51
|
Yao Z, Zou Y, Zheng W, Zhang Z, Li Y, Yu Y, Zhang Z, Fu Y, Shi J, Zhang W, Wu X, Hu B. Structural alterations of the brain preceded functional alterations in major depressive disorder patients: Evidence from multimodal connectivity. J Affect Disord 2019; 253:107-117. [PMID: 31035211 DOI: 10.1016/j.jad.2019.04.064] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 03/11/2019] [Accepted: 04/08/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND Recent studies showed that major depressive disorder (MDD) has been involved in abnormal functional and structural connections in specific brain regions. However, comprehensive researches on MDD-related alterations in the topological organization of brain functional and structural networks are still limited. METHODS Functional network (FN) was constructed from resting-state functional MRI temporal series correlations and structural network (SN) was established by Diffusion tensor imaging (DTI) data in 58 MDD patients and 71 healthy controls (HC). The measurements of the network properties were calculated for two networks respectively. Correlations were conducted between altered network parameters and Hamilton depression scale (HAMD) score. Additionally, network resilient analysis were conducted on FN and SN. RESULTS The losses of small-worldness charateristics and the decline of nodal efficiency across FN and SN were found in MDD patients. Based on network-based statistic (NBS) approach, the decreased connections in MDD patients were mainly found in the superior occipital gyrus, superior temporal gyrus for FN and SN, while the increased connections were distributed in putamen, superior frontal gyrus only for SN. Compared with the FN, the SN showed less resilient to targeted or random node failure. Besides, altered edges in NBS and regions with decreased nodal efficiency were negatively associated with HAMD score in MDD patients. LIMITATIONS The samples size is small and most of the MDD patients take different antidepressant medications. CONCLUSIONS Alterations of SN in the brain of MDD patients preceded that of FN to some extent, and reorganization of the brain network was a mechanism which compensated for functional and structural alterations during disease progression.
Collapse
Affiliation(s)
- Zhijun Yao
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, 730000, P.R. China
| | - Ying Zou
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, 730000, P.R. China
| | - Weihao Zheng
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, P.R. China
| | - Zhe Zhang
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, 730000, P.R. China
| | - Yuan Li
- School of Information Science and Engineering, Shandong Normal University, Jinan, Shandong Province, 250358, P.R. China
| | - Yue Yu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, 730000, P.R. China
| | - Zicheng Zhang
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, 730000, P.R. China
| | - Yu Fu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, 730000, P.R. China
| | - Jie Shi
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, 730000, P.R. China
| | - Wenwen Zhang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, Gansu Province, 730000, P.R. China
| | - Xia Wu
- College of Information Science and Technology, Beijing Normal University, Beijing, 100000, P.R. China.
| | - Bin Hu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, 730000, P.R. China.
| |
Collapse
|
52
|
Rolnick D, Dyer EL. Generative models and abstractions for large-scale neuroanatomy datasets. Curr Opin Neurobiol 2019; 55:112-120. [PMID: 30878806 PMCID: PMC8449855 DOI: 10.1016/j.conb.2019.02.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Revised: 01/09/2019] [Accepted: 02/07/2019] [Indexed: 01/09/2023]
Abstract
Neural datasets are increasing rapidly in both resolution and volume. In neuroanatomy, this trend has been accelerated by innovations in imaging technology. As full datasets are impractical and unnecessary for many applications, it is important to identify abstractions that distill useful features of neural structure, organization, and anatomy. In this review article, we discuss several such abstractions and highlight recent algorithmic advances in working with these models. In particular, we discuss the use of generative models in neuroanatomy; such models may be considered 'meta-abstractions' that capture distributions over other abstractions.
Collapse
Affiliation(s)
- David Rolnick
- School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Eva L Dyer
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, GA, USA; School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
| |
Collapse
|
53
|
Betzel RF, Griffa A, Hagmann P, Mišić B. Distance-dependent consensus thresholds for generating group-representative structural brain networks. Netw Neurosci 2019; 3:475-496. [PMID: 30984903 PMCID: PMC6444521 DOI: 10.1162/netn_a_00075] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2018] [Accepted: 12/03/2018] [Indexed: 12/16/2022] Open
Abstract
Large-scale structural brain networks encode white matter connectivity patterns among distributed brain areas. These connection patterns are believed to support cognitive processes and, when compromised, can lead to neurocognitive deficits and maladaptive behavior. A powerful approach for studying the organizing principles of brain networks is to construct group-representative networks from multisubject cohorts. Doing so amplifies signal to noise ratios and provides a clearer picture of brain network organization. Here, we show that current approaches for generating sparse group-representative networks overestimate the proportion of short-range connections present in a network and, as a result, fail to match subject-level networks along a wide range of network statistics. We present an alternative approach that preserves the connection-length distribution of individual subjects. We have used this method in previous papers to generate group-representative networks, though to date its performance has not been appropriately benchmarked and compared against other methods. As a result of this simple modification, the networks generated using this approach successfully recapitulate subject-level properties, outperforming similar approaches by better preserving features that promote integrative brain function rather than segregative. The method developed here holds promise for future studies investigating basic organizational principles and features of large-scale structural brain networks.
Collapse
Affiliation(s)
- Richard F. Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- Network Science Institute, Indiana University, Bloomington, IN, USA
| | - Alessandra Griffa
- Dutch Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University, Amsterdam, The Netherlands
| | - Patric Hagmann
- Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Bratislav Mišić
- Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
| |
Collapse
|
54
|
Macroscopic Cluster Organizations Change the Complexity of Neural Activity. ENTROPY 2019; 21:e21020214. [PMID: 33266930 PMCID: PMC7514695 DOI: 10.3390/e21020214] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 02/11/2019] [Accepted: 02/19/2019] [Indexed: 12/20/2022]
Abstract
In this study, simulations are conducted using a network model to examine how the macroscopic network in the brain is related to the complexity of activity for each region. The network model is composed of multiple neuron groups, each of which consists of spiking neurons with different topological properties of a macroscopic network based on the Watts and Strogatz model. The complexity of spontaneous activity is analyzed using multiscale entropy, and the structural properties of the network are analyzed using complex network theory. Experimental results show that a macroscopic structure with high clustering and high degree centrality increases the firing rates of neurons in a neuron group and enhances intraconnections from the excitatory neurons to inhibitory neurons in a neuron group. As a result, the intensity of the specific frequency components of neural activity increases. This decreases the complexity of neural activity. Finally, we discuss the research relevance of the complexity of the brain activity.
Collapse
|
55
|
Zuchowicz U, Wozniak-Kwasniewska A, Szekely D, Olejarczyk E, David O. EEG Phase Synchronization in Persons With Depression Subjected to Transcranial Magnetic Stimulation. Front Neurosci 2019; 12:1037. [PMID: 30692906 PMCID: PMC6340356 DOI: 10.3389/fnins.2018.01037] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2018] [Accepted: 12/21/2018] [Indexed: 12/26/2022] Open
Abstract
Aim: The main objective of this work was to study the impact of repetitive Transcranial Magnetic Stimulation (rTMS) treatment on brain activity in 8 patients with major depressive disorder (MDD) and 10 patients with bipolar disorder (BP). Changes due to rTMS stimulation of the left dorsolateral prefrontal cortex (DLPFC) were investigated considering separately responders and non-responders to therapy in each of both groups. The aim of the research is to determine whether non-responders differ from responders suffered from both diseases, as well as if any change occurred due to rTMS across consecutive rTMS sessions. Methods: The graph-theory-based connectivity analysis of non-linearity measure of phase interdependencies—Phase Locking Value (PLV)—was examined from EEG data. The approximately 15-min EEG recordings from each of participants were recorded before and after 1st, 10th, and 20th session, respectively. PLV calculated from data was analyzed using principal graph theory indices (strength and degree) within five physiological frequency bands and in individual channels separately. The impact of rTMS on the EEG connectivity in every group of patients evaluated by PLV was assessed. Results: Each of four groups reacted differently to rTMS treatment. The strength and degree of PLV increased in gamma band in both groups of responders. Moreover, an increase of indices in beta band for BP-responders was observed. While, in MDD-non-responders the indices decreased in gamma band and increased in beta band. Moreover, the index strength was lower in alpha band for BP- non-responders. The rTMS stimulation caused topographically specific changes, i.e., the increase of the activity in the left DLPFC as well as in other brain regions such as right parieto-occipital areas. Conclusions: The analysis of PLV allowed for evaluation of the rTMS impact on the EEG activity in each group of patients. The changes of PLV under stimulation might be a good indicator of response to depression treatment permitting to improve the effectiveness of therapy.
Collapse
Affiliation(s)
- Urszula Zuchowicz
- Department of Automatics and Biomedical Engineering, AGH University of Science and Technology, Cracow, Poland
| | - Agata Wozniak-Kwasniewska
- Inserm, U1216, Grenoble, France.,Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, Grenoble, France
| | - David Szekely
- Inserm, U1216, Grenoble, France.,Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, Grenoble, France.,Centre Hospitalier Univ. Grenoble Alpes, Service de Psychiatrie, Grenoble, France
| | - Elzbieta Olejarczyk
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland
| | - Olivier David
- Inserm, U1216, Grenoble, France.,Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, Grenoble, France
| |
Collapse
|
56
|
Closed-Loop Systems and In Vitro Neuronal Cultures: Overview and Applications. ADVANCES IN NEUROBIOLOGY 2019; 22:351-387. [DOI: 10.1007/978-3-030-11135-9_15] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
|
57
|
Khalil R, Karim AA, Khedr E, Moftah M, Moustafa AA. Dynamic Communications Between GABA A Switch, Local Connectivity, and Synapses During Cortical Development: A Computational Study. Front Cell Neurosci 2018; 12:468. [PMID: 30618625 PMCID: PMC6304749 DOI: 10.3389/fncel.2018.00468] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Accepted: 11/16/2018] [Indexed: 11/13/2022] Open
Abstract
Several factors regulate cortical development, such as changes in local connectivity and the influences of dynamical synapses. In this study, we simulated various factors affecting the regulation of neural network activity during cortical development. Previous studies have shown that during early cortical development, the reversal potential of GABAA shifts from depolarizing to hyperpolarizing. Here we provide the first integrative computational model to simulate the combined effects of these factors in a unified framework (building on our prior work: Khalil et al., 2017a,b). In the current study, we extend our model to monitor firing activity in response to the excitatory action of GABAA. Precisely, we created a Spiking Neural Network model that included certain biophysical parameters for lateral connectivity (distance between adjacent neurons) and nearby local connectivity (complex connections involving those between neuronal groups). We simulated different network scenarios (for immature and mature conditions) based on these biophysical parameters. Then, we implemented two forms of Short-term synaptic plasticity (depression and facilitation). Each form has two distinct kinds according to its synaptic time constant value. Finally, in both sets of networks, we compared firing rate activity responses before and after simulating dynamical synapses. Based on simulation results, we found that the modulation effect of dynamical synapses for evaluating and shaping the firing activity of the neural network is strongly dependent on the physiological state of GABAA. Moreover, the STP mechanism acts differently in every network scenario, mirroring the crucial modulating roles of these critical parameters during cortical development. Clinical implications for pathological alterations of GABAergic signaling in neurological and psychiatric disorders are discussed.
