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Liu Y, Wang M, Yu X, Han Y, Jiang J, Yan Z. An effective and robust lattice Boltzmann model guided by atlas for hippocampal subregions segmentation. Med Phys 2024; 51:4105-4120. [PMID: 38373278 DOI: 10.1002/mp.16984] [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: 09/05/2023] [Revised: 12/19/2023] [Accepted: 01/24/2024] [Indexed: 02/21/2024] Open
Abstract
BACKGROUND Given the varying vulnerability of the rostral and caudal regions of the hippocampus to neuropathology in the Alzheimer's disease (AD) continuum, accurately assessing structural changes in these subregions is crucial for early AD detection. The development of reliable and robust automatic segmentation methods for hippocampal subregions (HS) is of utmost importance. OBJECTIVE Our aim is to propose and validate a HS segmentation model that is both training-free and highly generalizable. This method should exhibit comparable accuracy and efficiency to state-of-the-art techniques. The segmented HS can serve as a biomarker for studying the progression of AD. METHODS We utilized the functional magnetic resonance imaging of the Brain's Integrated Registration and Segmentation Tool (FIRST) to segment the entire hippocampus. By intersecting the segmentation results with the Brainnetome (BN) atlas, we obtained coarse segmentation of the four HS regions. This coarse segmentation was then employed as a shape prior term in the lattice Boltzmann (LB) model, as well as for initializing contours. Additionally, image gradients and local gray levels were integrated into the external force terms of the LB model to refine the coarse segmentation results. We assessed the segmentation accuracy of the model using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and evaluated the potential of the segmentation results as AD biomarkers on both the ADNI and Xuanwu datasets. RESULTS The median Dice similarity coefficients (DSC) for the left caudal, right caudal, left rostral, and right rostral hippocampus were 0.87, 0.88, 0.88, and 0.89, respectively. The proportion of segmentation results with a DSC exceeding 0.8 was 77%, 78%, 77%, and 94% for the respective regions. In terms of volume, the correlation coefficients between the segmentation results of the four HS regions and the gold standard were 0.95, 0.93, 0.96, and 0.96, respectively. Regarding asymmetry, the correlation coefficient between the segmentation result's right caudal minus left caudal and the corresponding gold standard was 0.91, while for right rostral minus left rostral, it was 0.93. Over time, we observed a decline in the volumes of the four HS regions and the total hippocampal volume of mild cognitive impairment (MCI) converters. Analysis of inter-group differences revealed that, except for the right rostral region in the ADNI dataset, the p-values for the four HS regions in the normal controls (NC), MCI, and AD groups from both datasets were all below 0.05. The right caudal hippocampal volume demonstrated correlation coefficients of 0.47 and 0.43 with the mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA), respectively. Similarly, the left rostral hippocampal volume showed correlation coefficients of 0.50 and 0.58 with MMSE and MoCA, respectively. CONCLUSIONS Our framework allows for direct application to different brain magnetic resonance (MR) datasets without the need for training. It eliminates the requirement for complex image preprocessing steps while achieving segmentation accuracy comparable to deep learning (DL) methods even with small sample sizes. Compared to traditional active contour models (ACM) and atlas-based methods, our approach exhibits significant speed advantages. The segmented HS regions hold promise as potential biomarkers for studying the progression of AD.
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Affiliation(s)
- Yingqian Liu
- School of Communication and Information Engineering, Shanghai University, Shanghai, China
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, China
- School of Electrical Engineering, Shandong University of Aeronautics, Binzhou, China
| | - Min Wang
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, China
| | - Xianfeng Yu
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Jiehui Jiang
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, China
| | - Zhuangzhi Yan
- School of Communication and Information Engineering, Shanghai University, Shanghai, China
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, China
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Lim M, Kim DJ, Nascimento TD, DaSilva AF. High-definition tDCS over primary motor cortex modulates brain signal variability and functional connectivity in episodic migraine. Clin Neurophysiol 2024; 161:101-111. [PMID: 38460220 DOI: 10.1016/j.clinph.2024.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 02/08/2024] [Accepted: 02/10/2024] [Indexed: 03/11/2024]
Abstract
OBJECTIVE This study investigated how high-definition transcranial direct current stimulation (HD-tDCS) over the primary motor cortex (M1) affects brain signal variability and functional connectivity in the trigeminal pain pathway, and their association with changes in migraine attacks. METHODS Twenty-five episodic migraine patients were randomized for ten daily sessions of active or sham M1 HD-tDCS. Resting-state blood-oxygenation-level-dependent (BOLD) signal variability and seed-based functional connectivity were assessed pre- and post-treatment. A mediation analysis was performed to test whether BOLD signal variability mediates the relationship between treatment group and moderate-to-severe headache days. RESULTS The active M1 HD-tDCS group showed reduced BOLD variability in the spinal trigeminal nucleus (SpV) and thalamus, but increased variability in the rostral anterior cingulate cortex (rACC) compared to the sham group. Connectivity decreased between medial pulvinar-temporal pole, medial dorsal-precuneus, and the ventral posterior medial nucleus-SpV, but increased between the rACC-amygdala, and the periaqueductal gray-parahippocampal gyrus. Changes in medial pulvinar variability mediated the reduction in moderate-to-severe headache days at one-month post-treatment. CONCLUSIONS M1 HD-tDCS alters BOLD signal variability and connectivity in the trigeminal somatosensory and modulatory pain system, potentially alleviating migraine headache attacks. SIGNIFICANCE M1 HD-tDCS realigns brain signal variability and connectivity in migraineurs closer to healthy control levels.
