per i Servizi Sanitari (APSS) di Trento, Trento, Italy
| | - Flavio Nobili
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | | | - Laura Avanzino
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Experimental Medicine, Section of Human Physiology, University of Genoa, Genoa, Italy
| | | | - Paola Mandich
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- DINOGMI Department, University of Genoa, Genoa, Italy
| | | | - Mattia Pagano
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Cristina Campi
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Dipartimento Di Matematica, Università Di Genova, Genoa, Italy
| | - Michele Piana
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Dipartimento Di Matematica, Università Di Genova, Genoa, Italy
| | | | | | - Venet Osmani
- Fondazione Bruno Kessler Research Center, Trento, Italy
| |
Collapse
|
27
|
Gao C, Wu X, Cheng X, Madsen KH, Chu C, Yang Z, Fan L. Individualized brain mapping for navigated neuromodulation. Chin Med J (Engl) 2024; 137:508-523. [PMID: 38269482 PMCID: PMC10932519 DOI: 10.1097/cm9.0000000000002979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Indexed: 01/26/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Chaohong Gao
- Sino–Danish College, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Xia Wu
- Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Xinle Cheng
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Kristoffer Hougaard Madsen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby 2800, Denmark
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Hvidovre 2650, Denmark
| | - Congying Chu
- Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Zhengyi Yang
- Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Lingzhong Fan
- Sino–Danish College, University of Chinese Academy of Sciences, Beijing 100190, China
- Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao, Shandong 266000, China
| |
Collapse
|
28
|
Deuter D, Mederer T, Kohl Z, Forras P, Rosengarth K, Schlabeck M, Röhrl D, Wendl C, Fellner C, Schmidt NO, Schlaier J. Amelioration of Parkinsonian tremor evoked by DBS: which role play cerebello-(sub)thalamic fiber tracts? J Neurol 2024; 271:1451-1461. [PMID: 38032372 PMCID: PMC10896868 DOI: 10.1007/s00415-023-12095-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 11/01/2023] [Accepted: 11/02/2023] [Indexed: 12/01/2023]
Abstract
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.
Collapse
Affiliation(s)
- Daniel Deuter
- Department of Neurosurgery, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany.
- Center for Deep Brain Stimulation, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany.
| | - Tobias Mederer
- Department of Neurosurgery, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
- Center for Deep Brain Stimulation, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
| | - Zacharias Kohl
- Center for Deep Brain Stimulation, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
- Department of Neurology, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
- Department of Neurology, Regensburg Medbo District Hospital, Universitätsstraße 84, 93053, Regensburg, Germany
| | - Patricia Forras
- Center for Deep Brain Stimulation, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
- Department of Neurology, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
- Department of Neurology, Regensburg Medbo District Hospital, Universitätsstraße 84, 93053, Regensburg, Germany
| | - Katharina Rosengarth
- Department of Neurosurgery, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
| | - Mona Schlabeck
- Center for Deep Brain Stimulation, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
- Department of Anesthesiology, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
| | - Daniela Röhrl
- Center for Deep Brain Stimulation, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
- Department of Anesthesiology, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
| | - Christina Wendl
- Center for Deep Brain Stimulation, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
- Department of Radiology, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
- Department of Radiology, Regensburg Medbo District Hospital, Universitätsstraße 84, 93053, Regensburg, Germany
| | - Claudia Fellner
- Department of Radiology, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
| | - Nils-Ole Schmidt
- Department of Neurosurgery, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
| | - Jürgen Schlaier
- Department of Neurosurgery, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
- Center for Deep Brain Stimulation, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
| |
Collapse
|
29
|
Ooi LQR, Orban C, Nichols TE, Zhang S, Tan TWK, Kong R, Marek S, Dosenbach NU, Laumann T, Gordon EM, Zhou JH, Bzdok D, Eickhoff SB, Holmes AJ, Yeo BTT. MRI economics: Balancing sample size and scan duration in brain wide association studies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.16.580448. [PMID: 38405815 PMCID: PMC10889017 DOI: 10.1101/2024.02.16.580448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
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.
