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Sarnyai Z, Ben-Shachar D. Schizophrenia, a disease of impaired dynamic metabolic flexibility: A new mechanistic framework. Psychiatry Res 2024; 342:116220. [PMID: 39369460 DOI: 10.1016/j.psychres.2024.116220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 09/21/2024] [Accepted: 09/30/2024] [Indexed: 10/08/2024]
Abstract
Schizophrenia is a chronic, neurodevelopmental disorder with unknown aetiology and pathophysiology that emphasises the role of neurotransmitter imbalance and abnormalities in synaptic plasticity. The currently used pharmacological approach, the antipsychotic drugs, which have limited efficacy and an array of side-effects, have been developed based on the neurotransmitter hypothesis. Recent research has uncovered systemic and brain abnormalities in glucose and energy metabolism, focusing on altered glycolysis and mitochondrial oxidative phosphorylation. These findings call for a re-conceptualisation of schizophrenia pathophysiology as a progressing bioenergetics failure. In this review, we provide an overview of the fundamentals of brain bioenergetics and the changes identified in schizophrenia. We then propose a new explanatory framework positing that schizophrenia is a disease of impaired dynamic metabolic flexibility, which also reconciles findings of abnormal glucose and energy metabolism in the periphery and in the brain along the course of the disease. This evidence-based framework and testable hypothesis has the potential to transform the way we conceptualise this debilitating condition and to develop novel treatment approaches.
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Affiliation(s)
- Zoltán Sarnyai
- Laboratory of Psychobiology, Department of Neuroscience, The Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Department of Psychiatry, Rambam Health Campus, Haifa, Israel; Laboratory of Psychiatric Neuroscience, Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, QLD, Australia.
| | - Dorit Ben-Shachar
- Laboratory of Psychobiology, Department of Neuroscience, The Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Department of Psychiatry, Rambam Health Campus, Haifa, Israel.
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Kazinka R, Roediger D, Xuan L, Yu L, Mueller BA, Camchong J, Opitz A, MacDonald A, Lim KO. tDCS-enhanced cognitive training improves attention and alters connectivity in control and somatomotor networks: A triple blind study. Neuroimage 2024; 298:120792. [PMID: 39147294 PMCID: PMC11425656 DOI: 10.1016/j.neuroimage.2024.120792] [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: 07/02/2024] [Revised: 08/08/2024] [Accepted: 08/13/2024] [Indexed: 08/17/2024] Open
Abstract
BACKGROUND Executive dysfunction such as inattention or forgetfulness can lead to disruptions in a person's daily functioning and quality of life. OBJECTIVE/HYPOTHESIS This triple-blinded randomized clinical trial assessed the efficacy of bifrontal (over the forehead) transcranial direct current stimulation (tDCS) concurrent with cognitive training to improve cognitive performance in a healthy sample. METHODS Fifty-eight participants were randomly assigned to one of three stimulation conditions (2 mA left anode-right cathode, 2 mA right anode-left cathode, or sham), which was administered with cognitive training tasks 3x/week over 12 weeks with assessments at baseline, midpoint (6 weeks), and post-training (12 weeks). We assessed cognitive performance, functional connectivity, and the influence of individual differences in training advancement. RESULTS Forty participants completed training. We found that at midpoint and post, all groups improved significantly on overall cognitive performance. The left anode group's attention & vigilance score improved significantly at post, but the other two groups did not. Greater attention training advancement predicted attention improvement by post, most notably in the left anode group. Finally, within-network connectivity decreased in the control network and increased in the somatomotor network across all groups. CONCLUSIONS These results suggest that, given cognitive training, the left anode montage is more effective at improving attention than the right anode montage and sham. Future research may focus on the application of the left anode montage during cognitive training to assess its effectiveness in improving cognition in neuropsychiatric disorders.
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Affiliation(s)
- Rebecca Kazinka
- University of Minnesota, Department of Psychiatry and Behavioral Sciences, United States; University of Minnesota, Department of Biomedical Engineering, United States
| | - Donovan Roediger
- University of Minnesota, Department of Psychiatry and Behavioral Sciences, United States
| | - Lei Xuan
- University of Minnesota, Department of Psychiatry and Behavioral Sciences, United States
| | - Lingyan Yu
- University of Minnesota, Department of Psychology, United States
| | - Bryon A Mueller
- University of Minnesota, Department of Psychiatry and Behavioral Sciences, United States
| | - Jazmin Camchong
- University of Minnesota, Department of Psychiatry and Behavioral Sciences, United States
| | - Alexander Opitz
- University of Minnesota, Department of Biomedical Engineering, United States
| | - Angus MacDonald
- University of Minnesota, Department of Psychology, United States
| | - Kelvin O Lim
- University of Minnesota, Department of Psychiatry and Behavioral Sciences, United States
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Hirsch F, Bumanglag Â, Zhang Y, Wohlschlaeger A. Diverging functional connectivity timescales: Capturing distinct aspects of cognitive performance in early psychosis. Neuroimage Clin 2024; 43:103657. [PMID: 39208481 PMCID: PMC11401179 DOI: 10.1016/j.nicl.2024.103657] [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: 05/31/2024] [Revised: 08/05/2024] [Accepted: 08/16/2024] [Indexed: 09/04/2024]
Abstract
BACKGROUND Psychosis spectrum disorders (PSDs) are marked by cognitive impairments, the neurobiological correlates of which remain poorly understood. Here, we investigate the entropy of time-varying functional connectivity (TVFC) patterns from resting-state functional magnetic resonance imaging (rs-fMRI) as potential biomarker for cognitive performance in PSDs. By combining our results with multimodal reference data, we hope to generate new insights into the mechanisms underlying cognitive dysfunction in PSDs. We hypothesized that low-entropy TVFC patterns (LEN) would be more behaviorally informative than high-entropy TVFC patterns (HEN), especially for tasks that require extensive integration across diverse cognitive subdomains. METHODS rs-fMRI and behavioral data from 97 patients in the early phases of psychosis and 53 controls were analyzed. Positron emission tomography (PET) and magnetoencephalography (MEG) data were taken from a public repository (Hansen et al., 2022). Multivariate analyses were conducted to examine relationships between TVFC patterns at multiple spatial scales and cognitive performance in patients. RESULTS Compared to HEN, LEN explained significantly more cognitive variance on average in PSD patients, driven by superior encoding of information on psychometrically more integrated tasks. HEN better captured information in specific subdomains of executive functioning. Nodal HEN-LEN transitions were spatially aligned with neurobiological gradients reflecting monoaminergic transporter densities and MEG beta-power. Exploratory analyses revealed a close statistical relationship between LEN and positive symptom severity in patients. CONCLUSION Our entropy-based analysis of TVFC patterns dissociates distinct aspects of cognition in PSDs. By linking topographies of neurotransmission and oscillatory dynamics with cognitive performance, it enhances our understanding of the mechanisms underlying cognitive deficits in PSDs.
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Affiliation(s)
- Fabian Hirsch
- Department of Diagnostic and Interventional Neuroradiology, Klinikum R.d.Isar, Technical University Munich, Ismaninger Str. 22, Munich 81675, Germany.
| | - Ângelo Bumanglag
- Department of Diagnostic and Interventional Neuroradiology, Klinikum R.d.Isar, Technical University Munich, Ismaninger Str. 22, Munich 81675, Germany
| | - Yifei Zhang
- Department of Diagnostic and Interventional Neuroradiology, Klinikum R.d.Isar, Technical University Munich, Ismaninger Str. 22, Munich 81675, Germany
| | - Afra Wohlschlaeger
- Department of Diagnostic and Interventional Neuroradiology, Klinikum R.d.Isar, Technical University Munich, Ismaninger Str. 22, Munich 81675, Germany
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Trevino G, Lee JJ, Shimony JS, Luckett PH, Leuthardt EC. Complexity organization of resting-state functional-MRI networks. Hum Brain Mapp 2024; 45:e26809. [PMID: 39185729 PMCID: PMC11345701 DOI: 10.1002/hbm.26809] [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] [Revised: 05/28/2024] [Accepted: 07/20/2024] [Indexed: 08/27/2024] Open
Abstract
Entropy measures are increasingly being used to analyze the structure of neural activity observed by functional magnetic resonance imaging (fMRI), with resting-state networks (RSNs) being of interest for their reproducible descriptions of the brain's functional architecture. Temporal correlations have shown a dichotomy among these networks: those that engage with the environment, known as extrinsic, which include the visual and sensorimotor networks; and those associated with executive control and self-referencing, known as intrinsic, which include the default mode network and the frontoparietal control network. While these inter-voxel temporal correlations enable the assessment of synchrony among the components of individual networks, entropic measures introduce an intra-voxel assessment that quantifies signal features encoded within each blood oxygen level-dependent (BOLD) time series. As a result, this framework offers insights into comprehending the representation and processing of information within fMRI signals. Multiscale entropy (MSE) has been proposed as a useful measure for characterizing the entropy of neural activity across different temporal scales. This measure of temporal entropy in BOLD data is dependent on the length of the time series; thus, high-quality data with fine-grained temporal resolution and a sufficient number of time frames is needed to improve entropy precision. We apply MSE to the Midnight Scan Club, a highly sampled and well-characterized publicly available dataset, to analyze the entropy distribution of RSNs and evaluate its ability to distinguish between different functional networks. Entropy profiles are compared across temporal scales and RSNs. Our results have shown that the spatial distribution of entropy at infra-slow frequencies (0.005-0.1 Hz) reproduces known parcellations of RSNs. We found a complexity hierarchy between intrinsic and extrinsic RSNs, with intrinsic networks robustly exhibiting higher entropy than extrinsic networks. Finally, we found new evidence that the topography of entropy in the posterior cerebellum exhibits high levels of entropy comparable to that of intrinsic RSNs.
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Affiliation(s)
- Gabriel Trevino
- Department of Neurological SurgeryWashington University School of MedicineSt. LouisMissouriUSA
| | - John J. Lee
- Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Joshua S. Shimony
- Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Patrick H. Luckett
- Center for Innovation in Neuroscience and TechnologyWashington University School of MedicineSt. LouisMissouriUSA
- Division of NeurotechnologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Eric C. Leuthardt
- Department of Neurological SurgeryWashington University School of MedicineSt. LouisMissouriUSA
- Center for Innovation in Neuroscience and TechnologyWashington University School of MedicineSt. LouisMissouriUSA
- Division of NeurotechnologyWashington University School of MedicineSt. LouisMissouriUSA
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Tu D, Wrobel J, Satterthwaite TD, Goldsmith J, Gur RC, Gur RE, Gertheiss J, Bassett DS, Shinohara RT. Regression and alignment for functional data and network topology. Biostatistics 2024:kxae026. [PMID: 39140988 DOI: 10.1093/biostatistics/kxae026] [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: 07/13/2023] [Revised: 07/05/2024] [Accepted: 07/08/2024] [Indexed: 08/15/2024] Open
Abstract
In the brain, functional connections form a network whose topological organization can be described by graph-theoretic network diagnostics. These include characterizations of the community structure, such as modularity and participation coefficient, which have been shown to change over the course of childhood and adolescence. To investigate if such changes in the functional network are associated with changes in cognitive performance during development, network studies often rely on an arbitrary choice of preprocessing parameters, in particular the proportional threshold of network edges. Because the choice of parameter can impact the value of the network diagnostic, and therefore downstream conclusions, we propose to circumvent that choice by conceptualizing the network diagnostic as a function of the parameter. As opposed to a single value, a network diagnostic curve describes the connectome topology at multiple scales-from the sparsest group of the strongest edges to the entire edge set. To relate these curves to executive function and other covariates, we use scalar-on-function regression, which is more flexible than previous functional data-based models used in network neuroscience. We then consider how systematic differences between networks can manifest in misalignment of diagnostic curves, and consequently propose a supervised curve alignment method that incorporates auxiliary information from other variables. Our algorithm performs both functional regression and alignment via an iterative, penalized, and nonlinear likelihood optimization. The illustrated method has the potential to improve the interpretability and generalizability of neuroscience studies where the goal is to study heterogeneity among a mixture of function- and scalar-valued measures.
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Affiliation(s)
- Danni Tu
- The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, 423 Guardian Drive, University of Pennsylvania, Philadelphia, PA, 19104, United States
| | - Julia Wrobel
- Department of Biostatistics and Bioinformatics, 1518 Clifton Rd. NE, Emory University, Atlanta, GA, 30322, United States
| | - Theodore D Satterthwaite
- Department of Psychiatry, 3700 Hamilton Walk, Perelman School of Medicine, Philadelphia, PA, 19104, United States
- Penn Lifespan Informatics and Neuroimaging Center, 3700 Hamilton Walk, Philadelphia, PA, 19104, United States
| | - Jeff Goldsmith
- Department of Biostatistics, 722 W. 168th St, Columbia University, New York, NY, 10032, United States
| | - Ruben C Gur
- Department of Psychiatry, 3700 Hamilton Walk, Perelman School of Medicine, Philadelphia, PA, 19104, United States
- The Penn Medicine-CHOP Lifespan Brain Institute, 3700 Hamilton Walk, Philadelphia, PA, 19104, United States
| | - Raquel E Gur
- Department of Psychiatry, 3700 Hamilton Walk, Perelman School of Medicine, Philadelphia, PA, 19104, United States
- The Penn Medicine-CHOP Lifespan Brain Institute, 3700 Hamilton Walk, Philadelphia, PA, 19104, United States
| | - Jan Gertheiss
- Department of Mathematics and Statistics, School of Economics and Social Sciences, Holstenhofweg 85, Helmut Schmidt University, 22043 Hamburg, Germany
| | - Dani S Bassett
- Department of Bioengineering, 210 S 33rd St, University of Pennsylvania, Philadelphia, PA, 19104, United States
- Department of Physics and Astronomy, 209 S 33rd St, University of Pennsylvania, Philadelphia, PA, 19104, United States
- Department of Electrical and Systems Engineering, 200 S 33rd St, University of Pennsylvania, Philadelphia, PA, 19104, United States
- Department of Neurology, 3400 Spruce St, University of Pennsylvania, Philadelphia, PA, 19104, United States
| | - Russell T Shinohara
- The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, 423 Guardian Drive, University of Pennsylvania, Philadelphia, PA, 19104, United States
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Liu R, Huang ZA, Hu Y, Zhu Z, Wong KC, Tan KC. Spatial-Temporal Co-Attention Learning for Diagnosis of Mental Disorders From Resting-State fMRI Data. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:10591-10605. [PMID: 37027556 DOI: 10.1109/tnnls.2023.3243000] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Neuroimaging techniques have been widely adopted to detect the neurological brain structures and functions of the nervous system. As an effective noninvasive neuroimaging technique, functional magnetic resonance imaging (fMRI) has been extensively used in computer-aided diagnosis (CAD) of mental disorders, e.g., autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD). In this study, we propose a spatial-temporal co-attention learning (STCAL) model for diagnosing ASD and ADHD from fMRI data. In particular, a guided co-attention (GCA) module is developed to model the intermodal interactions of spatial and temporal signal patterns. A novel sliding cluster attention module is designed to address global feature dependency of self-attention mechanism in fMRI time series. Comprehensive experimental results demonstrate that our STCAL model can achieve competitive accuracies of 73.0 ± 4.5%, 72.0 ± 3.8%, and 72.5 ± 4.2% on the ABIDE I, ABIDE II, and ADHD-200 datasets, respectively. Moreover, the potential for feature pruning based on the co-attention scores is validated by the simulation experiment. The clinical interpretation analysis of STCAL can allow medical professionals to concentrate on the discriminative regions of interest and key time frames from fMRI data.
