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Pascarella A, Manzo L, Ferlazzo E. Modern neurophysiological techniques indexing normal or abnormal brain aging. Seizure 2024:S1059-1311(24)00194-8. [PMID: 38972778 DOI: 10.1016/j.seizure.2024.07.001] [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: 04/09/2024] [Accepted: 07/01/2024] [Indexed: 07/09/2024] Open
Abstract
Brain aging is associated with a decline in cognitive performance, motor function and sensory perception, even in the absence of neurodegeneration. The underlying pathophysiological mechanisms remain incompletely understood, though alterations in neurogenesis, neuronal senescence and synaptic plasticity are implicated. Recent years have seen advancements in neurophysiological techniques such as electroencephalography (EEG), magnetoencephalography (MEG), event-related potentials (ERP) and transcranial magnetic stimulation (TMS), offering insights into physiological and pathological brain aging. These methods provide real-time information on brain activity, connectivity and network dynamics. Integration of Artificial Intelligence (AI) techniques promise as a tool enhancing the diagnosis and prognosis of age-related cognitive decline. Our review highlights recent advances in these electrophysiological techniques (focusing on EEG, ERP, TMS and TMS-EEG methodologies) and their application in physiological and pathological brain aging. Physiological aging is characterized by changes in EEG spectral power and connectivity, ERP and TMS parameters, indicating alterations in neural activity and network function. Pathological aging, such as in Alzheimer's disease, is associated with further disruptions in EEG rhythms, ERP components and TMS measures, reflecting underlying neurodegenerative processes. Machine learning approaches show promise in classifying cognitive impairment and predicting disease progression. Standardization of neurophysiological methods and integration with other modalities are crucial for a comprehensive understanding of brain aging and neurodegenerative disorders. Advanced network analysis techniques and AI methods hold potential for enhancing diagnostic accuracy and deepening insights into age-related brain changes.
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Affiliation(s)
- Angelo Pascarella
- Department of Medical and Surgical Sciences, Magna Græcia University of Catanzaro, Italy; Regional Epilepsy Centre, Great Metropolitan "Bianchi-Melacrino-Morelli Hospital", Reggio Calabria, Italy.
| | - Lucia Manzo
- Regional Epilepsy Centre, Great Metropolitan "Bianchi-Melacrino-Morelli Hospital", Reggio Calabria, Italy
| | - Edoardo Ferlazzo
- Department of Medical and Surgical Sciences, Magna Græcia University of Catanzaro, Italy; Regional Epilepsy Centre, Great Metropolitan "Bianchi-Melacrino-Morelli Hospital", Reggio Calabria, Italy
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Alqaissi E, Alotaibi F, Sher Ramzan M, Algarni A. Novel graph-based machine-learning technique for viral infectious diseases: application to influenza and hepatitis diseases. Ann Med 2024; 55:2304108. [PMID: 38242107 PMCID: PMC10802812 DOI: 10.1080/07853890.2024.2304108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 12/18/2023] [Indexed: 01/21/2024] Open
Abstract
BACKGROUND Most infectious diseases are caused by viruses, fungi, bacteria and parasites. Their ability to easily infect humans and trigger large-scale epidemics makes them a public health concern. Methods for early detection of these diseases have been developed; however, they are hindered by the absence of a unified, interoperable and reusable model. This study seeks to create a holistic and real-time model for swift, preliminary detection of infectious diseases using symptoms and additional clinical data. MATERIALS AND METHODS In this study, we present a medical knowledge graph (MKG) that leverages multiple data sources to analyse connections between different nodes. Medical ontologies were used to enhance the MKG. We applied various graph algorithms to extract key features. The performance of multiple machine-learning (ML) techniques for influenza and hepatitis detection was assessed, selecting multi-layer perceptron (MLP) and random forest (RF) models due to their superior outcomes. The hyperparameters of both graph-based ML models were automatically fine-tuned. RESULTS Both the graph-based MLP and RF models showcased the least loss and error rates, along with the most specific, accurate recall, precision and F1 scores. Their Matthews correlation coefficients were also optimal. When compared with existing ML techniques and findings from the literature, these graph-based ML models manifested superior detection accuracy. CONCLUSIONS The graph-based MLP and RF models effectively diagnosed influenza and hepatitis, respectively. This underlines the potential of graph data science in enhancing ML model performance and uncovering concealed relationships in the MKG.
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Affiliation(s)
- Eman Alqaissi
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
- Computer Science and Information Systems, The Applied College, King Khalid University, Abha, Saudi Arabia
| | - Fahd Alotaibi
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Muhammad Sher Ramzan
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
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Zhang L, Qu J, Ma H, Chen T, Liu T, Zhu D. Exploring Alzheimer's disease: a comprehensive brain connectome-based survey. PSYCHORADIOLOGY 2024; 4:kkad033. [PMID: 38333558 PMCID: PMC10848159 DOI: 10.1093/psyrad/kkad033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 12/21/2023] [Accepted: 01/03/2024] [Indexed: 02/10/2024]
Abstract
Dementia is an escalating global health challenge, with Alzheimer's disease (AD) at its forefront. Substantial evidence highlights the accumulation of AD-related pathological proteins in specific brain regions and their subsequent dissemination throughout the broader area along the brain network, leading to disruptions in both individual brain regions and their interconnections. Although a comprehensive understanding of the neurodegeneration-brain network link is lacking, it is undeniable that brain networks play a pivotal role in the development and progression of AD. To thoroughly elucidate the intricate network of elements and connections constituting the human brain, the concept of the brain connectome was introduced. Research based on the connectome holds immense potential for revealing the mechanisms underlying disease development, and it has become a prominent topic that has attracted the attention of numerous researchers. In this review, we aim to systematically summarize studies on brain networks within the context of AD, critically analyze the strengths and weaknesses of existing methodologies, and offer novel perspectives and insights, intending to serve as inspiration for future research.
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Affiliation(s)
- Lu Zhang
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA
| | - Junqi Qu
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA
| | - Haotian Ma
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA
| | - Tong Chen
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA
| | - Tianming Liu
- Department of Computer Science, The University of Georgia, Athens, GA 30602, USA
| | - Dajiang Zhu
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA
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Alqaissi E, Alotaibi F, Ramzan MS. Graph data science and machine learning for the detection of COVID-19 infection from symptoms. PeerJ Comput Sci 2023; 9:e1333. [PMID: 37346701 PMCID: PMC10280642 DOI: 10.7717/peerj-cs.1333] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 03/16/2023] [Indexed: 06/23/2023]
Abstract
Background COVID-19 is an infectious disease caused by SARS-CoV-2. The symptoms of COVID-19 vary from mild-to-moderate respiratory illnesses, and it sometimes requires urgent medication. Therefore, it is crucial to detect COVID-19 at an early stage through specific clinical tests, testing kits, and medical devices. However, these tests are not always available during the time of the pandemic. Therefore, this study developed an automatic, intelligent, rapid, and real-time diagnostic model for the early detection of COVID-19 based on its symptoms. Methods The COVID-19 knowledge graph (KG) constructed based on literature from heterogeneous data is imported to understand the COVID-19 different relations. We added human disease ontology to the COVID-19 KG and applied a node-embedding graph algorithm called fast random projection to extract an extra feature from the COVID-19 dataset. Subsequently, experiments were conducted using two machine learning (ML) pipelines to predict COVID-19 infection from its symptoms. Additionally, automatic tuning of the model hyperparameters was adopted. Results We compared two graph-based ML models, logistic regression (LR) and random forest (RF) models. The proposed graph-based RF model achieved a small error rate = 0.0064 and the best scores on all performance metrics, including specificity = 98.71%, accuracy = 99.36%, precision = 99.65%, recall = 99.53%, and F1-score = 99.59%. Furthermore, the Matthews correlation coefficient achieved by the RF model was higher than that of the LR model. Comparative analysis with other ML algorithms and with studies from the literature showed that the proposed RF model exhibited the best detection accuracy. Conclusion The graph-based RF model registered high performance in classifying the symptoms of COVID-19 infection, thereby indicating that the graph data science, in conjunction with ML techniques, helps improve performance and accelerate innovations.
