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Li J, Li X, Chen F, Li W, Chen J, Zhang B. Studying the Alzheimer's disease continuum using EEG and fMRI in single-modality and multi-modality settings. Rev Neurosci 2024; 35:373-386. [PMID: 38157429 DOI: 10.1515/revneuro-2023-0098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 12/01/2023] [Indexed: 01/03/2024]
Abstract
Alzheimer's disease (AD) is a biological, clinical continuum that covers the preclinical, prodromal, and clinical phases of the disease. Early diagnosis and identification of the stages of Alzheimer's disease (AD) are crucial in clinical practice. Ideally, biomarkers should reflect the underlying process (pathological or otherwise), be reproducible and non-invasive, and allow repeated measurements over time. However, the currently known biomarkers for AD are not suitable for differentiating the stages and predicting the trajectory of disease progression. Some objective parameters extracted using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are widely applied to diagnose the stages of the AD continuum. While electroencephalography (EEG) has a high temporal resolution, fMRI has a high spatial resolution. Combined EEG and fMRI (EEG-fMRI) can overcome single-modality drawbacks and obtain multi-dimensional information simultaneously, and it can help explore the hemodynamic changes associated with the neural oscillations that occur during information processing. This technique has been used in the cognitive field in recent years. This review focuses on the different techniques available for studying the AD continuum, including EEG and fMRI in single-modality and multi-modality settings, and the possible future directions of AD diagnosis using EEG-fMRI.
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Affiliation(s)
- Jing Li
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Xin Li
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Futao Chen
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Weiping Li
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Jiu Chen
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Bing Zhang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
- Jiangsu Key Laboratory of Molecular Medicine, Nanjing, Jiangsu, 210008, China
- Institute of Brain Science, Nanjing University, Nanjing, Jiangsu, 210008, China
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2
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Li J, Yang F, Zhan F, Estin J, Iyer A, Zhao M, Niemeyer JE, Luo P, Li D, Lin W, Liou JY, Ma H, Schwartz TH. Mesoscopic mapping of hemodynamic responses and neuronal activity during pharmacologically induced interictal spikes in awake and anesthetized mice. J Cereb Blood Flow Metab 2024; 44:911-924. [PMID: 38230631 DOI: 10.1177/0271678x241226742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/18/2024]
Abstract
Imaging hemodynamic responses to interictal spikes holds promise for presurgical epilepsy evaluations. Understanding the hemodynamic response function is crucial for accurate interpretation. Prior interictal neurovascular coupling data primarily come from anesthetized animals, impacting reliability. We simultaneously monitored calcium fluctuations in excitatory neurons, hemodynamics, and local field potentials (LFP) during bicuculline-induced interictal events in both isoflurane-anesthetized and awake mice. Isoflurane significantly affected LFP amplitude but had little impact on the amplitude and area of the calcium signal. Anesthesia also dramatically blunted the amplitude and latency of the hemodynamic response, although not its area of spread. Cerebral blood volume change provided the best spatial estimation of excitatory neuronal activity in both states. Targeted silencing of the thalamus in awake mice failed to recapitulate the impact of anesthesia on hemodynamic responses suggesting that isoflurane's interruption of the thalamocortical loop did not contribute either to the dissociation between the LFP and the calcium signal nor to the alterations in interictal neurovascular coupling. The blood volume increase associated with interictal spikes represents a promising mapping signal in both the awake and anesthetized states.
