51
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Naik B, Mehta A, Shah M. Denouements of machine learning and multimodal diagnostic classification of Alzheimer's disease. Vis Comput Ind Biomed Art 2020; 3:26. [PMID: 33151420 PMCID: PMC7642580 DOI: 10.1186/s42492-020-00062-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 10/16/2020] [Indexed: 12/19/2022] Open
Abstract
Alzheimer's disease (AD) is the most common type of dementia. The exact cause and treatment of the disease are still unknown. Different neuroimaging modalities, such as magnetic resonance imaging (MRI), positron emission tomography, and single-photon emission computed tomography, have played a significant role in the study of AD. However, the effective diagnosis of AD, as well as mild cognitive impairment (MCI), has recently drawn large attention. Various technological advancements, such as robots, global positioning system technology, sensors, and machine learning (ML) algorithms, have helped improve the diagnostic process of AD. This study aimed to determine the influence of implementing different ML classifiers in MRI and analyze the use of support vector machines with various multimodal scans for classifying patients with AD/MCI and healthy controls. Conclusions have been drawn in terms of employing different classifier techniques and presenting the optimal multimodal paradigm for the classification of AD.
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Affiliation(s)
- Binny Naik
- Department of Computer Engineering, Indus University, Ahmedabad, Gujarat, 382115, India
| | - Ashir Mehta
- Department of Computer Engineering, Indus University, Ahmedabad, Gujarat, 382115, India
| | - Manan Shah
- Department of Chemical Engineering, School of Technology, Pandit Deendayal Petroleum University, Gandhinagar, Gujarat, 382007, India.
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52
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Dong GH, Wang Z, Dong H, Wang M, Zheng Y, Ye S, Zhang J, Potenza MN. More stringent criteria are needed for diagnosing internet gaming disorder: Evidence from regional brain features and whole-brain functional connectivity multivariate pattern analyses. J Behav Addict 2020; 9:642-653. [PMID: 33031057 PMCID: PMC8943664 DOI: 10.1556/2006.2020.00065] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 04/10/2020] [Accepted: 09/02/2020] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Internet gaming disorder (IGD) is included in the DSM-5 as a provisional diagnosis. Whether IGD should be regarded as a disorder and, if so, how it should be defined and thresholded have generated considerable debate. METHODS In the current study, machine learning was used, based on regional and interregional brain features. Resting-state data from 374 subjects (including 148 IGD subjects with DSM-5 scores ≥5 and 93 IGD subjects with DSM-5 scores ≥6) were collected, and multivariate pattern analysis (MVPA) was employed to classify IGD from recreational game use (RGU) subjects based on regional brain features (ReHo) and communication between brain regions (functional connectivity; FC). Permutation tests were used to assess classifier performance. RESULTS The results demonstrated that when using DSM-5 scores ≥5 as the inclusion criteria for IGD subjects, MVPA could not differentiate IGD subjects from RGU, whether based on ReHo or FC features or by using different templates. MVPA could differentiate IGD subjects from RGU better than expected by chance when using DSM-5 scores ≥6 with both ReHo and FC features. The brain regions involved in the default mode network and executive control network and the cerebellum exhibited high discriminative power during classification. DISCUSSION The current findings challenge the current IGD diagnostic criteria thresholding proposed in the DSM-5, suggesting that more stringent criteria may be needed for diagnosing IGD. The findings suggest that brain regions involved in the default mode network and executive control network relate importantly to the core criteria for IGD.
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Affiliation(s)
- Guang-Heng Dong
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, PR China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, PR China
| | - Ziliang Wang
- School of Psychology, Beijing Normal University, Beijing, PR China
| | - Haohao Dong
- Department of Psychology, Zhejiang Normal University, Jinhua, PR China
| | - Min Wang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, PR China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, PR China
| | - Yanbin Zheng
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, PR China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, PR China
| | - Shuer Ye
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, PR China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, PR China
| | - Jialin Zhang
- Department of Psychology, Zhejiang Normal University, Jinhua, PR China
| | - Marc N. Potenza
- Department of Psychiatry, Child Study Center, Yale University School of Medicine, New Haven, CT, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Council on Problem Gambling, Wethersfield, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
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53
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Mahmoud AM, Karamti H, Alrowais F. A Two Consequent Multi-layers Deep Discriminative Approach for Classifying fMRI Images. INT J ARTIF INTELL T 2020. [DOI: 10.1142/s021821302030001x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Functional Magnetic Resonance Imaging (fMRI), for many decades acts as a potential aiding method for diagnosing medical problems. Several successful machine learning algorithms have been proposed in literature to extract valuable knowledge from fMRI. One of these algorithms is the convolutional neural network (CNN) that competent with high capabilities for learning optimal abstractions of fMRI. This is because the CNN learns features similarly to human brain where it preserves local structure and avoids distortion of the global feature space. Focusing on the achievements of using the CNN for the fMRI, and accordingly, the Deep Convolutional Auto-Encoder (DCAE) benefits from the data-driven approach with CNN’s optimal features to strengthen the fMRI classification. In this paper, a new two consequent multi-layers DCAE deep discriminative approach for classifying fMRI Images is proposed. The first DCAE is unsupervised sub-model that is composed of four CNN. It focuses on learning weights to utilize discriminative characteristics of the extracted features for robust reconstruction of fMRI with lower dimensional considering tiny details and refining by its deep multiple layers. Then the second DCAE is a supervised sub-model that focuses on training labels to reach an outperformed results. The proposed approach proved its effectiveness and improved literately reported results on a large brain disorder fMRI dataset.
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Affiliation(s)
- Abeer M. Mahmoud
- Computer Sciences Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
| | - Hanen Karamti
- Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh, Saudi Arabia
- MIRACL Laboratory, ISIMS, University of Sfax, B. P. 242, 3021 Sakiet Ezzit, Sfax, Tunisia
| | - Fadwa Alrowais
- Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh, Saudi Arabia
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54
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Zhang W, Zhan L, Thompson P, Wang Y. Deep Representation Learning For Multimodal Brain Networks. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2020; 12267:613-624. [PMID: 34296225 PMCID: PMC8293685 DOI: 10.1007/978-3-030-59728-3_60] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Applying network science approaches to investigate the functions and anatomy of the human brain is prevalent in modern medical imaging analysis. Due to the complex network topology, for an individual brain, mining a discriminative network representation from the multimodal brain networks is non-trivial. The recent success of deep learning techniques on graph-structured data suggests a new way to model the non-linear cross-modality relationship. However, current deep brain network methods either ignore the intrinsic graph topology or require a network basis shared within a group. To address these challenges, we propose a novel end-to-end deep graph representation learning (Deep Multimodal Brain Networks - DMBN) to fuse multimodal brain networks. Specifically, we decipher the cross-modality relationship through a graph encoding and decoding process. The higher-order network mappings from brain structural networks to functional networks are learned in the node domain. The learned network representation is a set of node features that are informative to induce brain saliency maps in a supervised manner. We test our framework in both synthetic and real image data. The experimental results show the superiority of the proposed method over some other state-of-the-art deep brain network models.
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Affiliation(s)
- Wen Zhang
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, AZ, USA
| | - Liang Zhan
- Electrical and Computer Engineering, University of Pittsburgh, PA, USA
| | - Paul Thompson
- Imaging Genetics Center, University of Southern California, CA, USA
| | - Yalin Wang
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, AZ, USA
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55
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Xu X, Li W, Tao M, Xie Z, Gao X, Yue L, Wang P. Effective and Accurate Diagnosis of Subjective Cognitive Decline Based on Functional Connection and Graph Theory View. Front Neurosci 2020; 14:577887. [PMID: 33132832 PMCID: PMC7550635 DOI: 10.3389/fnins.2020.577887] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 09/02/2020] [Indexed: 12/12/2022] Open
Abstract
Subjective cognitive decline (SCD) is considered the earliest preclinical stage of Alzheimer’s disease (AD) that precedes mild cognitive impairment (MCI). Effective and accurate diagnosis of SCD is crucial for early detection of and timely intervention in AD. In this study, brain functional connectome (i.e., functional connections and graph theory metrics) based on the resting-state functional magnetic resonance imaging (rs-fMRI) provided multiple information about brain networks and has been used to distinguish individuals with SCD from normal controls (NCs). The consensus connections and the discriminative nodal graph metrics selected by group least absolute shrinkage and selection operator (LASSO) mainly distributed in the prefrontal and frontal cortices and the subcortical regions corresponded to default mode network (DMN) and frontoparietal task control network. Nodal efficiency and nodal shortest path showed the most significant discriminative ability among the selected nodal graph metrics. Furthermore, the comparison results of topological attributes suggested that the brain network integration function was weakened and network segregation function was enhanced in SCD patients. Moreover, the combination of brain connectome information based on multiple kernel-support vector machine (MK-SVM) achieved the best classification performance with 83.33% accuracy, 90.00% sensitivity, and an area under the curve (AUC) of 0.927. The findings of this study provided a new perspective to combine machine learning methods with exploration of brain pathophysiological mechanisms in SCD and offered potential neuroimaging biomarkers for diagnosis of early-stage AD.
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Affiliation(s)
- Xiaowen Xu
- Department of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China
| | - Weikai Li
- College of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing, China.,Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - Mengling Tao
- Department of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China
| | - Zhongfeng Xie
- Department of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China
| | - Xin Gao
- Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - Ling Yue
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China
| | - Peijun Wang
- Department of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China
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56
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Image-based state-of-the-art techniques for the identification and classification of brain diseases: a review. Med Biol Eng Comput 2020; 58:2603-2620. [PMID: 32960410 DOI: 10.1007/s11517-020-02256-z] [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: 01/30/2020] [Accepted: 08/28/2020] [Indexed: 12/22/2022]
Abstract
Detection and classification methods have a vital and important role in identifying brain diseases. Timely detection and classification of brain diseases enable an accurate identification and effective management of brain impairment. Brain disorders are commonly most spreadable diseases and the diagnosing process is time-consuming and highly expensive. There is an utmost need to develop effective and advantageous methods for brain diseases detection and characterization. Magnetic resonance imaging (MRI), computed tomography (CT), and other various brain imaging scans are used to identify different brain diseases and disorders. Brain imaging scans are the efficient tool to understand the anatomical changes in brain in fast and accurate manner. These different brain imaging scans used with segmentation techniques and along with machine learning and deep learning techniques give maximum accuracy and efficiency. This paper focuses on different conventional approaches, machine learning and deep learning techniques used for the detection, and classification of brain diseases and abnormalities. This paper also summarizes the research gap and problems in the existing techniques used for detection and classification of brain disorders. Comparison and evaluation of different machine learning and deep learning techniques in terms of efficiency and accuracy are also highlighted in this paper. Furthermore, different brain diseases like leukoariaosis, Alzheimer's, Parkinson's, and Wilson's disorder are studied in the scope of machine learning and deep learning techniques.
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57
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Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review. Processes (Basel) 2020. [DOI: 10.3390/pr8091071] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.