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Affiliation(s)
- Eman Alqaissi
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
- Information Systems, King Khalid University, Abha, Saudi Arabia
| | - Fahd Alotaibi
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Muhammad Sher Ramzan
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
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Yang Z, Chen Y, Hou X, Xu Y, Bai F. Topologically convergent and divergent large scale complex networks among Alzheimer's disease spectrum patients: A systematic review. Heliyon 2023; 9:e15389. [PMID: 37101638 PMCID: PMC10123263 DOI: 10.1016/j.heliyon.2023.e15389] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 03/16/2023] [Accepted: 04/05/2023] [Indexed: 04/28/2023] Open
Abstract
Alzheimer's disease (AD) is associated with disruption at the level of a large-scale complex network. To explore the underlying mechanisms in the progression of AD, graph theory was used to quantitatively analyze the topological properties of structural and functional connections. Although an increasing number of studies have shown altered global and nodal network properties, little is known about the topologically convergent and divergent patterns between structural and functional networks among AD-spectrum patients. In this review, we summarized the topological patterns of the large-scale complex networks using multimodal neuroimaging graph theory analysis in AD spectrum patients. Convergent deficits in the connectivity characteristics were primarily in the default mode network (DMN) itself both in the structural and functional networks, while a divergent changes in the neighboring regions of the DMN were also observed between the patient groups. Together, the application of graph theory to large-scale complex brain networks provides quantitative insights into topological principles of brain network organization, which may lead to increasing attention in identifying the underlying neuroimaging pathological changes and predicting the progression of AD.
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Affiliation(s)
- Zhiyuan Yang
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - Ya Chen
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing 210008, China
| | - Xinle Hou
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
- Department of Neurology, Nanjing Drum Tower Hospital, State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing 210008, China
| | - Feng Bai
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
- Geriatric Medicine Center, Affiliated Taikang Xianlin Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
- Correspondence to: 321 Zhongshan Road, Nanjing, 210008, China.
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Adebisi AT, Veluvolu KC. Brain network analysis for the discrimination of dementia disorders using electrophysiology signals: A systematic review. Front Aging Neurosci 2023; 15:1039496. [PMID: 36936496 PMCID: PMC10020520 DOI: 10.3389/fnagi.2023.1039496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 02/06/2023] [Indexed: 03/06/2023] Open
Abstract
Background Dementia-related disorders have been an age-long challenge to the research and healthcare communities as their various forms are expressed with similar clinical symptoms. These disorders are usually irreversible at their late onset, hence their lack of validated and approved cure. Since their prodromal stages usually lurk for a long period of time before the expression of noticeable clinical symptoms, a secondary prevention which has to do with treating the early onsets has been suggested as the possible solution. Connectivity analysis of electrophysiology signals has played significant roles in the diagnosis of various dementia disorders through early onset identification. Objective With the various applications of electrophysiology signals, the purpose of this study is to systematically review the step-by-step procedures of connectivity analysis frameworks for dementia disorders. This study aims at identifying the methodological issues involved in such frameworks and also suggests approaches to solve such issues. Methods In this study, ProQuest, PubMed, IEEE Xplore, Springer Link, and Science Direct databases are employed for exploring the evolution and advancement of connectivity analysis of electrophysiology signals of dementia-related disorders between January 2016 to December 2022. The quality of assessment of the studied articles was done using Cochrane guidelines for the systematic review of diagnostic test accuracy. Results Out of a total of 4,638 articles found to have been published on the review scope between January 2016 to December 2022, a total of 51 peer-review articles were identified to completely satisfy the review criteria. An increasing trend of research in this domain is identified within the considered time frame. The ratio of MEG and EEG utilization found within the reviewed articles is 1:8. Most of the reviewed articles employed graph theory metrics for their analysis with clustering coefficient (CC), global efficiency (GE), and characteristic path length (CPL) appearing more frequently compared to other metrics. Significance This study provides general insight into how to employ connectivity measures for the analysis of electrophysiology signals of dementia-related disorders in order to better understand their underlying mechanism and their differential diagnosis.
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Affiliation(s)
- Abdulyekeen T. Adebisi
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, Republic of Korea
| | - Kalyana C. Veluvolu
- School of Electronics Engineering, Kyungpook National University, Daegu, Republic of Korea
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Cheirdaris DG. Graph Theory-Based Approach in Brain Connectivity Modeling and Alzheimer's Disease Detection. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1424:49-58. [PMID: 37486478 DOI: 10.1007/978-3-031-31982-2_5] [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: 07/25/2023]
Abstract
There is strong evidence that the pathological findings of Alzheimer's disease (AD), consisting of accumulated amyloid plaques and neurofibrillary tangles, could spread around the brain through synapses and neural connections of neighboring brain sections. Graph theory is a helpful tool in depicting the complex human brain divided into various regions of interest (ROIs) and the connections among them. Thus, applying graph theory-based models in the study of brain connectivity comes natural in the study of AD propagation mechanisms. Moreover, graph theory-based computational approaches have been lately applied in order to boost data-driven analysis, extract model measures and robustness-effectiveness indexes, and provide insights on casual interactions between regions of interest (ROI), as imposed by the models' architecture.
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Affiliation(s)
- Dionysios G Cheirdaris
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.
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Stylianou O, Kaposzta Z, Czoch A, Stefanovski L, Yabluchanskiy A, Racz FS, Ritter P, Eke A, Mukli P. Scale-Free Functional Brain Networks Exhibit Increased Connectivity, Are More Integrated and Less Segregated in Patients with Parkinson's Disease following Dopaminergic Treatment. FRACTAL AND FRACTIONAL 2022; 6:737. [PMID: 38106971 PMCID: PMC10723163 DOI: 10.3390/fractalfract6120737] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Dopaminergic treatment (DT), the standard therapy for Parkinson's disease (PD), alters the dynamics of functional brain networks at specific time scales. Here, we explore the scale-free functional connectivity (FC) in the PD population and how it is affected by DT. We analyzed the electroencephalogram of: (i) 15 PD patients during DT (ON) and after DT washout (OFF) and (ii) 16 healthy control individuals (HC). We estimated FC using bivariate focus-based multifractal analysis, which evaluated the long-term memory ( H ( 2 ) ) and multifractal strength ( Δ H 15 ) of the connections. Subsequent analysis yielded network metrics (node degree, clustering coefficient and path length) based on FC estimated by H ( 2 ) or Δ H 15 . Cognitive performance was assessed by the Mini Mental State Examination (MMSE) and the North American Adult Reading Test (NAART). The node degrees of the Δ H 15 networks were significantly higher in ON, compared to OFF and HC, while clustering coefficient and path length significantly decreased. No alterations were observed in the H ( 2 ) networks. Significant positive correlations were also found between the metrics of H ( 2 ) networks and NAART scores in the HC group. These results demonstrate that DT alters the multifractal coupled dynamics in the brain, warranting the investigation of scale-free FC in clinical and pharmacological studies.
