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Taspinar G, Ozkurt N. A review of ADHD detection studies with machine learning methods using rsfMRI data. NMR IN BIOMEDICINE 2024; 37:e5138. [PMID: 38472163 DOI: 10.1002/nbm.5138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 02/05/2024] [Accepted: 02/11/2024] [Indexed: 03/14/2024]
Abstract
Attention deficit hyperactivity disorder (ADHD) is a common mental health condition that significantly affects school-age children, causing difficulties with learning and daily functioning. Early identification is crucial, and reliable and objective diagnostic tools are necessary. However, current clinical evaluations of behavioral symptoms can be inconsistent and subjective. Functional magnetic resonance imaging (fMRI) is a non-invasive technique that has proven effective in detecting brain abnormalities in individuals with ADHD. Recent studies have shown promising outcomes in using resting state fMRI (rsfMRI)-based brain functional networks to diagnose various brain disorders, including ADHD. Several review papers have examined the detection of other diseases using fMRI data and machine learning or deep learning methods. However, no review paper has specifically addressed ADHD. Therefore, this study aims to contribute to the literature by reviewing the use of rsfMRI data and machine learning methods for detection of ADHD. The study provides general information about fMRI databases and detailed knowledge of the ADHD-200 database, which is commonly used for ADHD detection. It also emphasizes the importance of examining all stages of the process, including network and atlas selection, feature extraction, and feature selection, before the classification stage. The study compares the performance, advantages, and disadvantages of previous studies in detail. This comprehensive approach may be a useful starting point for new researchers in this area.
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Affiliation(s)
| | - Nalan Ozkurt
- Electric and Electronic Engineering, Yasar University Izmir, Izmir, Turkey
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2
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Wong WW, Peel H, Cabeen R, Diaz-Fong JP, Feusner JD. Visual system structural and functional connections during face viewing in body dysmorphic disorder. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.12.603273. [PMID: 39071433 PMCID: PMC11275846 DOI: 10.1101/2024.07.12.603273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Background Individuals with body dysmorphic disorder (BDD) perceive distortions in their appearance, which could be due to imbalances in global and local visual processing. The vertical occipital fasciculus connects dorsal and ventral visual stream regions, integrating global and local information, yet the role of this structural connection in BDD has not been explored. Here, we investigated the vertical occipital fasciculus's white matter microstructure in those with BDD and healthy controls and tested associations with psychometric measures and effective connectivity while viewing their face during fMRI. Methods We analyzed diffusion MRI and fMRI data in 17 unmedicated adults with BDD and 21 healthy controls. For diffusion MRI, bundle-specific analysis was performed, enabling quantitative estimation of neurite density and orientation dispersion of the vertical occipital fasciculus. For task fMRI, participants naturalistically viewed photos of their own face, from which we computed effective connectivity from dorsal to ventral visual regions. Results In BDD, neurite density was negatively correlated with appearance dissatisfaction and negatively correlated with effective connectivity. Further, those with weaker effective connectivity while viewing their face had worse BDD symptoms and worse insight. In controls, no significant relationships were found between any of the measures. There were no significant group differences in neurite density or orientation dispersion. Conclusion Those with BDD with worse appearance dissatisfaction have a lower fraction of tissue having axons or dendrites along the vertical occipital fasciculus bundle, possibly reflecting impacting the degree of integration of global and local visual information between the dorsal and ventral visual streams. These results provide early insights into how the vertical occipital fasciculus's microstructure relates to the subjective experience of one's appearance, as well as the possibility of distinct functional-structural relationships in BDD.
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Affiliation(s)
- Wan-wa Wong
- Centre for Addiction and Mental Health, Toronto, ON, Canada
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong, China
| | - Hayden Peel
- Centre for Addiction and Mental Health, Toronto, ON, Canada
- La Trobe University, Melbourne, VIC, Australia
| | - Ryan Cabeen
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | | | - Jamie D. Feusner
- Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
- Department of Psychiatry, Division of Neurosciences & Clinical Translation, University of Toronto, Toronto, ON, Canada
- Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden
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Park Y, Yoon E, Park J, Kim JS, Han JW, Bae JB, Kim SS, Kim DW, Woo SJ, Park J, Lee W, Yoo S, Kim KW. White matter microstructural integrity as a key to effective propagation of gamma entrainment in humans. GeroScience 2024:10.1007/s11357-024-01281-2. [PMID: 39004653 DOI: 10.1007/s11357-024-01281-2] [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: 01/05/2024] [Accepted: 07/03/2024] [Indexed: 07/16/2024] Open
Abstract
Gamma entrainment through sensory stimulation has the potential to reduce the pathology of Alzheimer's disease in mouse models. However, clinical trials in Alzheimer's disease (AD) patients have yielded inconsistent results, necessitating further investigation. This single-center pre-post intervention study aims to explore the influence of white matter microstructural integrity on gamma rhythm propagation from the visual cortex to AD-affected regions in 31 cognitively normal volunteers aged ≥ 65. Gamma rhythm propagation induced by optimal FLS was measured. Diffusion tensor imaging was employed to assess the integrity of white matter tracts of interest. After excluding 5 participants with a deficit in steady-state visually evoked potentials, 26 participants were included in the final analysis. In the linear regression analyses, gamma entrainment was identified as a significant predictor of gamma propagation (p < 0.001). Furthermore, the study identified white matter microstructural integrity as a significant predictor of gamma propagation by flickering light stimulation (p < 0.05), which was specific to tracts that connect occipital and temporal or frontal regions. These findings indicate that, despite robust entrainment of gamma rhythms in the visual cortex, their propagation to other regions may be impaired if the microstructural integrity of the white matter tracts connecting the visual cortex to other areas is compromised. Consequently, our findings have expanded our understanding of the prerequisites for effective gamma entrainment and suggest that future clinical trials utilizing visual stimulation for gamma entrainment should consider white matter tract microstructural integrity for candidate selection and outcome analysis.
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Affiliation(s)
- Yeseung Park
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Brain and Cognitive Science, College of Natural Sciences, Seoul National University, Seoul, Korea
| | - Euisuk Yoon
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Brain and Cognitive Science, College of Natural Sciences, Seoul National University, Seoul, Korea
| | - Jieun Park
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Brain and Cognitive Science, College of Natural Sciences, Seoul National University, Seoul, Korea
| | - Jun Sung Kim
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Brain and Cognitive Science, College of Natural Sciences, Seoul National University, Seoul, Korea
| | - Ji Won Han
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Jong Bin Bae
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Sang-Su Kim
- Department of Biomedical Engineering, Chonnam National University, Yeosu, Republic of Korea
| | - Do-Won Kim
- Department of Biomedical Engineering, Chonnam National University, Yeosu, Republic of Korea
| | - Se Joon Woo
- Department of Ophthalmology, College of Medicine, Seoul National University, Seoul, Republic of Korea
- Department of Ophthalmology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Jaehyeok Park
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Wheesung Lee
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Brain and Cognitive Science, College of Natural Sciences, Seoul National University, Seoul, Korea
| | - Seunghyup Yoo
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Ki Woong Kim
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea.
- Department of Brain and Cognitive Science, College of Natural Sciences, Seoul National University, Seoul, Korea.
- Department of Psychiatry, College of Medicine, Seoul National University, Seoul, Korea.
- Department of Health Science and Technology, Seoul National University Graduate School of Convergence Science and Technology, Suwon, Korea.
- Department of Neuropsychiatry, College of Medicine, Seoul National University, Seoul, Korea.
- Seoul National University Bundang Hospital, 82, Gumi-ro 173 beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, Republic of Korea.
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4
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Bonanno M, Papa GA, Ruffoni P, Catalioto E, De Luca R, Maggio MG, Calabrò RS. The Effects of Osteopathic Manipulative Treatment on Brain Activity: A Scoping Review of MRI and EEG Studies. Healthcare (Basel) 2024; 12:1353. [PMID: 38998887 PMCID: PMC11241316 DOI: 10.3390/healthcare12131353] [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: 05/30/2024] [Revised: 07/01/2024] [Accepted: 07/04/2024] [Indexed: 07/14/2024] Open
Abstract
Osteopathic manipulative treatment (OMT) is a hands-on therapy aiming to achieve the global homeostasis of the patient. OMT focuses on treating the somatic dysfunctions characterized by tissue modifications, body asymmetry, and range-of-motion restrictions. The benefits related to OMT are thought to be associated with the interconnectedness of the body's systems and the inherent capacity for self-healing. However, whether OMT can influence brain activity, and, consequently, neurophysiological responses is an open research question. Our research investigates the literature to identify the effects of OMT on brain activity. The main purpose of the research question is: can OMT influence brain activity and consequently neurophysiological responses? A scoping review was conducted, searching the following databases: PubMed, Google Scholar, and OSTEOMED.DR (Osteopathic Medical Digital Repository), Scopus, Web of Science (WoS), and Science Direct. The initial search returned 114 articles, and after removing duplicates, 69 were considered eligible to be included in the final sample. In the end, eight studies (six randomized controlled trials, one pilot study, and one cross-over study) were finally included and analyzed in this review. In conclusion, OMT seems to have a role in influencing functional changes in brain activity in healthy individuals and even more in patients with chronic musculoskeletal pain. However, further RCT studies are needed to confirm these findings. Registration protocol: CRD42024525390.