Collapse
Affiliation(s)
- Leon Qi Rong Ooi
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
| | - Csaba Orban
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
| | - Thomas E Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Shaoshi Zhang
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
| | - Trevor Wei Kiat Tan
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
| | - Ru Kong
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
| | - Scott Marek
- Mallinckrodt Institute of Radiology, Washington University, School of Medicine, USA
| | - Nico U.F. Dosenbach
- Mallinckrodt Institute of Radiology, Washington University, School of Medicine, USA
- Department of Neurology, Washington University, School of Medicine, USA
- Department of Psychiatry, Washington University, School of Medicine, USA
- Deparments of Paediatrics, Biomedical Engineering, and Psychological and Brain Sciences, Washington University, School of Medicine, USA
| | - Timothy Laumann
- Department of Psychiatry, Washington University, School of Medicine, USA
| | - Evan M Gordon
- Mallinckrodt Institute of Radiology, Washington University, School of Medicine, USA
| | - Juan Helen Zhou
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | - Danilo Bzdok
- Department of Biomedical Engineering, McConnell Brain Imaging Centre, Montreal Neurological Institute, Canada
- Faculty of Medicine, School of Computer Science, McGill University, Montreal, QC, Canada
- Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Avram J Holmes
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA
| | - B. T. Thomas Yeo
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
| |
Collapse
|
30
|
Roalf DR, Figee M, Oathes DJ. Elevating the field for applying neuroimaging to individual patients in psychiatry. Transl Psychiatry 2024; 14:87. [PMID: 38341414 PMCID: PMC10858949 DOI: 10.1038/s41398-024-02781-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 12/06/2023] [Accepted: 01/15/2024] [Indexed: 02/12/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- David R Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Martijn Figee
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Desmond J Oathes
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Center for Brain Imaging and Stimulation, University of Pennsylvania, Philadelphia, PA, USA.
- Center for Neuromodulation in Depression and Stress, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Brain Science Translation, Innovation, and Modulation Center, University of Pennsylvania, Philadelphia, PA, USA.
| |
Collapse
|
31
|
O'Keeffe AB, Merla A, Ashkan K. Deep brain stimulation of the subthalamic nucleus in Parkinson disease 2013-2023: where are we a further 10 years on? Br J Neurosurg 2024:1-9. [PMID: 38323603 DOI: 10.1080/02688697.2024.2311128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 01/23/2024] [Indexed: 02/08/2024]
Abstract
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.
Collapse
Affiliation(s)
| | - Anca Merla
- King's College Hospital Department of Neurosurgery, Kings College Hospital, Denmark
| | - Keyoumars Ashkan
- King's College Hospital Department of Neurosurgery, Kings College Hospital, Denmark
| |
Collapse
|
32
|
Narayanan RP, Khaleghi A, Veletić M, Balasingham I. Multiphysics simulation of magnetoelectric micro core-shells for wireless cellular stimulation therapy via magnetic temporal interference. PLoS One 2024; 19:e0297114. [PMID: 38271467 PMCID: PMC10834063 DOI: 10.1371/journal.pone.0297114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 12/28/2023] [Indexed: 01/27/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Ram Prasadh Narayanan
- Institute of Electronic Systems, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ali Khaleghi
- Institute of Electronic Systems, Norwegian University of Science and Technology, Trondheim, Norway
- Intervention Center, Oslo University Hospital, Oslo, Norway
| | - Mladen Veletić
- Institute of Electronic Systems, Norwegian University of Science and Technology, Trondheim, Norway
- Intervention Center, Oslo University Hospital, Oslo, Norway
| | - Ilangko Balasingham
- Institute of Electronic Systems, Norwegian University of Science and Technology, Trondheim, Norway
- Intervention Center, Oslo University Hospital, Oslo, Norway
| |
Collapse
|
33
|
Wang J, Xue L, Jiang J, Liu F, Wu P, Lu J, Zhang H, Bao W, Xu Q, Ju Z, Chen L, Jiao F, Lin H, Ge J, Zuo C, Tian M. Diagnostic performance of artificial intelligence-assisted PET imaging for Parkinson's disease: a systematic review and meta-analysis. NPJ Digit Med 2024; 7:17. [PMID: 38253738 PMCID: PMC10803804 DOI: 10.1038/s41746-024-01012-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 01/10/2024] [Indexed: 01/24/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Jing Wang
- Huashan Hospital & Human Phenome Institute, Fudan University, Shanghai, China
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Le Xue
- Department of Nuclear Medicine, the Second Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jiehui Jiang
- Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai, China
| | - Fengtao Liu
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Aging and Medicine, & National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Ping Wu
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Jiaying Lu
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Huiwei Zhang
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Weiqi Bao
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Qian Xu
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Zizhao Ju
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Li Chen
- Department of Ultrasound Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Fangyang Jiao
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Huamei Lin
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Jingjie Ge
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China.