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Affiliation(s)
- Orestis Stylianou
- Department of Physiology, Semmelweis University, 1094 Budapest, Hungary
- Institute of Translational Medicine, Semmelweis University, 1094 Budapest, Hungary
- Berlin Institute of Health at Charité, University Hospital Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Department of Neurology with Experimental Neurology, Charité-University Hospital Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Zalan Kaposzta
- Department of Physiology, Semmelweis University, 1094 Budapest, Hungary
| | - Akos Czoch
- Department of Physiology, Semmelweis University, 1094 Budapest, Hungary
| | - Leon Stefanovski
- Berlin Institute of Health at Charité, University Hospital Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Department of Neurology with Experimental Neurology, Charité-University Hospital Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Andriy Yabluchanskiy
- Vascular Cognitive Impairment and Neurodegeneration Program, Oklahoma Center for Geroscience and Healthy Brain Aging, Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, 975 NE 10th Street, BRC, Oklahoma City, OK 73104, USA
| | - Frigyes Samuel Racz
- Department of Physiology, Semmelweis University, 1094 Budapest, Hungary
- Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, TX 78712, USA
| | - Petra Ritter
- Berlin Institute of Health at Charité, University Hospital Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Department of Neurology with Experimental Neurology, Charité-University Hospital Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience Berlin, 10115 Berlin, Germany
- Einstein Center for Neuroscience Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Einstein Center Digital Future, Wilhelmstraße 67, 10117 Berlin, Germany
| | - Andras Eke
- Department of Physiology, Semmelweis University, 1094 Budapest, Hungary
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 300 Cedar Street, New Haven, CT 06520, USA
| | - Peter Mukli
- Department of Physiology, Semmelweis University, 1094 Budapest, Hungary
- Vascular Cognitive Impairment and Neurodegeneration Program, Oklahoma Center for Geroscience and Healthy Brain Aging, Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, 975 NE 10th Street, BRC, Oklahoma City, OK 73104, USA
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Xefteris VR, Tsanousa A, Georgakopoulou N, Diplaris S, Vrochidis S, Kompatsiaris I. Graph Theoretical Analysis of EEG Functional Connectivity Patterns and Fusion with Physiological Signals for Emotion Recognition. SENSORS (BASEL, SWITZERLAND) 2022; 22:8198. [PMID: 36365896 PMCID: PMC9656224 DOI: 10.3390/s22218198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/22/2022] [Accepted: 10/24/2022] [Indexed: 06/16/2023]
Abstract
Emotion recognition is a key attribute for realizing advances in human-computer interaction, especially when using non-intrusive physiological sensors, such as electroencephalograph (EEG) and electrocardiograph. Although functional connectivity of EEG has been utilized for emotion recognition, the graph theory analysis of EEG connectivity patterns has not been adequately explored. The exploitation of brain network characteristics could provide valuable information regarding emotions, while the combination of EEG and peripheral physiological signals can reveal correlation patterns of human internal state. In this work, a graph theoretical analysis of EEG functional connectivity patterns along with fusion between EEG and peripheral physiological signals for emotion recognition has been proposed. After extracting functional connectivity from EEG signals, both global and local graph theory features are extracted. Those features are concatenated with statistical features from peripheral physiological signals and fed to different classifiers and a Convolutional Neural Network (CNN) for emotion recognition. The average accuracy on the DEAP dataset using CNN was 55.62% and 57.38% for subject-independent valence and arousal classification, respectively, and 83.94% and 83.87% for subject-dependent classification. Those scores went up to 75.44% and 78.77% for subject-independent classification and 88.27% and 90.84% for subject-dependent classification using a feature selection algorithm, exceeding the current state-of-the-art results.
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Shan X, Cao J, Huo S, Chen L, Sarrigiannis PG, Zhao Y. Spatial-temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram. Hum Brain Mapp 2022; 43:5194-5209. [PMID: 35751844 PMCID: PMC9812255 DOI: 10.1002/hbm.25994] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/19/2022] [Accepted: 06/08/2022] [Indexed: 01/15/2023] Open
Abstract
Functional connectivity of the human brain, representing statistical dependence of information flow between cortical regions, significantly contributes to the study of the intrinsic brain network and its functional mechanism. To fully explore its potential in the early diagnosis of Alzheimer's disease (AD) using electroencephalogram (EEG) recordings, this article introduces a novel dynamical spatial-temporal graph convolutional neural network (ST-GCN) for better classification performance. Different from existing studies that are based on either topological brain function characteristics or temporal features of EEG, the proposed ST-GCN considers both the adjacency matrix of functional connectivity from multiple EEG channels and corresponding dynamics of signal EEG channel simultaneously. Different from the traditional graph convolutional neural networks, the proposed ST-GCN makes full use of the constrained spatial topology of functional connectivity and the discriminative dynamic temporal information represented by the 1D convolution. We conducted extensive experiments on the clinical EEG data set of AD patients and Healthy Controls. The results demonstrate that the proposed method achieves better classification performance (92.3%) than the state-of-the-art methods. This approach can not only help diagnose AD but also better understand the effect of normal ageing on brain network characteristics before we can accurately diagnose the condition based on resting-state EEG.
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Affiliation(s)
- Xiaocai Shan
- Institute of Geology and GeophysicsChinese Academy of SciencesBeijingChina,School of Aerospace, Transport and ManufacturingCranfield UniversityCranfieldUK
| | - Jun Cao
- School of Aerospace, Transport and ManufacturingCranfield UniversityCranfieldUK
| | - Shoudong Huo
- Institute of Geology and GeophysicsChinese Academy of SciencesBeijingChina
| | - Liangyu Chen
- Department of NeurosurgeryShengjing Hospital of China Medical UniversityShenyangChina
| | | | - Yifan Zhao
- School of Aerospace, Transport and ManufacturingCranfield UniversityCranfieldUK
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Song Y, Wang K, Wei Y, Zhu Y, Wen J, Luo Y. Graph Theory Analysis of the Cortical Functional Network During Sleep in Patients With Depression. Front Physiol 2022; 13:858739. [PMID: 35721531 PMCID: PMC9199990 DOI: 10.3389/fphys.2022.858739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 04/19/2022] [Indexed: 11/24/2022] Open
Abstract
Depression, a common mental illness that seriously affects the psychological health of patients, is also thought to be associated with abnormal brain functional connectivity. This study aimed to explore the differences in the sleep-state functional network topology in depressed patients. A total of 25 healthy participants and 26 depressed patients underwent overnight 16-channel electroencephalography (EEG) examination. The cortical networks were constructed by using functional connectivity metrics of participants based on the weighted phase lag index (WPLI) between the EEG signals. The results indicated that depressed patients exhibited higher global efficiency and node strength than healthy participants. Furthermore, the depressed group indicated right-lateralization in the δ band. The top 30% of connectivity in both groups were shown in undirected connectivity graphs, revealing the distinct link patterns between the depressed and control groups. Links between the hemispheres were noted in the patient group, while the links in the control group were only observed within each hemisphere, and there were many long-range links inside the hemisphere. The altered sleep-state functional network topology in depressed patients may provide clues for a better understanding of the depression pathology. Overall, functional network topology may become a powerful tool for the diagnosis of depression.
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Affiliation(s)
- Yingjie Song
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Kejie Wang
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yu Wei
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yongpeng Zhu
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Jinfeng Wen
- Department of Psychology, Guangdong, 999 Brain Hospital, Guangzhou, China
| | - Yuxi Luo
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Sensing Technology and Biomedical Instruments, Sun Yat-sen University, Guangzhou, China
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Fu G, Xie Y, Pan J, Qiu Y, He H, Li Z, Li J, Feng Y, Lv X. Longitudinal study of irradiation-induced brain functional network alterations in patients with nasopharyngeal carcinoma. Radiother Oncol 2022; 173:277-284. [PMID: 35718009 DOI: 10.1016/j.radonc.2022.06.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 06/04/2022] [Accepted: 06/12/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND To investigate radiotherapy (RT)-related brain network changes in patients with nasopharyngeal carcinoma (NPC) over time and develop least absolute shrinkage and selection operator (LASSO)-based multivariable normal tissue complication probability (NTCP) models to predict RT-related brain network changes. METHODS 36 NPC patients were followed up at four timepoints: baseline, within 3 months (acute), 6 months (subacute), and 12 months (delayed) post-RT. 15 comparable healthy controls (HCs) were finally included and followed up in parallel. Functional neuroimaging data, dose-volume parameters of bilateral temporal lobes and Montreal Cognitive Assessment (MoCA) were acquired. Graph theoretical analysis and mixed-design analysis of variance were performed to investigate how the brain global and nodal changes were affected by RT. Multivariate logistic regression NTCP models were developed. LASSO with nested cross-validation strategy was used to select features. The relationships between network changes and MoCA changes were also examined. RESULTS Significant changes were detected in nodal efficiency (NE) in NPC patients but not in HCs over time. Altered NE was distributed in the bilateral frontal, temporal lobes and the right insula, which showed a "decrease-increase/recovery" pattern over time. Among all models, the model for predicting NE changes of STG.R showed a relatively good performance (area under the receiver operating curve: 0.68), and D20cc and V20 to right temporal lobe outperformed in this model. CONCLUSION Our findings indicate that RT-induced brain injury begin at the acute period and follow a recovery over time. Furthermore, our study presents prediction models for brain dysfunction based on the dosimetric and clinical parameters.