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7
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Parkes L, Kim JZ, Stiso J, Brynildsen JK, Cieslak M, Covitz S, Gur RE, Gur RC, Pasqualetti F, Shinohara RT, Zhou D, Satterthwaite TD, Bassett DS. A network control theory pipeline for studying the dynamics of the structural connectome. Nat Protoc 2024:10.1038/s41596-024-01023-w. [PMID: 39075309 DOI: 10.1038/s41596-024-01023-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 05/16/2024] [Indexed: 07/31/2024]
Abstract
Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains the dynamics of a system. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter the dynamics of a system in a desired way. An interesting development for NCT in the neuroscience field is its application to study behavior and mental health symptoms. To date, NCT has been validated to study different aspects of the human structural connectome. NCT outputs can be monitored throughout developmental stages to study the effects of connectome topology on neural dynamics and, separately, to test the coherence of empirical datasets with brain function and stimulation. Here, we provide a comprehensive pipeline for applying NCT to structural connectomes by following two procedures. The main procedure focuses on computing the control energy associated with the transitions between specific neural activity states. The second procedure focuses on computing average controllability, which indexes nodes' general capacity to control the dynamics of the system. We provide recommendations for comparing NCT outputs against null network models, and we further support this approach with a Python-based software package called 'network control theory for python'. The procedures in this protocol are appropriate for users with a background in network neuroscience and experience in dynamical systems theory.
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Affiliation(s)
- Linden Parkes
- Department of Psychiatry, Rutgers University, Piscataway, NJ, USA.
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Jason Z Kim
- Department of Physics, Cornell University, Ithaca, NY, USA
| | - Jennifer Stiso
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Julia K Brynildsen
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sydney Covitz
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Raquel E Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruben C Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, Riverside, CA, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Philadelphia, PA, USA
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dale Zhou
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
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Mana L, Schwartz-Pallejà M, Vila-Vidal M, Deco G. Overview on cognitive impairment in psychotic disorders: From impaired microcircuits to dysconnectivity. Schizophr Res 2024; 269:132-143. [PMID: 38788432 DOI: 10.1016/j.schres.2024.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 05/09/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024]
Abstract
Schizophrenia's cognitive deficits, often overshadowed by positive symptoms, significantly contribute to the disorder's morbidity. Increasing attention highlights these deficits as reflections of neural circuit dysfunction across various cortical regions. Numerous connectivity alterations linked to cognitive symptoms in psychotic disorders have been reported, both at the macroscopic and microscopic level, emphasizing the potential role of plasticity and microcircuits impairment during development and later stages. However, the heterogeneous clinical presentation of cognitive impairment and diverse connectivity findings pose challenges in summarizing them into a cohesive picture. This review aims to synthesize major cognitive alterations, recent insights into network structural and functional connectivity changes and proposed mechanisms and microcircuit alterations underpinning these symptoms, particularly focusing on neurodevelopmental impairment, E/I balance, and sleep disturbances. Finally, we will also comment on some of the most recent and promising therapeutic approaches that aim to target these mechanisms to address cognitive symptoms. Through this comprehensive exploration, we strive to provide an updated and nuanced overview of the multiscale connectivity impairment underlying cognitive impairment in psychotic disorders.
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Affiliation(s)
- L Mana
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain.
| | - M Schwartz-Pallejà
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain; Department of Experimental and Health Science, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain; Eurecat, Technology Center of Catalonia, Multimedia Technologies, Barcelona, Spain.
| | - M Vila-Vidal
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain; Computational Biology and Complex Systems Group, Department of Physics, Universitat Politècnica de Catalunya, Barcelona, Spain.
| | - G Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona 08010, Spain.
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Sharma CM, Chariar VM. Diagnosis of mental disorders using machine learning: Literature review and bibliometric mapping from 2012 to 2023. Heliyon 2024; 10:e32548. [PMID: 38975193 PMCID: PMC11225745 DOI: 10.1016/j.heliyon.2024.e32548] [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: 05/29/2024] [Revised: 06/05/2024] [Accepted: 06/05/2024] [Indexed: 07/09/2024] Open
Abstract
Background Mental disorders (MDs) are becoming a leading burden in non-communicable diseases (NCDs). As per the World Health Organization's 2022 assessment report, there was a steep increase of 25 % in MDs during the COVID-19 pandemic. Early diagnosis of MDs can significantly improve treatment outcome and save disability-adjusted life years (DALYs). In recent times, the application of machine learning (ML) and deep learning (DL)) has shown promising results in the diagnosis of MDs, and the field has witnessed a huge research output in the form of research publications. Therefore, a bibliometric mapping along with a review of recent advancements is required. Methods This study presents a bibliometric analysis and review of the research, published over the last 10 years. Literature searches were conducted in the Scopus database for the period from January 1, 2012, to June 9, 2023. The data was filtered and screened to include only relevant and reliable publications. A total of 2811 journal articles were found. The data was exported to a comma-separated value (CSV) format for further analysis. Furthermore, a review of 40 selected studies was performed. Results The popularity of ML techniques in diagnosing MDs has been growing, with an annual research growth rate of 17.05 %. The Journal of Affective Disorders published the most documents (n = 97), while Wang Y. (n = 64) has published the most articles. Lotka's law is observed, with a minority of authors contributing the majority of publications. The top affiliating institutes are the West China Hospital of Sichuan University followed by the University of California, with China and the US dominating the top 10 institutes. While China has more publications, papers affiliated with the US receive more citations. Depression and schizophrenia are the primary focuses of ML and deep learning (DL) in mental disease detection. Co-occurrence network analysis reveals that ML is associated with depression, schizophrenia, autism, anxiety, ADHD, obsessive-compulsive disorder, and PTSD. Popular algorithms include support vector machine (SVM) classifier, decision tree classifier, and random forest classifier. Furthermore, DL is linked to neuroimaging techniques such as MRI, fMRI, and EEG, as well as bipolar disorder. Current research trends encompass DL, LSTM, generalized anxiety disorder, feature fusion, and convolutional neural networks.
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Affiliation(s)
- Chandra Mani Sharma
- CRDT, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India
- School of Computer Science, UPES, Dehradun, Uttarakhand, India
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Blair DS, Miller RL, Calhoun VD. A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia. ENTROPY (BASEL, SWITZERLAND) 2024; 26:545. [PMID: 39056908 PMCID: PMC11275472 DOI: 10.3390/e26070545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/07/2024] [Accepted: 06/19/2024] [Indexed: 07/28/2024]
Abstract
Over the past decade and a half, dynamic functional imaging has revealed low-dimensional brain connectivity measures, identified potential common human spatial connectivity states, tracked the transition patterns of these states, and demonstrated meaningful transition alterations in disorders and over the course of development. Recently, researchers have begun to analyze these data from the perspective of dynamic systems and information theory in the hopes of understanding how these dynamics support less easily quantified processes, such as information processing, cortical hierarchy, and consciousness. Little attention has been paid to the effects of psychiatric disease on these measures, however. We begin to rectify this by examining the complexity of subject trajectories in state space through the lens of information theory. Specifically, we identify a basis for the dynamic functional connectivity state space and track subject trajectories through this space over the course of the scan. The dynamic complexity of these trajectories is assessed along each dimension of the proposed basis space. Using these estimates, we demonstrate that schizophrenia patients display substantially simpler trajectories than demographically matched healthy controls and that this drop in complexity concentrates along specific dimensions. We also demonstrate that entropy generation in at least one of these dimensions is linked to cognitive performance. Overall, the results suggest great value in applying dynamic systems theory to problems of neuroimaging and reveal a substantial drop in the complexity of schizophrenia patients' brain function.
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Affiliation(s)
- David Sutherland Blair
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory University, Atlanta, GA 30303, USA (V.D.C.)
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Zhao CL, Hou W, Jia Y, Sahakian BJ, Luo Q. Sex differences of signal complexity at resting-state functional magnetic resonance imaging and their associations with the estrogen-signaling pathway in the brain. Cogn Neurodyn 2024; 18:973-986. [PMID: 38826661 PMCID: PMC11143120 DOI: 10.1007/s11571-023-09954-y] [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: 10/06/2022] [Revised: 01/27/2023] [Accepted: 03/08/2023] [Indexed: 06/04/2024] Open
Abstract
Sex differences in the brain have been widely reported and may hold the key to elucidating sex differences in many medical conditions and drug response. However, the molecular correlates of these sex differences in structural and functional brain measures in the human brain remain unclear. Herein, we used sample entropy (SampEn) to quantify the signal complexity of resting-state functional magnetic resonance imaging (rsfMRI) in a large neuroimaging cohort (N = 1,642). The frontoparietal control network and the cingulo-opercular network had high signal complexity while the cerebellar and sensory motor networks had low signal complexity in both men and women. Compared with those in male brains, we found greater signal complexity in all functional brain networks in female brains with the default mode network exhibiting the largest sex difference. Using the gene expression data in brain tissues, we identified genes that were significantly associated with sex differences in brain signal complexity. The significant genes were enriched in the gene sets that were differentially expressed between the brain cortex and other tissues, the estrogen-signaling pathway, and the biological function of neural plasticity. In particular, the G-protein-coupled estrogen receptor 1 gene in the estrogen-signaling pathway was expressed more in brain regions with greater sex differences in SampEn. In conclusion, greater complexity in female brains may reflect the interactions between sex hormone fluctuations and neuromodulation of estrogen in women. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-023-09954-y.
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Affiliation(s)
- Cheng-li Zhao
- College of Science, National University of Defense Technology, Changsha, 410073 China
| | - Wenjie Hou
- National Clinical Research Center for Aging and Medicine at Huashan Hospital, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433 China
- Center for Computational Psychiatry, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Human Phenome Institute, Fudan University, Shanghai, 200438 China
| | - Yanbing Jia
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, 471000 China
| | - Barbara J. Sahakian
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB UK
| | - the DIRECT Consortium
- College of Science, National University of Defense Technology, Changsha, 410073 China
- National Clinical Research Center for Aging and Medicine at Huashan Hospital, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433 China
- Center for Computational Psychiatry, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Human Phenome Institute, Fudan University, Shanghai, 200438 China
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, 471000 China
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB UK
| | - Qiang Luo
- National Clinical Research Center for Aging and Medicine at Huashan Hospital, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433 China
- Center for Computational Psychiatry, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Human Phenome Institute, Fudan University, Shanghai, 200438 China
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12
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Hirsch F, Bumanglag Â, Zhang Y, Wohlschlaeger A. Diverging functional connectivity timescales: Capturing distinct aspects of cognitive performance in early psychosis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.07.24306932. [PMID: 38766002 PMCID: PMC11100938 DOI: 10.1101/2024.05.07.24306932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Background Psychosis spectrum disorders (PSDs) are marked by cognitive impairments, the neurobiological correlates of which remain poorly understood. Here, we investigate the entropy of time-varying functional connectivity (TVFC) patterns from resting-state fMRI (rfMRI) as potential biomarker for cognitive performance in PSDs. By combining our results with multimodal reference data, we hope to generate new insights into the mechanisms underlying cognitive dysfunction in PSDs. We hypothesized that low-entropy TVFC patterns (LEN) would be more behaviorally informative than high-entropy TVFC patterns (HEN), especially for tasks that require extensive integration across diverse cognitive subdomains. Methods rfMRI and behavioral data from 97 patients in the early phases of psychosis and 53 controls were analyzed. Positron-Emission Tomography (PET) and magnetoencephalography (MEG) data were taken from a public repository (Hansen et al., 2022). Multivariate analyses were conducted to examine relationships between TVFC patterns at multiple spatial scales and cognitive performance in patients. Results Compared to HEN, LEN explained significantly more cognitive variance on average in PSD patients, driven by superior encoding of information on psychometrically more integrated tasks. HEN better captured information in specific subdomains of executive functioning. Nodal HEN-LEN transitions were spatially aligned with neurobiological gradients reflecting monoaminergic transporter densities and MEG beta power. Exploratory analyses revealed a close statistical relationship between LEN and positive PSD symptoms. Conclusion Our entropy-based analysis of TVFC patterns dissociates distinct aspects of cognition in PSDs. By linking topographies of neurotransmission and oscillatory dynamics with cognitive performance, it enhances our understanding of the mechanisms underlying cognitive deficits in PSDs. CRediT Authorship Contribution Statement Fabian Hirsch: Conceptualization, Methodology, Software, Formal analysis, Writing - Original Draft, Writing - Review & Editing, Visualization; Ângelo Bumanglag: Methodology, Software, Formal analysis, Writing - Review & Editing; Yifei Zhang: Methodology, Software, Formal analysis, Writing - Review & Editing; Afra Wohlschlaeger: Methodology, Writing - Review & Editing, Supervision, Project administration.
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13
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Haiduk F, Zatorre RJ, Benjamin L, Morillon B, Albouy P. Spectrotemporal cues and attention jointly modulate fMRI network topology for sentence and melody perception. Sci Rep 2024; 14:5501. [PMID: 38448636 PMCID: PMC10917817 DOI: 10.1038/s41598-024-56139-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 03/01/2024] [Indexed: 03/08/2024] Open
Abstract
Speech and music are two fundamental modes of human communication. Lateralisation of key processes underlying their perception has been related both to the distinct sensitivity to low-level spectrotemporal acoustic features and to top-down attention. However, the interplay between bottom-up and top-down processes needs to be clarified. In the present study, we investigated the contribution of acoustics and attention to melodies or sentences to lateralisation in fMRI functional network topology. We used sung speech stimuli selectively filtered in temporal or spectral modulation domains with crossed and balanced verbal and melodic content. Perception of speech decreased with degradation of temporal information, whereas perception of melodies decreased with spectral degradation. Applying graph theoretical metrics on fMRI connectivity matrices, we found that local clustering, reflecting functional specialisation, linearly increased when spectral or temporal cues crucial for the task goal were incrementally degraded. These effects occurred in a bilateral fronto-temporo-parietal network for processing temporally degraded sentences and in right auditory regions for processing spectrally degraded melodies. In contrast, global topology remained stable across conditions. These findings suggest that lateralisation for speech and music partially depends on an interplay of acoustic cues and task goals under increased attentional demands.
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Affiliation(s)
- Felix Haiduk
- Department of Behavioral and Cognitive Biology, University of Vienna, Vienna, Austria.