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Affiliation(s)
- Manyoel Lim
- Food Processing Research Group, Korea Food Research Institute, Wanju-gun, Jeollabuk-do 55365, Republic of Korea; Department of Biologic and Materials Sciences & Prosthodontics, University of Michigan School of Dentistry, Ann Arbor, MI 48109, USA
| | - Dajung J Kim
- Department of Biologic and Materials Sciences & Prosthodontics, University of Michigan School of Dentistry, Ann Arbor, MI 48109, USA
| | - Thiago D Nascimento
- Department of Biologic and Materials Sciences & Prosthodontics, University of Michigan School of Dentistry, Ann Arbor, MI 48109, USA
| | - Alexandre F DaSilva
- Department of Biologic and Materials Sciences & Prosthodontics, University of Michigan School of Dentistry, Ann Arbor, MI 48109, USA; Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA.
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Lu Y, Cui Y, Cao L, Dong Z, Cheng L, Wu W, Wang C, Liu X, Liu Y, Zhang B, Li D, Zhao B, Wang H, Li K, Ma L, Shi W, Li W, Ma Y, Du Z, Zhang J, Xiong H, Luo N, Liu Y, Hou X, Han J, Sun H, Cai T, Peng Q, Feng L, Wang J, Paxinos G, Yang Z, Fan L, Jiang T. Macaque Brainnetome Atlas: A multifaceted brain map with parcellation, connection, and histology. Sci Bull (Beijing) 2024:S2095-9273(24)00187-7. [PMID: 38580551 DOI: 10.1016/j.scib.2024.03.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/18/2024] [Accepted: 03/11/2024] [Indexed: 04/07/2024]
Abstract
The rhesus macaque (Macaca mulatta) is a crucial experimental animal that shares many genetic, brain organizational, and behavioral characteristics with humans. A macaque brain atlas is fundamental to biomedical and evolutionary research. However, even though connectivity is vital for understanding brain functions, a connectivity-based whole-brain atlas of the macaque has not previously been made. In this study, we created a new whole-brain map, the Macaque Brainnetome Atlas (MacBNA), based on the anatomical connectivity profiles provided by high angular and spatial resolution ex vivo diffusion MRI data. The new atlas consists of 248 cortical and 56 subcortical regions as well as their structural and functional connections. The parcellation and the diffusion-based tractography were evaluated with invasive neuronal-tracing and Nissl-stained images. As a demonstrative application, the structural connectivity divergence between macaque and human brains was mapped using the Brainnetome atlases of those two species to uncover the genetic underpinnings of the evolutionary changes in brain structure. The resulting resource includes: (1) the thoroughly delineated Macaque Brainnetome Atlas (MacBNA), (2) regional connectivity profiles, (3) the postmortem high-resolution macaque diffusion and T2-weighted MRI dataset (Brainnetome-8), and (4) multi-contrast MRI, neuronal-tracing, and histological images collected from a single macaque. MacBNA can serve as a common reference frame for mapping multifaceted features across modalities and spatial scales and for integrative investigation and characterization of brain organization and function. Therefore, it will enrich the collaborative resource platform for nonhuman primates and facilitate translational and comparative neuroscience research.
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Affiliation(s)
- Yuheng Lu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yue Cui
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Long Cao
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China; Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhenwei Dong
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Luqi Cheng
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China; Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Wen Wu
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Changshuo Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Science, Beijing 100049, China
| | - Xinyi Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Youtong Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Baogui Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Deying Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Bokai Zhao
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Haiyan Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Kaixin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, China
| | - Liang Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weiyang Shi
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wen Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yawei Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Science, Beijing 100049, China
| | - Zongchang Du
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiaqi Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hui Xiong
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Na Luo
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Yanyan Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Xiaoxiao Hou
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Jinglu Han
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Science, Beijing 100049, China
| | - Hongji Sun
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Tao Cai
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Qiang Peng
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Linqing Feng
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Jiaojian Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
| | - George Paxinos
- Neuroscience Research Australia and The University of New South Wales, Sydney NSW 2031, Australia
| | - Zhengyi Yang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital, Yongzhou 425000, China.
