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Bell RP, Cohen JR, Towe SL, Gadde S, Al-Khalil K, Costello A, Song AW, Meade CS. Chronic cannabis use associated with subcortical topological reorganization of structural connectivity in adults. Drug Alcohol Depend 2024; 262:111405. [PMID: 39079224 DOI: 10.1016/j.drugalcdep.2024.111405] [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: 02/01/2024] [Revised: 06/16/2024] [Accepted: 07/20/2024] [Indexed: 08/17/2024]
Abstract
BACKGROUND Cannabis is commonly used in the United States. However, chronic cannabis use has been linked to alterations in white matter (WM) integrity. Studies investigating WM in people who use cannabis (PWC) have produced varying results, which may be due to a variety of factors, including a focus on individual WM tracts. Here, we examined WM connectivity using a module-based approach to help clarify whether cannabis use is associated with differences in WM organization. METHODS Connectomics is used to map complex networks of inter and intra-connected cortical and subcortical regions. A key concept of brain organization is the presence of groups of densely interconnected regions, referred to as modules. Here, we used WM structural connectivity estimates to compare connectome organization between adults who used cannabis regularly (n=53), and adults who did not use cannabis (n=60). We quantified aspects of network organization both across the whole brain and within specific modules. RESULTS There were no significant results between groups after correcting for multiple comparisons for whole-brain metrics. When considering group differences in network organization metrics for 10 identified modules, we observed that adult PWC showed higher within-module degree, local efficiency, and network strength in a right subcortical module relative to adults that did not use cannabis. CONCLUSIONS These results suggest that cannabis use in adults is associated with alterations of subcortical WM network organization. The observed differences in WM organization may be due to the involvement of the endocannabinoid system in the alteration of WM growth processes.
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Affiliation(s)
- R P Bell
- Department of Translational Neuroscience, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
| | - J R Cohen
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - S L Towe
- Department of Translational Neuroscience, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - S Gadde
- Brain Imaging and Analysis Center, Duke University Medical Center, Campus, Box 3918, Durham, NC 27710, USA
| | - K Al-Khalil
- Duke University School of Medicine, Department of Psychiatry and Behavioral Sciences, Campus Box 102848, Durham, NC 27710, USA
| | - A Costello
- Duke University School of Medicine, Department of Psychiatry and Behavioral Sciences, Campus Box 102848, Durham, NC 27710, USA
| | - A W Song
- Brain Imaging and Analysis Center, Duke University Medical Center, Campus, Box 3918, Durham, NC 27710, USA
| | - C S Meade
- Department of Translational Neuroscience, Wake Forest University School of Medicine, Winston-Salem, NC, USA; Brain Imaging and Analysis Center, Duke University Medical Center, Campus, Box 3918, Durham, NC 27710, USA; Department of Psychology and Neuroscience, Duke University, Campus Box 90086, Durham, NC 27708, USA
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2
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Huijsdens H, Leeftink D, Geerligs L, Hinne M. Robust Inference of Dynamic Covariance Using Wishart Processes and Sequential Monte Carlo. ENTROPY (BASEL, SWITZERLAND) 2024; 26:695. [PMID: 39202165 PMCID: PMC11353982 DOI: 10.3390/e26080695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Revised: 07/05/2024] [Accepted: 08/13/2024] [Indexed: 09/03/2024]
Abstract
Several disciplines, such as econometrics, neuroscience, and computational psychology, study the dynamic interactions between variables over time. A Bayesian nonparametric model known as the Wishart process has been shown to be effective in this situation, but its inference remains highly challenging. In this work, we introduce a Sequential Monte Carlo (SMC) sampler for the Wishart process, and show how it compares to conventional inference approaches, namely MCMC and variational inference. Using simulations, we show that SMC sampling results in the most robust estimates and out-of-sample predictions of dynamic covariance. SMC especially outperforms the alternative approaches when using composite covariance functions with correlated parameters. We further demonstrate the practical applicability of our proposed approach on a dataset of clinical depression (n=1), and show how using an accurate representation of the posterior distribution can be used to test for dynamics in covariance.
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Affiliation(s)
- Hester Huijsdens
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Thomas van Aquinostraat 4, 6525 GD Nijmegen, The Netherlands; (D.L.); (L.G.); (M.H.)
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3
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Merino Ruiz MC, Mortari MR. Neurotransmitters and Networks Interplay in Cognition and Gait Disturbances in Patients With Parkinson Disease: Beyond Lesion Localization. Neurology 2024; 103:e209704. [PMID: 38976804 DOI: 10.1212/wnl.0000000000209704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/10/2024] Open
Affiliation(s)
- Miguel Cesar Merino Ruiz
- From the Neurological Rehabilitation Unit (M.C.M.R.), Sarah Network of Rehabilitation Hospitals; and Department of Physiological Sciences (M.C.M.R., M.R.M.), University of Brasilia - Unb, Brasília, Distrito Federal, Brazil
| | - Márcia Renata Mortari
- From the Neurological Rehabilitation Unit (M.C.M.R.), Sarah Network of Rehabilitation Hospitals; and Department of Physiological Sciences (M.C.M.R., M.R.M.), University of Brasilia - Unb, Brasília, Distrito Federal, Brazil
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4
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Pontillo G, Cepas MB, Broeders TAA, Koubiyr I, Schoonheim MM. Network Analysis in Multiple Sclerosis and Related Disorders. Neuroimaging Clin N Am 2024; 34:375-384. [PMID: 38942522 DOI: 10.1016/j.nic.2024.03.008] [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] [Indexed: 06/30/2024]
Abstract
Multiple sclerosis (MS) is a neuroinflammatory and neurodegenerative disease of the central nervous system, commonly featuring disability and cognitive impairment. The pathologic hallmark of MS lies in demyelination and hence impaired structural and functional neuronal pathways. Recent studies have shown that MS shows extensive structural disconnection of key network hub areas like the thalamus, combined with a functional network reorganization that can mostly be related to poorer clinical functioning. As MS can, therefore, be considered a network disorder, this review outlines recent innovations in the field of network neuroscience in MS.
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Affiliation(s)
- Giuseppe Pontillo
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands; MS Center Amsterdam, Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands.
| | - Mar Barrantes Cepas
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands
| | - Tommy A A Broeders
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands
| | - Ismail Koubiyr
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands
| | - Menno M Schoonheim
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands
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5
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Ye C, Zhang Y, Ran C, Ma T. Recent Progress in Brain Network Models for Medical Applications: A Review. HEALTH DATA SCIENCE 2024; 4:0157. [PMID: 38979037 PMCID: PMC11227951 DOI: 10.34133/hds.0157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Accepted: 05/28/2024] [Indexed: 07/10/2024]
Abstract
Importance: Pathological perturbations of the brain often spread via connectome to fundamentally alter functional consequences. By integrating multimodal neuroimaging data with mathematical neural mass modeling, brain network models (BNMs) enable to quantitatively characterize aberrant network dynamics underlying multiple neurological and psychiatric disorders. We delved into the advancements of BNM-based medical applications, discussed the prevalent challenges within this field, and provided possible solutions and future directions. Highlights: This paper reviewed the theoretical foundations and current medical applications of computational BNMs. Composed of neural mass models, the BNM framework allows to investigate large-scale brain dynamics behind brain diseases by linking the simulated functional signals to the empirical neurophysiological data, and has shown promise in exploring neuropathological mechanisms, elucidating therapeutic effects, and predicting disease outcome. Despite that several limitations existed, one promising trend of this research field is to precisely guide clinical neuromodulation treatment based on individual BNM simulation. Conclusion: BNM carries the potential to help understand the mechanism underlying how neuropathology affects brain network dynamics, further contributing to decision-making in clinical diagnosis and treatment. Several constraints must be addressed and surmounted to pave the way for its utilization in the clinic.
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Affiliation(s)
- Chenfei Ye
- International Research Institute for Artificial Intelligence,
Harbin Institute of Technology at Shenzhen, Shenzhen, China
| | - Yixuan Zhang
- Department of Electronic and Information Engineering,
Harbin Institute of Technology at Shenzhen, Shenzhen, China
| | - Chen Ran
- Department of Electronic and Information Engineering,
Harbin Institute of Technology at Shenzhen, Shenzhen, China
| | - Ting Ma
- International Research Institute for Artificial Intelligence,
Harbin Institute of Technology at Shenzhen, Shenzhen, China
- Department of Electronic and Information Engineering,
Harbin Institute of Technology at Shenzhen, Shenzhen, China
- Peng Cheng Laboratory, Shenzhen, China
- Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology,
Harbin Institute of Technology at Shenzhen, China
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6
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Wu S, Zhan P, Wang G, Yu X, Liu H, Wang W. Changes of brain functional network in Alzheimer's disease and frontotemporal dementia: a graph-theoretic analysis. BMC Neurosci 2024; 25:30. [PMID: 38965489 PMCID: PMC11223280 DOI: 10.1186/s12868-024-00877-w] [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: 01/12/2024] [Accepted: 06/18/2024] [Indexed: 07/06/2024] Open
Abstract
BACKGROUND Alzheimer's disease (AD) and frontotemporal dementia (FTD) are the two most common neurodegenerative dementias, presenting with similar clinical features that challenge accurate diagnosis. Despite extensive research, the underlying pathophysiological mechanisms remain unclear, and effective treatments are limited. This study aims to investigate the alterations in brain network connectivity associated with AD and FTD to enhance our understanding of their pathophysiology and establish a scientific foundation for their diagnosis and treatment. METHODS We analyzed preprocessed electroencephalogram (EEG) data from the OpenNeuro public dataset, comprising 36 patients with AD, 23 patients with FTD, and 29 healthy controls (HC). Participants were in a resting state with eyes closed. We estimated the average functional connectivity using the Phase Lag Index (PLI) for lower frequencies (delta and theta) and the Amplitude Envelope Correlation with leakage correction (AEC-c) for higher frequencies (alpha, beta, and gamma). Graph theory was applied to calculate topological parameters, including mean node degree, clustering coefficient, characteristic path length, global and local efficiency. A permutation test was then utilized to assess changes in brain network connectivity in AD and FTD based on these parameters. RESULTS Both AD and FTD patients showed increased mean PLI values in the theta frequency band, along with increases in average node degree, clustering coefficient, global efficiency, and local efficiency. Conversely, mean AEC-c values in the alpha frequency band were notably diminished, which was accompanied by decreases average node degree, clustering coefficient, global efficiency, and local efficiency. Furthermore, AD patients in the occipital region showed an increase in theta band node degree and decreased alpha band clustering coefficient and local efficiency, a pattern not observed in FTD. CONCLUSIONS Our findings reveal distinct abnormalities in the functional network topology and connectivity in AD and FTD, which may contribute to a better understanding of the pathophysiological mechanisms of these diseases. Specifically, patients with AD demonstrated a more widespread change in functional connectivity, while those with FTD retained connectivity in the occipital lobe. These observations could provide valuable insights for developing electrophysiological markers to differentiate between the two diseases.
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Affiliation(s)
- Shijing Wu
- Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, 100853, China
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Beijing, 100853, China
| | - Ping Zhan
- Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, 100853, China
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Beijing, 100853, China
| | - Guojing Wang
- Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, 100853, China
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Beijing, 100853, China
| | - Xiaohua Yu
- Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, 100853, China
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Beijing, 100853, China
| | - Hongyun Liu
- Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, 100853, China.
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Beijing, 100853, China.
| | - Weidong Wang
- Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, 100853, China.
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Beijing, 100853, China.
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7
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De Ridder D, Siddiqi MA, Dauwels J, Serdijn WA, Strydis C. NeuroDots: From Single-Target to Brain-Network Modulation: Why and What Is Needed? Neuromodulation 2024; 27:711-729. [PMID: 38639704 DOI: 10.1016/j.neurom.2024.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 11/05/2023] [Accepted: 01/10/2024] [Indexed: 04/20/2024]
Abstract
OBJECTIVES Current techniques in brain stimulation are still largely based on a phrenologic approach that a single brain target can treat a brain disorder. Nevertheless, meta-analyses of brain implants indicate an overall success rate of 50% improvement in 50% of patients, irrespective of the brain-related disorder. Thus, there is still a large margin for improvement. The goal of this manuscript is to 1) develop a general theoretical framework of brain functioning that is amenable to surgical neuromodulation, and 2) describe the engineering requirements of the next generation of implantable brain stimulators that follow from this theoretic model. MATERIALS AND METHODS A neuroscience and engineering literature review was performed to develop a universal theoretical model of brain functioning and dysfunctioning amenable to surgical neuromodulation. RESULTS Even though a single target can modulate an entire network, research in network science reveals that many brain disorders are the consequence of maladaptive interactions among multiple networks rather than a single network. Consequently, targeting the main connector hubs of those multiple interacting networks involved in a brain disorder is theoretically more beneficial. We, thus, envision next-generation network implants that will rely on distributed, multisite neuromodulation targeting correlated and anticorrelated interacting brain networks, juxtaposing alternative implant configurations, and finally providing solid recommendations for the realization of such implants. In doing so, this study pinpoints the potential shortcomings of other similar efforts in the field, which somehow fall short of the requirements. CONCLUSION The concept of network stimulation holds great promise as a universal approach for treating neurologic and psychiatric disorders.
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Affiliation(s)
- Dirk De Ridder
- Section of Neurosurgery, Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand.
| | - Muhammad Ali Siddiqi
- Department of Electrical Engineering, Lahore University of Management Sciences, Lahore, Pakistan; Neuroscience Department, Erasmus Medical Center, Rotterdam, The Netherlands; Quantum and Computer Engineering Department, Delft University of Technology, Delft, The Netherlands
| | - Justin Dauwels
- Microelectronics Department, Delft University of Technology, Delft, The Netherlands
| | - Wouter A Serdijn
- Neuroscience Department, Erasmus Medical Center, Rotterdam, The Netherlands; Section Bioelectronics, Delft University of Technology, Delft, The Netherlands
| | - Christos Strydis
- Neuroscience Department, Erasmus Medical Center, Rotterdam, The Netherlands; Quantum and Computer Engineering Department, Delft University of Technology, Delft, The Netherlands
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8
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Xie Y, Li C, Guan M, Zhang T, Ma C, Wang Z, Ma Z, Wang H, Fang P. The efficacy of low frequency repetitive transcial magnetic stimulation for treating auditory verbal hallucinations in schizophrenia: Insights from functional gradient analyses. Heliyon 2024; 10:e30194. [PMID: 38707410 PMCID: PMC11066630 DOI: 10.1016/j.heliyon.2024.e30194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 04/20/2024] [Accepted: 04/22/2024] [Indexed: 05/07/2024] Open
Abstract
Background Auditory Verbal Hallucinations (AVH) constitute a prominent feature of schizophrenia. Although low-frequency repetitive transcranial magnetic stimulation (rTMS) has demonstrated therapeutic benefits in ameliorating AVH, the underlying mechanisms of its efficacy necessitate further elucidation. Objective This study investigated the cortical gradient characteristics and their associations with clinical responses in schizophrenia patients with AVH, mediated through 1 Hz rTMS targeting the left temporoparietal junction. Method Functional gradient metrics were employed to examine the hierarchy patterns of cortical organization, capturing whole-brain functional connectivity profiles in patients and controls. Results The 1 Hz rTMS treatment effectively ameliorated the positive symptoms in patients, specifically targeting AVH. Initial evaluations revealed expanded global gradient distribution patterns and specific principal gradient variations in certain brain regions in patients at baseline compared to a control cohort. Following treatment, these divergent global and local patterns showed signs of normalizing. Furthermore, there was observed a closer alignment in between-network dispersion among various networks after treatment, including the somatomotor, attention, and limbic networks, indicating a potential harmonization of brain functionality. Conclusion Low-frequency rTMS induces alternations in principal functional gradient patterns, may serve as imaging markers to elucidate the mechanisms underpinning the therapeutic efficacy of rTMS on AVH in schizophrenia.