- Department of General Psychology, University of Padua, Padua, Italy.
| | - Robert J Zatorre
- Cognitive Neuroscience Unit, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- International Laboratory for Brain, Music and Sound Research (BRAMS) - CRBLM, Montreal, QC, Canada
| | - Lucas Benjamin
- Cognitive Neuroscience Unit, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Cognitive Neuroimaging Unit, CNRS ERL 9003, INSERM U992, CEA, Université Paris-Saclay, NeuroSpin Center, 91191, Gif/Yvette, France
| | - Benjamin Morillon
- Aix Marseille University, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Philippe Albouy
- Cognitive Neuroscience Unit, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- International Laboratory for Brain, Music and Sound Research (BRAMS) - CRBLM, Montreal, QC, Canada
- CERVO Brain Research Centre, School of Psychology, Laval University, Quebec, QC, Canada
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Feng S, Zheng S, Dong L, Li Z, Zhu H, Liu S, Li X, Ning Y, Jia H. Effects of aripiprazole on resting-state functional connectivity of large-scale brain networks in first-episode drug-naïve schizophrenia patients. J Psychiatr Res 2024; 171:215-221. [PMID: 38309211 DOI: 10.1016/j.jpsychires.2024.01.020] [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: 10/24/2023] [Revised: 01/04/2024] [Accepted: 01/15/2024] [Indexed: 02/05/2024]
Abstract
Aripiprazole modulates functional connectivity (FC) between several brain regions in first-episode schizophrenia patients, contributing to improvement in clinical symptoms. However, the effects of aripiprazole on abnormal connections among extensive brain networks in schizophrenia patients remain unclear. We aimed to investigate the effects of 12 weeks of aripiprazole treatment on the FC of large-scale brain networks. Forty-five first-episode drug-naïve schizophrenia patients and 45 healthy controls were recruited for this longitudinal study. Resting-state functional magnetic resonance imaging (fMRI) data were collected at baseline and after 12 weeks of aripiprazole treatment. The patients were classified into those in response (SCHr group) and non-response (SCHnr group) according to the improvement of clinical symptoms after 12-weeks treatment. The FC were evaluated for seven large-scale brain networks. In addition, correlation analysis was performed to investigate associations between changes FC of large-scale brain networks and clinical symptoms. Before aripiprazole treatment, schizophrenia patients showed decreased FC of extensive brain networks compared to healthy controls. The 12-week aripiprazole treatment significantly prevented the constantly decreased FC of subcortical network, default mode network and other brain networks in patients with SCHr, in association with the improvement of clinical symptoms. Taken together, these findings have revealed the effects of aripiprazole on FC in large-scale networks in schizophrenia patients, which could provide new insight on interpreting symptom improvement in SCH.
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Affiliation(s)
- Sitong Feng
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Sisi Zheng
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Linrui Dong
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Ziyan Li
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Hong Zhu
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Shanshan Liu
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Xue Li
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yanzhe Ning
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Hongxiao Jia
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
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15
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Tu D, Wrobel J, Satterthwaite TD, Goldsmith J, Gur RC, Gur RE, Gertheiss J, Bassett DS, Shinohara RT. Regression and Alignment for Functional Data and Network Topology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.13.548836. [PMID: 37503017 PMCID: PMC10370026 DOI: 10.1101/2023.07.13.548836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
In the brain, functional connections form a network whose topological organization can be described by graph-theoretic network diagnostics. These include characterizations of the community structure, such as modularity and participation coefficient, which have been shown to change over the course of childhood and adolescence. To investigate if such changes in the functional network are associated with changes in cognitive performance during development, network studies often rely on an arbitrary choice of pre-processing parameters, in particular the proportional threshold of network edges. Because the choice of parameter can impact the value of the network diagnostic, and therefore downstream conclusions, we propose to circumvent that choice by conceptualizing the network diagnostic as a function of the parameter. As opposed to a single value, a network diagnostic curve describes the connectome topology at multiple scales-from the sparsest group of the strongest edges to the entire edge set. To relate these curves to executive function and other covariates, we use scalar-on-function regression, which is more flexible than previous functional data-based models used in network neuroscience. We then consider how systematic differences between networks can manifest in misalignment of diagnostic curves, and consequently propose a supervised curve alignment method that incorporates auxiliary information from other variables. Our algorithm performs both functional regression and alignment via an iterative, penalized, and nonlinear likelihood optimization. The illustrated method has the potential to improve the interpretability and generalizability of neuroscience studies where the goal is to study heterogeneity among a mixture of function- and scalar-valued measures.
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Affiliation(s)
- Danni Tu
- The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Julia Wrobel
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA
| | - Theodore D. Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA USA
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, PA, USA
| | - Jeff Goldsmith
- Department of Biostatistics, Columbia University, New York, NY, USA
| | - Ruben C. Gur
- Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA USA
- The Penn Medicine-CHOP Lifespan Brain Institute, Philadelphia, PA, USA
| | - Raquel E. Gur
- Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA USA
- The Penn Medicine-CHOP Lifespan Brain Institute, Philadelphia, PA, USA
| | - Jan Gertheiss
- Department of Mathematics and Statistics, School of Economics and Social Sciences, Helmut Schmidt University, Hamburg, Germany
| | - Dani S. Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T. Shinohara
- The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
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16
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Metzner C, Dimulescu C, Kamp F, Fromm S, Uhlhaas PJ, Obermayer K. Exploring global and local processes underlying alterations in resting-state functional connectivity and dynamics in schizophrenia. Front Psychiatry 2024; 15:1352641. [PMID: 38414495 PMCID: PMC10897003 DOI: 10.3389/fpsyt.2024.1352641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 01/19/2024] [Indexed: 02/29/2024] Open
Abstract
Introduction We examined changes in large-scale functional connectivity and temporal dynamics and their underlying mechanisms in schizophrenia (ScZ) through measurements of resting-state functional magnetic resonance imaging (rs-fMRI) data and computational modelling. Methods The rs-fMRI measurements from patients with chronic ScZ (n=38) and matched healthy controls (n=43), were obtained through the public schizConnect repository. Computational models were constructed based on diffusion-weighted MRI scans and fit to the experimental rs-fMRI data. Results We found decreased large-scale functional connectivity across sensory and association areas and for all functional subnetworks for the ScZ group. Additionally global synchrony was reduced in patients while metastability was unaltered. Perturbations of the computational model revealed that decreased global coupling and increased background noise levels both explained the experimentally found deficits better than local changes to the GABAergic or glutamatergic system. Discussion The current study suggests that large-scale alterations in ScZ are more likely the result of global rather than local network changes.
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Affiliation(s)
- Christoph Metzner
- Neural Information Processing Group, Institute of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Department of Child and Adolescent Psychiatry, Charité – Universitätsmedizin Berlin, Berlin, Germany
- School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, United Kingdom
| | - Cristiana Dimulescu
- Neural Information Processing Group, Institute of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Fabian Kamp
- Neural Information Processing Group, Institute of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Max Planck School of Cognition, Max Planck Institute for Human Cognitive and Brain Science, Leipzig, Germany
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Sophie Fromm
- Neural Information Processing Group, Institute of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Peter J. Uhlhaas
- Department of Child and Adolescent Psychiatry, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom
| | - Klaus Obermayer
- Neural Information Processing Group, Institute of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
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17
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Iglesias-Parro S, Soriano MF, Ibáñez-Molina AJ, Pérez-Matres AV, Ruiz de Miras J. Examining Neural Connectivity in Schizophrenia Using Task-Based EEG: A Graph Theory Approach. SENSORS (BASEL, SWITZERLAND) 2023; 23:8722. [PMID: 37960422 PMCID: PMC10647645 DOI: 10.3390/s23218722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 10/19/2023] [Accepted: 10/23/2023] [Indexed: 11/15/2023]
Abstract
Schizophrenia (SZ) is a complex disorder characterized by a range of symptoms and behaviors that have significant consequences for individuals, families, and society in general. Electroencephalography (EEG) is a valuable tool for understanding the neural dynamics and functional abnormalities associated with schizophrenia. Research studies utilizing EEG have identified specific patterns of brain activity in individuals diagnosed with schizophrenia that may reflect disturbances in neural synchronization and information processing in cortical circuits. Considering the temporal dynamics of functional connectivity provides a more comprehensive understanding of brain networks' organization and how they change during different cognitive states. This temporal perspective would enhance our understanding of the underlying mechanisms of schizophrenia. In the present study, we will use measures based on graph theory to obtain dynamic and static indicators in order to evaluate differences in the functional connectivity of individuals diagnosed with SZ and healthy controls using an ecologically valid task. At the static level, patients showed alterations in their ability to segregate information, particularly in the default mode network (DMN). As for dynamic measures, patients showed reduced values in most metrics (segregation, integration, centrality, and resilience), reflecting a reduced number of dynamic states of brain networks. Our results show the utility of combining static and dynamic indicators of functional connectivity from EEG sensors.
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Affiliation(s)
| | - María F. Soriano
- Mental Health Unit, San Agustín Hospital de Linares, 23700 Linares, Spain
| | | | - Ana V. Pérez-Matres
- Department of Software Engineering, University of Granada, 18071 Granada, Spain
| | - Juan Ruiz de Miras
- Department of Software Engineering, University of Granada, 18071 Granada, Spain
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18
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Parkes L, Kim JZ, Stiso J, Brynildsen JK, Cieslak M, Covitz S, Gur RE, Gur RC, Pasqualetti F, Shinohara RT, Zhou D, Satterthwaite TD, Bassett DS. Using network control theory to study the dynamics of the structural connectome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.23.554519. [PMID: 37662395 PMCID: PMC10473719 DOI: 10.1101/2023.08.23.554519] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains dynamics. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter dynamics in a desired way. We have extensively developed and validated the application of NCT to the human structural connectome. Through these efforts, we have studied (i) how different aspects of connectome topology affect neural dynamics, (ii) whether NCT outputs cohere with empirical data on brain function and stimulation, and (iii) how NCT outputs vary across development and correlate with behavior and mental health symptoms. In this protocol, we introduce a framework for applying NCT to structural connectomes following two main pathways. Our primary pathway focuses on computing the control energy associated with transitioning between specific neural activity states. Our second pathway focuses on computing average controllability, which indexes nodes' general capacity to control dynamics. We also provide recommendations for comparing NCT outputs against null network models. Finally, we support this protocol with a Python-based software package called network control theory for python (nctpy).
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Affiliation(s)
- Linden Parkes
- Department of Bioengineering, University of Pennsylvania, PA 19104, USA
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, Rutgers University, Piscataway, NJ 08854, USA
| | - Jason Z Kim
- Department of Physics, Cornell University, Ithaca, NY 14853, USA
| | - Jennifer Stiso
- Department of Bioengineering, University of Pennsylvania, PA 19104, USA
| | | | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sydney Covitz
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ruben C Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, Riverside, CA 92521, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dale Zhou
- Department of Bioengineering, University of Pennsylvania, PA 19104, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, Philadelphia, PA 19104, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, PA 19104, USA
- Department of Physics and Astronomy, University of Pennsylvania, PA 19104, USA
- Santa Fe Institute, Santa Fe, NM 87501, USA
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Bolton TAW, Van De Ville D, Amico E, Preti MG, Liégeois R. The arrow-of-time in neuroimaging time series identifies causal triggers of brain function. Hum Brain Mapp 2023; 44:4077-4087. [PMID: 37209360 PMCID: PMC10258533 DOI: 10.1002/hbm.26331] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 03/07/2023] [Accepted: 04/18/2023] [Indexed: 05/22/2023] Open
Abstract
Moving from association to causal analysis of neuroimaging data is crucial to advance our understanding of brain function. The arrow-of-time (AoT), that is, the known asymmetric nature of the passage of time, is the bedrock of causal structures shaping physical phenomena. However, almost all current time series metrics do not exploit this asymmetry, probably due to the difficulty to account for it in modeling frameworks. Here, we introduce an AoT-sensitive metric that captures the intensity of causal effects in multivariate time series, and apply it to high-resolution functional neuroimaging data. We find that causal effects underlying brain function are more distinctively localized in space and time than functional activity or connectivity, thereby allowing us to trace neural pathways recruited in different conditions. Overall, we provide a mapping of the causal brain that challenges the association paradigm of brain function.
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Affiliation(s)
- Thomas A. W. Bolton
- Connectomics Laboratory, Department of RadiologyCentre Hospitalier Universitaire VaudoisLausanneSwitzerland
- Department of Clinical NeurosciencesCentre Hospitalier Universitaire VaudoisLausanneSwitzerland
| | - Dimitri Van De Ville
- Neuro‐X InstituteÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland
- Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland
| | - Enrico Amico
- Neuro‐X InstituteÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland
- Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland
| | - Maria G. Preti
- Neuro‐X InstituteÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland
- Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland
- CIBM Center for Biomedical ImagingVaudSwitzerland
| | - Raphaël Liégeois
- Neuro‐X InstituteÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland
- Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland
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Xue K, Chen J, Wei Y, Chen Y, Han S, Wang C, Zhang Y, Song X, Cheng J. Impaired large-scale cortico-hippocampal network connectivity, including the anterior temporal and posterior medial systems, and its associations with cognition in patients with first-episode schizophrenia. Front Neurosci 2023; 17:1167942. [PMID: 37342466 PMCID: PMC10277613 DOI: 10.3389/fnins.2023.1167942] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 05/08/2023] [Indexed: 06/23/2023] Open
Abstract
Background and objective The cortico-hippocampal network is an emerging neural framework with striking evidence that it supports cognition in humans, especially memory; this network includes the anterior temporal (AT) system, the posterior medial (PM) system, the anterior hippocampus (aHIPPO), and the posterior hippocampus (pHIPPO). This study aimed to detect aberrant patterns of functional connectivity within and between large-scale cortico-hippocampal networks in first-episode schizophrenia patients compared with a healthy control group via resting-state functional magnetic resonance imaging (rs-fMRI) and to explore the correlations of these aberrant patterns with cognition. Methods A total of 86 first-episode, drug-naïve schizophrenia patients and 102 healthy controls (HC) were recruited to undergo rs-fMRI examinations and clinical evaluations. We conducted large-scale edge-based network analysis to characterize the functional architecture of the cortico-hippocampus network and investigate between-group differences in within/between-network functional connectivity. Additionally, we explored the associations of functional connectivity (FC) abnormalities with clinical characteristics, including scores on the Positive and Negative Syndrome Scale (PANSS) and cognitive scores. Results Compared with the HC group, schizophrenia patients exhibited widespread alterations to within-network FC of the cortico-hippocampal network, with decreases in FC involving the precuneus (PREC), amygdala (AMYG), parahippocampal cortex (PHC), orbitofrontal cortex (OFC), perirhinal cortex (PRC), retrosplenial cortex (RSC), posterior cingulate cortex (PCC), angular gyrus (ANG), aHIPPO, and pHIPPO. Schizophrenia patients also showed abnormalities in large-scale between-network FC of the cortico-hippocampal network, in the form of significantly decreased FC between the AT and the PM, the AT and the aHIPPO, the PM and the aHIPPO, and the aHIPPO and the pHIPPO. A number of these signatures of aberrant FC were correlated with PANSS score (positive, negative, and total score) and with scores on cognitive test battery items, including attention/vigilance (AV), working memory (WM), verbal learning and memory (Verb_Lrng), visual learning and memory (Vis_Lrng), reasoning and problem-solving (RPS), and social cognition (SC). Conclusion Schizophrenia patients show distinct patterns of functional integration and separation both within and between large-scale cortico-hippocampal networks, reflecting a network imbalance of the hippocampal long axis with the AT and PM systems, which regulate cognitive domains (mainly Vis_Lrng, Verb_Lrng, WM, and RPS), and particularly involving alterations to FC of the AT system and the aHIPPO. These findings provide new insights into the neurofunctional markers of schizophrenia.
