201
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Deng L, Wang Y. Hybrid diffusion tensor imaging feature-based AD classification. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2021; 29:151-169. [PMID: 33325450 DOI: 10.3233/xst-200771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
BACKGROUND Effective detection of Alzheimer's disease (AD) is still difficult in clinical practice. Therefore, establishment of AD detection model by means of machine learning is of great significance to assist AD diagnosis. OBJECTIVE To investigate and test a new detection model aiming to help doctors diagnose AD more accurately. METHODS Diffusion tensor images and the corresponding T1w images acquired from subjects (AD = 98, normal control (NC) = 100) are used to construct brain networks. Then, 9 types features (198×90×9 in total) are extracted from the 3D brain networks by a graph theory method. Features with low correction in both groups are selected through the Pearson correlation analysis. Finally, the selected features (198×33, 198×26, 198×30, 198×42, 198×36, 198×23, 198×29, 198×14, 198×25) are separately used into train 3 machine learning classifier based detection models in which 60% of study subjects are used for training, 20% for validation and 20% for testing. RESULTS The best detection accuracy levels of 3 models are 90%, 98% and 90% with the corresponding sensitivity of 92%, 96%, and 72% and specificity of 88%, 100% and 94% when using a random forest classifier trained with the Shortest Path Length (SPL) features (198×14), a support vector machine trained with the Degree Centrality features (198×33), and a convolution neural network trained with SPL features, respectively. CONCLUSIONS This study demonstrates that the new method and models not only improve the accuracy of detecting AD, but also avoid bias caused by the method of direct dimensionality reduction from high dimensional data.
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Affiliation(s)
- Lan Deng
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Yuanjun Wang
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
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202
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Gutierrez CE, Skibbe H, Nakae K, Tsukada H, Lienard J, Watakabe A, Hata J, Reisert M, Woodward A, Yamaguchi Y, Yamamori T, Okano H, Ishii S, Doya K. Optimization and validation of diffusion MRI-based fiber tracking with neural tracer data as a reference. Sci Rep 2020; 10:21285. [PMID: 33339834 PMCID: PMC7749185 DOI: 10.1038/s41598-020-78284-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Accepted: 11/24/2020] [Indexed: 11/29/2022] Open
Abstract
Diffusion-weighted magnetic resonance imaging (dMRI) allows non-invasive investigation of whole-brain connectivity, which can reveal the brain’s global network architecture and also abnormalities involved in neurological and mental disorders. However, the reliability of connection inferences from dMRI-based fiber tracking is still debated, due to low sensitivity, dominance of false positives, and inaccurate and incomplete reconstruction of long-range connections. Furthermore, parameters of tracking algorithms are typically tuned in a heuristic way, which leaves room for manipulation of an intended result. Here we propose a general data-driven framework to optimize and validate parameters of dMRI-based fiber tracking algorithms using neural tracer data as a reference. Japan’s Brain/MINDS Project provides invaluable datasets containing both dMRI and neural tracer data from the same primates. A fundamental difference when comparing dMRI-based tractography and neural tracer data is that the former cannot specify the direction of connectivity; therefore, evaluating the fitting of dMRI-based tractography becomes challenging. The framework implements multi-objective optimization based on the non-dominated sorting genetic algorithm II. Its performance is examined in two experiments using data from ten subjects for optimization and six for testing generalization. The first uses a seed-based tracking algorithm, iFOD2, and objectives for sensitivity and specificity of region-level connectivity. The second uses a global tracking algorithm and a more refined set of objectives: distance-weighted coverage, true/false positive ratio, projection coincidence, and commissural passage. In both experiments, with optimized parameters compared to default parameters, fiber tracking performance was significantly improved in coverage and fiber length. Improvements were more prominent using global tracking with refined objectives, achieving an average fiber length from 10 to 17 mm, voxel-wise coverage of axonal tracts from 0.9 to 15%, and the correlation of target areas from 40 to 68%, while minimizing false positives and impossible cross-hemisphere connections. Optimized parameters showed good generalization capability for test brain samples in both experiments, demonstrating the flexible applicability of our framework to different tracking algorithms and objectives. These results indicate the importance of data-driven adjustment of fiber tracking algorithms and support the validity of dMRI-based tractography, if appropriate adjustments are employed.