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Affiliation(s)
- Gui Fu
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Yuanyao Xie
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Jie Pan
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Yingwei Qiu
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Haoqiang He
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Zhipeng Li
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Jing Li
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Yanqiu Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China; Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou, China; Department of Rehabilitation, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
| | - Xiaofei Lv
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China.
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13
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Javaid H, Kumarnsit E, Chatpun S. Age-Related Alterations in EEG Network Connectivity in Healthy Aging. Brain Sci 2022; 12:brainsci12020218. [PMID: 35203981 PMCID: PMC8870284 DOI: 10.3390/brainsci12020218] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 01/28/2022] [Accepted: 02/01/2022] [Indexed: 02/01/2023] Open
Abstract
Emerging studies have reported that functional brain networks change with increasing age. Graph theory is applied to understand the age-related differences in brain behavior and function, and functional connectivity between the regions is examined using electroencephalography (EEG). The effect of normal aging on functional networks and inter-regional synchronization during the working memory (WM) state is not well known. In this study, we applied graph theory to investigate the effect of aging on network topology in a resting state and during performing a visual WM task to classify aging EEG signals. We recorded EEGs from 20 healthy middle-aged and 20 healthy elderly subjects with their eyes open, eyes closed, and during a visual WM task. EEG signals were used to construct the functional network; nodes are represented by EEG electrodes; and edges denote the functional connectivity. Graph theory matrices including global efficiency, local efficiency, clustering coefficient, characteristic path length, node strength, node betweenness centrality, and assortativity were calculated to analyze the networks. We applied the three classifiers of K-nearest neighbor (KNN), a support vector machine (SVM), and random forest (RF) to classify both groups. The analyses showed the significantly reduced network topology features in the elderly group. Local efficiency, global efficiency, and clustering coefficient were significantly lower in the elderly group with the eyes-open, eyes-closed, and visual WM task states. KNN achieved its highest accuracy of 98.89% during the visual WM task and depicted better classification performance than other classifiers. Our analysis of functional network connectivity and topological characteristics can be used as an appropriate technique to explore normal age-related changes in the human brain.
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Affiliation(s)
- Hamad Javaid
- Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand;
| | - Ekkasit Kumarnsit
- Physiology Program, Division of Health and Applied Science, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand;
- Biosignal Research Centre for Health, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand
| | - Surapong Chatpun
- Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand;
- Biosignal Research Centre for Health, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand
- Institute of Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand
- Correspondence:
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14
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Weighted Brain Network Analysis on Different Stages of Clinical Cognitive Decline. Bioengineering (Basel) 2022; 9:bioengineering9020062. [PMID: 35200415 PMCID: PMC8869328 DOI: 10.3390/bioengineering9020062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/26/2022] [Accepted: 01/29/2022] [Indexed: 11/25/2022] Open
Abstract
This study addresses brain network analysis over different clinical severity stages of cognitive dysfunction using electroencephalography (EEG). We exploit EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients and Alzheimer’s disease (AD) patients. We propose a new framework to study the topological networks with a spatiotemporal entropy measure for estimating the connectivity. Our results show that functional connectivity and graph analysis are frequency-band dependent, and alterations start at the MCI stage. In delta, the SCI group exhibited a decrease of clustering coefficient and an increase of path length compared to MCI and AD. In alpha, the opposite behavior appeared, suggesting a rapid and high efficiency in information transmission across the SCI network. Modularity analysis showed that electrodes of the same brain region were distributed over several modules, and some obtained modules in SCI were extended from anterior to posterior regions. These results demonstrate that the SCI network was more resilient to neuronal damage compared to that of MCI and even more compared to that of AD. Finally, we confirm that MCI is a transitional stage between SCI and AD, with a predominance of high-strength intrinsic connectivity, which may reflect the compensatory response to the neuronal damage occurring early in the disease process.
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15
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Kotlarz P, Nino JC, Febo M. Connectomic analysis of Alzheimer's disease using percolation theory. Netw Neurosci 2022; 6:213-233. [PMID: 36605889 PMCID: PMC9810282 DOI: 10.1162/netn_a_00221] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 12/08/2021] [Indexed: 01/09/2023] Open
Abstract
Alzheimer's disease (AD) is a severe neurodegenerative disorder that affects a growing worldwide elderly population. Identification of brain functional biomarkers is expected to help determine preclinical stages for targeted mechanistic studies and development of therapeutic interventions to deter disease progression. Connectomic analysis, a graph theory-based methodology used in the analysis of brain-derived connectivity matrices was used in conjunction with percolation theory targeted attack model to investigate the network effects of AD-related amyloid deposition. We used matrices derived from resting-state functional magnetic resonance imaging collected on mice with extracellular amyloidosis (TgCRND8 mice, n = 17) and control littermates (n = 17). Global, nodal, spatial, and percolation-based analysis was performed comparing AD and control mice. These data indicate a short-term compensatory response to neurodegeneration in the AD brain via a strongly connected core network with highly vulnerable or disconnected hubs. Targeted attacks demonstrated a greater vulnerability of AD brains to all types of attacks and identified progression models to mimic AD brain functional connectivity through betweenness centrality and collective influence metrics. Furthermore, both spatial analysis and percolation theory identified a key disconnect between the anterior brain of the AD mice to the rest of the brain network.
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Affiliation(s)
- Parker Kotlarz
- Department of Materials Science and Engineering, University of Florida, Gainesville, FL, USA,* Corresponding Author:
| | - Juan C. Nino
- Department of Materials Science and Engineering, University of Florida, Gainesville, FL, USA
| | - Marcelo Febo
- Department of Psychiatry, University of Florida, Gainesville, FL, USA
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16
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Bigham B, Zamanpour SA, Zare H. Features of the superficial white matter as biomarkers for the detection of Alzheimer's disease and mild cognitive impairment: A diffusion tensor imaging study. Heliyon 2022; 8:e08725. [PMID: 35071808 PMCID: PMC8761704 DOI: 10.1016/j.heliyon.2022.e08725] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 10/02/2021] [Accepted: 01/05/2022] [Indexed: 10/25/2022] Open
Abstract
BACKGROUND With the development of medical imaging and processing tools, accurate diagnosis of diseases has been made possible by intelligent systems. Owing to the remarkable ability of support vector machines (SVMs) for diseases diagnosis, extensive research has been conducted using the SVM algorithm for the classification of Alzheimer's disease (AD) and mild cognitive impairment (MCI). OBJECTIVES In this study, we applied an automated method to classify patients with AD and MCI and healthy control (HC) subjects based on the diffusion tensor imaging (DTI) features in the superficial white matter (SWM). PARTICIPANTS For this purpose, DTI data were downloaded from the Alzheimer's Disease Neuroimaging Initiative (ADNI). This method employed DTI data from 72 subjects: 24 subjects as HC, 24 subjects with MCI, and 24 subjects with AD. MEASURE ments: DTI processing was performed using DSI Studio software and all machine learning analyses were performed using MATLAB software. RESULTS The linear kernel of SVM was the best classifier, with an accuracy of 95.8% between the AD and HC groups, followed by the quadratic kernel of SVM with an accuracy of 83.3% between the MCI and HC groups and the Gaussian kernel of SVM with an accuracy of 83.3% between the AD and MCI groups. CONCLUSIONS Given the importance of diagnosing AD and MCI as well as the role of superficial white matter in the diagnosis of neurodegenerative diseases, in this study, the features of different DTI methods of the SWM are discussed, which could be a useful tool to assist in the diagnosis of AD and MCI.