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China; Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital, Yongzhou 425000, China.
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Gao C, Wu X, Wang Y, Li G, Ma L, Wang C, Xie S, Chu C, Madsen KH, Hou Z, Fan L. Prior-guided individualized thalamic parcellation based on local diffusion characteristics. Hum Brain Mapp 2024; 45:e26646. [PMID: 38433705 PMCID: PMC10910286 DOI: 10.1002/hbm.26646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 02/10/2024] [Accepted: 02/19/2024] [Indexed: 03/05/2024] Open
Abstract
Comprising numerous subnuclei, the thalamus intricately interconnects the cortex and subcortex, orchestrating various facets of brain functions. Extracting personalized parcellation patterns for these subnuclei is crucial, as different thalamic nuclei play varying roles in cognition and serve as therapeutic targets for neuromodulation. However, accurately delineating the thalamic nuclei boundary at the individual level is challenging due to intersubject variability. In this study, we proposed a prior-guided parcellation (PG-par) method to achieve robust individualized thalamic parcellation based on a central-boundary prior. We first constructed probabilistic atlas of thalamic nuclei using high-quality diffusion MRI datasets based on the local diffusion characteristics. Subsequently, high-probability voxels in the probabilistic atlas were utilized as prior guidance to train unique multiple classification models for each subject based on a multilayer perceptron. Finally, we employed the trained model to predict the parcellation labels for thalamic voxels and construct individualized thalamic parcellation. Through a test-retest assessment, the proposed prior-guided individualized thalamic parcellation exhibited excellent reproducibility and the capacity to detect individual variability. Compared with group atlas registration and individual clustering parcellation, the proposed PG-par demonstrated superior parcellation performance under different scanning protocols and clinic settings. Furthermore, the prior-guided individualized parcellation exhibited better correspondence with the histological staining atlas. The proposed prior-guided individualized thalamic parcellation method contributes to the personalized modeling of brain parcellation.
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Affiliation(s)
- Chaohong Gao
- Sino‐Danish CollegeSino‐Danish Center for Education and ResearchUniversity of Chinese Academy of SciencesBeijingChina
- Brainnetome Center, Institute of AutomationChinese Academy of SciencesBeijingChina
- Department of Applied Mathematics and Computer ScienceTechnical University of DenmarkKongens LyngbyDenmark
| | - Xia Wu
- Brainnetome Center, Institute of AutomationChinese Academy of SciencesBeijingChina
| | - Yaping Wang
- Sino‐Danish CollegeSino‐Danish Center for Education and ResearchUniversity of Chinese Academy of SciencesBeijingChina
- Brainnetome Center, Institute of AutomationChinese Academy of SciencesBeijingChina
- Department of Applied Mathematics and Computer ScienceTechnical University of DenmarkKongens LyngbyDenmark
| | - Gang Li
- Brainnetome Center, Institute of AutomationChinese Academy of SciencesBeijingChina
| | - Liang Ma
- Brainnetome Center, Institute of AutomationChinese Academy of SciencesBeijingChina
| | - Changshuo Wang
- Sino‐Danish CollegeSino‐Danish Center for Education and ResearchUniversity of Chinese Academy of SciencesBeijingChina
- Brainnetome Center, Institute of AutomationChinese Academy of SciencesBeijingChina
| | - Sangma Xie
- Institute of Biomedical Engineering and Instrumentation, School of AutomationHangzhou Dianzi UniversityHangzhouChina
| | - Congying Chu
- Brainnetome Center, Institute of AutomationChinese Academy of SciencesBeijingChina
| | - Kristoffer Hougaard Madsen
- Sino‐Danish CollegeSino‐Danish Center for Education and ResearchUniversity of Chinese Academy of SciencesBeijingChina
- Department of Applied Mathematics and Computer ScienceTechnical University of DenmarkKongens LyngbyDenmark
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and ResearchCopenhagen University Hospital—Amager and HvidovreHvidovreDenmark
| | - Zhongyu Hou
- Department of Medical ImagingShandong Provincial Hospital Affiliated to Shandong First Medical UniversityJinanChina
| | - Lingzhong Fan
- Sino‐Danish CollegeSino‐Danish Center for Education and ResearchUniversity of Chinese Academy of SciencesBeijingChina
- Brainnetome Center, Institute of AutomationChinese Academy of SciencesBeijingChina
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of AutomationChinese Academy of SciencesBeijingChina
- School of Health and Life SciencesUniversity of Health and Rehabilitation SciencesQingdaoShandongChina
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Biesbroek JM, Verhagen MG, van der Stigchel S, Biessels GJ. When the central integrator disintegrates: A review of the role of the thalamus in cognition and dementia. Alzheimers Dement 2024; 20:2209-2222. [PMID: 38041861 PMCID: PMC10984498 DOI: 10.1002/alz.13563] [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: 06/22/2023] [Revised: 10/18/2023] [Accepted: 10/29/2023] [Indexed: 12/04/2023]
Abstract
The thalamus is a complex neural structure with numerous anatomical subdivisions and intricate connectivity patterns. In recent decades, the traditional view of the thalamus as a relay station and "gateway to the cortex" has expanded in recognition of its role as a central integrator of inputs from sensory systems, cortex, basal ganglia, limbic systems, brain stem nuclei, and cerebellum. As such, the thalamus is critical for numerous aspects of human cognition, mood, and behavior, as well as serving sensory processing and motor functions. Thalamus pathology is an important contributor to cognitive and functional decline, and it might be argued that the thalamus has been somewhat overlooked as an important player in dementia. In this review, we provide a comprehensive overview of thalamus anatomy and function, with an emphasis on human cognition and behavior, and discuss emerging insights on the role of thalamus pathology in dementia.