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Affiliation(s)
- Jing Li
- Department of Neurology, The First Hospital of Jilin University, Changchun, China
- Department of Neurological Surgery and Brain and Mind Research Institute, Weill Cornell Medicine of Cornell University, New York Presbyterian Hospital, New York, USA
| | - Fan Yang
- Department of Neurology, The First Hospital of Jilin University, Changchun, China
- Department of Neurological Surgery and Brain and Mind Research Institute, Weill Cornell Medicine of Cornell University, New York Presbyterian Hospital, New York, USA
| | - Fengrui Zhan
- Department of Neurological Surgery and Brain and Mind Research Institute, Weill Cornell Medicine of Cornell University, New York Presbyterian Hospital, New York, USA
| | - Joshua Estin
- Department of Neurological Surgery and Brain and Mind Research Institute, Weill Cornell Medicine of Cornell University, New York Presbyterian Hospital, New York, USA
| | - Aditya Iyer
- Department of Anesthesiology, Weill Cornell Medicine, New York, USA
| | - Mingrui Zhao
- Department of Neurological Surgery and Brain and Mind Research Institute, Weill Cornell Medicine of Cornell University, New York Presbyterian Hospital, New York, USA
| | - James E Niemeyer
- Department of Neurological Surgery and Brain and Mind Research Institute, Weill Cornell Medicine of Cornell University, New York Presbyterian Hospital, New York, USA
| | - Peijuan Luo
- Department of Neurology, The First Hospital of Jilin University, Changchun, China
- Department of Neurological Surgery and Brain and Mind Research Institute, Weill Cornell Medicine of Cornell University, New York Presbyterian Hospital, New York, USA
| | - Dan Li
- Department of Radiology, The First Hospital of Jilin University, Changchun, China
| | - Weihong Lin
- Department of Neurology, The First Hospital of Jilin University, Changchun, China
| | - Jyun-You Liou
- Department of Anesthesiology, Weill Cornell Medicine, New York, USA
| | - Hongtao Ma
- Department of Neurological Surgery and Brain and Mind Research Institute, Weill Cornell Medicine of Cornell University, New York Presbyterian Hospital, New York, USA
| | - Theodore H Schwartz
- Department of Neurological Surgery and Brain and Mind Research Institute, Weill Cornell Medicine of Cornell University, New York Presbyterian Hospital, New York, USA
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3
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Wang M, Wang T, Li X, Yuan Y. Low-intensity ultrasound stimulation modulates cortical neurovascular coupling in an attention deficit hyperactivity disorder rat model. Cereb Cortex 2023; 33:11646-11655. [PMID: 37874023 DOI: 10.1093/cercor/bhad398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 09/26/2023] [Accepted: 09/27/2023] [Indexed: 10/25/2023] Open
Abstract
Attention deficit hyperactivity disorder is accompanied by changes in cranial nerve function and cerebral blood flow (CBF). Low-intensity ultrasound stimulation can modulate brain neural activity in attention deficit hyperactivity disorder. However, to date, the modulatory effects of low-intensity ultrasound stimulation on CBF and neurovascular coupling in attention deficit hyperactivity disorder have not been reported. To address this question, Sprague-Dawley, Wistar-Kyoto, and spontaneously hypertensive (attention deficit hyperactivity disorder (ADHD) rat model) rats were divided into the control and low-intensity ultrasound stimulation (LIUS) groups. Cortical electrical stimulation was used to induce cortical excitability in different types of rats, and a penetrable laser speckle contrast imaging (LSCI) system and electrodes were used to evaluate the electrical stimulation-induced CBF, cortical excitability, and neurovascular coupling in free-moving rats. The CBF, cortical excitability, and neurovascular coupling (NVC) under cortical electrical stimulation in the attention deficit hyperactivity disorder rats were significantly different from those in the Sprague-Dawley and Wistar-Kyoto rats. We also found that low-intensity ultrasound stimulation significantly interfered with the cortical excitability and neurovascular coupling induced by cortical electrical stimulation in rats with attention deficit hyperactivity disorder. Our findings suggest that neurovascular coupling is a potential biomarker for attention deficit hyperactivity disorder. Furthermore, low-intensity ultrasound stimulation can improve abnormal brain function in attention deficit hyperactivity disorder and lay a research foundation for its application in the clinical treatment of attention deficit hyperactivity disorder.