| | - Chuantao Zuo
- Huashan Hospital & Human Phenome Institute, Fudan University, Shanghai, China.
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China.
- National Clinical Research Center for Aging and Medicine, & National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China.
| | - Mei Tian
- Huashan Hospital & Human Phenome Institute, Fudan University, Shanghai, China.
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China.
| |
Collapse
|
34
|
Santyr B, Loh A, Vetkas A, Gwun D, Fung WK, Qazi S, Germann J, Boutet A, Sarica C, Yang A, Elias G, Kalia SK, Fasano A, Lozano AM. Uncovering neuroanatomical correlates of impaired coordinated movement after pallidal deep brain stimulation. J Neurol Neurosurg Psychiatry 2024; 95:167-170. [PMID: 37438098 DOI: 10.1136/jnnp-2022-330734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 05/30/2023] [Indexed: 07/14/2023]
Abstract
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.
Collapse
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
| |
Collapse
|
35
|
Sellers KK, Cohen JL, Khambhati AN, Fan JM, Lee AM, Chang EF, Krystal AD. Closed-loop neurostimulation for the treatment of psychiatric disorders. Neuropsychopharmacology 2024; 49:163-178. [PMID: 37369777 PMCID: PMC10700557 DOI: 10.1038/s41386-023-01631-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/31/2023] [Accepted: 06/02/2023] [Indexed: 06/29/2023]
Abstract
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.
Collapse
Affiliation(s)
- Kristin K Sellers
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Joshua L Cohen
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Ankit N Khambhati
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Joline M Fan
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
- Department of Neurology, University of California, San Francisco, CA, USA
| | - A Moses Lee
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Edward F Chang
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Andrew D Krystal
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA.
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA.
| |
Collapse
|
36
|
Bobin M, Sulzer N, Bründler G, Staib M, Imbach LL, Stieglitz LH, Krauss P, Bichsel O, Baumann CR, Frühholz S. Direct subthalamic nucleus stimulation influences speech and voice quality in Parkinson's disease patients. Brain Stimul 2024; 17:112-124. [PMID: 38272256 DOI: 10.1016/j.brs.2024.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 12/21/2023] [Accepted: 01/16/2024] [Indexed: 01/27/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Marine Bobin
- Cognitive and Affective Neuroscience Unit, University of Zürich, 8050 Zürich, Switzerland; Neuroscience Center Zurich, University of Zurich and ETH Zurich, 8057 Zurich, Switzerland
| | - Neil Sulzer
- Cognitive and Affective Neuroscience Unit, University of Zürich, 8050 Zürich, Switzerland
| | - Gina Bründler
- Cognitive and Affective Neuroscience Unit, University of Zürich, 8050 Zürich, Switzerland
| | - Matthias Staib
- Cognitive and Affective Neuroscience Unit, University of Zürich, 8050 Zürich, Switzerland; Neuroscience Center Zurich, University of Zurich and ETH Zurich, 8057 Zurich, Switzerland
| | - Lukas L Imbach
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, 8057 Zurich, Switzerland; Department of Neurology, University Hospital Zurich, 8091 Zurich, Switzerland; Swiss Epilepsy Center, Klinik Lengg, 8008 Zurich, Switzerland
| | - Lennart H Stieglitz
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Philipp Krauss
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland; Department of Neurosurgery, University Hospital Augsburg, 86159 Augsburg, Germany
| | - Oliver Bichsel
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Christian R Baumann
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, 8057 Zurich, Switzerland; Department of Neurology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Sascha Frühholz
- Cognitive and Affective Neuroscience Unit, University of Zürich, 8050 Zürich, Switzerland; Neuroscience Center Zurich, University of Zurich and ETH Zurich, 8057 Zurich, Switzerland; Department of Psychology, University of Oslo, 0373 Oslo, Norway.