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Affiliation(s)
- Bahare Bigham
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Seyed Amir Zamanpour
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hoda Zare
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
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17
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Davoudi S, Parto Dezfouli M, Knight RT, Daliri MR, Johnson EL. Prefrontal Lesions Disrupt Posterior Alpha-Gamma Coordination of Visual Working Memory Representations. J Cogn Neurosci 2021; 33:1798-1810. [PMID: 34375418 DOI: 10.1162/jocn_a_01715] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
How does the human brain prioritize different visual representations in working memory (WM)? Here, we define the oscillatory mechanisms supporting selection of "where" and "when" features from visual WM storage and investigate the role of pFC in feature selection. Fourteen individuals with lateral pFC damage and 20 healthy controls performed a visuospatial WM task while EEG was recorded. On each trial, two shapes were presented sequentially in a top/bottom spatial orientation. A retro-cue presented mid-delay prompted which of the two shapes had been in either the top/bottom spatial position or first/second temporal position. We found that cross-frequency coupling between parieto-occipital alpha (α; 8-12 Hz) oscillations and topographically distributed gamma (γ; 30-50 Hz) activity tracked selection of the distinct cued feature in controls. This signature of feature selection was disrupted in patients with pFC lesions, despite intact α-γ coupling independent of feature selection. These findings reveal a pFC-dependent parieto-occipital α-γ mechanism for the rapid selection of visual WM representations.
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Affiliation(s)
- Saeideh Davoudi
- University of Montréal, Quebec, Canada.,CHU Sainte-Justine Research Center, Montréal, Quebec, Canada.,Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran
| | - Mohsen Parto Dezfouli
- Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.,School of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | | | - Mohammad Reza Daliri
- Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.,School of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Elizabeth L Johnson
- University of California, Berkeley.,Wayne State University, Detroit, Michigan
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18
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Resting state EEG biomarkers of cognitive decline associated with Alzheimer's disease and mild cognitive impairment. PLoS One 2021; 16:e0244180. [PMID: 33544703 PMCID: PMC7864432 DOI: 10.1371/journal.pone.0244180] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 12/05/2020] [Indexed: 02/03/2023] Open
Abstract
In this paper, we explore the utility of resting-state EEG measures as potential biomarkers for the detection and assessment of cognitive decline in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Neurophysiological biomarkers of AD derived from EEG and FDG-PET, once characterized and validated, would expand the set of existing diagnostic molecular biomarkers of AD pathology with associated biomarkers of disease progression and neural dysfunction. Since symptoms of AD often begin to appear later in life, successful identification of EEG-based biomarkers must account for age-related neurophysiological changes that occur even in healthy individuals. To this end, we collected EEG data from individuals with AD (n = 26), MCI (n = 53), and cognitively normal healthy controls stratified by age into three groups: 18-40 (n = 129), 40-60 (n = 62) and 60-90 (= 55) years old. For each participant, we computed power spectral density at each channel and spectral coherence between pairs of channels. Compared to age matched controls, in the AD group, we found increases in both spectral power and coherence at the slower frequencies (Delta, Theta). A smaller but significant increase in power of slow frequencies was observed for the MCI group, localized to temporal areas. These effects on slow frequency spectral power opposed that of normal aging observed by a decrease in the power of slow frequencies in our control groups. The AD group showed a significant decrease in the spectral power and coherence in the Alpha band consistent with the same effect in normal aging. However, the MCI group did not show any significant change in the Alpha band. Overall, Theta to Alpha ratio (TAR) provided the largest and most significant differences between the AD group and controls. However, differences in the MCI group remained small and localized. We proposed a novel method to quantify these small differences between Theta and Alpha bands' power using empirically derived distributions of spectral power across the time domain as opposed to averaging power across time. We defined Power Distribution Distance Measure (PDDM) as a distance measure between probability distribution functions (pdf) of Theta and Alpha power. Compared to average TAR, using PDDF enhanced the statistical significance, the effect size, and the spatial distribution of significant effects in the MCI group. We designed classifiers for differentiating individual MCI and AD participants from age-matched controls. The classification performance measured by the area under ROC curve after cross-validation were AUC = 0.85 and AUC = 0.6, for AD and MCI classifiers, respectively. Posterior probability of AD, TAR, and the proposed PDDM measure were all significantly correlated with MMSE score and neuropsychological tests in the AD group.
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19
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Ahmadi H, Fatemizadeh E, Motie-Nasrabadi A. Identifying brain functional connectivity alterations during different stages of Alzheimer's disease. Int J Neurosci 2020; 132:1005-1013. [PMID: 33297814 DOI: 10.1080/00207454.2020.1860037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Purpose: Alzheimer's disease (AD) starts years before its signs and symptoms including the dementia become apparent. Diagnosis of the AD in the early stages is important to reduce the speed of brain decline. Aim of the study: Identifying the alterations in the functional connectivity of the brain during the disease stages is among the main important issues in this regard. Therefore, in this study, the changes in the functional connectivity during the AD stages were analyzed.Materials and methods: By employing the functional magnetic resonance imaging (fMRI) data and graph theory, weighted undirected graphs of the whole-brain and default mode network (DMN) network were investigated individually in the early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), AD, and control subjects. Results: In the whole-brain analysis, during one year of disease progression, no significant changes were observed in none of the study groups. However, the intergroup comparison showed that in different stages (from healthy to AD) the efficiencies, clustering coefficient, transitivity, and modularity of the brain network have significantly changed. In the DMN network analysis, the EMCI subjects demonstrated significant alterations but no significant changes were observed in other study groups. In the nodal analysis of the DMN, the participation, clustering, and degree were among the measures significantly changed with the AD progression. Conclusions: Functional connectivity alterations are more in the first stage of AD. Since AD progresses slowly whole brain alterations are not significant in one year but DMN exhibits significant changes. Cingulum anterior and posterior areas were the first affected regions of interest (ROI) in the DMN network afterwards, the frontal superior medial ROI was declined in the functional connectivity.
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Affiliation(s)
- Hessam Ahmadi
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Emad Fatemizadeh
- School of Electrical Engineering, Sharif University of Technology, Tehran, Iran
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20
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Zhang L, Ni H, Yu Z, Wang J, Qin J, Hou F, Yang A. Investigation on the Alteration of Brain Functional Network and Its Role in the Identification of Mild Cognitive Impairment. Front Neurosci 2020; 14:558434. [PMID: 33100958 PMCID: PMC7556272 DOI: 10.3389/fnins.2020.558434] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Accepted: 09/04/2020] [Indexed: 01/13/2023] Open
Abstract
Mild cognitive impairment (MCI) is generally regarded as a prodromal stage of Alzheimer’s disease (AD). In coping with the challenges caused by AD, we analyzed resting-state functional magnetic resonance imaging data of 82 MCI subjects and 93 normal controls (NCs). The alteration of brain functional network in MCI was investigated on three scales, including global metrics, nodal characteristics, and modular properties. The results supported the existence of small worldness, hubs, and community structure in the brain functional networks of both groups. Compared with NCs, the network altered in MCI over all the three scales. In scale I, we found significantly decreased characteristic path length and increased global efficiency in MCI. Moreover, altered global network metrics were associated with cognitive level evaluated by neuropsychological assessments. In scale II, the nodal betweenness centrality of some global hubs, such as the right Crus II of cerebellar hemisphere (CERCRU2.R) and fusiform gyrus (FFG.R), changed significantly and associated with the severity and cognitive impairment in MCI. In scale III, although anatomically adjacent regions tended to be clustered into the same module regardless of group, discrepancies existed in the composition of modules in both groups, with a prominent separation of the cerebellum and a less localized organization of community structure in MCI compared with NC. Taking advantages of random forest approach, we achieved an accuracy of 91.4% to discriminate MCI patients from NCs by integrating cognitive assessments and network analysis. The importance of the used features fed into the classifier further validated the nodal characteristics of CERCRU2.R and FFG.R could be potential biomarkers in the identification of MCI. In conclusion, the present study demonstrated that the brain functional connectome data altered at the stage of MCI and could assist the automatic diagnosis of MCI patients.