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58
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Huang J, Zhou L, Wang L, Zhang D. Attention-Diffusion-Bilinear Neural Network for Brain Network Analysis. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:2541-2552. [PMID: 32070948 DOI: 10.1109/tmi.2020.2973650] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Brain network provides essential insights in diagnosing many brain disorders. Integrative analysis of multiple types of connectivity, e.g, functional connectivity (FC) and structural connectivity (SC), can take advantage of their complementary information and therefore may help to identify patients. However, traditional brain network methods usually focus on either FC or SC for describing node interactions and only consider the interaction between paired network nodes. To tackle this problem, in this paper, we propose an Attention-Diffusion-Bilinear Neural Network (ADB-NN) framework for brain network analysis, which is trained in an end-to-end manner. The proposed network seamlessly couples FC and SC to learn wider node interactions and generates a joint representation of FC and SC for diagnosis. Specifically, a brain network (graph) is first defined, where each node corresponding to a brain region is governed by the features of brain activities (i.e., FC) extracted from functional magnetic resonance imaging (fMRI), and the presence of edges is determined by neural fiber physical connections (i.e., SC) extracted from Diffusion Tensor Imaging (DTI). Based on this graph, we train two Attention-Diffusion-Bilinear (ADB) modules jointly. In each module, an attention model is utilized to automatically learn the strength of node interactions. This information further guides a diffusion process that generates new node representations by considering the influence from other nodes as well. After that, the second-order statistics of these node representations are extracted by bilinear pooling to form connectivity-based features for disease prediction. The two ADB modules correspond to the one-step and two-step diffusion, respectively. Experiments on a real epilepsy dataset demonstrate the effectiveness and advantages of our proposed method.
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59
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Zilcha-Mano S, Zhu X, Suarez-Jimenez B, Pickover A, Tal S, Such S, Marohasy C, Chrisanthopoulos M, Salzman C, Lazarov A, Neria Y, Rutherford BR. Diagnostic and Predictive Neuroimaging Biomarkers for Posttraumatic Stress Disorder. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:688-696. [PMID: 32507508 PMCID: PMC7354213 DOI: 10.1016/j.bpsc.2020.03.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Revised: 03/29/2020] [Accepted: 03/30/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND Comorbidity between posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) has been commonly overlooked by studies examining resting-state functional connectivity patterns in PTSD. The current study used a data-driven approach to identify resting-state functional connectivity biomarkers to 1) differentiate individuals with PTSD (with or without MDD) from trauma-exposed healthy control subjects (TEHCs), 2) compare individuals with PTSD alone with those with comorbid PTSD+MDD, and 3) explore the clinical utility of the identified biomarkers by testing their associations with clinical symptoms and treatment response. METHODS Resting-state magnetic resonance images were obtained from 51 individuals with PTSD alone, 52 individuals with PTSD+MDD, and 76 TEHCs. Of the 103 individuals with PTSD, 55 were enrolled in prolonged exposure treatment. A support vector machine model was used to identify resting-state functional connectivity biomarkers differentiating individuals with PTSD (with or without MDD) from TEHCs and differentiating individuals with PTSD alone from those with PTSD+MDD. The associations between the identified features and symptomatology were tested with Pearson correlations. RESULTS The support vector machine model achieved 70.6% accuracy in discriminating between individuals with PTSD and TEHCs and achieved 76.7% accuracy in discriminating between individuals with PTSD alone and those with PTSD+MDD for out-of-sample prediction. Within-network connectivity in the executive control network, prefrontal network, and salience network discriminated individuals with PTSD from TEHCs. The basal ganglia network played an important role in differentiating individuals with PTSD alone from those with PTSD+MDD. PTSD scores were inversely correlated with within-executive control network connectivity (p < .001), and executive control network connectivity was positively correlated with treatment response (p < .001). CONCLUSIONS Results suggest that unique brain-based abnormalities differentiate individuals with PTSD from TEHCs, differentiate individuals with PTSD from those with PTSD+MDD, and demonstrate clinical utility in predicting levels of symptomatology and treatment response.
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Affiliation(s)
- Sigal Zilcha-Mano
- Department of Psychology, University of Haifa, Mount Carmel, Haifa, Israel.
| | - Xi Zhu
- Department of Psychiatry, Columbia University, New York, New York; New York State Psychiatric Institute, Columbia University Medical Center, New York, New York
| | - Benjamin Suarez-Jimenez
- Department of Psychiatry, Columbia University, New York, New York; New York State Psychiatric Institute, Columbia University Medical Center, New York, New York
| | - Alison Pickover
- Department of Psychiatry, Columbia University, New York, New York; New York State Psychiatric Institute, Columbia University Medical Center, New York, New York
| | - Shachaf Tal
- Department of Psychology, University of Haifa, Mount Carmel, Haifa, Israel
| | - Sara Such
- New York State Psychiatric Institute, Columbia University Medical Center, New York, New York
| | - Caroline Marohasy
- New York State Psychiatric Institute, Columbia University Medical Center, New York, New York
| | - Marika Chrisanthopoulos
- New York State Psychiatric Institute, Columbia University Medical Center, New York, New York; Columbia University Vagelos College of Physicians and Surgeons, New York, New York
| | - Chloe Salzman
- New York State Psychiatric Institute, Columbia University Medical Center, New York, New York; Columbia University Vagelos College of Physicians and Surgeons, New York, New York
| | - Amit Lazarov
- School of Psychological Sciences, Tel-Aviv University, Tel-Aviv, Israel; Department of Psychiatry, Columbia University, New York, New York
| | - Yuval Neria
- Department of Psychiatry, Columbia University, New York, New York; New York State Psychiatric Institute, Columbia University Medical Center, New York, New York
| | - Bret R Rutherford
- New York State Psychiatric Institute, Columbia University Medical Center, New York, New York; Columbia University Vagelos College of Physicians and Surgeons, New York, New York
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60
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Castellazzi G, Cuzzoni MG, Cotta Ramusino M, Martinelli D, Denaro F, Ricciardi A, Vitali P, Anzalone N, Bernini S, Palesi F, Sinforiani E, Costa A, Micieli G, D'Angelo E, Magenes G, Gandini Wheeler-Kingshott CAM. A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features. Front Neuroinform 2020; 14:25. [PMID: 32595465 PMCID: PMC7300291 DOI: 10.3389/fninf.2020.00025] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 04/24/2020] [Indexed: 12/28/2022] Open
Abstract
Among dementia-like diseases, Alzheimer disease (AD) and vascular dementia (VD) are two of the most frequent. AD and VD may share multiple neurological symptoms that may lead to controversial diagnoses when using conventional clinical and MRI criteria. Therefore, other approaches are needed to overcome this issue. Machine learning (ML) combined with magnetic resonance imaging (MRI) has been shown to improve the diagnostic accuracy of several neurodegenerative diseases, including dementia. To this end, in this study, we investigated, first, whether different kinds of ML algorithms, combined with advanced MRI features, could be supportive in classifying VD from AD and, second, whether the developed approach might help in predicting the prevalent disease in subjects with an unclear profile of AD or VD. Three ML categories of algorithms were tested: artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). Multiple regional metrics from resting-state fMRI (rs-fMRI) and diffusion tensor imaging (DTI) of 60 subjects (33 AD, 27 VD) were used as input features to train the algorithms and find the best feature pattern to classify VD from AD. We then used the identified VD–AD discriminant feature pattern as input for the most performant ML algorithm to predict the disease prevalence in 15 dementia patients with a “mixed VD–AD dementia” (MXD) clinical profile using their baseline MRI data. ML predictions were compared with the diagnosis evidence from a 3-year clinical follow-up. ANFIS emerged as the most efficient algorithm in discriminating AD from VD, reaching a classification accuracy greater than 84% using a small feature pattern. Moreover, ANFIS showed improved classification accuracy when trained with a multimodal input feature data set (e.g., DTI + rs-fMRI metrics) rather than a unimodal feature data set. When applying the best discriminant pattern to the MXD group, ANFIS achieved a correct prediction rate of 77.33%. Overall, results showed that our approach has a high discriminant power to classify AD and VD profiles. Moreover, the same approach also showed potential in predicting earlier the prevalent underlying disease in dementia patients whose clinical profile is uncertain between AD and VD, therefore suggesting its usefulness in supporting physicians' diagnostic evaluations.
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Affiliation(s)
- Gloria Castellazzi
- NMR Research Unit, Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square MS Centre, UCL Queen Square Institute of Neurology, London, United Kingdom.,Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.,Brain MRI 3T Research Center, IRCCS Mondino Foundation, Pavia, Italy
| | | | - Matteo Cotta Ramusino
- Laboratory of Neuropsychology and Unit of Behavioral Neurology, IRCCS Mondino Foundation, Pavia, Italy.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Daniele Martinelli
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Headache Center, IRCCS Mondino Foundation, Pavia, Italy
| | | | - Antonio Ricciardi
- NMR Research Unit, Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square MS Centre, UCL Queen Square Institute of Neurology, London, United Kingdom.,Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Paolo Vitali
- Brain MRI 3T Research Center, IRCCS Mondino Foundation, Pavia, Italy.,Radiology Unit, IRCCS Policlinico San Donato, Milan, Italy
| | - Nicoletta Anzalone
- Scientific Institute H.S. Raffaele Vita e Salute University, Milan, Italy
| | - Sara Bernini
- Laboratory of Neuropsychology and Unit of Behavioral Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | - Fulvia Palesi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Elena Sinforiani
- Laboratory of Neuropsychology and Unit of Behavioral Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | - Alfredo Costa
- Laboratory of Neuropsychology and Unit of Behavioral Neurology, IRCCS Mondino Foundation, Pavia, Italy.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Giuseppe Micieli
- Department of Emergency Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Giovanni Magenes
- Brain MRI 3T Research Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square MS Centre, UCL Queen Square Institute of Neurology, London, United Kingdom.,Brain MRI 3T Research Center, IRCCS Mondino Foundation, Pavia, Italy.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
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61
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Dou X, Yao H, Feng F, Wang P, Zhou B, Jin D, Yang Z, Li J, Zhao C, Wang L, An N, Liu B, Zhang X, Liu Y. Characterizing white matter connectivity in Alzheimer's disease and mild cognitive impairment: An automated fiber quantification analysis with two independent datasets. Cortex 2020; 129:390-405. [PMID: 32574842 DOI: 10.1016/j.cortex.2020.03.032] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Revised: 12/13/2019] [Accepted: 03/31/2020] [Indexed: 12/28/2022]
Abstract
Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive dementia. Diffusion tensor imaging (DTI) has been widely used to show structural integrity and delineate white matter (WM) degeneration in AD. The automated fiber quantification (AFQ) method is a fully automated approach that can rapidly and reliably identify major WM fiber tracts and evaluate WM properties. The main aim of this study was to assess WM integrity and abnormities in a cohort of patients with amnestic mild cognitive impairment (aMCI) and AD as well as normal controls (NCs). For this purpose, we first used AFQ to identify 20 major WM tracts and assessed WM integrity and abnormalities in a cohort of 120 subjects (39 NCs, 34 aMCI patients and 47 AD patients) in a discovery dataset and 122 subjects (43 NCs, 37 aMCI patients and 42 AD patients) in a replicated dataset. Pointwise differences along WM tracts were identified in the discovery dataset and simultaneously confirmed in the replicated dataset. Next, we investigated the utility of DTI measures along WM tracts as features to distinguish patients with AD from NCs via multilevel cross validation using a support vector machine. Correlation analysis revealed the identified microstructural WM alterations and classification output to be highly associated with cognitive ability in the patient groups, suggesting that they may be a robust biomarker of AD. This systematic study provides a pipeline to examine WM integrity and its potential clinical application in AD and may be useful for studying other neurological and psychiatric disorders.
