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Zhang C, Yu S. The Technology to Enhance Patient Motivation in Virtual Reality Rehabilitation: A Review. Games Health J 2024; 13:215-233. [PMID: 39159237 DOI: 10.1089/g4h.2023.0069] [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] [Indexed: 08/21/2024] Open
Abstract
Virtual reality (VR) technology has experienced a steady rise and has been widely applied in the field of rehabilitation. The integration of VR technology in rehabilitation has shown promising results in enhancing their motivation for treatment, thereby enabling patients to actively engage in rehab training. Despite the advancement, there is a dearth of comprehensive summary and analysis on the use of VR technology to enhance patient motivation in rehabilitation. Thus, this narrative review aims to evaluate the potential of VR technology in enhancing patient motivation during motor rehabilitation training. This review commences with an explanation of how enhancing motivation through the VR rehabilitation system could improve the efficiency and effectiveness of rehabilitation training. Then, the technology was analyzed to improve patient motivation in the present VR rehabilitation system in detail. Furthermore, these technologies are classified and summarized to provide a comprehensive overview of the state-of-the-art approaches for enhancing patient motivation in VR rehabilitation. Findings showed VR rehabilitation training utilizes game-like exercises to enhance the engagement and enjoyment of rehabilitation training. By immersing patients in a simulated environment with multisensory feedback, VR systems offer a unique approach to rehabilitation that can lead to improved patient motivation. Both ultimately lead to improved patient outcomes, which is not typically achievable with traditional rehabilitation methods. The review concludes that VR rehabilitation presents an opportunity to improve patient motivation and adherence to long-term rehabilitation training. However, to further enhance patient self-efficacy, VR rehabilitation should integrate psychology and incorporate methods. Moreover, it is necessary to build a game design theory for rehabilitation games, and the latest VR feedback technology should also be introduced.
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Affiliation(s)
- Chengjie Zhang
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Suiran Yu
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
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Hajebrahimi F, Sangoi A, Scheiman M, Santos E, Gohel S, Alvarez TL. From convergence insufficiency to functional reorganization: A longitudinal randomized controlled trial of treatment-induced connectivity plasticity. CNS Neurosci Ther 2024; 30:e70007. [PMID: 39185637 PMCID: PMC11345633 DOI: 10.1111/cns.70007] [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: 03/26/2024] [Revised: 07/11/2024] [Accepted: 08/08/2024] [Indexed: 08/27/2024] Open
Abstract
INTRODUCTION Convergence Insufficiency (CI) is the most prevalent oculomotor dysfunction of binocular vision that negatively impacts quality of life when performing visual near tasks. Decreased resting-state functional connectivity (RSFC) is reported in the CI participants compared to binocularly normal control participants. Studies report that therapeutic interventions such as office-based vergence and accommodative therapy (OBVAT) can improve CI participants' clinical signs, visual symptoms, and task-related functional activity. However, longitudinal studies investigating the RSFC changes after such treatments in participants with CI have not been conducted. This study aimed to investigate the neural basis of OBVAT using RSFC in CI participants compared to the placebo treatment to understand how OBVAT improves visual function and symptoms. METHODS A total of 51 CI participants between 18 and 35 years of age were included in the study and randomly allocated to receive either 12 one-hour sessions of OBVAT or placebo treatment for 6 to 8 weeks (1 to 2 sessions per week). Resting-state functional magnetic resonance imaging and clinical assessments were evaluated at baseline and outcome for each treatment group. Region of interest (ROI) analysis was conducted in nine ROIs of the oculomotor vergence network, including the following: cerebellar vermis (CV), frontal eye fields (FEF), supplementary eye fields (SEF), parietal eye fields (PEF), and primary visual cortices (V1). Paired t-tests assessed RSFC changes in each group. A linear regression analysis was conducted for significant ROI pairs in the group-level analysis for correlations with clinical measures. RESULTS Paired t-test results showed increased RSFC in 10 ROI pairs after the OBVAT but not placebo treatment (p < 0.05, false discovery rate corrected). These ROI pairs included the following: Left (L)-SEF-Right (R)-V1, L-SEF-CV, R-SEF-R-PEF, R-SEF-L-V1, R-SEF-R-V1, R-SEF-CV, R-PEF-CV, L-V1-CV, R-V1-CV, and L-V1-R-V1. Significant correlations were observed between the RSFC strength of the R-SEF-R-PEF ROI pair and the following clinical visual function parameters: positive fusional vergence and near point of convergence (p < 0.05). CONCLUSION OBVAT, but not placebo treatment, increased the RSFC in the ROIs of the oculomotor vergence network, which was correlated with the improvements in the clinical measures of the CI participants.