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Affiliation(s)
- Lulu Zhang
- Key Laboratory of Biomedical Functional Materials, School of Science, China Pharmaceutical University, Nanjing, China
| | - Huangjing Ni
- Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Zhinan Yu
- Key Laboratory of Biomedical Functional Materials, School of Science, China Pharmaceutical University, Nanjing, China
| | - Jun Wang
- Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Jiaolong Qin
- Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| | - Fengzhen Hou
- Key Laboratory of Biomedical Functional Materials, School of Science, China Pharmaceutical University, Nanjing, China
| | - Albert Yang
- Division of Interdisciplinary Medicine and Biotechnology, Department of Medicine, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA, United States
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21
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Krishnagopal S. Multi-layer Trajectory Clustering: a Network Algorithm for Disease Subtyping. Biomed Phys Eng Express 2020; 6. [PMID: 35046146 DOI: 10.1088/2057-1976/abad8f] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 08/06/2020] [Indexed: 01/16/2023]
Abstract
Many diseases display heterogeneity in clinical features and their progression, indicative of the existence of disease subtypes. Extracting patterns of disease variable progression for subtypes has tremendous application in medicine, for example, in early prognosis and personalized medical therapy. This work presents a novel, data-driven, network-based Trajectory Clustering (TC) algorithm for identifying Parkinson's subtypes based on disease trajectory. Modeling patient-variable interactions as a bipartite network, TC first extracts communities of co-expressing disease variables at different stages of progression. Then, it identifies Parkinson's subtypes by clustering similar patient trajectories that are characterized by severity of disease variables through a multi-layer network. Determination of trajectory similarity accounts for direct overlaps between trajectories as well as second-order similarities, i.e., common overlap with a third set of trajectories. This work clusters trajectories across two types of layers: (a) temporal, and (b) ranges of independent outcome variable (representative of disease severity), both of which yield four distinct subtypes. The former subtypes exhibit differences in progression of disease domains (Cognitive, Mental Health etc.), whereas the latter subtypes exhibit different degrees of progression, i.e., some remain mild, whereas others show significant deterioration after 5 years. The TC approach is validated through statistical analyses and consistency of the identified subtypes with medical literature. This generalizable and robust method can easily be extended to other progressive multi-variate disease datasets, and can effectively assist in targeted subtype-specific treatment in the field of personalized medicine.
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Affiliation(s)
- Sanjukta Krishnagopal
- Department of Physics, University of Maryland, College Park, Maryland, 20742, United States of America.,Gatsby Computational Neuroscience Unit, University College London, London, W1T4JG, United Kingdom
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22
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Ahmadi H, Fatemizadeh E, Motie-Nasrabadi A. Multiclass classification of patients during different stages of Alzheimer's disease using fMRI time-series. Biomed Phys Eng Express 2020; 6:055022. [PMID: 33444253 DOI: 10.1088/2057-1976/abaf5e] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Alzheimer's Disease (AD) begins several years before the symptoms develop. It starts with Mild Cognitive Impairment (MCI) which can be separated into Early MCI and Late MCI (EMCI and LMCI). Functional connectivity analysis and classification are done among the different stages of illness with Functional Magnetic Resonance Imaging (fMRI). In this study, in addition to the four stages including healthy, EMCI, LMCI, and AD, the patients have been tracked for a year. Indeed, the classification has been done among 7 groups to analyze the functional connectivity changes in one year in different stages. After generating the functional connectivity graphs for eliminating the weak links, three different sparsification methods were used. In addition to simple thresholding, spectral sparsification based on effective resistance and sparse autoencoder were performed in order to analyze the effect of sparsification routine on classification results. Also, instead of extracting common features, the correlation matrices were reshaped to a correlation vector and used as a feature vector to enter the classifier. Since the correlation matrix is symmetric, in another analysis half of the feature vector was used, moreover, the Genetic Algorithm (GA) also utilized for feature vector dimension reduction. The non-linear SVM classifier with a polynomial kernel applied. The results showed that the autoencoder sparsification method had the greatest discrimination power with the accuracy of 98.35% for classification when the feature vector was the full correlation matrix.
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Affiliation(s)
- Hessam Ahmadi
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
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23
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Farahani FV, Fafrowicz M, Karwowski W, Douglas PK, Domagalik A, Beldzik E, Oginska H, Marek T. Effects of Chronic Sleep Restriction on the Brain Functional Network, as Revealed by Graph Theory. Front Neurosci 2019; 13:1087. [PMID: 31680823 PMCID: PMC6807652 DOI: 10.3389/fnins.2019.01087] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 09/27/2019] [Indexed: 12/30/2022] Open
Abstract
Sleep is a complex and dynamic process for maintaining homeostasis, and a lack of sleep can disrupt whole-body functioning. No organ is as vulnerable to the loss of sleep as the brain. Accordingly, we examined a set of task-based functional magnetic resonance imaging (fMRI) data by using graph theory to assess brain topological changes in subjects in a state of chronic sleep restriction, and then identified diurnal variability in the graph-theoretic measures. Task-based fMRI data were collected in a 1.5T MR scanner from the same participants on two days: after a week of fully restorative sleep and after a week with 35% sleep curtailment. Each day included four scanning sessions throughout the day (at approximately 10:00 AM, 2:00 PM, 6:00 PM, and 10:00 PM). A modified spatial cueing task was applied to evaluate sustained attention. After sleep restriction, the characteristic path length significantly increased at all measurement times, and small-worldness significantly decreased. Assortativity, a measure of network fault tolerance, diminished over the course of the day in both conditions. Local graph measures were altered primarily across the limbic system (particularly in the hippocampus, parahippocampal gyrus, and amygdala), default mode network, and visual network.
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Affiliation(s)
- Farzad V. Farahani
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering & Management Systems, University of Central Florida, Orlando, FL, United States
| | - Magdalena Fafrowicz
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Kraków, Poland
| | - Waldemar Karwowski
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering & Management Systems, University of Central Florida, Orlando, FL, United States
| | - Pamela K. Douglas
- Institute for Simulation and Training, University of Central Florida, Orlando, FL, United States
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States
| | - Aleksandra Domagalik
- Brain Imaging Core Facility, Malopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland
| | - Ewa Beldzik
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Kraków, Poland
| | - Halszka Oginska
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Kraków, Poland
| | - Tadeusz Marek
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Kraków, Poland
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24
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Hojjati SH, Ebrahimzadeh A, Babajani-Feremi A. Identification of the Early Stage of Alzheimer's Disease Using Structural MRI and Resting-State fMRI. Front Neurol 2019; 10:904. [PMID: 31543860 PMCID: PMC6730495 DOI: 10.3389/fneur.2019.00904] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 08/05/2019] [Indexed: 12/29/2022] Open
Abstract
Accurate prediction of the early stage of Alzheimer's disease (AD) is important but very challenging. The goal of this study was to utilize predictors for diagnosis conversion to AD based on integrating resting-state functional MRI (rs-fMRI) connectivity analysis and structural MRI (sMRI). We included 177 subjects in this study and aimed at identifying patients with mild cognitive impairment (MCI) who progress to AD, MCI converter (MCI-C), patients with MCI who do not progress to AD, MCI non-converter (MCI-NC), patients with AD, and healthy controls (HC). The graph theory was used to characterize different aspects of the rs-fMRI brain network by calculating measures of integration and segregation. The cortical and subcortical measurements, e.g., cortical thickness, were extracted from sMRI data. The rs-fMRI graph measures were combined with the sMRI measures to construct input features of a support vector machine (SVM) and classify different groups of subjects. Two feature selection algorithms [i.e., the discriminant correlation analysis (DCA) and sequential feature collection (SFC)] were used for feature reduction and selecting a subset of optimal features. Maximum accuracy of 67 and 56% for three-group (“AD, MCI-C, and MCI-NC” or “MCI-C, MCI-NC, and HC”) and four-group (“AD, MCI-C, MCI-NC, and HC”) classification, respectively, were obtained with the SFC feature selection algorithm. We also identified hub nodes in the rs-fMRI brain network which were associated with the early stage of AD. Our results demonstrated the potential of the proposed method based on integration of the functional and structural MRI for identification of the early stage of AD.