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Affiliation(s)
- Xuejiao Dou
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, China
| | - Hongxiang Yao
- Department of Radiology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China
| | - Feng Feng
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, 300350, China; Department of Neurology, Nankai University Huanhu Hospital, Tianjin, China
| | - Bo Zhou
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China
| | - Dan Jin
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, China
| | - Zhengyi Yang
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jin Li
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Cui Zhao
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China
| | - Luning Wang
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China
| | - Ningyu An
- Department of Radiology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China
| | - Bing Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Xi Zhang
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China.
| | - Yong Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
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62
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Zhang T, Shi M. Multi-modal neuroimaging feature fusion for diagnosis of Alzheimer's disease. J Neurosci Methods 2020; 341:108795. [PMID: 32446943 DOI: 10.1016/j.jneumeth.2020.108795] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 05/19/2020] [Accepted: 05/19/2020] [Indexed: 11/24/2022]
Abstract
BACKGROUND Compared with single-modal neuroimages classification of AD, multi-modal classification can achieve better performance by fusing different information. Exploring synergy among various multi-modal neuroimages is contributed to identifying the pathological process of neurological disorders. However, it is still problematic to effectively exploit multi-modal information since the lack of an effective fusion method. NEW METHOD In this paper, we propose a deep multi-modal fusion network based on the attention mechanism, which can selectively extract features from MRI and PET branches and suppress irrelevant information. In the attention model, the fusion ratio of each modality is assigned automatically according to the importance of the data. A hierarchical fusion method is adopted to ensure the effectiveness of Multi-modal Fusion. RESULTS Evaluating the model on the ADNI dataset, the experimental results show that it outperforms the state-of-the-art methods. In particular, the final classification results of the NC/AD, SMCI/PMCI and Four-Class are 95.21 %, 89.79 %, and 86.15 %, respectively. COMPARISON WITH EXISTING METHODS Different from the early fusion and the late fusion, the hierarchical fusion method contributes to learning the synergy between the multi-modal data. Compared with some other prominent algorithms, the attention model enables our network to focus on the regions of interest and effectively fuse the multi-modal data. CONCLUSION Benefit from the hierarchical structure with attention model, the proposed network is capable of exploiting low-level and high-level features extracted from the multi-modal data and improving the accuracy of AD diagnosis. Results show its promising performance.
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Affiliation(s)
- Tao Zhang
- School of Electronic and Information Engineering, Tianjin University, 300387, Tianjin, China
| | - Mingyang Shi
- School of Electronic and Information Engineering, Tianjin University, 300387, Tianjin, China.
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63
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Wang Z, Dong H, Du X, Zhang JT, Dong GH. Decreased effective connection from the parahippocampal gyrus to the prefrontal cortex in Internet gaming disorder: A MVPA and spDCM study. J Behav Addict 2020; 9:105-115. [PMID: 32359234 PMCID: PMC8935187 DOI: 10.1556/2006.2020.00012] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
OBJECTIVES Understanding the neural mechanisms underlying Internet gaming disorder (IGD) is essential for the condition's diagnosis and treatment. Nevertheless, the pathological mechanisms of IGD remain elusive at present. Hence, we employed multi-voxel pattern analysis (MVPA) and spectral dynamic causal modeling (spDCM) to explore this issue. METHODS Resting-state fMRI data were collected from 103 IGD subjects (male = 57) and 99 well-matched recreational game users (RGUs, male = 51). Regional homogeneity was calculated as the feature for MVPA based on the support vector machine (SVM) with leave-one- out cross-validation. Mean time series data extracted from the brain regions in accordance with the MVPA results were used for further spDCM analysis. RESULTS Results display a high accuracy of 82.67% (sensitivity of 83.50% and specificity of 81.82%) in the classification of the two groups. The most discriminative brain regions that contributed to the classification were the bilateral parahippocampal gyrus (PG), right anterior cingulate cortex (ACC), and middle frontal gyrus (MFG). Significant correlations were found between addiction severity (IAT and DSM scores) and the ReHo values of the brain regions that contributed to the classification. Moreover, the results of spDCM showed that compared with RGU, IGD showed decreased effective connectivity from the left PG to the right MFG and from the right PG to the ACC and decreased self-connection in the right PG. CONCLUSIONS These results show that the weakening of the PG and its connection with the prefrontal cortex, including the ACC and MFG, may be an underlying mechanism of IGD.
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Affiliation(s)
- Ziliang Wang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, PR China,Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
| | - Haohao Dong
- Department of Psychology, Zhejiang Normal University, Jinhua, PR China
| | - Xiaoxia Du
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, PR China
| | - Jin-Tao Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, PR China,Corresponding author. Tel./fax: +86 10 58800728. E-mail:
| | - Guang-Heng Dong
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China,Corresponding author. Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China. Tel.: +86 15 867949909. E-mail:
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64
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Zheng W, Cui B, Sun Z, Li X, Han X, Yang Y, Li K, Hu L, Wang Z. Application of Generalized Split Linearized Bregman Iteration algorithm for Alzheimer's disease prediction. Aging (Albany NY) 2020; 12:6206-6224. [PMID: 32248185 PMCID: PMC7185109 DOI: 10.18632/aging.103017] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 02/25/2020] [Indexed: 12/16/2022]
Abstract
In this paper, we applied a novel method for the detection of Alzheimer's disease (AD) based on a structural magnetic resonance imaging (sMRI) dataset. Specifically, the method involved a new classification algorithm of machine learning, named Generalized Split Linearized Bregman Iteration (GSplit LBI). It combines logistic regression and structural sparsity regularizations. In the study, 57 AD patients and 47 normal controls (NCs) were enrolled. We first extracted the entire brain gray matter volume values of all subjects and then used GSplit LBI to build a predictive classification model with a 10-fold full cross-validation method. The model accuracy achieved 90.44%. To further verify which voxels in the dataset have greater impact on the prediction results, we ranked the weight parameters and obtained the top 6% of the model parameters. To verify the generalization of model prediction and the stability of feature selection, we performed a cross-test on the Alzheimer's Disease Neuroimaging Initiative (ADNI) and a Chinese dataset and achieved good performances on different cohorts. Conclusively, based on the sMRI dataset, our algorithm not only had good performance in a local cohort with high accuracy but also had good generalization of model prediction and stability of feature selection in different cohorts.
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Affiliation(s)
- Weimin Zheng
- Department of Radiology, Aerospace Center Hospital, Beijing 100049, China
| | - Bin Cui
- Department of Radiology, Aerospace Center Hospital, Beijing 100049, China
| | - Zeyu Sun
- Deepwise AI lab, Beijing 100080, China
| | - Xiuli Li
- Deepwise AI lab, Beijing 100080, China
| | - Xu Han
- Department of Radiology, Aerospace Center Hospital, Beijing 100049, China
| | - Yu Yang
- Beijing Huading Jialiang Technology Co, Beijing 100000, China
| | - Kuncheng Li
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
| | - Lingjing Hu
- Yanjing Medical College, Capital Medical University, Beijing 101300, China
| | - Zhiqun Wang
- Department of Radiology, Aerospace Center Hospital, Beijing 100049, China
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65
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The Neural Mechanism of the Social Framing Effect: Evidence from fMRI and tDCS Studies. J Neurosci 2020; 40:3646-3656. [PMID: 32238480 DOI: 10.1523/jneurosci.1385-19.2020] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 03/18/2020] [Accepted: 03/19/2020] [Indexed: 12/13/2022] Open
Abstract
As an important cognitive bias, the framing effect shows that our decision preferences are sensitive to the verbal description (i.e., frame) of options. This study focuses on the neural underpinnings of the social framing effect, which is based on decision-making regarding other people. A novel paradigm was used in which participants made a trade-off between economic benefits and the feelings of others. This decision was described as either a "harm" to, or "not helping," other persons in two conditions (Harm frame vs Help frame). Both human males and females were recruited. Participants behaved more prosocially for Harm frame compared with Help frame, resulting in a significant social framing effect. Using functional magnetic resonance imaging, Experiment 1 showed that the social framing effect was associated with stronger activation in the temporoparietal junction (TPJ), especially its right part. The functional connectivity between the right TPJ (rTPJ) and medial prefrontal cortex predicted the social framing effect on the group level. In Experiment 2, we used transcranial direct current stimulation to modulate the activity of the rTPJ and found that the social framing effect became more prominent under anodal (excitatory) stimulation, while the nonsocial framing effect elicited by the economic gain/loss gambling frame remained unaffected. The rTPJ results might be associated with moral conflicts modulated by the social consequences of an action or different levels of mentalizing with others under different frame conditions, but alternative interpretations are also worth noting. These findings could help elucidate the psychological mechanisms of the social framing effect.SIGNIFICANCE STATEMENT Previous studies have suggested that the framing effect is generated from an interaction between the amygdala and anterior cingulate cortex. This opinion, however, is based on findings from nonsocial framing tasks. Recent research has highlighted the importance of distinguishing between the social and nonsocial framing effects. The current study focuses on the social framing effect and finds out that the temporoparietal junction and its functional connectivity with the medial prefrontal cortex play a significant role. Additionally, modulating the activity of this region leads to changes in social (but not nonsocial) framing effect. Broadly speaking, these findings help understand the difference in neural mechanisms between social and nonsocial decision-making. Meanwhile, they might be illuminating to promote helping behavior in society.
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66
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Kernel based locality – Sensitive discriminative sparse representation for face recognition. SCIENTIFIC AFRICAN 2020. [DOI: 10.1016/j.sciaf.2019.e00249] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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67
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Machine Learning and DWI Brain Communicability Networks for Alzheimer’s Disease Detection. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10030934] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Signal processing and machine learning techniques are changing the clinical practice based on medical imaging from many perspectives. A major topic is related to (i) the development of computer aided diagnosis systems to provide clinicians with novel, non-invasive and low-cost support-tools, and (ii) to the development of new methodologies for the analysis of biomedical data for finding new disease biomarkers. Advancements have been recently achieved in the context of Alzheimer’s disease (AD) diagnosis through the use of diffusion weighted imaging (DWI) data. When combined with tractography algorithms, this imaging modality enables the reconstruction of the physical connections of the brain that can be subsequently investigated through a complex network-based approach. A graph metric particularly suited to describe the disruption of the brain connectivity due to AD is communicability. In this work, we develop a machine learning framework for the classification and feature importance analysis of AD based on communicability at the whole brain level. We fairly compare the performance of three state-of-the-art classification models, namely support vector machines, random forests and artificial neural networks, on the connectivity networks of a balanced cohort of healthy control subjects and AD patients from the ADNI database. Moreover, we clinically validate the information content of the communicability metric by performing a feature importance analysis. Both performance comparison and feature importance analysis provide evidence of the robustness of the method. The results obtained confirm that the whole brain structural communicability alterations due to AD are a valuable biomarker for the characterization and investigation of pathological conditions.