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Affiliation(s)
- Mirjam Bonanno
- IRCCS Centro Neurolesi Bonino-Pulejo, Cda Casazza, SS 113, 98124 Messina, Italy
| | | | - Paola Ruffoni
- International College of Osteopathic Medicine, 20092 Milan, Italy
| | | | - Rosaria De Luca
- IRCCS Centro Neurolesi Bonino-Pulejo, Cda Casazza, SS 113, 98124 Messina, Italy
| | - Maria Grazia Maggio
- IRCCS Centro Neurolesi Bonino-Pulejo, Cda Casazza, SS 113, 98124 Messina, Italy
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5
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Qi G, Liu R, Guan W, Huang A. Augmented Recognition of Distracted Driving State Based on Electrophysiological Analysis of Brain Network. CYBORG AND BIONIC SYSTEMS 2024; 5:0130. [PMID: 38966123 PMCID: PMC11222012 DOI: 10.34133/cbsystems.0130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 04/25/2024] [Indexed: 07/06/2024] Open
Abstract
In this study, we propose an electrophysiological analysis-based brain network method for the augmented recognition of different types of distractions during driving. Driver distractions, such as cognitive processing and visual disruptions during driving, lead to distinct alterations in the electroencephalogram (EEG) signals and the extracted brain networks. We designed and conducted a simulated experiment comprising 4 distracted driving subtasks. Three connectivity indices, including both linear and nonlinear synchronization measures, were chosen to construct the brain network. By computing connectivity strengths and topological features, we explored the potential relationship between brain network configurations and states of driver distraction. Statistical analysis of network features indicates substantial differences between normal and distracted states, suggesting a reconfiguration of the brain network under distracted conditions. Different brain network features and their combinations are fed into varied machine learning classifiers to recognize the distracted driving states. The results indicate that XGBoost demonstrates superior adaptability, outperforming other classifiers across all selected network features. For individual networks, features constructed using synchronization likelihood (SL) achieved the highest accuracy in distinguishing between cognitive and visual distraction. The optimal feature set from 3 network combinations achieves an accuracy of 95.1% for binary classification and 88.3% for ternary classification of normal, cognitively distracted, and visually distracted driving states. The proposed method could accomplish the augmented recognition of distracted driving states and may serve as a valuable tool for further optimizing driver assistance systems with distraction control strategies, as well as a reference for future research on the brain-computer interface in autonomous driving.
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Affiliation(s)
- Geqi Qi
- Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport,
Beijing Jiaotong University, Beijing, China
- Key Laboratory of Brain-Machine Intelligence for Information Behavior—Ministry of Education,
Shanghai International Studies University, Shanghai, China
| | - Rui Liu
- Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport,
Beijing Jiaotong University, Beijing, China
| | - Wei Guan
- Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport,
Beijing Jiaotong University, Beijing, China
- School of Systems Science,
Beijing Jiaotong University, Beijing, China
| | - Ailing Huang
- Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport,
Beijing Jiaotong University, Beijing, China
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Fouladivanda M, Iraji A, Wu L, van Erp TG, Belger A, Hawamdeh F, Pearlson GD, Calhoun VD. A spatially constrained independent component analysis jointly informed by structural and functional network connectivity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.13.553101. [PMID: 38853973 PMCID: PMC11160563 DOI: 10.1101/2023.08.13.553101] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
There are a growing number of neuroimaging studies motivating joint structural and functional brain connectivity. Brain connectivity of different modalities provides insight into brain functional organization by leveraging complementary information, especially for brain disorders such as schizophrenia. In this paper, we propose a multi-modal independent component analysis (ICA) model that utilizes information from both structural and functional brain connectivity guided by spatial maps to estimate intrinsic connectivity networks (ICNs). Structural connectivity is estimated through whole-brain tractography on diffusion-weighted MRI (dMRI), while functional connectivity is derived from resting-state functional MRI (rs-fMRI). The proposed structural-functional connectivity and spatially constrained ICA (sfCICA) model estimates ICNs at the subject level using a multi-objective optimization framework. We evaluated our model using synthetic and real datasets (including dMRI and rs-fMRI from 149 schizophrenia patients and 162 controls). Multi-modal ICNs revealed enhanced functional coupling between ICNs with higher structural connectivity, improved modularity, and network distinction, particularly in schizophrenia. Statistical analysis of group differences showed more significant differences in the proposed model compared to the unimodal model. In summary, the sfCICA model showed benefits from being jointly informed by structural and functional connectivity. These findings suggest advantages in simultaneously learning effectively and enhancing connectivity estimates using structural connectivity.
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Affiliation(s)
- Mahshid Fouladivanda
- Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Georgia State University, Atlanta, GA, USA
| | - Armin Iraji
- Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Georgia State University, Atlanta, GA, USA
| | - Lei Wu
- Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Theodorus G.M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior School of Medicine, University of California, Irvine, CA, USA
| | - Aysenil Belger
- Department of Psychiatry Director, Neuroimaging Research in Psychiatry Director, Clinical Translational Core, UNC Intellectual and Developmental Disabilities Research Center, University of North Carolina, Chapel Hill, NC, USA
| | - Faris Hawamdeh
- Center for Disaster Informatics and Computational Epidemiology (DICE), Georgia State University, Atlanta, GA, USA
| | - Godfrey D. Pearlson
- Olin Neuropsychiatry Research Center, Department of Psychiatry and Neuroscience, Yale University, School of Medicine, New Haven, CT, USA
| | - Vince D. Calhoun
- Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Georgia State University, Atlanta, GA, USA
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7
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Peña Pino I, Fellows E, McGovern RA, Chen CC, Sandoval-Garcia C. Structural and functional connectivity in hydrocephalus: a scoping review. Neurosurg Rev 2024; 47:201. [PMID: 38695962 DOI: 10.1007/s10143-024-02430-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 04/01/2024] [Accepted: 04/22/2024] [Indexed: 06/26/2024]
Abstract
Optimizing the treatment of hydrocephalus remains a major challenge in adult and pediatric neurosurgery. Currently, clinical treatment relies heavily on anatomic imaging of ventricular size and clinical presentation. The emergence of functional and structural brain connectivity imaging has provided the basis for a new paradigm in the management of hydrocephalus. Here we review the pertinent advances in this field. Following PRISMA-ScR guidelines for scoping reviews, we searched PubMed for relevant literature from 1994 to April 2023 using hydrocephalus and MRI-related terms. Included articles reported original MRI data on human subjects with hydrocephalus, while excluding non-English or pre-1994 publications that didn't match the study framework. The review identified 44 studies that investigated functional and/or structural connectivity using various MRI techniques across different hydrocephalus populations. While there is significant heterogeneity in imaging technology and connectivity analysis, there is broad consensus in the literature that 1) hydrocephalus is associated with disruption of functional and structural connectivity, 2) this disruption in cerebral connectivity can be further associated with neurologic compromise 3) timely treatment of hydrocephalus restores both cerebral connectivity and neurologic compromise. The robustness and consistency of these findings vary as a function of patient age, hydrocephalus etiology, and the connectivity region of interest studied. Functional and structural brain connectivity imaging shows potential as an imaging biomarker that may facilitate optimization of hydrocephalus treatment. Future research should focus on standardizing regions of interest as well as identifying connectivity analysis most pertinent to clinical outcome.
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Affiliation(s)
- Isabela Peña Pino
- Department of Neurosurgery, University of Minnesota, Minneapolis, MN, USA
| | - Emily Fellows
- University of Minnesota Medical School, Minneapolis, MN, USA
| | - Robert A McGovern
- Department of Neurosurgery, University of Minnesota, Minneapolis, MN, USA
| | - Clark C Chen
- Department of Neurosurgery, University of Minnesota, Minneapolis, MN, USA
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Park BS, Heo CM, Lee YJ, Park S, Kim YW, Son S, Kwon H, Park Y, Kim Y, Lee DA, Park KM. Difference in functional connectivity between end-stage renal disease patients with and without restless legs syndrome: A prospective study. Sleep Breath 2024; 28:673-681. [PMID: 37889458 DOI: 10.1007/s11325-023-02943-9] [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: 06/26/2023] [Revised: 10/11/2023] [Accepted: 10/18/2023] [Indexed: 10/28/2023]
Abstract
PURPOSE The purpose of this study was to examine differences in functional connectivity between patients with end-stage renal disease (ESRD) with and without restless legs syndrome (RLS). In addition, the study aimed to identify any potential associations between RLS severity and functional connectivity. METHODS We enrolled patients with ESRD who had been undergoing hemodialysis. Patients with and without RLS were separated into two groups. Using functional near-infrared spectroscopy (fNIRS) and a graph theory approach, we determined the functional connectivity of patients with ESRD. The data were collected during a 300-s resting state evaluation performed in the dialysis room prior to dialysis. RESULTS Eighteen of 48 patients with ESRD were diagnosed with RLS, whereas 30 patients did not exhibit RLS symptoms. Notably, functional connectivity metrics differed significantly between patients with and without RLS. Specifically, patients with ESRD and RLS displayed higher values for mean clustering coefficient (0.474 vs. 0.352, p = 0.001), global efficiency (0.520 vs. 0.414, p = 0.001), strength (6.538 vs. 4.783, p = 0.001), and transitivity (0.714 vs. 0.521, p = 0.001), while values for diameter (5.451 vs. 7.338, p = 0.002), eccentricity (4.598 vs. 5.985, p = 0.004), and characteristic path length (2.520 vs. 3.271, p = 0.002) were lower in patients with ESRD and RLS compared to those without RLS. In addition, there were correlations between the RLS severity score and the assortative coefficient (r = 0.479, p = 0.044), the small-worldness index (r = -0.475, p = 0.046), and transitivity (r = 0.500, p = 0.034). CONCLUSIONS We demonstrated differences in functional connectivity between patients with ESRD with and without RLS, which may shed light on the pathophysiology of RLS. Notably, a number of functional connectivity metrics demonstrated strong associations with RLS severity. Our study also confirmed the applicability of fNIRS as a tool for investigating functional connectivity in patients with RLS.