Collapse
Affiliation(s)
- Radwa Khalil
- Department of Psychology and Methods, Jacobs University Bremen, Bremen, Germany
| | - Ahmed A Karim
- Department of Psychology and Methods, Jacobs University Bremen, Bremen, Germany.,University Clinic of Psychiatry and Psychotherapy, Tübingen, Germany
| | - Eman Khedr
- Department of Neuropsychiatry, Faculty of Medicine, Assiut University, Assiut, Egypt
| | - Marie Moftah
- Zoology Department, Faculty of Science, Alexandria University, Alexandria, Egypt
| | - Ahmed A Moustafa
- MARCS Institute for Brain and Behaviour, Western Sydney University, Sydney, NSW, Australia.,Department of Social Sciences, College of Arts and Sciences, Qatar University, Doha, Qatar
| |
Collapse
|
58
|
Sokolov AA, Zeidman P, Erb M, Ryvlin P, Pavlova MA, Friston KJ. Linking structural and effective brain connectivity: structurally informed Parametric Empirical Bayes (si-PEB). Brain Struct Funct 2018; 224:205-217. [PMID: 30302538 PMCID: PMC6373362 DOI: 10.1007/s00429-018-1760-8] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Accepted: 09/21/2018] [Indexed: 12/13/2022]
Abstract
Despite the potential for better understanding functional neuroanatomy, the complex relationship between neuroimaging measures of brain structure and function has confounded integrative, multimodal analyses of brain connectivity. This is particularly true for task-related effective connectivity, which describes the causal influences between neuronal populations. Here, we assess whether measures of structural connectivity may usefully inform estimates of effective connectivity in larger scale brain networks. To this end, we introduce an integrative approach, capitalising on two recent statistical advances: Parametric Empirical Bayes, which provides group-level estimates of effective connectivity, and Bayesian model reduction, which enables rapid comparison of competing models. Crucially, we show that structural priors derived from high angular resolution diffusion imaging on a dynamic causal model of a 12-region network-based on functional MRI data from the same subjects-substantially improve model evidence (posterior probability 1.00). This provides definitive evidence that structural and effective connectivity depend upon each other in mediating distributed, large-scale interactions in the brain. Furthermore, this work offers novel perspectives for understanding normal brain architecture and its disintegration in clinical conditions.
Collapse
Affiliation(s)
- Arseny A Sokolov
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK. .,Service de Neurologie, Département des Neurosciences Cliniques, Centre Hospitalier Universitaire Vaudois (CHUV), 1011, Lausanne, Switzerland.
| | - Peter Zeidman
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK
| | - Michael Erb
- Department of Biomedical Magnetic Resonance, Department of Radiology, University of Tübingen Medical School, 72076, Tübingen, Germany
| | - Philippe Ryvlin
- Service de Neurologie, Département des Neurosciences Cliniques, Centre Hospitalier Universitaire Vaudois (CHUV), 1011, Lausanne, Switzerland
| | - Marina A Pavlova
- Department of Psychiatry and Psychotherapy, University of Tübingen Medical School, 72076, Tübingen, Germany
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK
| |
Collapse
|
59
|
Frässle S, Lomakina EI, Kasper L, Manjaly ZM, Leff A, Pruessmann KP, Buhmann JM, Stephan KE. A generative model of whole-brain effective connectivity. Neuroimage 2018; 179:505-529. [DOI: 10.1016/j.neuroimage.2018.05.058] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Revised: 05/16/2018] [Accepted: 05/24/2018] [Indexed: 12/17/2022] Open
|
60
|
Roy N, Sanz-Leon P, Robinson PA. Spectrum of connectivity fluctuations including the effect of activity-dependent feedback. Phys Rev E 2018; 98:022319. [PMID: 30253627 DOI: 10.1103/physreve.98.022319] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Indexed: 11/07/2022]
Abstract
The spatiotemporal spectrum of feedback-driven fluctuations of brain connectivity is investigated using nonlinear neural field theory of the corticothalamic system. Weakly nonlinear dynamics of neural feedbacks are expanded in terms of first order perturbations of neural activity relative to a fixed point. Susceptibilities are used to quantify the change in connectivity per unit change in presynaptic or postsynaptic activity caused by nonlinear feedbacks such as facilitation, depression, sensitization, potentiation, and the effects of discrete eigenmode structure are included for a spherical brain geometry. Spectral signatures such as resonances are identified that allow the presence of particular presynaptic and postsynaptic feedback effects to be inferred. These include additional resonances at high frequencies and shifts of existing spectral peaks, mostly visible in the lowest spatial modes of the response.
Collapse
Affiliation(s)
- N Roy
- School of Physics, University of Sydney, New South Wales 2006, Australia and Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia
| | - P Sanz-Leon
- School of Physics, University of Sydney, New South Wales 2006, Australia and Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia
| | - P A Robinson
- School of Physics, University of Sydney, New South Wales 2006, Australia and Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia
| |
Collapse
|
61
|
Leming M, Su L, Chattopadhyay S, Suckling J. Normative pathways in the functional connectome. Neuroimage 2018; 184:317-334. [PMID: 30223061 DOI: 10.1016/j.neuroimage.2018.09.028] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Revised: 08/22/2018] [Accepted: 09/10/2018] [Indexed: 02/06/2023] Open
Abstract
Functional connectivity is frequently derived from fMRI data to reduce a complex image of the brain to a graph, or "functional connectome". Often shortest-path algorithms are used to characterize and compare functional connectomes. Previous work on the identification and measurement of semi-metric (shortest circuitous) pathways in the functional connectome has discovered cross-sectional differences in major depressive disorder (MDD), autism spectrum disorder (ASD), and Alzheimer's disease. However, while measurements of shortest path length have been analyzed in functional connectomes, less work has been done to investigate the composition of the pathways themselves, or whether the edges composing pathways differ between individuals. Developments in this area would help us understand how pathways might be organized in mental disorders, and if a consistent pattern can be found. Furthermore, studies in structural brain connectivity and other real-world graphs suggest that shortest pathways may not be as important in functional connectivity studies as previously assumed. In light of this, we present a novel measurement of the consistency of pathways across functional connectomes, and an algorithm for improvement by selecting the most frequently occurring "normative pathways" from the k shortest paths, instead of just the shortest path. We also look at this algorithm's effect on various graph measurements, using randomized matrix simulations to support the efficacy of this method and demonstrate our algorithm on the resting-state fMRI (rs-fMRI) of a group of 34 adolescent control participants. Additionally, a comparison of normative pathways is made with a group of 82 age-matched participants, diagnosed with MDD, and in doing so we find the normative pathways that are most disrupted. Our results, which are carried out with estimates of connectivity derived from correlation, partial correlation, and normalized mutual information connectomes, suggest disruption to the default mode, affective, and ventral attention networks. Normative pathways, especially with partial correlation, make greater use of critical anatomical pathways through the striatum, cingulum, and the cerebellum. In summary, MDD is characterized by a disruption of normative pathways of the ventral attention network, increases in alternative pathways in the frontoparietal network in MDD, and a mixture of both in the default mode network. Additionally, within- and between-groups findings depend on the estimate of connectivity.
Collapse
Affiliation(s)
- Matthew Leming
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
| | - Li Su
- Department of Psychiatry, University of Cambridge, Cambridge, UK; China-UK Centre for Cognition and Ageing Research, Faculty of Psychology, Southwest University, Chongqing, China
| | | | - John Suckling
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| |
Collapse
|
62
|
Kim SY, Lim W. Burst synchronization in a scale-free neuronal network with inhibitory spike-timing-dependent plasticity. Cogn Neurodyn 2018; 13:53-73. [PMID: 30728871 DOI: 10.1007/s11571-018-9505-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Revised: 08/19/2018] [Accepted: 08/28/2018] [Indexed: 01/09/2023] Open
Abstract
We are concerned about burst synchronization (BS), related to neural information processes in health and disease, in the Barabási-Albert scale-free network (SFN) composed of inhibitory bursting Hindmarsh-Rose neurons. This inhibitory neuronal population has adaptive dynamic synaptic strengths governed by the inhibitory spike-timing-dependent plasticity (iSTDP). In previous works without considering iSTDP, BS was found to appear in a range of noise intensities for fixed synaptic inhibition strengths. In contrast, in our present work, we take into consideration iSTDP and investigate its effect on BS by varying the noise intensity. Our new main result is to find occurrence of a Matthew effect in inhibitory synaptic plasticity: good BS gets better via LTD, while bad BS get worse via LTP. This kind of Matthew effect in inhibitory synaptic plasticity is in contrast to that in excitatory synaptic plasticity where good (bad) synchronization gets better (worse) via LTP (LTD). We note that, due to inhibition, the roles of LTD and LTP in inhibitory synaptic plasticity are reversed in comparison with those in excitatory synaptic plasticity. Moreover, emergences of LTD and LTP of synaptic inhibition strengths are intensively investigated via a microscopic method based on the distributions of time delays between the pre- and the post-synaptic burst onset times. Finally, in the presence of iSTDP we investigate the effects of network architecture on BS by varying the symmetric attachment degree l ∗ and the asymmetry parameter Δ l in the SFN.
Collapse
Affiliation(s)
- Sang-Yoon Kim
- Institute for Computational Neuroscience and Department of Science Education, Daegu National University of Education, Daegu, 42411 Korea
| | - Woochang Lim
- Institute for Computational Neuroscience and Department of Science Education, Daegu National University of Education, Daegu, 42411 Korea
| |
Collapse
|
63
|
Brain white matter structural networks in patients with non-neuropsychiatric systemic lupus erythematosus. Brain Imaging Behav 2018; 12:142-155. [PMID: 28190161 DOI: 10.1007/s11682-017-9681-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Previous neuroimaging studies have revealed cognitive dysfunction in patients with systemic lupus erythematosus (SLE) and suggested that it may be related to disrupted brain white matter (WM) connectivity. However, no study has examined the topological properties of brain WM structural networks in SLE patients, especially in patients with non-neuropsychiatric SLE (non-NPSLE). In this study, we acquired DTI datasets from 28 non-NPSLE patients and 24 healthy controls, constructed their brain WM structural networks by using a deterministic fiber tracking approach, estimated the topological parameters of their structural networks, and compared their group differences. We reached the following results: 1) At the global level, the non-NPSLE patients showed significantly increased characteristic path length, normalized clustering coefficient and small-worldness, but significantly decreased global efficiency and local efficiency compared to the controls; 2) At the nodal level, the non-NPSLE patients had significantly decreased nodal efficiency in regions related to movement control, executive control, and working memory (bilateral precentral gyri, bilateral middle frontal gyri, bilateral inferior parietal lobes, left median cingulate gyrus and paracingulate gyrus, and right middle temporal gyrus). In addition, to pinpointing the injured WM fiber tracts in the non-NPSLE patients, we reconstructed the major brain WM pathways connecting the abnormal regions at the nodal level with the corticospinal tract (CST), superior longitudinal fasciculus-parietal terminations (SLFP), and superior longitudinal fasciculus-temporal terminations (SLFT). By analyzing the diffusion parameters along these WM fiber pathways, we detected abnormal diffusion parameters in the bilateral CST and right SLFT in the non-NPSLE patients. These results seem to indicate that injured brain WM connectivity exists in SLE patients even in the absence of neuropsychiatric symptoms.
