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Ahmadi A, Saadatmand M, Wallois F. Evaluation of potential alterations related to ADHD in the effective connectivity between the default mode network and cerebellum, hippocampus, thalamus, and primary visual cortex. Cereb Cortex 2024; 34:bhae335. [PMID: 39147392 DOI: 10.1093/cercor/bhae335] [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: 04/09/2024] [Revised: 07/18/2024] [Accepted: 07/31/2024] [Indexed: 08/17/2024] Open
Abstract
Hyperactivity in children with attention-deficit/hyperactivity disorder (ADHD) leads to restlessness and impulse-control impairments. Nevertheless, the relation between ADHD symptoms and brain regions interactions remains unclear. We focused on dynamic causal modeling to study the effective connectivity in a fully connected network comprised of four regions of the default mode network (DMN) (linked to response control behaviors) and four other regions with previously-reported structural alterations due to ADHD. Then, via the parametric empirical Bayes analysis, the most significant connections, with the highest correlation to the covariates ADHD/control, age, and sex were extracted. Our results demonstrated a positive correlation between ADHD and effective connectivity between the right cerebellum and three DMN nodes (intrinsically inhibitory connections). Therefore, an increase in the effective connectivity leads to more inhibition imposition from the right cerebellum to DMN that reduces this network activation. The lower DMN activity makes leaving the resting-state easier, which may be involved in the restlessness symptom. Furthermore, our results indicated a negative correlation between age and these connections. We showed that the difference between the average of effective connectivities of ADHD and control groups in the age-range of 7-11 years disappeared after 14 years-old. Therefore, aging tends to alleviate ADHD-specific symptoms.
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Affiliation(s)
- Amirhossein Ahmadi
- Ferdowsi Cognitive Science and Technology Center & Medical Imaging Lab, Department of Electrical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Vakil-Abad Blv., Bahonar St., Mashhad, 9177948974, Iran
| | - Mahdi Saadatmand
- Ferdowsi Cognitive Science and Technology Center & Medical Imaging Lab, Department of Electrical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Vakil-Abad Blv., Bahonar St., Mashhad, 9177948974, Iran
| | - Fabrice Wallois
- INSERM U1105, Université de Picardie, CURS, Avenue Laennec, 80054, Amiens, France
- INSERM U1105, Unit Exploration Fonctionnelles du Système Nerveux Pèdiatrique, South University Hospital, Avenue Laennec, 80054, Amiens, France
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Moazeni O, Northoff G, Batouli SAH. The subcortical brain regions influence the cortical areas during resting-state: an fMRI study. Front Hum Neurosci 2024; 18:1363125. [PMID: 39055533 PMCID: PMC11271203 DOI: 10.3389/fnhum.2024.1363125] [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: 12/29/2023] [Accepted: 06/06/2024] [Indexed: 07/27/2024] Open
Abstract
Introduction Numerous modes or patterns of neural activity can be seen in the brain of individuals during the resting state. However, those functions do not persist long, and they are continuously altering in the brain. We have hypothesized that the brain activations during the resting state should themselves be responsible for this alteration of the activities. Methods Using the resting-state fMRI data of 63 healthy young individuals, we estimated the causality effects of each resting-state activation map on all other networks. The resting-state networks were identified, their causality effects on the other components were extracted, the networks with the top 20% of the causality were chosen, and the networks which were under the influence of those causal networks were also identified. Results Our results showed that the influence of each activation component over other components is different. The brain areas which showed the highest causality coefficients were subcortical regions, such as the brain stem, thalamus, and amygdala. On the other hand, nearly all the areas which were mostly under the causal effects were cortical regions. Discussion In summary, our results suggest that subcortical brain areas exert a higher influence on cortical regions during the resting state, which could help in a better understanding the dynamic nature of brain functions.