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Affiliation(s)
- Bong Soo Park
- Department of Internal Medicine, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Chang Min Heo
- Department of Internal Medicine, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Yoo Jin Lee
- Department of Internal Medicine, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Sihyung Park
- Department of Internal Medicine, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Yang Wook Kim
- Department of Internal Medicine, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - SungHyun Son
- Department of Internal Medicine, BMS Hanseo Hospital, Busan, Korea
| | - Hyukyong Kwon
- Department of Internal Medicine, BMS Hanseo Hospital, Busan, Korea
| | - Youngchan Park
- Department of Internal Medicine, BMS Hanseo Hospital, Busan, Korea
| | - Yunmi Kim
- Department of Internal Medicine, Busan Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Dong Ah Lee
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Kang Min Park
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea.
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Dam S, Batail JM, Robert GH, Drapier D, Maurel P, Coloigner J. Structural Brain Connectivity and Treatment Improvement in Mood Disorder. Brain Connect 2024; 14:239-251. [PMID: 38534988 DOI: 10.1089/brain.2023.0063] [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: 04/25/2024] Open
Abstract
Background: The treatment of depressive episodes is well established, with clearly demonstrated effectiveness of antidepressants and psychotherapies. However, more than one-third of depressed patients do not respond to treatment. Identifying the brain structural basis of treatment-resistant depression could prevent useless pharmacological prescriptions, adverse events, and lost therapeutic opportunities. Methods: Using diffusion magnetic resonance imaging, we performed structural connectivity analyses on a cohort of 154 patients with mood disorder (MD) and 77 sex- and age-matched healthy control (HC) participants. To assess illness improvement, the patients with MD went through two clinical interviews at baseline and at 6-month follow-up and were classified based on the Clinical Global Impression-Improvement score into improved or not-improved (NI). First, the threshold-free network-based statistics (NBS) was conducted to measure the differences in regional network architecture. Second, nonparametric permutations tests were performed on topological metrics based on graph theory to examine differences in connectome organization. Results: The threshold-free NBS revealed impaired connections involving regions of the basal ganglia in patients with MD compared with HC. Significant increase of local efficiency and clustering coefficient was found in the lingual gyrus, insula, and amygdala in the MD group. Compared with the NI, the improved displayed significantly reduced network integration and segregation, predominately in the default-mode regions, including the precuneus, middle temporal lobe, and rostral anterior cingulate. Conclusions: This study highlights the involvement of regions belonging to the basal ganglia, the fronto-limbic network, and the default mode network, leading to a better understanding of MD disease and its unfavorable outcome.
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Affiliation(s)
- Sébastien Dam
- Univ Rennes, Inria, CNRS, IRISA, INSERM, Empenn U1228 ERL, Rennes, France
| | - Jean-Marie Batail
- Academic Psychiatry Department, Centre Hospitalier Guillaume Régnier, Rennes, France
- CIC 1414, CHU de Rennes, INSERM, Rennes, France
| | - Gabriel H Robert
- Univ Rennes, Inria, CNRS, IRISA, INSERM, Empenn U1228 ERL, Rennes, France
- Academic Psychiatry Department, Centre Hospitalier Guillaume Régnier, Rennes, France
- CIC 1414, CHU de Rennes, INSERM, Rennes, France
| | - Dominique Drapier
- Academic Psychiatry Department, Centre Hospitalier Guillaume Régnier, Rennes, France
- CIC 1414, CHU de Rennes, INSERM, Rennes, France
| | - Pierre Maurel
- Univ Rennes, Inria, CNRS, IRISA, INSERM, Empenn U1228 ERL, Rennes, France
| | - Julie Coloigner
- Univ Rennes, Inria, CNRS, IRISA, INSERM, Empenn U1228 ERL, Rennes, France
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Schmid W, Danstrom IA, Crespo Echevarria M, Adkinson J, Mattar L, Banks GP, Sheth SA, Watrous AJ, Heilbronner SR, Bijanki KR, Alabastri A, Bartoli E. A biophysically constrained brain connectivity model based on stimulation-evoked potentials. J Neurosci Methods 2024; 405:110106. [PMID: 38453060 PMCID: PMC11233030 DOI: 10.1016/j.jneumeth.2024.110106] [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: 11/03/2023] [Revised: 01/24/2024] [Accepted: 03/04/2024] [Indexed: 03/09/2024]
Abstract
BACKGROUND Single-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies. NEW METHOD Using intracranial electrophysiology data recorded from a single patient undergoing stereo-electroencephalography (sEEG) evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D electrical conductivity to infer neural pathways from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features. RESULTS The proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlated with additional PEP features and displayed stable, weak correlations with tractography measures. COMPARISON WITH EXISTING METHOD Existing methods for estimating neural signal pathways are imaging-based and thus rely on anatomical inferences. CONCLUSIONS These results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.
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Affiliation(s)
- William Schmid
- Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston, TX 77005, USA
| | - Isabel A Danstrom
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Maria Crespo Echevarria
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Joshua Adkinson
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Layth Mattar
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Garrett P Banks
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Andrew J Watrous
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Sarah R Heilbronner
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Kelly R Bijanki
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Alessandro Alabastri
- Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston, TX 77005, USA.
| | - Eleonora Bartoli
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA.
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11
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Lee DA, Kim SE, Park KM. Increased Thalamic Connectivity in Juvenile Myoclonic Epilepsy Based on Electroencephalography Source-Level Analysis. Brain Connect 2024; 14:182-188. [PMID: 38343360 DOI: 10.1089/brain.2023.0084] [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: 03/23/2024] Open
Abstract
Background: This study investigated alterations in the intrinsic thalamic network of patients with juvenile myoclonic epilepsy (JME) based on an electroencephalography (EEG) source-level analysis. Materials and Methods: We enrolled patients newly diagnosed with JME as well as healthy controls. The assessments were conducted in the resting state. We computed sources based on the scalp electrical potentials using a minimum-norm imaging method and a standardized, low-resolution, brain electromagnetic tomography approach. To create a functional connectivity matrix, we used the Talairach atlas to define thalamic nodes and applied the coherence method to measure brain synchronization as edges. We then calculated the intrinsic thalamic network using graph theory. We compared the intrinsic thalamic network of patients with JME with those of healthy controls. Results: This study included 67 patients with JME and 66 healthy controls. EEG source-level analysis revealed significant differences in the intrinsic thalamic networks between patients with JME and healthy controls. The measures of functional connectivity (radius, diameter, and characteristic path length) were significantly lower in patients with JME than in healthy controls (radius: 2.769 vs. 3.544, p = 0.015; diameter: 4.464 vs. 5.443, p = 0.024; and characteristic path length: 2.248 vs. 2.616, p = 0.046). Conclusions: We demonstrated alterations in the intrinsic thalamic network in patients with JME compared with those in healthy controls based on the EEG source-level analysis. These findings indicated increased thalamic connectivity in the JME group. These intrinsic thalamic network changes may be related to the pathophysiology of JME.