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Affiliation(s)
- J. Matthijs Biesbroek
- Department of NeurologyUMC Utrecht Brain CenterUniversity Medical Center UtrechtUtrechtThe Netherlands
- Department of NeurologyDiakonessenhuis HospitalUtrechtThe Netherlands
| | - Marieke G. Verhagen
- VIB Center for Brain and DiseaseLeuvenBelgium
- Department of NeurosciencesKatholieke Universiteit (KU) LeuvenLeuvenBelgium
| | - Stefan van der Stigchel
- Department of Experimental PsychologyHelmholtz InstituteUtrecht UniversityUtrechtThe Netherlands
| | - Geert Jan Biessels
- Department of NeurologyUMC Utrecht Brain CenterUniversity Medical Center UtrechtUtrechtThe Netherlands
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Mukherjee A, Halassa MM. The Associative Thalamus: A Switchboard for Cortical Operations and a Promising Target for Schizophrenia. Neuroscientist 2024; 30:132-147. [PMID: 38279699 PMCID: PMC10822032 DOI: 10.1177/10738584221112861] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2024]
Abstract
Schizophrenia is a brain disorder that profoundly perturbs cognitive processing. Despite the success in treating many of its symptoms, the field lacks effective methods to measure and address its impact on reasoning, inference, and decision making. Prefrontal cortical abnormalities have been well documented in schizophrenia, but additional dysfunction in the interactions between the prefrontal cortex and thalamus have recently been described. This dysfunction may be interpreted in light of parallel advances in neural circuit research based on nonhuman animals, which show critical thalamic roles in maintaining and switching prefrontal activity patterns in various cognitive tasks. Here, we review this basic literature and connect it to emerging innovations in clinical research. We highlight the value of focusing on associative thalamic structures not only to better understand the very nature of cognitive processing but also to leverage these circuits for diagnostic and therapeutic development in schizophrenia. We suggest that the time is right for building close bridges between basic thalamic research and its clinical translation, particularly in the domain of cognition and schizophrenia.
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Affiliation(s)
- Arghya Mukherjee
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Michael M Halassa
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
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Zhao G, Yu L, Chen P, Zhu K, Yang L, Lin W, Luo Y, Dou Z, Xu H, Zhang P, Zhu T, Yu S. Neural mechanisms of attentional bias to emotional faces in patients with chronic insomnia disorder. J Psychiatr Res 2024; 169:49-57. [PMID: 38000184 DOI: 10.1016/j.jpsychires.2023.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 10/27/2023] [Accepted: 11/15/2023] [Indexed: 11/26/2023]
Abstract
OBJECTIVE This study used event-related potential (ERP) and resting-state functional connectivity (rs-FC) approaches to investigate the neural mechanisms underlying the emotional attention bias in patients with chronic insomnia disorder (CID). METHODS Twenty-five patients with CID and thirty-three demographically matched healthy controls (HCs) completed clinical questionnaires and underwent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) scans. EEG analysis examined the group differences in terms of reaction times, P3 amplitudes, event-related spectral perturbations, and inter-trial phase synchrony. Subsequently, seed-based rs-FC analysis of the amygdala nuclei (including the central-medial amygdala [CMA] and basolateral amygdala [BLA]) was performed. The relationship between P3 amplitude, rs-FC and clinical symptom severity in patients with CID was further investigated by correlation analysis. RESULTS CID patients exhibited shorter reaction times than HCs in both standard and deviant stimuli, with the abnormalities becoming more pronounced as attention allocation increased. Compared to HCs, ERP analysis revealed increased P3 amplitude, theta wave power, and inter-trial synchrony in CID patients. The rs-FC analysis showed increased connectivity of the BLA-occipital pole, CMA-precuneus, and CMA-angular gyrus and decreased connectivity of the CMA-thalamus in CID patients. Notably, correlation analysis of the EEG and fMRI measurements showed a significant positive correlation between the P3 amplitude and the rs-FC of the CMA-PCU. CONCLUSION This study confirms an emotional attention bias in CID, specifically in the neural mechanisms of attention processing that vary depending on the allocation of attentional resources. Abnormal connectivity in the emotion-cognition networks may constitute the neural basis of the abnormal scalp activation pattern.