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Affiliation(s)
- Omid Moazeni
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Georg Northoff
- Mind, Brain Imaging and Neuroethics Research Unit, The Royal’s Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada
| | - Seyed Amir Hossein Batouli
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
- BrainEE Research Group, Tehran University of Medical Sciences, Tehran, Iran
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Zhang Y, Yin X, Chen YC, Chen H, Jin M, Ma Y, Yong W, Muthaiah VPK, Xia W, Yin X. Aberrant Brain Triple-Network Effective Connectivity Patterns in Type 2 Diabetes Mellitus. Diabetes Ther 2024; 15:1215-1229. [PMID: 38578396 PMCID: PMC11043308 DOI: 10.1007/s13300-024-01565-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 03/12/2024] [Indexed: 04/06/2024] Open
Abstract
INTRODUCTION Aberrant brain functional connectivity network is thought to be related to cognitive impairment in patients with type 2 diabetes mellitus (T2DM). This study aims to investigate the triple-network effective connectivity patterns in patients with T2DM within and between the default mode network (DMN), salience network (SN), and executive control network (ECN) and their associations with cognitive declines. METHODS In total, 92 patients with T2DM and 98 matched healthy controls (HCs) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Spectral dynamic causal modeling (spDCM) was used for effective connectivity analysis within the triple network. The posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), lateral prefrontal cortex (LPFC), supramarginal gyrus (SMG), and anterior insula (AINS) were selected as the regions of interest. Group comparisons were performed for effective connectivity calculated using the fully connected model, and the relationships between effective connectivity alterations and cognitive impairment as well as clinical parameters were detected. RESULTS Compared to HCs, patients with T2DM exhibited increased or decreased effective connectivity patterns within the triple network. Furthermore, diabetes duration was significantly negatively correlated with increased effective connectivity from the r-LPFC to the mPFC, while body mass index (BMI) was significantly positively correlated with increased effective connectivity from the l-LPFC to the l-AINS (r = - 0.353, p = 0.001; r = 0.377, p = 0.004). CONCLUSION These results indicate abnormal effective connectivity patterns within the triple network model in patients with T2DM and provide new insight into the neurological mechanisms of T2DM and related cognitive dysfunction.
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Affiliation(s)
- Yujie Zhang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | - Xiao Yin
- Department of Endocrinology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | - Huiyou Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | - Mingxu Jin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | - Yuehu Ma
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | - Wei Yong
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | | | - Wenqing Xia
- Department of Endocrinology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China.
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China.
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Xing C, Chang W, Liu Y, Tong Z, Xu X, Yin X, Wu Y, Chen YC, Fang X. Alteration in resting-state effective connectivity within the Papez circuit in Presbycusis. Eur J Neurosci 2023; 58:3026-3036. [PMID: 37337805 DOI: 10.1111/ejn.16067] [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: 01/22/2023] [Revised: 05/14/2023] [Accepted: 06/06/2023] [Indexed: 06/21/2023]
Abstract
Previous studies have suggested that the Papez circuit may be involved in the cognitive impairment observed after hearing loss in presbycusis patients, yet relatively little is known about the pattern of changes in effective connectivity within the circuit. The aim of this study was to investigate abnormal alterations in resting-state effective connectivity within the Papez circuit and their association with cognitive decline in presbycusis patients. The spectral dynamic causal modelling (spDCM) approach was used for resting-state effective connectivity analysis in 61 presbycusis patients and 52 healthy controls (HCs) within the Papez circuit. The hippocampus (HPC), mamillary body (MB), anterior thalamic nuclei (ATN), anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), entorhinal cortex (ERC), subiculum (Sub) and parahippocampal gyrus (PHG) were selected as the regions of interest (ROIs). The fully connected model difference in effective connectivity between the two groups was assessed, and the correlation between effective connectivity alteration and cognitive scale was analysed. We found that presbycusis patients demonstrated decreased effective connectivity from MB, PCC, and Sub to ACC relative to HCs, whereas higher effective connectivity strength was shown from HPC to MB, from ATN to PHG and from PHG to Sub. The effective connectivity from PHG to Sub was significantly negatively correlated with the complex figure test (CFT)-delay score (rho = -0.259, p = 0.044). The results support and reinforce the role of abnormal effective connectivity within the Papez circuit in the pathophysiology of presbycusis-related cognitive impairment and reveal its potential as a novel imaging marker.