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Affiliation(s)
- Dong Ah Lee
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Sung Eun Kim
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Kang Min Park
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
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Yang L, Xu C, Qin Y, Chen K, Xie Y, Zhou X, Liu T, Tan S, Liu J, Yao D. Exploring resting-state EEG oscillations in patients with Neuromyelitis Optica Spectrum Disorder. Brain Res Bull 2024; 208:110900. [PMID: 38364986 DOI: 10.1016/j.brainresbull.2024.110900] [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: 11/13/2023] [Revised: 01/24/2024] [Accepted: 02/11/2024] [Indexed: 02/18/2024]
Abstract
BACKGROUND AND OBJECTIVE Quantitative resting-state electroencephalography (rs-EEG) is a convenient method for characterizing the functional impairments and adaptations of the brain that has been shown to be valuable for assessing many neurological and psychiatric disorders, especially in monitoring disease status and assisting neuromodulation treatment. However, it has not yet been explored in patients with neuromyelitis optica spectrum disorder (NMOSD). This study aimed to investigate the rs-EEG features of NMOSD patients and explore the rs-EEG features related to disease characteristics and complications (such as anxiety, depression, and fatigue). METHODS A total of 32 NMOSD patients and 20 healthy controls (HCs) were recruited; their demographic and disease information were collected, and their anxiety, depression, and fatigue symptoms were evaluated. The rs-EEG power spectra of all the participants were obtained. After excluding the participants with low-quality rs-EEG data during processing, statistical analysis was conducted based on the clinical information and rs-EEG data of 29 patients and 19 HCs. The rs-EEG power (the mean spectral energy (MSE) of absolute power and relative power in all frequency bands, as well as the specific power for all electrode sites) of NMOSD patients and HCs was compared. Furthermore, correlation analyses were performed between rs-EEG power and other variables for NMOSD patients (including the disease characteristics and complications). RESULTS The distribution of the rs-EEG power spectra in NMOSD patients was similar to that in HCs. The dominant alpha-peaks shifted significantly towards a lower frequency for patients when compared to HCs. The delta and theta power was significantly increased in the NMOSD group compared to that in the HC group. The alpha oscillation power was found to be significantly negatively associated with the degree of anxiety (reflected by the anxiety subscore of hospital anxiety and depression scale (HADS)) and the degree of depression (reflected by the depression subscore of HADS). The gamma oscillation power was revealed to be significantly positively correlated with the fatigue severity scale (FSS) score, while further analysis indicated that the electrode sites of almost the whole brain region showing correlations with fatigue. Regarding the disease variables, no statistically significant rs-EEG features were related to the main disease features in NMOSD patients. CONCLUSION The results of this study suggest that the rs-EEG power spectra of NMOSD patients show increased slow oscillations and are potential biomarkers of widespread white matter microstructural damage in NMOSD. Moreover, this study revealed the rs-EEG features associated with anxiety, depression, and fatigue in NMOSD patients, which might help in the evaluation of these complications and the development of neuromodulation treatment. Quantitative rs-EEG analysis may play an important role in the management of NMOSD patients, and future studies are warranted to more comprehensively understand its application value.
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Affiliation(s)
- Lili Yang
- Department of Neurology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Congyu Xu
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yun Qin
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Kai Chen
- Department of Neurology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yan Xie
- Department of Neurology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaobo Zhou
- Department of Psychosomatic, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Tiejun Liu
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Song Tan
- Department of Neurology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; Sichuan Provincial Key Laboratory for Human Disease Gene Study, Chengdu, China.
| | - Jie Liu
- Department of Neurology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
| | - Dezhong Yao
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
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Riedel D, Lorke N, Fellerhoff T, Mierau A, Strüder HK, Wolf D, Fischer F, Fellgiebel A, Tüscher O, Kollmann B, Knaepen K. Interhemispheric transfer time correlates with white matter integrity of the corpus callosum in healthy older adults. Neuropsychologia 2024; 193:108761. [PMID: 38104856 DOI: 10.1016/j.neuropsychologia.2023.108761] [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: 09/10/2023] [Revised: 12/10/2023] [Accepted: 12/14/2023] [Indexed: 12/19/2023]
Abstract
The corpus callosum (CC) has been identified as an important structure in the context of cognitive aging (Fling et al., 2011). Interhemispheric transfer time (IHTT) is regularly used in order to estimate interhemispheric integration enabled by the CC (Marzi, 2010; Nowicka and Tacikowski, 2011). However, only little is known with regards to the relationship between IHTT and the structural properties of the CC with only few studies with specific samples and methods available (Whitford et al., 2011). Thus, the present study aimed at investigating this relationship applying an event-related potentials (ERP) based approach of estimating IHTT as well as diffusion weighted imaging (DWI) with fractional anisotropy (FA) as an indicator of white matter integrity (WMI) of the genu, corpus and splenium of the CC. 56 healthy older adults performed a Dimond Task while ERPs were recorded and underwent DWI scanning. IHTT derived from posterior electrode sites correlated significantly with FA of the splenium (r = -0.286*, p = .03) but not the corpus (r = -0.187, p = .08) or genu (r = -0.189, p = .18). The present results support the notion that IHTT is related to WMI of the posterior CC. It may be concluded that ERP based IHTT is a suitable indicator of CC structure and function, however, likely specific to the interhemispheric transfer of visual information. Future studies may wish to confirm these findings in a more divers sample further exploring the precise interrelation between IHTT and structural or functional properties of the CC.
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Affiliation(s)
- David Riedel
- Institute of Movement and Neurosciences, German Sport University Cologne, Address: Am Sportpark Müngersdorf 6, 50933, Cologne, Germany.
| | - Nicolai Lorke
- Institute of Movement and Neurosciences, German Sport University Cologne, Address: Am Sportpark Müngersdorf 6, 50933, Cologne, Germany
| | - Tim Fellerhoff
- Institute of Movement and Neurosciences, German Sport University Cologne, Address: Am Sportpark Müngersdorf 6, 50933, Cologne, Germany
| | - Andreas Mierau
- Institute of Movement and Neurosciences, German Sport University Cologne, Address: Am Sportpark Müngersdorf 6, 50933, Cologne, Germany; Department of Exercise and Sport Science, LUNEX International University of Health, Exercise and Sports, Address: 50, Avenue du Parc des Sports, L-4671, Differdange, Luxembourg
| | - Heiko K Strüder
- Institute of Movement and Neurosciences, German Sport University Cologne, Address: Am Sportpark Müngersdorf 6, 50933, Cologne, Germany
| | - Dominik Wolf
- Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Address: Untere Zahlbacher Str. 8, 55131, Mainz, Germany
| | - Florian Fischer
- Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Address: Untere Zahlbacher Str. 8, 55131, Mainz, Germany
| | - Andreas Fellgiebel
- Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Address: Untere Zahlbacher Str. 8, 55131, Mainz, Germany; Center for Mental Health in Old Age, Landeskrankenhaus (AöR), Address: Hartmühlenweg 2-4, 55122, Mainz, Germany
| | - Oliver Tüscher
- Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Address: Untere Zahlbacher Str. 8, 55131, Mainz, Germany; Leibniz Institute for Resilience Research (LIR), Address: Wallstraße 7, 55122, Mainz, Germany; Institute of Molecular Biology (IMB), Mainz, Address: Ackermannweg 4, 55128, Mainz, Germany
| | - Bianca Kollmann
- Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Address: Untere Zahlbacher Str. 8, 55131, Mainz, Germany; Leibniz Institute for Resilience Research (LIR), Address: Wallstraße 7, 55122, Mainz, Germany
| | - Kristel Knaepen
- Institute of Movement and Neurosciences, German Sport University Cologne, Address: Am Sportpark Müngersdorf 6, 50933, Cologne, Germany
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14
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Bröhl T, Rings T, Pukropski J, von Wrede R, Lehnertz K. The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 3:1338864. [PMID: 38293249 PMCID: PMC10825060 DOI: 10.3389/fnetp.2023.1338864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 12/19/2023] [Indexed: 02/01/2024]
Abstract
Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.
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Affiliation(s)
- Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Thorsten Rings
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Jan Pukropski
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Randi von Wrede
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
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15
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Udayakumar P, Subhashini R. Connectome-based schizophrenia prediction using structural connectivity - Deep Graph Neural Network(sc-DGNN). JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024; 32:1041-1059. [PMID: 38820060 DOI: 10.3233/xst-230426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2024]
Abstract
BACKGROUND Connectome is understanding the complex organization of the human brain's structural and functional connectivity is essential for gaining insights into cognitive processes and disorders. OBJECTIVE To improve the prediction accuracy of brain disorder issues, the current study investigates dysconnected subnetworks and graph structures associated with schizophrenia. METHOD By using the proposed structural connectivity-deep graph neural network (sc-DGNN) model and compared with machine learning (ML) and deep learning (DL) models.This work attempts to focus on eighty-eight subjects of diffusion magnetic resonance imaging (dMRI), three classical ML, and five DL models. RESULT The structural connectivity-deep graph neural network (sc-DGNN) model is proposed to effectively predict dysconnectedness associated with schizophrenia and exhibits superior performance compared to traditional ML and DL (GNNs) methods in terms of accuracy, sensitivity, specificity, precision, F1-score, and Area under receiver operating characteristic (AUC). CONCLUSION The classification task on schizophrenia using structural connectivity matrices and experimental results showed that linear discriminant analysis (LDA) performed 72% accuracy rate in ML models and sc-DGNN performed at a 93% accuracy rate in DL models to distinguish between schizophrenia and healthy patients.