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Affiliation(s)
- Mengran Wang
- School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Yanshan University, Qinhuangdao 066004, China
| | - Teng Wang
- School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Yanshan University, Qinhuangdao 066004, China
| | - Xin Li
- School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
| | - Yi Yuan
- School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Yanshan University, Qinhuangdao 066004, China
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Liu Y, Zhang Y, Jiang Z, Kong W, Zou L. Exploring Neural Mechanisms of Reward Processing Using Coupled Matrix Tensor Factorization: A Simultaneous EEG-fMRI Investigation. Brain Sci 2023; 13:485. [PMID: 36979295 PMCID: PMC10046863 DOI: 10.3390/brainsci13030485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/06/2023] [Accepted: 03/08/2023] [Indexed: 03/14/2023] Open
Abstract
BACKGROUND It is crucial to understand the neural feedback mechanisms and the cognitive decision-making of the brain during the processing of rewards. Here, we report the first attempt for a simultaneous electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) study in a gambling task by utilizing tensor decomposition. METHODS First, the single-subject EEG data are represented as a third-order spectrogram tensor to extract frequency features. Next, the EEG and fMRI data are jointly decomposed into a superposition of multiple sources characterized by space-time-frequency profiles using coupled matrix tensor factorization (CMTF). Finally, graph-structured clustering is used to select the most appropriate model according to four quantitative indices. RESULTS The results clearly show that not only are the regions of interest (ROIs) found in other literature activated, but also the olfactory cortex and fusiform gyrus which are usually ignored. It is found that regions including the orbitofrontal cortex and insula are activated for both winning and losing stimuli. Meanwhile, regions such as the superior orbital frontal gyrus and anterior cingulate cortex are activated upon winning stimuli, whereas the inferior frontal gyrus, cingulate cortex, and medial superior frontal gyrus are activated upon losing stimuli. CONCLUSION This work sheds light on the reward-processing progress, provides a deeper understanding of brain function, and opens a new avenue in the investigation of neurovascular coupling via CMTF.
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Affiliation(s)
- Yuchao Liu
- School of Computer and Artificial Intelligence, Changzhou University, Changzhou 213164, China
| | - Yin Zhang
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, China
| | - Zhongyi Jiang
- School of Computer and Artificial Intelligence, Changzhou University, Changzhou 213164, China
| | - Wanzeng Kong
- College of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China
- Key Laboratory of Brain Machine Collaborative Intelligence Foundation of Zhejiang Province, Hangzhou 310018, China
| | - Ling Zou
- School of Computer and Artificial Intelligence, Changzhou University, Changzhou 213164, China
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, China
- Key Laboratory of Brain Machine Collaborative Intelligence Foundation of Zhejiang Province, Hangzhou 310018, China
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5
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Erol A, Soloukey C, Generowicz B, van Dorp N, Koekkoek S, Kruizinga P, Hunyadi B. Deconvolution of the Functional Ultrasound Response in the Mouse Visual Pathway Using Block-Term Decomposition. Neuroinformatics 2022; 21:247-265. [PMID: 36378467 PMCID: PMC10085969 DOI: 10.1007/s12021-022-09613-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/06/2022] [Indexed: 11/16/2022]
Abstract
Functional ultrasound (fUS) indirectly measures brain activity by detecting changes in cerebral blood volume following neural activation. Conventional approaches model such functional neuroimaging data as the convolution between an impulse response, known as the hemodynamic response function (HRF), and a binarized representation of the input signal based on the stimulus onsets, the so-called experimental paradigm (EP). However, the EP may not characterize the whole complexity of the activity-inducing signals that evoke the hemodynamic changes. Furthermore, the HRF is known to vary across brain areas and stimuli. To achieve an adaptable framework that can capture such dynamics of the brain function, we model the multivariate fUS time-series as convolutive mixtures and apply block-term decomposition on a set of lagged fUS autocorrelation matrices, revealing both the region-specific HRFs and the source signals that induce the hemodynamic responses. We test our approach on two mouse-based fUS experiments. In the first experiment, we present a single type of visual stimulus to the mouse, and deconvolve the fUS signal measured within the mouse brain's lateral geniculate nucleus, superior colliculus and visual cortex. We show that the proposed method is able to recover back the time instants at which the stimulus was displayed, and we validate the estimated region-specific HRFs based on prior studies. In the second experiment, we alter the location of the visual stimulus displayed to the mouse, and aim at differentiating the various stimulus locations over time by identifying them as separate sources.