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Affiliation(s)
- Farzin Hajebrahimi
- Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkNew JerseyUSA
| | - Ayushi Sangoi
- Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkNew JerseyUSA
| | - Mitchell Scheiman
- Pennsylvania College of OptometrySalus UniversityPhiladelphiaPennsylvaniaUSA
| | - Elio Santos
- Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkNew JerseyUSA
| | - Suril Gohel
- Department of Health InformaticsRutgers University School of Health ProfessionsNewarkNew JerseyUSA
| | - Tara L. Alvarez
- Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkNew JerseyUSA
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Capato TTC, Chen J, Miranda JDA, Chien HF. Assisted technology in Parkinson's disease gait: what's up? ARQUIVOS DE NEURO-PSIQUIATRIA 2024; 82:1-10. [PMID: 38395424 PMCID: PMC10890908 DOI: 10.1055/s-0043-1777782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 11/21/2023] [Indexed: 02/25/2024]
Abstract
BACKGROUND Gait disturbances are prevalent and debilitating symptoms, diminishing mobility and quality of life for Parkinson's disease (PD) individuals. While traditional treatments offer partial relief, there is a growing interest in alternative interventions to address this challenge. Recently, a remarkable surge in assisted technology (AT) development was witnessed to aid individuals with PD. OBJECTIVE To explore the burgeoning landscape of AT interventions tailored to alleviate PD-related gait impairments and describe current research related to such aim. METHODS In this review, we searched on PubMed for papers published in English (2018-2023). Additionally, the abstract of each study was read to ensure inclusion. Four researchers searched independently, including studies according to our inclusion and exclusion criteria. RESULTS We included studies that met all inclusion criteria. We identified key trends in assistive technology of gait parameters analysis in PD. These encompass wearable sensors, gait analysis, real-time feedback and cueing techniques, virtual reality, and robotics. CONCLUSION This review provides a resource for guiding future research, informing clinical decisions, and fostering collaboration among researchers, clinicians, and policymakers. By delineating this rapidly evolving field's contours, it aims to inspire further innovation, ultimately improving the lives of PD patients through more effective and personalized interventions.
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Affiliation(s)
- Tamine T. C. Capato
- Universidade de São Paulo, Faculdade de Medicina, Departamento de Neurologia, Centro de Distúrbios do Movimento, São Paulo SP, Brazil.
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Nijmegen, The Netherlands.
| | - Janini Chen
- Universidade de São Paulo, Faculdade de Medicina FMUSP, Departamento de Ortopedia e Traumatologia, São Paulo, SP, Brazil.
| | - Johnny de Araújo Miranda
- Universidade de São Paulo, Faculdade de Medicina, Departamento de Neurologia, Centro de Distúrbios do Movimento, São Paulo SP, Brazil.
| | - Hsin Fen Chien
- Universidade de São Paulo, Faculdade de Medicina FMUSP, Departamento de Ortopedia e Traumatologia, São Paulo, SP, Brazil.