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Affiliation(s)
- P Udayakumar
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India
| | - R Subhashini
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India
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16
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Shin S, Nam HY. Characteristics of brain glucose metabolism and metabolic connectivity in noise-induced hearing loss. Sci Rep 2023; 13:21889. [PMID: 38081979 PMCID: PMC10713681 DOI: 10.1038/s41598-023-48911-x] [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: 02/21/2023] [Accepted: 12/01/2023] [Indexed: 12/18/2023] Open
Abstract
The purpose of this study was to evaluate the differences in cerebral glucose metabolism and metabolic connectivity between noise-induced hearing loss (NIHL) subjects and normal subjects. Eighty-nine subjects who needed close observation for NIHL or were diagnosed with NIHL and 89 normal subjects were enrolled. After pre-processing of positron emission tomography images including co-registration, spatial normalization, and smoothing, a two-sample t-test was conducted to compare cerebral glucose metabolism between the two groups. To evaluate metabolic connectivity between two groups, BRAPH-BRain Analysis using graPH theory, a software package to perform graph theory analysis of the brain connectome was used. NIHL subjects showed hypometabolism compared to normal subjects in both insulae (x - 38, y - 18, z 4; × 42, y - 12, z 4) and right superior temporal gyrus (× 44, y 16, z - 20). No brain regions showed hypermetabolism in the NIHL subjects. In metabolic connectivity analysis, NIHL subjects showed decreased average strength, global efficiency, local efficiency, and mean clustering coefficient when compared with normal subjects. Decreased glucose metabolism and metabolic connectivity in NIHL subject might reflect decreased auditory function. It might be characteristic of sensorineural hearing loss.
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Affiliation(s)
- Seunghyeon Shin
- Department of Nuclear Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Republic of Korea
| | - Hyun-Yeol Nam
- Department of Nuclear Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Republic of Korea.
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17
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Schmid W, Danstrom IA, Echevarria MC, Adkinson J, Mattar L, Banks GP, Sheth SA, Watrous AJ, Heilbronner SR, Bijanki KR, Alabastri A, Bartoli E. A biophysically constrained brain connectivity model based on stimulation-evoked potentials. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.03.565525. [PMID: 37986830 PMCID: PMC10659345 DOI: 10.1101/2023.11.03.565525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Background Single-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies. New Method Using intracranial electrophysiology data recorded from a single patient undergoing sEEG evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D conductivity propagation from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features. Results The proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlates with additional PEP features and displayed stable, weak correlations with tractography measures. Comparison with existing methods Existing methods for estimating conductivity propagation are imaging-based and thus rely on anatomical inferences. Conclusions These results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.
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Affiliation(s)
- William Schmid
- Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston 77005, Texas, USA
| | - Isabel A. Danstrom
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Maria Crespo Echevarria
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Joshua Adkinson
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Layth Mattar
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Garrett P. Banks
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Sameer A. Sheth
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Andrew J. Watrous
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Sarah R. Heilbronner
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Kelly R. Bijanki
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Alessandro Alabastri
- Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston 77005, Texas, USA
| | - Eleonora Bartoli
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
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18
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Phunruangsakao C, Achanccaray D, Bhattacharyya S, Izumi SI, Hayashibe M. Effects of visual-electrotactile stimulation feedback on brain functional connectivity during motor imagery practice. Sci Rep 2023; 13:17752. [PMID: 37853020 PMCID: PMC10584917 DOI: 10.1038/s41598-023-44621-6] [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: 06/01/2023] [Accepted: 10/10/2023] [Indexed: 10/20/2023] Open
Abstract
The use of neurofeedback is an important aspect of effective motor rehabilitation as it offers real-time sensory information to promote neuroplasticity. However, there is still limited knowledge about how the brain's functional networks reorganize in response to such feedback. To address this gap, this study investigates the reorganization of the brain network during motor imagery tasks when subject to visual stimulation or visual-electrotactile stimulation feedback. This study can provide healthcare professionals with a deeper understanding of the changes in the brain network and help develop successful treatment approaches for brain-computer interface-based motor rehabilitation applications. We examine individual edges, nodes, and the entire network, and use the minimum spanning tree algorithm to construct a brain network representation using a functional connectivity matrix. Furthermore, graph analysis is used to detect significant features in the brain network that might arise in response to the feedback. Additionally, we investigate the power distribution of brain activation patterns using power spectral analysis and evaluate the motor imagery performance based on the classification accuracy. The results showed that the visual and visual-electrotactile stimulation feedback induced subject-specific changes in brain activation patterns and network reorganization in the [Formula: see text] band. Thus, the visual-electrotactile stimulation feedback significantly improved the integration of information flow between brain regions associated with motor-related commands and higher-level cognitive functions, while reducing cognitive workload in the sensory areas of the brain and promoting positive emotions. Despite these promising results, neither neurofeedback modality resulted in a significant improvement in classification accuracy, compared with the absence of feedback. These findings indicate that multimodal neurofeedback can modulate imagery-mediated rehabilitation by enhancing motor-cognitive communication and reducing cognitive effort. In future interventions, incorporating this technique to ease cognitive demands for participants could be crucial for maintaining their motivation to engage in rehabilitation.
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Affiliation(s)
- Chatrin Phunruangsakao
- Neuro-Robotics Laboratory, Graduate School of Biomedical Engineering, Tohoku University, Sendai, Japan.
| | - David Achanccaray
- Presence Media Research Group, Hiroshi Ishiguro Laboratory, Advanced Telecommunications Research Institute International, Kyoto, Japan
| | - Saugat Bhattacharyya
- School of Computing, Engineering and Intelligent Systems, Ulster University, Northland Road, Londonderry, BT48 7JL, UK
| | - Shin-Ichi Izumi
- Department of Physical Medicine and Rehabilitation, Graduate School of Biomedical Engineering, Tohoku University, Sendai, Japan
| | - Mitsuhiro Hayashibe
- Neuro-Robotics Laboratory, Graduate School of Biomedical Engineering, Tohoku University, Sendai, Japan
- Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai, Japan
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Ladas AI, Gravalas T, Stoneham T, Frantzidis CA. Towards a hybrid approach to unveil the Chimaira of neurosciences: philosophy, aperiodic activity and the neural correlates of consciousness. Front Hum Neurosci 2023; 17:1245868. [PMID: 37900726 PMCID: PMC10603270 DOI: 10.3389/fnhum.2023.1245868] [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: 06/23/2023] [Accepted: 10/02/2023] [Indexed: 10/31/2023] Open
Abstract
Contemporary theories of consciousness, although very efficient in postulating testable hypotheses, seem to either neglect its relational aspect or to have a profound difficulty in operationalizing this aspect in a measurable manner. We further argue that the analysis of periodic brain activity is inadequate to reveal consciousness's subjective facet. This creates an important epistemic gap in the quest for the neural correlates of consciousness. We suggest a possible solution to bridge this gap, by analysing aperiodic brain activity. We further argue for the imperative need to inform neuroscientific theories of consciousness with relevant philosophical endeavours, in an effort to define, and therefore operationalise, consciousness thoroughly.
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Affiliation(s)
- Aristea I. Ladas
- Department of Psychology, CITY College, University of York Europe Campus, Thessaloniki, Greece
| | - Triantafyllos Gravalas
- Department of Psychology, CITY College, University of York Europe Campus, Thessaloniki, Greece
| | - Tom Stoneham
- Department of Philosophy, University of York, York, United Kingdom
| | - Christos A. Frantzidis
- School of Computer Science, University of Lincoln, Lincoln, United Kingdom
- Laboratory of Medical Physics and Digital Innovation, Biomedical Engineering and Aerospace Neuroscience (BEAN), Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
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20
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Márquez-García AV, Ng BK, Iarocci G, Moreno S, Vakorin VA, Doesburg SM. Atypical Associations between Functional Connectivity during Pragmatic and Semantic Language Processing and Cognitive Abilities in Children with Autism. Brain Sci 2023; 13:1448. [PMID: 37891816 PMCID: PMC10605927 DOI: 10.3390/brainsci13101448] [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: 08/30/2023] [Revised: 09/26/2023] [Accepted: 10/01/2023] [Indexed: 10/29/2023] Open
Abstract
Autism Spectrum Disorder (ASD) is characterized by both atypical functional brain connectivity and cognitive challenges across multiple cognitive domains. The relationship between task-dependent brain connectivity and cognitive abilities, however, remains poorly understood. In this study, children with ASD and their typically developing (TD) peers engaged in semantic and pragmatic language tasks while their task-dependent brain connectivity was mapped and compared. A multivariate statistical approach revealed associations between connectivity and psychometric assessments of relevant cognitive abilities. While both groups exhibited brain-behavior correlations, the nature of these associations diverged, particularly in the directionality of overall correlations across various psychometric categories. Specifically, greater disparities in functional connectivity between the groups were linked to larger differences in Autism Questionnaire, BRIEF, MSCS, and SRS-2 scores but smaller differences in WASI, pragmatic language, and Theory of Mind scores. Our findings suggest that children with ASD utilize distinct neural communication patterns for language processing. Although networks recruited by children with ASD may appear less efficient than those typically engaged, they could serve as compensatory mechanisms for potential disruptions in conventional brain networks.
