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Gao Y, Zhu Z, Fang F, Zhang Y, Meng M. EEG emotion recognition based on data-driven signal auto-segmentation and feature fusion. J Affect Disord 2024; 361:356-366. [PMID: 38885847 DOI: 10.1016/j.jad.2024.06.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 05/27/2024] [Accepted: 06/14/2024] [Indexed: 06/20/2024]
Abstract
Pattern recognition based on network connections has recently been applied to the brain-computer interface (BCI) research, offering new ideas for emotion recognition using Electroencephalogram (EEG) signal. However unified standards are currently lacking for selecting emotional signals in emotion recognition research, and potential associations between activation differences in brain regions and network connectivity pattern are often being overlooked. To bridge this technical gap, a data-driven signal auto-segmentation and feature fusion algorithm (DASF) is proposed in this paper. First, the Phase Locking Value (PLV) method was used to construct the brain functional adjacency matrix of each subject, and the dynamic brain functional network across subjects was then constructed. Next, tucker decomposition was performed and the Grassmann distance of the connectivity submatrix was calculated. Subsequently, different brain network states were distinguished and signal segments under emotional states were automatically extract using data-driven methods. Then, tensor sparse representation was adopted on the intercepted EEG signals to effectively extract functional connections under different emotional states. Finally, power-distribution related features (differential entropy and energy feature) and brain functional connection features were effectively combined for classification using the support vector machines (SVM) classifier. The proposed method was validated on ERN and DEAP datasets. The single-feature emotion classification accuracy of 86.57 % and 87.74 % were achieved on valence and arousal dimensions, respectively. The accuracy of the proposed feature fusion method was achieved at 89.14 % and 89.65 %, accordingly, demonstrating an improvement in emotion recognition accuracy. The results demonstrated the superior classification performance of the proposed data-driven signal auto-segmentation and feature fusion algorithm in emotion recognition compared to state-of-the-art classification methods.
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Affiliation(s)
- Yunyuan Gao
- College of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Zehao Zhu
- College of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Feng Fang
- Department of Biomedical Engineering, University of Houston, Houston, USA
| | - Yingchun Zhang
- Department of Biomedical Engineering, University of Houston, Houston, USA
| | - Ming Meng
- College of Automation, Hangzhou Dianzi University, Hangzhou, China.
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2
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Demeter DV, Greene DJ. The promise of precision functional mapping for neuroimaging in psychiatry. Neuropsychopharmacology 2024:10.1038/s41386-024-01941-z. [PMID: 39085426 DOI: 10.1038/s41386-024-01941-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 06/14/2024] [Accepted: 07/17/2024] [Indexed: 08/02/2024]
Abstract
Precision functional mapping (PFM) is a neuroimaging approach to reliably estimate metrics of brain function from individual people via the collection of large amounts of fMRI data (hours per person). This method has revealed much about the inter-individual variation of functional brain networks. While standard group-level studies, in which we average brain measures across groups of people, are important in understanding the generalizable neural underpinnings of neuropsychiatric disorders, many disorders are heterogeneous in nature. This heterogeneity often complicates clinical care, leading to patient uncertainty when considering prognosis or treatment options. We posit that PFM methods may help streamline clinical care in the future, fast-tracking the choice of personalized treatment that is most compatible with the individual. In this review, we provide a history of PFM studies, foundational results highlighting the benefits of PFM methods in the pursuit of an advanced understanding of individual differences in functional network organization, and possible avenues where PFM can contribute to clinical translation of neuroimaging research results in the way of personalized treatment in psychiatry.
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Affiliation(s)
- Damion V Demeter
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA.
| | - Deanna J Greene
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA.
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Pan C, Zhang Q, Zhu Y, Kong S, Liu J, Zhang C, Wang F, Zhang X. Module control of network analysis in psychopathology. iScience 2024; 27:110302. [PMID: 39045106 PMCID: PMC11263636 DOI: 10.1016/j.isci.2024.110302] [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: 02/06/2024] [Revised: 04/12/2024] [Accepted: 06/14/2024] [Indexed: 07/25/2024] Open
Abstract
The network approach to characterizing psychopathology departs from traditional latent categorical and dimensional approaches. Causal interplay among symptoms contributed to dynamic psychopathology system. Therefore, analyzing the symptom clusters is critical for understanding mental disorders. Furthermore, despite extensive research studying the topological features of symptom networks, the control relationships between symptoms remain largely unclear. Here, we present a novel systematizing concept, module control, to analyze the control principle of the symptom network at a module level. We introduce Module Control Network (MCN) to identify key modules that regulate the network's behavior. By applying our approach to a multivariate psychological dataset, we discover that non-emotional modules, such as sleep-related and stress-related modules, are the primary controlling modules in the symptom network. Our findings indicate that module control can expose central symptom cluster governing psychopathology network, offering novel insights into the underlying mechanisms of mental disorders and individualized approach to psychological interventions.