Collapse
|
64
|
Gollo LL, Roberts JA, Cropley VL, Di Biase MA, Pantelis C, Zalesky A, Breakspear M. Fragility and volatility of structural hubs in the human connectome. Nat Neurosci 2018; 21:1107-1116. [PMID: 30038275 DOI: 10.1038/s41593-018-0188-z] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Accepted: 05/30/2018] [Indexed: 11/09/2022]
Abstract
Brain structure reflects the influence of evolutionary processes that pit the costs of its anatomical wiring against the computational advantages conferred by its complexity. We show that cost-neutral 'mutations' of the human connectome almost inevitably degrade its complexity and disconnect high-strength connections to prefrontal network hubs. Conversely, restoring the peripheral location and strong connectivity of empirically observed hubs confers a wiring cost that the brain appears to minimize. Progressive cost-neutral randomization yields daughter networks that differ substantially from one another and results in a topologically unstable phenomenon consistent with a phase transition in complex systems. The fragility of hubs to disconnection shows a significant association with the acceleration of gray matter loss in schizophrenia. Together with effects on wiring cost, we suggest that fragile prefrontal hub connections and topological volatility act as evolutionary influences on brain networks whose optimal set point may be perturbed in neuropsychiatric disorders.
Collapse
Affiliation(s)
- Leonardo L Gollo
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.,Centre of Excellence for Integrative Brain Function, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - James A Roberts
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.,Centre of Excellence for Integrative Brain Function, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Vanessa L Cropley
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia
| | - Maria A Di Biase
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia.,Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, MA, USA
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia.,Centre for Neural Engineering, Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, Victoria, Australia.,Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia.,Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia
| | - Michael Breakspear
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia. .,Centre of Excellence for Integrative Brain Function, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia. .,Metro North Mental Health Service, Brisbane, Queensland, Australia.
| |
Collapse
|
65
|
Kim SY, Lim W. Effect of inhibitory spike-timing-dependent plasticity on fast sparsely synchronized rhythms in a small-world neuronal network. Neural Netw 2018; 106:50-66. [PMID: 30025272 DOI: 10.1016/j.neunet.2018.06.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 05/14/2018] [Accepted: 06/25/2018] [Indexed: 02/06/2023]
Abstract
We consider the Watts-Strogatz small-world network (SWN) consisting of inhibitory fast spiking Izhikevich interneurons. This inhibitory neuronal population has adaptive dynamic synaptic strengths governed by the inhibitory spike-timing-dependent plasticity (iSTDP). In previous works without iSTDP, fast sparsely synchronized rhythms, associated with diverse cognitive functions, were found to appear in a range of large noise intensities for fixed strong synaptic inhibition strengths. Here, we investigate the effect of iSTDP on fast sparse synchronization (FSS) by varying the noise intensity D. We employ an asymmetric anti-Hebbian time window for the iSTDP update rule [which is in contrast to the Hebbian time window for the excitatory STDP (eSTDP)]. Depending on values of D, population-averaged values of saturated synaptic inhibition strengths are potentiated [long-term potentiation (LTP)] or depressed [long-term depression (LTD)] in comparison with the initial mean value, and dispersions from the mean values of LTP/LTD are much increased when compared with the initial dispersion, independently of D. In most cases of LTD where the effect of mean LTD is dominant in comparison with the effect of dispersion, good synchronization (with higher spiking measure) is found to get better via LTD, while bad synchronization (with lower spiking measure) is found to get worse via LTP. This kind of Matthew effect in inhibitory synaptic plasticity is in contrast to that in excitatory synaptic plasticity where good (bad) synchronization gets better (worse) via LTP (LTD). Emergences of LTD and LTP of synaptic inhibition strengths are intensively investigated via a microscopic method based on the distributions of time delays between the pre- and the post-synaptic spike times. Furthermore, we also investigate the effects of network architecture on FSS by changing the rewiring probability p of the SWN in the presence of iSTDP.
Collapse
Affiliation(s)
- Sang-Yoon Kim
- Institute for Computational Neuroscience and Department of Science Education, Daegu National University of Education, Daegu 42411, Republic of Korea.
| | - Woochang Lim
- Institute for Computational Neuroscience and Department of Science Education, Daegu National University of Education, Daegu 42411, Republic of Korea.
| |
Collapse
|
66
|
Zheng W, Yao Z, Xie Y, Fan J, Hu B. Identification of Alzheimer's Disease and Mild Cognitive Impairment Using Networks Constructed Based on Multiple Morphological Brain Features. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2018; 3:887-897. [PMID: 30077576 DOI: 10.1016/j.bpsc.2018.06.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Revised: 06/13/2018] [Accepted: 06/14/2018] [Indexed: 01/24/2023]
Abstract
Structural brain markers are important for characterizing the pathology of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Here, we constructed a multifeature-based network (MFN) for each individual using a sparse linear regression performed on six types of morphological features to promote the structure-based autodiagnosis. The categorization performance of the MFN was evaluated in 165 normal control subjects, 221 patients with MCI, and 142 patients with AD. We achieved 96.42% and 96.37% accuracy, respectively, in distinguishing the patients with AD and MCI from the normal control subjects, and reasonable discrimination of the two disease cohorts (70.52%) and prediction of the MCI to AD progression (65.61%). The performance was further improved by combining the properties of the MFN with the morphological features. Our results demonstrate the effectiveness of the MFN in combination with morphological features obtained from single imaging modality, serving as robust biomarkers in the diagnosis of AD and MCI.
Collapse
Affiliation(s)
- Weihao Zheng
- School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Zhijun Yao
- School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Yuanwei Xie
- School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Jin Fan
- Department of Psychology, Queens College, City University of New York, Queens; Department of Neuroscience, Queens College, City University of New York, Queens; Department of Psychiatry, Icahn School of Medicine at Mont Sinai, New York, New York; Friedman Brain Institute, Icahn School of Medicine at Mont Sinai, New York, New York.
| | - Bin Hu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, China; Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou, China.
| |
Collapse
|
67
|
Reimann MW, Horlemann AL, Ramaswamy S, Muller EB, Markram H. Morphological Diversity Strongly Constrains Synaptic Connectivity and Plasticity. Cereb Cortex 2018. [PMID: 28637203 DOI: 10.1093/cercor/bhx150] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
Synaptic connectivity between neurons is naturally constrained by the anatomical overlap of neuronal arbors, the space on the axon available for synapses, and by physiological mechanisms that form synapses at a subset of potential synapse locations. What is not known is how these constraints impact emergent connectivity in a circuit with diverse morphologies. We investigated the role of morphological diversity within and across neuronal types on emergent connectivity in a model of neocortical microcircuitry. We found that the average overlap between the dendritic and axonal arbors of different types of neurons determines neuron-type specific patterns of distance-dependent connectivity, severely constraining the space of possible connectomes. However, higher order connectivity motifs depend on the diverse branching patterns of individual arbors of neurons belonging to the same type. Morphological diversity across neuronal types, therefore, imposes a specific structure on first order connectivity, and morphological diversity within neuronal types imposes a higher order structure of connectivity. We estimate that the morphological constraints resulting from diversity within and across neuron types together lead to a 10-fold reduction of the entropy of possible connectivity configurations, revealing an upper bound on the space explored by structural plasticity.
Collapse
Affiliation(s)
- Michael W Reimann
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Anna-Lena Horlemann
- Faculty of Mathematics and Statistics, University of St. Gallen, Bodanstrasse 6, CH-9000 St. Gallen, Switzerland
| | - Srikanth Ramaswamy
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Eilif B Muller
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Henry Markram
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| |
Collapse
|
68
|
Betzel RF, Bassett DS. Specificity and robustness of long-distance connections in weighted, interareal connectomes. Proc Natl Acad Sci U S A 2018; 115:E4880-E4889. [PMID: 29739890 PMCID: PMC6003515 DOI: 10.1073/pnas.1720186115] [Citation(s) in RCA: 105] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Brain areas' functional repertoires are shaped by their incoming and outgoing structural connections. In empirically measured networks, most connections are short, reflecting spatial and energetic constraints. Nonetheless, a small number of connections span long distances, consistent with the notion that the functionality of these connections must outweigh their cost. While the precise function of long-distance connections is unknown, the leading hypothesis is that they act to reduce the topological distance between brain areas and increase the efficiency of interareal communication. However, this hypothesis implies a nonspecificity of long-distance connections that we contend is unlikely. Instead, we propose that long-distance connections serve to diversify brain areas' inputs and outputs, thereby promoting complex dynamics. Through analysis of five weighted interareal network datasets, we show that long-distance connections play only minor roles in reducing average interareal topological distance. In contrast, areas' long-distance and short-range neighbors exhibit marked differences in their connectivity profiles, suggesting that long-distance connections enhance dissimilarity between areal inputs and outputs. Next, we show that-in isolation-areas' long-distance connectivity profiles exhibit nonrandom levels of similarity, suggesting that the communication pathways formed by long connections exhibit redundancies that may serve to promote robustness. Finally, we use a linearization of Wilson-Cowan dynamics to simulate the covariance structure of neural activity and show that in the absence of long-distance connections a common measure of functional diversity decreases. Collectively, our findings suggest that long-distance connections are necessary for supporting diverse and complex brain dynamics.