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Affiliation(s)
- Yuanjun Xie
- Military Medical Psychology School, Fourth Military Medical University, Xi'an, China
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Chenxi Li
- Military Medical Psychology School, Fourth Military Medical University, Xi'an, China
| | - Muzhen Guan
- Department of Mental Health, Xi'an Medical College, Xi'an, China
| | - Tian Zhang
- Military Medical Psychology School, Fourth Military Medical University, Xi'an, China
| | - Chaozong Ma
- Military Medical Psychology School, Fourth Military Medical University, Xi'an, China
| | - Zhongheng Wang
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Zhujing Ma
- Military Medical Psychology School, Fourth Military Medical University, Xi'an, China
| | - Huaning Wang
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Peng Fang
- Military Medical Psychology School, Fourth Military Medical University, Xi'an, China
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, China
- Military Medical Innovation Center, Fourth Military Medical University, Xi'an, China
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9
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Carozza S, Holmes J, Akarca D, Astle DE. Global topology of human connectome is insensitive to early life environments - A prospective longitudinal study of the general population. Dev Sci 2024:e13490. [PMID: 38494672 DOI: 10.1111/desc.13490] [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: 05/02/2023] [Revised: 01/26/2024] [Accepted: 01/28/2024] [Indexed: 03/19/2024]
Abstract
The widely acknowledged detrimental impact of early adversity on child development has driven efforts to understand the underlying mechanisms that may mediate these effects within the developing brain. Recent efforts have begun to move beyond associating adversity with the morphology of individual brain regions towards determining if and how adversity might shape their interconnectivity. However, whether adversity effects a global shift in the organisation of whole-brain networks remains unclear. In this study, we assessed this possibility using parental questionnaire and diffusion imaging data from The Avon Longitudinal Study of Parents and Children (ALSPAC, N = 913), a prospective longitudinal study spanning more than 20 years. We tested whether a wide range of adversities-including experiences of abuse, domestic violence, physical and emotional cruelty, poverty, neglect, and parental separation-measured by questionnaire within the first seven years of life were significantly associated with the tractography-derived connectome in young adulthood. We tested this across multiple measures of organisation and using a computational model that simulated the wiring economy of the brain. We found no significant relationships between early exposure to any form of adversity and the global organisation of the structural connectome in young adulthood. We did detect local differences in the medial prefrontal cortex, as well as an association between weaker brain wiring constraints and greater externalising behaviour in adolescence. Our results indicate that further efforts are necessary to delimit the magnitude and functional implications of adversity-related differences in connectomic organization. RESEARCH HIGHLIGHTS: Diverse prospective measures of the early-life environment do not predict the organisation of the DTI tractography-derived connectome in young adulthood Wiring economy of the connectome is weakly associated with externalising in adolescence, but not internalising or cognitive ability Further work is needed to establish the scope and significance of global adversity-related differences in the structural connectome.
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Affiliation(s)
- Sofia Carozza
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
- Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Joni Holmes
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
- School of Psychology, University of East Anglia, Norwich, UK
| | - Danyal Akarca
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Duncan E Astle
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
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Schindler H, Jawinski P, Arnatkevičiūtė A, Markett S. Molecular signatures of attention networks. Hum Brain Mapp 2024; 45:e26588. [PMID: 38401136 PMCID: PMC10893969 DOI: 10.1002/hbm.26588] [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: 07/04/2023] [Revised: 11/10/2023] [Accepted: 12/22/2023] [Indexed: 02/26/2024] Open
Abstract
Attention network theory proposes three distinct types of attention-alerting, orienting, and control-that are supported by separate brain networks and modulated by different neurotransmitters, that is, norepinephrine, acetylcholine, and dopamine. Here, we explore the extent of cortical, genetic, and molecular dissociation of these three attention systems using multimodal neuroimaging. We evaluated the spatial overlap between fMRI activation maps from the attention network test (ANT) and cortex-wide gene expression data from the Allen Human Brain Atlas. The goal was to identify genes associated with each of the attention networks in order to determine whether specific groups of genes were co-expressed with the corresponding attention networks. Furthermore, we analyzed publicly available PET-maps of neurotransmitter receptors and transporters to investigate their spatial overlap with the attention networks. Our analyses revealed a substantial number of genes (3871 for alerting, 6905 for orienting, 2556 for control) whose cortex-wide expression co-varied with the activation maps, prioritizing several molecular functions such as the regulation of protein biosynthesis, phosphorylation, and receptor binding. Contrary to the hypothesized associations, the ANT activation maps neither aligned with the distribution of norepinephrine, acetylcholine, and dopamine receptor and transporter molecules, nor with transcriptomic profiles that would suggest clearly separable networks. Independence of the attention networks appeared additionally constrained by a high level of spatial dependency between the network maps. Future work may need to reconceptualize the attention networks in terms of their segregation and reevaluate the presumed independence at the neural and neurochemical level.
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Affiliation(s)
| | | | - Aurina Arnatkevičiūtė
- Turner Institute for Brain and Mental Health, School of Psychological SciencesMonash UniversityMelbourneAustralia
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11
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Spalek K, Coynel D, de Quervain D, Milnik A. Sex-dependent differences in connectivity patterns are related to episodic memory recall. Hum Brain Mapp 2023; 44:5612-5623. [PMID: 37647201 PMCID: PMC10619411 DOI: 10.1002/hbm.26465] [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: 04/03/2023] [Revised: 07/12/2023] [Accepted: 08/08/2023] [Indexed: 09/01/2023] Open
Abstract
Previous studies have shown that females typically outperform males on episodic memory tasks. In this study, we investigated if (1) there are differences between males and females in their connectome characteristics, (2) if these connectivity patterns are associated with memory performance, and (3) if these brain connectome characteristics contribute to the differences in episodic memory performance between sexes. In a sample of 655 healthy young subjects (n = 391 females; n = 264 males), we derived brain network characteristics from diffusion-weighted imaging (DWI) data using models of crossing fibers within each voxel of the brain and probabilistic tractography (graph strength, shortest path length, global efficiency, and weighted transitivity). Group differences were analysed with linear models and mediation analyses were used to explore how connectivity patterns might relate to sex-dependent differences in memory performance. Our results show significant sex-dependent differences in weighted transitivity (d = 0.42), with males showing higher values. Further, we observed a negative association between weighted transitivity and memory performance (r = -0.12). Finally, these distinct connectome characteristics partially mediated the observed differences in memory performance (effect size of the indirect effect r = 0.02). Our findings indicate a higher interconnectedness in females compared to males. Additionally, we demonstrate that the sex-dependent differences in episodic memory performance can be partially explained by the differences in this connectome measure. These results further underscore the importance of sex-dependent differences in brain connectivity and their impact on cognitive function.
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Affiliation(s)
- Klara Spalek
- Division of Cognitive NeuroscienceDepartment of BiomedicineUniversity of BaselBaselSwitzerland
- Division of Molecular NeuroscienceDepartment of BiomedicineUniversity of BaselBaselSwitzerland
- Hoekzema Lab, Adult PsychiatryUniversity Medical Centre AmsterdamAmsterdamNetherlands
| | - David Coynel
- Division of Cognitive NeuroscienceDepartment of BiomedicineUniversity of BaselBaselSwitzerland
- Research Cluster Molecular and Cognitive NeurosciencesUniversity of BaselBaselSwitzerland
| | - Dominique de Quervain
- Division of Cognitive NeuroscienceDepartment of BiomedicineUniversity of BaselBaselSwitzerland
- Research Cluster Molecular and Cognitive NeurosciencesUniversity of BaselBaselSwitzerland
- Psychiatric University Clinics, University of BaselBaselSwitzerland
| | - Annette Milnik
- Division of Cognitive NeuroscienceDepartment of BiomedicineUniversity of BaselBaselSwitzerland
- Division of Molecular NeuroscienceDepartment of BiomedicineUniversity of BaselBaselSwitzerland
- Psychiatric University Clinics, University of BaselBaselSwitzerland
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12
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Ma Y, Fu S, Ye X, Yang Y, Yin Y, Xu G, Liu M, Jiang G. Aberrant single-subject morphological cerebellar connectome in chronic insomnia. Neuroimage Clin 2023; 39:103492. [PMID: 37603949 PMCID: PMC10458694 DOI: 10.1016/j.nicl.2023.103492] [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: 06/01/2023] [Revised: 07/24/2023] [Accepted: 08/06/2023] [Indexed: 08/23/2023]
Abstract
BACKGROUND To systematically investigate the topological organisation of morphological networks of the cerebellum using structural MRI and examine their clinical relevance in chronic insomnia (CI). METHODS One hundred and one patients with CI and 102 healthy controls (HCs) were recruited in this study. Individual morphological networks of the cerebellum were constructed based on regional grey matter volume, and topologically characterised using weighted graph theory-based network approaches. Between-group comparisons were performed using permutation tests, and Spearman's correlation was used to examine the relationships between topological alterations and clinical variables. RESULTS Compared with HCs, patients with CI exhibited a lower normalised clustering coefficient. Locally, CI patients exhibited lower nodal efficiency in the cerebellar lobule VIIb and vermis regions, but higher nodal efficiency in the right cerebellar lobule VIIIa regions. No correlations were observed between network alterations and clinical variables. CONCLUSIONS Individual morphological network analysis provides a new strategy for investigating cerebellar morphometric changes in CI, and our findings may have important implications in establishing diagnostic and categorical biomarkers.
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Affiliation(s)
- Yuqin Ma
- Guangzhou Medical University, Guangzhou 51495, PR China
| | - Shishun Fu
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China
| | - Xi Ye
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China
| | - Yuping Yang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510317, PR China
| | - Yi Yin
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China
| | - Guang Xu
- Department of Neurology, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China
| | - Mengchen Liu
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China
| | - Guihua Jiang
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China.
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13
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Sadat Shahabi M, Nobakhsh B, Shalbaf A, Rostami R, Kazemi R. Prediction of treatment outcome for repetitive transcranial magnetic stimulation in major depressive disorder using connectivity measures and ensemble of pre-trained deep learning models. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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14
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Messé A, Hollensteiner KJ, Delettre C, Dell-Brown LA, Pieper F, Nentwig LJ, Galindo-Leon EE, Larrat B, Mériaux S, Mangin JF, Reillo I, de Juan Romero C, Borrell V, Engler G, Toro R, Engel AK, Hilgetag CC. Structural basis of envelope and phase intrinsic coupling modes in the cerebral cortex. Neuroimage 2023; 276:120212. [PMID: 37269959 PMCID: PMC10300241 DOI: 10.1016/j.neuroimage.2023.120212] [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: 12/09/2022] [Revised: 05/09/2023] [Accepted: 05/30/2023] [Indexed: 06/05/2023] Open
Abstract
Intrinsic coupling modes (ICMs) can be observed in ongoing brain activity at multiple spatial and temporal scales. Two families of ICMs can be distinguished: phase and envelope ICMs. The principles that shape these ICMs remain partly elusive, in particular their relation to the underlying brain structure. Here we explored structure-function relationships in the ferret brain between ICMs quantified from ongoing brain activity recorded with chronically implanted micro-ECoG arrays and structural connectivity (SC) obtained from high-resolution diffusion MRI tractography. Large-scale computational models were used to explore the ability to predict both types of ICMs. Importantly, all investigations were conducted with ICM measures that are sensitive or insensitive to volume conduction effects. The results show that both types of ICMs are significantly related to SC, except for phase ICMs when using measures removing zero-lag coupling. The correlation between SC and ICMs increases with increasing frequency which is accompanied by reduced delays. Computational models produced results that were highly dependent on the specific parameter settings. The most consistent predictions were derived from measures solely based on SC. Overall, the results demonstrate that patterns of cortical functional coupling as reflected in both phase and envelope ICMs are both related, albeit to different degrees, to the underlying structural connectivity in the cerebral cortex.
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Affiliation(s)
- Arnaud Messé
- Institute of Computational Neuroscience, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany.
| | - Karl J Hollensteiner
- Department of Neurophysiology and Pathophysiology, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany
| | - Céline Delettre
- Institute of Computational Neuroscience, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany; Unité de Neuroanatomie Appliquée et Théorique, Unité de Génétique Humaine et Fonctions Cognitives, Institut Pasteur, Université Paris Cité, 25-28 rue du Dr Roux, Paris 75015, France
| | - Leigh-Anne Dell-Brown
- Institute of Computational Neuroscience, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany
| | - Florian Pieper
- Department of Neurophysiology and Pathophysiology, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany
| | - Lena J Nentwig
- Institute of Computational Neuroscience, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany
| | - Edgar E Galindo-Leon
- Department of Neurophysiology and Pathophysiology, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany
| | - Benoît Larrat
- NeuroSpin, CEA, Paris-Saclay University, Centre d'études de Saclay, Bâtiment 145, Gif-sur-Yvette 91191, France
| | - Sébastien Mériaux
- NeuroSpin, CEA, Paris-Saclay University, Centre d'études de Saclay, Bâtiment 145, Gif-sur-Yvette 91191, France
| | - Jean-François Mangin
- NeuroSpin, CEA, Paris-Saclay University, Centre d'études de Saclay, Bâtiment 145, Gif-sur-Yvette 91191, France
| | - Isabel Reillo
- Developmental Neurobiology Unit, Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas - Universidad Miguel Hernández, Sant Joan d'Alacant, Av. Santiago Ramón y Cajal s/n, Sant Joan d'Alacant 03550, Spain
| | - Camino de Juan Romero
- Developmental Neurobiology Unit, Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas - Universidad Miguel Hernández, Sant Joan d'Alacant, Av. Santiago Ramón y Cajal s/n, Sant Joan d'Alacant 03550, Spain
| | - Víctor Borrell
- Developmental Neurobiology Unit, Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas - Universidad Miguel Hernández, Sant Joan d'Alacant, Av. Santiago Ramón y Cajal s/n, Sant Joan d'Alacant 03550, Spain
| | - Gerhard Engler
- Department of Neurophysiology and Pathophysiology, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany
| | - Roberto Toro
- Unité de Neuroanatomie Appliquée et Théorique, Unité de Génétique Humaine et Fonctions Cognitives, Institut Pasteur, Université Paris Cité, 25-28 rue du Dr Roux, Paris 75015, France; Center for Research and Interdisciplinarity, Paris Descartes University, 24, rue du Faubourg Saint Jacques, Paris 75014, France
| | - Andreas K Engel
- Department of Neurophysiology and Pathophysiology, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany
| | - Claus C Hilgetag
- Institute of Computational Neuroscience, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany; Department of Health Sciences, Boston University, 635 Commonwealth Avenue, Boston, Massachusetts 02215, USA
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15
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Rong B, Huang H, Gao G, Sun L, Zhou Y, Xiao L, Wang H, Wang G. Widespread Intra- and Inter-Network Dysconnectivity among Large-Scale Resting State Networks in Schizophrenia. J Clin Med 2023; 12:jcm12093176. [PMID: 37176617 PMCID: PMC10179370 DOI: 10.3390/jcm12093176] [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: 01/27/2023] [Revised: 03/08/2023] [Accepted: 04/07/2023] [Indexed: 05/15/2023] Open
Abstract
Schizophrenia is characterized by the distributed dysconnectivity of resting-state multiple brain networks. However, the abnormalities of intra- and inter-network functional connectivity (FC) in schizophrenia and its relationship to symptoms remain unknown. The aim of the present study is to compare the intra- and inter-connectivity of the intrinsic networks between a large sample of patients with schizophrenia and healthy controls. Using the Region of interest (ROI) to ROI FC analyses, the intra- and inter-network FC of the eight resting state networks [default mode network (DMN); salience network (SN); frontoparietal network (FPN); dorsal attention network (DAN); language network (LN); visual network (VN); sensorimotor network (SMN); and cerebellar network (CN)] were investigated in 196 schizophrenia and 169-healthy controls. Compared to the healthy control group, the schizophrenia group exhibited increased intra-network FC in the DMN and decreased intra-network FC in the CN. Additionally, the schizophrenia group showed the decreased inter-network FC mainly involved the SN-DMN, SN-LN and SN-CN while increased inter-network FC in the SN-SMN and SN-DAN (p < 0.05, FDR-corrected). Our study suggests widespread intra- and inter-network dysconnectivity among large-scale RSNs in schizophrenia, mainly involving the DMN, SN and SMN, which may further contribute to the dysconnectivity hypothesis of schizophrenia.