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Affiliation(s)
- Chunyu Pan
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210024, China
- Northeastern University, Shenyang, Liaoning 110169, China
| | - Quan Zhang
- Vanke School of Public Health, Tsinghua University, Beijing 100084, China
- Institute for Healthy China, Tsinghua University, Beijing 100084, China
| | - Yue Zhu
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210024, China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu 210024, China
| | - Shengzhou Kong
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210024, China
| | - Juan Liu
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210024, China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu 210024, China
| | | | - Fei Wang
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210024, China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu 210024, China
- Department of Mental Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Xizhe Zhang
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 210033, China
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Cash RFH, Zalesky A. Personalized and Circuit-Based Transcranial Magnetic Stimulation: Evidence, Controversies, and Opportunities. Biol Psychiatry 2024; 95:510-522. [PMID: 38040047 DOI: 10.1016/j.biopsych.2023.11.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 11/13/2023] [Accepted: 11/18/2023] [Indexed: 12/03/2023]
Abstract
The development of neuroimaging methodologies to map brain connectivity has transformed our understanding of psychiatric disorders, the distributed effects of brain stimulation, and how transcranial magnetic stimulation can be best employed to target and ameliorate psychiatric symptoms. In parallel, neuroimaging research has revealed that higher-order brain regions such as the prefrontal cortex, which represent the most common therapeutic brain stimulation targets for psychiatric disorders, show some of the highest levels of interindividual variation in brain connectivity. These findings provide the rationale for personalized target site selection based on person-specific brain network architecture. Recent advances have made it possible to determine reproducible personalized targets with millimeter precision in clinically tractable acquisition times. These advances enable the potential advantages of spatially personalized transcranial magnetic stimulation targeting to be evaluated and translated to basic and clinical applications. In this review, we outline the motivation for target site personalization, preliminary support (mostly in depression), convergent evidence from other brain stimulation modalities, and generalizability beyond depression and the prefrontal cortex. We end by detailing methodological recommendations, controversies, and notable alternatives. Overall, while this research area appears highly promising, the value of personalized targeting remains unclear, and dedicated large prospective randomized clinical trials using validated methodology are critical.
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Affiliation(s)
- Robin F H Cash
- Melbourne Neuropsychiatry Centre and Department of Biomedical Engineering, University of Melbourne, Parkville, Victoria, Australia.
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre and Department of Biomedical Engineering, University of Melbourne, Parkville, Victoria, Australia
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Dengler J, Deck BL, Stoll H, Fernandez-Nunez G, Kelkar AS, Rich RR, Erickson BA, Erani F, Faseyitan O, Hamilton RH, Medaglia JD. Enhancing cognitive control with transcranial magnetic stimulation in subject-specific frontoparietal networks. Cortex 2024; 172:141-158. [PMID: 38330778 DOI: 10.1016/j.cortex.2023.11.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 10/26/2023] [Accepted: 11/28/2023] [Indexed: 02/10/2024]
Abstract
BACKGROUND Cognitive control processes, including those involving frontoparietal networks, are highly variable between individuals, posing challenges to basic and clinical sciences. While distinct frontoparietal networks have been associated with specific cognitive control functions such as switching, inhibition, and working memory updating functions, there have been few basic tests of the role of these networks at the individual level. METHODS To examine the role of cognitive control at the individual level, we conducted a within-subject excitatory transcranial magnetic stimulation (TMS) study in 19 healthy individuals that targeted intrinsic ("resting") frontoparietal networks. Person-specific intrinsic networks were identified with resting state functional magnetic resonance imaging scans to determine TMS targets. The participants performed three cognitive control tasks: an adapted Navon figure-ground task (requiring set switching), n-back (working memory), and Stroop color-word (inhibition). OBJECTIVE Hypothesis: We predicted that stimulating a network associated with externally oriented control [the "FPCN-B" (fronto-parietal control network)] would improve performance on the set switching and working memory task relative to a network associated with attention (the Dorsal Attention Network, DAN) and cranial vertex in a full within-subjects crossover design. RESULTS We found that set switching performance was enhanced by FPCN-B stimulation along with some evidence of enhancement in the higher-demand n-back conditions. CONCLUSION Higher task demands or proactive control might be a distinguishing role of the FPCN-B, and personalized intrinsic network targeting is feasible in TMS designs.