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Costa V, Prati JM, de Oliveira Barreto Suassuna A, Souza Silva Brito T, Frigo da Rocha T, Gianlorenço AC. Physical Exercise for Treating the Anxiety and Depression Symptoms of Parkinson's Disease: Systematic Review and Meta-Analysis. J Geriatr Psychiatry Neurol 2024:8919887241237223. [PMID: 38445606 DOI: 10.1177/08919887241237223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
BACKGROUND Depression and anxiety are non-motor symptoms of Parkinson's disease (PD). Physical exercise is a promising approach to reducing neuropsychological burden. We aimed to comprehensively synthesize evidence regarding the use of exercise for treating depression and anxiety symptoms in PD. METHODS Systematic review and meta-analysis following PRISMA recommendations. Searches on PubMed, Cochrane Library, Scopus, Web of Science, Embase, and Physiotherapy Evidence Database (PEDro) was conducted. The random-effects model was employed for all analyses with the standardized mean difference as the effect estimate. RESULTS Fifty records were retrieved, but only 17 studies met the criteria for the meta-analyses. A moderate to large effect was observed for depression (-.71 [95% CI = -.96 to -.46], 11 studies, 728 individuals), and a small to moderate effect for anxiety (-.39 [95% CI = -.65 to -.14], 6 studies, 241 individuals), when comparing exercise to non-exercise controls. Subgroup analysis revealed significant effects from aerobic (-.95 [95% CI = -1.60, -.31]), mind-body (-1.85 [95% CI = -2.63, -1.07]), and resistance modalities (-1.61 [95% CI = -2.40, -.83]) for depression, and from mind-body (-.67 [95% CI = -1.19 to -.15]) and resistance exercises (-1.00 [95% CI = -1.70 to -.30]) for anxiety. CONCLUSION Physical exercise has a relevant clinical impact on depression and anxiety in PD. We discuss the level of the evidence, the methodological limitations of the studies, and give recommendations.
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Affiliation(s)
- Valton Costa
- Neurosciences Laboratory, Physical Therapy Graduate Program, Federal University of Sao Carlos, Sao Carlos, Brazil
| | - José Mario Prati
- Neurosciences Laboratory, Physical Therapy Graduate Program, Federal University of Sao Carlos, Sao Carlos, Brazil
| | - Alice de Oliveira Barreto Suassuna
- Centre for Innovation and Technology Assessment in Health, Postgraduate Program in Electrical and Biomedical Engineering, Federal University of Uberlandia, Uberlandia, Brazil
| | - Thanielle Souza Silva Brito
- Neurosciences Laboratory, Physical Therapy Graduate Program, Federal University of Sao Carlos, Sao Carlos, Brazil
| | - Thalita Frigo da Rocha
- Neurosciences Laboratory, Physical Therapy Graduate Program, Federal University of Sao Carlos, Sao Carlos, Brazil
| | - Anna Carolyna Gianlorenço
- Neurosciences Laboratory, Physical Therapy Graduate Program, Federal University of Sao Carlos, Sao Carlos, Brazil
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Yu J, Wu J, Liu B, Zheng K, Ren Z. Efficacy of virtual reality technology interventions for cognitive and mental outcomes in older people with cognitive disorders: An umbrella review comprising meta-analyses of randomized controlled trials. Ageing Res Rev 2024; 94:102179. [PMID: 38163517 DOI: 10.1016/j.arr.2023.102179] [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: 07/05/2023] [Revised: 12/25/2023] [Accepted: 12/25/2023] [Indexed: 01/03/2024]
Abstract
We conducted an umbrella review of virtual reality (VR) technology interventions and cognitive improvement in older adults with cognitive disorders to establish a hierarchy of evidence. We systematically searched PubMed, Web of Science, Scopus, and PsycINFO databases from database creation to February 2023. We included meta-analyses relevant to our study objectives for the overall review. We assessed the methodological quality according to AMSTAR2, and we used the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) method to assess the credibility of the evidence. This overall review was registered with the International Prospective Register of Systematic Reviews (CRD42023423063). We identified six meta-analyses that included 12 cognitive outcomes, but only memory (Standardized Mean Difference(SMD) = 0.27, 95% confidence interval (CI): 0.04 to 0.49), depression (SMD = -1.26, 95% CI: -1.8 to -0.72), and global cognition (SMD = 0.42, 95% CI: 0.18 to 0.66) improved through the VR technology intervention. Using the 95% prediction interval (PI) results, we found that VR technology did not significantly affect the cognitive abilities of people with cognitive decline despite increasing the subject size. We conclude that the VR technology intervention improved only specific cognitive abilities.