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69
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Bucholc M, Ding X, Wang H, Glass DH, Wang H, Prasad G, Maguire LP, Bjourson AJ, McClean PL, Todd S, Finn DP, Wong-Lin K. A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual. EXPERT SYSTEMS WITH APPLICATIONS 2019; 130:157-171. [PMID: 31402810 PMCID: PMC6688646 DOI: 10.1016/j.eswa.2019.04.022] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Computerized clinical decision support systems can help to provide objective, standardized, and timely dementia diagnosis. However, current computerized systems are mainly based on group analysis, discrete classification of disease stages, or expensive and not readily accessible biomarkers, while current clinical practice relies relatively heavily on cognitive and functional assessments (CFA). In this study, we developed a computational framework using a suite of machine learning tools for identifying key markers in predicting the severity of Alzheimer's disease (AD) from a large set of biological and clinical measures. Six machine learning approaches, namely Kernel Ridge Regression (KRR), Support Vector Regression, and k-Nearest Neighbor for regression and Support Vector Machine (SVM), Random Forest, and k-Nearest Neighbor for classification, were used for the development of predictive models. We demonstrated high predictive power of CFA. Predictive performance of models incorporating CFA was shown to consistently have higher accuracy than those based solely on biomarker modalities. We found that KRR and SVM were the best performing regression and classification methods respectively. The optimal SVM performance was observed for a set of four CFA test scores (FAQ, ADAS13, MoCA, MMSE) with multi-class classification accuracy of 83.0%, 95%CI = (72.1%, 93.8%) while the best performance of the KRR model was reported with combined CFA and MRI neuroimaging data, i.e., R 2 = 0.874, 95%CI = (0.827, 0.922). Given the high predictive power of CFA and their widespread use in clinical practice, we then designed a data-driven and self-adaptive computerized clinical decision support system (CDSS) prototype for evaluating the severity of AD of an individual on a continuous spectrum. The system implemented an automated computational approach for data pre-processing, modelling, and validation and used exclusively the scores of selected cognitive measures as data entries. Taken together, we have developed an objective and practical CDSS to aid AD diagnosis.
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Affiliation(s)
- Magda Bucholc
- Intelligent Systems Research Centre, School of Computing, Engineering & Intelligent Systems, Ulster University, Magee campus, Northern Ireland, United Kingdom
| | - Xuemei Ding
- Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Magee campus, Northern Ireland, United Kingdom
- Fujian Provincial Engineering Technology Research Centre for Public Service Big Data Mining and Application, College of Mathematics and Informatics, Fujian Normal University, Fuzhou, Fujian, 350108, China
| | - Haiying Wang
- School of Computing and Mathematics, Ulster University, Jordanstown campus, Northern Ireland, United Kingdom
| | - David H. Glass
- School of Computing and Mathematics, Ulster University, Jordanstown campus, Northern Ireland, United Kingdom
| | - Hui Wang
- School of Computing and Mathematics, Ulster University, Jordanstown campus, Northern Ireland, United Kingdom
| | - Girijesh Prasad
- Intelligent Systems Research Centre, School of Computing, Engineering & Intelligent Systems, Ulster University, Magee campus, Northern Ireland, United Kingdom
| | - Liam P. Maguire
- Intelligent Systems Research Centre, School of Computing, Engineering & Intelligent Systems, Ulster University, Magee campus, Northern Ireland, United Kingdom
| | - Anthony J. Bjourson
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Northern Ireland, United Kingdom
| | - Paula L. McClean
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Northern Ireland, United Kingdom
| | - Stephen Todd
- Altnagelvin Area Hospital, Western Health and Social Care Trust, Northern Ireland, United Kingdom
| | - David P. Finn
- Pharmacology and Therapeutics, School of Medicine, and NCBES Galway Neuroscience Centre, National University of Ireland, Galway, Republic of Ireland
| | - KongFatt Wong-Lin
- Intelligent Systems Research Centre, School of Computing, Engineering & Intelligent Systems, Ulster University, Magee campus, Northern Ireland, United Kingdom
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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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71
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Fu Z, Iraji A, Caprihan A, Adair JC, Sui J, Rosenberg GA, Calhoun VD. In search of multimodal brain alterations in Alzheimer's and Binswanger's disease. NEUROIMAGE-CLINICAL 2019; 26:101937. [PMID: 31351845 PMCID: PMC7229329 DOI: 10.1016/j.nicl.2019.101937] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Revised: 05/16/2019] [Accepted: 07/14/2019] [Indexed: 11/07/2022]
Abstract
Structural and functional brain abnormalities have been widely identified in dementia, but with variable replicability and significant overlap. Alzheimer's disease (AD) and Binswanger's disease (BD) share similar symptoms and common brain changes that can confound diagnosis. In this study, we aimed to investigate correlated structural and functional brain changes in AD and BD by combining resting-state functional magnetic resonance imaging (fMRI) and diffusion MRI. A group independent component analysis was first performed on the fMRI data to extract 49 intrinsic connectivity networks (ICNs). Then we conducted a multi-set canonical correlation analysis on three features, functional network connectivity (FNC) between ICNs, fractional anisotropy (FA) and mean diffusivity (MD). Two inter-correlated components show significant group differences. The first component demonstrates distinct brain changes between AD and BD. AD shows increased cerebellar FNC but decreased thalamic and hippocampal FNC. Such FNC alterations are linked to the decreased corpus callosum FA. AD also has increased MD in the frontal and temporal cortex, but BD shows opposite alterations. The second component demonstrates specific brain changes in BD. Increased FNC is mainly between default mode and sensory regions, while decreased FNC is mainly within the default mode domain and related to auditory regions. The FNC changes are associated with FA changes in posterior/middle cingulum cortex and visual cortex and increased MD in thalamus and hippocampus. Our findings provide evidence of linked functional and structural deficits in dementia and suggest that AD and BD have both common and distinct changes in white matter integrity and functional connectivity. This is the first study to explore multi-modalities changes in different dementia. A multimodal fusion method is applied to identify joint components. Brain abnormalities in different modalities are highly correlated. Alzheimer's and Binswanger's disease share similar brain changes. Alzheimer's and Binswanger's disease also have distinct brain changes.
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Affiliation(s)
- Zening Fu
- The Mind Research Network, Albuquerque, NM, United States.
| | - Armin Iraji
- The Mind Research Network, Albuquerque, NM, United States
| | | | - John C Adair
- Department of Neurology, University of New Mexico Health Sciences Center, Albuquerque, NM, United States
| | - Jing Sui
- The Mind Research Network, Albuquerque, NM, United States; Chinese Academy of Sciences (CAS) Centre for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, China
| | - Gary A Rosenberg
- Department of Neurology, University of New Mexico Health Sciences Center, Albuquerque, NM, United States
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM, United States; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, United States
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72
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Bouts MJRJ, Möller C, Hafkemeijer A, van Swieten JC, Dopper E, van der Flier WM, Vrenken H, Wink AM, Pijnenburg YAL, Scheltens P, Barkhof F, Schouten TM, de Vos F, Feis RA, van der Grond J, de Rooij M, Rombouts SARB. Single Subject Classification of Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia Using Anatomical, Diffusion Tensor, and Resting-State Functional Magnetic Resonance Imaging. J Alzheimers Dis 2019; 62:1827-1839. [PMID: 29614652 DOI: 10.3233/jad-170893] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND/OBJECTIVE Overlapping clinical symptoms often complicate differential diagnosis between patients with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). Magnetic resonance imaging (MRI) reveals disease specific structural and functional differences that aid in differentiating AD from bvFTD patients. However, the benefit of combining structural and functional connectivity measures to-on a subject-basis-differentiate these dementia-types is not yet known. METHODS Anatomical, diffusion tensor (DTI), and resting-state functional MRI (rs-fMRI) of 30 patients with early stage AD, 23 with bvFTD, and 35 control subjects were collected and used to calculate measures of structural and functional tissue status. All measures were used separately or selectively combined as predictors for training an elastic net regression classifier. Each classifier's ability to accurately distinguish dementia-types was quantified by calculating the area under the receiver operating characteristic curves (AUC). RESULTS Highest AUC values for AD and bvFTD discrimination were obtained when mean diffusivity, full correlations between rs-fMRI-derived independent components, and fractional anisotropy (FA) were combined (0.811). Similarly, combining gray matter density (GMD), FA, and rs-fMRI correlations resulted in highest AUC of 0.922 for control and bvFTD classifications. This, however, was not observed for control and AD differentiations. Classifications with GMD (0.940) and a GMD and DTI combination (0.941) resulted in similar AUC values (p = 0.41). CONCLUSION Combining functional and structural connectivity measures improve dementia-type differentiations and may contribute to more accurate and substantiated differential diagnosis of AD and bvFTD patients. Imaging protocols for differential diagnosis may benefit from also including DTI and rs-fMRI.
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Affiliation(s)
- Mark J R J Bouts
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Christiane Möller
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Anne Hafkemeijer
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - John C van Swieten
- Department of Clinical Genetics, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.,Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Elise Dopper
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands.,Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.,Department of Epidemiology and Biostatistics, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Hugo Vrenken
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.,Department of Physics and Medical Technology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Alle Meije Wink
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Yolande A L Pijnenburg
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.,Institute of Neurology and Healthcare Engineering, University College London, London, UK
| | - Tijn M Schouten
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Frank de Vos
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Rogier A Feis
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Jeroen van der Grond
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Mark de Rooij
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Serge A R B Rombouts
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
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Amoroso N, La Rocca M, Bellantuono L, Diacono D, Fanizzi A, Lella E, Lombardi A, Maggipinto T, Monaco A, Tangaro S, Bellotti R. Deep Learning and Multiplex Networks for Accurate Modeling of Brain Age. Front Aging Neurosci 2019; 11:115. [PMID: 31178715 PMCID: PMC6538815 DOI: 10.3389/fnagi.2019.00115] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 05/01/2019] [Indexed: 12/27/2022] Open
Abstract
Recent works have extensively investigated the possibility to predict brain aging from T1-weighted MRI brain scans. The main purposes of these studies are the investigation of subject-specific aging mechanisms and the development of accurate models for age prediction. Deviations between predicted and chronological age are known to occur in several neurodegenerative diseases; as a consequence, reaching higher levels of age prediction accuracy is of paramount importance to develop diagnostic tools. In this work, we propose a novel complex network model for brain based on segmenting T1-weighted MRI scans in rectangular boxes, called patches, and measuring pairwise similarities using Pearson's correlation to define a subject-specific network. We fed a deep neural network with nodal metrics, evaluating both the intensity and the uniformity of connections, to predict subjects' ages. Our model reaches high accuracies which compare favorably with state-of-the-art approaches. We observe that the complex relationships involved in this brain description cannot be accurately modeled with standard machine learning approaches, such as Ridge and Lasso regression, Random Forest, and Support Vector Machines, instead a deep neural network has to be used.