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Affiliation(s)
- Carlos Enrique Gutierrez
- Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan.
| | - Henrik Skibbe
- Brain Image Analysis Unit, RIKEN Center for Brain Science, Wako, Japan
| | - Ken Nakae
- Integrated Systems Biology Laboratory, Department of Systems Science, Graduate School of Informatics, Kyoto University, Kyoto, Japan
| | - Hiromichi Tsukada
- Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
| | - Jean Lienard
- Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
| | - Akiya Watakabe
- Laboratory for Molecular Analysis of Higher Brain Function, RIKEN Center for Brain Science, Wako, Japan
| | - Junichi Hata
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Japan.,Division of Regenerative Medicine, The Jikei University School of Medicine, Tokyo, Japan.,Department of Physiology, Keio University School of Medicine, Tokyo, Japan
| | - Marco Reisert
- Department of Medical Physics and Stereotaxy, Medical Center, Faculty of Medicine, University Freiburg, Freiburg, Germany
| | | | - Yoko Yamaguchi
- Applied Electronics Laboratory, Kanazawa Institute of Technology, Nonoichi, Japan.,Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.,Laboratory for Cognitive Brain Mapping, RIKEN Center for Brain Science, Wako, Japan
| | - Tetsuo Yamamori
- Laboratory for Molecular Analysis of Higher Brain Function, RIKEN Center for Brain Science, Wako, Japan
| | - Hideyuki Okano
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Japan.,Department of Physiology, Keio University School of Medicine, Tokyo, Japan
| | - Shin Ishii
- Integrated Systems Biology Laboratory, Department of Systems Science, Graduate School of Informatics, Kyoto University, Kyoto, Japan
| | - Kenji Doya
- Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
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203
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Valdés Hernández MDC, Smith K, Bastin ME, Nicole Amft E, Ralston SH, Wardlaw JM, Wiseman SJ. Brain network reorganisation and spatial lesion distribution in systemic lupus erythematosus. Lupus 2020; 30:285-298. [PMID: 33307988 PMCID: PMC7854491 DOI: 10.1177/0961203320979045] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Objective This work investigates network organisation of brain structural connectivity
in systemic lupus erythematosus (SLE) relative to healthy controls and its
putative association with lesion distribution and disease indicators. Methods White matter hyperintensity (WMH) segmentation and connectomics were
performed in 47 patients with SLE and 47 healthy age-matched controls from
structural and diffusion MRI data. Network nodes were divided into
hierarchical tiers based on numbers of connections. Results were compared
between patients and controls to assess for differences in brain network
organisation. Voxel-based analyses of the spatial distribution of WMH in
relation to network measures and SLE disease indicators were conducted. Results Despite inter-individual differences in brain network organization observed
across the study sample, the connectome networks of SLE patients had larger
proportion of connections in the peripheral nodes. SLE patients had
statistically larger numbers of links in their networks with generally
larger fractional anisotropy weights (i.e. a measure of white matter
integrity) and less tendency to aggregate than those of healthy controls.
The voxels exhibiting connectomic differences were coincident with WMH
clusters, particularly the left hemisphere’s intersection between the
anterior limb of the internal and external capsules. Moreover, these voxels
also associated more strongly with disease indicators. Conclusion Our results indicate network differences reflective of compensatory
reorganization of the neural circuits, reflecting adaptive or extended
neuroplasticity in SLE.
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Affiliation(s)
- Maria Del C Valdés Hernández
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Keith Smith
- Usher Institute for Population Health Science and Informatics, University of Edinburgh, Edinburgh, UK.,Health Data Research UK, London, UK
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - E Nicole Amft
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Stuart H Ralston
- Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, UK
| | - Joanna M Wardlaw
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Stewart J Wiseman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
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204
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Protachevicz PR, Iarosz KC, Caldas IL, Antonopoulos CG, Batista AM, Kurths J. Influence of Autapses on Synchronization in Neural Networks With Chemical Synapses. Front Syst Neurosci 2020; 14:604563. [PMID: 33328913 PMCID: PMC7734146 DOI: 10.3389/fnsys.2020.604563] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 11/05/2020] [Indexed: 11/29/2022] Open
Abstract
A great deal of research has been devoted on the investigation of neural dynamics in various network topologies. However, only a few studies have focused on the influence of autapses, synapses from a neuron onto itself via closed loops, on neural synchronization. Here, we build a random network with adaptive exponential integrate-and-fire neurons coupled with chemical synapses, equipped with autapses, to study the effect of the latter on synchronous behavior. We consider time delay in the conductance of the pre-synaptic neuron for excitatory and inhibitory connections. Interestingly, in neural networks consisting of both excitatory and inhibitory neurons, we uncover that synchronous behavior depends on their synapse type. Our results provide evidence on the synchronous and desynchronous activities that emerge in random neural networks with chemical, inhibitory and excitatory synapses where neurons are equipped with autapses.