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Affiliation(s)
- Guangli Zhao
- School of Rehabilitation and Health Preservation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Liyong Yu
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Peixin Chen
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Keli Zhu
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Lu Yang
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Wenting Lin
- School of Rehabilitation and Health Preservation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yucai Luo
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Zeyang Dou
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Hao Xu
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China; Center of Interventional Medicine, Affiliated Hospital of North Sichuan Medical College, North Sichuan Medical College, Nanchong, China
| | - Pan Zhang
- Nervous System Disease Treatment Center, Traditional Chinese Medicine Hospital of Meishan, Meishan, China.
| | - Tianmin Zhu
- School of Rehabilitation and Health Preservation, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
| | - Siyi Yu
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China; Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
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Delli Pizzi S, Chiacchiaretta P, Sestieri C, Ferretti A, Tullo MG, Della Penna S, Martinotti G, Onofrj M, Roseman L, Timmermann C, Nutt DJ, Carhart-Harris RL, Sensi SL. LSD-induced changes in the functional connectivity of distinct thalamic nuclei. Neuroimage 2023; 283:120414. [PMID: 37858906 DOI: 10.1016/j.neuroimage.2023.120414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 09/05/2023] [Accepted: 10/16/2023] [Indexed: 10/21/2023] Open
Abstract
The role of the thalamus in mediating the effects of lysergic acid diethylamide (LSD) was recently proposed in a model of communication and corroborated by imaging studies. However, a detailed analysis of LSD effects on nuclei-resolved thalamocortical connectivity is still missing. Here, in a group of healthy volunteers, we evaluated whether LSD intake alters the thalamocortical coupling in a nucleus-specific manner. Structural and resting-state functional Magnetic Resonance Imaging (MRI) data were acquired in a placebo-controlled study on subjects exposed to acute LSD administration. Structural MRI was used to parcel the thalamus into its constituent nuclei based on individual anatomy. Nucleus-specific changes of resting-state functional MRI (rs-fMRI) connectivity were mapped using a seed-based approach. LSD intake selectively increased the thalamocortical functional connectivity (FC) of the ventral complex, pulvinar, and non-specific nuclei. Functional coupling was increased between these nuclei and sensory cortices that include the somatosensory and auditory networks. The ventral and pulvinar nuclei also exhibited increased FC with parts of the associative cortex that are dense in serotonin type 2A receptors. These areas are hyperactive and hyper-connected upon LSD intake. At subcortical levels, LSD increased the functional coupling among the thalamus's ventral, pulvinar, and non-specific nuclei, but decreased the striatal-thalamic connectivity. These findings unravel some LSD effects on the modulation of subcortical-cortical circuits and associated behavioral outputs.