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Affiliation(s)
- Bei Rong
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Neuropsychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Huan Huang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Guoqing Gao
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Limin Sun
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Neuropsychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yuan Zhou
- Institute of Psychology, CAS Key Laboratory of Behavioral Science, Beijing 100101, China
| | - Ling Xiao
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Neuropsychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Huiling Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Gaohua Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Neuropsychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan 430071, China
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16
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Gnörich J, Reifschneider A, Wind K, Zatcepin A, Kunte ST, Beumers P, Bartos LM, Wiedemann T, Grosch M, Xiang X, Fard MK, Ruch F, Werner G, Koehler M, Slemann L, Hummel S, Briel N, Blume T, Shi Y, Biechele G, Beyer L, Eckenweber F, Scheifele M, Bartenstein P, Albert NL, Herms J, Tahirovic S, Haass C, Capell A, Ziegler S, Brendel M. Depletion and activation of microglia impact metabolic connectivity of the mouse brain. J Neuroinflammation 2023; 20:47. [PMID: 36829182 PMCID: PMC9951492 DOI: 10.1186/s12974-023-02735-8] [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: 07/03/2022] [Accepted: 02/13/2023] [Indexed: 02/26/2023] Open
Abstract
AIM We aimed to investigate the impact of microglial activity and microglial FDG uptake on metabolic connectivity, since microglial activation states determine FDG-PET alterations. Metabolic connectivity refers to a concept of interacting metabolic brain regions and receives growing interest in approaching complex cerebral metabolic networks in neurodegenerative diseases. However, underlying sources of metabolic connectivity remain to be elucidated. MATERIALS AND METHODS We analyzed metabolic networks measured by interregional correlation coefficients (ICCs) of FDG-PET scans in WT mice and in mice with mutations in progranulin (Grn) or triggering receptor expressed on myeloid cells 2 (Trem2) knockouts (-/-) as well as in double mutant Grn-/-/Trem2-/- mice. We selected those rodent models as they represent opposite microglial signatures with disease associated microglia in Grn-/- mice and microglia locked in a homeostatic state in Trem2-/- mice; however, both resulting in lower glucose uptake of the brain. The direct influence of microglia on metabolic networks was further determined by microglia depletion using a CSF1R inhibitor in WT mice at two different ages. Within maps of global mean scaled regional FDG uptake, 24 pre-established volumes of interest were applied and assigned to either cortical or subcortical networks. ICCs of all region pairs were calculated and z-transformed prior to group comparisons. FDG uptake of neurons, microglia, and astrocytes was determined in Grn-/- and WT mice via assessment of single cell tracer uptake (scRadiotracing). RESULTS Microglia depletion by CSF1R inhibition resulted in a strong decrease of metabolic connectivity defined by decrease of mean cortical ICCs in WT mice at both ages studied (6-7 m; p = 0.0148, 9-10 m; p = 0.0191), when compared to vehicle-treated age-matched WT mice. Grn-/-, Trem2-/- and Grn-/-/Trem2-/- mice all displayed reduced FDG-PET signals when compared to WT mice. However, when analyzing metabolic networks, a distinct increase of ICCs was observed in Grn-/- mice when compared to WT mice in cortical (p < 0.0001) and hippocampal (p < 0.0001) networks. In contrast, Trem2-/- mice did not show significant alterations in metabolic connectivity when compared to WT. Furthermore, the increased metabolic connectivity in Grn-/- mice was completely suppressed in Grn-/-/Trem2-/- mice. Grn-/- mice exhibited a severe loss of neuronal FDG uptake (- 61%, p < 0.0001) which shifted allocation of cellular brain FDG uptake to microglia (42% in Grn-/- vs. 22% in WT). CONCLUSIONS Presence, absence, and activation of microglia have a strong impact on metabolic connectivity of the mouse brain. Enhanced metabolic connectivity is associated with increased microglial FDG allocation.
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Affiliation(s)
- Johannes Gnörich
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Anika Reifschneider
- grid.5252.00000 0004 1936 973XMetabolic Biochemistry, Faculty of Medicine, Biomedical Center (BMC), Ludwig-Maximilians-Universität München, Munich, Germany
| | - Karin Wind
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Artem Zatcepin
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Sebastian T. Kunte
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany
| | - Philipp Beumers
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany
| | - Laura M. Bartos
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany
| | - Thomas Wiedemann
- grid.5252.00000 0004 1936 973XMetabolic Biochemistry, Faculty of Medicine, Biomedical Center (BMC), Ludwig-Maximilians-Universität München, Munich, Germany
| | - Maximilian Grosch
- grid.5252.00000 0004 1936 973XGerman Center for Vertigo and Balance Disorders, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Xianyuan Xiang
- grid.5252.00000 0004 1936 973XMetabolic Biochemistry, Faculty of Medicine, Biomedical Center (BMC), Ludwig-Maximilians-Universität München, Munich, Germany ,grid.9227.e0000000119573309CAS Key Laboratory of Brain Connectome and Manipulation, The Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, 518055 China
| | - Maryam K. Fard
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Francois Ruch
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany
| | - Georg Werner
- grid.5252.00000 0004 1936 973XMetabolic Biochemistry, Faculty of Medicine, Biomedical Center (BMC), Ludwig-Maximilians-Universität München, Munich, Germany
| | - Mara Koehler
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany
| | - Luna Slemann
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany
| | - Selina Hummel
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany
| | - Nils Briel
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany ,grid.5252.00000 0004 1936 973XCenter for Neuropathology and Prion Research, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Tanja Blume
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany ,grid.5252.00000 0004 1936 973XCenter for Neuropathology and Prion Research, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Yuan Shi
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany ,grid.5252.00000 0004 1936 973XCenter for Neuropathology and Prion Research, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Gloria Biechele
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany
| | - Leonie Beyer
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany
| | - Florian Eckenweber
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany
| | - Maximilian Scheifele
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany
| | - Peter Bartenstein
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany ,grid.452617.3Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Nathalie L. Albert
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany
| | - Jochen Herms
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany ,grid.5252.00000 0004 1936 973XCenter for Neuropathology and Prion Research, Ludwig-Maximilians-Universität München, Munich, Germany ,grid.452617.3Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Sabina Tahirovic
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Christian Haass
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany ,grid.5252.00000 0004 1936 973XMetabolic Biochemistry, Faculty of Medicine, Biomedical Center (BMC), Ludwig-Maximilians-Universität München, Munich, Germany ,grid.452617.3Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Anja Capell
- grid.5252.00000 0004 1936 973XMetabolic Biochemistry, Faculty of Medicine, Biomedical Center (BMC), Ludwig-Maximilians-Universität München, Munich, Germany
| | - Sibylle Ziegler
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany ,grid.452617.3Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Matthias Brendel
- Department of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377, Munich, Germany. .,German Center for Neurodegenerative Diseases (DZNE), Munich, Germany. .,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.
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Oikonomou VP, Georgiadis K, Kalaganis F, Nikolopoulos S, Kompatsiaris I. A Sparse Representation Classification Scheme for the Recognition of Affective and Cognitive Brain Processes in Neuromarketing. SENSORS (BASEL, SWITZERLAND) 2023; 23:2480. [PMID: 36904683 PMCID: PMC10007402 DOI: 10.3390/s23052480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 02/09/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
In this work, we propose a novel framework to recognize the cognitive and affective processes of the brain during neuromarketing-based stimuli using EEG signals. The most crucial component of our approach is the proposed classification algorithm that is based on a sparse representation classification scheme. The basic assumption of our approach is that EEG features from a cognitive or affective process lie on a linear subspace. Hence, a test brain signal can be represented as a linear (or weighted) combination of brain signals from all classes in the training set. The class membership of the brain signals is determined by adopting the Sparse Bayesian Framework with graph-based priors over the weights of linear combination. Furthermore, the classification rule is constructed by using the residuals of linear combination. The experiments on a publicly available neuromarketing EEG dataset demonstrate the usefulness of our approach. For the two classification tasks offered by the employed dataset, namely affective state recognition and cognitive state recognition, the proposed classification scheme manages to achieve a higher classification accuracy compared to the baseline and state-of-the art methods (more than 8% improvement in classification accuracy).
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Affiliation(s)
- Vangelis P. Oikonomou
- Information Technologies Institute, Centre for Research and Technology Hellas, CERTH-ITI, 6th km Charilaou-Thermi Road, 57001 Thessaloniki, Greece
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18
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Schurman CA, Burton JB, Rose J, Ellerby LM, Alliston T, Schilling B. Molecular and Cellular Crosstalk between Bone and Brain: Accessing Bidirectional Neural and Musculoskeletal Signaling during Aging and Disease. J Bone Metab 2023; 30:1-29. [PMID: 36950837 PMCID: PMC10036181 DOI: 10.11005/jbm.2023.30.1.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 12/15/2022] [Accepted: 12/20/2022] [Indexed: 03/24/2023] Open
Abstract
Molecular omics technologies, including proteomics, have enabled the elucidation of key signaling pathways that mediate bidirectional communication between the brain and bone tissues. Here we provide a brief summary of the clinical and molecular evidence of the need to study the bone-brain axis of cross-tissue cellular communication. Clear clinical and molecular evidence suggests biological interactions and similarities between bone and brain cells. Here we review the current mass spectrometric techniques for studying brain and bone diseases with an emphasis on neurodegenerative diseases and osteoarthritis/osteoporosis, respectively. Further study of the bone-brain axis on a molecular level and evaluation of the role of proteins, neuropeptides, osteokines, and hormones in molecular pathways linked to bone and brain diseases is critically needed. The use of mass spectrometry and other omics technologies to analyze these cross-tissue signaling events and interactions will help us better understand disease progression and comorbidities and potentially identify new pathways and targets for therapeutic interventions. Proteomic measurements are particularly favorable for investigating the role of signaling and secreted and circulating analytes and identifying molecular and metabolic pathways implicated in age-related diseases.
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Affiliation(s)
| | | | - Jacob Rose
- Buck Institute for Research on Aging, Novato, CA,
USA
| | | | - Tamara Alliston
- Department of Orthopaedic Surgery, University of California San Francisco, San Francisco, CA,
USA
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19
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Vedaei F, Mashhadi N, Zabrecky G, Monti D, Navarreto E, Hriso C, Wintering N, Newberg AB, Mohamed FB. Identification of chronic mild traumatic brain injury using resting state functional MRI and machine learning techniques. Front Neurosci 2023; 16:1099560. [PMID: 36699521 PMCID: PMC9869678 DOI: 10.3389/fnins.2022.1099560] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 12/21/2022] [Indexed: 01/11/2023] Open
Abstract
Mild traumatic brain injury (mTBI) is a major public health concern that can result in a broad spectrum of short-term and long-term symptoms. Recently, machine learning (ML) algorithms have been used in neuroscience research for diagnostics and prognostic assessment of brain disorders. The present study aimed to develop an automatic classifier to distinguish patients suffering from chronic mTBI from healthy controls (HCs) utilizing multilevel metrics of resting-state functional magnetic resonance imaging (rs-fMRI). Sixty mTBI patients and forty HCs were enrolled and allocated to training and testing datasets with a ratio of 80:20. Several rs-fMRI metrics including fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), functional connectivity strength (FCS), and seed-based FC were generated from two main analytical categories: local measures and network measures. Statistical two-sample t-test was employed comparing between mTBI and HCs groups. Then, for each rs-fMRI metric the features were selected extracting the mean values from the clusters showing significant differences. Finally, the support vector machine (SVM) models based on separate and multilevel metrics were built and the performance of the classifiers were assessed using five-fold cross-validation and via the area under the receiver operating characteristic curve (AUC). Feature importance was estimated using Shapley additive explanation (SHAP) values. Among local measures, the range of AUC was 86.67-100% and the optimal SVM model was obtained based on combined multilevel rs-fMRI metrics and DC as a separate model with AUC of 100%. Among network measures, the range of AUC was 80.42-93.33% and the optimal SVM model was obtained based on the combined multilevel seed-based FC metrics. The SHAP analysis revealed the DC value in the left postcentral and seed-based FC value between the motor ventral network and right superior temporal as the most important local and network features with the greatest contribution to the classification models. Our findings demonstrated that different rs-fMRI metrics can provide complementary information for classifying patients suffering from chronic mTBI. Moreover, we showed that ML approach is a promising tool for detecting patients with mTBI and might serve as potential imaging biomarker to identify patients at individual level. Clinical trial registration [clinicaltrials.gov], identifier [NCT03241732].