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Affiliation(s)
| | - Kelly C Iarosz
- Faculdade de Telêmaco Borba, FATEB, Telêmaco Borba, Brazil.,Graduate Program in Chemical Engineering, Federal University of Technology Paraná, Ponta Grossa, Brazil
| | - Iberê L Caldas
- Institute of Physics, University of São Paulo, São Paulo, Brazil
| | - Chris G Antonopoulos
- Department of Mathematical Sciences, University of Essex, Colchester, United Kingdom
| | - Antonio M Batista
- Institute of Physics, University of São Paulo, São Paulo, Brazil.,Department of Mathematics and Statistics, State University of Ponta Grossa, Ponta Grossa, Brazil
| | - Jurgen Kurths
- Department Complexity Science, Potsdam Institute for Climate Impact Research, Potsdam, Germany.,Department of Physics, Humboldt University, Berlin, Germany.,Centre for Analysis of Complex Systems, Sechenov First Moscow State Medical University, Moscow, Russia
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205
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Howell AL, Osher DE, Li J, Saygin ZM. The intrinsic neonatal hippocampal network: rsfMRI findings. J Neurophysiol 2020; 124:1458-1468. [DOI: 10.1152/jn.00362.2020] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Although both animal data and human data suggest that the hippocampus is immature at birth, to date, there are no direct assessments of human hippocampal functional connectivity (FC) very early in life. Our study explores the FC of the hippocampus to the cortex at birth, allowing insight into the development of human memory systems. In particular, we find that adults and neonates exhibit vastly different hippocampal connectivity profiles—a finding that likely has large developmental implications.
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Affiliation(s)
- Athena L. Howell
- Department of Neuroscience, The Ohio State University, Columbus, Ohio
| | - David E. Osher
- Department of Psychology, The Ohio State University, Columbus, Ohio
| | - Jin Li
- Department of Psychology, The Ohio State University, Columbus, Ohio
| | - Zeynep M. Saygin
- Department of Psychology, The Ohio State University, Columbus, Ohio
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206
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Shadi K, Dyer E, Dovrolis C. Multisensory integration in the mouse cortical connectome using a network diffusion model. Netw Neurosci 2020; 4:1030-1054. [PMID: 33195947 PMCID: PMC7655044 DOI: 10.1162/netn_a_00164] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 08/03/2020] [Indexed: 01/05/2023] Open
Abstract
Having a structural network representation of connectivity in the brain is instrumental in analyzing communication dynamics and neural information processing. In this work, we make steps towards understanding multisensory information flow and integration using a network diffusion approach. In particular, we model the flow of evoked activity, initiated by stimuli at primary sensory regions, using the asynchronous linear threshold (ALT) diffusion model. The ALT model captures how evoked activity that originates at a given region of the cortex “ripples through” other brain regions (referred to as an activation cascade). We find that a small number of brain regions–the claustrum and the parietal temporal cortex being at the top of the list–are involved in almost all cortical sensory streams. This suggests that the cortex relies on an hourglass architecture to first integrate and compress multisensory information from multiple sensory regions, before utilizing that lower dimensionality representation in higher level association regions and more complex cognitive tasks. Having a structural network representation of connectivity in the brain is instrumental in analyzing communication dynamics and neural information processing. In this work, we make steps towards understanding multisensory information flow and integration using a network diffusion approach. In particular, we model the flow of evoked activity, initiated by stimuli at primary sensory regions, using the asynchronous linear threshold (ALT) diffusion model. The ALT model captures how evoked activity that originates at a given region of the cortex “ripples through” other brain regions (referred to as an activation cascade). We apply the ALT model to the mouse connectome provided by the Allen Institute for Brain Science. A first result, using functional datasets based on voltage-sensitive dye (VSD) imaging, is that the ALT model, despite its simplicity, predicts the temporal ordering of each sensory activation cascade quite accurately. We further apply this model to study multisensory integration and find that a small number of brain regionsthe claustrum and the parietal temporal cortex being at the top of the listare involved in almost all cortical sensory streams. This suggests that the cortex relies on an hourglass architecture to first integrate and compress multisensory information from multiple sensory regions, before utilizing that lower dimensionality representation in higher level association regions and more complex cognitive tasks.