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Affiliation(s)
- Nicola Amoroso
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli studi di Bari "A. Moro", Bari, Italy.,Istituto Nazionale di Fisica Nucleare, Bari, Italy
| | - Marianna La Rocca
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Loredana Bellantuono
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli studi di Bari "A. Moro", Bari, Italy
| | | | | | - Eufemia Lella
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli studi di Bari "A. Moro", Bari, Italy.,Istituto Nazionale di Fisica Nucleare, Bari, Italy
| | | | - Tommaso Maggipinto
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli studi di Bari "A. Moro", Bari, Italy.,Istituto Nazionale di Fisica Nucleare, Bari, Italy
| | | | | | - Roberto Bellotti
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli studi di Bari "A. Moro", Bari, Italy.,Istituto Nazionale di Fisica Nucleare, Bari, Italy
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74
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Li Y, Meng F, Shi J. Learning using privileged information improves neuroimaging-based CAD of Alzheimer's disease: a comparative study. Med Biol Eng Comput 2019; 57:1605-1616. [PMID: 31028606 DOI: 10.1007/s11517-019-01974-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Accepted: 03/19/2019] [Indexed: 12/26/2022]
Abstract
The neuroimaging-based computer-aided diagnosis (CAD) for Alzheimer's disease (AD) has shown its effectiveness in recent years. In general, the multimodal neuroimaging-based CAD always outperforms the approaches based on a single modality. However, single-modal neuroimaging is more favored in clinical practice for diagnosis due to the limitations of imaging devices, especially in rural hospitals. Learning using privileged information (LUPI) is a new learning paradigm that adopts additional privileged information (PI) modality to help to train a more effective learning model during the training stage, but PI itself is not available in the testing stage. Since PI is generally related to the training samples, it is then transferred to the learned model. In this work, a LUPI-based CAD framework for AD is proposed. It can flexibly perform a classifier- or feature-level LUPI, in which the information is transferred from the additional PI modality to the diagnosis modality. A thorough comparison has been made among three classifier-level algorithms and five feature-level LUPI algorithms. The experimental results on the ADNI dataset show that all classifier-level and deep learning based feature-level LUPI algorithms can improve the performance of a single-modal neuroimaging-based CAD for AD by transferring PI. Graphical abstract Graphical abstract for the framework of the LUPI-based CAD for AD.
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Affiliation(s)
- Yan Li
- Shenzhen City Key Laboratory of Embedded System Design, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
| | - Fanqing Meng
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, No. 99 Shangda Road, Shanghai, 200444, People's Republic of China
| | - Jun Shi
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, No. 99 Shangda Road, Shanghai, 200444, People's Republic of China.
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75
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Multimodal classification of drug-naïve first-episode schizophrenia combining anatomical, diffusion and resting state functional resonance imaging. Neurosci Lett 2019; 705:87-93. [PMID: 31022433 DOI: 10.1016/j.neulet.2019.04.039] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Revised: 04/16/2019] [Accepted: 04/17/2019] [Indexed: 01/15/2023]
Abstract
The accurate diagnosis in the early stage of schizophrenia (SZ) is of great importance yet remains challenging. The classification between SZ and control groups based on magnetic resonance imaging (MRI) data using machine learning method could be helpful for SZ diagnosis. Increasing evidence showed that the combination of multimodal MRI data might further improve the classification performance However, medication effect has a profound influence on patients' anatomical and functional features and may reduce the classification efficiency. In this paper, we proposed a multimodal classification method to discriminate drug-naïve first-episode schizophrenia patients from healthy controls (HCs) by a combined structural MRI, diffusion tensor imaging (DTI) and resting state-functional MRI data. To reduce the feature dimension of multimodal data, we applied sparse coding (SC) for feature selection and multi-kernel support vector machine (SVM) for feature combination and classification. The best classification performance with the classification accuracy of 84.29% and area under the receiver operating characteristic (ROC) curve (AUC) of 81.64% was achieved when all modality data were combined. Interestingly, the identified functional markers were mainly found in default mode network (DMN) and cerebellar connections, while the structural markers were within limbic system and prefrontal-thalamo-hippocampal circuit.
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76
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Fritz HJ, Ray N, Dyrba M, Sorg C, Teipel S, Grothe MJ. The corticotopic organization of the human basal forebrain as revealed by regionally selective functional connectivity profiles. Hum Brain Mapp 2019; 40:868-878. [PMID: 30311315 PMCID: PMC6865372 DOI: 10.1002/hbm.24417] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Revised: 08/06/2018] [Accepted: 10/01/2018] [Indexed: 12/25/2022] Open
Abstract
The cholinergic basal forebrain (CBF), comprising different groups of cortically projecting cholinergic neurons, plays a crucial role in higher cognitive processes and has been implicated in diverse neuropsychiatric disorders. A distinct corticotopic organization of CBF projections has been revealed in animal studies, but little is known about their organization in the human brain. We explored regional differences in functional connectivity (FC) profiles within the human CBF by applying a clustering approach to resting-state functional magnetic resonance imaging (rs-fMRI) data of healthy adult individuals (N = 85; 19-85 years). We further examined effects of age on FC of the identified CBF clusters and assessed the reproducibility of cluster-specific FC profiles in independent data from healthy older individuals (N = 25; 65-89 years). Results showed that the human CBF is functionally organized into distinct anterior-medial and posterior-lateral subdivisions that largely follow anatomically defined boundaries of the medial septum/diagonal band and nucleus basalis Meynert. The anterior-medial CBF subdivision was characterized by connectivity with the hippocampus and interconnected nodes of an extended medial cortical memory network, whereas the posterior-lateral subdivision was specifically connected to anterior insula and dorsal anterior cingulate components of a salience/attention network. FC of both CBF subdivisions declined with increasing age, but the overall topography of subregion-specific FC profiles was reproduced in independent rs-fMRI data of healthy older individuals acquired in a typical clinical setting. Rs-fMRI-based assessments of subregion-specific CBF function may complement established volumetric approaches for the in vivo study of CBF involvement in neuropsychiatric disorders.
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Affiliation(s)
- Hans‐Christian J. Fritz
- Clinical Dementia Research SectionGerman Center for Neurodegenerative Diseases (DZNE)RostockGermany
- Department of Psychosomatic and Psychotherapeutic MedicineRostock University Medical CenterRostockGermany
| | - Nicola Ray
- Department of PsychologyManchester Metropolitan UniversityManchesterUK
| | - Martin Dyrba
- Clinical Dementia Research SectionGerman Center for Neurodegenerative Diseases (DZNE)RostockGermany
| | - Christian Sorg
- Departments of Neuroradiology and Psychiatry, TUM‐Neuroimaging Center of Klinikum rechts der IsarTechnische Universität München TUMMunichGermany
| | - Stefan Teipel
- Clinical Dementia Research SectionGerman Center for Neurodegenerative Diseases (DZNE)RostockGermany
- Department of Psychosomatic and Psychotherapeutic MedicineRostock University Medical CenterRostockGermany
| | - Michel J. Grothe
- Clinical Dementia Research SectionGerman Center for Neurodegenerative Diseases (DZNE)RostockGermany
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77
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Qureshi MNI, Ryu S, Song J, Lee KH, Lee B. Evaluation of Functional Decline in Alzheimer's Dementia Using 3D Deep Learning and Group ICA for rs-fMRI Measurements. Front Aging Neurosci 2019; 11:8. [PMID: 30804774 PMCID: PMC6378312 DOI: 10.3389/fnagi.2019.00008] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Accepted: 01/10/2019] [Indexed: 12/21/2022] Open
Abstract
Purpose: To perform automatic assessment of dementia severity using a deep learning framework applied to resting-state functional magnetic resonance imaging (rs-fMRI) data. Method: We divided 133 Alzheimer’s disease (AD) patients with clinical dementia rating (CDR) scores from 0.5 to 3 into two groups based on dementia severity; the groups with very mild/mild (CDR: 0.5–1) and moderate to severe (CDR: 2–3) dementia consisted of 77 and 56 subjects, respectively. We used rs-fMRI to extract functional connectivity features, calculated using independent component analysis (ICA), and performed automated severity classification with three-dimensional convolutional neural networks (3D-CNNs) based on deep learning. Results: The mean balanced classification accuracy was 0.923 ± 0.042 (p < 0.001) with a specificity of 0.946 ± 0.019 and sensitivity of 0.896 ± 0.077. The rs-fMRI data indicated that the medial frontal, sensorimotor, executive control, dorsal attention, and visual related networks mainly correlated with dementia severity. Conclusions: Our CDR-based novel classification using rs-fMRI is an acceptable objective severity indicator. In the absence of trained neuropsychologists, dementia severity can be objectively and accurately classified using a 3D-deep learning framework with rs-fMRI independent components.
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Affiliation(s)
- Muhammad Naveed Iqbal Qureshi
- Department of Biomedical Science and Engineering (BMSE), Institute of Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea.,Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada.,Alzheimer's Disease Research Unit, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada.,Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Seungjun Ryu
- Department of Biomedical Science and Engineering (BMSE), Institute of Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea
| | - Joonyoung Song
- Department of Biomedical Science and Engineering (BMSE), Institute of Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea
| | - Kun Ho Lee
- National Research Center for Dementia, Chosun University, Gwangju, South Korea.,Department of Biomedical Science, Chosun University, Gwangju, South Korea
| | - Boreom Lee
- Department of Biomedical Science and Engineering (BMSE), Institute of Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea
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78
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Kazeminejad A, Sotero RC. Topological Properties of Resting-State fMRI Functional Networks Improve Machine Learning-Based Autism Classification. Front Neurosci 2019; 12:1018. [PMID: 30686984 PMCID: PMC6335365 DOI: 10.3389/fnins.2018.01018] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Accepted: 12/18/2018] [Indexed: 01/16/2023] Open
Abstract
Automatic algorithms for disease diagnosis are being thoroughly researched for use in clinical settings. They usually rely on pre-identified biomarkers to highlight the existence of certain problems. However, finding such biomarkers for neurodevelopmental disorders such as Autism Spectrum Disorder (ASD) has challenged researchers for many years. With enough data and computational power, machine learning (ML) algorithms can be used to interpret the data and extract the best biomarkers from thousands of candidates. In this study, we used the fMRI data of 816 individuals enrolled in the Autism Brain Imaging Data Exchange (ABIDE) to introduce a new biomarker extraction pipeline for ASD that relies on the use of graph theoretical metrics of fMRI-based functional connectivity to inform a support vector machine (SVM). Furthermore, we split the dataset into 5 age groups to account for the effect of aging on functional connectivity. Our methodology achieved better results than most state-of-the-art investigations on this dataset with the best model for the >30 years age group achieving an accuracy, sensitivity, and specificity of 95, 97, and 95%, respectively. Our results suggest that measures of centrality provide the highest contribution to the classification power of the models.