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Affiliation(s)
- Amparo V. Márquez-García
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Bonnie K. Ng
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
- School of Interactive Arts and Technology, Simon Fraser University, Surrey, BC V3T 0A3, Canada
| | - Grace Iarocci
- Department of Psychology, Simon Fraser University, Burnaby, BC V5A 1S6, Canada;
| | - Sylvain Moreno
- School of Interactive Arts and Technology, Simon Fraser University, Surrey, BC V3T 0A3, Canada
| | - Vasily A. Vakorin
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Sam M. Doesburg
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
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Jeong KY. Editorial for "An MRI Study Combining Virtual Brain Grafting and Surface-Based Morphometry Analysis to Investigate Contralateral Alterations in Cortical Morphology in Patients With Diffuse Low-Grade Glioma". J Magn Reson Imaging 2023; 58:750-751. [PMID: 36510417 DOI: 10.1002/jmri.28561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 11/29/2022] [Indexed: 12/14/2022] Open
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22
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Christensen JD, Bell MA, Deater-Deckard KD. Maternal age differences in cognitive regulation: examination of associations and interactions between RSA and EEG frontoparietal alpha power coherence. Front Hum Neurosci 2023; 17:1188820. [PMID: 37694174 PMCID: PMC10483125 DOI: 10.3389/fnhum.2023.1188820] [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: 03/22/2023] [Accepted: 07/31/2023] [Indexed: 09/12/2023] Open
Abstract
Strong cognitive regulation is advantageous for flexible, responsive parenting. Optimal cognitive regulation is reliant on associations between physiological mechanisms of central and peripheral nervous system functioning. Across middle adulthood there may be shifts in how cognitive regulation functions, reflecting changes in the associations and interactions between these physiological mechanisms. Two physiological indicators of cognitive regulation are autonomic regulation of the heart (e.g., respiratory sinus arrhythmia, RSA) and activity of the brain's frontoparietal network (e.g., frontoparietal EEG alpha power coherence, FPc). In the current study we examined maternal age differences (N = 90, age M = 32.35 years, SD = 5.86 years) in correlations and interactions between RSA and FPc in the statistical prediction of cognitive regulation [i.e., executive function (EF), effortful control (EC), cognitive reappraisal (CR)]. Age-related patterns involving interaction between RSA and FPc were found, pointing to a potential shift from optimization to compensation for changes with aging or alternately, the effects of age-based decrements in functioning. Findings are discussed in the context of adult developmental changes in maternal caregiving.
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Affiliation(s)
- Jennifer D. Christensen
- Department of Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, MA, United States
| | - Martha Ann Bell
- Virginia Tech, Department of Psychology, Blacksburg, VA, United States
| | - Kirby D. Deater-Deckard
- Department of Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, MA, United States
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23
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Mahani FSN, Kalantari A, Fink GR, Hoehn M, Aswendt M. A systematic review of the relationship between magnetic resonance imaging based resting-state and structural networks in the rodent brain. Front Neurosci 2023; 17:1194630. [PMID: 37554291 PMCID: PMC10405456 DOI: 10.3389/fnins.2023.1194630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 07/05/2023] [Indexed: 08/10/2023] Open
Abstract
Recent developments in rodent brain imaging have enabled translational characterization of functional and structural connectivity at the whole brain level in vivo. Nevertheless, fundamental questions about the link between structural and functional networks remain unsolved. In this review, we systematically searched for experimental studies in rodents investigating both structural and functional network measures, including studies correlating functional connectivity using resting-state functional MRI with diffusion tensor imaging or viral tracing data. We aimed to answer whether functional networks reflect the architecture of the structural connectome, how this reciprocal relationship changes throughout a disease, how structural and functional changes relate to each other, and whether changes follow the same timeline. We present the knowledge derived exclusively from studies that included in vivo imaging of functional and structural networks. The limited number of available reports makes it difficult to draw general conclusions besides finding a spatial and temporal decoupling between structural and functional networks during brain disease. Data suggest that when overcoming the currently limited evidence through future studies with combined imaging in various disease models, it will be possible to explore the interaction between both network systems as a disease or recovery biomarker.
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Affiliation(s)
- Fatemeh S. N. Mahani
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Aref Kalantari
- Department of Neurology, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Gereon R. Fink
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Mathias Hoehn
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
| | - Markus Aswendt
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
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24
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Soman SM, Vijayakumar N, Thomson P, Ball G, Hyde C, Silk TJ. Cortical structural and functional coupling during development and implications for attention deficit hyperactivity disorder. Transl Psychiatry 2023; 13:252. [PMID: 37433763 DOI: 10.1038/s41398-023-02546-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 06/21/2023] [Accepted: 06/26/2023] [Indexed: 07/13/2023] Open
Abstract
Functional connectivity is scaffolded by the structural connections of the brain. Disruptions of either structural or functional connectivity can lead to deficits in cognitive functions and increase the risk for neurodevelopmental disorders such as attention deficit hyperactivity disorder (ADHD). To date, very little research has examined the association between structural and functional connectivity in typical development, while no studies have attempted to understand the development of structure-function coupling in children with ADHD. 175 individuals (84 typically developing children and 91 children with ADHD) participated in a longitudinal neuroimaging study with up to three waves. In total, we collected 278 observations between the ages 9 and 14 (139 each in typically developing controls and ADHD). Regional measures of structure-function coupling were calculated at each timepoint using Spearman's rank correlation and mixed effect models were used to determine group differences and longitudinal changes in coupling over time. In typically developing children, we observed increases in structure-function coupling strength across multiple higher-order cognitive and sensory regions. Overall, weaker coupling was observed in children with ADHD, mainly in the prefrontal cortex, superior temporal gyrus, and inferior parietal cortex. Further, children with ADHD showed an increased rate of coupling strength predominantly in the inferior frontal gyrus, superior parietal cortex, precuneus, mid-cingulate, and visual cortex, compared to no corresponding change over time in typically developing controls. This study provides evidence of the joint maturation of structural and functional brain connections in typical development across late childhood to mid-adolescence, particularly in regions that support cognitive maturation. Findings also suggest that children with ADHD exhibit different patterns of structure-function coupling, suggesting atypical patterns of coordinated white matter and functional connectivity development predominantly in the regions overlapping with the default mode network, salience network, and dorsal attention network during late childhood to mid-adolescence.
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Affiliation(s)
- Shania Mereen Soman
- Centre for Social and Early Emotional Development and School of Psychology, Deakin University, Burwood, VIC, 3125, Australia.
| | - Nandita Vijayakumar
- Centre for Social and Early Emotional Development and School of Psychology, Deakin University, Burwood, VIC, 3125, Australia
| | - Phoebe Thomson
- Child Mind Institute, New York, NY, 10022, USA
- Department of Paediatrics, University of Melbourne, Parkville, VIC, 3010, Australia
| | - Gareth Ball
- Department of Paediatrics, University of Melbourne, Parkville, VIC, 3010, Australia
- Developmental Imaging, Murdoch Children's Research Institute, Flemington Road, Parkville, VIC, 3052, Australia
| | - Christian Hyde
- Centre for Social and Early Emotional Development and School of Psychology, Deakin University, Burwood, VIC, 3125, Australia
| | - Timothy J Silk
- Centre for Social and Early Emotional Development and School of Psychology, Deakin University, Burwood, VIC, 3125, Australia.
- Developmental Imaging, Murdoch Children's Research Institute, Flemington Road, Parkville, VIC, 3052, Australia.
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25
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Chauhan N, Choi BJ. Classification of Alzheimer's Disease Using Maximal Information Coefficient-Based Functional Connectivity with an Extreme Learning Machine. Brain Sci 2023; 13:1046. [PMID: 37508978 PMCID: PMC10377329 DOI: 10.3390/brainsci13071046] [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: 06/21/2023] [Revised: 07/03/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023] Open
Abstract
Alzheimer's disease (AD) is a progressive chronic illness that leads to cognitive decline and dementia. Neuroimaging technologies, such as functional magnetic resonance imaging (fMRI), and deep learning approaches offer promising avenues for AD classification. In this study, we investigate the use of fMRI-based functional connectivity (FC) measures, including the Pearson correlation coefficient (PCC), maximal information coefficient (MIC), and extended maximal information coefficient (eMIC), combined with extreme learning machines (ELM) for AD classification. Our findings demonstrate that employing non-linear techniques, such as MIC and eMIC, as features for classification yields accurate results. Specifically, eMIC-based features achieve a high accuracy of 94% for classifying cognitively normal (CN) and mild cognitive impairment (MCI) individuals, outperforming PCC (81%) and MIC (85%). For MCI and AD classification, MIC achieves higher accuracy (81%) compared to PCC (58%) and eMIC (78%). In CN and AD classification, eMIC exhibits the best accuracy of 95% compared to MIC (90%) and PCC (87%). These results underscore the effectiveness of fMRI-based features derived from non-linear techniques in accurately differentiating AD and MCI individuals from CN individuals, emphasizing the potential of neuroimaging and machine learning methods for improving AD diagnosis and classification.