| |
Collapse
|
37
|
Hossain MA, Amenta F. Machine Learning-Based Classification of Parkinson's Disease Patients Using Speech Biomarkers. JOURNAL OF PARKINSON'S DISEASE 2024; 14:95-109. [PMID: 38160364 PMCID: PMC10836572 DOI: 10.3233/jpd-230002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/09/2023] [Indexed: 01/03/2024]
Abstract
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.
Collapse
Affiliation(s)
- Mohammad Amran Hossain
- Telemedicine and Telepharmacy Centre, School of Medicinal and Health Products Sciences, University of Camerino, Camerino, Italy
| | - Francesco Amenta
- Telemedicine and Telepharmacy Centre, School of Medicinal and Health Products Sciences, University of Camerino, Camerino, Italy
| |
Collapse
|
38
|
Chen H, Liu P, Chen Z, Chen Q, Wen Z, Xie Z. Predicting sequenced dental treatment plans from electronic dental records using deep learning. Artif Intell Med 2024; 147:102734. [PMID: 38184358 DOI: 10.1016/j.artmed.2023.102734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 02/26/2023] [Accepted: 11/28/2023] [Indexed: 01/08/2024]
Abstract
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.
Collapse
Affiliation(s)
- Haifan Chen
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China; Xiangjiang Laboratory, Changsha, PR China
| | - Pufan Liu
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, PR China
| | - Zhaoxing Chen
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China; Xiangjiang Laboratory, Changsha, PR China
| | - Qingxiao Chen
- Peking University School and Hospital of Stomatology, Beijing, PR China; Georgia Institute of Technology, College of Computing, USA.
| | - Zaiwen Wen
- Beijing International Center for Mathematical Research, Peking University, Beijing, PR China
| | - Ziqing Xie
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China; Xiangjiang Laboratory, Changsha, PR China
| |
Collapse
|
39
|
Nguyen HH, Blaschko MB, Saarakkala S, Tiulpin A. Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting From Multimodal Data. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:529-541. [PMID: 37672368 PMCID: PMC10880139 DOI: 10.1109/tmi.2023.3312524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
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.
Collapse
|
40
|
Song W, Wu F, Yan Y, Li Y, Wang Q, Hu X, Li Y. Gut microbiota landscape and potential biomarker identification in female patients with systemic lupus erythematosus using machine learning. Front Cell Infect Microbiol 2023; 13:1289124. [PMID: 38169617 PMCID: PMC10758415 DOI: 10.3389/fcimb.2023.1289124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 11/28/2023] [Indexed: 01/05/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Wenzhu Song
- School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Feng Wu
- Department of Nephrology, Shanxi Provincial People's Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan, China
| | - Yan Yan
- Department of Nephrology, Shanxi Provincial People's Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan, China
| | - Yaheng Li
- Shanxi Provincial Key Laboratory of Kidney Disease, Taiyuan, Shanxiuan, China
| | - Qian Wang
- Shanxi Provincial Key Laboratory of Kidney Disease, Taiyuan, Shanxiuan, China
| | - Xueli Hu
- Department of Nephrology, Hejin People’s Hospital, Yuncheng, Shanxi, China
| | - Yafeng Li
- Department of Nephrology, Shanxi Provincial People's Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan, China
- Shanxi Provincial Key Laboratory of Kidney Disease, Taiyuan, Shanxiuan, China
- Core Laboratory, Shanxi Provincial People's Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan, China
- Academy of Microbial Ecology, Shanxi Medical University, Taiyuan, China
| |
Collapse
|
41
|
Smith CM, Weathers AL, Lewis SL. An overview of clinical machine learning applications in neurology. J Neurol Sci 2023; 455:122799. [PMID: 37979413 DOI: 10.1016/j.jns.2023.122799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 10/26/2023] [Accepted: 11/12/2023] [Indexed: 11/20/2023]
Abstract
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.