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Affiliation(s)
- Stefano Delli Pizzi
- Department of Neuroscience, Imaging, and Clinical Sciences, University "G. d'Annunzio" of Chieti-Pescara, Italy; Molecular Neurology Unit, Center for Advanced Studies and Technology (CAST), University "G. d'Annunzio" of Chieti-Pescara, Italy
| | - Piero Chiacchiaretta
- Department of Innovative Technologies in Medicine and Dentistry, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Carlo Sestieri
- Department of Neuroscience, Imaging, and Clinical Sciences, University "G. d'Annunzio" of Chieti-Pescara, Italy; Institute for Advanced Biomedical Technologies (ITAB), "G. d'Annunzio" University, Chieti-Pescara, Italy
| | - Antonio Ferretti
- Department of Neuroscience, Imaging, and Clinical Sciences, University "G. d'Annunzio" of Chieti-Pescara, Italy; Institute for Advanced Biomedical Technologies (ITAB), "G. d'Annunzio" University, Chieti-Pescara, Italy; UdA-TechLab, Research Center, University "G. d'Annunzio" of Chieti-Pescara, 66100 Chieti, Italy
| | - Maria Giulia Tullo
- Department of Neuroscience, Imaging, and Clinical Sciences, University "G. d'Annunzio" of Chieti-Pescara, Italy
| | - Stefania Della Penna
- Department of Neuroscience, Imaging, and Clinical Sciences, University "G. d'Annunzio" of Chieti-Pescara, Italy; Institute for Advanced Biomedical Technologies (ITAB), "G. d'Annunzio" University, Chieti-Pescara, Italy
| | - Giovanni Martinotti
- Department of Neuroscience, Imaging, and Clinical Sciences, University "G. d'Annunzio" of Chieti-Pescara, Italy
| | - Marco Onofrj
- Department of Neuroscience, Imaging, and Clinical Sciences, University "G. d'Annunzio" of Chieti-Pescara, Italy
| | - Leor Roseman
- Centre for Psychedelic Research, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Christopher Timmermann
- Centre for Psychedelic Research, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - David J Nutt
- Centre for Psychedelic Research, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Robin L Carhart-Harris
- Centre for Psychedelic Research, Faculty of Medicine, Imperial College London, London, United Kingdom; Psychedelics Division, Neuroscape, Neurology, University of California San Francisco
| | - Stefano L Sensi
- Department of Neuroscience, Imaging, and Clinical Sciences, University "G. d'Annunzio" of Chieti-Pescara, Italy; Molecular Neurology Unit, Center for Advanced Studies and Technology (CAST), University "G. d'Annunzio" of Chieti-Pescara, Italy; Institute for Advanced Biomedical Technologies (ITAB), "G. d'Annunzio" University, Chieti-Pescara, Italy.
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9
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Wang BA, Drammis S, Hummos A, Halassa MM, Pleger B. Modulation of prefrontal couplings by prior belief-related responses in ventromedial prefrontal cortex. Front Neurosci 2023; 17:1278096. [PMID: 38033544 PMCID: PMC10684683 DOI: 10.3389/fnins.2023.1278096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 10/30/2023] [Indexed: 12/02/2023] Open
Abstract
Humans and other animals can maintain constant payoffs in an uncertain environment by steadily re-evaluating and flexibly adjusting current strategy, which largely depends on the interactions between the prefrontal cortex (PFC) and mediodorsal thalamus (MD). While the ventromedial PFC (vmPFC) represents the level of uncertainty (i.e., prior belief about external states), it remains unclear how the brain recruits the PFC-MD network to re-evaluate decision strategy based on the uncertainty. Here, we leverage non-linear dynamic causal modeling on fMRI data to test how prior belief-dependent activity in vmPFC gates the information flow in the PFC-MD network when individuals switch their decision strategy. We show that the prior belief-related responses in vmPFC had a modulatory influence on the connections from dorsolateral PFC (dlPFC) to both, lateral orbitofrontal (lOFC) and MD. Bayesian parameter averaging revealed that only the connection from the dlPFC to lOFC surpassed the significant threshold, which indicates that the weaker the prior belief, the less was the inhibitory influence of the vmPFC on the strength of effective connections from dlPFC to lOFC. These findings suggest that the vmPFC acts as a gatekeeper for the recruitment of processing resources to re-evaluate the decision strategy in situations of high uncertainty.
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Affiliation(s)
- Bin A. Wang
- Department of Neurology, BG University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
- Collaborative Research Centre 874 "Integration and Representation of Sensory Processes", Ruhr-University Bochum, Bochum, Germany
- Guangdong Key Laboratory of Mental Health and Cognitive Science, Ministry of Education Key Laboratory of Brain Cognition and Educational Science, School of Psychology, Center for Studies of Psychological Application, South China Normal University, Guangzhou, China
| | - Sabrina Drammis
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Ali Hummos
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Michael M. Halassa
- Department of Neuroscience, Tufts University School of Medicine, Boston, MA, United States
| | - Burkhard Pleger
- Department of Neurology, BG University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
- Collaborative Research Centre 874 "Integration and Representation of Sensory Processes", Ruhr-University Bochum, Bochum, Germany
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10
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Mengxing L, Lerma-Usabiaga G, Clascá F, Paz-Alonso PM. High-Resolution Tractography Protocol to Investigate the Pathways between Human Mediodorsal Thalamic Nucleus and Prefrontal Cortex. J Neurosci 2023; 43:7780-7798. [PMID: 37709539 PMCID: PMC10648582 DOI: 10.1523/jneurosci.0721-23.2023] [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: 04/23/2023] [Revised: 09/07/2023] [Accepted: 09/08/2023] [Indexed: 09/16/2023] Open