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Affiliation(s)
- Chunhua Xing
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Wei Chang
- Department of Laboratory Medicine, Nanjing Yuhua Hospital, Yuhua Branch of Nanjing First Hospital, Nanjing, China
| | - Yin Liu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Zhaopeng Tong
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiaomin Xu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yuanqing Wu
- Department of Otolaryngology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xiangming Fang
- Department of Medical Imaging, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, China
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Eo J, Kang J, Youn T, Park HJ. Neuropharmacological computational analysis of longitudinal electroencephalograms in clozapine-treated patients with schizophrenia using hierarchical dynamic causal modeling. Neuroimage 2023; 275:120161. [PMID: 37172662 DOI: 10.1016/j.neuroimage.2023.120161] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/15/2023] [Accepted: 05/09/2023] [Indexed: 05/15/2023] Open
Abstract
The hierarchical characteristics of the brain are prominent in the pharmacological treatment of psychiatric diseases, primarily targeting cellular receptors that extend upward to intrinsic connectivity within a region, interregional connectivity, and, consequently, clinical observations such as an electroencephalogram (EEG). To understand the long-term effects of neuropharmacological intervention on neurobiological properties at different hierarchical levels, we explored long-term changes in neurobiological parameters of an N-methyl-D-aspartate canonical microcircuit model (CMM-NMDA) in the default mode network (DMN) and auditory hallucination network (AHN) using dynamic causal modeling of longitudinal EEG in clozapine-treated patients with schizophrenia. The neurobiological properties of the CMM-NMDA model associated with symptom improvement in schizophrenia were found across hierarchical levels, from a reduced membrane capacity of the deep pyramidal cell and intrinsic connectivity with the inhibitory population in DMN and intrinsic and extrinsic connectivity in AHN. The medication duration mainly affects the intrinsic connectivity and NMDA time constant in DMN. Virtual perturbation analysis specified the contribution of each parameter to the cross-spectral density (CSD) of the EEG, particularly intrinsic connectivity and membrane capacitances for CSD frequency shifts and progression. It further reveals that excitatory and inhibitory connectivity complements frequency-specific CSD changes, notably the alpha frequency band in DMN. Positive and negative synergistic interactions exist between neurobiological properties primarily within the same region in patients treated with clozapine. The current study shows how computational neuropharmacology helps explore the multiscale link between neurobiological properties and clinical observations and understand the long-term mechanism of neuropharmacological intervention reflected in clinical EEG.
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Affiliation(s)
- Jinseok Eo
- Graduate School of Medical Science, Brain Korea 21 Project, Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea; Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea
| | - Jiyoung Kang
- Department of Scientific Computing, Pukyong National University, Busan, Republic of Korea; Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea
| | - Tak Youn
- Department of Psychiatry and Electroconvulsive Therapy Center, Dongguk University International Hospital, Goyang, Republic of Korea; Institute of Buddhism and Medicine, Dongguk University, Seoul, Republic of Korea
| | - Hae-Jeong Park
- Graduate School of Medical Science, Brain Korea 21 Project, Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea; Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Cognitive Science, Yonsei University, Seoul, Republic of Korea.
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Li G, Yap PT. From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis. Front Hum Neurosci 2022; 16:940842. [PMID: 36061504 PMCID: PMC9428697 DOI: 10.3389/fnhum.2022.940842] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 07/28/2022] [Indexed: 01/28/2023] Open
Abstract
As a newly emerging field, connectomics has greatly advanced our understanding of the wiring diagram and organizational features of the human brain. Generative modeling-based connectome analysis, in particular, plays a vital role in deciphering the neural mechanisms of cognitive functions in health and dysfunction in diseases. Here we review the foundation and development of major generative modeling approaches for functional magnetic resonance imaging (fMRI) and survey their applications to cognitive or clinical neuroscience problems. We argue that conventional structural and functional connectivity (FC) analysis alone is not sufficient to reveal the complex circuit interactions underlying observed neuroimaging data and should be supplemented with generative modeling-based effective connectivity and simulation, a fruitful practice that we term "mechanistic connectome." The transformation from descriptive connectome to mechanistic connectome will open up promising avenues to gain mechanistic insights into the delicate operating principles of the human brain and their potential impairments in diseases, which facilitates the development of effective personalized treatments to curb neurological and psychiatric disorders.
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Affiliation(s)
- Guoshi Li
- Department of Radiology, University of North Carolina, Chapel Hill, NC, United States,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, United States,*Correspondence: Guoshi Li,
| | - Pew-Thian Yap
- Department of Radiology, University of North Carolina, Chapel Hill, NC, United States,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, United States
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