Collapse
Affiliation(s)
- Richard F Betzel
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
| | - Danielle S Bassett
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104;
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104
| |
Collapse
|
69
|
Weber R, Alicea B, Huskey R, Mathiak K. Network Dynamics of Attention During a Naturalistic Behavioral Paradigm. Front Hum Neurosci 2018; 12:182. [PMID: 29780313 PMCID: PMC5946671 DOI: 10.3389/fnhum.2018.00182] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Accepted: 04/17/2018] [Indexed: 11/13/2022] Open
Abstract
This study investigates the dynamics of attention during continuous, naturalistic interactions in a video game. Specifically, the effect of repeated distraction on a continuous primary task is related to a functional model of network connectivity. We introduce the Non-linear Attentional Saturation Hypothesis (NASH), which predicts that effective connectivity within attentional networks increases non-linearly with decreasing distraction over time, and exhibits dampening at critical parameter values. Functional magnetic resonance imaging (fMRI) data collected using a naturalistic behavioral paradigm coupled with an interactive video game is used to test the hypothesis. As predicted, connectivity in pre-defined regions corresponding to attentional networks increases as distraction decreases. Moreover, the functional relationship between connectivity and distraction is convex, that is, network connectivity somewhat increases as distraction decreases during the continuous primary task, however, connectivity increases considerably as distraction falls below critical levels. This result characterizes the non-linear pattern of connectivity within attentional networks, particularly with respect to their dynamics during behavior. These results are also summarized in the form of a network structure analysis, which underscores the role of various nodes in regulating the global network state. In conclusion, we situate the implications of this research in the context of cognitive complexity and an emerging theory of flow during media exposure.
Collapse
Affiliation(s)
- René Weber
- Media Neuroscience Lab, Department of Communication, University of California, Santa Barbara, Santa Barbara, CA, United States
| | - Bradly Alicea
- Orthogonal Research and Teaching Laboratory, Champaign, IL, United States
| | - Richard Huskey
- Cognitive Communication Science Lab, School of Communication, The Ohio State University, Columbus, OH, United States
| | - Klaus Mathiak
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
| |
Collapse
|
70
|
Amyloid causes intermittent network disruptions in cognitively intact older subjects. Brain Imaging Behav 2018; 13:699-716. [DOI: 10.1007/s11682-018-9869-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
|
71
|
Gilson M, Kouvaris NE, Deco G, Zamora-López G. Framework based on communicability and flow to analyze complex network dynamics. Phys Rev E 2018; 97:052301. [PMID: 29906867 DOI: 10.1103/physreve.97.052301] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Indexed: 06/08/2023]
Abstract
Graph theory constitutes a widely used and established field providing powerful tools for the characterization of complex networks. The intricate topology of networks can also be investigated by means of the collective dynamics observed in the interactions of self-sustained oscillations (synchronization patterns) or propagationlike processes such as random walks. However, networks are often inferred from real-data-forming dynamic systems, which are different from those employed to reveal their topological characteristics. This stresses the necessity for a theoretical framework dedicated to the mutual relationship between the structure and dynamics in complex networks, as the two sides of the same coin. Here we propose a rigorous framework based on the network response over time (i.e., Green function) to study interactions between nodes across time. For this purpose we define the flow that describes the interplay between the network connectivity and external inputs. This multivariate measure relates to the concepts of graph communicability and the map equation. We illustrate our theory using the multivariate Ornstein-Uhlenbeck process, which describes stable and non-conservative dynamics, but the formalism can be adapted to other local dynamics for which the Green function is known. We provide applications to classical network examples, such as small-world ring and hierarchical networks. Our theory defines a comprehensive framework that is canonically related to directed and weighted networks, thus paving a way to revise the standards for network analysis, from the pairwise interactions between nodes to the global properties of networks including community detection.
Collapse
Affiliation(s)
- M Gilson
- Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer Ramon Trias Fargas, 25-27, 08005 Barcelona, Spain
| | - N E Kouvaris
- Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer Ramon Trias Fargas, 25-27, 08005 Barcelona, Spain
- Namur Institute for Complex Systems (naXys), Department of Mathematics, University of Namur, Rempart de la Vierge 8, B 5000 Namur, Belgium
| | - G Deco
- Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer Ramon Trias Fargas, 25-27, 08005 Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avanats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona, 08010, Spain
| | - G Zamora-López
- Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer Ramon Trias Fargas, 25-27, 08005 Barcelona, Spain
| |
Collapse
|
72
|
Garcia JO, Ashourvan A, Muldoon SF, Vettel JM, Bassett DS. Applications of community detection techniques to brain graphs: Algorithmic considerations and implications for neural function. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2018; 106:846-867. [PMID: 30559531 PMCID: PMC6294140 DOI: 10.1109/jproc.2017.2786710] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
The human brain can be represented as a graph in which neural units such as cells or small volumes of tissue are heterogeneously connected to one another through structural or functional links. Brain graphs are parsimonious representations of neural systems that have begun to offer fundamental insights into healthy human cognition, as well as its alteration in disease. A critical open question in network neuroscience lies in how neural units cluster into densely interconnected groups that can provide the coordinated activity that is characteristic of perception, action, and adaptive behaviors. Tools that have proven particularly useful for addressing this question are community detection approaches, which can identify communities or modules: groups of neural units that are densely interconnected with other units in their own group but sparsely interconnected with units in other groups. In this paper, we describe a common community detection algorithm known as modularity maximization, and we detail its applications to brain graphs constructed from neuroimaging data. We pay particular attention to important algorithmic considerations, especially in recent extensions of these techniques to graphs that evolve in time. After recounting a few fundamental insights that these techniques have provided into brain function, we highlight potential avenues of methodological advancements for future studies seeking to better characterize the patterns of coordinated activity in the brain that accompany human behavior. This tutorial provides a naive reader with an introduction to theoretical considerations pertinent to the generation of brain graphs, an understanding of modularity maximization for community detection, a resource of statistical measures that can be used to characterize community structure, and an appreciation of the usefulness of these approaches in uncovering behaviorally-relevant network dynamics in neuroimaging data.
Collapse
Affiliation(s)
- Javier O Garcia
- U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005 USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104 USA
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Mathematics and CDSE Program, University at Buffalo, Buffalo, NY 14260 USA
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, CA, 93106 USA
- Department of Electrical & Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Arian Ashourvan
- U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005 USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104 USA
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Mathematics and CDSE Program, University at Buffalo, Buffalo, NY 14260 USA
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, CA, 93106 USA
- Department of Electrical & Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Sarah F Muldoon
- U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005 USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104 USA
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Mathematics and CDSE Program, University at Buffalo, Buffalo, NY 14260 USA
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, CA, 93106 USA
- Department of Electrical & Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Jean M Vettel
- U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005 USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104 USA
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Mathematics and CDSE Program, University at Buffalo, Buffalo, NY 14260 USA
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, CA, 93106 USA
- Department of Electrical & Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Danielle S Bassett
- U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005 USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104 USA
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Mathematics and CDSE Program, University at Buffalo, Buffalo, NY 14260 USA
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, CA, 93106 USA
- Department of Electrical & Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104 USA
| |
Collapse
|
73
|
|
74
|
Chen J, Rashid B, Yu Q, Liu J, Lin D, Du Y, Sui J, Calhoun VD. Variability in Resting State Network and Functional Network Connectivity Associated With Schizophrenia Genetic Risk: A Pilot Study. Front Neurosci 2018; 12:114. [PMID: 29545739 PMCID: PMC5838400 DOI: 10.3389/fnins.2018.00114] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Accepted: 02/13/2018] [Indexed: 12/19/2022] Open
Abstract
Imaging genetics posits a valuable strategy for elucidating genetic influences on brain abnormalities in psychiatric disorders. However, association analysis between 2D genetic data (subject × genetic variable) and 3D first-level functional magnetic resonance imaging (fMRI) data (subject × voxel × time) has been challenging given the asymmetry in data dimension. A summary feature needs to be derived for the imaging modality to compute inter-modality association at subject level. In this work, we propose to use variability in resting state networks (RSNs) and functional network connectivity (FNC) as potential features for purpose of association analysis. We conducted a pilot study to investigate the proposed features in a dataset of 171 healthy controls and 134 patients with schizophrenia (SZ). We computed variability in RSN and FNC in a group independent component analysis framework and tested three types of variability metrics, namely Euclidean distance, Pearson correlation and Kullback-Leibler (KL) divergence. Euclidean distance and Pearson correlation metrics more effectively discriminated controls from patients than KL divergence. The group differences observed with variability in RSN and FNC were highly consistent, indicating patients presenting increased deviation from the cohort-common pattern of RSN and FNC than controls. The variability in RSN and FNC showed significant associations with network global efficiency, the more the deviation, the lower the efficiency. Furthermore, the RSN and FNC variability were found to associate with individual SZ risk SNPs as well as cumulative polygenic risk score for SZ. Collectively the current findings provide preliminary evidence for variability in RSN and FNC being promising imaging features that may find applications as biomarkers and in imaging genetic association analysis.
Collapse
Affiliation(s)
- Jiayu Chen
- Mind Research Network, Albuquerque, NM, United States
| | - Barnaly Rashid
- Mind Research Network, Albuquerque, NM, United States
- Harvard Medical School, Harvard University, Boston, MA, United States
| | - Qingbao Yu
- Mind Research Network, Albuquerque, NM, United States
| | - Jingyu Liu
- Mind Research Network, Albuquerque, NM, United States
- Department of Electrical Engineering, University of New Mexico, Albuquerque, NM, United States
| | - Dongdong Lin
- Mind Research Network, Albuquerque, NM, United States
| | - Yuhui Du
- Mind Research Network, Albuquerque, NM, United States
- School of Computer & Information Technology, Shanxi University, Taiyuan, China
| | - Jing Sui
- Mind Research Network, Albuquerque, NM, United States
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Vince D. Calhoun
- Mind Research Network, Albuquerque, NM, United States
- Department of Electrical Engineering, University of New Mexico, Albuquerque, NM, United States
- Departments of Neurosciences and Psychiatry, University of New Mexico School of Medicine, Albuquerque, NM, United States
| |
Collapse
|
75
|
Chen Z, Guo Y, Suo T, Feng T. Coupling and segregation of large-scale brain networks predict individual differences in delay discounting. Biol Psychol 2018; 133:63-71. [PMID: 29382543 DOI: 10.1016/j.biopsycho.2018.01.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2017] [Revised: 01/20/2018] [Accepted: 01/21/2018] [Indexed: 01/11/2023]
Abstract
Decision-making about rewards, which requires us to choose between different time points, generally refers to intertemporal choice. Converging evidence suggests that some of the brain networks recruited in the delay discounting task have been well characterized for intertemporal choice. However, little is known about how the connectivity patterns of these large-scale brain networks are associated with delay discounting. Here, we use a resting-state functional connectivity MRI (rs-fcMRI) and a graph theoretical analysis to address this question. We found that the delay discounting rates showed a positive correlation with the functional network connectivity (FNC) between the cingulo-opercular network (CON) and the default mode network (DMN), while they showed a negative correlation with the FNC of both the CON-SAN (salience network) and the SAN-FPN (fronto-parietal network). Our results showed the association of both coupling and segregating processes with large-scale brain networks in delay discounting. Thus, the present study highlights the pivotal role of the functional connectivity patterns of intrinsic large-scale brain networks in delay discounting and extends our perspective on the neural mechanism of delay discounting.