Abstract
Animal studies have established that the mediodorsal nucleus (MD) of the thalamus is heavily and reciprocally connected with all areas of the prefrontal cortex (PFC). In humans, however, these connections are difficult to investigate. High-resolution imaging protocols capable of reliably tracing the axonal tracts linking the human MD with each of the PFC areas may thus be key to advance our understanding of the variation, development, and plastic changes of these important circuits, in health and disease. Here, we tested in adult female and male humans the reliability of a new reconstruction protocol based on in vivo diffusion MRI to trace, measure, and characterize the fiber tracts interconnecting the MD with 39 human PFC areas per hemisphere. Our protocol comprised the following three components: (1) defining regions of interest; (2) preprocessing diffusion data; and, (3) modeling white matter tracts and tractometry. This analysis revealed largely separate PFC territories of reciprocal MD-PFC tracts bearing striking resemblance with the topographic layout observed in macaque connection-tracing studies. We then examined whether our protocol could reliably reconstruct each of these MD-PFC tracts and their profiles across test and retest sessions. Results revealed that this protocol was able to trace and measure, in both left and right hemispheres, the trajectories of these 39 area-specific axon bundles with good-to-excellent test-retest reproducibility. This protocol, which has been made publicly available, may be relevant for cognitive neuroscience and clinical studies of normal and abnormal PFC function, development, and plasticity.SIGNIFICANCE STATEMENT Reciprocal MD-PFC interactions are critical for complex human cognition and learning. Reliably tracing, measuring and characterizing MD-PFC white matter tracts using high-resolution noninvasive methods is key to assess individual variation of these systems in humans. Here, we propose a high-resolution tractography protocol that reliably reconstructs 39 area-specific MD-PFC white matter tracts per hemisphere and quantifies structural information from diffusion MRI data. This protocol revealed a detailed mapping of thalamocortical and corticothalamic MD-PFC tracts in four different PFC territories (dorsal, medial, orbital/frontal pole, inferior frontal) showing structural connections resembling those observed in tracing studies with macaques. Furthermore, our automated protocol revealed high test-retest reproducibility and is made publicly available, constituting a step forward in mapping human MD-PFC circuits in clinical and academic research.
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Affiliation(s)
- Liu Mengxing
- Basque Center on Cognition, Brain and Language, 20009 Donostia-San Sebastián, Spain
| | - Garikoitz Lerma-Usabiaga
- Basque Center on Cognition, Brain and Language, 20009 Donostia-San Sebastián, Spain
- Ikerbasque, Basque Foundation for Science, 48013 Bilbao, Spain
| | - Francisco Clascá
- Department of Anatomy and Neuroscience, School of Medicine, Autónoma de Madrid University, 28029 Madrid, Spain
| | - Pedro M Paz-Alonso
- Basque Center on Cognition, Brain and Language, 20009 Donostia-San Sebastián, Spain
- Ikerbasque, Basque Foundation for Science, 48013 Bilbao, Spain
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11
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Aiello G, Ledergerber D, Dubcek T, Stieglitz L, Baumann C, Polanìa R, Imbach L. Functional network dynamics between the anterior thalamus and the cortex in deep brain stimulation for epilepsy. Brain 2023; 146:4717-4735. [PMID: 37343140 DOI: 10.1093/brain/awad211] [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: 01/13/2023] [Revised: 05/10/2023] [Accepted: 06/08/2023] [Indexed: 06/23/2023] Open
Abstract
Owing to its unique connectivity profile with cortical brain regions, and its suggested role in the subcortical propagation of seizures, the anterior nucleus of the thalamus (ANT) has been proposed as a key deep brain stimulation (DBS) target in drug-resistant epilepsy. However, the spatio-temporal interaction dynamics of this brain structure, and the functional mechanisms underlying ANT DBS in epilepsy remain unknown. Here, we study how the ANT interacts with the neocortex in vivo in humans and provide a detailed neurofunctional characterization of mechanisms underlying the effectiveness of ANT DBS, aiming at defining intraoperative neural biomarkers of responsiveness to therapy, assessed at 6 months post-implantation as the reduction in seizure frequency. A cohort of 15 patients with drug-resistant epilepsy (n = 6 males, age = 41.6 ± 13.79 years) underwent bilateral ANT DBS implantation. Using intraoperative cortical and ANT simultaneous electrophysiological recordings, we found that the ANT is characterized by high amplitude θ (4-8 Hz) oscillations, mostly in its superior part. The strongest functional connectivity between the ANT and the scalp EEG was also found in the θ band in ipsilateral centro-frontal regions. Upon intraoperative stimulation in the ANT, we found a decrease in higher EEG frequencies (20-70 Hz) and a generalized increase in scalp-to-scalp connectivity. Crucially, we observed that responders to ANT DBS treatment were characterized by higher EEG θ oscillations, higher θ power in the ANT, and stronger ANT-to-scalp θ connectivity, highlighting the crucial role of θ oscillations in the dynamical network characterization of these structures. Our study provides a comprehensive characterization of the interaction dynamic between the ANT and the cortex, delivering crucial information to optimize and predict clinical DBS response in patients with drug-resistant epilepsy.