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Affiliation(s)
- Amirali Kazeminejad
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada
| | - Roberto C Sotero
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada.,Department of Radiology, University of Calgary, Calgary, AB, Canada
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79
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Qiao J, Lv Y, Cao C, Wang Z, Li A. Multivariate Deep Learning Classification of Alzheimer's Disease Based on Hierarchical Partner Matching Independent Component Analysis. Front Aging Neurosci 2018; 10:417. [PMID: 30618723 PMCID: PMC6304436 DOI: 10.3389/fnagi.2018.00417] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Accepted: 12/03/2018] [Indexed: 12/11/2022] Open
Abstract
Machine learning and pattern recognition have been widely investigated in order to look for the biomarkers of Alzheimer’s disease (AD). However, most existing methods extract features by seed-based correlation, which not only requires prior information but also ignores the relationship between resting state functional magnetic resonance imaging (rs-fMRI) voxels. In this study, we proposed a deep learning classification framework with multivariate data-driven based feature extraction for automatic diagnosis of AD. Specifically, a three-level hierarchical partner matching independent components analysis (3LHPM-ICA) approach was proposed first in order to address the issues in spatial individual ICA, including the uncertainty of the numbers of components, the randomness of initial values, and the correspondence of ICs of multiple subjects, resulting in stable and reliable ICs which were applied as the intrinsic brain functional connectivity (FC) features. Second, Granger causality (GC) was utilized to infer directional interaction between the ICs that were identified by the 3LHPM-ICA method and extract the effective connectivity features. Finally, a deep learning classification framework was developed to distinguish AD from controls by fusing the functional and effective connectivities. A resting state fMRI dataset containing 34 AD patients and 34 normal controls (NCs) was applied to the multivariate deep learning platform, leading to a classification accuracy of 95.59%, with a sensitivity of 97.06% and a specificity of 94.12% with leave-one-out cross validation (LOOCV). The experimental results demonstrated that the measures of neural connectivities of ICA and GC followed by deep learning classification represented the most powerful methods of distinguishing AD clinical data from NCs, and these aberrant brain connectivities might serve as robust brain biomarkers for AD. This approach also allows for expansion of the methodology to classify other psychiatric disorders.
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Affiliation(s)
- Jianping Qiao
- Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, Institute of Data Science and Technology, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Yingru Lv
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Chongfeng Cao
- Department of Emergency, Jinan Central Hospital Affiliated to Shandong University, Jinan, China
| | - Zhishun Wang
- Department of Psychiatry, Columbia University, New York, NY, United States
| | - Anning Li
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
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80
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Zeng N, Qiu H, Wang Z, Liu W, Zhang H, Li Y. A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer’s disease. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.09.001] [Citation(s) in RCA: 102] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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81
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Sakai K, Yamada K. Machine learning studies on major brain diseases: 5-year trends of 2014–2018. Jpn J Radiol 2018; 37:34-72. [DOI: 10.1007/s11604-018-0794-4] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Accepted: 11/14/2018] [Indexed: 12/17/2022]
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82
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Bi K, Luo G, Tian S, Zhang S, Liu X, Wang Q, Lu Q, Yao Z. An enriched granger causal model allowing variable static anatomical constraints. Neuroimage Clin 2018; 21:101592. [PMID: 30448217 PMCID: PMC6411584 DOI: 10.1016/j.nicl.2018.11.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Revised: 10/08/2018] [Accepted: 11/03/2018] [Indexed: 01/08/2023]
Abstract
The anatomical connectivity constrains but does not fully determine functional connectivity, especially when one explores into the dynamics over the course of a trial. Therefore, an enriched granger causal model (GCM) integrated with anatomical prior information is proposed in this study, to describe the dynamic effective connectivity to distinguish the depression and explore the pathogenesis of depression. In the proposed frame, the anatomical information was converted via an optimized transformation model, which was then integrated into the normal GCM by variational bayesian model. Magnetoencephalography (MEG) signals and diffusion tensor imaging (DTI) of 24 depressive patients and 24 matched controls were utilized for performance comparison. Together with the sliding windowed MEG signals under sad facial stimuli, the enriched GCM was applied to calculate the regional-pair dynamic effective connectivity, which were repeatedly sifted via feature selection and fed into different classifiers. From the aspects of model errors and recognition accuracy rates, results supported the superiority of the enriched GCM with anatomical priors over the normal GCM. For the effective connectivity with anatomical priors, the best subject discrimination accuracy of SVM was 85.42% (the sensitivity was 87.50% and the specificity was 83.33%). Furthermore, discriminative feature analysis suggested that the enriched GCM that detect the variable anatomical constraint on function could better detect more stringent and less dynamic brain function in depression. The proposed approach is valuable in dynamic functional dysfunction exploration in depression and could be useful for depression recognition.
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Affiliation(s)
- Kun Bi
- Key Laboratory of Child Development and Learning Science, School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China
| | - Guoping Luo
- Key Laboratory of Child Development and Learning Science, School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China
| | - Shui Tian
- Key Laboratory of Child Development and Learning Science, School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China
| | - Siqi Zhang
- Key Laboratory of Child Development and Learning Science, School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China
| | - Xiaoxue Liu
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China
| | - Qiang Wang
- Medical School of Nanjing University, Nanjing University, Nanjing 210093, China
| | - Qing Lu
- Key Laboratory of Child Development and Learning Science, School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China.
| | - Zhijian Yao
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China; Medical School of Nanjing University, Nanjing University, Nanjing 210093, China.
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83
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Hojjati SH, Ebrahimzadeh A, Khazaee A, Babajani-Feremi A. Predicting conversion from MCI to AD by integrating rs-fMRI and structural MRI. Comput Biol Med 2018; 102:30-39. [DOI: 10.1016/j.compbiomed.2018.09.004] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 09/06/2018] [Accepted: 09/09/2018] [Indexed: 12/21/2022]
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84
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Dyrba M, Grothe MJ, Mohammadi A, Binder H, Kirste T, Teipel SJ. Comparison of Different Hypotheses Regarding the Spread of Alzheimer’s Disease Using Markov Random Fields and Multimodal Imaging. J Alzheimers Dis 2018; 65:731-746. [DOI: 10.3233/jad-161197] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Rostock, Germany
| | - Michel J. Grothe
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Rostock, Germany
| | - Abdolreza Mohammadi
- Department of Methodology and Statistics, Tilburg University, Tilburg, The Netherlands
| | - Harald Binder
- Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Thomas Kirste
- Mobile Multimedia Information Systems Group (MMIS), University of Rostock, Rostock, Germany
| | - Stefan J. Teipel
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Rostock, Germany
- Clinic for Psychosomatic and Psychotherapeutic Medicine, University Medical Center Rostock, Rostock, Germany
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85
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Mateos-Pérez JM, Dadar M, Lacalle-Aurioles M, Iturria-Medina Y, Zeighami Y, Evans AC. Structural neuroimaging as clinical predictor: A review of machine learning applications. NEUROIMAGE-CLINICAL 2018; 20:506-522. [PMID: 30167371 PMCID: PMC6108077 DOI: 10.1016/j.nicl.2018.08.019] [Citation(s) in RCA: 90] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2017] [Revised: 01/22/2018] [Accepted: 08/09/2018] [Indexed: 11/26/2022]
Abstract
In this paper, we provide an extensive overview of machine learning techniques applied to structural magnetic resonance imaging (MRI) data to obtain clinical classifiers. We specifically address practical problems commonly encountered in the literature, with the aim of helping researchers improve the application of these techniques in future works. Additionally, we survey how these algorithms are applied to a wide range of diseases and disorders (e.g. Alzheimer's disease (AD), Parkinson's disease (PD), autism, multiple sclerosis, traumatic brain injury, etc.) in order to provide a comprehensive view of the state of the art in different fields.
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Affiliation(s)
| | - Mahsa Dadar
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | | | | | - Yashar Zeighami
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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86
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Du Y, Fu Z, Calhoun VD. Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging. Front Neurosci 2018; 12:525. [PMID: 30127711 PMCID: PMC6088208 DOI: 10.3389/fnins.2018.00525] [Citation(s) in RCA: 166] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Accepted: 07/12/2018] [Indexed: 12/13/2022] Open
Abstract
Brain functional imaging data, especially functional magnetic resonance imaging (fMRI) data, have been employed to reflect functional integration of the brain. Alteration in brain functional connectivity (FC) is expected to provide potential biomarkers for classifying or predicting brain disorders. In this paper, we present a comprehensive review in order to provide guidance about the available brain FC measures and typical classification strategies. We survey the state-of-the-art FC analysis methods including widely used static functional connectivity (SFC) and more recently proposed dynamic functional connectivity (DFC). Temporal correlations among regions of interest (ROIs), data-driven spatial network and functional network connectivity (FNC) are often computed to reflect SFC from different angles. SFC can be extended to DFC using a sliding-window framework, and intrinsic connectivity states along the time-varying connectivity patterns are typically extracted using clustering or decomposition approaches. We also briefly summarize window-less DFC approaches. Subsequently, we highlight various strategies for feature selection including the filter, wrapper and embedded methods. In terms of model building, we include traditional classifiers as well as more recently applied deep learning methods. Moreover, we review representative applications with remarkable classification accuracy for psychosis and mood disorders, neurodevelopmental disorder, and neurological disorders using fMRI data. Schizophrenia, bipolar disorder, autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer's disease and mild cognitive impairment (MCI) are discussed. Finally, challenges in the field are pointed out with respect to the inaccurate diagnosis labeling, the abundant number of possible features and the difficulty in validation. Some suggestions for future work are also provided.
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Affiliation(s)
- Yuhui Du
- The Mind Research Network, Albuquerque, NM, United States
- School of Computer & Information Technology, Shanxi University, Taiyuan, China
| | - Zening Fu
- The Mind Research Network, Albuquerque, NM, United States
| | - Vince D. Calhoun
- The Mind Research Network, Albuquerque, NM, United States
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, United States
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87
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La Rocca M, Amoroso N, Monaco A, Bellotti R, Tangaro S. A novel approach to brain connectivity reveals early structural changes in Alzheimer's disease. Physiol Meas 2018; 39:074005. [PMID: 29943735 DOI: 10.1088/1361-6579/aacf1f] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
OBJECTIVE Recent studies have shown that complex networks along with diffusion weighted imaging (DWI) can be efficient and promising techniques for early detection of structural pathological changes in Alzheimer's disease. Besides, connectivity studies, specifically assessing the organization of a graph and its topology, could represent the best chance to discover how brain activity is shaped and driven. Accordingly, we propose a methodology to evaluate how Alzheimer's disease affects brain networks through a novel way to look at graph connectivity. In fact, we use the combination of network features related to brain organization with network features related to the variations in connectivity between several subjects. APPROACH From a DWI brain scan we reconstruct a probabilistic tractography by evaluating the number of white matter fibers connecting two anatomical districts, thus obtaining a weighted undirected network. The nodes of this network are the cerebral regions provided by the reference brain atlas, the weights are the intensity of linkage among them. We investigated brain connectivity graphs retrieved from a set of 222 publicly available DWI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI): 47 Alzheimer's disease (AD) patients, 52 normal controls (NC) and 123 mild cognitive impairment (MCI) subjects, this latter cohort includes 85 early and 38 late MCI subjects, EMCI and LMCI respectively. MAIN RESULTS The proposed brain connectivity approach effectively characterizes Alzheimer's disease, especially in its early stages. In fact, MCI is a prodromal phase of Alzheimer's disease. We report a [Formula: see text] accuracy for the discrimination of NC and AD subjects and accuracies of [Formula: see text] and [Formula: see text] for the discrimination of MCI from respectively NC and AD subjects. SIGNIFICANCE Our complex network approach offers an innovative and effective instrument to model brain connectivity and detect in DWI tractographies early changes due to Alzheimer's.