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Affiliation(s)
- Kangkang Xue
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Jingli Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Caihong Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
| | - Xueqin Song
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, China
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21
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Tao B, Xiao Y, Li B, Yu W, Zhu F, Gao Z, Cao H, Gong Q, Gu S, Qiu C, Lui S. Linked patterns of interhemispheric functional connectivity and microstructural characteristics of the corpus callosum in antipsychotic-naive first-episode schizophrenia. Asian J Psychiatr 2023; 86:103659. [PMID: 37327564 DOI: 10.1016/j.ajp.2023.103659] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 06/01/2023] [Accepted: 06/02/2023] [Indexed: 06/18/2023]
Abstract
OBJECTIVE Many magnetic resonance imaging (MRI) studies have showed significant structural abnormalities of the corpus callosum (CC) and dysregulated interhemispheric functional connectivity (FC) in schizophrenia. Although the hemispheres are mainly linked through CC, few studies directly examined the relationship between aberrant interhemispheric FC and the white matter deficits of the CC in schizophrenia. METHODS One hundred and sixty-nine antipsychotic-naive first-episode schizophrenia patients (AN-FES) and 214 healthy controls (HCs) were recruited. Diffusional and functional MRI data were obtained for each participant, and fractional anisotropy (FA) values of the five CC subregions and interhemispheric FC for each participant were acquired. Between-group differences in these metrics were compared using multivariate analysis of covariance (MANCOVA). Moreover, sparse canonical correlation analysis (sCCA) was conducted to explore correlations of fibers integrity of the CC subregions with dysregulated interhemispheric FC in patients. RESULTS Compared with HCs, the patients with schizophrenia showed significantly reduced FA values of the CC subregions and dysregulated connectivity between two cerebral hemispheres. The canonical correlation coefficients identified five significant sCCA modes between FA and FC (r > 0.75, p < 0.001), suggesting strong relationships between FA values of the CC subregions and interhemispheric FC in patients. CONCLUSION Our findings support a key role of CC in maintaining ongoing functional communication between two cerebral hemispheres, and suggest that microstructural changes of white matter fibers crossing different CC subregions may affect special interhemispheric FC in schizophrenia.
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Affiliation(s)
- Bo Tao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Yuan Xiao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Bin Li
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, No. 2006 Xiyuan Avenue, West Hi-Tech Zone, 611731, Chengdu, China
| | - Wei Yu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Fei Zhu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Ziyang Gao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Hengyi Cao
- Center for Psychiatry Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, USA; Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
| | - Shi Gu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, No. 2006 Xiyuan Avenue, West Hi-Tech Zone, 611731, Chengdu, China..
| | - Changjian Qiu
- Mental Health Center, West China Hospital of Sichuan University, 28 Dianxin Street, Chengdu, China.
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
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Yadav Y, Elumalai P, Williams N, Jost J, Samal A. Discrete Ricci curvatures capture age-related changes in human brain functional connectivity networks. Front Aging Neurosci 2023; 15:1120846. [PMID: 37293668 PMCID: PMC10244515 DOI: 10.3389/fnagi.2023.1120846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 05/02/2023] [Indexed: 06/10/2023] Open
Abstract
Introduction Geometry-inspired notions of discrete Ricci curvature have been successfully used as markers of disrupted brain connectivity in neuropsychiatric disorders, but their ability to characterize age-related changes in functional connectivity is unexplored. Methods We apply Forman-Ricci curvature and Ollivier-Ricci curvature to compare functional connectivity networks of healthy young and older subjects from the Max Planck Institute Leipzig Study for Mind-Body-Emotion Interactions (MPI-LEMON) dataset (N = 225). Results We found that both Forman-Ricci curvature and Ollivier-Ricci curvature can capture whole-brain and region-level age-related differences in functional connectivity. Meta-analysis decoding demonstrated that those brain regions with age-related curvature differences were associated with cognitive domains known to manifest age-related changes-movement, affective processing, and somatosensory processing. Moreover, the curvature values of some brain regions showing age-related differences exhibited correlations with behavioral scores of affective processing. Finally, we found an overlap between brain regions showing age-related curvature differences and those brain regions whose non-invasive stimulation resulted in improved movement performance in older adults. Discussion Our results suggest that both Forman-Ricci curvature and Ollivier-Ricci curvature correctly identify brain regions that are known to be functionally or clinically relevant. Our results add to a growing body of evidence demonstrating the sensitivity of discrete Ricci curvature measures to changes in the organization of functional connectivity networks, both in health and disease.
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Affiliation(s)
- Yasharth Yadav
- The Institute of Mathematical Sciences (IMSc), Chennai, India
- Indian Institute of Science Education and Research (IISER), Pune, India
| | | | - Nitin Williams
- Department of Computer Science, Helsinki Institute of Information Technology, Aalto University, Espoo, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Jürgen Jost
- Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany
- The Santa Fe Institute, Santa Fe, NM, United States
| | - Areejit Samal
- The Institute of Mathematical Sciences (IMSc), Chennai, India
- Homi Bhabha National Institute (HBNI), Mumbai, India
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23
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Yang J, Tao H, Sun F, Fan Z, Yang J, Liu Z, Xue Z, Chen X. The anatomical networks based on probabilistic structurally connectivity in bipolar disorder across mania, depression, and euthymic states. J Affect Disord 2023; 329:42-49. [PMID: 36842653 DOI: 10.1016/j.jad.2023.02.109] [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: 04/10/2022] [Revised: 02/17/2023] [Accepted: 02/21/2023] [Indexed: 02/27/2023]
Abstract
BACKGROUNDS There have pieces of evidence of the distinct aberrant functional network topology profile in bipolar disorder (BD) across mania, depression, and euthymic episodes. However, the underlying anatomical network topology pattern in BD across different episodes is unclear. METHODS We calculated the whole-brain probabilistic structurally connectivity across 143 subjects (72 with BD [34 depression; 13 mania; 25 euthymic] and 53 healthy controls), and used graph theory to examine the trait- and state-related topology alterations of the structural connectome in BD. The correlation analysis was further conducted to explore the relationship between detected network measures and clinical symptoms. RESULTS There no omnibus alteration of any global network metrics were observed across all diagnostic groups. In the regional network metrics level, bipolar depression showed increased clustering coefficient in the right lingual gyrus compared with all other groups, and the increased clustering coefficient in the right lingual gyrus positively correlated with depression, anxiety, and illness burden symptoms but negatively correlated with mania symptoms; manic and euthymic patients showed decreased clustering coefficient in the left inferior occipital gyrus compared with HCs. LIMITATIONS The moderate sample size of all patient groups (especially for subjects with mania) might have contributed to the negative findings of the trait feature in this study. CONCLUSIONS We demonstrated the altered regional connectivity pattern in the occipital lobe of the bipolar depression and mania episode, especially the lingual gyrus. The association of the clustering coefficient in the lingual gyrus with clinical symptoms helps monitor the state of BD.
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Affiliation(s)
- Jie Yang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Haojuan Tao
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Fuping Sun
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Zebin Fan
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Jun Yang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Zhening Liu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Zhimin Xue
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Xudong Chen
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China.
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24
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Deleglise A, Donnelly-Kehoe PA, Yeffal A, Jacobacci F, Jovicich J, Amaro E, Armony JL, Doyon J, Della-Maggiore V. Human motor sequence learning drives transient changes in network topology and hippocampal connectivity early during memory consolidation. Cereb Cortex 2023; 33:6120-6131. [PMID: 36587288 DOI: 10.1093/cercor/bhac489] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 11/03/2022] [Accepted: 11/20/2022] [Indexed: 01/02/2023] Open
Abstract
In the last decade, the exclusive role of the hippocampus in human declarative learning has been challenged. Recently, we have shown that gains in performance observed in motor sequence learning (MSL) during the quiet rest periods interleaved with practice are associated with increased hippocampal activity, suggesting a role of this structure in motor memory reactivation. Yet, skill also develops offline as memory stabilizes after training and overnight. To examine whether the hippocampus contributes to motor sequence memory consolidation, here we used a network neuroscience strategy to track its functional connectivity offline 30 min and 24 h post learning using resting-state functional magnetic resonance imaging. Using a graph-analytical approach we found that MSL transiently increased network modularity, reflected in an increment in local information processing at 30 min that returned to baseline at 24 h. Within the same time window, MSL decreased the connectivity of a hippocampal-sensorimotor network, and increased the connectivity of a striatal-premotor network in an antagonistic manner. Finally, a supervised classification identified a low-dimensional pattern of hippocampal connectivity that discriminated between control and MSL data with high accuracy. The fact that changes in hippocampal connectivity were detected shortly after training supports a relevant role of the hippocampus in early stages of motor memory consolidation.
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Affiliation(s)
- Alvaro Deleglise
- University of Buenos Aires, CONICET, Institute of Physiology and Biophysics (IFIBIO) Bernardo Houssay, Buenos Aires C1121ABG, Argentina
| | | | - Abraham Yeffal
- University of Buenos Aires, CONICET, Institute of Physiology and Biophysics (IFIBIO) Bernardo Houssay, Buenos Aires C1121ABG, Argentina
| | - Florencia Jacobacci
- University of Buenos Aires, CONICET, Institute of Physiology and Biophysics (IFIBIO) Bernardo Houssay, Buenos Aires C1121ABG, Argentina
| | - Jorge Jovicich
- Center for Mind/Brain Sciences, University of Trento, 38068 Trento, Italy
| | - Edson Amaro
- Plataforma de Imagens na Sala de Autopsia (PISA), Instituto de Radiologia, Facultade de Medicina, Universidade de Sao Paulo, Sao Paulo 05403-000, Brazil
| | - Jorge L Armony
- Douglas Mental Health Research Institute, McGill University, Montreal, QC H4H 1R3, Canada
| | - Julien Doyon
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC H3A 2B4, Canada
| | - Valeria Della-Maggiore
- University of Buenos Aires, CONICET, Institute of Physiology and Biophysics (IFIBIO) Bernardo Houssay, Buenos Aires C1121ABG, Argentina
- School of Science and Technology (ECyT), National University of San Martin, B1650 Villa Lynch, Buenos Aires, Argentina
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25
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Vecchio D, Piras F, Ciullo V, Piras F, Natalizi F, Ducci G, Ambrogi S, Spalletta G, Banaj N. Brain Network Topology in Deficit and Non-Deficit Schizophrenia: Application of Graph Theory to Local and Global Indices. J Pers Med 2023; 13:jpm13050799. [PMID: 37240969 DOI: 10.3390/jpm13050799] [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: 03/29/2023] [Revised: 04/27/2023] [Accepted: 05/03/2023] [Indexed: 05/28/2023] Open
Abstract
Patients with deficit schizophrenia (SZD) suffer from primary and enduring negative symptoms. Limited pieces of evidence and neuroimaging studies indicate they differ from patients with non-deficit schizophrenia (SZND) in neurobiological aspects, but the results are far from conclusive. We applied for the first time, graph theory analyses to discriminate local and global indices of brain network topology in SZD and SZND patients compared with healthy controls (HC). High-resolution T1-weighted images were acquired for 21 SZD patients, 21 SZND patients, and 21 HC to measure cortical thickness from 68 brain regions. Graph-based metrics (i.e., centrality, segregation, and integration) were computed and compared among groups, at both global and regional networks. When compared to HC, at the regional level, SZND were characterized by temporoparietal segregation and integration differences, while SZD showed widespread alterations in all network measures. SZD also showed less segregated network topology at the global level in comparison to HC. SZD and SZND differed in terms of centrality and integration measures in nodes belonging to the left temporoparietal cortex and to the limbic system. SZD is characterized by topological features in the network architecture of brain regions involved in negative symptomatology. Such results help to better define the neurobiology of SZD (SZD: Deficit Schizophrenia; SZND: Non-Deficit Schizophrenia; SZ: Schizophrenia; HC: healthy controls; CC: clustering coefficient; L: characteristic path length; E: efficiency; D: degree; CCnode: CC of a node; CCglob: the global CC of the network; Eloc: efficiency of the information transfer flow either within segregated subgraphs or neighborhoods nodes; Eglob: efficiency of the information transfer flow among the global network; FDA: Functional Data Analysis; and Dmin: estimated minimum densities).
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Affiliation(s)
- Daniela Vecchio
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy
| | - Valentina Ciullo
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy
| | - Federica Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy
| | - Federica Natalizi
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy
- Department of Psychology, "Sapienza" University of Rome, Via dei Marsi 78, 00185 Rome, Italy
- PhD Program in Behavioral Neuroscience, Sapienza University of Rome, 00161 Rome, Italy
| | - Giuseppe Ducci
- Department of Mental Health, ASL Roma 1, 00135 Rome, Italy
| | - Sonia Ambrogi
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy
| | - Gianfranco Spalletta
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX 77030, USA
| | - Nerisa Banaj
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy
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Li X, Kaur Y, Wilhelm O, Reuter M, Montag C, Sommer W, Zhou C, Hildebrandt A. Resting-state brain signal complexity discriminates young healthy APOE e4 carriers from non-e4 carriers. Eur J Neurosci 2023; 57:854-866. [PMID: 36656069 DOI: 10.1111/ejn.15915] [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: 06/25/2022] [Revised: 01/10/2023] [Accepted: 01/11/2023] [Indexed: 01/20/2023]
Abstract
It is well established that the e4 allele of the APOE gene is associated with impaired brain functionality and cognitive decline in humans at elder age. However, it is controversial whether and how the APOE e4 allele is associated with superior brain function among young healthy individuals, thus indicates a case of antagonistic pleiotropy of APOE e4 allele. Signal complexity is a critical aspect of brain activity that has been associated with brain function. In this study, the multiscale entropy (MSE) of resting-state EEG signals among a sample of young healthy adults (N = 260) as an indicator of brain signal complexity was investigated. It was of interest whether MSE differs across APOE genotype groups while age and education level were controlled for and whether the APOE genotype effect on MSE interacts with MSE time scale, as well as EEG recording condition. Results of linear mixed models indicate overall larger MSE in APOE e4 carriers. This genotype-dependent difference is larger at high as compared with low time scales. The interaction effect between APOE genotype and recording condition indicates increased between-state MSE change in young healthy APOE e4 carriers as compared with non-carriers. Because higher complexity is commonly taken to be associated with better cognitive functioning, the present results complement previous findings and therefore point to a pleiotropic spectrum of the APOE gene polymorphism.
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Affiliation(s)
- Xiaojing Li
- Chinese Academy of Disability Data Science, Nanjing Normal University of Special Education, Nanjing, China.,Department of Physics, Centre for Nonlinear Studies, Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Hong Kong.,Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
| | - Yadwinder Kaur
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
| | | | - Martin Reuter
- Centre for Economics and Neuroscience, University of Bonn, Bonn, Germany.,Department of Psychology, University of Bonn, Bonn, Germany
| | - Christian Montag
- Department of Psychology, Ulm University, Ulm, Germany.,The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Werner Sommer
- Department of Physics, Centre for Nonlinear Studies, Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Hong Kong.,Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany.,Department of Psychology, Zhejiang Normal University, Jinhua, China
| | - Changsong Zhou
- Department of Physics, Centre for Nonlinear Studies, Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Hong Kong
| | - Andrea Hildebrandt
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany.,Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
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Zhou Y, Müller HG, Zhu C, Chen Y, Wang JL, O'Muircheartaigh J, Bruchhage M, Deoni S, Bruchhage M, Carnell S, Deoni S, D’Sa V, Huentelman M, Klepac-Ceraj V, LeBourgeois M, Müller HG, O’Muircheartaigh J, Wang JL. Network evolution of regional brain volumes in young children reflects neurocognitive scores and mother's education. Sci Rep 2023; 13:2984. [PMID: 36804963 PMCID: PMC9941570 DOI: 10.1038/s41598-023-29797-1] [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: 10/19/2022] [Accepted: 02/10/2023] [Indexed: 02/22/2023] Open
Abstract
The maturation of regional brain volumes from birth to preadolescence is a critical developmental process that underlies emerging brain structural connectivity and function. Regulated by genes and environment, the coordinated growth of different brain regions plays an important role in cognitive development. Current knowledge about structural network evolution is limited, partly due to the sparse and irregular nature of most longitudinal neuroimaging data. In particular, it is unknown how factors such as mother's education or sex of the child impact the structural network evolution. To address this issue, we propose a method to construct evolving structural networks and study how the evolving connections among brain regions as reflected at the network level are related to maternal education and biological sex of the child and also how they are associated with cognitive development. Our methodology is based on applying local Fréchet regression to longitudinal neuroimaging data acquired from the RESONANCE cohort, a cohort of healthy children (245 females and 309 males) ranging in age from 9 weeks to 10 years. Our findings reveal that sustained highly coordinated volume growth across brain regions is associated with lower maternal education and lower cognitive development. This suggests that higher neurocognitive performance levels in children are associated with increased variability of regional growth patterns as children age.