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Affiliation(s)
- Faezeh Vedaei
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
| | - Najmeh Mashhadi
- Department of Computer Science and Engineering, University of California Santa Cruz, Santa Cruz, CA, United States
| | - George Zabrecky
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Daniel Monti
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Emily Navarreto
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Chloe Hriso
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Nancy Wintering
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Andrew B. Newberg
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Feroze B. Mohamed
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
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20
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Cao Z, Xiao X, Zhao Y, Jiang Y, Xie C, Paillère-Martinot ML, Artiges E, Li Z, Daskalakis ZJ, Yang Y, Zhu C. Targeting the pathological network: Feasibility of network-based optimization of transcranial magnetic stimulation coil placement for treatment of psychiatric disorders. Front Neurosci 2023; 16:1079078. [PMID: 36685239 PMCID: PMC9846047 DOI: 10.3389/fnins.2022.1079078] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 12/05/2022] [Indexed: 01/06/2023] Open
Abstract
It has been recognized that the efficacy of TMS-based modulation may depend on the network profile of the stimulated regions throughout the brain. However, what profile of this stimulation network optimally benefits treatment outcomes is yet to be addressed. The answer to the question is crucial for informing network-based optimization of stimulation parameters, such as coil placement, in TMS treatments. In this study, we aimed to investigate the feasibility of taking a disease-specific network as the target of stimulation network for guiding individualized coil placement in TMS treatments. We present here a novel network-based model for TMS targeting of the pathological network. First, combining E-field modeling and resting-state functional connectivity, stimulation networks were modeled from locations and orientations of the TMS coil. Second, the spatial anti-correlation between the stimulation network and the pathological network of a given disease was hypothesized to predict the treatment outcome. The proposed model was validated to predict treatment efficacy from the position and orientation of TMS coils in two depression cohorts and one schizophrenia cohort with auditory verbal hallucinations. We further demonstrate the utility of the proposed model in guiding individualized TMS treatment for psychiatric disorders. In this proof-of-concept study, we demonstrated the feasibility of the novel network-based targeting strategy that uses the whole-brain, system-level abnormity of a specific psychiatric disease as a target. Results based on empirical data suggest that the strategy may potentially be utilized to identify individualized coil parameters for maximal therapeutic effects.
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Affiliation(s)
- Zhengcao Cao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xiang Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States
| | - Yang Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yihan Jiang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Cong Xie
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Marie-Laure Paillère-Martinot
- Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, APHP.Sorbonne Université, Paris, France
- INSERM U A10 Developmental Trajectories and Psychiatry, Ecole Normale Supérieure Paris-Saclay, CNRS, Center Borelli, University of Paris-Saclay, Gif-sur-Yvette, France
| | - Eric Artiges
- INSERM U A10 Developmental Trajectories and Psychiatry, Ecole Normale Supérieure Paris-Saclay, CNRS, Center Borelli, University of Paris-Saclay, Gif-sur-Yvette, France
- Department of Psychiatry, Etablissement Public de Santé (EPS) Barthélemy Durand, tampes, France
| | - Zheng Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Center for Cognition and Neuroergonomics, Beijing Normal University at Zhuhai, Zhuhai, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Zafiris J. Daskalakis
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States
| | - Chaozhe Zhu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China
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21
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Deery HA, Di Paolo R, Moran C, Egan GF, Jamadar SD. The older adult brain is less modular, more integrated, and less efficient at rest: A systematic review of large-scale resting-state functional brain networks in aging. Psychophysiology 2023; 60:e14159. [PMID: 36106762 PMCID: PMC10909558 DOI: 10.1111/psyp.14159] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 07/22/2022] [Accepted: 07/25/2022] [Indexed: 12/23/2022]
Abstract
The literature on large-scale resting-state functional brain networks across the adult lifespan was systematically reviewed. Studies published between 1986 and July 2021 were retrieved from PubMed. After reviewing 2938 records, 144 studies were included. Results on 11 network measures were summarized and assessed for certainty of the evidence using a modified GRADE method. The evidence provides high certainty that older adults display reduced within-network and increased between-network functional connectivity. Older adults also show lower segregation, modularity, efficiency and hub function, and decreased lateralization and a posterior to anterior shift at rest. Higher-order functional networks reliably showed age differences, whereas primary sensory and motor networks showed more variable results. The inflection point for network changes is often the third or fourth decade of life. Age effects were found with moderate certainty for within- and between-network altered patterns and speed of dynamic connectivity. Research on within-subject bold variability and connectivity using glucose uptake provides low certainty of age differences but warrants further study. Taken together, these age-related changes may contribute to the cognitive decline often seen in older adults.
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Affiliation(s)
- Hamish A. Deery
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
| | - Robert Di Paolo
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
| | - Chris Moran
- Peninsula Clinical School, Central Clinical SchoolMonash UniversityFrankstonVictoriaAustralia
- Department of Geriatric MedicinePeninsula HealthFrankstonVictoriaAustralia
| | - Gary F. Egan
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
- Australian Research Council Centre of Excellence for Integrative Brain FunctionMelbourneVictoriaAustralia
| | - Sharna D. Jamadar
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
- Australian Research Council Centre of Excellence for Integrative Brain FunctionMelbourneVictoriaAustralia
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22
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Kandilarova S, Stoyanov D, Aryutova K, Paunova R, Mantarkov M, Mitrev I, Todeva-Radneva A, Specht K. Effective Connectivity Between the Orbitofrontal Cortex and the Precuneus Differentiates Major Psychiatric Disorders: Results from a Transdiagnostic Spectral DCM Study. CNS & NEUROLOGICAL DISORDERS DRUG TARGETS 2023; 22:180-190. [PMID: 34533450 DOI: 10.2174/1871527320666210917142815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 08/09/2021] [Accepted: 08/12/2021] [Indexed: 12/16/2022]
Abstract
BACKGROUND & OBJECTIVE We have previously identified aberrant connectivity of the left precuneus, ventrolateral prefrontal cortex, anterior cingulate cortex, and anterior insula in patients with either a paranoid (schizophrenia), or a depressive syndrome (both unipolar and bipolar). In the current study, we attempted to replicate and expand these findings by including a healthy control sample and separating the patients in a depressive episode into two groups: unipolar and bipolar depression. We hypothesized that the connections between those major nodes of the resting state networks would demonstrate different patterns in the three patient groups compared to the healthy subjects. METHODS Resting-state functional MRI was performed on a sample of 101 participants, of which 26 patients with schizophrenia (current psychotic episodes), 24 subjects with Bipolar Disorder (BD), 33 with Major Depressive Disorder (MDD) (both BD and MDD patients were in a current depressive episode), and 21 healthy controls. Spectral Dynamic Causal Modeling was used to calculate the coupling values between eight regions of interest, including the anterior precuneus (PRC), anterior hippocampus, anterior insula, angular gyrus, lateral Orbitofrontal Cortex (OFC), middle frontal gyrus, planum temporale, and anterior thalamus. RESULTS & CONCLUSION We identified disturbed effective connectivity from the left lateral orbitofrontal cortex to the left anterior precuneus that differed significantly between unipolar depression, where the influence was inhibitory, and bipolar depression, where the effect was excitatory. A logistic regression analysis correctly classified 75% of patients with unipolar and bipolar depression based solely on the coupling values of this connection. In addition, patients with schizophrenia demonstrated negative effective connectivity from the anterior PRC to the lateral OFC, which distinguished them from healthy controls and patients with major depression. Future studies with unmedicated patients will be needed to establish the replicability of our findings.
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Affiliation(s)
- Sevdalina Kandilarova
- Department of Psychiatry and Medical Psychology, Medical University-Plovdiv, Plovdiv, Bulgaria
- Division of Translational Neuroscience, Research Institute at Medical University-Plovdiv, Plovdiv, Bulgaria
| | - Drozdstoy Stoyanov
- Department of Psychiatry and Medical Psychology, Medical University-Plovdiv, Plovdiv, Bulgaria
- Division of Translational Neuroscience, Research Institute at Medical University-Plovdiv, Plovdiv, Bulgaria
| | - Katrin Aryutova
- Department of Psychiatry and Medical Psychology, Medical University-Plovdiv, Plovdiv, Bulgaria
- Division of Translational Neuroscience, Research Institute at Medical University-Plovdiv, Plovdiv, Bulgaria
| | - Rossitsa Paunova
- Department of Psychiatry and Medical Psychology, Medical University-Plovdiv, Plovdiv, Bulgaria
- Division of Translational Neuroscience, Research Institute at Medical University-Plovdiv, Plovdiv, Bulgaria
| | - Mladen Mantarkov
- Department of Psychiatry and Medical Psychology, Medical University-Plovdiv, Plovdiv, Bulgaria
| | - Ivo Mitrev
- Department of Psychiatry and Medical Psychology, Medical University-Plovdiv, Plovdiv, Bulgaria
| | - Anna Todeva-Radneva
- Department of Psychiatry and Medical Psychology, Medical University-Plovdiv, Plovdiv, Bulgaria
- Division of Translational Neuroscience, Research Institute at Medical University-Plovdiv, Plovdiv, Bulgaria
| | - Karsten Specht
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
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23
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Brain network architecture constrains age-related cortical thinning. Neuroimage 2022; 264:119721. [PMID: 36341953 DOI: 10.1016/j.neuroimage.2022.119721] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 10/23/2022] [Accepted: 10/28/2022] [Indexed: 11/06/2022] Open
Abstract
Age-related cortical atrophy, approximated by cortical thickness measurements from magnetic resonance imaging, follows a characteristic pattern over the lifespan. Although its determinants remain unknown, mounting evidence demonstrates correspondence between the connectivity profiles of structural and functional brain networks and cortical atrophy in health and neurological disease. Here, we performed a cross-sectional multimodal neuroimaging analysis of 2633 individuals from a large population-based cohort to characterize the association between age-related differences in cortical thickness and functional as well as structural brain network topology. We identified a widespread pattern of age-related cortical thickness differences including "hotspots" of pronounced age effects in sensorimotor areas. Regional age-related differences were strongly correlated within the structurally defined node neighborhood. The overall pattern of thickness differences was found to be anchored in the functional network hierarchy as encoded by macroscale functional connectivity gradients. Lastly, the identified difference pattern covaried significantly with cognitive and motor performance. Our findings indicate that connectivity profiles of functional and structural brain networks act as organizing principles behind age-related cortical thinning as an imaging surrogate of cortical atrophy.
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24
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Wang S, Wen H, Qiu S, Xie P, Qiu J, He H. Driving brain state transitions in major depressive disorder through external stimulation. Hum Brain Mapp 2022; 43:5326-5339. [PMID: 35808927 PMCID: PMC9812249 DOI: 10.1002/hbm.26006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 05/27/2022] [Accepted: 06/22/2022] [Indexed: 01/15/2023] Open
Abstract
Major depressive disorder (MDD) as a dysfunction of neural circuits and brain networks has been established in modern neuroimaging sciences. However, the brain state transitions between MDD and health through external stimulation remain unclear, which limits translation to clinical contexts and demonstrable clinical utility. We propose a framework of the large-scale whole-brain network model for MDD linking the underlying anatomical connectivity with functional dynamics obtained from diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI). Then, we further explored the optimal brain regions to promote the transition of brain states between MDD and health through external stimulation of the model. Based on the whole-brain model successfully fitting the brain state space in MDD and the health, we demonstrated that the transition from MDD to health is achieved by the excitatory activation of the limbic system and from health to MDD by the inhibitory stimulation of the reward circuit. Our finding provides novel biophysical evidence for the neural mechanism of MDD and its recovery and allows the discovery of new stimulation targets for MDD recovery.
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Affiliation(s)
- Shengpei Wang
- Research Centre for Brain‐inspired Intelligence and National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina
| | - Hongwei Wen
- Key Laboratory of Cognition and Personality (Ministry of Education)ChongqingChina
- School of PsychologySouthwest UniversityChongqingChina
| | - Shuang Qiu
- Research Centre for Brain‐inspired Intelligence and National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina
| | - Peng Xie
- Institute of NeuroscienceChongqing Medical UniversityChongqingChina
- Chongqing Key Laboratory of NeurobiologyChongqingChina
- Department of Neurologythe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (Ministry of Education)ChongqingChina
- School of PsychologySouthwest UniversityChongqingChina
| | - Huiguang He
- Research Centre for Brain‐inspired Intelligence and National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
- Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesBeijingChina
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25
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Zovetti N, Bellani M, Chowdury A, Alessandrini F, Zoccatelli G, Perlini C, Ricciardi GK, Marzi CA, Diwadkar VA, Brambilla P. Inefficient white matter activity in Schizophrenia evoked during intra and inter-hemispheric communication. Transl Psychiatry 2022; 12:449. [PMID: 36244980 PMCID: PMC9573867 DOI: 10.1038/s41398-022-02200-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 09/13/2022] [Accepted: 09/22/2022] [Indexed: 11/23/2022] Open
Abstract
Intensive cognitive tasks induce inefficient regional and network responses in schizophrenia (SCZ). fMRI-based studies have naturally focused on gray matter, but appropriately titrated visuo-motor integration tasks reliably activate inter- and intra-hemispheric white matter pathways. Such tasks can assess network inefficiency without demanding intensive cognitive effort. Here, we provide the first application of this framework to the study of white matter functional responses in SCZ. Event-related fMRI data were acquired from 28 patients (nine females, mean age 43.3, ±11.7) and 28 age- and gender-comparable controls (nine females, mean age 42.1 ± 10.1), using the Poffenberger paradigm, a rapid visual detection task used to induce intra- (ipsi-lateral visual and motor cortex) or inter-hemispheric (contra-lateral visual and motor cortex) transfer. fMRI data were pre- and post-processed to reliably isolate activations in white matter, using probabilistic tractography-based white matter tracts. For intra- and inter-hemispheric transfer conditions, SCZ evinced hyper-activations in longitudinal and transverse white matter tracts, with hyper-activation in sub-regions of the corpus callosum primarily observed during inter-hemispheric transfer. Evidence for the functional inefficiency of white matter was observed in conjunction with small (~50 ms) but significant increases in response times. Functional inefficiencies in SCZ are (1) observable in white matter, with the degree of inefficiency contextually related to task-conditions, and (2) are evoked by simple detection tasks without intense cognitive processing. These cumulative results while expanding our understanding of this dys-connection syndrome, also extend the search of biomarkers beyond the traditional realm of fMRI studies of gray matter.