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Affiliation(s)
- Jingxuan Yu
- College of Physical Education, Shenzhen University, Shenzhen 518060, China
| | - Jinlong Wu
- College of Physical Education, Southwest University, Chongqing 400715, China
| | - Bowen Liu
- College of Physical Education, Shenzhen University, Shenzhen 518060, China
| | - Kangyong Zheng
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, 999077, Hong Kong, China
| | - Zhanbing Ren
- College of Physical Education, Shenzhen University, Shenzhen 518060, China.
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Chang H, Liu B, Zong Y, Lu C, Wang X. EEG-Based Parkinson's Disease Recognition via Attention-Based Sparse Graph Convolutional Neural Network. IEEE J Biomed Health Inform 2023; 27:5216-5224. [PMID: 37405893 DOI: 10.1109/jbhi.2023.3292452] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/07/2023]
Abstract
Parkinson's disease (PD) is a complicated neurological ailment that affects both the physical and mental wellness of elderly individuals which makes it problematic to diagnose in its initial stages. Electroencephalogram (EEG) promises to be an efficient and cost-effective method for promptly detecting cognitive impairment in PD. Nevertheless, prevailing diagnostic practices utilizing EEG features have failed to examine the functional connectivity among EEG channels and the response of associated brain areas causing an unsatisfactory level of precision. Here, we construct an attention-based sparse graph convolutional neural network (ASGCNN) for diagnosing PD. Our ASGCNN model uses a graph structure to represent channel relationships, the attention mechanism for selecting channels, and the L1 norm to capture channel sparsity. We conduct extensive experiments on the publicly available PD auditory oddball dataset, which consists of 24 PD patients (under ON/OFF drug status) and 24 matched controls, to validate the effectiveness of our method. Our results show that the proposed method provides better results compared to the publicly available baselines. The achieved scores for Recall, Precision, F1-score, Accuracy and Kappa measures are 90.36%, 88.43%, 88.41%, 87.67%, and 75.24%, respectively. Our study reveals that the frontal and temporal lobes show significant differences between PD patients and healthy individuals. In addition, EEG features extracted by ASGCNN demonstrate significant asymmetry in the frontal lobe among PD patients. These findings can offer a basis for the establishment of a clinical system for intelligent diagnosis of PD by using auditory cognitive impairment features.
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Conceição ISR, Garcia-Burgos D, de Macêdo PFC, Nepomuceno CMM, Pereira EM, Cunha CDM, Ribeiro CDF, de Santana MLP. Habits and Persistent Food Restriction in Patients with Anorexia Nervosa: A Scoping Review. Behav Sci (Basel) 2023; 13:883. [PMID: 37998630 PMCID: PMC10669471 DOI: 10.3390/bs13110883] [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: 09/06/2023] [Revised: 10/11/2023] [Accepted: 10/17/2023] [Indexed: 11/25/2023] Open
Abstract
The aetiology of anorexia nervosa (AN) presents a puzzle for researchers. Recent research has sought to understand the behavioural and neural mechanisms of these patients' persistent choice of calorie restriction. This scoping review aims to map the literature on the contribution of habit-based learning to food restriction in AN. PRISMA-ScR guidelines were adopted. The search strategy was applied to seven databases and to grey literature. A total of 35 studies were included in this review. The results indicate that the habit-based learning model has gained substantial attention in current research, employing neuroimaging methods, scales, and behavioural techniques. Food choices were strongly associated with dorsal striatum activity, and habitual food restriction based on the self-report restriction index was associated with clinical impairment in people chronically ill with restricting AN. High-frequency repetitive transcranial magnetic stimulation (HF-rTMS) and Regulating Emotions and Changing Habits (REaCH) have emerged as potential treatments. Future research should employ longitudinal studies to investigate the time required for habit-based learning and analyse how developmental status, such as adolescence, influences the role of habits in the progression and severity of diet-related illnesses. Ultimately, seeking effective strategies to modify persistent dietary restrictions controlled by habits remains essential.