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Affiliation(s)
- Aybüke Erol
- Circuits and Systems (CAS), Department of Microelectronics, Delft University of Technology, Mekelweg 5, Delft, 2628 CD, The Netherlands.
| | - Chagajeg Soloukey
- Center for Ultrasound and Brain imaging at Erasmus MC (CUBE), Department of Neuroscience, Erasmus Medical Center, Doctor Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands
| | - Bastian Generowicz
- Center for Ultrasound and Brain imaging at Erasmus MC (CUBE), Department of Neuroscience, Erasmus Medical Center, Doctor Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands
| | - Nikki van Dorp
- Center for Ultrasound and Brain imaging at Erasmus MC (CUBE), Department of Neuroscience, Erasmus Medical Center, Doctor Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands
| | - Sebastiaan Koekkoek
- Center for Ultrasound and Brain imaging at Erasmus MC (CUBE), Department of Neuroscience, Erasmus Medical Center, Doctor Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands
| | - Pieter Kruizinga
- Center for Ultrasound and Brain imaging at Erasmus MC (CUBE), Department of Neuroscience, Erasmus Medical Center, Doctor Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands
| | - Borbála Hunyadi
- Circuits and Systems (CAS), Department of Microelectronics, Delft University of Technology, Mekelweg 5, Delft, 2628 CD, The Netherlands
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6
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Abstract
PURPOSE OF REVIEW We review significant advances in epilepsy imaging in recent years. RECENT FINDINGS Structural MRI at 7T with optimization of acquisition and postacquisition image processing increases the diagnostic yield but artefactual findings remain a challenge. MRI analysis from multiple sites indicates different atrophy patterns and white matter diffusion abnormalities in temporal lobe and generalized epilepsies, with greater abnormalities close to the presumed seizure source. Structural and functional connectivity relate to seizure spread and generalization; longitudinal studies are needed to clarify the causal relationship of these associations. Diffusion MRI may help predict surgical outcome and network abnormalities extending beyond the epileptogenic zone. Three-dimensional multimodal imaging can increase the precision of epilepsy surgery, improve seizure outcome and reduce complications. Language and memory fMRI are useful predictors of postoperative deficits, and lead to risk minimization. FDG PET is useful for clinical studies and specific ligands probe the pathophysiology of neurochemical fluxes and receptor abnormalities. SUMMARY Improved structural MRI increases detection of abnormalities that may underlie epilepsy. Diffusion, structural and functional MRI indicate the widespread associations of epilepsy syndromes. These can assist stratification of surgical outcome and minimize risk. PET has continued utility clinically and for research into the pathophysiology of epilepsies.
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Affiliation(s)
- John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London
- MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, UK
| | - Karin Trimmel
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London
- MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, UK
- Department of Neurology, Medical University of Vienna, Vienna, Austria
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7
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Chatzichristos C, Kofidis E, Van Paesschen W, De Lathauwer L, Theodoridis S, Van Huffel S. Early soft and flexible fusion of electroencephalography and functional magnetic resonance imaging via double coupled matrix tensor factorization for multisubject group analysis. Hum Brain Mapp 2021; 43:1231-1255. [PMID: 34806255 PMCID: PMC8837580 DOI: 10.1002/hbm.25717] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 09/29/2021] [Accepted: 10/18/2021] [Indexed: 11/12/2022] Open
Abstract
Data fusion refers to the joint analysis of multiple datasets that provide different (e.g., complementary) views of the same task. In general, it can extract more information than separate analyses can. Jointly analyzing electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) measurements has been proved to be highly beneficial to the study of the brain function, mainly because these neuroimaging modalities have complementary spatiotemporal resolution: EEG offers good temporal resolution while fMRI is better in its spatial resolution. The EEG–fMRI fusion methods that have been reported so far ignore the underlying multiway nature of the data in at least one of the modalities and/or rely on very strong assumptions concerning the relation of the respective datasets. For example, in multisubject analysis, it is commonly assumed that the hemodynamic response function is a priori known for all subjects and/or the coupling across corresponding modes is assumed to be exact (hard). In this article, these two limitations are overcome by adopting tensor models for both modalities and by following soft and flexible coupling approaches to implement the multimodal fusion. The obtained results are compared against those of parallel independent component analysis and hard coupling alternatives, with both synthetic and real data (epilepsy and visual oddball paradigm). Our results demonstrate the clear advantage of using soft and flexible coupled tensor decompositions in scenarios that do not conform with the hard coupling assumption.