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Affiliation(s)
- Yidong Zhou
- Department of Statistics, University of California, Davis, Davis, CA, 95616, USA.
| | - Hans-Georg Müller
- Department of Statistics, University of California, Davis, Davis, CA, 95616, USA
| | - Changbo Zhu
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, 46556, USA
| | - Yaqing Chen
- Department of Statistics, Rutgers University, New Brunswick, NJ, 08901, USA
| | - Jane-Ling Wang
- Department of Statistics, University of California, Davis, Davis, CA, 95616, USA
| | - Jonathan O'Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Muriel Bruchhage
- Department of Pediatrics, Warren Alpert Medical School at Brown University, Providence, USA.,Department of Diagnostic Imaging, Rhode Island Hospital, Providence, USA.,Institute of Social Sciences, Stavanger University, Stavanger, 4021, Norway
| | - Sean Deoni
- Maternal, Newborn, and Child Health Discovery and Tools, Bill and Melinda Gates Foundation, Seattle, WA, USA
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Schizophrenia and psychedelic state: Dysconnection versus hyper-connection. A perspective on two different models of psychosis stemming from dysfunctional integration processes. Mol Psychiatry 2023; 28:59-67. [PMID: 35931756 DOI: 10.1038/s41380-022-01721-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 07/15/2022] [Accepted: 07/22/2022] [Indexed: 01/07/2023]
Abstract
Psychotic symptoms are a cross-sectional dimension affecting multiple diagnostic categories, despite schizophrenia represents the prototype of psychoses. Initially, dopamine was considered the most involved molecule in the neurobiology of schizophrenia. Over the next years, several biological factors were added to the discussion helping to constitute the concept of schizophrenia as a disease marked by a deficit of functional integration, contributing to the formulation of the Dysconnection Hypothesis in 1995. Nowadays the notion of dysconnection persists in the conceptualization of schizophrenia enriched by neuroimaging findings which corroborate the hypothesis. At the same time, in recent years, psychedelics received a lot of attention by the scientific community and astonishing findings emerged about the rearrangement of brain networks under the effect of these compounds. Specifically, a global decrease in functional connectivity was found, highlighting the disintegration of preserved and functional circuits and an increase of overall connectivity in the brain. The aim of this paper is to compare the biological bases of dysconnection in schizophrenia with the alterations of neuronal cyto-architecture induced by psychedelics and the consequent state of cerebral hyper-connection. These two models of psychosis, despite diametrically opposed, imply a substantial deficit of integration of neural signaling reached through two opposite paths.
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Associations between polygenic risk, negative symptoms, and functional connectome topology during a working memory task in early-onset schizophrenia. SCHIZOPHRENIA 2022; 8:54. [PMID: 35853905 PMCID: PMC9261080 DOI: 10.1038/s41537-022-00260-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 05/12/2022] [Indexed: 11/18/2022]
Abstract
Working memory (WM) deficit in schizophrenia is thought to arise from a widespread neural inefficiency. However, we do not know if this deficit results from the illness-related genetic risk and influence the symptom burden in various domains, especially in patients who have an early onset illness. We used graph theory to examine the topology of the functional connectome in 99 subjects (27 early-onset schizophrenia (EOS), 24 asymptomatic siblings, and 48 healthy subjects) during an n-back task, and calculated their polygenic risk score (PRS) for susceptibility to schizophrenia. Linear regression analysis was used to test associations of the PRS, clinical symptoms, altered connectomic properties, and WM accuracy in EOS. Indices of small-worldness and segregation were elevated in EOS during the WM task compared with the other two groups; these connectomic aberrations correlated with increased PRS and negative symptoms. In patients with higher polygenic risk, WM performance was lower only when both the connectomic aberrations and the burden of negative symptoms were higher. Negative symptoms had a stronger moderating role in this relationship. Our findings suggest that the aberrant connectomic topology is a feature of WM task performance in schizophrenia; this relates to higher polygenic risk score as well as higher burden of negative symptoms. The deleterious effects of polygenic risk on cognition are played out via its effects on the functional connectome, as well as negative symptoms.
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Cheng P, Liu Z, Yang J, Sun F, Fan Z, Yang J. Decreased integration of default-mode network during a working memory task in schizophrenia with severe attention deficits. Front Cell Neurosci 2022; 16:1006797. [PMID: 36425664 PMCID: PMC9679280 DOI: 10.3389/fncel.2022.1006797] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 10/18/2022] [Indexed: 11/10/2022] Open
Abstract
Background Working memory (WM) and attention deficits are both important features of schizophrenia. WM is closely related to attention, for it acted as an important characteristic in activating and manipulating WM. However, the knowledge of neural mechanisms underlying the relationship between WM and attention deficits in schizophrenia is poorly investigated. Methods Graph theory was used to examine the network topology at the whole-brain and large-scale network levels among 125 schizophrenia patients with different severity of attention deficits (65 mild attention deficits; 46 moderate attention deficits; and 14 severe attention deficits) and 53 healthy controls (HCs) during an N-back WM task. These analyses were repeated in the same participants during the resting state. Results In the WM task, there were omnibus differences in small-worldness and normalized clustering coefficient at a whole-brain level and normalized characterized path length of the default-mode network (DMN) among all groups. Post hoc analysis further indicated that all patient groups showed increased small-worldness and normalized clustering coefficient of the whole brain compared with HCs, and schizophrenia with severe attention deficits showed increased normalized characterized path length of the DMN compared with schizophrenia with mild attention deficits and HCs. However, these observations were not persisted under the resting state. Further correlation analyses indicated that the increased normalized characterized path length of the DMN was correlated with more severe attentional deficits and poorer accuracy of the WM task. Conclusion Our research demonstrated that, compared with the schizophrenia patients with less attention deficits, disrupted integration of the DMN may more particularly underlie the WM deficits in schizophrenia patients with severe attention deficits.
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Affiliation(s)
- Peng Cheng
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- National Clinical Research Center for Mental Disorders, Changsha, China
| | - Zhening Liu
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- National Clinical Research Center for Mental Disorders, Changsha, China
- *Correspondence: Zhening Liu,
| | - Jun Yang
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- National Clinical Research Center for Mental Disorders, Changsha, China
| | - Fuping Sun
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- National Clinical Research Center for Mental Disorders, Changsha, China
| | - Zebin Fan
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- National Clinical Research Center for Mental Disorders, Changsha, China
- Zebin Fan,
| | - Jie Yang
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- National Clinical Research Center for Mental Disorders, Changsha, China
- Jie Yang,
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31
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Adamovich T, Zakharov I, Tabueva A, Malykh S. The thresholding problem and variability in the EEG graph network parameters. Sci Rep 2022; 12:18659. [PMID: 36333413 PMCID: PMC9636266 DOI: 10.1038/s41598-022-22079-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022] Open
Abstract
Graph thresholding is a frequently used practice of eliminating the weak connections in brain functional connectivity graphs. The main aim of the procedure is to delete the spurious connections in the data. However, the choice of the threshold is arbitrary, and the effect of the threshold choice is not fully understood. Here we present the description of the changes in the global measures of a functional connectivity graph depending on the different proportional thresholds based on the 146 resting-state EEG recordings. The dynamics is presented in five different synchronization measures (wPLI, ImCoh, Coherence, ciPLV, PPC) in sensors and source spaces. The analysis shows significant changes in the graph's global connectivity measures as a function of the chosen threshold which may influence the outcome of the study. The choice of the threshold could lead to different study conclusions; thus it is necessary to improve the reasoning behind the choice of the different analytic options and consider the adoption of different analytic approaches. We also proposed some ways of improving the procedure of thresholding in functional connectivity research.
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Affiliation(s)
- Timofey Adamovich
- Psychological Institute of Russian Academy of Education, Moscow, Russia.
- Ural Federal University Named After the First President of Russia B. N. Yeltsin, Yekaterinburg, Russia.
| | - Ilya Zakharov
- Psychological Institute of Russian Academy of Education, Moscow, Russia
- Ural Federal University Named After the First President of Russia B. N. Yeltsin, Yekaterinburg, Russia
| | - Anna Tabueva
- Psychological Institute of Russian Academy of Education, Moscow, Russia
- Ural Federal University Named After the First President of Russia B. N. Yeltsin, Yekaterinburg, Russia
| | - Sergey Malykh
- Psychological Institute of Russian Academy of Education, Moscow, Russia
- Ural Federal University Named After the First President of Russia B. N. Yeltsin, Yekaterinburg, Russia
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32
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Tooley UA, Park AT, Leonard JA, Boroshok AL, McDermott CL, Tisdall MD, Bassett DS, Mackey AP. The Age of Reason: Functional Brain Network Development during Childhood. J Neurosci 2022; 42:8237-8251. [PMID: 36192151 PMCID: PMC9653278 DOI: 10.1523/jneurosci.0511-22.2022] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 07/25/2022] [Accepted: 09/03/2022] [Indexed: 01/27/2023] Open
Abstract
Human childhood is characterized by dramatic changes in the mind and brain. However, little is known about the large-scale intrinsic cortical network changes that occur during childhood because of methodological challenges in scanning young children. Here, we overcome this barrier by using sophisticated acquisition and analysis tools to investigate functional network development in children between the ages of 4 and 10 years ([Formula: see text]; 50 female, 42 male). At multiple spatial scales, age is positively associated with brain network segregation. At the system level, age was associated with segregation of systems involved in attention from those involved in abstract cognition, and with integration among attentional and perceptual systems. Associations between age and functional connectivity are most pronounced in visual and medial prefrontal cortex, the two ends of a gradient from perceptual, externally oriented cortex to abstract, internally oriented cortex. These findings suggest that both ends of the sensory-association gradient may develop early, in contrast to the classical theories that cortical maturation proceeds from back to front, with sensory areas developing first and association areas developing last. More mature patterns of brain network architecture, controlling for age, were associated with better visuospatial reasoning abilities. Our results suggest that as cortical architecture becomes more specialized, children become more able to reason about the world and their place in it.SIGNIFICANCE STATEMENT Anthropologists have called the transition from early to middle childhood the "age of reason", when children across cultures become more independent. We employ cutting-edge neuroimaging acquisition and analysis approaches to investigate associations between age and functional brain architecture in childhood. Age was positively associated with segregation between cortical systems that process the external world and those that process abstract phenomena like the past, future, and minds of others. Surprisingly, we observed pronounced development at both ends of the sensory-association gradient, challenging the theory that sensory areas develop first and association areas develop last. Our results open new directions for research into how brains reorganize to support rapid gains in cognitive and socioemotional skills as children reach the age of reason.
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Affiliation(s)
- Ursula A Tooley
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Anne T Park
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Julia A Leonard
- Department of Psychology, Yale University, New Haven, Connecticut 06520
| | - Austin L Boroshok
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Cassidy L McDermott
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Matthew D Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Dani S Bassett
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Department of Physics and Astronomy, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Santa Fe Institute, Santa Fe, New Mexico 87501
| | - Allyson P Mackey
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
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33
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Riazi AH, Rabbani H, Kafieh R. Dynamic Brain Connectivity in Resting-State FMRI Using Spectral ICA and Graph Approach: Application to Healthy Controls and Multiple Sclerosis. Diagnostics (Basel) 2022; 12:diagnostics12092263. [PMID: 36140663 PMCID: PMC9497797 DOI: 10.3390/diagnostics12092263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 09/07/2022] [Accepted: 09/10/2022] [Indexed: 11/27/2022] Open
Abstract
Multiple sclerosis (MS) is a neuroinflammatory disease that involves structural and functional damage to the brain. It changes the functional connectivity of the brain between and within networks. Resting-state functional magnetic resonance imaging (fMRI) enables us to measure functional correlation and independence between different brain regions. In recent years, statistical methods, including independent component analysis (ICA) and graph-based analysis, have been widely used in fMRI studies. Furthermore, topological properties of the brain have been appeared as significant features of neuroscience studies. Most studies are focused on graph analysis and ICA methods, rather than considering spectral approaches. Here, we developed a new framework to measure brain connectivity (in static and dynamic formats) and incorporate it to study fMRI data from MS patients and healthy controls (HCs). For this purpose, a spectral ICA method is proposed to extract the nodes of the brain graph. Spectral ICA extracts more reliable components and decreases the processing time in calculation of the static brain connectivity. Compared to Infomax ICA, dynamic range and low-frequency to high-frequency power ratio (fALFF) show better results using the proposed ICA. It is also helpful in selection of the states for dynamic connectivity. Furthermore, the dynamic connectivity-based extracted components from spectral ICA are estimated using a mutual information method and based on correlation of sliding time-windowed on selected IC time courses. First-level and second-level connectivity states are calculated using correlations of connectivity strength between graph nodes (spectral ICA components). Finally, static and dynamic connectivity are analyzed based on correlation nodes percolated by an anatomical automatic labeling (AAL) atlas. Despite static and dynamic connectivity results of AAL correlations not showing any significant changes between MS and HC, our results based on spectral ICA in static and dynamic connectivity showed significantly decreased connectivity in MS patients in the anterior cingulate cortex, whereas it was significantly weaker in the core but stronger at the periphery of the posterior cingulate cortex.
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Affiliation(s)
- Amir Hosein Riazi
- Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
| | - Hossein Rabbani
- Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
| | - Rahele Kafieh
- Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
- Department of Engineering, Durham University, South Road, Durham DH1 3LE, UK
- Correspondence:
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34
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Liu X, He Y, Gao Y, Booth JR, Zhang L, Zhang S, Lu C, Liu L. Developmental differences of large-scale functional brain networks for spoken word processing. BRAIN AND LANGUAGE 2022; 231:105149. [PMID: 35777141 DOI: 10.1016/j.bandl.2022.105149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 06/03/2022] [Accepted: 06/13/2022] [Indexed: 06/15/2023]
Abstract
A dual-stream dissociation for separate phonological and semantic processing has been implicated in adults' language processing, but it is unclear how this dissociation emerges with development. By employing a graph-theory based brain network analysis, we compared functional interaction architecture during a rhyming and meaning judgment task of children (aged 8-12) with adults (aged 19-26). We found adults had stronger functional connectivity strength than children between bilateral inferior frontal gyri and left inferior parietal lobule in the rhyming task, between middle frontal gyrus and angular gyrus, and within occipital areas in the meaning task. Meanwhile, adults but not children manifested between-task differences in these properties. In contrast, children had stronger functional connectivity strength or nodal degree in Heschl's gyrus, superior temporal gyrus, and subcortical areas. Our findings indicated spoken word processing development is characterized by increased functional specialization, relying on the dorsal and ventral pathways for phonological and semantic processing respectively.