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Affiliation(s)
- Seyed Hani Hojjati
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, United States.,Department of Electrical Engineering, Babol University of Technology, Babol, Iran.,Neuroscience Institute and Children's Foundation Research Institute, Le Bonheur Children's Hospital, Memphis, TN, United States
| | - Ata Ebrahimzadeh
- Department of Electrical Engineering, Babol University of Technology, Babol, Iran
| | - Abbas Babajani-Feremi
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, United States.,Neuroscience Institute and Children's Foundation Research Institute, Le Bonheur Children's Hospital, Memphis, TN, United States.,Department of Anatomy and Neurobiology, University of Tennessee Health Science Center, Memphis, TN, United States
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Mishra VR, Sreenivasan KR, Zhuang X, Yang Z, Cordes D, Banks SJ, Bernick C. Understanding white matter structural connectivity differences between cognitively impaired and nonimpaired active professional fighters. Hum Brain Mapp 2019; 40:5108-5122. [PMID: 31403734 DOI: 10.1002/hbm.24761] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2019] [Revised: 07/20/2019] [Accepted: 07/31/2019] [Indexed: 11/06/2022] Open
Abstract
Long-term traumatic brain injury due to repeated head impacts (RHI) has been shown to be a risk factor for neurodegenerative disorders, characterized by a loss in cognitive performance. Establishing the correlation between changes in the white matter (WM) structural connectivity measures and neuropsychological test scores might help to identify the neural correlates of the scores that are used in daily clinical setting to investigate deficits due to repeated head blows. Hence, in this study, we utilized high angular diffusion MRI (dMRI) of 69 cognitively impaired and 70 nonimpaired active professional fighters from the Professional Fighters Brain Health Study, and constructed structural connectomes to understand: (a) whether there is a difference in the topological WM organization between cognitively impaired and nonimpaired active professional fighters, and (b) whether graph-theoretical measures exhibit correlations with neuropsychological scores in these groups. A dMRI derived structural connectome was constructed for every participant using brain regions defined in AAL atlas as nodes, and the product of fiber number and average fractional anisotropy of the tracts connecting the nodes as edges. Our study identified a topological WM reorganization due to RHI in fighters prone to cognitive decline that was correlated with neuropsychological scores. Furthermore, graph-theoretical measures were correlated differentially with neuropsychological scores between groups. We also found differentiated WM connectivity involving regions of hippocampus, precuneus, and insula within our cohort of cognitively impaired fighters suggesting that there is a discernible WM topological reorganization in fighters prone to cognitive decline.
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Affiliation(s)
- Virendra R Mishra
- Lou Ruvo Center for Brain Health, Cleveland Clinic Foundation, Las Vegas, Nevada
| | | | - Xiaowei Zhuang
- Lou Ruvo Center for Brain Health, Cleveland Clinic Foundation, Las Vegas, Nevada
| | - Zhengshi Yang
- Lou Ruvo Center for Brain Health, Cleveland Clinic Foundation, Las Vegas, Nevada
| | - Dietmar Cordes
- Lou Ruvo Center for Brain Health, Cleveland Clinic Foundation, Las Vegas, Nevada.,Department of Psychology and Neuroscience, University of Colorado, Boulder, Colorado
| | - Sarah J Banks
- Department of Neurosciences, University of California at San Diego, San Diego, California
| | - Charles Bernick
- Lou Ruvo Center for Brain Health, Cleveland Clinic Foundation, Las Vegas, Nevada
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26
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Lo Giudice P, Mammone N, Morabito FC, Pizzimenti RG, Ursino D, Virgili L. Leveraging network analysis to support experts in their analyses of subjects with MCI and AD. Med Biol Eng Comput 2019; 57:1961-1983. [PMID: 31301007 DOI: 10.1007/s11517-019-02004-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 06/09/2019] [Indexed: 11/25/2022]
Abstract
In this paper, we propose a network analysis-based approach to help experts in their analyses of subjects with mild cognitive impairment (hereafter, MCI) and Alzheimer's disease (hereafter, AD) and to investigate the evolution of these subjects over time. The inputs of our approach are the electroencephalograms (hereafter, EEGs) of the patients to analyze, performed at a certain time and, again, 3 months later. Given an EEG of a subject, our approach constructs a network with nodes that represent the electrodes and edges that denote connections between electrodes. Then, it applies several network-based techniques allowing the investigation of subjects with MCI and AD and the analysis of their evolution over time. (i) A connection coefficient, supporting experts to distinguish patients with MCI from patients with AD; (ii) A conversion coefficient, supporting experts to verify if a subject with MCI is converting to AD; (iii) Some network motifs, i.e., network patterns very frequent in one kind of patient and absent, or very rare, in the other. Patients with AD, just by the very nature of their condition, cannot be forced to stay motionless while undergoing examinations for a long time. EEG is a non-invasive examination that can be easily done on them. Since AD and MCI, if prodromal to AD, are associated with a loss of cortical connections, the adoption of network analysis appears suitable to investigate the effects of the progression of the disease on EEG. This paper confirms the suitability of this idea Graphical Abstract Ability of our proposed model to distinguish a control subject from a patient with MCI and a patient with AD. Blue edges represent strong connections among the corresponding brain areas; red edges denote middle connections, whereas green edges indicate weak connections. In the control subject (at the top), most connections are blue. In the patient with MCI (at the middle), most connections are red and green. In the patient with AD (at the bottom), most connections are either absent or green. .
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Affiliation(s)
- Paolo Lo Giudice
- DIIES, University Mediterranea of Reggio Calabria, Reggio Calabria, Italy
| | - Nadia Mammone
- IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy
| | | | | | | | - Luca Virgili
- DII, Polytechnic University of Marche, Ancona, Italy
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Trevisan K, Cristina-Pereira R, Silva-Amaral D, Aversi-Ferreira TA. Theories of Aging and the Prevalence of Alzheimer's Disease. BIOMED RESEARCH INTERNATIONAL 2019; 2019:9171424. [PMID: 31317043 PMCID: PMC6601487 DOI: 10.1155/2019/9171424] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Revised: 04/22/2019] [Accepted: 05/14/2019] [Indexed: 01/09/2023]
Abstract
OBJECTIVE Aging and AD are associated in some way, then it is reasonable to ask whether or not it is possible to age without AD inexorably appearing at any moment, depending on the period of life. Therefore, the goal of this review is to verify, in light of some aging theories, the prevalence of AD. METHODS For the purpose of this manuscript, the indexers Alzheimer, aging, Alzheimer, and aging were considered; theories of aging were researched. The research was conducted using PubMed, Medline, Scopus, Elsevier, and Google Scholar. RESULTS The most common subjects in the papers analyzed for this manuscript were aging and Alzheimer's disease. The association between Alzheimer and theories of aging seems inconclusive. CONCLUSIONS Accordingly, the general idea is that AD is associated with aging in such a way that almost all people will present this disease; however, it is plausible to consider that the increase in life expectancy will generate a high prevalence of AD. In a general sense, it seems that the theories of aging explain the origin of AD under superlative and catastrophic considerations and use more biomolecular data than social or behavioral data as the bases of analysis, which may be the problem.