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Affiliation(s)
- Christos Chatzichristos
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Eleftherios Kofidis
- Department of Statistics and Insurance Science, University of Piraeus, Piraeus, Greece.,Computer Technology Institute and Press "Diophantus" (CTI), Patras, Greece
| | | | - Lieven De Lathauwer
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium.,Engineering, Science and Technology, KU Leuven Kulak, Kortrijk, Belgium
| | - Sergios Theodoridis
- Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece.,Department of Electronic Systems, University of Aalborg, Aalborg, Denmark
| | - Sabine Van Huffel
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
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8
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Qi S, Silva RF, Zhang D, Plis SM, Miller R, Vergara VM, Jiang R, Zhi D, Sui J, Calhoun VD. Three-way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data. Hum Brain Mapp 2021; 43:1280-1294. [PMID: 34811846 PMCID: PMC8837596 DOI: 10.1002/hbm.25720] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 10/17/2021] [Accepted: 11/07/2021] [Indexed: 01/24/2023] Open
Abstract
Advances in imaging acquisition techniques allow multiple imaging modalities to be collected from the same subject. Each individual modality offers limited yet unique views of the functional, structural, or dynamic temporal features of the brain. Multimodal fusion provides effective ways to leverage these complementary perspectives from multiple modalities. However, the majority of current multimodal fusion approaches involving functional magnetic resonance imaging (fMRI) are limited to 3D feature summaries that do not incorporate its rich temporal information. Thus, we propose a novel three‐way parallel group independent component analysis (pGICA) fusion method that incorporates the first‐level 4D fMRI data (temporal information included) by parallelizing group ICA into parallel ICA via a unified optimization framework. A new variability matrix was defined to capture subject‐wise functional variability and then link it to the mixing matrices of the other two modalities. Simulation results show that the three‐way pGICA provides highly accurate cross‐modality linkage estimation under both weakly and strongly correlated conditions, as well as comparable source estimation under different noise levels. Results using real brain imaging data identified one linked functional–structural–diffusion component associated to differences between schizophrenia and controls. This was replicated in an independent cohort, and the identified components were also correlated with major cognitive domains. Functional network connectivity revealed visual–subcortical and default mode‐cerebellum pairs that discriminate between schizophrenia and controls. Overall, both simulation and real data results support the use of three‐way pGICA to identify multimodal spatiotemporal links and to pursue the study of brain disorders under a single unifying multimodal framework.
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Affiliation(s)
- Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Rogers F Silva
- Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, Georgia, USA
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Sergey M Plis
- Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, Georgia, USA
| | - Robyn Miller
- Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, Georgia, USA
| | - Victor M Vergara
- Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, Georgia, USA
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, Georgia, USA
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9
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Suarez A, Valdés-Hernández PA, Bernal B, Dunoyer C, Khoo HM, Bosch-Bayard J, Riera JJ. Identification of Negative BOLD Responses in Epilepsy Using Windkessel Models. Front Neurol 2021; 12:659081. [PMID: 34690906 PMCID: PMC8531269 DOI: 10.3389/fneur.2021.659081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 09/03/2021] [Indexed: 11/16/2022] Open
Abstract
Alongside positive blood oxygenation level–dependent (BOLD) responses associated with interictal epileptic discharges, a variety of negative BOLD responses (NBRs) are typically found in epileptic patients. Previous studies suggest that, in general, up to four mechanisms might underlie the genesis of NBRs in the brain: (i) neuronal disruption of network activity, (ii) altered balance of neurometabolic/vascular couplings, (iii) arterial blood stealing, and (iv) enhanced cortical inhibition. Detecting and classifying these mechanisms from BOLD signals are pivotal for the improvement of the specificity of the electroencephalography–functional magnetic resonance imaging (EEG-fMRI) image modality to identify the seizure-onset zones in refractory local epilepsy. This requires models with physiological interpretation that furnish the understanding of how these mechanisms are fingerprinted by their BOLD responses. Here, we used a Windkessel model with viscoelastic compliance/inductance in combination with dynamic models of both neuronal population activity and tissue/blood O2 to classify the hemodynamic response functions (HRFs) linked to the above mechanisms in the irritative zones of epileptic patients. First, we evaluated the most relevant imprints on the BOLD response caused by variations of key model parameters. Second, we demonstrated that a general linear model is enough to accurately represent the four different types of NBRs. Third, we tested the ability of a machine learning classifier, built from a simulated ensemble of HRFs, to predict the mechanism underlying the BOLD signal from irritative zones. Cross-validation indicates that these four mechanisms can be classified from realistic fMRI BOLD signals. To demonstrate proof of concept, we applied our methodology to EEG-fMRI data from five epileptic patients undergoing neurosurgery, suggesting the presence of some of these mechanisms. We concluded that a proper identification and interpretation of NBR mechanisms in epilepsy can be performed by combining general linear models and biophysically inspired models.