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Affiliation(s)
- Xin Liu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/ McGovern, Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
| | - Yin He
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/ McGovern, Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yue Gao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/ McGovern, Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - James R Booth
- Department of Psychology and Human Development, Vanderbilt University, Nashville, TN 37203, USA
| | - Lihuan Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/ McGovern, Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Shudong Zhang
- Faculty of Education, Beijing Normal University, Beijing 100875, China
| | - Chunming Lu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/ McGovern, Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Li Liu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/ McGovern, Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
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35
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Huang H, Zhang B, Mi L, Liu M, Chang X, Luo Y, Li C, He H, Zhou J, Yang R, Li H, Jiang S, Yao D, Li Q, Duan M, Luo C. Reconfiguration of Functional Dynamics in Cortico-Thalamo-Cerebellar Circuit in Schizophrenia Following High-Frequency Repeated Transcranial Magnetic Stimulation. Front Hum Neurosci 2022; 16:928315. [PMID: 35959244 PMCID: PMC9359206 DOI: 10.3389/fnhum.2022.928315] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 06/22/2022] [Indexed: 11/20/2022] Open
Abstract
Schizophrenia is a serious mental illness characterized by a disconnection between brain regions. Transcranial magnetic stimulation is a non-invasive brain intervention technique that can be used as a new and safe treatment option for patients with schizophrenia with drug-refractory symptoms, such as negative symptoms and cognitive impairment. However, the therapeutic effects of transcranial magnetic stimulation remain unclear and would be investigated using non-invasive tools, such as functional connectivity (FC). A longitudinal design was adopted to investigate the alteration in FC dynamics using a dynamic functional connectivity (dFC) approach in patients with schizophrenia following high-frequency repeated transcranial magnetic stimulation (rTMS) with the target at the left dorsolateral prefrontal cortex (DLPFC). Two groups of schizophrenia inpatients were recruited. One group received a 4-week high-frequency rTMS together with antipsychotic drugs (TSZ, n = 27), while the other group only received antipsychotic drugs (DSZ, n = 26). Resting-state functional magnetic resonance imaging (fMRI) and psychiatric symptoms were obtained from the patients with schizophrenia twice at baseline (t1) and after 4-week treatment (t2). The dynamics was evaluated using voxel- and region-wise FC temporal variability resulting from fMRI data. The pattern classification technique was used to verify the clinical application value of FC temporal variability. For the voxel-wise FC temporary variability, the repeated measures ANCOVA analysis showed significant treatment × time interaction effects on the FC temporary variability between the left DLPFC and several regions, including the thalamus, cerebellum, precuneus, and precentral gyrus, which are mainly located within the cortico-thalamo-cerebellar circuit (CTCC). For the ROI-wise FC temporary variability, our results found a significant interaction effect on the FC among CTCC. rTMS intervention led to a reduced FC temporary variability. In addition, higher alteration in FC temporal variability between left DLPFC and right posterior parietal thalamus predicted a higher remission ratio of negative symptom scores, indicating that the decrease of FC temporal variability between the brain regions was associated with the remission of schizophrenia severity. The support vector regression (SVR) results suggested that the baseline pattern of FC temporary variability between the regions in CTCC could predict the efficacy of high-frequency rTMS intervention on negative symptoms in schizophrenia. These findings confirm the potential relationship between the reduction in whole-brain functional dynamics induced by high-frequency rTMS and the improvement in psychiatric scores, suggesting that high-frequency rTMS affects psychiatric symptoms by coordinating the heterogeneity of activity between the brain regions. Future studies would examine the clinical utility of using functional dynamics patterns between specific brain regions as a biomarker to predict the treatment response of high-frequency rTMS.
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Affiliation(s)
- Huan Huang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Bei Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Li Mi
- Department of Psychiatry, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Meiqing Liu
- Department of Neurology, First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Xin Chang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuling Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Cheng Li
- Department of Psychiatry, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Psychiatry, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Jingyu Zhou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ruikun Yang
- University of Science and Technology Beijing, Beijing, China
| | - Hechun Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neurology, First Affiliated Hospital of Hainan Medical University, Haikou, China
- Research Unit of Neuroinformation, Chinese Academy of Medical Sciences, Chengdu, China
| | - Qifu Li
- Department of Neurology, First Affiliated Hospital of Hainan Medical University, Haikou, China
- *Correspondence: Qifu Li,
| | - Mingjun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Psychiatry, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of Neuroinformation, Chinese Academy of Medical Sciences, Chengdu, China
- Mingjun Duan,
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of Neuroinformation, Chinese Academy of Medical Sciences, Chengdu, China
- Cheng Luo,
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Al-Shargie F, Katmah R, Tariq U, Babiloni F, Al-Mughairbi F, Al-Nashash H. Stress management using fNIRS and binaural beats stimulation. BIOMEDICAL OPTICS EXPRESS 2022; 13:3552-3575. [PMID: 35781942 PMCID: PMC9208616 DOI: 10.1364/boe.455097] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 04/21/2022] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
In this study, we investigate the effectiveness of binaural beats stimulation (BBs) in enhancing cognitive vigilance and mitigating mental stress level at the workplace. We developed an experimental protocol under four cognitive conditions: high vigilance (HV), vigilance enhancement (VE), mental stress (MS) and stress mitigation (SM). The VE and SM conditions were achieved by listening to 16 Hz of BBs. We assessed the four cognitive conditions using salivary alpha-amylase, behavioral responses, and Functional Near-Infrared Spectroscopy (fNIRS). We quantified the vigilance and stress levels using the reaction time (RT) to stimuli, accuracy of detection, and the functional connectivity metrics of the fNIRS estimated by Phase Locking Values (PLV). We propose using the orthogonal minimum spanning tree (OMST) to determine the true connectivity network patterns of the PLV. Our results show that listening to 16-Hz BBs has significantly reduced the level of alpha amylase by 44%, reduced the RT to stimuli by 20% and increased the accuracy of target detection by 25%, (p < 0.001). The analysis of the connectivity network across the four different cognitive conditions revealed several statistically significant trends. Specifically, a significant increase in connectivity between the right and left dorsolateral prefrontal cortex (DLPFC) areas and left orbitofrontal cortex was found during the vigilance enhancement condition compared to the high vigilance. Likewise, similar patterns were found between the right and left DLPFC, orbitofrontal cortex, right ventrolateral prefrontal cortex (VLPFC) and right frontopolar PFC (prefrontal cortex) area during stress mitigation compared to mental stress. Furthermore, the connectivity network under stress condition alone showed significant connectivity increase between the VLPFC and DLPFC compared to other areas. The laterality index demonstrated left frontal laterality under high vigilance and VE conditions, and right DLPFC and left frontopolar PFC while under mental stress. Overall, our results showed that BBs can be used for vigilance enhancement and stress mitigation.
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Affiliation(s)
- Fares Al-Shargie
- Department of Electrical Engineering, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
- Biomedical Engineering Graduate Program, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
| | - Rateb Katmah
- Biomedical Engineering Graduate Program, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
| | - Usman Tariq
- Department of Electrical Engineering, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
- Biomedical Engineering Graduate Program, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
| | - Fabio Babiloni
- Department Molecular Medicine, University of Sapienza Rome, 00185 Rome, Italy
| | - Fadwa Al-Mughairbi
- Department of Clinical Psychology, College of Medicines and Health Sciences, UAE University, Al Ain, United Arab Emirates
| | - Hasan Al-Nashash
- Department of Electrical Engineering, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
- Biomedical Engineering Graduate Program, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
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Guan XY, Zheng WJ, Fan KY, Han X, Li X, Yan ZH, Lu Z, Gong J. Changes in a sensorimotor network, occipital network, and psychomotor speed within three months after focal surgical injury in pediatric patients with intracranial space-occupying lesions. BMC Pediatr 2022; 22:321. [PMID: 35650566 PMCID: PMC9158303 DOI: 10.1186/s12887-022-03348-5] [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: 01/18/2022] [Accepted: 05/09/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Studies on cognition and brain networks after various forms of brain injury mainly involve traumatic brain injury, neurological disease, tumours, and mental disease. There are few related studies on surgical injury and even fewer pediatric studies. This study aimed to preliminarily explore the cognitive and brain network changes in children with focal, unilateral, well-bounded intracranial space-occupying lesions (ISOLs) in the short term period after surgery. METHODS We enrolled 15 patients (6-14 years old) with ISOLs admitted to the Department of Pediatric Neurosurgery of the Beijing Tiantan Hospital between July 2020 and August 2021. Cognitive assessment and resting-state functional magnetic resonance imaging (rs-fMRI) were performed. Regional homogeneity (Reho), seed-based analysis (SBA) and graph theory analysis (GTA) were performed. Paired T-test was used for statistical analysis of cognitive assessment and rs-fMRI. Gaussian random-field theory correction (voxel p-value < 0.001, cluster p-value < 0.05) was used for Reho and SBA. False discovery rate correction (corrected p value < 0.05) for GTA. RESULTS Our results showed that psychomotor speed decreased within three months after surgery. Further, rs-fMRI data analysis suggested that sensorimotor and occipital network activation decreased with low information transmission efficiency. CONCLUSION We prudently concluded that the changes in cognitive function and brain network within three months after surgery may be similar to ageing and that the brain is vulnerable during this period.
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Affiliation(s)
- Xue-Yi Guan
- Department of Pediatric Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, (100070), China
| | - Wen-Jian Zheng
- Department of Pediatric Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, (100070), China
| | - Kai-Yu Fan
- Department of Pediatric Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, (100070), China
| | - Xu Han
- Department of Pediatric Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, (100070), China
| | - Xiang Li
- Department of Pediatric Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, (100070), China
| | - Zi-Han Yan
- Department of Pediatric Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, (100070), China
| | - Zheng Lu
- Department of Pediatric Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, (100070), China
| | - Jian Gong
- Department of Pediatric Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, (100070), China.
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38
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Elumalai P, Yadav Y, Williams N, Saucan E, Jost J, Samal A. Graph Ricci curvatures reveal atypical functional connectivity in autism spectrum disorder. Sci Rep 2022; 12:8295. [PMID: 35585156 PMCID: PMC9117309 DOI: 10.1038/s41598-022-12171-y] [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] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 05/04/2022] [Indexed: 11/20/2022] Open
Abstract
While standard graph-theoretic measures have been widely used to characterize atypical resting-state functional connectivity in autism spectrum disorder (ASD), geometry-inspired network measures have not been applied. In this study, we apply Forman-Ricci and Ollivier-Ricci curvatures to compare networks of ASD and typically developing individuals (N = 1112) from the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset. We find brain-wide and region-specific ASD-related differences for both Forman-Ricci and Ollivier-Ricci curvatures, with region-specific differences concentrated in Default Mode, Somatomotor and Ventral Attention networks for Forman-Ricci curvature. We use meta-analysis decoding to demonstrate that brain regions with curvature differences are associated to those cognitive domains known to be impaired in ASD. Further, we show that brain regions with curvature differences overlap with those brain regions whose non-invasive stimulation improves ASD-related symptoms. These results suggest the utility of graph Ricci curvatures in characterizing atypical connectivity of clinically relevant regions in ASD and other neurodevelopmental disorders.
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Affiliation(s)
| | - Yasharth Yadav
- The Institute of Mathematical Sciences (IMSc), Chennai, India
- Indian Institute of Science Education and Research (IISER), Pune, India
| | - Nitin Williams
- Department of Computer Science, Helsinki Institute of Information Technology, Aalto University, Espoo, Finland.
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland.
| | - Emil Saucan
- Department of Applied Mathematics, ORT Braude College, Karmiel, Israel
| | - Jürgen Jost
- Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany
- The Santa Fe Institute, Santa Fe, NM, USA
| | - Areejit Samal
- The Institute of Mathematical Sciences (IMSc), Chennai, India.
- Homi Bhabha National Institute (HBNI), Mumbai, India.
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39
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Wu Y, Zheng Y, Li J, Liu Y, Liang X, Chen Y, Zhang H, Wang N, Weng X, Qiu S, Wang J. Subregion-specific, modality-dependent and timescale-sensitive hippocampal connectivity alterations in patients with first-episode, drug-naïve major depression disorder. J Affect Disord 2022; 305:159-172. [PMID: 35218862 DOI: 10.1016/j.jad.2022.02.052] [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: 10/22/2021] [Revised: 01/11/2022] [Accepted: 02/18/2022] [Indexed: 12/25/2022]
Abstract
BACKGROUND Despite accumulating evidence for the hippocampus as a key dysfunctional node in major depressive disorder (MDD), previous findings are controversial possibly due to heterogeneous and small clinical samples, complicated hippocampal structure, and different imaging modalities and analytical methods. METHODS We collected structural and resting-state functional MRI data from 100 first-episode, drug-naïve MDD patients and 99 healthy controls. A subset of the participants (34 patients and 33 controls) also completed a battery of neuropsychological tests and childhood trauma questionnaires. Seed-based morphological and functional (static and dynamic) connectivity were calculated for ten hippocampal subregions, followed by analyses of dynamic functional connectivity states (k-means clustering), connectivity cross-modality relationships (cosine similarity), and connectivity associations with clinical and neuropsychological variables (Spearman correlation). RESULTS Between-group comparisons revealed abnormal hippocampal connectivity in the patients that depended on 1) hippocampal subdivisions: the cornu ammonis (CA) was the most seriously affected subregion, in particular the right CA1 for functional connectivity alterations; 2) imaging modality: morphological connectivity revealed seldom and sporadic alterations with different lobes, while functional connectivity identified numerous and convergent alterations with prefrontal regions; and 3) time scale: dynamic functional connectivity was more sensitive than static functional connectivity, in particular in revealing alterations between the right CA1 and contralateral prefrontal cortex. Among the 34 patients, functional connectivity alterations of the CA1 were related to the history of childhood trauma in the patients. LIMITATIONS Only a subset of the patients completed the neuropsychological tests, which may cause underestimation of cognitive relevance of hippocampal connectivity alterations. CONCLUSIONS Disrupted hippocampal CA1 functional connectivity plays key roles in the pathophysiology of MDD and may act as a potential diagnostic biomarker for the disease.
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Affiliation(s)
- Yujie Wu
- Institute for Brain Research and Rehabilitation, South China Normal University, 510631 Guangzhou, China; School of Psychology, South China Normal University, Guangzhou, China
| | - Yanting Zheng
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong 510405, China; Department of Radiology, Guangzhou First People's Hospital, Guangdong 510180, China
| | - Junle Li
- Institute for Brain Research and Rehabilitation, South China Normal University, 510631 Guangzhou, China
| | - Yujie Liu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong 510405, China; Department of Radiology, Guangzhou First People's Hospital, Guangdong 510180, China
| | - Xinyu Liang
- The First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, China
| | - Yaoping Chen
- The Third Affliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong 510630, China
| | - Hanyue Zhang
- Department of Radiology, Guangzhou First People's Hospital, Guangdong 510180, China
| | - Ningkai Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, 510631 Guangzhou, China
| | - Xuchu Weng
- Institute for Brain Research and Rehabilitation, South China Normal University, 510631 Guangzhou, China; Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China; Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong 510405, China.
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, 510631 Guangzhou, China; Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China; Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China.