Collapse
Affiliation(s)
- Colin M Smith
- Lehigh Valley Fleming Neuroscience Institute, 1250 S Cedar Crest Blvd., Allentown, PA 18103, USA
| | - Allison L Weathers
- Cleveland Clinic Information Technology Division, 9500 Euclid Ave. Cleveland, OH 44195, USA
| | - Steven L Lewis
- Lehigh Valley Fleming Neuroscience Institute, 1250 S Cedar Crest Blvd., Allentown, PA 18103, USA.
| |
Collapse
|
42
|
Zhang J, Zhou W, Yu H, Wang T, Wang X, Liu L, Wen Y. Prediction of Parkinson's Disease Using Machine Learning Methods. Biomolecules 2023; 13:1761. [PMID: 38136632 PMCID: PMC10741603 DOI: 10.3390/biom13121761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 11/29/2023] [Accepted: 12/06/2023] [Indexed: 12/24/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Jiayu Zhang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, No. 56 Xinjian South Road, Yingze District, Taiyuan 030001, China; (J.Z.); (W.Z.); (H.Y.); (T.W.)
| | - Wenchao Zhou
- Department of Health Statistics, School of Public Health, Shanxi Medical University, No. 56 Xinjian South Road, Yingze District, Taiyuan 030001, China; (J.Z.); (W.Z.); (H.Y.); (T.W.)
| | - Hongmei Yu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, No. 56 Xinjian South Road, Yingze District, Taiyuan 030001, China; (J.Z.); (W.Z.); (H.Y.); (T.W.)
| | - Tong Wang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, No. 56 Xinjian South Road, Yingze District, Taiyuan 030001, China; (J.Z.); (W.Z.); (H.Y.); (T.W.)
| | - Xiaqiong Wang
- Department of Epidemiology and Biostatistics, Southeast University, 87 Ding Jiaqiao Road, Nanjing 210009, China;
| | - Long Liu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, No. 56 Xinjian South Road, Yingze District, Taiyuan 030001, China; (J.Z.); (W.Z.); (H.Y.); (T.W.)
| | - Yalu Wen
- Department of Statistics, University of Auckland, 38 Princes Street, Auckland Central, Auckland 1010, New Zealand
| |
Collapse
|
43
|
Jiang W, Li Q, Zhang R, Li J, Lin Q, Li J, Zhou X, Yan X, Fan K. Chiral metal-organic frameworks incorporating nanozymes as neuroinflammation inhibitors for managing Parkinson's disease. Nat Commun 2023; 14:8137. [PMID: 38065945 PMCID: PMC10709450 DOI: 10.1038/s41467-023-43870-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 11/21/2023] [Indexed: 12/18/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Wei Jiang
- Application Center for Precision Medicine, the Second Affiliated Hospital of Zhengzhou University, Henan, 450052, China
- Nanozyme Medical Center, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, 450001, China
| | - Qing Li
- Application Center for Precision Medicine, the Second Affiliated Hospital of Zhengzhou University, Henan, 450052, China.