Collapse
Affiliation(s)
- Zhiyi Chen
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Yiqun Guo
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Tao Suo
- Institute of Psychology and Behavior, School of Education, Henan University, Kaifeng, China.
| | - Tingyong Feng
- Faculty of Psychology, Southwest University, Chongqing, China; Key Laboratory of Cognition and Personality, Ministry of Education, China.
| |
Collapse
|
76
|
Abstract
The underlying neural mechanisms of implicit and explicit facial emotion recognition (FER) were studied in children and adolescents with autism spectrum disorder (ASD) compared to matched typically developing controls (TDC). EEG was obtained from N = 21 ASD and N = 16 TDC. Task performance, visual (P100, N170) and cognitive (late positive potential) event-related-potentials, as well as coherence were compared across groups. TDC showed a task-dependent increase and a stronger lateralization of P100 amplitude during the explicit task and task-dependent modulation of intra-hemispheric coherence in the beta band. In contrast, the ASD group showed no task dependent modulation. Results indicate disruptions in early visual processing and top-down attentional processes as contributing factors to FER deficits in ASD.
Collapse
|
77
|
Betzel RF, Medaglia JD, Bassett DS. Diversity of meso-scale architecture in human and non-human connectomes. Nat Commun 2018; 9:346. [PMID: 29367627 PMCID: PMC5783945 DOI: 10.1038/s41467-017-02681-z] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2017] [Accepted: 12/19/2017] [Indexed: 12/21/2022] Open
Abstract
Brain function is reflected in connectome community structure. The dominant view is that communities are assortative and segregated from one another, supporting specialized information processing. However, this view precludes the possibility of non-assortative communities whose complex inter-community interactions could engender a richer functional repertoire. We use weighted stochastic blockmodels to uncover the meso-scale architecture of Drosophila, mouse, rat, macaque, and human connectomes. We find that most communities are assortative, though others form core-periphery and disassortative structures, which better recapitulate observed patterns of functional connectivity and gene co-expression in human and mouse connectomes compared to standard community detection techniques. We define measures for quantifying the diversity of communities in which brain regions participate, showing that this measure is peaked in control and subcortical systems in humans, and that inter-individual differences are correlated with cognitive performance. Our report paints a more diverse portrait of connectome communities and demonstrates their cognitive relevance.
Collapse
Affiliation(s)
- Richard F Betzel
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - John D Medaglia
- Department of Psychology, Drexel University, Philadelphia, PA, 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| |
Collapse
|
78
|
Effect of spike-timing-dependent plasticity on stochastic burst synchronization in a scale-free neuronal network. Cogn Neurodyn 2018; 12:315-342. [PMID: 29765480 DOI: 10.1007/s11571-017-9470-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Revised: 11/29/2017] [Accepted: 12/26/2017] [Indexed: 01/02/2023] Open
Abstract
We consider an excitatory population of subthreshold Izhikevich neurons which cannot fire spontaneously without noise. As the coupling strength passes a threshold, individual neurons exhibit noise-induced burstings. This neuronal population has adaptive dynamic synaptic strengths governed by the spike-timing-dependent plasticity (STDP). However, STDP was not considered in previous works on stochastic burst synchronization (SBS) between noise-induced burstings of sub-threshold neurons. Here, we study the effect of additive STDP on SBS by varying the noise intensity D in the Barabási-Albert scale-free network (SFN). One of our main findings is a Matthew effect in synaptic plasticity which occurs due to a positive feedback process. Good burst synchronization (with higher bursting measure) gets better via long-term potentiation (LTP) of synaptic strengths, while bad burst synchronization (with lower bursting measure) gets worse via long-term depression (LTD). Consequently, a step-like rapid transition to SBS occurs by changing D, in contrast to a relatively smooth transition in the absence of STDP. We also investigate the effects of network architecture on SBS by varying the symmetric attachment degree [Formula: see text] and the asymmetry parameter [Formula: see text] in the SFN, and Matthew effects are also found to occur by varying [Formula: see text] and [Formula: see text]. Furthermore, emergences of LTP and LTD of synaptic strengths are investigated in details via our own microscopic methods based on both the distributions of time delays between the burst onset times of the pre- and the post-synaptic neurons and the pair-correlations between the pre- and the post-synaptic instantaneous individual burst rates (IIBRs). Finally, a multiplicative STDP case (depending on states) with soft bounds is also investigated in comparison with the additive STDP case (independent of states) with hard bounds. Due to the soft bounds, a Matthew effect with some quantitative differences is also found to occur for the case of multiplicative STDP.
Collapse
|
79
|
Zheng X, Dai W, Alsop DC, Schlaug G. Modulating transcallosal and intra-hemispheric brain connectivity with tDCS: Implications for interventions in Aphasia. Restor Neurol Neurosci 2018; 34:519-30. [PMID: 27472845 DOI: 10.3233/rnn-150625] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND/OBJECTIVE Transcranial direct current stimulation (tDCS) can enhance or diminish cortical excitability levels depending on the polarity of the stimulation. One application of non-invasive brain-stimulation has been to modulate a possible inter-hemispheric disinhibition after a stroke. This disinhibition model has been developed mainly for the upper extremity motor system, but it is not known whether the language/speech-motor system shows a similar inter-hemispheric interaction. We aimed to examine physiological evidence of inter- and intra-hemispheric connectivity changes induced by tDCS of the right inferior frontal gyrus (IFG) using arterial-spin labeling (ASL) MRI. METHODS Using an MR-compatible DC-Stimulator, we applied anodal stimulation to the right IFG region of nine healthy adults while undergoing non-invasive cerebral blood flow imaging with arterial-spin labeling (ASL) before, during, and after the stimulation. All ASL images were then normalized and timecourses were extracted in regions of interest (ROIs), which were the left and right IFG regions, and the right supramarginal gyrus (SMG) in the inferior parietal lobule. Two additional ROIs (the right occipital lobe and the left fronto-orbital region) were taken as control regions. RESULTS Using regional correlation coefficients as a surrogate marker of connectivity, we could show that inter-hemispheric connectivity (right IFG with left IFG) decreased significantly (p < 0.05; r-scores from 0.67 to 0.53) between baseline and post-stimulation, while the intra-hemispheric connectivity (right IFG with right SMG) increased significantly (p < 0.05;r-scores from 0.74 to 0.81). A 2 × 2 ANOVA found a significant main effect of HEMISPHERE (F(8) = 6.83, p < 0.01) and a significant HEMISPHERE-by-TIME interaction (F(8) = 4.24, p < 0.05) in connectivity changes. The correlation scores did not change significantly in the control region pairs (right IFG with right occipital and right IFG with left fronto-orbital) over time. CONCLUSION Using an MR-compatible DC stimulator we showed that ASL-MRI can detect tDCS-induced modulation of brain connectivity within and between hemispheres. These findings might affect trial designs focusing on modulating the non-dominant hemisphere to enhance language/speech-motor functions.
Collapse
Affiliation(s)
- Xin Zheng
- Department of Neurology, Neuroimaging and Stroke Recovery Laboratory, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Weiying Dai
- Division of MR Research, Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - David C Alsop
- Division of MR Research, Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Gottfried Schlaug
- Department of Neurology, Neuroimaging and Stroke Recovery Laboratory, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| |
Collapse
|
80
|
Iordan AD, Cooke KA, Moored KD, Katz B, Buschkuehl M, Jaeggi SM, Jonides J, Peltier SJ, Polk TA, Reuter-Lorenz PA. Aging and Network Properties: Stability Over Time and Links with Learning during Working Memory Training. Front Aging Neurosci 2018; 9:419. [PMID: 29354048 PMCID: PMC5758500 DOI: 10.3389/fnagi.2017.00419] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Accepted: 12/07/2017] [Indexed: 12/20/2022] Open
Abstract
Growing evidence suggests that healthy aging affects the configuration of large-scale functional brain networks. This includes reducing network modularity and local efficiency. However, the stability of these effects over time and their potential role in learning remain poorly understood. The goal of the present study was to further clarify previously reported age effects on "resting-state" networks, to test their reliability over time, and to assess their relation to subsequent learning during training. Resting-state fMRI data from 23 young (YA) and 20 older adults (OA) were acquired in 2 sessions 2 weeks apart. Graph-theoretic analyses identified both consistencies in network structure and differences in module composition between YA and OA, suggesting topological changes and less stability of functional network configuration with aging. Brain-wide, OA showed lower modularity and local efficiency compared to YA, consistent with the idea of age-related functional dedifferentiation, and these effects were replicable over time. At the level of individual networks, OA consistently showed greater participation and lower local efficiency and within-network connectivity in the cingulo-opercular network, as well as lower intra-network connectivity in the default-mode network and greater participation of the somato-sensorimotor network, suggesting age-related differential effects at the level of specialized brain modules. Finally, brain-wide network properties showed associations, albeit limited, with learning rates, as assessed with 10 days of computerized working memory training administered after the resting-state sessions, suggesting that baseline network configuration may influence subsequent learning outcomes. Identification of neural mechanisms associated with learning-induced plasticity is important for further clarifying whether and how such changes predict the magnitude and maintenance of training gains, as well as the extent and limits of cognitive transfer in both younger and older adults.
Collapse
Affiliation(s)
- Alexandru D. Iordan
- Department of Psychology, University of Michigan, Ann Arbor, MI, United States
| | - Katherine A. Cooke
- Department of Psychology, University of Michigan, Ann Arbor, MI, United States
| | - Kyle D. Moored
- Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
| | - Benjamin Katz
- Department of Human Development and Family Science, Virginia Tech, Blacksburg, VA, United States
| | | | - Susanne M. Jaeggi
- School of Education, University of California, Irvine, Irvine, CA, United States
| | - John Jonides
- Department of Psychology, University of Michigan, Ann Arbor, MI, United States
| | - Scott J. Peltier
- Functional MRI Laboratory, Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Thad A. Polk
- Department of Psychology, University of Michigan, Ann Arbor, MI, United States
| | | |
Collapse
|
81
|
Goh JOS, Hung HY, Su YS. A conceptual consideration of the free energy principle in cognitive maps: How cognitive maps help reduce surprise. PSYCHOLOGY OF LEARNING AND MOTIVATION 2018. [DOI: 10.1016/bs.plm.2018.09.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
82
|
Wu J, Skilling QM, Maruyama D, Li C, Ognjanovski N, Aton S, Zochowski M. Functional network stability and average minimal distance - A framework to rapidly assess dynamics of functional network representations. J Neurosci Methods 2017; 296:69-83. [PMID: 29294309 DOI: 10.1016/j.jneumeth.2017.12.021] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 12/21/2017] [Accepted: 12/24/2017] [Indexed: 01/31/2023]
Abstract
BACKGROUND Recent advances in neurophysiological recording techniques have increased both the spatial and temporal resolution of data. New methodologies are required that can handle large data sets in an efficient manner as well as to make quantifiable, and realistic, predictions about the global modality of the brain from under-sampled recordings. NEW METHOD To rectify both problems, we first propose an analytical modification to an existing functional connectivity algorithm, Average Minimal Distance (AMD), to rapidly capture functional network connectivity. We then complement this algorithm by introducing Functional Network Stability (FuNS), a metric that can be used to quickly assess the global network dynamic changes over time, without being constrained by the activities of a specific set of neurons. RESULTS We systematically test the performance of AMD and FuNS (1) on artificial spiking data with different statistical characteristics, (2) from spiking data generated using a neural network model, and (3) using in vivo data recorded from mouse hippocampus during fear learning. Our results show that AMD and FuNS are able to monitor the change in network dynamics during memory consolidation. COMPARISON WITH OTHER METHODS AMD outperforms traditional bootstrapping and cross-correlation (CC) methods in both significance and computation time. Simultaneously, FuNS provides a reliable way to establish a link between local structural network changes, global dynamics of network-wide representations activity, and behavior. CONCLUSIONS The AMD-FuNS framework should be universally useful in linking long time-scale, global network dynamics and cognitive behavior.