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40
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Caciagli L, Paquola C, He X, Vollmar C, Centeno M, Wandschneider B, Braun U, Trimmel K, Vos SB, Sidhu MK, Thompson PJ, Baxendale S, Winston GP, Duncan JS, Bassett DS, Koepp MJ, Bernhardt BC. Disorganization of language and working memory systems in frontal versus temporal lobe epilepsy. Brain 2022; 146:935-953. [PMID: 35511160 PMCID: PMC9976988 DOI: 10.1093/brain/awac150] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 02/28/2022] [Accepted: 03/12/2022] [Indexed: 02/06/2023] Open
Abstract
Cognitive impairment is a common comorbidity of epilepsy and adversely impacts people with both frontal lobe (FLE) and temporal lobe (TLE) epilepsy. While its neural substrates have been investigated extensively in TLE, functional imaging studies in FLE are scarce. In this study, we profiled the neural processes underlying cognitive impairment in FLE and directly compared FLE and TLE to establish commonalities and differences. We investigated 172 adult participants (56 with FLE, 64 with TLE and 52 controls) using neuropsychological tests and four functional MRI tasks probing expressive language (verbal fluency, verb generation) and working memory (verbal and visuo-spatial). Patient groups were comparable in disease duration and anti-seizure medication load. We devised a multiscale approach to map brain activation and deactivation during cognition and track reorganization in FLE and TLE. Voxel-based analyses were complemented with profiling of task effects across established motifs of functional brain organization: (i) canonical resting-state functional systems; and (ii) the principal functional connectivity gradient, which encodes a continuous transition of regional connectivity profiles, anchoring lower-level sensory and transmodal brain areas at the opposite ends of a spectrum. We show that cognitive impairment in FLE is associated with reduced activation across attentional and executive systems, as well as reduced deactivation of the default mode system, indicative of a large-scale disorganization of task-related recruitment. The imaging signatures of dysfunction in FLE are broadly similar to those in TLE, but some patterns are syndrome-specific: altered default-mode deactivation is more prominent in FLE, while impaired recruitment of posterior language areas during a task with semantic demands is more marked in TLE. Functional abnormalities in FLE and TLE appear overall modulated by disease load. On balance, our study elucidates neural processes underlying language and working memory impairment in FLE, identifies shared and syndrome-specific alterations in the two most common focal epilepsies and sheds light on system behaviour that may be amenable to future remediation strategies.
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Affiliation(s)
- Lorenzo Caciagli
- Correspondence to: Lorenzo Caciagli, MD, PhD Department of Bioengineering University of Pennsylvania, 240 Skirkanich Hall 210 South 33rd Street, Philadelphia, PA 19104, USA E-mail: ;
| | - Casey Paquola
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Xiaosong He
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Christian Vollmar
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK,MRI Unit, Epilepsy Society,Chalfont St Peter, Buckinghamshire SL9 0RJ, UK,Department of Neurology, Ludwig-Maximilians-Universität, 81377 Munich, Germany
| | - Maria Centeno
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK,MRI Unit, Epilepsy Society,Chalfont St Peter, Buckinghamshire SL9 0RJ, UK,Epilepsy Unit, Hospital Clínic de Barcelona, IDIBAPS, 08036 Barcelona, Spain
| | - Britta Wandschneider
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK,MRI Unit, Epilepsy Society,Chalfont St Peter, Buckinghamshire SL9 0RJ, UK
| | - Urs Braun
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA,Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Karin Trimmel
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK,MRI Unit, Epilepsy Society,Chalfont St Peter, Buckinghamshire SL9 0RJ, UK,Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Sjoerd B Vos
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK,MRI Unit, Epilepsy Society,Chalfont St Peter, Buckinghamshire SL9 0RJ, UK,Centre for Medical Image Computing, University College London, London, UK,Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Meneka K Sidhu
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK,MRI Unit, Epilepsy Society,Chalfont St Peter, Buckinghamshire SL9 0RJ, UK
| | - Pamela J Thompson
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK,MRI Unit, Epilepsy Society,Chalfont St Peter, Buckinghamshire SL9 0RJ, UK
| | - Sallie Baxendale
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK,MRI Unit, Epilepsy Society,Chalfont St Peter, Buckinghamshire SL9 0RJ, UK
| | - Gavin P Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK,MRI Unit, Epilepsy Society,Chalfont St Peter, Buckinghamshire SL9 0RJ, UK,Department of Medicine, Division of Neurology, Queen’s University, Kingston, Ontario, Canada
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK,MRI Unit, Epilepsy Society,Chalfont St Peter, Buckinghamshire SL9 0RJ, UK
| | - Dani S Bassett
- Correspondence may also be addressed to: Dani S. Bassett, PhD E-mail:
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Phillips NS, Rao V, Kmetz L, Vela R, Medick S, Krull K, Kesler SR. Changes in Brain Functional and Effective Connectivity After Treatment for Breast Cancer and Implications for Intervention Targets. Brain Connect 2022; 12:385-397. [PMID: 34210168 PMCID: PMC9131353 DOI: 10.1089/brain.2021.0049] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background: Patients with breast cancer frequently report cognitive impairment both during and after completion of therapy. Evidence suggests that cancer-related cognitive impairments are related to widespread neural network dysfunction. The default mode network (DMN) is a large conserved network that plays a critical role in integrating the functions of various neural systems. Disruption of the network may play a key role in the development of cognitive impairment. Methods: We compared neuroimaging and neurocognitive data from 43 newly diagnosed primary breast cancer patients (mean age = 48, standard deviation [SD] = 8.9 years) and 50 frequency-matched healthy female controls (mean age = 50, SD = 10 years) before treatment and 1 year after treatment completion. Functional and effective connectivity measures of the DMN were obtained using graph theory and Bayesian network analysis methods, respectively. Results: Compared with healthy females, the breast cancer group displayed higher global efficiency and path length post-treatment (p < 0.03, corrected). Breast cancer survivors showed significantly lower performance on measures of verbal memory, attention, and verbal fluency (p < 0.05) at both time points. Within the DMN, local brain network organization, as measured by edge-betweenness centralities, was significantly altered in the breast cancer group compared with controls at both time points (p < 0.0001, corrected), with several connections showing a significant group-by-time effect (p < 0.003, corrected). Effective connectivity demonstrated significantly altered patterns of neuronal coupling in patients with breast cancer (p < 0.05). Significant correlations were seen between hormone blockade therapy, radiation therapy, chemotherapy cycles, memory, and verbal fluency test and edge-betweenness centralities. Discussion: This pattern of altered network organization in the default mode is believed to result in reduced network efficiency and disrupted communication. Subregions of the DMN, the orbital prefrontal cortex and posterior memory network, appear to be at the center of this disruption and this could inform future interventions. Impact statement This prospective study is the first to investigate how post-treatment changes in functional and effective connectivity in the regions of default mode network are related to cancer therapy and measures of memory and verbal learning in breast cancer patients. We demonstrate that the interactions between treatment, brain connectivity, and neurocognitive outcomes coalesce around a subgroup of brain structures in the orbital frontal and parietal lobe. This would suggest that interventions that target these regions may improve neurocognitive outcomes in breast cancer survivors.
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Affiliation(s)
- Nicholas S. Phillips
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Vikram Rao
- School of Nursing, University of Texas at Austin, Austin, Texas, USA
| | - Lorie Kmetz
- School of Nursing, University of Texas at Austin, Austin, Texas, USA
| | - Ruben Vela
- School of Nursing, University of Texas at Austin, Austin, Texas, USA
- Department of Diagnostic Medicine, Dell School of Medicine, University of Texas at Austin, Austin, Texas, USA
| | - Sarah Medick
- School of Nursing, University of Texas at Austin, Austin, Texas, USA
| | - Kevin Krull
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Shelli R. Kesler
- School of Nursing, University of Texas at Austin, Austin, Texas, USA
- Department of Diagnostic Medicine, Dell School of Medicine, University of Texas at Austin, Austin, Texas, USA
- Center for Computational Oncology, Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas, USA
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42
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Yang J, Hellerstein DJ, Chen Y, McGrath PJ, Stewart JW, Peterson BS, Wang Z. Serotonin-norepinephrine reuptake inhibitor antidepressant effects on regional connectivity of the thalamus in persistent depressive disorder: evidence from two randomized, double-blind, placebo-controlled clinical trials. Brain Commun 2022; 4:fcac100. [PMID: 35592490 PMCID: PMC9113244 DOI: 10.1093/braincomms/fcac100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 02/02/2022] [Accepted: 04/12/2022] [Indexed: 11/13/2022] Open
Abstract
Previous neuroimaging studies have shown that serotonin-norepinephrine reuptake inhibitor antidepressants alter functional activity in large expanses of brain regions. However, it is not clear how these regions are systemically organized on a connectome level with specific topological properties, which may be crucial to revealing neural mechanisms underlying serotonin-norepinephrine reuptake inhibitor treatment of persistent depressive disorder. To investigate the effect of serotonin-norepinephrine reuptake inhibitor antidepressants on brain functional connectome reconfiguration in persistent depressive disorder and whether this reconfiguration promotes the improvement of clinical symptoms, we combined resting-state functional magnetic resonance imaging (fMRI) scans acquired in two randomized, double-blind, placebo-controlled trial studies of serotonin-norepinephrine reuptake inhibitor antidepressant treatment of patients with persistent depressive disorder. One was a randomized, double-blind, placebo-controlled trial of 10-week duloxetine medication treatment, which included 17 patients in duloxetine group and 17 patients in placebo group (ClinicalTrials.gov Identifier: NCT00360724); the other one was a randomized, double-blind, placebo-controlled trial of 12-week desvenlafaxine medication treatment, which included 16 patients in desvenlafaxine group and 15 patients in placebo group (ClinicalTrials.gov Identifier: NCT01537068). The 24-item Hamilton Depression Rating Scale was used to measure clinical symptoms, and graph theory was employed to examine serotonin-norepinephrine reuptake inhibitor antidepressant treatment effects on the topological properties of whole-brain functional connectome of patients with persistent depressive disorder. We adopted a hierarchical strategy to examine the topological property changes caused by serotonin-norepinephrine reuptake inhibitor antidepressant treatment, calculated their small-worldness, global integration, local segregation and nodal clustering coefficient in turn. Linear regression analysis was used to test associations of treatment, graph properties changes and clinical symptom response. Symptom scores were more significantly reduced after antidepressant than placebo administration (η 2 = 0.18). There was a treatment-by-time effect that optimized the functional connectome in a small-world manner, with increased global integration and increased nodal clustering coefficient in the bilateral thalamus (left thalamus η 2 = 0.21; right thalamus η 2 = 0.23). The nodal clustering coefficient increment of the right thalamus (ratio = 29.86; 95% confidence interval, -4.007 to -0.207) partially mediated the relationship between treatment and symptom improvement, and symptom improvement partially mediated (ratio = 21.21; 95% confidence interval, 0.0243-0.444) the relationship between treatment and nodal clustering coefficient increments of the right thalamus. Our study may indicate a putative mutually reinforcing association between nodal clustering coefficient increment of the right thalamus and symptom improvement from serotonin-norepinephrine reuptake inhibitor antidepressant treatments with duloxetine or desvenlafaxine.
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Affiliation(s)
- Jie Yang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Department of Depression Evaluation Service, New York State Psychiatric Institute, 1051 Riverside Drive, Unit #51, New York, NY 10032, USA
| | - David J. Hellerstein
- Department of Depression Evaluation Service, New York State Psychiatric Institute, 1051 Riverside Drive, Unit #51, New York, NY 10032, USA
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
| | - Ying Chen
- Department of Depression Evaluation Service, New York State Psychiatric Institute, 1051 Riverside Drive, Unit #51, New York, NY 10032, USA
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
- Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Patrick J. McGrath
- Department of Depression Evaluation Service, New York State Psychiatric Institute, 1051 Riverside Drive, Unit #51, New York, NY 10032, USA
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
| | - Jonathan W. Stewart
- Department of Depression Evaluation Service, New York State Psychiatric Institute, 1051 Riverside Drive, Unit #51, New York, NY 10032, USA
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
| | - Bradley S. Peterson
- Institute for the Developing Mind, Children’s Hospital Los Angeles, Los Angeles, CA 90027, USA
- Keck School of Medicine, University of Southern California, Los Angeles, CA 90089-9021, USA
| | - Zhishun Wang
- Department of Depression Evaluation Service, New York State Psychiatric Institute, 1051 Riverside Drive, Unit #51, New York, NY 10032, USA
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
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43
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Dorfer C, Pletschko T, Seiger R, Chocholous M, Kasprian G, Krajnik J, Roessler K, Kollndorfer K, Schöpf V, Leiss U, Slavc I, Prayer D, Lanzenberger R, Czech T. Impact of childhood cerebellar tumor surgery on cognition revealed by precuneus hyperconnectivity. Neurooncol Adv 2022; 4:vdac050. [PMID: 35571986 PMCID: PMC9092637 DOI: 10.1093/noajnl/vdac050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Background Childhood cerebellar pilocytic astrocytomas harbor excellent overall survival rates after surgical resection, but the patients may exhibit specific cognitive and behavioral problems. Functional MRI has catalyzed insights into brain functional systems and has already been linked with the neuropsychological performance. We aimed to exploit the question of whether resting-state functional MRI can be used as a biomarker for the cognitive outcome assessment of these patients. Methods We investigated 13 patients (median age 22.0 years; range 14.9-31.3) after a median interval between surgery and examination of 15.0 years (range 4.2-20.5) and 16 matched controls. All subjects underwent functional 3-Tesla MRI scans in a resting-state condition and battery neuropsychological tests. Results Patients showed a significantly increased functional connectivity in the precuneus compared with controls (P < .05) and at the same time impairments in various domains of neuropsychological functioning such as a lower mean Wechsler Intelligenztest für Erwachsene (WIE) IQ percentile (mean [M] = 48.62, SD = 29.14), lower scores in the Trail Making Test (TMT) letter sequencing (M = 49.54, SD = 30.66), worse performance on the WIE subtest Digit Symbol Coding (M = 38.92, SD = 35.29), subtest Symbol Search (M = 40.75, SD = 35.28), and test battery for attentional performance (TAP) divided attention task (M = 783.92, SD = 73.20). Conclusion Childhood cerebellar tumor treated by resection only strongly impacts the development of precuneus/posterior cingulate cortex functional connectivity. Functional MRI has the potential to help deciphering the pathophysiology of cerebellar-related cognitive impairments in these patients and could be an additional tool in their individual assessment and follow-up.