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Affiliation(s)
- Ismara Santos Rocha Conceição
- Graduate Program in Food, Nutrition and Health, School of Nutrition, Federal University of Bahia, Salvador 40110-907, Brazil; (I.S.R.C.); (P.F.C.d.M.)
| | - David Garcia-Burgos
- Department of Psychobiology, The “Federico Olóriz” Institute of Neurosciences, Biomedical Research Centre, University of Granada, 18071 Granada, Spain;
| | - Patrícia Fortes Cavalcanti de Macêdo
- Graduate Program in Food, Nutrition and Health, School of Nutrition, Federal University of Bahia, Salvador 40110-907, Brazil; (I.S.R.C.); (P.F.C.d.M.)
| | | | | | - Carla de Magalhães Cunha
- School of Nutrition, Federal University of Bahia, Salvador 40110-907, Brazil; (C.d.M.C.); (C.D.F.R.)
| | - Camila Duarte Ferreira Ribeiro
- School of Nutrition, Federal University of Bahia, Salvador 40110-907, Brazil; (C.d.M.C.); (C.D.F.R.)
- Graduate Program in Food Science, Faculty of Pharmacy, Federal University of Bahia, Salvador 40170-115, Brazil
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Bayram E, Batzu L, Tilley B, Gandhi R, Jagota P, Biundo R, Garon M, Prasertpan T, Lazcano-Ocampo C, Chaudhuri KR, Weil RS. Clinical trials for cognition in Parkinson's disease: Where are we and how can we do better? Parkinsonism Relat Disord 2023; 112:105385. [PMID: 37031010 PMCID: PMC10330317 DOI: 10.1016/j.parkreldis.2023.105385] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 03/24/2023] [Accepted: 03/25/2023] [Indexed: 03/31/2023]
Abstract
BACKGROUND Cognitive impairment is common in Parkinson's disease (PD) and has a substantial impact on quality of life. Despite numerous trials targeting various PD features, we still lack effective treatments for cognition beyond cholinesterase inhibitors. OBJECTIVE To identify the gaps in recent clinical trials with cognitive outcomes in PD and consider areas for improvement. METHODS We examined recent clinical trials with cognitive outcomes in PD registered on ClinicalTrials.gov, excluding trials without cognitive outcomes, non-interventional studies, and in atypical Parkinsonian disorders. Included trials were categorized by treatment approach (investigational medicinal product, behavioral, physical activity, device-based). Details of trial design and outcomes were collected. RESULTS 178 trials at different stages of trial completion were considered. 46 trials were completed, 25 had available results. Mean follow-up duration was 29.9 weeks. Most common cognitive measure was Montreal Cognitive Assessment. Most were performed in North America or Europe. Majority of the participants identified as non-Hispanic and White. Only eight trials showed improvement in cognition, none showed improvement beyond four months. These included trials of international medicinal products, cognitive and physical interventions and devices. GRADE certainty levels ranged from Moderate to Very Low. Only mevidalen had a Moderate certainty for potential clinical effectiveness. CONCLUSIONS Amongst a large number of trials for cognition in PD, only a small proportion were completed. Few showed significant improvement, with no proven long-lasting effects. Trial design, lack of enrichment for at-risk groups, short follow-up duration, insensitive outcome measures likely contribute to lack of detectable benefit and should be considered in future trials.