Collapse
Affiliation(s)
- Jiaxing Wu
- Applied Physics Program, University of Michigan, Ann Arbor, MI, 48109, USA
| | | | - Daniel Maruyama
- Department of Physics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Chenguang Li
- R.E.U program in Biophysics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Nicolette Ognjanovski
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Sara Aton
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Michal Zochowski
- Applied Physics Program, University of Michigan, Ann Arbor, MI, 48109, USA; Biophysics Program, University of Michigan, Ann Arbor, MI, 48109, USA; Department of Physics, University of Michigan, Ann Arbor, MI, 48109, USA.
| |
Collapse
|
83
|
Wig GS. Segregated Systems of Human Brain Networks. Trends Cogn Sci 2017; 21:981-996. [DOI: 10.1016/j.tics.2017.09.006] [Citation(s) in RCA: 128] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Revised: 09/06/2017] [Accepted: 09/11/2017] [Indexed: 12/17/2022]
|
84
|
Barbey AK. Network Neuroscience Theory of Human Intelligence. Trends Cogn Sci 2017; 22:8-20. [PMID: 29167088 DOI: 10.1016/j.tics.2017.10.001] [Citation(s) in RCA: 128] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Revised: 10/05/2017] [Accepted: 10/06/2017] [Indexed: 10/18/2022]
Abstract
An enduring aim of research in the psychological and brain sciences is to understand the nature of individual differences in human intelligence, examining the stunning breadth and diversity of intellectual abilities and the remarkable neurobiological mechanisms from which they arise. This Opinion article surveys recent neuroscience evidence to elucidate how general intelligence, g, emerges from individual differences in the network architecture of the human brain. The reviewed findings motivate new insights about how network topology and dynamics account for individual differences in g, represented by the Network Neuroscience Theory. According to this framework, g emerges from the small-world topology of brain networks and the dynamic reorganization of its community structure in the service of system-wide flexibility and adaptation.
Collapse
Affiliation(s)
- Aron K Barbey
- Decision Neuroscience Laboratory, University of Illinois at Urbana-Champaign, IL, USA; Department of Psychology, University of Illinois at Urbana-Champaign, IL, USA; Department of Bioengineering, University of Illinois at Urbana-Champaign, IL, USA; Neuroscience Program, University of Illinois at Urbana-Champaign, IL, USA; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, IL, USA; https://www.DecisionNeuroscienceLab.org.
| |
Collapse
|
85
|
Ball KR, Grant C, Mundy WR, Shafer TJ. A multivariate extension of mutual information for growing neural networks. Neural Netw 2017; 95:29-43. [DOI: 10.1016/j.neunet.2017.07.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2016] [Revised: 05/26/2017] [Accepted: 07/07/2017] [Indexed: 10/19/2022]
|
86
|
Moyer D, Gutman BA, Faskowitz J, Jahanshad N, Thompson PM. Continuous representations of brain connectivity using spatial point processes. Med Image Anal 2017; 41:32-39. [PMID: 28487128 PMCID: PMC5559296 DOI: 10.1016/j.media.2017.04.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Revised: 04/15/2017] [Accepted: 04/27/2017] [Indexed: 01/25/2023]
Abstract
We present a continuous model for structural brain connectivity based on the Poisson point process. The model treats each streamline curve in a tractography as an observed event in connectome space, here the product space of the gray matter/white matter interfaces. We approximate the model parameter via kernel density estimation. To deal with the heavy computational burden, we develop a fast parameter estimation method by pre-computing associated Legendre products of the data, leveraging properties of the spherical heat kernel. We show how our approach can be used to assess the quality of cortical parcellations with respect to connectivity. We further present empirical results that suggest that "discrete" connectomes derived from our model have substantially higher test-retest reliability compared to standard methods. In this, the expanded form of this paper for journal publication, we also explore parcellation free analysis techniques that avoid the use of explicit partitions of the cortical surface altogether. We provide an analysis of sex effects on our proposed continuous representation, demonstrating the utility of this approach.
Collapse
Affiliation(s)
- Daniel Moyer
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging & Informatics, University of Southern California, United States; Information Sciences Institute, University of Southern California, United States; Department of Computer Science, University of Southern California, United States.
| | - Boris A Gutman
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging & Informatics, University of Southern California, United States
| | - Joshua Faskowitz
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging & Informatics, University of Southern California, United States; Department of Psychological and Brain Sciences, Indiana University, United States
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging & Informatics, University of Southern California, United States
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging & Informatics, University of Southern California, United States.
| |
Collapse
|
87
|
Tan TL, Cheong SA. Statistical complexity is maximized in a small-world brain. PLoS One 2017; 12:e0183918. [PMID: 28850587 PMCID: PMC5574548 DOI: 10.1371/journal.pone.0183918] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2017] [Accepted: 08/14/2017] [Indexed: 01/03/2023] Open
Abstract
In this paper, we study a network of Izhikevich neurons to explore what it means for a brain to be at the edge of chaos. To do so, we first constructed the phase diagram of a single Izhikevich excitatory neuron, and identified a small region of the parameter space where we find a large number of phase boundaries to serve as our edge of chaos. We then couple the outputs of these neurons directly to the parameters of other neurons, so that the neuron dynamics can drive transitions from one phase to another on an artificial energy landscape. Finally, we measure the statistical complexity of the parameter time series, while the network is tuned from a regular network to a random network using the Watts-Strogatz rewiring algorithm. We find that the statistical complexity of the parameter dynamics is maximized when the neuron network is most small-world-like. Our results suggest that the small-world architecture of neuron connections in brains is not accidental, but may be related to the information processing that they do.
Collapse
Affiliation(s)
- Teck Liang Tan
- Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Republic of Singapore
- Complexity Institute, Nanyang Technological University, Block 2 Innovation Centre, Level 2 Unit 245, 18 Nanyang Drive, Singapore 637723, Republic of Singapore
- * E-mail:
| | - Siew Ann Cheong
- Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Republic of Singapore
- Complexity Institute, Nanyang Technological University, Block 2 Innovation Centre, Level 2 Unit 245, 18 Nanyang Drive, Singapore 637723, Republic of Singapore
| |
Collapse
|
88
|
Kalinosky BT, Berrios Barillas R, Schmit BD. Structurofunctional resting-state networks correlate with motor function in chronic stroke. NEUROIMAGE-CLINICAL 2017; 16:610-623. [PMID: 28971011 PMCID: PMC5619927 DOI: 10.1016/j.nicl.2017.07.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Revised: 06/12/2017] [Accepted: 07/03/2017] [Indexed: 12/26/2022]
Abstract
Purpose Motor function and recovery after stroke likely rely directly on the residual anatomical connections in the brain and its resting-state functional connectivity. Both structural and functional properties of cortical networks after stroke are revealed using multimodal magnetic resonance imaging (MRI). Specifically, functional connectivity MRI (fcMRI) can extract functional networks of the brain at rest, while structural connectivity can be estimated from white matter fiber orientations measured with high angular-resolution diffusion imaging (HARDI). A model that marries these two techniques may be the key to understanding functional recovery after stroke. In this study, a novel set of voxel-level measures of structurofunctional correlations (SFC) was developed and tested in a group of chronic stroke subjects. Methods A fully automated method is presented for modeling the structure-function relationship of brain connectivity in individuals with stroke. Brains from ten chronic stroke subjects and nine age-matched controls were imaged with a structural T1-weighted scan, resting-state fMRI, and HARDI. Each subject's T1-weighted image was nonlinearly registered to a T1-weighted 152-brain MNI template using a local histogram-matching technique that alleviates distortions caused by brain lesions. Fractional anisotropy maps and mean BOLD images of each subject were separately registered to the individual's T1-weighted image using affine transformations. White matter fiber orientations within each voxel were estimated with the q-ball model, which approximates the orientation distribution function (ODF) from the diffusion-weighted measurements. Deterministic q-ball tractography was performed in order to obtain a set of fiber trajectories. The new structurofunctional correlation method assigns each voxel a new BOLD time course based on a summation of its structural connections with a common fiber length interval. Then, the voxel's original time-course was correlated with this fiber-distance BOLD signal to derive a novel structurofunctional correlation coefficient. These steps were repeated for eight fiber distance intervals, and the maximum of these correlations was used to define an intrinsic structurofunctional correlation (iSFC) index. A network-based SFC map (nSFC) was also developed in order to enhance resting-state functional networks derived from conventional functional connectivity analyses. iSFC and nSFC maps were individually compared between stroke subjects and controls using a voxel-based two-tailed Student's t-test (alpha = 0.01). A linear regression was also performed between the SFC metrics and the Box and Blocks Score, a clinical measure of arm motor function. Results Significant decreases (p < 0.01) in iSFC were found in stroke subjects within functional hubs of the brain, including the precuneus, prefrontal cortex, posterior parietal cortex, and cingulate gyrus. Many of these differences were significantly correlated with the Box and Blocks Score. The nSFC maps of prefrontal networks in control subjects revealed localized increases within the cerebellum, and these enhancements were diminished in stroke subjects. This finding was further supported by a reduction in functional connectivity between the prefrontal cortex and cerebellum. Default-mode network nSFC maps were higher in the contralesional hemisphere of lower-functioning stroke subjects. Conclusion The results demonstrate that changes after a stroke in both intrinsic and network-based structurofunctional correlations at rest are correlated with motor function, underscoring the importance of residual structural connectivity in cortical networks.