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Affiliation(s)
- Christian Dorfer
- Department of Neurosurgery, Medical University of Vienna, Austria
| | - Thomas Pletschko
- Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Austria
| | - Rene Seiger
- Department of Psychiatry and Psychotherapy Medical University of Vienna, Austria
| | - Monika Chocholous
- Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Austria
| | - Gregor Kasprian
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
| | - Jacqueline Krajnik
- Department of Neurosurgery, Medical University of Vienna, Austria
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
| | - Karl Roessler
- Department of Neurosurgery, Medical University of Vienna, Austria
| | - Kathrin Kollndorfer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
| | - Veronika Schöpf
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
| | - Ulrike Leiss
- Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Austria
| | - Irene Slavc
- Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Austria
| | - Daniela Prayer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy Medical University of Vienna, Austria
| | - Thomas Czech
- Department of Neurosurgery, Medical University of Vienna, Austria
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Aberrant integrity of the cortico-limbic-striatal circuit in major depressive disorder with suicidal ideation. J Psychiatr Res 2022; 148:277-285. [PMID: 35180634 DOI: 10.1016/j.jpsychires.2022.02.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 02/08/2022] [Accepted: 02/10/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND Suicidal ideation is a common symptom of major depressive disorder (MDD) that reflects a cognitive alteration in the background of intense emotional dysregulation. Amygdala is a critical emotion processing center that facilitates moving from emotional appraisal to action. However, whether MDD patients with suicidal ideation show dysconnectivity of the amygdala within a large-scale neurocognitive circuitry remains unknown. METHODS Participants were 22 MDD patients without suicidal ideation (MDD-NSI), 59 MDD patients with suicidal ideation (MDD-SI), and 60 healthy controls (HCs). We compared the amygdala-based resting-state functional connectivity of four amygdala subregions across the three groups. We selected brain regions with significant between-group differences in amygdalar conectivity as the regions of interest (ROI) and performed ROI-to-ROI and graph-theoretical analyses to explore dysconnectivity patterns at various granularity levels. RESULTS Brain regions showing omnibus differences across the three groups were distributed across a cortico-limbic-striatal circuitry. MDD-SI had unique dysconnectivity of the lateral amygdala with caudate, middle temporal gyrus, and postcentral gyrus compared with the other two groups. MDD-SI and MDD-NSI had shared dysconnectivity of the medial amygdala with medial superior frontal gyrus and middle temporal gyrus. Within the derived cortico-limbic-striatal circuitry, MDD-SI exhibited lower global connectivity, reduced sigma (small-worldness), but increased lambda (path-length) than HCs. Reduced sigma correlated with increased severity of suicidal ideation. We achieved high classification accuracy (84.09%, with AUC 0.82) in distinguishing MDD-SI from MDD-NSI. CONCLUSIONS Aberrant integrity of the cortico-limbic-striatal circuit centered on the amygdala provides a promising neural substrate for suicidal ideation that requires further investigation in MDD.
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Deng S, Li J, Thomas Yeo BT, Gu S. Control theory illustrates the energy efficiency in the dynamic reconfiguration of functional connectivity. Commun Biol 2022; 5:295. [PMID: 35365757 PMCID: PMC8975837 DOI: 10.1038/s42003-022-03196-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 02/22/2022] [Indexed: 11/09/2022] Open
Abstract
The brain's functional connectivity fluctuates over time instead of remaining steady in a stationary mode even during the resting state. This fluctuation establishes the dynamical functional connectivity that transitions in a non-random order between multiple modes. Yet it remains unexplored how the transition facilitates the entire brain network as a dynamical system and what utility this mechanism for dynamic reconfiguration can bring over the widely used graph theoretical measurements. To address these questions, we propose to conduct an energetic analysis of functional brain networks using resting-state fMRI and behavioral measurements from the Human Connectome Project. Through comparing the state transition energy under distinct adjacent matrices, we justify that dynamic functional connectivity leads to 60% less energy cost to support the resting state dynamics than static connectivity when driving the transition through default mode network. Moreover, we demonstrate that combining graph theoretical measurements and our energy-based control measurements as the feature vector can provide complementary prediction power for the behavioral scores (Combination vs. Control: t = 9.41, p = 1.64e-13; Combination vs. Graph: t = 4.92, p = 3.81e-6). Our approach integrates statistical inference and dynamical system inspection towards understanding brain networks.
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Affiliation(s)
- Shikuang Deng
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Jingwei Li
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
| | - Shi Gu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
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Scharwächter L, Schmitt FJ, Pallast N, Fink GR, Aswendt M. Network analysis of neuroimaging in mice. Neuroimage 2022; 253:119110. [PMID: 35311664 DOI: 10.1016/j.neuroimage.2022.119110] [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: 12/17/2021] [Revised: 03/01/2022] [Accepted: 03/15/2022] [Indexed: 10/18/2022] Open
Abstract
Graph theory allows assessing changes of neuronal connectivity and interactions of brain regions in response to local lesions, e.g., after stroke, and global perturbations, e.g., due to psychiatric dysfunctions or neurodegenerative disorders. Consequently, network analysis based on constructing graphs from structural and functional MRI connectivity matrices is increasingly used in clinical studies. In contrast, in mouse neuroimaging, the focus is mainly on basic connectivity parameters, i.e., the correlation coefficient or fiber counts, whereas more advanced network analyses remain rarely used. This review summarizes graph theoretical measures and their interpretation to describe networks derived from recent in vivo mouse brain studies. To facilitate the entry into the topic, we explain the related mathematical definitions, provide a dedicated software toolkit, and discuss practical considerations for the application to rs-fMRI and DTI. This way, we aim to foster cross-species comparisons and the application of standardized measures to classify and interpret network changes in translational brain disease studies.
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Affiliation(s)
- Leon Scharwächter
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany
| | - Felix J Schmitt
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany; University of Cologne, Institute of Zoology, Dept. of Computational Systems Neuroscience, Cologne, Germany
| | - Niklas Pallast
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany
| | - Gereon R Fink
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Germany
| | - Markus Aswendt
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Germany.
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Tooley UA, Bassett DS, Mackey AP. Functional brain network community structure in childhood: Unfinished territories and fuzzy boundaries. Neuroimage 2022; 247:118843. [PMID: 34952233 PMCID: PMC8920293 DOI: 10.1016/j.neuroimage.2021.118843] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 12/01/2021] [Accepted: 12/19/2021] [Indexed: 12/23/2022] Open
Abstract
Adult cortex is organized into distributed functional communities. Yet, little is known about community architecture of children's brains. Here, we uncovered the community structure of cortex in childhood using fMRI data from 670 children aged 9-11 years (48% female, replication sample n=544, 56% female) from the Adolescent Brain and Cognitive Development study. We first applied a data-driven community detection approach to cluster cortical regions into communities, then employed a generative model-based approach called the weighted stochastic block model to further probe community interactions. Children showed similar community structure to adults, as defined by Yeo and colleagues in 2011, in early-developing sensory and motor communities, but differences emerged in transmodal areas. Children have more cortical territory in the limbic community, which is involved in emotion processing, than adults. Regions in association cortex interact more flexibly across communities, creating uncertainty for the model-based assignment algorithm, and perhaps reflecting cortical boundaries that are not yet solidified. Uncertainty was highest for cingulo-opercular areas involved in flexible deployment of cognitive control. Activation and deactivation patterns during a working memory task showed that both the data-driven approach and a set of adult communities statistically capture functional organization in middle childhood. Collectively, our findings suggest that community boundaries are not solidified by middle childhood.
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Affiliation(s)
- Ursula A Tooley
- Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104, Pennsylvania, US; Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, 19104, Pennsylvania, US
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, 19104, Pennsylvania, US; Department of Electrical & Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, 19104, Pennsylvania, US; Department of Physics & Astronomy, School of Arts and Sciences, University of Pennsylvania, Philadelphia, 19104, Pennsylvania,USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104, Pennsylvania, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104, Pennsylvania, USA; Santa Fe Institute, Santa Fe, 87501, New Mexico, USA
| | - Allyson P Mackey
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, 19104, Pennsylvania, US.
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Improving Functional Connectivity in Developmental Dyslexia through Combined Neurofeedback and Visual Training. Symmetry (Basel) 2022. [DOI: 10.3390/sym14020369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
This study examined the effects of combined neurofeedback (NF) and visual training (VT) on children with developmental dyslexia (DD). Although NF is the first noninvasive approach to support neurological disorders, the mechanisms of its effects on the brain functional connectivity are still unclear. A key question is whether the functional connectivities of the EEG frequency networks change after the combined NF–VT training of DD children (postD). NF sessions of voluntary α/θ rhythm control were applied in a low-spatial-frequency (LSF) illusion contrast discrimination, which provides feedback with visual cues to improve the brain signals and cognitive abilities in DD children. The measures of connectivity, which are defined by small-world propensity, were sensitive to the properties of the brain electrical oscillations in the quantitative EEG-NF training. In the high-contrast LSF illusion, the z-NF reduced the α/θ scores in the frontal areas, and in the right ventral temporal, occipital–temporal, and middle occipital areas in the postD (vs. the preD) because of their suppression in the local hub θ-network and the altered global characteristics of the functional θ-frequency network. In the low-contrast condition, the z-NF stimulated increases in the α/θ scores, which induced hubs in the left-side α-frequency network of the postD, and changes in the global characteristics of the functional α-frequency network. Because of the anterior, superior, and middle temporal deficits affecting the ventral and occipital–temporal pathways, the z-NF–VT compensated for the more ventral brain regions, mainly in the left hemispheres of the postD group in the low-contrast LSF illusion. Compared to pretraining, the NF–VT increased the segregation of the α, β (low-contrast), and θ networks (high-contrast), as well as the γ2-network integration (both contrasts) after the termination of the training of the children with developmental dyslexia. The remediation compensated more for the dorsal (prefrontal, premotor, occipital–parietal connectivities) dysfunction of the θ network in the developmental dyslexia in the high-contrast LSF illusion. Our findings provide neurobehavioral evidence for the exquisite brain functional plasticity and direct effect of NF–VT on cognitive disabilities in DD children.
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Hoptman MJ, Tural U, Lim KO, Javitt DC, Oberlin LE. Relationships between Diffusion Tensor Imaging and Resting State Functional Connectivity in Patients with Schizophrenia and Healthy Controls: A Preliminary Study. Brain Sci 2022; 12:brainsci12020156. [PMID: 35203920 PMCID: PMC8870342 DOI: 10.3390/brainsci12020156] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 01/02/2022] [Accepted: 01/19/2022] [Indexed: 11/16/2022] Open
Abstract
Schizophrenia is widely seen as a disorder of dysconnectivity. Neuroimaging studies have examined both structural and functional connectivity in the disorder, but these modalities have rarely been integrated directly. We scanned 29 patients with schizophrenia and 25 healthy control subjects, and we acquired resting state fMRI and diffusion tensor imaging. We used the Functional and Tractographic Connectivity Analysis Toolbox (FATCAT) to estimate functional and structural connectivity of the default mode network. Correlations between modalities were investigated, and multimodal connectivity scores (MCS) were created using principal component analysis. Of the 28 possible region pairs, 9 showed consistent (>80%) tracts across participants. Correlations between modalities were found among those with schizophrenia for the prefrontal cortex, posterior cingulate, and lateral temporal lobes, with frontal and parietal regions, consistent with frontotemporoparietal network involvement in the disorder. In patients, MCS correlated with several aspects of the Positive and Negative Syndrome Scale, with higher multimodal connectivity associated with outward-directed (externalizing) behavior and lower multimodal connectivity related to psychosis per se. In this preliminary sample, we found FATCAT to be a useful toolbox to directly integrate and examine connectivity between imaging modalities. A consideration of conjoint structural and functional connectivity can provide important information about the network mechanisms of schizophrenia.
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Affiliation(s)
- Matthew J. Hoptman
- Clinical Research Division, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA;
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- Correspondence: or ; Tel.: +1-845-398-6569
| | - Umit Tural
- Clinical Research Division, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA;
| | - Kelvin O. Lim
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55454, USA;
| | - Daniel C. Javitt
- Schizophrenia Research Division, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA; or
- Division of Experimental Therapeutics, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
| | - Lauren E. Oberlin
- Department of Psychiatry, Weill Cornell Medicine, New York, NY 10065, USA;
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Dupuy M, Abdallah M, Swendsen J, N’Kaoua B, Chanraud S, Schweitzer P, Fatseas M, Serre F, Barse E, Auriacombe M, Misdrahi D. Real-time cognitive performance and positive symptom expression in schizophrenia. Eur Arch Psychiatry Clin Neurosci 2022; 272:415-425. [PMID: 34287696 PMCID: PMC8938338 DOI: 10.1007/s00406-021-01296-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 07/04/2021] [Indexed: 11/28/2022]
Abstract
Deficits in cognitive functions are frequent in schizophrenia and are often conceptualized as stable characteristics of this disorder. However, cognitive capacities may fluctuate over the course of a day and it is unknown if such variation may be linked to the dynamic expression of psychotic symptoms. This investigation used Ecological Momentary Assessment (EMA) to provide mobile tests of cognitive functions and positive symptoms in real time. Thirty-three individuals with schizophrenia completed five EMA assessments per day for a one-week period that included real-time assessments of cognitive performance and psychotic symptoms. A subsample of patients and 31 healthy controls also completed a functional MRI examination. Relative to each individual's average score, moments of worsened cognitive performance on the mobile tests were associated with an increased probability of positive symptom occurrence over subsequent hours of the day (coef = 0.06, p < 0.05), adjusting for the presence of psychotic symptoms at the moment of mobile test administration. These prospective associations varied as a function of graph theory indices in MRI analyses. These findings demonstrate that cognitive performance is prospectively linked to psychotic symptom expression in daily life, and that underlying brain markers may be observed in the Executive Control Network. While the potential causal nature of this association remains to be investigated, our results offer promising prospects for a better understanding of the underlying mechanisms of symptom expression in schizophrenia.
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Affiliation(s)
- Maud Dupuy
- Institut de Neurosciences Cognitives et Intégratives d'Aquitaine (INCIA), University of Bordeaux/CNRS-UMR 5287, Bordeaux, France.
| | - Majd Abdallah
- Institut de Neurosciences Cognitives et Intégratives d’Aquitaine (INCIA), University of Bordeaux/CNRS-UMR 5287, Bordeaux, France
| | - Joel Swendsen
- Institut de Neurosciences Cognitives et Intégratives d’Aquitaine (INCIA), University of Bordeaux/CNRS-UMR 5287, Bordeaux, France ,EPHE, PSL Research University, Paris, France
| | - Bernard N’Kaoua
- Handicap, Activity, Cognition, Health, Inserm/University of Bordeaux, Talence, France
| | - Sandra Chanraud
- Institut de Neurosciences Cognitives et Intégratives d’Aquitaine (INCIA), University of Bordeaux/CNRS-UMR 5287, Bordeaux, France ,EPHE, PSL Research University, Paris, France
| | - Pierre Schweitzer
- Institut de Neurosciences Cognitives et Intégratives d’Aquitaine (INCIA), University of Bordeaux/CNRS-UMR 5287, Bordeaux, France
| | - Melina Fatseas
- Institut de Neurosciences Cognitives et Intégratives d’Aquitaine (INCIA), University of Bordeaux/CNRS-UMR 5287, Bordeaux, France ,CHU Bordeaux, Bordeaux, France
| | - Fuschia Serre
- Addiction and Neuropsychiatry (SANPSY), University of Bordeaux, CNRS USR 3413 – Sleep, Bordeaux, France
| | - Elodie Barse
- Institut de Neurosciences Cognitives et Intégratives d’Aquitaine (INCIA), University of Bordeaux/CNRS-UMR 5287, Bordeaux, France ,EPHE, PSL Research University, Paris, France
| | - Marc Auriacombe
- Addiction and Neuropsychiatry (SANPSY), University of Bordeaux, CNRS USR 3413 – Sleep, Bordeaux, France ,CH Charles Perrens, Bordeaux, France ,CHU Bordeaux, Bordeaux, France
| | - David Misdrahi
- Institut de Neurosciences Cognitives et Intégratives d’Aquitaine (INCIA), University of Bordeaux/CNRS-UMR 5287, Bordeaux, France ,CH Charles Perrens, Bordeaux, France
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