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Affiliation(s)
- Niccolò Zovetti
- grid.5611.30000 0004 1763 1124Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy
| | - Marcella Bellani
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy.
| | - Asadur Chowdury
- grid.254444.70000 0001 1456 7807Department of Psychiatry & Behavioral Neurosciences, Wayne State University, Detroit, MI USA
| | - Franco Alessandrini
- grid.411475.20000 0004 1756 948XNeuroradiology Department, Azienda Ospedaliera Universitaria Integrata di Verona, Verona, Italy
| | - Giada Zoccatelli
- grid.411475.20000 0004 1756 948XNeuroradiology Department, Azienda Ospedaliera Universitaria Integrata di Verona, Verona, Italy
| | - Cinzia Perlini
- grid.5611.30000 0004 1763 1124Department of Neurosciences, Biomedicine and Movement Sciences, Section of Clinical Psychology, University of Verona, Verona, Italy
| | - Giuseppe K. Ricciardi
- Pathology and Diagnostics, Section of Neuroradiology, Hospital Trust Verona, Verona, Italy
| | - Carlo A. Marzi
- grid.5611.30000 0004 1763 1124Physiology and Psychology Section, Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy ,National Institute of Neuroscience, Verona, Italy
| | - Vaibhav A. Diwadkar
- grid.254444.70000 0001 1456 7807Department of Psychiatry & Behavioral Neurosciences, Wayne State University, Detroit, MI USA
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy. .,Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
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26
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Chino B, Cuesta P, Pacios J, de Frutos-Lucas J, Torres-Simón L, Doval S, Marcos A, Bruña R, Maestú F. Episodic memory dysfunction and hypersynchrony in brain functional networks in cognitively intact subjects and MCI: a study of 379 individuals. GeroScience 2022; 45:477-489. [PMID: 36109436 PMCID: PMC9886758 DOI: 10.1007/s11357-022-00656-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 09/01/2022] [Indexed: 02/03/2023] Open
Abstract
Delayed recall (DR) impairment is one of the most significant predictive factors in defining the progression to Alzheimer's disease (AD). Changes in brain functional connectivity (FC) could accompany this decline in the DR performance even in a resting state condition from the preclinical stages to the diagnosis of AD itself, so the characterization of the relationship between the two phenomena has attracted increasing interest. Another aspect to contemplate is the potential moderator role of the APOE genotype in this association, considering the evidence about their implication for the disease. 379 subjects (118 mild cognitive impairment (MCI) and 261 cognitively intact (CI) individuals) underwent an extensive evaluation, including MEG recording. Applying cluster-based permutation test, we identified a cluster of differences in FC and studied which connections drove such an effect in DR. The moderation effect of APOE genotype between FC results and delayed recall was evaluated too. Higher FC in beta band in the right occipital region is associated with lower DR scores in both groups. A significant anteroposterior link emerged in the seed-based analysis with higher values in MCI. Moreover, APOE genotype appeared as a moderator between beta FC and DR performance only in the CI group. An increased beta FC in the anteroposterior brain region appears to be associated with lower memory performance in MCI. This finding could help discriminate the pattern of the progression of healthy aging to MCI and the relation between resting state and memory performance.
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Affiliation(s)
- Brenda Chino
- Institute of Neuroscience, Autonomous University of Barcelona, Barcelona, Spain. .,Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain.
| | - Pablo Cuesta
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain ,Department of Radiology, Rehabilitation, and Physiotherapy, Complutense University of Madrid, Madrid, Spain ,Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain
| | - Javier Pacios
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain ,Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain ,Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
| | - Jaisalmer de Frutos-Lucas
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain ,Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain ,Centre for Precision Health, Edith Cowan University, Joondalup, WA 6027 Australia ,Centro de Investigación Nebrija en Cognición (CINC), Universidad de Nebrija, Madrid, Spain
| | - Lucía Torres-Simón
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain ,Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
| | - Sandra Doval
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain ,Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
| | - Alberto Marcos
- Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain ,Neurology Department, Hospital Clinico San Carlos, Madrid, Spain
| | - Ricardo Bruña
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain ,Department of Radiology, Rehabilitation, and Physiotherapy, Complutense University of Madrid, Madrid, Spain ,Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain
| | - Fernando Maestú
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain ,Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain ,Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
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27
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Zahnert F, Kräling G, Melms L, Belke M, Kleinholdermann U, Timmermann L, Hirsch M, Jansen A, Mross P, Menzler K, Habermehl L, Knake S. Diffusion magnetic resonance imaging connectome features are predictive of functional lateralization of semantic processing in the anterior temporal lobes. Hum Brain Mapp 2022; 44:496-508. [PMID: 36098483 PMCID: PMC9842893 DOI: 10.1002/hbm.26074] [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: 03/16/2022] [Revised: 07/22/2022] [Accepted: 08/18/2022] [Indexed: 01/25/2023] Open
Abstract
Assessment of regional language lateralization is crucial in many scenarios, but not all populations are suited for its evaluation via task-functional magnetic resonance imaging (fMRI). In this study, the utility of structural connectome features for the classification of language lateralization in the anterior temporal lobes (ATLs) was investigated. Laterality indices for semantic processing in the ATL were computed from task-fMRI in 1038 subjects from the Human Connectome Project who were labeled as stronger rightward lateralized (RL) or stronger leftward to bilaterally lateralized (LL) in a data-driven approach. Data of unrelated subjects (n = 432) were used for further analyses. Structural connectomes were generated from diffusion-MRI tractography, and graph theoretical metrics (node degree, betweenness centrality) were computed. A neural network (NN) and a random forest (RF) classifier were trained on these metrics to classify subjects as RL or LL. After classification, comparisons of network measures were conducted via permutation testing. Degree-based classifiers produced significant above-chance predictions both during cross-validation (NN: AUC-ROC[CI] = 0.68[0.64-0.73], accuracy[CI] = 68.34%[63-73.2%]; RF: AUC-ROC[CI] = 0.7[0.66-0.73], accuracy[CI] = 64.81%[60.9-68.5]) and testing (NN: AUC-ROC[CI] = 0.69[0.53-0.84], accuracy[CI] = 68.09[53.2-80.9]; RF: AUC-ROC[CI] = 0.68[0.53-0.84], accuracy[CI] = 68.09[55.3-80.9]). Comparison of network metrics revealed small effects of increased node degree within the right posterior middle temporal gyrus (pMTG) in subjects with RL, while degree was decreased in the right posterior cingulate cortex (PCC). Above-chance predictions of functional language lateralization in the ATL are possible based on diffusion-MRI connectomes alone. Increased degree within the right pMTG as a right-sided homologue of a known semantic hub, and decreased hubness of the right PCC may form a structural basis for rightward-lateralized semantic processing.
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Affiliation(s)
- Felix Zahnert
- Epilepsy Center Hesse, Department for NeurologyUniversity Hospital Marburg, Philipps University MarburgMarburgGermany
| | - Gunter Kräling
- Department of Medical TechnologyUniversity Hospital MarburgMarburgGermany
| | - Leander Melms
- Institute for Artificial IntelligenceUniversity Hospital Marburg, Philipps University MarburgMarburgGermany
| | - Marcus Belke
- Epilepsy Center Hesse, Department for NeurologyUniversity Hospital Marburg, Philipps University MarburgMarburgGermany,LOEWE Center for Personalized Translational Epilepsy Research (CePTER)Goethe‐University FrankfurtFrankfurt Am MainGermany
| | - Urs Kleinholdermann
- Department for NeurologyUniversity Hospital Marburg, Philipps University MarburgMarburgGermany
| | - Lars Timmermann
- Department for NeurologyUniversity Hospital Marburg, Philipps University MarburgMarburgGermany,Center for Mind, Brain and Behavior (CMBB)Philipps‐University MarburgMarburgGermany,Core Facility Brainimaging, Faculty of MedicineUniversity of MarburgMarburgGermany
| | - Martin Hirsch
- Institute for Artificial IntelligenceUniversity Hospital Marburg, Philipps University MarburgMarburgGermany
| | - Andreas Jansen
- Center for Mind, Brain and Behavior (CMBB)Philipps‐University MarburgMarburgGermany,Core Facility Brainimaging, Faculty of MedicineUniversity of MarburgMarburgGermany,Department for Psychiatry and PsychotherapyUniversity Hospital Marburg, Philipps University MarburgMarburgGermany
| | - Peter Mross
- Epilepsy Center Hesse, Department for NeurologyUniversity Hospital Marburg, Philipps University MarburgMarburgGermany
| | - Katja Menzler
- Epilepsy Center Hesse, Department for NeurologyUniversity Hospital Marburg, Philipps University MarburgMarburgGermany,Center for Mind, Brain and Behavior (CMBB)Philipps‐University MarburgMarburgGermany,Core Facility Brainimaging, Faculty of MedicineUniversity of MarburgMarburgGermany
| | - Lena Habermehl
- Epilepsy Center Hesse, Department for NeurologyUniversity Hospital Marburg, Philipps University MarburgMarburgGermany
| | - Susanne Knake
- Epilepsy Center Hesse, Department for NeurologyUniversity Hospital Marburg, Philipps University MarburgMarburgGermany,LOEWE Center for Personalized Translational Epilepsy Research (CePTER)Goethe‐University FrankfurtFrankfurt Am MainGermany,Center for Mind, Brain and Behavior (CMBB)Philipps‐University MarburgMarburgGermany,Core Facility Brainimaging, Faculty of MedicineUniversity of MarburgMarburgGermany
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28
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Fathian A, Jamali Y, Raoufy MR. The trend of disruption in the functional brain network topology of Alzheimer's disease. Sci Rep 2022; 12:14998. [PMID: 36056059 PMCID: PMC9440254 DOI: 10.1038/s41598-022-18987-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 08/23/2022] [Indexed: 12/19/2022] Open
Abstract
Alzheimer's disease (AD) is a progressive disorder associated with cognitive dysfunction that alters the brain's functional connectivity. Assessing these alterations has become a topic of increasing interest. However, a few studies have examined different stages of AD from a complex network perspective that cover different topological scales. This study used resting state fMRI data to analyze the trend of functional connectivity alterations from a cognitively normal (CN) state through early and late mild cognitive impairment (EMCI and LMCI) and to Alzheimer's disease. The analyses had been done at the local (hubs and activated links and areas), meso (clustering, assortativity, and rich-club), and global (small-world, small-worldness, and efficiency) topological scales. The results showed that the trends of changes in the topological architecture of the functional brain network were not entirely proportional to the AD progression. There were network characteristics that have changed non-linearly regarding the disease progression, especially at the earliest stage of the disease, i.e., EMCI. Further, it has been indicated that the diseased groups engaged somatomotor, frontoparietal, and default mode modules compared to the CN group. The diseased groups also shifted the functional network towards more random architecture. In the end, the methods introduced in this paper enable us to gain an extensive understanding of the pathological changes of the AD process.
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Affiliation(s)
- Alireza Fathian
- Biomathematics Laboratory, Department of Applied Mathematics, School of Mathematical Science, Tarbiat Modares University, Tehran, Iran
| | - Yousef Jamali
- Biomathematics Laboratory, Department of Applied Mathematics, School of Mathematical Science, Tarbiat Modares University, Tehran, Iran.
- Applied Systems Biology, Leibniz-Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute, Jena, Germany.
| | - Mohammad Reza Raoufy
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
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Liu J, Wang W, Wang Y, Liu M, Liu D, Li R, Cai C, Sun L, Gao Q, Li H. Structural network alterations induced by ART-naive and ART-treated subjects infected with HIV. Biochem Biophys Res Commun 2022; 622:115-121. [PMID: 35849952 DOI: 10.1016/j.bbrc.2022.06.078] [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: 05/05/2022] [Revised: 05/20/2022] [Accepted: 06/23/2022] [Indexed: 11/24/2022]
Abstract
OBJECTIVE To investigate how the structural connectivity altered in combined antiretroviral therapy-treated (cART+) HIV patients and cART-naive (cART-) HIV patients by conducting Network analysis of Diffusion Tensor Imaging (DTI) data. METHODS We enrolled 22 cART-, 23 cART+ and 28 normal controls (NC) in our current study. Firstly, the DTI imaging data pre-processing was conducted and the asymmetric 90 × 90 matrix for each participant from their DTI data was obtained with the use of PANDA. Then, we applied a graph-theoretical network analysis toolkit, GRETNA v2.0, to calculate metrics such as small-"worldness," characteristic path length, clustering coefficient, global efficiency, local efficiency, and nodal "betweenness". Finally, we took comparisons among the three groups to investigate topological alterations. RESULTS Results (1) the regional characteristics (nodal efficiency) were altered in cART- and cART+ patients predominantly in the frontal cortical regions; (2) changes in various network properties in cART+treat and cART-patients were associated with the performance of behavior functions; (3) Hubs redistributed in HIV subjects especially in cART+ patients. CONCLUSION The regional characteristics (nodal efficiency) were altered in cART- and cART+ patients predominantly in the frontal cortical region, and changes in various network properties in cART- and cART+ patients were associated with the performance of behavior functions. In addition, Hubs redistributed in HIV subjects especially in cART+ patients.
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Affiliation(s)
- Jiaojiao Liu
- Beijign Youan Hospital, Capital Medical University, Beijing, 100069, China
| | - Wei Wang
- Beijign Youan Hospital, Capital Medical University, Beijing, 100069, China
| | | | | | - Dan Liu
- Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200000, China
| | - Ruili Li
- Beijing Xuanwu Hospital, Capital Medical University, Beijing, 100000, China
| | - Chao Cai
- Beijign Youan Hospital, Capital Medical University, Beijing, 100069, China
| | - Lijun Sun
- Beijign Youan Hospital, Capital Medical University, Beijing, 100069, China
| | - Quansheng Gao
- Institute of Environmental Medicine and Occupational Medicine, Academy of Military Medical Sciences, Tianjin, 300050, China.
| | - Hongjun Li
- Beijign Youan Hospital, Capital Medical University, Beijing, 100069, China.
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Nigro S, Filardi M, Tafuri B, De Blasi R, Cedola A, Gigli G, Logroscino G. The Role of Graph Theory in Evaluating Brain Network Alterations in Frontotemporal Dementia. Front Neurol 2022; 13:910054. [PMID: 35837233 PMCID: PMC9275562 DOI: 10.3389/fneur.2022.910054] [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: 03/31/2022] [Accepted: 06/02/2022] [Indexed: 11/21/2022] Open
Abstract
Frontotemporal dementia (FTD) is a spectrum of clinical syndromes that affects personality, behavior, language, and cognition. The current diagnostic criteria recognize three main clinical subtypes: the behavioral variant of FTD (bvFTD), the semantic variant of primary progressive aphasia (svPPA), and the non-fluent/agrammatic variant of PPA (nfvPPA). Patients with FTD display heterogeneous clinical and neuropsychological features that highly overlap with those presented by psychiatric syndromes and other types of dementia. Moreover, up to now there are no reliable disease biomarkers, which makes the diagnosis of FTD particularly challenging. To overcome this issue, different studies have adopted metrics derived from magnetic resonance imaging (MRI) to characterize structural and functional brain abnormalities. Within this field, a growing body of scientific literature has shown that graph theory analysis applied to MRI data displays unique potentialities in unveiling brain network abnormalities of FTD subtypes. Here, we provide a critical overview of studies that adopted graph theory to examine the topological changes of large-scale brain networks in FTD. Moreover, we also discuss the possible role of information arising from brain network organization in the diagnostic algorithm of FTD-spectrum disorders and in investigating the neural correlates of clinical symptoms and cognitive deficits experienced by patients.