| | - Ruofei Zhang
- CAS Engineering Laboratory for Nanozyme, Key Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Beijing, 100101, China
| | - Jianru Li
- CAS Engineering Laboratory for Nanozyme, Key Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Beijing, 100101, China
| | - Qianyu Lin
- Application Center for Precision Medicine, the Second Affiliated Hospital of Zhengzhou University, Henan, 450052, China
| | - Jingyun Li
- Nanozyme Medical Center, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, 450001, China
- CAS Engineering Laboratory for Nanozyme, Key Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Beijing, 100101, China
| | - Xinyao Zhou
- School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, 19104, USA
| | - Xiyun Yan
- Nanozyme Medical Center, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, 450001, China.
- CAS Engineering Laboratory for Nanozyme, Key Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Beijing, 100101, China.
| | - Kelong Fan
- Nanozyme Medical Center, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, 450001, China.
- CAS Engineering Laboratory for Nanozyme, Key Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Beijing, 100101, China.
| |
Collapse
|
44
|
Ragothaman A, Mancini M, Nutt JG, Wang J, Fair DA, Horak FB, Miranda-Dominguez O. Motor networks, but also non-motor networks predict motor signs in Parkinson's disease. Neuroimage Clin 2023; 40:103541. [PMID: 37972450 PMCID: PMC10685308 DOI: 10.1016/j.nicl.2023.103541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 10/31/2023] [Accepted: 11/10/2023] [Indexed: 11/19/2023]
Abstract
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.
Collapse
Affiliation(s)
| | - Martina Mancini
- Department of Neurology, Oregon Health and Science University, Portland, OR 97239, USA
| | - John G Nutt
- Department of Neurology, Oregon Health and Science University, Portland, OR 97239, USA
| | - Junping Wang
- Department of Radiology, Tianjin Medical University General Hospital, China
| | - Damien A Fair
- Masonic Institute for the Developing Brain (MIDB), University of Minnesota, Minneapolis, MN 55455, USA; Institute of Child Development, College of Education and Human Development, University of Minnesota, Minneapolis, MN 55455, USA; Department of Pediatrics, University of Minnesota Medical School, University of Minnesota, Minneapolis, MN 55455, USA; Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR 97239, USA
| | - Fay B Horak
- Department of Neurology, Oregon Health and Science University, Portland, OR 97239, USA; Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR 97239, USA.
| | - Oscar Miranda-Dominguez
- Masonic Institute for the Developing Brain (MIDB), University of Minnesota, Minneapolis, MN 55455, USA; Department of Pediatrics, University of Minnesota Medical School, University of Minnesota, Minneapolis, MN 55455, USA
| |
Collapse
|
45
|
Sandoval-Pistorius SS, Hacker ML, Waters AC, Wang J, Provenza NR, de Hemptinne C, Johnson KA, Morrison MA, Cernera S. Advances in Deep Brain Stimulation: From Mechanisms to Applications. J Neurosci 2023; 43:7575-7586. [PMID: 37940596 PMCID: PMC10634582 DOI: 10.1523/jneurosci.1427-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/14/2023] [Accepted: 08/15/2023] [Indexed: 11/10/2023] Open
Abstract
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.
Collapse
Affiliation(s)
| | - Mallory L Hacker
- Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee 37232
| | - Allison C Waters
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York 10029
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York 10029
| | - Jing Wang
- Department of Neurology, University of Minnesota, Minneapolis, Minnesota 55455
| | - Nicole R Provenza
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas 77030
| | - Coralie de Hemptinne
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, Florida 32608
| | - Kara A Johnson
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, Florida 32608
| | - Melanie A Morrison
- Department of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, California 94143
| | - Stephanie Cernera
- Department of Neurological Surgery, University of California-San Francisco, San Francisco, California 94143
| |
Collapse
|
46
|
Finn ES, Poldrack RA, Shine JM. Functional neuroimaging as a catalyst for integrated neuroscience. Nature 2023; 623:263-273. [PMID: 37938706 DOI: 10.1038/s41586-023-06670-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 09/22/2023] [Indexed: 11/09/2023]
Abstract
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.