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Affiliation(s)
- Alejandro Suarez
- Neuronal Mass Dynamics Laboratory, Florida International University, Miami, FL, United States
| | | | - Byron Bernal
- Nicklaus Children Hospital, Miami, FL, United States
| | | | - Hui Ming Khoo
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada.,Department of Neurosurgery, Osaka University, Suita, Japan
| | - Jorge Bosch-Bayard
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Jorge J Riera
- Neuronal Mass Dynamics Laboratory, Florida International University, Miami, FL, United States
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10
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Dron N, Navarro-Cáceres M, Chin RF, Escudero J. Functional, structural, and phenotypic data fusion to predict developmental scores of pre-school children based on Canonical Polyadic Decomposition. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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11
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Labounek R, Wu Z, Bridwell DA, Brázdil M, Jan J, Nestrašil I. Blind Visualization of Task-Related Networks From Visual Oddball Simultaneous EEG-fMRI Data: Spectral or Spatiospectral Model? Front Neurol 2021; 12:644874. [PMID: 33981283 PMCID: PMC8107237 DOI: 10.3389/fneur.2021.644874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 03/22/2021] [Indexed: 02/01/2023] Open
Abstract
Various disease conditions can alter EEG event-related responses and fMRI-BOLD signals. We hypothesized that event-related responses and their clinical alterations are imprinted in the EEG spectral domain as event-related (spatio)spectral patterns (ERSPat). We tested four EEG-fMRI fusion models utilizing EEG power spectra fluctuations (i.e., absolute spectral model - ASM; relative spectral model - RSM; absolute spatiospectral model - ASSM; and relative spatiospectral model - RSSM) for fully automated and blind visualization of task-related neural networks. Two (spatio)spectral patterns (high δ 4 band and low β 1 band) demonstrated significant negative linear relationship (p FWE < 0.05) to the frequent stimulus and three patterns (two low δ 2 and δ 3 bands, and narrow θ 1 band) demonstrated significant positive relationship (p < 0.05) to the target stimulus. These patterns were identified as ERSPats. EEG-fMRI F-map of each δ 4 model showed strong engagement of insula, cuneus, precuneus, basal ganglia, sensory-motor, motor and dorsal part of fronto-parietal control (FPCN) networks with fast HRF peak and noticeable trough. ASM and RSSM emphasized spatial statistics, and the relative power amplified the relationship to the frequent stimulus. For the δ 4 model, we detected a reduced HRF peak amplitude and a magnified HRF trough amplitude in the frontal part of the FPCN, default mode network (DMN) and in the frontal white matter. The frequent-related β 1 patterns visualized less significant and distinct suprathreshold spatial associations. Each θ 1 model showed strong involvement of lateralized left-sided sensory-motor and motor networks with simultaneous basal ganglia co-activations and reduced HRF peak and amplified HRF trough in the frontal part of the FPCN and DMN. The ASM θ 1 model preserved target-related EEG-fMRI associations in the dorsal part of the FPCN. For δ 4, β 1, and θ 1 bands, all models provided high local F-statistics in expected regions. The most robust EEG-fMRI associations were observed for ASM and RSSM.
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Affiliation(s)
- René Labounek
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, United States
| | - Zhuolin Wu
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, United States
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | | | - Milan Brázdil
- Central European Institute of Technology, Masaryk University, Brno, Czechia
| | - Jiří Jan
- Department of Biomedical Engineering, Brno University of Technology, Brno, Czechia
| | - Igor Nestrašil
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, United States
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States
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