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Affiliation(s)
- Marianna La Rocca
- Dipartimento Interateneo di Fisica 'M. Merlin', Università degli studi di Bari 'A. Moro', Italy. Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Italy
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88
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Li W, Zhao Y, Chen X, Xiao Y, Qin Y. Detecting Alzheimer's Disease on Small Dataset: A Knowledge Transfer Perspective. IEEE J Biomed Health Inform 2018; 23:1234-1242. [PMID: 29994324 DOI: 10.1109/jbhi.2018.2839771] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Computer-aided diagnosis (CAD) is an attractive topic in Alzheimer's disease (AD) research. Many algorithms are based on a relatively large training dataset. However, small hospitals are usually unable to collect sufficient training samples for robust classification. Although data sharing is expanding in scientific research, it is unclear whether a model based on one dataset is well suited for other data sources. Using a small dataset from a local hospital and a large shared dataset from the AD neuroimaging initiative, we conducted a heterogeneity analysis and found that different functional magnetic resonance imaging data sources show different sample distributions in feature space. In addition, we proposed an effective knowledge transfer method to diminish the disparity among different datasets and improve the classification accuracy on datasets with insufficient training samples. The accuracy increased by approximately 20% compared with that of a model based only on the original small dataset. The results demonstrated that the proposed approach is a novel and effective method for CAD in hospitals with only small training datasets. It solved the challenge of limited sample size in detection of AD, which is a common issue but lack of adequate attention. Furthermore, this paper sheds new light on effective use of multi-source data for neurological disease diagnosis.
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89
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Long Z, Jing B, Guo R, Li B, Cui F, Wang T, Chen H. A Brainnetome Atlas Based Mild Cognitive Impairment Identification Using Hurst Exponent. Front Aging Neurosci 2018; 10:103. [PMID: 29692721 PMCID: PMC5902491 DOI: 10.3389/fnagi.2018.00103] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Accepted: 03/27/2018] [Indexed: 11/15/2022] Open
Abstract
Mild cognitive impairment (MCI), which generally represents the transition state between normal aging and the early changes related to Alzheimer’s disease (AD), has drawn increasing attention from neuroscientists due that efficient AD treatments need early initiation ahead of irreversible brain tissue damage. Thus effective MCI identification methods are desperately needed, which may be of great importance for the clinical intervention of AD. In this article, the range scaled analysis, which could effectively detect the temporal complexity of a time series, was utilized to calculate the Hurst exponent (HE) of functional magnetic resonance imaging (fMRI) data at a voxel level from 64 MCI patients and 60 healthy controls (HCs). Then the average HE values of each region of interest (ROI) in brainnetome atlas were extracted and compared between MCI and HC. At last, the abnormal average HE values were adopted as the classification features for a proposed support vector machine (SVM) based identification algorithm, and the classification performance was estimated with leave-one-out cross-validation (LOOCV). Our results indicated 83.1% accuracy, 82.8% sensitivity and 83.3% specificity, and an area under curve of 0.88, suggesting that the HE index could serve as an effective feature for the MCI identification. Furthermore, the abnormal HE brain regions in MCI were predominately involved in left middle frontal gyrus, right hippocampus, bilateral parahippocampal gyrus, bilateral amygdala, left cingulate gyrus, left insular gyrus, left fusiform gyrus, left superior parietal gyrus, left orbital gyrus and left basal ganglia.
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Affiliation(s)
- Zhuqing Long
- Medical Apparatus and Equipment Deployment, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Bin Jing
- School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Ru Guo
- Department of Tuberculosis, Beijing Chest Hospital Capital Medical University, Beijing, China
| | - Bo Li
- Department of Traditional Chinese Medicine, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Feiyi Cui
- Medical Apparatus and Equipment Deployment, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Tingting Wang
- Medical Apparatus and Equipment Deployment, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Hongwen Chen
- Medical Apparatus and Equipment Deployment, Nanfang Hospital, Southern Medical University, Guangzhou, China
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90
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Snow WM, Dale R, O'Brien-Moran Z, Buist R, Peirson D, Martin M, Albensi BC. In Vivo Detection of Gray Matter Neuropathology in the 3xTg Mouse Model of Alzheimer's Disease with Diffusion Tensor Imaging. J Alzheimers Dis 2018; 58:841-853. [PMID: 28505976 DOI: 10.3233/jad-170136] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
A diagnosis of Alzheimer's disease (AD), a neurodegenerative disorder accompanied by severe functional and cognitive decline, is based on clinical findings, with final confirmation of the disease at autopsy by the presence of amyloid-β (Aβ) plaques and neurofibrillary tangles. Given that microstructural brain alterations occur years prior to clinical symptoms, efforts to detect brain changes early could significantly enhance our ability to diagnose AD sooner. Diffusion tensor imaging (DTI), a type of MRI that characterizes the magnitude, orientation, and anisotropy of the diffusion of water in tissues, has been used to infer neuropathological changes in vivo. Its utility in AD, however, is still under investigation. The current study used DTI to examine brain regions susceptible to AD-related pathology; the cerebral cortex, entorhinal cortex, and hippocampus, in 12-14-month-old 3xTg AD mice that possess both Aβ plaques and neurofibrillary tangles. Mean diffusivity did not differ between 3xTg and control mice in any region. Decreased fractional anisotropy (p < 0.01) and axial diffusivity (p < 0.05) were detected only in the hippocampus, in which both congophilic Aβ plaques and hyperphosphorylated tau accumulation, consistent with neurofibrillary tangle formation, were detected. Pathological tau accumulation was seen in the cortex. The entorhinal cortex was largely spared from AD-related neuropathology. This is the first study to demonstrate DTI abnormalities in gray matter in a mouse model of AD in which both pathological hallmarks are present, suggesting the feasibility of DTI as a non-invasive means of detecting brain pathology in vivo in early-stage AD.
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Affiliation(s)
- Wanda M Snow
- Division of Neurodegenerative Disorders, St. Boniface Hospital Albrechtsen Research Centre, Winnipeg, MB, Canada.,Department of Pharmacology & Therapeutics, University of Manitoba, Winnipeg, MB, Canada
| | - Ryan Dale
- Division of Neurodegenerative Disorders, St. Boniface Hospital Albrechtsen Research Centre, Winnipeg, MB, Canada.,Department of Pharmacology & Therapeutics, University of Manitoba, Winnipeg, MB, Canada
| | | | - Richard Buist
- Department of Radiology, University of Manitoba, Winnipeg, MB, Canada
| | - Danial Peirson
- Division of Neurodegenerative Disorders, St. Boniface Hospital Albrechtsen Research Centre, Winnipeg, MB, Canada.,Department of Pharmacology & Therapeutics, University of Manitoba, Winnipeg, MB, Canada
| | - Melanie Martin
- Department of Pharmacology & Therapeutics, University of Manitoba, Winnipeg, MB, Canada.,Department of Physics, University of Winnipeg, Winnipeg, MB, Canada.,Department of Radiology, University of Manitoba, Winnipeg, MB, Canada
| | - Benedict C Albensi
- Division of Neurodegenerative Disorders, St. Boniface Hospital Albrechtsen Research Centre, Winnipeg, MB, Canada.,Department of Pharmacology & Therapeutics, University of Manitoba, Winnipeg, MB, Canada
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91
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Yang J, Yin Y, Zhang Z, Long J, Dong J, Zhang Y, Xu Z, Li L, Liu J, Yuan Y. Predictive brain networks for major depression in a semi-multimodal fusion hierarchical feature reduction framework. Neurosci Lett 2018; 665:163-169. [PMID: 29217258 DOI: 10.1016/j.neulet.2017.12.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Revised: 11/05/2017] [Accepted: 12/04/2017] [Indexed: 12/26/2022]
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92
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He L, Li H, Holland SK, Yuan W, Altaye M, Parikh NA. Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework. NEUROIMAGE-CLINICAL 2018; 18:290-297. [PMID: 29876249 PMCID: PMC5987842 DOI: 10.1016/j.nicl.2018.01.032] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Revised: 01/22/2018] [Accepted: 01/24/2018] [Indexed: 12/15/2022]
Abstract
Investigation of the brain's functional connectome can improve our understanding of how an individual brain's organizational changes influence cognitive function and could result in improved individual risk stratification. Brain connectome studies in adults and older children have shown that abnormal network properties may be useful as discriminative features and have exploited machine learning models for early diagnosis in a variety of neurological conditions. However, analogous studies in neonates are rare and with limited significant findings. In this paper, we propose an artificial neural network (ANN) framework for early prediction of cognitive deficits in very preterm infants based on functional connectome data from resting state fMRI. Specifically, we conducted feature selection via stacked sparse autoencoder and outcome prediction via support vector machine (SVM). The proposed ANN model was unsupervised learned using brain connectome data from 884 subjects in autism brain imaging data exchange database and SVM was cross-validated on 28 very preterm infants (born at 23-31 weeks of gestation and without brain injury; scanned at term-equivalent postmenstrual age). Using 90 regions of interests, we found that the ANN model applied to functional connectome data from very premature infants can predict cognitive outcome at 2 years of corrected age with an accuracy of 70.6% and area under receiver operating characteristic curve of 0.76. We also noted that several frontal lobe and somatosensory regions, significantly contributed to prediction of cognitive deficits 2 years later. Our work can be considered as a proof of concept for utilizing ANN models on functional connectome data to capture the individual variability inherent in the developing brains of preterm infants. The full potential of ANN will be realized and more robust conclusions drawn when applied to much larger neuroimaging datasets, as we plan to do.
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Affiliation(s)
- Lili He
- Perinatal Institute, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States; Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.
| | - Hailong Li
- Perinatal Institute, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Scott K Holland
- Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Weihong Yuan
- Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Mekibib Altaye
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Nehal A Parikh
- Perinatal Institute, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States; Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
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93
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Li Q, Wu X, Xu L, Chen K, Yao L. Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Cognitively Unimpaired Individuals Using Multi-feature Kernel Discriminant Dictionary Learning. Front Comput Neurosci 2018; 11:117. [PMID: 29375356 PMCID: PMC5767247 DOI: 10.3389/fncom.2017.00117] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Accepted: 12/19/2017] [Indexed: 01/03/2023] Open
Abstract
Accurate classification of either patients with Alzheimer's disease (AD) or patients with mild cognitive impairment (MCI), the prodromal stage of AD, from cognitively unimpaired (CU) individuals is important for clinical diagnosis and adequate intervention. The current study focused on distinguishing AD or MCI from CU based on the multi-feature kernel supervised within-Class-similar discriminative dictionary learning algorithm (MKSCDDL), which we introduced in a previous study, demonstrating that MKSCDDL had superior performance in face recognition. Structural magnetic resonance imaging (sMRI), fluorodeoxyglucose (FDG) positron emission tomography (PET), and florbetapir-PET data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were all included for classification of AD vs. CU, MCI vs. CU, as well as AD vs. MCI (113 AD patients, 110 MCI patients, and 117 CU subjects). By adopting MKSCDDL, we achieved a classification accuracy of 98.18% for AD vs. CU, 78.50% for MCI vs. CU, and 74.47% for AD vs. MCI, which in each instance was superior to results obtained using several other state-of-the-art approaches (MKL, JRC, mSRC, and mSCDDL). In addition, testing time results outperformed other high quality methods. Therefore, the results suggested that the MKSCDDL procedure is a promising tool for assisting early diagnosis of diseases using neuroimaging data.