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Affiliation(s)
- Nishant Chauhan
- Department of Electronic Engineering, Daegu University, Gyeongsan 38453, Republic of Korea
| | - Byung-Jae Choi
- Department of Electronic Engineering, Daegu University, Gyeongsan 38453, Republic of Korea
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26
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Zdorovtsova N, Jones J, Akarca D, Benhamou E, The Calm Team, Astle DE. Exploring neural heterogeneity in inattention and hyperactivity. Cortex 2023; 164:90-111. [PMID: 37207412 DOI: 10.1016/j.cortex.2023.04.001] [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: 10/17/2022] [Revised: 02/21/2023] [Accepted: 04/04/2023] [Indexed: 05/21/2023]
Abstract
Inattention and hyperactivity are cardinal symptoms of Attention Deficit Hyperactivity Disorder (ADHD). These characteristics have also been observed across a range of other neurodevelopmental conditions, such as autism and dyspraxia, suggesting that they might best be studied across diagnostic categories. Here, we evaluated the associations between inattention and hyperactivity behaviours and features of the structural brain network (connectome) in a large transdiagnostic sample of children (Centre for Attention, Learning, and Memory; n = 383). In our sample, we found that a single latent factor explains 77.6% of variance in scores across multiple questionnaires measuring inattention and hyperactivity. Partial Least-Squares (PLS) regression revealed that variability in this latent factor could not be explained by a linear component representing nodewise properties of connectomes. We then investigated the type and extent of neural heterogeneity in a subset of our sample with clinically-elevated levels of inattention and hyperactivity. Multidimensional scaling combined with k-means clustering revealed two neural subtypes in children with elevated levels of inattention and hyperactivity (n = 232), differentiated primarily by nodal communicability-a measure which demarcates the extent to which neural signals propagate through specific brain regions. These different clusters had similar behavioural profiles, which included high levels of inattention and hyperactivity. However, one of the clusters scored higher on multiple cognitive assessment measures of executive function. We conclude that inattention and hyperactivity are so common in children with neurodevelopmental difficulties because they emerge through multiple different trajectories of brain development. In our own data, we can identify two of these possible trajectories, which are reflected by measures of structural brain network topology and cognition.
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Affiliation(s)
- Natalia Zdorovtsova
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
| | - Jonathan Jones
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Danyal Akarca
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Elia Benhamou
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - The Calm Team
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Duncan E Astle
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK; Department of Psychiatry, University of Cambridge, Cambridge, UK
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27
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Sadoun MSN, Laleg-Kirati TM. Seizure Onset Localization in Focal Epilepsy using intracranial-EEG data and the Schrodinger Operator's Spectrum. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-5. [PMID: 38083081 DOI: 10.1109/embc40787.2023.10339987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Epilepsy is a neurological disorder characterized by recurrent, unprovoked seizures that vary from short attention failure to convulsions. Despite its threats and limitations, existing medications target only specific types of seizures while up to 33% of epileptic conditions are drug-resistant. The best available treatment is surgical resection or neurostimulation and both require accurate localization of the Seizure Onset Zone. Its delineation is performed by analyzing neuronal activity by epileptologists, however, it is time-consuming and error-prone. Therefore, if the said zone could be located faster and more accurately, the seizure freedom of patients would be significantly enhanced. An effort within the field is aiming at developing computer-aided methods to assist medical experts and this starts with characterizing electrical neural activity. In the present paper, a new method for characterizing the epileptic intracranial EEG is proposed. The method is based on a semi-classical signal analysis (SCSA) method. Functional connectivity measures are used to compare patterns observed when feeding these measures with the raw time-series and when feeding them with SCSA features. The obtained results are undeniably promising for further investigation and improvement of the framework.Clinical relevance- The paper contributes to the design methods and algorithms to build reliable software solutions to assist medical experts in identifying Seizure Onset Zone in focal epilepsy.
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28
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Ozay M. Editorial: Novel perspectives and improvements in fMRI functional connectivity analysis methods used to investigate brain networks and cognitive mechanisms in humans. Front Neurosci 2023; 17:1222586. [PMID: 37325039 PMCID: PMC10265644 DOI: 10.3389/fnins.2023.1222586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 05/17/2023] [Indexed: 06/17/2023] Open
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29
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Novitskiy N, Chan PHY, Chan M, Lai CM, Leung TY, Leung TF, Bornstein MH, Lam HS, Wong PCM. Deficits in neural encoding of speech in preterm infants. Dev Cogn Neurosci 2023; 61:101259. [PMID: 37257249 DOI: 10.1016/j.dcn.2023.101259] [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: 01/11/2023] [Revised: 05/19/2023] [Accepted: 05/25/2023] [Indexed: 06/02/2023] Open
Abstract
Preterm children show developmental cognitive and language deficits that can be subtle and sometimes undetectable until later in life. Studies of brain development in children who are born preterm have largely focused on vascular and gross anatomical characteristics rather than pathophysiological processes that may contribute to these developmental deficits. Neural encoding of speech as reflected in EEG recordings is predictive of future language development and could provide insights into those pathophysiological processes. We recorded EEG from 45 preterm (≤ 34 weeks of gestation) and 45 term (≥ 38 weeks) Chinese-learning infants 0-12 months of (corrected) age during natural sleep. Each child listened to three speech stimuli that differed in lexically meaningful pitch (2 native and 1 non-native speech categories). EEG measures associated with synchronization and gross power of the frequency following response (FFR) were examined. ANCOVAs revealed no main effect of stimulus nativeness but main effects of age, consistent with earlier studies. A main effect of prematurity also emerged, with synchronization measures showing stronger group differences than power. By detailing differences in FFR measures related to synchronization and power, this study brings us closer to identifying the pathophysiological pathway to often subtle language problems experienced by preterm children.
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Affiliation(s)
- Nikolay Novitskiy
- Brain and Mind Institute, The Chinese University of Hong Kong, Hong Kong, China
| | - Peggy H Y Chan
- Brain and Mind Institute, The Chinese University of Hong Kong, Hong Kong, China; Department of Paediatrics, The Chinese University of Hong Kong, Hong Kong, China
| | - Mavis Chan
- Brain and Mind Institute, The Chinese University of Hong Kong, Hong Kong, China
| | - Chin Man Lai
- Brain and Mind Institute, The Chinese University of Hong Kong, Hong Kong, China
| | - Tak Yeung Leung
- Department of Obsterics and Gynaecology, The Chinese University of Hong Kong, Hong Kong, China
| | - Ting Fan Leung
- Department of Paediatrics, The Chinese University of Hong Kong, Hong Kong, China
| | - Marc H Bornstein
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, USA; UNICEF, USA; Institute for Fiscal Studies, UK
| | - Hugh S Lam
- Department of Paediatrics, The Chinese University of Hong Kong, Hong Kong, China.
| | - Patrick C M Wong
- Brain and Mind Institute, The Chinese University of Hong Kong, Hong Kong, China.
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30
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Wang S, Li X. A revisit of the amygdala theory of autism: Twenty years after. Neuropsychologia 2023; 183:108519. [PMID: 36803966 PMCID: PMC10824605 DOI: 10.1016/j.neuropsychologia.2023.108519] [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: 07/09/2022] [Revised: 01/23/2023] [Accepted: 02/16/2023] [Indexed: 02/19/2023]
Abstract
The human amygdala has long been implicated to play a key role in autism spectrum disorder (ASD). Yet it remains unclear to what extent the amygdala accounts for the social dysfunctions in ASD. Here, we review studies that investigate the relationship between amygdala function and ASD. We focus on studies that employ the same task and stimuli to directly compare people with ASD and patients with focal amygdala lesions, and we also discuss functional data associated with these studies. We show that the amygdala can only account for a limited number of deficits in ASD (primarily face perception tasks but not social attention tasks), a network view is, therefore, more appropriate. We next discuss atypical brain connectivity in ASD, factors that can explain such atypical brain connectivity, and novel tools to analyze brain connectivity. Lastly, we discuss new opportunities from multimodal neuroimaging with data fusion and human single-neuron recordings that can enable us to better understand the neural underpinnings of social dysfunctions in ASD. Together, the influential amygdala theory of autism should be extended with emerging data-driven scientific discoveries such as machine learning-based surrogate models to a broader framework that considers brain connectivity at the global scale.
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Affiliation(s)
- Shuo Wang
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, USA; Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA.
| | - Xin Li
- Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA.
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31
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Maher C, Yang Y, Truong ND, Wang C, Nikpour A, Kavehei O. Seizure detection with reduced electroencephalogram channels: research trends and outlook. ROYAL SOCIETY OPEN SCIENCE 2023; 10:230022. [PMID: 37153360 PMCID: PMC10154941 DOI: 10.1098/rsos.230022] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 04/11/2023] [Indexed: 05/09/2023]
Abstract
Epilepsy is a prevalent condition characterized by recurrent, unpredictable seizures. Monitoring with surface electroencephalography (EEG) is the gold standard for diagnosing epilepsy, but a time-consuming, uncomfortable and sometimes ineffective process for patients. Further, using EEG over a brief monitoring period has variable success, dependent on patient tolerance and seizure frequency. The availability of hospital resources and hardware and software specifications inherently restrict the options for comfortable, long-term data collection, resulting in limited data for training machine-learning models. This mini-review examines the current patient journey, providing an overview of the current state of EEG monitoring with reduced electrodes and automated channel reduction methods. Opportunities for improving data reliability through multi-modal data fusion are suggested. We assert the need for further research in electrode reduction to advance brain monitoring solutions towards portable, reliable devices that simultaneously offer patient comfort, perform ultra-long-term monitoring and expedite the diagnosis process.