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Affiliation(s)
- Julia Dengler
- School of Biomedical Engineering Science and Health Systems, Drexel University, Philadelphia, PA, USA
| | - Benjamin L Deck
- Department of Psychological & Brain Sciences, Drexel University, Philadelphia, PA, USA
| | - Harrison Stoll
- Department of Psychological & Brain Sciences, Drexel University, Philadelphia, PA, USA
| | | | - Apoorva S Kelkar
- Department of Psychological & Brain Sciences, Drexel University, Philadelphia, PA, USA
| | - Ryan R Rich
- Department of Psychological & Brain Sciences, Drexel University, Philadelphia, PA, USA
| | - Brian A Erickson
- Department of Psychological & Brain Sciences, Drexel University, Philadelphia, PA, USA
| | - Fareshte Erani
- Department of Psychological & Brain Sciences, Drexel University, Philadelphia, PA, USA
| | | | - Roy H Hamilton
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - John D Medaglia
- Department of Psychological & Brain Sciences, Drexel University, Philadelphia, PA, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
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Fang F, Teixeira AL, Li R, Zou L, Zhang Y. The control patterns of affective processing and cognitive reappraisal: insights from brain controllability analysis. Cereb Cortex 2024; 34:bhad500. [PMID: 38216523 DOI: 10.1093/cercor/bhad500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/03/2023] [Accepted: 12/05/2023] [Indexed: 01/14/2024] Open
Abstract
Perceiving and modulating emotions is vital for cognitive function and is often impaired in neuropsychiatric conditions. Current tools for evaluating emotional dysregulation suffer from subjectivity and lack of precision, especially when it comes to understanding emotion from a regulatory or control-based perspective. To address these limitations, this study leverages an advanced methodology known as functional brain controllability analysis. We simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data from 17 healthy subjects engaged in emotion processing and regulation tasks. We then employed a novel EEG/fMRI integration technique to reconstruct cortical activity in a high spatiotemporal resolution manner. Subsequently, we conducted functional brain controllability analysis to explore the neural network control patterns underlying different emotion conditions. Our findings demonstrated that the dorsolateral and ventrolateral prefrontal cortex exhibited increased controllability during the processing and regulation of negative emotions compared to processing of neutral emotion. Besides, the anterior cingulate cortex was notably more active in managing negative emotion than in either controlling neutral emotion or regulating negative emotion. Finally, the posterior parietal cortex emerged as a central network controller for the regulation of negative emotion. This study offers valuable insights into the cortical control mechanisms that support emotion perception and regulation.