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Affiliation(s)
- Ece Bayram
- Parkinson and Other Movement Disorders Center, Department of Neurosciences, University of California San Diego, La Jolla, CA, USA.
| | - Lucia Batzu
- Department of Basic and Clinical Neurosciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Parkinson's Foundation Centre of Excellence, King's College Hospital, London, UK.
| | - Bension Tilley
- Dementia Research Centre, University College London, London, UK; Department of Brain Sciences, Imperial College London, London, UK
| | - Rhea Gandhi
- Parkinson and Other Movement Disorders Center, Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
| | - Priya Jagota
- Chulalongkorn Centre of Excellence for Parkinson's Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Roberta Biundo
- Department of General Psychology, University of Padua, Padua, Italy; Study Center for Neurodegeneration (CESNE), University of Padua, Padua, Italy
| | - Michela Garon
- Parkinson and Movement Disorders Unit, Department of Neuroscience, University of Padua, Padua, Italy
| | - Tittaya Prasertpan
- Chulalongkorn Centre of Excellence for Parkinson's Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Claudia Lazcano-Ocampo
- Department of Basic and Clinical Neurosciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Department of Neurology, Hospital Sotero del Rio, Santiago, Chile
| | - K Ray Chaudhuri
- Department of Basic and Clinical Neurosciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Parkinson's Foundation Centre of Excellence, King's College Hospital, London, UK
| | - Rimona S Weil
- Dementia Research Centre, University College London, London, UK; Movement Disorders Centre, University College London, London, UK
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Alves CL, Toutain TGLDO, de Carvalho Aguiar P, Pineda AM, Roster K, Thielemann C, Porto JAM, Rodrigues FA. Diagnosis of autism spectrum disorder based on functional brain networks and machine learning. Sci Rep 2023; 13:8072. [PMID: 37202411 DOI: 10.1038/s41598-023-34650-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 05/04/2023] [Indexed: 05/20/2023] Open
Abstract
Autism is a multifaceted neurodevelopmental condition whose accurate diagnosis may be challenging because the associated symptoms and severity vary considerably. The wrong diagnosis can affect families and the educational system, raising the risk of depression, eating disorders, and self-harm. Recently, many works have proposed new methods for the diagnosis of autism based on machine learning and brain data. However, these works focus on only one pairwise statistical metric, ignoring the brain network organization. In this paper, we propose a method for the automatic diagnosis of autism based on functional brain imaging data recorded from 500 subjects, where 242 present autism spectrum disorder considering the regions of interest throughout Bootstrap Analysis of Stable Cluster map. Our method can distinguish the control group from autism spectrum disorder patients with high accuracy. Indeed the best performance provides an AUC near 1.0, which is higher than that found in the literature. We verify that the left ventral posterior cingulate cortex region is less connected to an area in the cerebellum of patients with this neurodevelopment disorder, which agrees with previous studies. The functional brain networks of autism spectrum disorder patients show more segregation, less distribution of information across the network, and less connectivity compared to the control cases. Our workflow provides medical interpretability and can be used on other fMRI and EEG data, including small data sets.
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Affiliation(s)
- Caroline L Alves
- Institute of Mathematical and Computer Sciences (ICMC), University of São Paulo (USP), São Paulo, Brazil.
- BioMEMS Lab, Aschaffenburg University of Applied Sciences, Aschaffenburg, Germany.
| | | | - Patricia de Carvalho Aguiar
- Hospital Israelita Albert Einstein, São Paulo, Brazil
- Department of Neurology and Neurosurgery, Federal University of São Paulo, São Paulo, Brazil
| | - Aruane M Pineda
- Institute of Mathematical and Computer Sciences (ICMC), University of São Paulo (USP), São Paulo, Brazil
| | - Kirstin Roster
- Institute of Mathematical and Computer Sciences (ICMC), University of São Paulo (USP), São Paulo, Brazil
| | | | | | - Francisco A Rodrigues
- Institute of Mathematical and Computer Sciences (ICMC), University of São Paulo (USP), São Paulo, Brazil
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