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Affiliation(s)
- Christina Maher
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales 2006, Australia
| | - Yikai Yang
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
| | - Nhan Duy Truong
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
| | - Chenyu Wang
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales 2006, Australia
- Translational Research Collective, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales 2050, Australia
- Sydney Neuroimaging Analysis Centre, Camperdown, New South Wales 2050, Australia
| | - Armin Nikpour
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales 2006, Australia
- Central Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales 2006, Australia
- Translational Research Collective, Faculty of Medicine and Health, The University of Sydney, Camperdown, New South Wales 2050, Australia
| | - Omid Kavehei
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
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32
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Wan Z, Cheng W, Li M, Zhu R, Duan W. GDNet-EEG: An attention-aware deep neural network based on group depth-wise convolution for SSVEP stimulation frequency recognition. Front Neurosci 2023; 17:1160040. [PMID: 37123356 PMCID: PMC10133471 DOI: 10.3389/fnins.2023.1160040] [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: 02/06/2023] [Accepted: 03/27/2023] [Indexed: 05/02/2023] Open
Abstract
Background Steady state visually evoked potentials (SSVEPs) based early glaucoma diagnosis requires effective data processing (e.g., deep learning) to provide accurate stimulation frequency recognition. Thus, we propose a group depth-wise convolutional neural network (GDNet-EEG), a novel electroencephalography (EEG)-oriented deep learning model tailored to learn regional characteristics and network characteristics of EEG-based brain activity to perform SSVEPs-based stimulation frequency recognition. Method Group depth-wise convolution is proposed to extract temporal and spectral features from the EEG signal of each brain region and represent regional characteristics as diverse as possible. Furthermore, EEG attention consisting of EEG channel-wise attention and specialized network-wise attention is designed to identify essential brain regions and form significant feature maps as specialized brain functional networks. Two publicly SSVEPs datasets (large-scale benchmark and BETA dataset) and their combined dataset are utilized to validate the classification performance of our model. Results Based on the input sample with a signal length of 1 s, the GDNet-EEG model achieves the average classification accuracies of 84.11, 85.93, and 93.35% on the benchmark, BETA, and combination datasets, respectively. Compared with the average classification accuracies achieved by comparison baselines, the average classification accuracies of the GDNet-EEG trained on a combination dataset increased from 1.96 to 18.2%. Conclusion Our approach can be potentially suitable for providing accurate SSVEP stimulation frequency recognition and being used in early glaucoma diagnosis.
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Affiliation(s)
- Zhijiang Wan
- The First Affiliated Hospital of Nanchang University, Nanchang University, Nanchang, Jiangxi, China
- School of Information Engineering, Nanchang University, Nanchang, Jiangxi, China
- Industrial Institute of Artificial Intelligence, Nanchang University, Nanchang, Jiangxi, China
| | - Wangxinjun Cheng
- Queen Mary College of Nanchang University, Nanchang University, Nanchang, Jiangxi, China
| | - Manyu Li
- School of Information Engineering, Nanchang University, Nanchang, Jiangxi, China
| | - Renping Zhu
- School of Information Engineering, Nanchang University, Nanchang, Jiangxi, China
- Industrial Institute of Artificial Intelligence, Nanchang University, Nanchang, Jiangxi, China
- School of Information Management, Wuhan University, Wuhan, China
| | - Wenfeng Duan
- The First Affiliated Hospital of Nanchang University, Nanchang University, Nanchang, Jiangxi, China
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33
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Alghamdi AJ. The Value of Various Post-Processing Modalities of Diffusion Weighted Imaging in the Detection of Multiple Sclerosis. Brain Sci 2023; 13:brainsci13040622. [PMID: 37190587 DOI: 10.3390/brainsci13040622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 03/31/2023] [Accepted: 04/04/2023] [Indexed: 04/08/2023] Open
Abstract
Diffusion tensor imaging (DTI) showed its adequacy in evaluating the normal-appearing white matter (NAWM) and lesions in the brain that are difficult to evaluate with routine clinical magnetic resonance imaging (MRI) in multiple sclerosis (MS). Recently, MRI systems have been developed with regard to software and hardware, leading to different proposed diffusion analysis methods such as diffusion tensor imaging, q-space imaging, diffusional kurtosis imaging, neurite orientation dispersion and density imaging, and axonal diameter measurement. These methods have the ability to better detect in vivo microstructural changes in the brain than DTI. These different analysis modalities could provide supplementary inputs for MS disease characterization and help in monitoring the disease’s progression as well as treatment efficacy. This paper reviews some of the recent diffusion MRI methods used for the assessment of MS in vivo.
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Affiliation(s)
- Ahmad Joman Alghamdi
- Radiological Sciences Department, College of Applied Medical Sciences, Taif University, Taif 21944, Saudi Arabia
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34
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Graph-generative neural network for EEG-based epileptic seizure detection via discovery of dynamic brain functional connectivity. Sci Rep 2022; 12:18998. [PMID: 36348082 PMCID: PMC9643358 DOI: 10.1038/s41598-022-23656-1] [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: 08/15/2022] [Accepted: 11/03/2022] [Indexed: 11/09/2022] Open
Abstract
Dynamic complexity in brain functional connectivity has hindered the effective use of signal processing or machine learning methods to diagnose neurological disorders such as epilepsy. This paper proposed a new graph-generative neural network (GGN) model for the dynamic discovery of brain functional connectivity via deep analysis of scalp electroencephalogram (EEG) signals recorded from various regions of a patient's scalp. Brain functional connectivity graphs are generated for the extraction of spatial-temporal resolution of various onset epilepsy seizure patterns. Our supervised GGN model was substantiated by seizure detection and classification experiments. We train the GGN model using a clinically proven dataset of over 3047 epileptic seizure cases. The GGN model achieved a 91% accuracy in classifying seven types of epileptic seizure attacks, which outperformed the 65%, 74%, and 82% accuracy in using the convolutional neural network (CNN), graph neural networks (GNN), and transformer models, respectively. We present the GGN model architecture and operational steps to assist neuroscientists or brain specialists in using dynamic functional connectivity information to detect neurological disorders. Furthermore, we suggest to merge our spatial-temporal graph generator design in upgrading the conventional CNN and GNN models with dynamic convolutional kernels for accuracy enhancement.
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35
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DeRamus TP, Wu L, Qi S, Iraji A, Silva R, Du Y, Pearlson G, Mayer A, Bustillo JR, Stromberg SF, Calhoun VD. Multimodal data fusion of cortical-subcortical morphology and functional network connectivity in psychotic spectrum disorder. Neuroimage Clin 2022; 35:103056. [PMID: 35709557 PMCID: PMC9207350 DOI: 10.1016/j.nicl.2022.103056] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 04/18/2022] [Accepted: 05/21/2022] [Indexed: 11/20/2022]
Abstract
Overlap has been noted disorders which fall on the psychotic spectrum. Univariate studies may miss joint brain features across diagnostic categories. mCCA with jICA is paired with features across the psychotic spectrum to produce joint components. One joint component displayed a significant relationship with cognitive scores. The replicate trends of cortical-subcortical irregularity in psychotic spectrum disorders.
Multiple authors have noted overlapping symptoms and alterations across clinical, anatomical, and functional brain features in schizophrenia (SZ), schizoaffective disorder (SZA), and bipolar disorder (BPI). However, regarding brain features, few studies have approached this line of inquiry using analytical techniques optimally designed to extract the shared features across anatomical and functional information in a simultaneous manner. Univariate studies of anatomical or functional alterations across these disorders can be limited and run the risk of omitting small but potentially crucial overlapping or joint neuroanatomical (e.g., structural images) and functional features (e.g., fMRI-based features) which may serve as informative clinical indicators of across multiple diagnostic categories. To address this limitation, we paired an unsupervised multimodal canonical correlation analysis (mCCA) together with joint independent component analysis (jICA) to identify linked spatial gray matter (GM), resting-state functional network connectivity (FNC), and white matter fractional anisotropy (FA) features across these diagnostic categories. We then calculated associations between the identified linked features and trans-diagnostic behavioral measures (MATRICs Consensus Cognitive Battery, MCCB). Component number 4 of the 13 identified displayed a statistically significant relationship with overall MCCB scores across GM, resting-state FNC, and FA. These linked modalities of component 4 consisted primarily of positive correlations within subcortical structures including the caudate and putamen in the GM maps with overall MCCB, sparse negative correlations within subcortical and cortical connection tracts (e.g., corticospinal tract, superior longitudinal fasciculus) in the FA maps with overall MCCB, and negative relationships with MCCB values and loading parameters with FNC matrices displaying increased FNC in subcortical-cortical regions with auditory, somatomotor, and visual regions.
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Affiliation(s)
- T P DeRamus
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) - Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA.
| | - L Wu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) - Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - S Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - A Iraji
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) - Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - R Silva
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) - Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Y Du
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) - Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA; School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - G Pearlson
- Olin Neuropsychiatry Research Center, Institute of Living at Hartford Hospital, Hartford, CT, USA; Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - A Mayer
- The Mind Research Network, Lovelace Biomedical and Environmental Research Institute, Albuquerque, USA
| | - J R Bustillo
- Department of Psychiatry, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - S F Stromberg
- Psychiatry and Behavioral Health Clinical Program, Presbyterian Healthcare System, Albuquerque, NM, USA
| | - V D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) - Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA; The Mind Research Network, Lovelace Biomedical and Environmental Research Institute, Albuquerque, USA; Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA; Department of Computer Science, Georgia State University, Atlanta, USA; Department of Psychology, Georgia State University, Atlanta, USA
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