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Affiliation(s)
- Feng Fang
- Department of Biomedical Engineering, University of Houston, Houston, TX, United States
| | - Antonio L Teixeira
- Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Rihui Li
- Center for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macau, China
| | - Ling Zou
- School of Information Science and Engineering, Changzhou University, Changzhou, Jiangsu, China
| | - Yingchun Zhang
- Department of Biomedical Engineering, University of Houston, Houston, TX, United States
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Fang F, Cammon J, Li R, Zhang Y. Test and re-test reliability of optimal stimulation targets and parameters for personalized neuromodulation. Front Neurosci 2023; 17:1153786. [PMID: 37250412 PMCID: PMC10213310 DOI: 10.3389/fnins.2023.1153786] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 04/25/2023] [Indexed: 05/31/2023] Open
Abstract
Protocols have been proposed to optimize neuromodulation targets and parameters to increase treatment efficacies for different neuropsychiatric diseases. However, no study has investigated the temporal effects of optimal neuromodulation targets and parameters simultaneously via exploring the test-retest reliability of the optimal neuromodulation protocols. In this study, we employed a publicly available structural and resting-state functional magnetic resonance imaging (fMRI) dataset to investigate the temporal effects of the optimal neuromodulation targets and parameters inferred from our customized neuromodulation protocol and examine the test-retest reliability over scanning time. 57 healthy young subjects were included in this study. Each subject underwent a repeated structural and resting state fMRI scan in two visits with an interval of 6 weeks between two scanning visits. Brain controllability analysis was performed to determine the optimal neuromodulation targets and optimal control analysis was further applied to calculate the optimal neuromodulation parameters for specific brain states transition. Intra-class correlation (ICC) measure was utilized to examine the test-retest reliability. Our results demonstrated that the optimal neuromodulation targets and parameters had excellent test-retest reliability (both ICCs > 0.80). The test-retest reliability of model fitting accuracies between the actual final state and the simulated final state also showed a good test-retest reliability (ICC > 0.65). Our results indicated the validity of our customized neuromodulation protocol to reliably identify the optimal neuromodulation targets and parameters between visits, which may be reliably extended to optimize the neuromodulation protocols to efficiently treat different neuropsychiatric disorders.
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Affiliation(s)
- Feng Fang
- Department of Biomedical Engineering, University of Houston, Houston, TX, United States
| | - Jared Cammon
- Department of Biomedical Engineering, University of Houston, Houston, TX, United States
| | - Rihui Li
- Department of Psychiatry and Behavioral Sciences, Center for Interdisciplinary Brain Sciences Research, Stanford University, Stanford, CA, United States
| | - Yingchun Zhang
- Department of Biomedical Engineering, University of Houston, Houston, TX, United States
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Alavi SMM, Vila-Rodriguez F, Mahdi A, Goetz SM. Closed-loop optimal and automatic tuning of pulse amplitude and width in EMG-guided controllable transcranial magnetic stimulation. Biomed Eng Lett 2023; 13:119-127. [PMID: 37124104 PMCID: PMC10130260 DOI: 10.1007/s13534-022-00259-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 11/26/2022] [Accepted: 12/20/2022] [Indexed: 01/01/2023] Open
Abstract
This paper proposes an efficient algorithm for automatic and optimal tuning of pulse amplitude and width for sequential parameter estimation (SPE) of the neural membrane time constant and input-output (IO) curve parameters in closed-loop electromyography-guided (EMG-guided) controllable transcranial magnetic stimulation (cTMS). The proposed SPE is performed by administering a train of optimally tuned TMS pulses and updating the estimations until a stopping rule is satisfied or the maximum number of pulses is reached. The pulse amplitude is computed by the Fisher information maximization. The pulse width is chosen by maximizing a normalized depolarization factor, which is defined to separate the optimization and tuning of the pulse amplitude and width. The normalized depolarization factor maximization identifies the critical pulse width, which is an important parameter in the identifiability analysis, without any prior neurophysiological or anatomical knowledge of the neural membrane. The effectiveness of the proposed algorithm is evaluated through simulation. The results confirm satisfactory estimation of the membrane time constant and IO curve parameters for the simulation case. By defining the stopping rule based on the satisfaction of the convergence criterion with tolerance of 0.01 for 5 consecutive times for all parameters, the IO curve parameters are estimated with 52 TMS pulses, with absolute relative estimation errors (AREs) of less than 7%. The membrane time constant is estimated with 0.67% ARE, and the pulse width value tends to the critical pulse width with 0.16% ARE with 52 TMS pulses. The results confirm that the pulse width and amplitude can be tuned optimally and automatically to estimate the membrane time constant and IO curve parameters in real-time with closed-loop EMG-guided cTMS.
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Affiliation(s)
- S. M. Mahdi Alavi
- The Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, BC Canada
| | - Fidel Vila-Rodriguez
- The Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, BC Canada
| | - Adam Mahdi
- Surrey Institute for People-Centred AI, University of Surrey, Surrey, UK
- Oxford Internet Institute, University of Oxford, Oxford, UK
| | - Stefan M. Goetz
- Department of Engineering, University of Cambridge, Cambridge, UK
- Department of Psychiatry & Behavioral Sciences, Duke University, Durham, NC USA
- Department of Electrical and Computer Engineering, Duke University, Durham, NC USA
- Department of Neurosurgery, Duke University, Durham, NC USA
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