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Affiliation(s)
- Salvatore Nigro
- Institute of Nanotechnology (NANOTEC), National Research Council, Lecce, Italy
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Tricase, Italy
- Salvatore Nigro
| | - Marco Filardi
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Tricase, Italy
- Department of Basic Medicine, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Benedetta Tafuri
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Tricase, Italy
- Department of Basic Medicine, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Roberto De Blasi
- Department of Radiology, “Pia Fondazione Cardinale G. Panico”, Tricase, Lecce, Italy
| | - Alessia Cedola
- Institute of Nanotechnology (NANOTEC), National Research Council, Lecce, Italy
| | - Giuseppe Gigli
- Institute of Nanotechnology (NANOTEC), National Research Council, Lecce, Italy
- Department of Mathematics and Physics “Ennio De Giorgi”, University of Salento, Lecce, Italy
| | - Giancarlo Logroscino
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Tricase, Italy
- Department of Basic Medicine, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
- *Correspondence: Giancarlo Logroscino
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Lei D, Qin K, Pinaya WHL, Young J, Van Amelsvoort T, Marcelis M, Donohoe G, Mothersill DO, Corvin A, Vieira S, Lui S, Scarpazza C, Arango C, Bullmore E, Gong Q, McGuire P, Mechelli A. Graph Convolutional Networks Reveal Network-Level Functional Dysconnectivity in Schizophrenia. Schizophr Bull 2022; 48:881-892. [PMID: 35569019 PMCID: PMC9212102 DOI: 10.1093/schbul/sbac047] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia is increasingly understood as a disorder of brain dysconnectivity. Recently, graph-based approaches such as graph convolutional network (GCN) have been leveraged to explore complex pairwise similarities in imaging features among brain regions, which can reveal abstract and complex relationships within brain networks. STUDY DESIGN We used GCN to investigate topological abnormalities of functional brain networks in schizophrenia. Resting-state functional magnetic resonance imaging data were acquired from 505 individuals with schizophrenia and 907 controls across 6 sites. Whole-brain functional connectivity matrix was extracted for each individual. We examined the performance of GCN relative to support vector machine (SVM), extracted the most salient regions contributing to both classification models, investigated the topological profiles of identified salient regions, and explored correlation between nodal topological properties of each salient region and severity of symptom. STUDY RESULTS GCN enabled nominally higher classification accuracy (85.8%) compared with SVM (80.9%). Based on the saliency map, the most discriminative brain regions were located in a distributed network including striatal areas (ie, putamen, pallidum, and caudate) and the amygdala. Significant differences in the nodal efficiency of bilateral putamen and pallidum between patients and controls and its correlations with negative symptoms were detected in post hoc analysis. CONCLUSIONS The present study demonstrates that GCN allows classification of schizophrenia at the individual level with high accuracy, indicating a promising direction for detection of individual patients with schizophrenia. Functional topological deficits of striatal areas may represent a focal neural deficit of negative symptomatology in schizophrenia.
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Affiliation(s)
| | | | - Walter H L Pinaya
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Jonathan Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
| | - Therese Van Amelsvoort
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Machteld Marcelis
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
- Mental Health Care Institute Eindhoven (GGzE), Eindhoven, The Netherlands
| | - Gary Donohoe
- School of Psychology & Center for Neuroimaging and Cognitive Genomics, NUI Galway University, Galway, Ireland
| | - David O Mothersill
- Psychology Department, School of Business, National College of Ireland, Dublin, Ireland
| | - Aiden Corvin
- Department of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Sandra Vieira
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Cristina Scarpazza
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Department of General Psychology, University of Padova, Padova, Italy
- Padova Neuroscience Centre, University of Padova, Padova, Italy
| | - Celso Arango
- Institute of Psychiatry and Mental Health, Department of Child and Adolescent Psychiatry, Hospital General Universitario Gregorio Marañon, School of Medicine, Universidad Complutense Madrid, IiSGM, CIBERSAM, Madrid, Spain
| | - Ed Bullmore
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Qiyong Gong
- To whom correspondence should be addressed; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, No 37 Guo Xue Xiang, Chengdu, 610041, China; tel: 86-18980601593, fax: 028-85423503,
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
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Liu Y, Varoquaux G. Understanding Brain Network Dynamics in Autism Begs for Generalization. Biol Psychiatry 2022; 91:916-917. [PMID: 35589311 DOI: 10.1016/j.biopsych.2022.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 03/11/2022] [Indexed: 11/26/2022]
Affiliation(s)
- Yong Liu
- School of Artificial Intelligence and the Center for Artificial Intelligence in Medical Imaging, Beijing University of Posts and Telecommunications, Beijing, China.
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Luhmann HJ, Kanold PO, Molnár Z, Vanhatalo S. Early brain activity: Translations between bedside and laboratory. Prog Neurobiol 2022; 213:102268. [PMID: 35364141 PMCID: PMC9923767 DOI: 10.1016/j.pneurobio.2022.102268] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 03/01/2022] [Accepted: 03/25/2022] [Indexed: 01/29/2023]
Abstract
Neural activity is both a driver of brain development and a readout of developmental processes. Changes in neuronal activity are therefore both the cause and consequence of neurodevelopmental compromises. Here, we review the assessment of neuronal activities in both preclinical models and clinical situations. We focus on issues that require urgent translational research, the challenges and bottlenecks preventing translation of biomedical research into new clinical diagnostics or treatments, and possibilities to overcome these barriers. The key questions are (i) what can be measured in clinical settings versus animal experiments, (ii) how do measurements relate to particular stages of development, and (iii) how can we balance practical and ethical realities with methodological compromises in measurements and treatments.
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Affiliation(s)
- Heiko J. Luhmann
- Institute of Physiology, University Medical Center of the Johannes Gutenberg University Mainz, Duesbergweg 6, Mainz, Germany.,Correspondence:, , ,
| | - Patrick O. Kanold
- Department of Biomedical Engineering and Kavli Neuroscience Discovery Institute, Johns Hopkins University, School of Medicine, 720 Rutland Avenue / Miller 379, Baltimore, MD 21205, USA.,Correspondence:, , ,
| | - Zoltán Molnár
- Department of Physiology, Anatomy and Genetics, Sherrington Building, University of Oxford, Parks Road, Oxford OX1 3PT, UK.
| | - Sampsa Vanhatalo
- BABA Center, Departments of Physiology and Clinical Neurophysiology, Children's Hospital, Helsinki University Hospital, Helsinki, Finland.
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Hall SA, Bell RP, Gadde S, Towe SL, Nadeem MT, McCann PS, Song AW, Meade CS. Strengthened and posterior-shifted structural rich-club organization in people who use cocaine. Drug Alcohol Depend 2022; 235:109436. [PMID: 35413558 PMCID: PMC9948276 DOI: 10.1016/j.drugalcdep.2022.109436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 03/18/2022] [Accepted: 03/30/2022] [Indexed: 02/04/2023]
Abstract
BACKGROUND People with cocaine use disorder (CUD) often have abnormal cognitive function and brain structure. Cognition is supported by brain networks that typically have characteristics like rich-club organization, which is a group of regions that are highly connected across the brain and to each other, and small worldness, which is a balance between local and long-distance connections. However, it is unknown whether there are abnormalities in structural brain network connectivity of CUD. METHODS Using diffusion-weighted imaging, we measured structural connectivity in 37 people with CUD and 38 age-matched controls. We identified differences in rich-club organization and whether such differences related to small worldness and behavior. We also tested whether rich-club reorganization was associated with caudate and putamen structural connectivity due to the relevance of the dopamine system to cocaine use. RESULTS People with CUD had a higher normalized rich-club coefficient than controls, more edges connecting rich-club nodes to each other and to non-rich-club nodes, and fewer edges connecting non-rich-club nodes. Rich-club nodes were shifted posterior and lateral. Rich-club reorganization was related to lower clustered connectivity around individual nodes found in CUD, to increased impulsivity, and to a decrease in caudate connectivity. CONCLUSIONS These findings are consistent with previous work showing increased rich-club connectivity in conditions associated with a hypofunctional dopamine system. The posterior shift in rich-club nodes in CUD suggests that the structural connectivity of posterior regions may be more impacted than previously recognized in models based on brain function and morphology.
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Affiliation(s)
- Shana A Hall
- Duke University School of Medicine, Department of Psychiatry and Behavioral Sciences, Campus Box 102848, Durham, NC 27710, USA
| | - Ryan P Bell
- Duke University School of Medicine, Department of Psychiatry and Behavioral Sciences, Campus Box 102848, Durham, NC 27710, USA
| | - Syam Gadde
- Brain Imaging and Analysis Center, Duke University Medical Center, Campus Box 3918, Durham, NC 27710, USA
| | - Sheri L Towe
- Duke University School of Medicine, Department of Psychiatry and Behavioral Sciences, Campus Box 102848, Durham, NC 27710, USA
| | - Muhammad Tauseef Nadeem
- Duke University School of Medicine, Department of Psychiatry and Behavioral Sciences, Campus Box 102848, Durham, NC 27710, USA
| | - Peter S McCann
- Duke University Hospital, 2301 Erwin Rd, Durham, NC 27710, USA
| | - Allen W Song
- Brain Imaging and Analysis Center, Duke University Medical Center, Campus Box 3918, Durham, NC 27710, USA
| | - Christina S Meade
- Duke University School of Medicine, Department of Psychiatry and Behavioral Sciences, Campus Box 102848, Durham, NC 27710, USA; Brain Imaging and Analysis Center, Duke University Medical Center, Campus Box 3918, Durham, NC 27710, USA.
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Li L, Han X, Ji E, Tao X, Shen M, Zhu D, Zhang L, Li L, Yang H, Zhang Z. Altered task-modulated functional connectivity during emotional face processing in euthymic bipolar patients: A whole-brain psychophysiological interaction study. J Affect Disord 2022; 301:162-171. [PMID: 35031332 DOI: 10.1016/j.jad.2022.01.045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 12/10/2021] [Accepted: 01/10/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND Patients with bipolar disorder (BD) show deficits of facial emotion processing even in the euthymic phase. However, the large-scale functional brain network mechanism underlying the emotional deficit of BD remains unclear. Specifically, it is of importance to understand how the task-modulated functional connectivity (FC) was alternated over distributed brain networks in BD. METHODS In this study, we analyzed functional MRI data of a face-matching task from 29 euthymic BD patients and 29 healthy controls (HC), and performed whole-brain psychophysiological interaction (PPI) analysis to obtain task-modulated FC. Abnormal FC patterns were identified through support vector machine-based classification. The topological organization of task-modulated FC networks was estimated by the graph theoretical analysis and compared between BD and HC. RESULTS BD exhibited widely distributed aberrant task-modulated FC patterns not only in core neurocognitive intrinsic brain networks (the fronto-parietal, cingulo-opercular, and default mode networks), but also in the cerebellum and primary processing networks (sensorimotor and visual). Furthermore, the local efficiency of the frontal-parietal network was significantly increased in BD. LIMITATIONS The modest sample size. Only face pictures with negative emotion were used. Only unidirectional task-modulated FC was investigated. CONCLUSIONS BD patients showed a widely distributed aberrant task-modulated FC pattern. Particularly, the fronto-parietal network, as one of the core neurocognitive intrinsic brain networks, was the primary network that demonstrated changes of both FC strength and local efficiency in BD. These findings on the task-modulated FC between these intrinsic brain networks might be considered an endophenotype of the BD condition persistent in the euthymic state.
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Affiliation(s)
- Linling Li
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China.
| | - Xue Han
- Department of Mental Health, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen 518060, China.
| | - Erni Ji
- Department for Bipolar Disorders, Shenzhen Mental Health Centre, Shenzhen Key Lab for Psychological Healthcare, Shenzhen 518020, China.
| | - Xiangrong Tao
- Department for Depression, Shenzhen Mental Health Centre, Shenzhen Key Lab for Psychological Healthcare, Shenzhen 518020, China
| | - Manjun Shen
- Department of Mental Health, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen 518060, China
| | - Dongjian Zhu
- Department of Mental Health, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen 518060, China
| | - Li Zhang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China.
| | - Lingjiang Li
- Department of Mental Health, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen 518060, China; National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China
| | - Haichen Yang
- Department for Bipolar Disorders, Shenzhen Mental Health Centre, Shenzhen Key Lab for Psychological Healthcare, Shenzhen 518020, China.
| | - Zhiguo Zhang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China; Peng Cheng Laboratory, Shenzhen 518055, China; Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen 518055, China.
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Naro A, Pignolo L, Calabrò RS. Brain Network Organization Following Post-Stroke Neurorehabilitation. Int J Neural Syst 2022; 32:2250009. [PMID: 35139774 DOI: 10.1142/s0129065722500095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Brain network analysis can offer useful information to guide the rehabilitation of post-stroke patients. We applied functional network connection models based on multiplex-multilayer network analysis (MMN) to explore functional network connectivity changes induced by robot-aided gait training (RAGT) using the Ekso, a wearable exoskeleton, and compared it to conventional overground gait training (COGT) in chronic stroke patients. We extracted the coreness of individual nodes at multiple locations in the brain from EEG recordings obtained before and after gait training in a resting state. We found that patients provided with RAGT achieved a greater motor function recovery than those receiving COGT. This difference in clinical outcome was paralleled by greater changes in connectivity patterns among different brain areas central to motor programming and execution, as well as a recruitment of other areas beyond the sensorimotor cortices and at multiple frequency ranges, contemporarily. The magnitude of these changes correlated with motor function recovery chances. Our data suggest that the use of RAGT as an add-on treatment to COGT may provide post-stroke patients with a greater modification of the functional brain network impairment following a stroke. This might have potential clinical implications if confirmed in large clinical trials.
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Affiliation(s)
- Antonino Naro
- IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy. Via Palermo, SS 113, Ctr. Casazza, 98124, Messina, Italy
| | - Loris Pignolo
- Sant'Anna Institute, Via Siris, 11, 88900 Crotone, Italy
| | - Rocco Salvatore Calabrò
- IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy. Via Palermo, SS 113, Ctr. Casazza, 98124, Messina, Italy
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Xiao J, Hornburg KJ, Cofer G, Cook JJ, Pratson F, Qi Y, Johnson GA. A time-course study of actively stained mouse brains: Diffusion tensor imaging parameters and connectomic stability over 1 year. NMR IN BIOMEDICINE 2022; 35:e4611. [PMID: 34558744 PMCID: PMC10461792 DOI: 10.1002/nbm.4611] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 07/21/2021] [Accepted: 08/10/2021] [Indexed: 06/13/2023]
Abstract
While the application of diffusion tensor imaging (DTI), tractography, and connectomics to fixed tissue is a common practice today, there have been limited studies examining the effects of fixation on brain microstructure over extended periods. This mouse model time-course study reports the changes of regional brain volumes and diffusion scalar parameters, such as fractional anisotropy, across 12 representative brain regions as measures of brain structural stability. The scalar DTI parameters and regional volumes were highly variable over the first 2 weeks after fixation. The same parameters were consistent over a 2-8-week window after fixation, which means confounds from tissue stability over that scanning window were minimal. Quantitative connectomes were analyzed over the same time with extension out to 1 year. While there was some change in the scalar metrics at 1 year after fixation, these changes were sufficiently small, particularly in white matter, to support reproducible connectomes over a period ranging from 2-weeks to 1-year post-fixation. These findings delineate a scanning period, during which brain volumes, diffusion scalar metrics, and connectomes are remarkably consistent.