Collapse
Affiliation(s)
| | | | - Brian D. Schmit
- Department of Biomedical Engineering, Marquette University, Milwaukee, WI, USA
- Corresponding author at: Department of Biomedical Engineering, Marquette University, PO Box 1881, Milwaukee, WI 53201-1881, USA.
| |
Collapse
|
89
|
Cognitive performance in healthy older adults relates to spontaneous switching between states of functional connectivity during rest. Sci Rep 2017; 7:5135. [PMID: 28698644 PMCID: PMC5506029 DOI: 10.1038/s41598-017-05425-7] [Citation(s) in RCA: 169] [Impact Index Per Article: 24.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2016] [Accepted: 05/30/2017] [Indexed: 12/25/2022] Open
Abstract
Growing evidence has shown that brain activity at rest slowly wanders through a repertoire of different states, where whole-brain functional connectivity (FC) temporarily settles into distinct FC patterns. Nevertheless, the functional role of resting-state activity remains unclear. Here, we investigate how the switching behavior of resting-state FC relates with cognitive performance in healthy older adults. We analyse resting-state fMRI data from 98 healthy adults previously categorized as being among the best or among the worst performers in a cohort study of >1000 subjects aged 50+ who underwent neuropsychological assessment. We use a novel approach focusing on the dominant FC pattern captured by the leading eigenvector of dynamic FC matrices. Recurrent FC patterns - or states - are detected and characterized in terms of lifetime, probability of occurrence and switching profiles. We find that poorer cognitive performance is associated with weaker FC temporal similarity together with altered switching between FC states. These results provide new evidence linking the switching dynamics of FC during rest with cognitive performance in later life, reinforcing the functional role of resting-state activity for effective cognitive processing.
Collapse
|
90
|
de Pasquale F, Della Penna S, Sabatini U, Caravasso Falletta C, Peran P. The anatomical scaffold underlying the functional centrality of known cortical hubs. Hum Brain Mapp 2017; 38:5141-5160. [PMID: 28681960 DOI: 10.1002/hbm.23721] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Revised: 05/17/2017] [Accepted: 06/27/2017] [Indexed: 11/10/2022] Open
Abstract
Cortical hubs play a fundamental role in the functional architecture of brain connectivity at rest. However, the anatomical scaffold underlying their centrality is still under debate. Certainly, the brain function and anatomy are significantly entwined through synaptogenesis and pruning mechanisms that continuously reshape structural and functional connections. Thus, if hubs are expected to exhibit a large number of direct anatomical connections with the rest of the brain, such a dense wiring is extremely inefficient in energetic terms. In this work, we investigate these aspects on fMRI and DTI data from a set of know resting-state networks, starting from the hypothesis that to promote integration, functional, and anatomical connections link different areas at different scales or hierarchies. Thus, we focused on the role of functional hubs in this hierarchical organization of functional and anatomical architectures. We found that these regions, from a structural point of view, are first linked to each other and successively to the rest of the brain. Thus, functionally central nodes seem to show few strong anatomical connections. These findings suggest an efficient strategy of the investigated cortical hubs in exploiting few direct anatomical connections to link functional hubs among each other that eventually reach the rest of the considered nodes through local indirect tracts. Hum Brain Mapp 38:5141-5160, 2017. © 2017 Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Francesco de Pasquale
- Faculty of Veterinary Medicine, University of Teramo, Italy.,Department of Radiology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Stefania Della Penna
- Department of Neuroscience Imaging and Clinical Science, University of Chieti, Italy
| | - Umberto Sabatini
- Neuroradiology Unit, Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Italy
| | | | - Patrice Peran
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France
| |
Collapse
|
91
|
Jacquemont T, De Vico Fallani F, Bertrand A, Epelbaum S, Routier A, Dubois B, Hampel H, Durrleman S, Colliot O. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiol Aging 2017; 55:177-189. [DOI: 10.1016/j.neurobiolaging.2017.03.023] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Revised: 03/17/2017] [Accepted: 03/19/2017] [Indexed: 01/01/2023]
|
92
|
Mirzakhalili E, Gourgou E, Booth V, Epureanu B. Synaptic Impairment and Robustness of Excitatory Neuronal Networks with Different Topologies. Front Neural Circuits 2017; 11:38. [PMID: 28659765 PMCID: PMC5468411 DOI: 10.3389/fncir.2017.00038] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Accepted: 05/22/2017] [Indexed: 11/13/2022] Open
Abstract
Synaptic deficiencies are a known hallmark of neurodegenerative diseases, but the diagnosis of impaired synapses on the cellular level is not an easy task. Nonetheless, changes in the system-level dynamics of neuronal networks with damaged synapses can be detected using techniques that do not require high spatial resolution. This paper investigates how the structure/topology of neuronal networks influences their dynamics when they suffer from synaptic loss. We study different neuronal network structures/topologies by specifying their degree distributions. The modes of the degree distribution can be used to construct networks that consist of rich clubs and resemble small world networks, as well. We define two dynamical metrics to compare the activity of networks with different structures: persistent activity (namely, the self-sustained activity of the network upon removal of the initial stimulus) and quality of activity (namely, percentage of neurons that participate in the persistent activity of the network). Our results show that synaptic loss affects the persistent activity of networks with bimodal degree distributions less than it affects random networks. The robustness of neuronal networks enhances when the distance between the modes of the degree distribution increases, suggesting that the rich clubs of networks with distinct modes keep the whole network active. In addition, a tradeoff is observed between the quality of activity and the persistent activity. For a range of distributions, both of these dynamical metrics are considerably high for networks with bimodal degree distribution compared to random networks. We also propose three different scenarios of synaptic impairment, which may correspond to different pathological or biological conditions. Regardless of the network structure/topology, results demonstrate that synaptic loss has more severe effects on the activity of the network when impairments are correlated with the activity of the neurons.
Collapse
Affiliation(s)
- Ehsan Mirzakhalili
- Department of Mechanical Engineering, University of MichiganAnn Arbor, MI, United States
| | - Eleni Gourgou
- Department of Mechanical Engineering, University of MichiganAnn Arbor, MI, United States.,Division of Geriatrics, Department of Internal Medicine, Medical School, University of MichiganAnn Arbor, MI, United States
| | - Victoria Booth
- Department of Mathematics, University of MichiganAnn Arbor, MI, United States.,Department of Anesthesiology, Medical School, University of MichiganAnn Arbor, MI, United States
| | - Bogdan Epureanu
- Department of Mechanical Engineering, University of MichiganAnn Arbor, MI, United States
| |
Collapse
|
93
|
Deco G, Kringelbach ML. Hierarchy of Information Processing in the Brain: A Novel ‘Intrinsic Ignition’ Framework. Neuron 2017; 94:961-968. [DOI: 10.1016/j.neuron.2017.03.028] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Revised: 01/30/2017] [Accepted: 03/22/2017] [Indexed: 11/25/2022]
|
94
|
Tremblay S, Iturria-Medina Y, Mateos-Pérez JM, Evans AC, De Beaumont L. Defining a multimodal signature of remote sports concussions. Eur J Neurosci 2017; 46:1956-1967. [DOI: 10.1111/ejn.13583] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Revised: 03/28/2017] [Accepted: 03/30/2017] [Indexed: 12/11/2022]
Affiliation(s)
| | - Yasser Iturria-Medina
- Montreal Neurological Institute; McGill University; Montreal QC Canada
- Ludmer Center for Neuroinformatics and Mental Health; McGill University; Montreal QC Canada
| | - José María Mateos-Pérez
- Montreal Neurological Institute; McGill University; Montreal QC Canada
- Ludmer Center for Neuroinformatics and Mental Health; McGill University; Montreal QC Canada
| | - Alan C. Evans
- Montreal Neurological Institute; McGill University; Montreal QC Canada
- Ludmer Center for Neuroinformatics and Mental Health; McGill University; Montreal QC Canada
| | - Louis De Beaumont
- Centre de Recherche de l'Hôpital du Sacré-Coeur de Montréal; Montreal QC Canada
- Department of Surgery; Université de Montréal; Montreal QC H3C 3J7 Canada
| |
Collapse
|
95
|
Kim SY, Lim W. Emergence of ultrafast sparsely synchronized rhythms and their responses to external stimuli in an inhomogeneous small-world complex neuronal network. Neural Netw 2017; 93:57-75. [PMID: 28544891 DOI: 10.1016/j.neunet.2017.04.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2016] [Revised: 02/22/2017] [Accepted: 04/11/2017] [Indexed: 10/19/2022]
Abstract
We consider an inhomogeneous small-world network (SWN) composed of inhibitory short-range (SR) and long-range (LR) interneurons, and investigate the effect of network architecture on emergence of synchronized brain rhythms by varying the fraction of LR interneurons plong. The betweenness centralities of the LR and SR interneurons (characterizing the potentiality in controlling communication between other interneurons) are distinctly different. Hence, in view of the betweenness, SWNs we consider are inhomogeneous, unlike the "canonical" Watts-Strogatz SWN with nearly the same betweenness centralities. For small plong, the load of communication traffic is much concentrated on a few LR interneurons. However, as plong is increased, the number of LR connections (coming from LR interneurons) increases, and then the load of communication traffic is less concentrated on LR interneurons, which leads to better efficiency of global communication between interneurons. Sparsely synchronized rhythms are thus found to emerge when passing a small critical value plong(c)(≃0.16). The population frequency of the sparsely synchronized rhythm is ultrafast (higher than 100 Hz), while the mean firing rate of individual interneurons is much lower (∼30 Hz) due to stochastic and intermittent neural discharges. These dynamical behaviors in the inhomogeneous SWN are also compared with those in the homogeneous Watts-Strogatz SWN, in connection with their network topologies. Particularly, we note that the main difference between the two types of SWNs lies in the distribution of betweenness centralities. Unlike the case of the Watts-Strogatz SWN, dynamical responses to external stimuli vary depending on the type of stimulated interneurons in the inhomogeneous SWN. We consider two cases of external time-periodic stimuli applied to sub-populations of the LR and SR interneurons, respectively. Dynamical responses (such as synchronization suppression and enhancement) to these two cases of stimuli are studied and discussed in relation to the betweenness centralities of stimulated interneurons, representing the effectiveness for transfer of stimulation effect in the whole network.
Collapse
Affiliation(s)
- Sang-Yoon Kim
- Institute for Computational Neuroscience and Department of Science Education, Daegu National University of Education, Daegu 42411, Republic of Korea.
| | - Woochang Lim
- Institute for Computational Neuroscience and Department of Science Education, Daegu National University of Education, Daegu 42411, Republic of Korea.
| |
Collapse
|
96
|
Olejarczyk E, Marzetti L, Pizzella V, Zappasodi F. Comparison of connectivity analyses for resting state EEG data. J Neural Eng 2017; 14:036017. [PMID: 28378705 DOI: 10.1088/1741-2552/aa6401] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
OBJECTIVE In the present work, a nonlinear measure (transfer entropy, TE) was used in a multivariate approach for the analysis of effective connectivity in high density resting state EEG data in eyes open and eyes closed. Advantages of the multivariate approach in comparison to the bivariate one were tested. Moreover, the multivariate TE was compared to an effective linear measure, i.e. directed transfer function (DTF). Finally, the existence of a relationship between the information transfer and the level of brain synchronization as measured by phase synchronization value (PLV) was investigated. APPROACH The comparison between the connectivity measures, i.e. bivariate versus multivariate TE, TE versus DTF, TE versus PLV, was performed by means of statistical analysis of indexes based on graph theory. MAIN RESULTS The multivariate approach is less sensitive to false indirect connections with respect to the bivariate estimates. The multivariate TE differentiated better between eyes closed and eyes open conditions compared to DTF. Moreover, the multivariate TE evidenced non-linear phenomena in information transfer, which are not evidenced by the use of DTF. We also showed that the target of information flow, in particular the frontal region, is an area of greater brain synchronization. SIGNIFICANCE Comparison of different connectivity analysis methods pointed to the advantages of nonlinear methods, and indicated a relationship existing between the flow of information and the level of synchronization of the brain.