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Affiliation(s)
- Giovanna Aiello
- Decision Neuroscience Lab, Department of Health Sciences and Technology, ETH Zurich, 8092 Zurich, Switzerland
- Swiss Epilepsy Center (Klinik Lengg), 8008 Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, 8057 Zurich, Switzerland
| | - Debora Ledergerber
- Swiss Epilepsy Center (Klinik Lengg), 8008 Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, 8057 Zurich, Switzerland
| | - Tena Dubcek
- Decision Neuroscience Lab, Department of Health Sciences and Technology, ETH Zurich, 8092 Zurich, Switzerland
- Swiss Epilepsy Center (Klinik Lengg), 8008 Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, 8057 Zurich, Switzerland
| | - Lennart Stieglitz
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Christian Baumann
- Department of Neurology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Rafael Polanìa
- Decision Neuroscience Lab, Department of Health Sciences and Technology, ETH Zurich, 8092 Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, 8057 Zurich, Switzerland
| | - Lukas Imbach
- Swiss Epilepsy Center (Klinik Lengg), 8008 Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, 8057 Zurich, Switzerland
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12
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Poli A, Cappellini F, Sala J, Miccoli M. The integrative process promoted by EMDR in dissociative disorders: neurobiological mechanisms, psychometric tools, and intervention efficacy on the psychological impact of the COVID-19 pandemic. Front Psychol 2023; 14:1164527. [PMID: 37727746 PMCID: PMC10505816 DOI: 10.3389/fpsyg.2023.1164527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 08/07/2023] [Indexed: 09/21/2023] Open
Abstract
Dissociative disorders (DDs) are characterized by a discontinuity in the normal integration of consciousness, memory, identity, emotion, perception, bodily representation, motor control, and action. The life-threatening coronavirus disease 2019 (COVID-19) pandemic has been identified as a potentially traumatic event and may produce a wide range of mental health problems, such as depression, anxiety disorders, sleep disorders, and DD, stemming from pandemic-related events, such as sickness, isolation, losing loved ones, and fear for one's life. In our conceptual analysis, we introduce the contribution of the structural dissociation of personality (SDP) theory and polyvagal theory to the conceptualization of the COVID-19 pandemic-triggered DD and the importance of assessing perceived safety in DD through neurophysiologically informed psychometric tools. In addition, we analyzed the contribution of eye movement desensitization and reprocessing (EMDR) to the treatment of the COVID-19 pandemic-triggered DD and suggest possible neurobiological mechanisms of action of the EMDR. In particular, we propose that, through slow eye movements, the EMDR may promote an initial non-rapid-eye-movement sleep stage 1-like activity, a subsequent access to a slow-wave sleep activity, and an oxytocinergic neurotransmission that, in turn, may foster the functional coupling between paraventricular nucleus and both sympathetic and parasympathetic cardioinhibitory nuclei. Neurophysiologically informed psychometric tools for safety evaluation in DDs are discussed. Furthermore, clinical and public health implications are considered, combining the EMDR, SDP theory, and polyvagal conceptualizations in light of the potential dissociative symptomatology triggered by the COVID-19 pandemic.
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Wang Q, Wang Y, Xu W, Chen X, Li X, Li Q, Li H. Corresponding anatomical of the macaque superior parietal lobule areas 5 (PE) subdivision reveal similar connectivity patterns with humans. Front Neurosci 2022; 16:964310. [PMID: 36267237 PMCID: PMC9577089 DOI: 10.3389/fnins.2022.964310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 08/26/2022] [Indexed: 11/25/2022] Open
Abstract
Using the animal brain as a cross-species tool for human brain research based on imaging features can provide more potential to reveal comprehensive human brain analysis. Previous studies have shown that human Brodmann area 5 (BA5) and macaque PE are homologous regions. They are both involved in processes depth and direction information during the touch process in the arm movement. However, recent studies show that both BA5 and PE are not homogeneous. According to the cytoarchitecture, BA5 is subdivided into three different subregions, and PE can be subdivided into PEl, PEla, and PEm. The species homologous relationship among the subregions is not clear between BA5 and PE. At the same time, the subdivision of PE based on the anatomical connection of white matter fiber bundles needs more verification. This research subdivided the PE of macaques based on the anatomical connection of white matter fiber bundles. Two PE subregions are defined based on probabilistic fiber tracking, one on the anterior side and the other on the dorsal side. Finally, the research draws connectivity fingerprints with predefined homologous target areas for the BA5 and PE subregions to reveal the characteristics of structure and functions and gives the homologous correspondence identified.
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