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Affiliation(s)
- Jaclyn Xiao
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Kathryn J. Hornburg
- Duke Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Gary Cofer
- Duke Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - James J. Cook
- Duke Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Forrest Pratson
- Duke Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Yi Qi
- Duke Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - G. Allan Johnson
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
- Duke Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
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Guan F, Ni T, Zhu W, Williams LK, Cui LB, Li M, Tubbs J, Sham PC, Gui H. Integrative omics of schizophrenia: from genetic determinants to clinical classification and risk prediction. Mol Psychiatry 2022; 27:113-126. [PMID: 34193973 PMCID: PMC11018294 DOI: 10.1038/s41380-021-01201-2] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 06/15/2021] [Accepted: 06/17/2021] [Indexed: 02/06/2023]
Abstract
Schizophrenia (SCZ) is a debilitating neuropsychiatric disorder with high heritability and complex inheritance. In the past decade, successful identification of numerous susceptibility loci has provided useful insights into the molecular etiology of SCZ. However, applications of these findings to clinical classification and diagnosis, risk prediction, or intervention for SCZ have been limited, and elucidating the underlying genomic and molecular mechanisms of SCZ is still challenging. More recently, multiple Omics technologies - genomics, transcriptomics, epigenomics, proteomics, metabolomics, connectomics, and gut microbiomics - have all been applied to examine different aspects of SCZ pathogenesis. Integration of multi-Omics data has thus emerged as an approach to provide a more comprehensive view of biological complexity, which is vital to enable translation into assessments and interventions of clinical benefit to individuals with SCZ. In this review, we provide a broad survey of the single-omics studies of SCZ, summarize the advantages and challenges of different Omics technologies, and then focus on studies in which multiple omics data are integrated to unravel the complex pathophysiology of SCZ. We believe that integration of multi-Omics technologies would provide a roadmap to create a more comprehensive picture of interactions involved in the complex pathogenesis of SCZ, constitute a rich resource for elucidating the potential molecular mechanisms of the illness, and eventually improve clinical assessments and interventions of SCZ to address clinical translational questions from bench to bedside.
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Affiliation(s)
- Fanglin Guan
- Department of Forensic Psychiatry, School of Medicine & Forensics, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Tong Ni
- Department of Forensic Psychiatry, School of Medicine & Forensics, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Weili Zhu
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, China
| | - L Keoki Williams
- Center for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Health System, Detroit, MI, USA
| | - Long-Biao Cui
- Department of Clinical Psychology, School of Medical Psychology, Air Force Medical University, Xi'an, Shaanxi, China
| | - Ming Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Justin Tubbs
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Centre for PanorOmic Sciences, The University of Hong Kong, Hong Kong SAR, China
| | - Pak-Chung Sham
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
- Centre for PanorOmic Sciences, The University of Hong Kong, Hong Kong SAR, China.
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China.
| | - Hongsheng Gui
- Center for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Health System, Detroit, MI, USA.
- Behavioral Health Services, Henry Ford Health System, Detroit, MI, USA.
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Ponticorvo S, Manara R, Cassandro E, Canna A, Scarpa A, Troisi D, Cassandro C, Cuoco S, Cappiello A, Pellecchia MT, Salle FD, Esposito F. Cross-modal connectivity effects in age-related hearing loss. Neurobiol Aging 2021; 111:1-13. [PMID: 34915240 DOI: 10.1016/j.neurobiolaging.2021.09.024] [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: 05/27/2021] [Revised: 08/31/2021] [Accepted: 09/01/2021] [Indexed: 10/19/2022]
Abstract
Age-related sensorineural hearing loss (HL) leads to localized brain changes in the primary auditory cortex, long-range functional alterations, and is considered a risk factor for dementia. Nonhuman studies have repeatedly highlighted cross-modal brain plasticity in sensorial brain networks other than those primarily involved in the peripheral damage, thus in this study, the possible cortical alterations associated with HL have been analyzed using a whole-brain multimodal connectomic approach. Fifty-two HL and 30 normal hearing participants were examined in a 3T MRI study along with audiological and neurological assessments. Between-regions functional connectivity and whole-brain probabilistic tractography were calculated in a connectome-based manner and graph theory was used to obtain low-dimensional features for the analysis of brain connectivity at global and local levels. The HL condition was associated with a different functional organization of the visual subnetwork as revealed by a significant increase in global efficiency, density, and clustering coefficient. These functional effects were mirrored by similar (but more subtle) structural effects suggesting that a functional repurposing of visual cortical centers occurs to compensate for age-related loss of hearing abilities.
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Affiliation(s)
- Sara Ponticorvo
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi, Italy
| | - Renzo Manara
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi, Italy; Department of Neuroscience, University of Padova, Padova, Italy
| | - Ettore Cassandro
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi, Italy; University Hospital "San Giovanni di Dio e Ruggi D'Aragona", Scuola Medica Salernitana, Salerno, Italy
| | - Antonietta Canna
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Napoli, Italy
| | - Alfonso Scarpa
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi, Italy; University Hospital "San Giovanni di Dio e Ruggi D'Aragona", Scuola Medica Salernitana, Salerno, Italy
| | - Donato Troisi
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi, Italy; University Hospital "San Giovanni di Dio e Ruggi D'Aragona", Scuola Medica Salernitana, Salerno, Italy
| | - Claudia Cassandro
- University Hospital "San Giovanni di Dio e Ruggi D'Aragona", Scuola Medica Salernitana, Salerno, Italy
| | - Sofia Cuoco
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi, Italy; University Hospital "San Giovanni di Dio e Ruggi D'Aragona", Scuola Medica Salernitana, Salerno, Italy
| | - Arianna Cappiello
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi, Italy; University Hospital "San Giovanni di Dio e Ruggi D'Aragona", Scuola Medica Salernitana, Salerno, Italy
| | - Maria Teresa Pellecchia
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi, Italy; University Hospital "San Giovanni di Dio e Ruggi D'Aragona", Scuola Medica Salernitana, Salerno, Italy
| | - Francesco Di Salle
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi, Italy; University Hospital "San Giovanni di Dio e Ruggi D'Aragona", Scuola Medica Salernitana, Salerno, Italy
| | - Fabrizio Esposito
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Napoli, Italy.
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40
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Kiar G, Chatelain Y, de Oliveira Castro P, Petit E, Rokem A, Varoquaux G, Misic B, Evans AC, Glatard T. Numerical uncertainty in analytical pipelines lead to impactful variability in brain networks. PLoS One 2021; 16:e0250755. [PMID: 34724000 PMCID: PMC8559953 DOI: 10.1371/journal.pone.0250755] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 08/25/2021] [Indexed: 11/19/2022] Open
Abstract
The analysis of brain-imaging data requires complex processing pipelines to support findings on brain function or pathologies. Recent work has shown that variability in analytical decisions, small amounts of noise, or computational environments can lead to substantial differences in the results, endangering the trust in conclusions. We explored the instability of results by instrumenting a structural connectome estimation pipeline with Monte Carlo Arithmetic to introduce random noise throughout. We evaluated the reliability of the connectomes, the robustness of their features, and the eventual impact on analysis. The stability of results was found to range from perfectly stable (i.e. all digits of data significant) to highly unstable (i.e. 0 - 1 significant digits). This paper highlights the potential of leveraging induced variance in estimates of brain connectivity to reduce the bias in networks without compromising reliability, alongside increasing the robustness and potential upper-bound of their applications in the classification of individual differences. We demonstrate that stability evaluations are necessary for understanding error inherent to brain imaging experiments, and how numerical analysis can be applied to typical analytical workflows both in brain imaging and other domains of computational sciences, as the techniques used were data and context agnostic and globally relevant. Overall, while the extreme variability in results due to analytical instabilities could severely hamper our understanding of brain organization, it also affords us the opportunity to increase the robustness of findings.
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Affiliation(s)
- Gregory Kiar
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Yohan Chatelain
- Department of Computer Science and Software Engineering, Concordia University, Montréal, QC, Canada
| | | | - Eric Petit
- Exascale Computing Lab, Intel, Paris, France
| | - Ariel Rokem
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, United States of America
| | - Gaël Varoquaux
- Parietal Project-team, INRIA Saclay-ile de France, Paris, France
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Alan C Evans
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Tristan Glatard
- Department of Computer Science and Software Engineering, Concordia University, Montréal, QC, Canada
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41
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Svaldi DO, Goñi J, Abbas K, Amico E, Clark DG, Muralidharan C, Dzemidzic M, West JD, Risacher SL, Saykin AJ, Apostolova LG. Optimizing differential identifiability improves connectome predictive modeling of cognitive deficits from functional connectivity in Alzheimer's disease. Hum Brain Mapp 2021; 42:3500-3516. [PMID: 33949732 PMCID: PMC8249900 DOI: 10.1002/hbm.25448] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 03/07/2021] [Accepted: 04/06/2021] [Indexed: 12/29/2022] Open
Abstract
Functional connectivity, as estimated using resting state functional MRI, has shown potential in bridging the gap between pathophysiology and cognition. However, clinical use of functional connectivity biomarkers is impeded by unreliable estimates of individual functional connectomes and lack of generalizability of models predicting cognitive outcomes from connectivity. To address these issues, we combine the frameworks of connectome predictive modeling and differential identifiability. Using the combined framework, we show that enhancing the individual fingerprint of resting state functional connectomes leads to robust identification of functional networks associated to cognitive outcomes and also improves prediction of cognitive outcomes from functional connectomes. Using a comprehensive spectrum of cognitive outcomes associated to Alzheimer's disease (AD), we identify and characterize functional networks associated to specific cognitive deficits exhibited in AD. This combined framework is an important step in making individual level predictions of cognition from resting state functional connectomes and in understanding the relationship between cognition and connectivity.
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Affiliation(s)
| | - Joaquín Goñi
- School of Industrial EngineeringPurdue UniversityWest LafayetteIndianaUSA
- Purdue Institute for Integrative Neuroscience, Purdue UniversityWest LafayetteIndianaUSA
- Weldon School of Biomedical EngineeringPurdue UniversityWest LafayetteIndianaUSA
| | - Kausar Abbas
- School of Industrial EngineeringPurdue UniversityWest LafayetteIndianaUSA
- Purdue Institute for Integrative Neuroscience, Purdue UniversityWest LafayetteIndianaUSA
| | - Enrico Amico
- School of Industrial EngineeringPurdue UniversityWest LafayetteIndianaUSA
- Purdue Institute for Integrative Neuroscience, Purdue UniversityWest LafayetteIndianaUSA
| | - David G. Clark
- Indiana University School of MedicineIndianapolisIndianaUSA
| | | | | | - John D. West
- Indiana University School of MedicineIndianapolisIndianaUSA
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42
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Zou Y, Ma H, Liu B, Li D, Liu D, Wang X, Wang S, Fan W, Han P. Disrupted Topological Organization in White Matter Networks in Unilateral Sudden Sensorineural Hearing Loss. Front Neurosci 2021; 15:666651. [PMID: 34321993 PMCID: PMC8312563 DOI: 10.3389/fnins.2021.666651] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 05/10/2021] [Indexed: 12/12/2022] Open
Abstract
Sudden sensorineural hearing loss (SSNHL) is a sudden-onset hearing impairment that rapidly develops within 72 h and is mostly unilateral. Only a few patients can be identified with a defined cause by routine clinical examinations. Recently, some studies have shown that unilateral SSNHL is associated with alterations in the central nervous system. However, little is known about the topological organization of white matter (WM) networks in unilateral SSNHL patients in the acute phase. In this study, 145 patients with SSNHL and 91 age-, gender-, and education-matched healthy controls were evaluated using diffusion tensor imaging (DTI) and graph theoretical approaches. The topological properties of WM networks, including global and nodal parameters, were investigated. At the global level, SSNHL patients displayed decreased clustering coefficient, local efficiency, global efficiency, normalized clustering coefficient, normalized characteristic path length, and small-worldness and increased characteristic path length (p < 0.05) compared with healthy controls. At the nodal level, altered nodal centralities in brain regions involved the auditory network, visual network, attention network, default mode network (DMN), sensorimotor network, and subcortical network (p < 0.05, Bonferroni corrected). These findings indicate a shift of the WM network topology in SSNHL patients toward randomization, which is characterized by decreased global network integration and segregation and is reflected by decreased global connectivity and altered nodal centralities. This study could help us understand the potential pathophysiology of unilateral SSNHL.
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Affiliation(s)
- Yan Zou
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Key Laboratory of Molecular Imaging, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hui Ma
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Key Laboratory of Molecular Imaging, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Bo Liu
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Dan Li
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Dingxi Liu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Key Laboratory of Molecular Imaging, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | | | - Siqi Wang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Key Laboratory of Molecular Imaging, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenliang Fan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Key Laboratory of Molecular Imaging, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ping Han
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Key Laboratory of Molecular Imaging, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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43
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Sareen E, Zahar S, Ville DVD, Gupta A, Griffa A, Amico E. Exploring MEG brain fingerprints: Evaluation, pitfalls, and interpretations. Neuroimage 2021; 240:118331. [PMID: 34237444 DOI: 10.1016/j.neuroimage.2021.118331] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 06/22/2021] [Accepted: 07/01/2021] [Indexed: 12/16/2022] Open
Abstract
Individual characterization of subjects based on their functional connectome (FC), termed "FC fingerprinting", has become a highly sought-after goal in contemporary neuroscience research. Recent functional magnetic resonance imaging (fMRI) studies have demonstrated unique characterization and accurate identification of individuals as an accomplished task. However, FC fingerprinting in magnetoencephalography (MEG) data is still widely unexplored. Here, we study resting-state MEG data from the Human Connectome Project to assess the MEG FC fingerprinting and its relationship with several factors including amplitude- and phase-coupling functional connectivity measures, spatial leakage correction, frequency bands, and behavioral significance. To this end, we first employ two identification scoring methods, differential identifiability and success rate, to provide quantitative fingerprint scores for each FC measurement. Secondly, we explore the edgewise and nodal MEG fingerprinting patterns across the different frequency bands (delta, theta, alpha, beta, and gamma). Finally, we investigate the cross-modality fingerprinting patterns obtained from MEG and fMRI recordings from the same subjects. We assess the behavioral significance of FC across connectivity measures and imaging modalities using partial least square correlation analyses. Our results suggest that fingerprinting performance is heavily dependent on the functional connectivity measure, frequency band, identification scoring method, and spatial leakage correction. We report higher MEG fingerprinting performances in phase-coupling methods, central frequency bands (alpha and beta), and in the visual, frontoparietal, dorsal-attention, and default-mode networks. Furthermore, cross-modality comparisons reveal a certain degree of spatial concordance in fingerprinting patterns between the MEG and fMRI data, especially in the visual system. Finally, the multivariate correlation analyses show that MEG connectomes have strong behavioral significance, which however depends on the considered connectivity measure and temporal scale. This comprehensive, albeit preliminary investigation of MEG connectome test-retest identifiability offers a first characterization of MEG fingerprinting in relation to different methodological and electrophysiological factors and contributes to the understanding of fingerprinting cross-modal relationships. We hope that this first investigation will contribute to setting the grounds for MEG connectome identification.
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Affiliation(s)
- Ekansh Sareen
- Signal Processing and Biomedical Imaging, Dept. of Electronics and Communication Engineering, IIIT-Delhi, New Delhi, India
| | - Sélima Zahar
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland
| | - Dimitri Van De Ville
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Anubha Gupta
- Signal Processing and Biomedical Imaging, Dept. of Electronics and Communication Engineering, IIIT-Delhi, New Delhi, India
| | - Alessandra Griffa
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland; Department of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland.