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Affiliation(s)
- Qing Li
- Department of Electronics, College of Information Science and Technology, Beijing Normal University, Beijing, China
| | - Xia Wu
- Department of Electronics, College of Information Science and Technology, Beijing Normal University, Beijing, China.,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Lele Xu
- Department of Electronics, College of Information Science and Technology, Beijing Normal University, Beijing, China
| | - Kewei Chen
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, United States
| | - Li Yao
- Department of Electronics, College of Information Science and Technology, Beijing Normal University, Beijing, China.,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
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94
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Zhou C, Cheng Y, Ping L, Xu J, Shen Z, Jiang L, Shi L, Yang S, Lu Y, Xu X. Support Vector Machine Classification of Obsessive-Compulsive Disorder Based on Whole-Brain Volumetry and Diffusion Tensor Imaging. Front Psychiatry 2018; 9:524. [PMID: 30405461 PMCID: PMC6206075 DOI: 10.3389/fpsyt.2018.00524] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2018] [Accepted: 10/03/2018] [Indexed: 01/17/2023] Open
Abstract
Magnetic resonance imaging (MRI) methods have been used to detect cerebral anatomical distinction between obsessive-compulsive disorder (OCD) patients and healthy controls (HC). Machine learning approach allows for the possibility of discriminating patients on the individual level. However, few studies have used this automatic technique based on multiple modalities to identify potential biomarkers of OCD. High-resolution structural MRI and diffusion tensor imaging (DTI) data were acquired from 48 OCD patients and 45 well-matched HC. Gray matter volume (GMV), white matter volume (WMV), fractional anisotropy (FA), and mean diffusivity (MD) were extracted as four features were examined using support vector machine (SVM). Ten brain regions of each feature contributed most to the classification were also estimated. Using different algorithms, the classifier achieved accuracies of 72.08, 61.29, 80.65, and 77.42% for GMV, WMV, FA, and MD, respectively. The most discriminative gray matter regions that contributed to the classification were mainly distributed in the orbitofronto-striatal "affective" circuit, the dorsolateral, prefronto-striatal "executive" circuit and the cerebellum. For WMV feature and the two feature sets of DTI, the shared regions contributed the most to the discrimination mainly included the uncinate fasciculus, the cingulum in the hippocampus, corticospinal tract, as well as cerebellar peduncle. Based on whole-brain volumetry and DTI images, SVM algorithm revealed high accuracies for distinguishing OCD patients from healthy subjects at the individual level. Computer-aided method is capable of providing accurate diagnostic information and might provide a new perspective for clinical diagnosis of OCD.
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Affiliation(s)
- Cong Zhou
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China.,Postgraduate College, Kunming Medical University, Kunming, China
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Liangliang Ping
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China.,Postgraduate College, Kunming Medical University, Kunming, China
| | - Jian Xu
- Department of Internal Medicine, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zonglin Shen
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Linling Jiang
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Li Shi
- Department of Internal Medicine, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Shuran Yang
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yi Lu
- Department of Medical Imaging, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xiufeng Xu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
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95
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Shi J, Zheng X, Li Y, Zhang Q, Ying S. Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease. IEEE J Biomed Health Inform 2018; 22:173-183. [DOI: 10.1109/jbhi.2017.2655720] [Citation(s) in RCA: 222] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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96
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Dimitriadis SI, Liparas D, Tsolaki MN. Random forest feature selection, fusion and ensemble strategy: Combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI and alzheimer's disease patients: From the alzheimer's disease neuroimaging initiative (ADNI) database. J Neurosci Methods 2017; 302:14-23. [PMID: 29269320 DOI: 10.1016/j.jneumeth.2017.12.010] [Citation(s) in RCA: 73] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2017] [Revised: 12/14/2017] [Accepted: 12/17/2017] [Indexed: 02/06/2023]
Abstract
BACKGROUND In the era of computer-assisted diagnostic tools for various brain diseases, Alzheimer's disease (AD) covers a large percentage of neuroimaging research, with the main scope being its use in daily practice. However, there has been no study attempting to simultaneously discriminate among Healthy Controls (HC), early mild cognitive impairment (MCI), late MCI (cMCI) and stable AD, using features derived from a single modality, namely MRI. NEW METHOD Based on preprocessed MRI images from the organizers of a neuroimaging challenge,3 we attempted to quantify the prediction accuracy of multiple morphological MRI features to simultaneously discriminate among HC, MCI, cMCI and AD. We explored the efficacy of a novel scheme that includes multiple feature selections via Random Forest from subsets of the whole set of features (e.g. whole set, left/right hemisphere etc.), Random Forest classification using a fusion approach and ensemble classification via majority voting. From the ADNI database, 60 HC, 60 MCI, 60 cMCI and 60 CE were used as a training set with known labels. An extra dataset of 160 subjects (HC: 40, MCI: 40, cMCI: 40 and AD: 40) was used as an external blind validation dataset to evaluate the proposed machine learning scheme. RESULTS In the second blind dataset, we succeeded in a four-class classification of 61.9% by combining MRI-based features with a Random Forest-based Ensemble Strategy. We achieved the best classification accuracy of all teams that participated in this neuroimaging competition. COMPARISON WITH EXISTING METHOD(S) The results demonstrate the effectiveness of the proposed scheme to simultaneously discriminate among four groups using morphological MRI features for the very first time in the literature. CONCLUSIONS Hence, the proposed machine learning scheme can be used to define single and multi-modal biomarkers for AD.
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Affiliation(s)
- S I Dimitriadis
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK; Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK; MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff School of Medicine, Cardiff University, Cardiff, UK; Neuroinformatics Group, (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK; School of Psychology, Cardiff University, Cardiff, UK; 3rd Department of Neurology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece.
| | - Dimitris Liparas
- High Performance Computing Center Stuttgart (HLRS), University of Stuttgart, Stuttgart, Germany; Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.
| | - Magda N Tsolaki
- School of Psychology, Cardiff University, Cardiff, UK; 3rd Department of Neurology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
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97
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Machine Learning Applications to Resting-State Functional MR Imaging Analysis. Neuroimaging Clin N Am 2017; 27:609-620. [DOI: 10.1016/j.nic.2017.06.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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98
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Zhou J, Liu S, Ng KK, Wang J. Applications of Resting-State Functional Connectivity to Neurodegenerative Disease. Neuroimaging Clin N Am 2017; 27:663-683. [DOI: 10.1016/j.nic.2017.06.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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99
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Cosa A, Moreno A, Pacheco-Torres J, Ciccocioppo R, Hyytiä P, Sommer WH, Moratal D, Canals S. Multi-modal MRI classifiers identify excessive alcohol consumption and treatment effects in the brain. Addict Biol 2017; 22:1459-1472. [PMID: 27273582 DOI: 10.1111/adb.12418] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2015] [Revised: 04/22/2016] [Accepted: 05/13/2016] [Indexed: 12/14/2022]
Abstract
Robust neuroimaging markers of neuropsychiatric disorders have proven difficult to obtain. In alcohol use disorders, profound brain structural deficits can be found in severe alcoholic patients, but the heterogeneity of unimodal MRI measurements has so far precluded the identification of selective biomarkers, especially for early diagnosis. In the present work we used a combination of multiple MRI modalities to provide comprehensive and insightful descriptions of brain tissue microstructure. We performed a longitudinal experiment using Marchigian-Sardinian (msP) rats, an established model of chronic excessive alcohol consumption, and acquired multi-modal images before and after 1 month of alcohol consumption (6.8 ± 1.4 g/kg/day, mean ± SD), as well as after 1 week of abstinence with or without concomitant treatment with the antirelapse opioid antagonist naltrexone (2.5 mg/kg/day). We found remarkable sensitivity and selectivity to accurately classify brains affected by alcohol even after the relative short exposure period. One month drinking was enough to imprint a highly specific signature of alcohol consumption. Brain alterations were regionally specific and affected both gray and white matter and persisted into the early abstinence state without any detectable recovery. Interestingly, naltrexone treatment during early abstinence resulted in subtle brain changes that could be distinguished from non-treated abstinent brains, suggesting the existence of an intermediate state associated with brain recovery from alcohol exposure induced by medication. The presented framework is a promising tool for the development of biomarkers for clinical diagnosis of alcohol use disorders, with capacity to further inform about its progression and response to treatment.
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Affiliation(s)
- Alejandro Cosa
- Instituto de Neurociencias; Consejo Superior de Investigaciones Científicas and Universidad Miguel Hernández; Sant Joan d'Alacant Spain
- Center for Biomaterials and Tissue Engineering; Universitat Politècnica de València; Valencia Spain
| | - Andrea Moreno
- Instituto de Neurociencias; Consejo Superior de Investigaciones Científicas and Universidad Miguel Hernández; Sant Joan d'Alacant Spain
| | - Jesús Pacheco-Torres
- Instituto de Neurociencias; Consejo Superior de Investigaciones Científicas and Universidad Miguel Hernández; Sant Joan d'Alacant Spain
| | | | - Petri Hyytiä
- Department of Pharmacology, Faculty of Medicine; University of Helsinki; Helsinki Finland
| | - Wolfgang H. Sommer
- Department of Psychopharmacology, Central Institute of Mental Health; University of Heidelberg; Mannheim Germany
| | - David Moratal
- Center for Biomaterials and Tissue Engineering; Universitat Politècnica de València; Valencia Spain
| | - Santiago Canals
- Instituto de Neurociencias; Consejo Superior de Investigaciones Científicas and Universidad Miguel Hernández; Sant Joan d'Alacant Spain
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100
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Onoda K, Yada N, Ozasa K, Hara S, Yamamoto Y, Kitagaki H, Yamaguchi S. Can a Resting-State Functional Connectivity Index Identify Patients with Alzheimer's Disease and Mild Cognitive Impairment Across Multiple Sites? Brain Connect 2017; 7:391-400. [PMID: 28666395 DOI: 10.1089/brain.2017.0507] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Resting-state functional connectivity is one promising biomarker for Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, it is still not known how accurately network analysis identifies AD and MCI across multiple sites. In this study, we examined whether resting-state functional connectivity data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) could identify patients with AD and MCI at our site. We implemented an index based on the functional connectivity frequency distribution and compared performance for AD and MCI identification with multivoxel pattern analysis. The multivoxel pattern analysis using a connectivity map of the default mode network showed good performance, with an accuracy of 81.9% for AD and MCI identification within the ADNI, but the classification model obtained from the ADNI failed to classify AD, MCI, and healthy elderly adults from our site, with an accuracy of only 43.1%. In contrast, a functional connectivity index of the medial temporal lobe based on the frequency distribution showed moderate performance, with an accuracy of 76.5-80.3% for AD identification within the ADNI. The performance of this index was similar for our data, with an accuracy of 73.9-82.6%. The frequency distribution-based index of functional connectivity could be a good biomarker for AD across multiple sites.
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Affiliation(s)
- Keiichi Onoda
- Department of Neurology, Shimane University, Izumo, Japan
| | - Nobuhiro Yada
- Department of Radiology, Shimane University, Izumo, Japan
| | - Kentaro Ozasa
- Department of Radiology, Shimane University, Izumo, Japan
| | - Shinji Hara
- Department of Radiology, Shimane University, Izumo, Japan
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