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Affiliation(s)
- Kaynara Trevisan
- Laboratory of Physical Anthropology and Biomathematics, Department of Anatomy, Institute of Biomedical Science, Federal University of Alfenas, Alfenas, Brazil
| | - Renata Cristina-Pereira
- Laboratory of Physical Anthropology and Biomathematics, Department of Anatomy, Institute of Biomedical Science, Federal University of Alfenas, Alfenas, Brazil
| | - Danyelle Silva-Amaral
- Laboratory of Physical Anthropology and Biomathematics, Department of Anatomy, Institute of Biomedical Science, Federal University of Alfenas, Alfenas, Brazil
| | - Tales Alexandre Aversi-Ferreira
- Laboratory of Physical Anthropology and Biomathematics, Department of Anatomy, Institute of Biomedical Science, Federal University of Alfenas, Alfenas, Brazil
- Department of Physiology, School of Medicine and Pharmaceutical Sciences, System Emotional Science, University of Toyama, Toyama, Japan
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Farahani FV, Karwowski W, Lighthall NR. Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review. Front Neurosci 2019; 13:585. [PMID: 31249501 PMCID: PMC6582769 DOI: 10.3389/fnins.2019.00585] [Citation(s) in RCA: 281] [Impact Index Per Article: 56.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 05/23/2019] [Indexed: 12/20/2022] Open
Abstract
Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.
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Affiliation(s)
- Farzad V Farahani
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Waldemar Karwowski
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Nichole R Lighthall
- Department of Psychology, University of Central Florida, Orlando, FL, United States
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Pournoor E, Elmi N, Masoudi-Nejad A. CatbNet: A Multi Network Analyzer for Comparing and Analyzing the Topology of Biological Networks. Curr Genomics 2019; 20:69-75. [PMID: 31015793 PMCID: PMC6446483 DOI: 10.2174/1389202919666181213101540] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2018] [Revised: 10/19/2018] [Accepted: 12/02/2018] [Indexed: 12/17/2022] Open
Abstract
Background Complexity and dynamicity of biological events is a reason to use comprehen-sive and holistic approaches to deal with their difficulty. Currently with advances in omics data genera-tion, network-based approaches are used frequently in different areas of computational biology and bio-informatics to solve problems in a systematic way. Also, there are many applications and tools for net-work data analysis and manipulation which their goal is to facilitate the way of improving our under-standings of inter/intra cellular interactions. Methods In this article, we introduce CatbNet, a multi network analyzer application which is prepared for network comparison objectives. Result and Conclusion CatbNet uses many topological features of networks to compare their structure and foundations. One of the most prominent properties of this application is classified network analysis in which groups of networks are compared with each other.
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Affiliation(s)
- Ehsan Pournoor
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Naser Elmi
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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Disease global behavior: A systematic study of the human interactome network reveals conserved topological features among categories of diseases. INFORMATICS IN MEDICINE UNLOCKED 2019. [DOI: 10.1016/j.imu.2019.100249] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
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31
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Farahani FV, Karwowski W, Lighthall NR. Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review. Front Neurosci 2019. [PMID: 31249501 DOI: 10.3389/fnins.2019.00585/bibtex] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2023] Open
Abstract
Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.
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Affiliation(s)
- Farzad V Farahani
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Waldemar Karwowski
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Nichole R Lighthall
- Department of Psychology, University of Central Florida, Orlando, FL, United States
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Si SZ, Liu X, Wang JF, Wang B, Zhao H. Brain networks modeling for studying the mechanism underlying the development of Alzheimer's disease. Neural Regen Res 2019; 14:1805-1813. [PMID: 31169199 PMCID: PMC6585551 DOI: 10.4103/1673-5374.257538] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Alzheimer's disease is a primary age-related neurodegenerative disorder that can result in impaired cognitive and memory functions. Although connections between changes in brain networks of Alzheimer's disease patients have been established, the mechanisms that drive these alterations remain incompletely understood. This study, which was conducted in 2018 at Northeastern University in China, included data from 97 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset covering genetics, imaging, and clinical data. All participants were divided into two groups: normal control (n = 52; 20 males and 32 females; mean age 73.90 ± 4.72 years) and Alzheimer's disease (n = 45, 23 males and 22 females; mean age 74.85 ± 5.66). To uncover the wiring mechanisms that shaped changes in the topology of human brain networks of Alzheimer's disease patients, we proposed a local naïve Bayes brain network model based on graph theory. Our results showed that the proposed model provided an excellent fit to observe networks in all properties examined, including clustering coefficient, modularity, characteristic path length, network efficiency, betweenness, and degree distribution compared with empirical methods. This proposed model simulated the wiring changes in human brain networks between controls and Alzheimer's disease patients. Our results demonstrate its utility in understanding relationships between brain tissue structure and cognitive or behavioral functions. The ADNI was performed in accordance with the Good Clinical Practice guidelines, US 21CFR Part 50-Protection of Human Subjects, and Part 56-Institutional Review Boards (IRBs)/Research Good Clinical Practice guidelines Institutional Review Boards (IRBs)/Research Ethics Boards (REBs).
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Affiliation(s)
- Shuai-Zong Si
- School of Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Xiao Liu
- School of Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Jin-Fa Wang
- School of Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Bin Wang
- School of Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Hai Zhao
- School of Computer Science and Engineering, Northeastern University, Shenyang, China
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33
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Cai L, Wei X, Wang J, Yu H, Deng B, Wang R. Reconstruction of functional brain network in Alzheimer's disease via cross-frequency phase synchronization. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.07.019] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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35
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Szenkovits A, Meszlényi R, Buza K, Gaskó N, Lung RI, Suciu M. Feature Selection with a Genetic Algorithm for Classification of Brain Imaging Data. ADVANCES IN FEATURE SELECTION FOR DATA AND PATTERN RECOGNITION 2018. [DOI: 10.1007/978-3-319-67588-6_10] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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36
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Sulaimany S, Khansari M, Zarrineh P, Daianu M, Jahanshad N, Thompson PM, Masoudi-Nejad A. Predicting brain network changes in Alzheimer's disease with link prediction algorithms. MOLECULAR BIOSYSTEMS 2017; 13:725-735. [PMID: 28197591 PMCID: PMC6167930 DOI: 10.1039/c6mb00815a] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Link prediction is a promising research area for modeling various types of networks and has mainly focused on predicting missing links. Link prediction methods may be valuable for describing brain connectivity, as it changes in Alzheimer's disease (AD) and its precursor, mild cognitive impairment (MCI). Here, we analyzed 3-tesla whole-brain diffusion-weighted images from 202 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) - 50 healthy controls, 72 with earlyMCI (eMCI) and 38 with lateMCI (lMCI) and 42 AD patients. We introduce a novel approach for Mixed Link Prediction (MLP) to test and define the percent of predictability of each heightened stage of dementia from its previous, less impaired stage, in the simplest case. Using well-known link prediction algorithms as the core of MLP, we propose a new approach that predicts stages of cognitive impairment by simultaneously adding and removing links in the brain networks of elderly individuals. We found that the optimal algorithm, called "Adamic and Adar", had the best fit and most accurately predicted the stages of AD from their previous stage. When compared to the other link prediction algorithms, that mainly only predict the added links, our proposed approach can more inclusively simulate the brain changes during disease by both adding and removing links of the network. Our results are also in line with computational neuroimaging and clinical findings and can be improved for better results.
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Affiliation(s)
- Sadegh Sulaimany
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
| | - Mohammad Khansari
- Department of New Sciences and Technologies (FNST), University of Tehran, Tehran, Iran
| | - Peyman Zarrineh
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
| | - Madelaine Daianu
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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