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44
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Bas-Hoogendam JM, van Steenbergen H, Cohen Kadosh K, Westenberg PM, van der Wee NJA. Intrinsic functional connectivity in families genetically enriched for social anxiety disorder - an endophenotype study. EBioMedicine 2021; 69:103445. [PMID: 34161885 PMCID: PMC8237289 DOI: 10.1016/j.ebiom.2021.103445] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 05/18/2021] [Accepted: 06/04/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Social anxiety disorder (SAD) is a serious psychiatric condition with a high prevalence, and a typical onset during childhood/adolescence. The condition runs in families, but it is largely unknown which neurobiological characteristics transfer this genetic vulnerability ('endophenotypes'). Using data from the Leiden Family Lab study on SAD, including two generations of families genetically enriched for SAD, we investigated whether social anxiety (SA) co-segregated with changes in intrinsic functional connectivity (iFC), and examined heritability. METHODS Functional MRI data were acquired during resting-state in 109 individuals (56 males; mean age: 31·5, range 9·2-61·5 years). FSL's tool MELODIC was used to perform independent component analysis. Six networks of interest (default mode, dorsal attention, executive control, frontoparietal, limbic and salience) were identified at the group-level and used to generate subject-specific spatial maps. Voxel-wise regression models, with SA-level as predictor and voxel-wise iFC as candidate endophenotypes, were performed to investigate the association with SA, within masks of the networks of interest. Subsequently, heritability was estimated. FINDINGS SA co-segregated with iFC within the dorsal attention network (positive association in left middle frontal gyrus and right postcentral gyrus) and frontoparietal network (positive association within left middle temporal gyrus) (cluster-forming-threshold z>2·3, cluster-corrected extent-threshold p<0·05). Furthermore, iFC of multiple voxels within these clusters was at least moderately heritable. INTERPRETATION These findings provide initial evidence for increased iFC as candidate endophenotype of SAD, particularly within networks involved in attention. These changes might underlie attentional biases commonly present in SAD. FUNDING Leiden University Research Profile 'Health, Prevention and the Human Lifecycle'.
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Affiliation(s)
- Janna Marie Bas-Hoogendam
- Institute of Psychology, Leiden University, Wassenaarseweg 52, 2333, AK, Leiden, the Netherlands; Department of Psychiatry, Leiden University Medical Center, Albinusdreef 2, 2333, ZA, Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden, the Netherlands.
| | - Henk van Steenbergen
- Institute of Psychology, Leiden University, Wassenaarseweg 52, 2333, AK, Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden, the Netherlands.
| | | | - P Michiel Westenberg
- Institute of Psychology, Leiden University, Wassenaarseweg 52, 2333, AK, Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden, the Netherlands.
| | - Nic J A van der Wee
- Department of Psychiatry, Leiden University Medical Center, Albinusdreef 2, 2333, ZA, Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden, the Netherlands.
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45
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Abbas K, Liu M, Venkatesh M, Amico E, Kaplan AD, Ventresca M, Pessoa L, Harezlak J, Goñi J. Geodesic Distance on Optimally Regularized Functional Connectomes Uncovers Individual Fingerprints. Brain Connect 2021; 11:333-348. [PMID: 33470164 PMCID: PMC8215418 DOI: 10.1089/brain.2020.0881] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background: Functional connectomes (FCs) have been shown to provide a reproducible individual fingerprint, which has opened the possibility of personalized medicine for neuro/psychiatric disorders. Thus, developing accurate ways to compare FCs is essential to establish associations with behavior and/or cognition at the individual level. Methods: Canonically, FCs are compared using Pearson's correlation coefficient of the entire functional connectivity profiles. Recently, it has been proposed that the use of geodesic distance is a more accurate way of comparing FCs, one which reflects the underlying non-Euclidean geometry of the data. Computing geodesic distance requires FCs to be positive-definite and hence invertible matrices. As this requirement depends on the functional magnetic resonance imaging scanning length and the parcellation used, it is not always attainable and sometimes a regularization procedure is required. Results: In the present work, we show that regularization is not only an algebraic operation for making FCs invertible, but also that an optimal magnitude of regularization leads to systematically higher fingerprints. We also show evidence that optimal regularization is data set-dependent and varies as a function of condition, parcellation, scanning length, and the number of frames used to compute the FCs. Discussion: We demonstrate that a universally fixed regularization does not fully uncover the potential of geodesic distance on individual fingerprinting and indeed could severely diminish it. Thus, an optimal regularization must be estimated on each data set to uncover the most differentiable across-subject and reproducible within-subject geodesic distances between FCs. The resulting pairwise geodesic distances at the optimal regularization level constitute a very reliable quantification of differences between subjects.
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Affiliation(s)
- Kausar Abbas
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, USA
- School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Mintao Liu
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, USA
- School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Manasij Venkatesh
- Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland, USA
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | | | - Mario Ventresca
- School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Luiz Pessoa
- Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland, USA
- Department of Psychology and Maryland Neuroimaging Center, University of Maryland, College Park, Maryland, USA
| | - Jaroslaw Harezlak
- Department of Epidemiology and Biostatistics, Indiana University, Bloomington, Indiana, USA
| | - Joaquín Goñi
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, USA
- School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA
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46
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Garbarino S, Lorenzi M. Investigating hypotheses of neurodegeneration by learning dynamical systems of protein propagation in the brain. Neuroimage 2021; 235:117980. [PMID: 33823273 DOI: 10.1016/j.neuroimage.2021.117980] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 02/20/2021] [Accepted: 03/12/2021] [Indexed: 11/28/2022] Open
Abstract
We introduce a theoretical framework for estimating, comparing and interpreting mechanistic hypotheses on long term protein propagation across brain networks in neurodegenerative disorders (ND). The model is expressed within a Bayesian non-parametric regression setting, where mechanisms of protein dynamics are inferred by means of gradient matching on dynamical systems (DS). The Bayesian formalism, combined with stochastic variational inference, naturally allows for model comparison via assessment of model evidence, while providing uncertainty quantification of causal relationship underlying protein progressions. When applied to in-vivo AV45-PET brain imaging data measuring topographic amyloid deposition in Alzheimer's disease (AD), our model identified the mechanisms of accumulation, clearance and propagation as the best suited DS for bio-mechanical description of amyloid dynamics in AD, enabling realistic and accurate personalized simulation of amyloidosis.
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Affiliation(s)
- Sara Garbarino
- Universitè Côte d'Azur, Inria, Epione Research Project, France.
| | - Marco Lorenzi
- Universitè Côte d'Azur, Inria, Epione Research Project, France.
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- Universitè Côte d'Azur, Inria, Epione Research Project, France
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47
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Frey BM, Petersen M, Schlemm E, Mayer C, Hanning U, Engelke K, Fiehler J, Borof K, Jagodzinski A, Gerloff C, Thomalla G, Cheng B. White matter integrity and structural brain network topology in cerebral small vessel disease: The Hamburg city health study. Hum Brain Mapp 2021; 42:1406-1415. [PMID: 33289924 PMCID: PMC7927298 DOI: 10.1002/hbm.25301] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Revised: 11/08/2020] [Accepted: 11/16/2020] [Indexed: 12/13/2022] Open
Abstract
Cerebral small vessel disease is a common finding in the elderly and associated with various clinical sequelae. Previous studies suggest disturbances in the integration capabilities of structural brain networks as a mediating link between imaging and clinical presentations. To what extent cerebral small vessel disease might interfere with other measures of global network topology is not well understood. Connectomes were reconstructed via diffusion weighted imaging in a sample of 930 participants from a population based epidemiologic study. Linear models were fitted testing for an association of graph-theoretical measures reflecting integration and segregation with both the Peak width of Skeletonized Mean Diffusivity (PSMD) and the load of white matter hyperintensities of presumed vascular origin (WMH). The latter were subdivided in periventricular and deep for an analysis of localisation-dependent correlations of cerebral small vessel disease. The median WMH volume was 0.6 mL (1.4) and the median PSMD 2.18 mm2 /s x 10-4 (0.5). The connectomes showed a median density of 0.880 (0.030), the median values for normalised global efficiency, normalised clustering coefficient, modularity Q and small-world propensity were 0.780 (0.045), 1.182 (0.034), 0.593 (0.026) and 0.876 (0.040) respectively. An increasing burden of cerebral small vessel disease was significantly associated with a decreased integration and increased segregation and thus decreased small-worldness of structural brain networks. Even in rather healthy subjects increased cerebral small vessel disease burden is accompanied by topological brain network disturbances. Segregation parameters and small-worldness might as well contribute to the understanding of the known clinical sequelae of cerebral small vessel disease.
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Affiliation(s)
- Benedikt M. Frey
- Department of NeurologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Marvin Petersen
- Department of NeurologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Eckhard Schlemm
- Department of NeurologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Carola Mayer
- Department of NeurologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Uta Hanning
- Department of Diagnostic and Interventional NeuroradiologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Kristin Engelke
- Department of Diagnostic and Interventional NeuroradiologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Jens Fiehler
- Department of Diagnostic and Interventional NeuroradiologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Katrin Borof
- Epidemiological study centerUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Annika Jagodzinski
- Epidemiological study centerUniversity Medical Center Hamburg‐EppendorfHamburgGermany
- Department of General and Interventional CardiologyUniversity Heart and Vascular CenterHamburgGermany
| | - Christian Gerloff
- Department of NeurologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Götz Thomalla
- Department of NeurologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Bastian Cheng
- Department of NeurologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
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48
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Advances in functional and diffusion neuroimaging research into the long-term consequences of very preterm birth. J Perinatol 2021; 41:689-706. [PMID: 33099576 DOI: 10.1038/s41372-020-00865-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 09/21/2020] [Accepted: 10/12/2020] [Indexed: 11/08/2022]
Abstract
Very preterm birth (<32 weeks of gestation) has been associated with lifelong difficulties in a variety of neurocognitive functions. Magnetic resonance imaging (MRI) combined with advanced analytical approaches have been employed in order to increase our understanding of the neurodevelopmental problems that many very preterm born individuals face as they grow up. In this review, we will focus on two novel imaging techniques that have explored relationships between specific brain mechanisms and behavioural outcomes. These are functional MRI, which maps regional, time-varying changes in brain metabolism and diffusion-weighted MRI, which measures the displacement of water molecules in tissue and provides quantitative information about tissue microstructure. Identifying the neurobiological underpinning of the long-term sequelae associated with very preterm birth could inform the development and implementation of preventative interventions (before any cognitive problem emerges) and could facilitate the identification of behavioural targets for improving the life course outcomes of very preterm individuals.
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Khastkhodaei Z, Muthuraman M, Yang JW, Groppa S, Luhmann HJ. Functional and directed connectivity of the cortico-limbic network in mice in vivo. Brain Struct Funct 2021; 226:685-700. [PMID: 33442810 PMCID: PMC7981333 DOI: 10.1007/s00429-020-02202-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 12/16/2020] [Indexed: 11/22/2022]
Abstract
Higher cognitive processes and emotional regulation depend on densely interconnected telencephalic and limbic areas. Central structures of this cortico-limbic network are ventral hippocampus (vHC), medial prefrontal cortex (PFC), basolateral amygdala (BLA) and nucleus accumbens (NAC). Human and animal studies have revealed both anatomical and functional alterations in specific connections of this network in several psychiatric disorders. However, it is often not clear whether functional alterations within these densely interconnected brain areas are caused by modifications in the direct pathways, or alternatively through indirect interactions. We performed multi-site extracellular recordings of spontaneous activity in three different brain regions to study the functional connectivity in the BLA-NAC-PFC-vHC network of the lightly anesthetized mouse in vivo. We show that BLA, NAC, PFC and vHC are functionally connected in distinct frequency bands and determined the influence of a third brain region on this connectivity. In addition to describing mutual synchronicity, we determined the strength of functional connectivity for each region in the BLA-NAC-PFC-vHC network. We find a region-specificity in the strength of feedforward and feedback connections for each region in its interaction with other areas in the network. Our results provide insights into functional and directed connectivity in the cortico-limbic network of adult wild-type mice, which may be helpful to further elucidate the pathophysiological changes of this network in psychiatric disorders and to develop target-specific therapeutic interventions.
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Affiliation(s)
- Zeinab Khastkhodaei
- Institute of Physiology, University Medical Center of the Johannes Gutenberg University Mainz, Duesbergweg 6, 55128, Mainz, Germany
| | - Muthuraman Muthuraman
- Section of Movement Disorders and Neurostimulation, Biomedical Statistics and MULTIMODAL Signal Processing Unit, Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Duesbergweg 6, 55128, Mainz, Germany
| | - Jenq-Wei Yang
- Institute of Physiology, University Medical Center of the Johannes Gutenberg University Mainz, Duesbergweg 6, 55128, Mainz, Germany
| | - Sergiu Groppa
- Section of Movement Disorders and Neurostimulation, Biomedical Statistics and MULTIMODAL Signal Processing Unit, Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Duesbergweg 6, 55128, Mainz, Germany
| | - Heiko J Luhmann
- Institute of Physiology, University Medical Center of the Johannes Gutenberg University Mainz, Duesbergweg 6, 55128, Mainz, Germany.
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Carli G, Tondo G, Boccalini C, Perani D. Brain Molecular Connectivity in Neurodegenerative Conditions. Brain Sci 2021; 11:brainsci11040433. [PMID: 33800680 PMCID: PMC8067093 DOI: 10.3390/brainsci11040433] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 03/15/2021] [Accepted: 03/23/2021] [Indexed: 12/28/2022] Open
Abstract
Positron emission tomography (PET) allows for the in vivo assessment of early brain functional and molecular changes in neurodegenerative conditions, representing a unique tool in the diagnostic workup. The increased use of multivariate PET imaging analysis approaches has provided the chance to investigate regional molecular processes and long-distance brain circuit functional interactions in the last decade. PET metabolic and neurotransmission connectome can reveal brain region interactions. This review is an overview of concepts and methods for PET molecular and metabolic covariance assessment with evidence in neurodegenerative conditions, including Alzheimer’s disease and Lewy bodies disease spectrum. We highlight the effects of environmental and biological factors on brain network organization. All of the above might contribute to innovative diagnostic tools and potential disease-modifying interventions.
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Affiliation(s)
- Giulia Carli
- School of Psychology, Vita-Salute San Raffaele University, 20121 Milan, Italy; (G.C.); (G.T.); (C.B.)
- In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, 20121 Milan, Italy
| | - Giacomo Tondo
- School of Psychology, Vita-Salute San Raffaele University, 20121 Milan, Italy; (G.C.); (G.T.); (C.B.)
- In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, 20121 Milan, Italy
| | - Cecilia Boccalini
- School of Psychology, Vita-Salute San Raffaele University, 20121 Milan, Italy; (G.C.); (G.T.); (C.B.)
- In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, 20121 Milan, Italy
| | - Daniela Perani
- School of Psychology, Vita-Salute San Raffaele University, 20121 Milan, Italy; (G.C.); (G.T.); (C.B.)
- In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, 20121 Milan, Italy
- Nuclear Medicine Unit, San Raffaele Hospital, 20121 Milan, Italy
- Correspondence: ; Tel.: +39-02-26432224
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