Collapse
Affiliation(s)
- Emily S Finn
- Department of Psychological and Brain Sciences, Dartmouth College, Dartmouth, NH, USA.
| | | | - James M Shine
- School of Medical Sciences, University of Sydney, Sydney, New South Wales, Australia.
| |
Collapse
|
47
|
Chen H, Li M, Qin Z, Yang Z, Lv T, Yao W, Hu Z, Qin R, Zhao H, Bai F. Functional network connectivity patterns predicting the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer's disease. Eur Radiol Exp 2023; 7:63. [PMID: 37872457 PMCID: PMC10593644 DOI: 10.1186/s41747-023-00376-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 08/17/2023] [Indexed: 10/25/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Haifeng Chen
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Mengyun Li
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Zhiming Qin
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Zhiyuan Yang
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Tingyu Lv
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Weina Yao
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Zheqi Hu
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Ruomeng Qin
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Hui Zhao
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Feng Bai
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China.
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China.
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China.
- Geriatric Medicine Center, Affiliated Hospital of Medical School, Taikang Xianlin Drum Tower Hospital, Nanjing University, Nanjing, China.
| |
Collapse
|
48
|
Staudt MD, Yaghi NK, Mazur-Hart DJ, Shirvalkar P. Editorial: Advancements in deep brain stimulation for chronic pain control. FRONTIERS IN PAIN RESEARCH 2023; 4:1293919. [PMID: 37936962 PMCID: PMC10627217 DOI: 10.3389/fpain.2023.1293919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 10/16/2023] [Indexed: 11/09/2023] Open
Affiliation(s)
- Michael D. Staudt
- Department of Neurosurgery, Beaumont Neuroscience Center, Royal Oak, MI, United States
- Department of Neurosurgery, Oakland University William Beaumont School of Medicine, Rochester, MI, United States
| | - Nasser K. Yaghi
- Department of Neurosurgery, Barrow Neurological Institute, Phoenix, AZ, United States
| | - David J. Mazur-Hart
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, United States
| | - Prasad Shirvalkar
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, United States
- Department of Anesthesiology and Perioperative Care, Division of Pain Medicine, University of California San Francisco, San Francisco, CA, United States
| |
Collapse
|
49
|
Diao Y, Hu T, Xie H, Fan H, Meng F, Yang A, Bai Y, Zhang J. Premature drug reduction after subthalamic nucleus deep brain stimulation leading to worse depression in patients with Parkinson's disease. Front Neurol 2023; 14:1270746. [PMID: 37928164 PMCID: PMC10620523 DOI: 10.3389/fneur.2023.1270746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 09/26/2023] [Indexed: 11/07/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Yu Diao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tianqi Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Hutao Xie
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Houyou Fan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Fangang Meng
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Anchao Yang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Yutong Bai
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| |
Collapse
|
50
|
Lin C, Liu D, Liu Y, Chen Z, Wei X, Liu H, Wang K, Liu T, Xiao L, Rong L. Altered functional activity of the precuneus and superior temporal gyrus in patients with residual dizziness caused by benign paroxysmal positional vertigo. Front Neurosci 2023; 17:1221579. [PMID: 37901419 PMCID: PMC10600499 DOI: 10.3389/fnins.2023.1221579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 09/29/2023] [Indexed: 10/31/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Cunxin Lin
- Department of Neurology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
- Graduate School of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Dan Liu
- Department of Neurology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
- Graduate School of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Yueji Liu
- Department of Neurology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
- Graduate School of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Zhengwei Chen
- Department of Neurology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Xiue Wei
- Department of Neurology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Haiyan Liu
- Department of Neurology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Kai Wang
- Department of Neurology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Tengfei Liu
- Department of Neurology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Lijie Xiao
- Department of Neurology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Liangqun Rong
- Department of Neurology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| |
Collapse
|