Collapse
Affiliation(s)
- Elzbieta Olejarczyk
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland
| | | | | | | |
Collapse
|
97
|
Mehta-Pandejee G, Robinson PA, Henderson JA, Aquino KM, Sarkar S. Inference of direct and multistep effective connectivities from functional connectivity of the brain and of relationships to cortical geometry. J Neurosci Methods 2017; 283:42-54. [PMID: 28342831 DOI: 10.1016/j.jneumeth.2017.03.014] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 03/15/2017] [Accepted: 03/18/2017] [Indexed: 01/26/2023]
Abstract
BACKGROUND The problem of inferring effective brain connectivity from functional connectivity is under active investigation, and connectivity via multistep paths is poorly understood. NEW METHOD A method is presented to calculate the direct effective connection matrix (deCM), which embodies direct connection strengths between brain regions, from functional CMs (fCMs) by minimizing the difference between an experimental fCM and one calculated via neural field theory from an ansatz deCM based on an experimental anatomical CM. RESULTS The best match between fCMs occurs close to a critical point, consistent with independent published stability estimates. Residual mismatch between fCMs is identified to be largely due to interhemispheric connections that are poorly estimated in an initial ansatz deCM due to experimental limitations; improved ansatzes substantially reduce the mismatch and enable interhemispheric connections to be estimated. Various levels of significant multistep connections are then imaged via the neural field theory (NFT) result that these correspond to powers of the deCM; these are shown to be predictable from geometric distances between regions. COMPARISON WITH EXISTING METHODS This method gives insight into direct and multistep effective connectivity from fCMs and relating to physiology and brain geometry. This contrasts with other methods, which progressively adjust connections without an overarching physiologically based framework to deal with multistep or poorly estimated connections. CONCLUSIONS deCMs can be usefully estimated using this method and the results enable multistep connections to be investigated systematically.
Collapse
Affiliation(s)
- Grishma Mehta-Pandejee
- School of Physics, The University of Sydney, Sydney, New South Wales 2006, Australia; Center of Excellence for Integrative Brain Function, The University of Sydney, New South Wales 2006, Australia.
| | - P A Robinson
- School of Physics, The University of Sydney, Sydney, New South Wales 2006, Australia; Center of Excellence for Integrative Brain Function, The University of Sydney, New South Wales 2006, Australia
| | - James A Henderson
- School of Physics, The University of Sydney, Sydney, New South Wales 2006, Australia; Center of Excellence for Integrative Brain Function, The University of Sydney, New South Wales 2006, Australia; School of Information Technology and Electrical Engineering, University of Queensland, St Lucia, Queensland 4072, Australia
| | - K M Aquino
- School of Physics, The University of Sydney, Sydney, New South Wales 2006, Australia; Center of Excellence for Integrative Brain Function, The University of Sydney, New South Wales 2006, Australia; Sir Peter Mansfield Imaging Center, University of Nottingham, Nottingham NG7 2RD, United Kingdom
| | - Somwrita Sarkar
- Center of Excellence for Integrative Brain Function, The University of Sydney, New South Wales 2006, Australia; Design Lab, University of Sydney, Sydney, New South Wales 2006, Australia
| |
Collapse
|
98
|
Xu J, Zhang J, Zhang J, Wang Y, Zhang Y, Wang J, Li G, Hu Q, Zhang Y. Abnormalities in Structural Covariance of Cortical Gyrification in Parkinson's Disease. Front Neuroanat 2017; 11:12. [PMID: 28326021 PMCID: PMC5339339 DOI: 10.3389/fnana.2017.00012] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Accepted: 02/17/2017] [Indexed: 01/21/2023] Open
Abstract
Although abnormal cortical morphology and connectivity between brain regions (structural covariance) have been reported in Parkinson's disease (PD), the topological organizations of large-scale structural brain networks are still poorly understood. In this study, we investigated large-scale structural brain networks in a sample of 37 PD patients and 34 healthy controls (HC) by assessing the structural covariance of cortical gyrification with local gyrification index (lGI). We demonstrated prominent small-world properties of the structural brain networks for both groups. Compared with the HC group, PD patients showed significantly increased integrated characteristic path length and integrated clustering coefficient, as well as decreased integrated global efficiency in structural brain networks. Distinct distributions of hub regions were identified between the two groups, showing more hub regions in the frontal cortex in PD patients. Moreover, the modular analyses revealed significantly decreased integrated regional efficiency in lateral Fronto-Insula-Temporal module, and increased integrated regional efficiency in Parieto-Temporal module in the PD group as compared to the HC group. In summary, our study demonstrated altered topological properties of structural networks at a global, regional and modular level in PD patients. These findings suggests that the structural networks of PD patients have a suboptimal topological organization, resulting in less effective integration of information between brain regions.
Collapse
Affiliation(s)
- Jinping Xu
- Key Laboratory for Neuroinformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengdu, China; Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhen, China
| | - Jiuquan Zhang
- Department of Radiology, Southwest Hospital, Third Military Medical University Chongqing, China
| | - Jinlei Zhang
- Key Laboratory for Neuroinformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu, China
| | - Yue Wang
- Key Laboratory for Neuroinformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu, China
| | - Yanling Zhang
- Department of Neurology, Southwest Hospital, Third Military Medical University Chongqing, China
| | - Jian Wang
- Department of Radiology, Southwest Hospital, Third Military Medical University Chongqing, China
| | - Guanglin Li
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhen, China; Key Laboratory of Human-Machine Intelligence Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhen, China
| | - Qingmao Hu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhen, China; Key Laboratory of Human-Machine Intelligence Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhen, China
| | - Yuanchao Zhang
- Key Laboratory for Neuroinformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengdu, China; Center for Information in Medicine, University of Electronic Science and Technology of ChinaChengdu, China
| |
Collapse
|
99
|
O'Donoghue S, Holleran L, Cannon DM, McDonald C. Anatomical dysconnectivity in bipolar disorder compared with schizophrenia: A selective review of structural network analyses using diffusion MRI. J Affect Disord 2017; 209:217-228. [PMID: 27930915 DOI: 10.1016/j.jad.2016.11.015] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Revised: 08/16/2016] [Accepted: 11/14/2016] [Indexed: 11/15/2022]
Abstract
BACKGROUND The dysconnectivity hypothesis suggests that psychotic illnesses arise not from regionally specific focal pathophysiology, but rather from impaired neuroanatomical integration across networks of brain regions. Decreased white matter organization has been hypothesized to be a feature of psychotic illnesses in general, which is supported by meta-analyses of DTI studies in bipolar disorder and schizophrenia. Although many diffusion MRI studies investigate bipolar disorder and schizophrenia alone, relatively few studies directly compare structural features in these psychotic illnesses. Recently, the application of graph theory analyses to DTI data has supported the dysconnectivity hypothesis in bipolar disorder and schizophrenia, employing topological properties to assess neuroanatomical dysconnectivity. METHODS This selective review evaluates white matter alterations using Diffusion Tensor Imaging (DTI) in bipolar disorder and schizophrenia, with a focus upon direct comparison DTI studies in both psychotic illnesses. We then expand in more detail on the development of network analyses and the application of these techniques in bipolar disorder and schizophrenia. RESULTS Converging evidence indicates that frontal connectivity alterations are common to both disorders, with prominent fronto-temporal deficits identified in schizophrenia and inter-hemispheric and limbic alterations reported in bipolar disorder. LIMITATIONS In bipolar disorder, most connectome reports use cortical maps alone, which given the importance of the limbic system in emotional regulation may limit the scope of network approaches in mood disorders. CONCLUSIONS Further direct connectivity comparisons between these psychotic illnesses may assist in unravelling the neuroanatomical deviations underpinning the overlapping features of psychosis and cognitive impairment, and the more diagnostically distinctive features of affective disturbance in bipolar disorder and deficit syndrome in schizophrenia.
Collapse
Affiliation(s)
- Stefani O'Donoghue
- The Centre for Neuroimaging & Cognitive Genomics (NICOG) and NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland.
| | - Laurena Holleran
- The Centre for Neuroimaging & Cognitive Genomics (NICOG) and NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland
| | - Dara M Cannon
- The Centre for Neuroimaging & Cognitive Genomics (NICOG) and NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland
| | - Colm McDonald
- The Centre for Neuroimaging & Cognitive Genomics (NICOG) and NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland
| |
Collapse
|
100
|
Xie S, Yang J, Zhang Z, Zhao C, Bi Y, Zhao Q, Pan H, Gong G. The Effects of the X Chromosome on Intrinsic Functional Connectivity in the Human Brain: Evidence from Turner Syndrome Patients. Cereb Cortex 2017; 27:474-484. [PMID: 26494797 DOI: 10.1093/cercor/bhv240] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
Turner syndrome (TS), a disorder caused by the congenital absence of one of the 2 X chromosomes in female humans, provides a valuable human "knockout model" for studying the functions of the X chromosome. At present, it remains unknown whether and how the loss of the X chromosome influences intrinsic functional connectivity (FC), a fundamental phenotype of the human brain. To address this, we performed resting-state functional magnetic resonance imaging and specific cognitive assessments on 22 TS patients and 17 age-matched control girls. A novel data-driven approach was applied to identify the disrupted patterns of intrinsic FC in TS. The TS girls exhibited significantly reduced whole-brain FC strength within the bilateral postcentral gyrus/intraparietal sulcus, angular gyrus, and cuneus and the right cerebellum. Furthermore, a specific functional subnetwork was identified in which the intrinsic FC between nodes was mostly reduced in TS patients. Particularly, this subnetwork is composed of 3 functional modules, and the disruption of intrinsic FC within one of these modules was associated with the deficits of TS patients in math-related cognition. Taken together, these findings provide novel insight into how the X chromosome affects the human brain and cognition, and emphasize an important role of X-linked genes in intrinsic neural coupling.
Collapse
Affiliation(s)
| | - Jiaotian Yang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Zhixin Zhang
- Department of Pediatrics, China-Japan Friendship Hospital, Beijing 100029, China
| | - Chenxi Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yanchao Bi
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Qiuling Zhao
- Department of Pediatrics, China-Japan Friendship Hospital, Beijing 100029, China
| | - Hui Pan
- Key Laboratory of Endocrinology, Ministry of Health, Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| |
Collapse
|