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Affiliation(s)
- Kamal Shadi
- School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA
| | - Eva Dyer
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
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207
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Birle C, Slavoaca D, Balea M, Livint Popa L, Muresanu I, Stefanescu E, Vacaras V, Dina C, Strilciuc S, Popescu BO, Muresanu DF. Cognitive function: holarchy or holacracy? Neurol Sci 2020; 42:89-99. [PMID: 33070201 DOI: 10.1007/s10072-020-04737-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 09/17/2020] [Indexed: 12/24/2022]
Abstract
Cognition is the most complex function of the brain. When exploring the inner workings of cognitive processes, it is crucial to understand the complexity of the brain's dynamics. This paper aims to describe the integrated framework of the cognitive function, seen as the result of organization and interactions between several systems and subsystems. We briefly describe several organizational concepts, spanning from the reductionist hierarchical approach, up to the more dynamic theory of open complex systems. The homeostatic regulation of the mechanisms responsible for cognitive processes is showcased as a dynamic interplay between several anticorrelated mechanisms, which can be found at every level of the brain's organization, from molecular and cellular level to large-scale networks (e.g., excitation-inhibition, long-term plasticity-long-term depression, synchronization-desynchronization, segregation-integration, order-chaos). We support the hypothesis that cognitive function is the consequence of multiple network interactions, integrating intricate relationships between several systems, in addition to neural circuits.
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Affiliation(s)
- Codruta Birle
- Department of Neurosciences, "Iuliu Hatieganu" University of Medicine and Pharmacy, No. 37 Mircea Eliade Street, 400486, Cluj-Napoca, Romania.,"RoNeuro" Institute for Neurological Research and Diagnostic, Cluj-Napoca, Romania
| | - Dana Slavoaca
- Department of Neurosciences, "Iuliu Hatieganu" University of Medicine and Pharmacy, No. 37 Mircea Eliade Street, 400486, Cluj-Napoca, Romania. .,"RoNeuro" Institute for Neurological Research and Diagnostic, Cluj-Napoca, Romania.
| | - Maria Balea
- Department of Neurosciences, "Iuliu Hatieganu" University of Medicine and Pharmacy, No. 37 Mircea Eliade Street, 400486, Cluj-Napoca, Romania.,"RoNeuro" Institute for Neurological Research and Diagnostic, Cluj-Napoca, Romania
| | - Livia Livint Popa
- Department of Neurosciences, "Iuliu Hatieganu" University of Medicine and Pharmacy, No. 37 Mircea Eliade Street, 400486, Cluj-Napoca, Romania.,"RoNeuro" Institute for Neurological Research and Diagnostic, Cluj-Napoca, Romania
| | - Ioana Muresanu
- Department of Neurosciences, "Iuliu Hatieganu" University of Medicine and Pharmacy, No. 37 Mircea Eliade Street, 400486, Cluj-Napoca, Romania.,"RoNeuro" Institute for Neurological Research and Diagnostic, Cluj-Napoca, Romania
| | - Emanuel Stefanescu
- "RoNeuro" Institute for Neurological Research and Diagnostic, Cluj-Napoca, Romania
| | - Vitalie Vacaras
- Department of Neurosciences, "Iuliu Hatieganu" University of Medicine and Pharmacy, No. 37 Mircea Eliade Street, 400486, Cluj-Napoca, Romania.,"RoNeuro" Institute for Neurological Research and Diagnostic, Cluj-Napoca, Romania
| | - Constantin Dina
- Department of Clinical Neurosciences, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| | - Stefan Strilciuc
- Department of Neurosciences, "Iuliu Hatieganu" University of Medicine and Pharmacy, No. 37 Mircea Eliade Street, 400486, Cluj-Napoca, Romania.,"RoNeuro" Institute for Neurological Research and Diagnostic, Cluj-Napoca, Romania
| | - Bogdan Ovidiu Popescu
- Department of Radiology, Faculty of Medicine, "Ovidius" University, Constanta, Romania
| | - Dafin F Muresanu
- Department of Neurosciences, "Iuliu Hatieganu" University of Medicine and Pharmacy, No. 37 Mircea Eliade Street, 400486, Cluj-Napoca, Romania.,"RoNeuro" Institute for Neurological Research and Diagnostic, Cluj-Napoca, Romania
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208
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Andjelković M, Tadić B, Melnik R. The topology of higher-order complexes associated with brain hubs in human connectomes. Sci Rep 2020; 10:17320. [PMID: 33057130 PMCID: PMC7560876 DOI: 10.1038/s41598-020-74392-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 09/28/2020] [Indexed: 02/07/2023] Open
Abstract
Higher-order connectivity in complex systems described by simplexes of different orders provides a geometry for simplex-based dynamical variables and interactions. Simplicial complexes that constitute a functional geometry of the human connectome can be crucial for the brain complex dynamics. In this context, the best-connected brain areas, designated as hub nodes, play a central role in supporting integrated brain function. Here, we study the structure of simplicial complexes attached to eight global hubs in the female and male connectomes and identify the core networks among the affected brain regions. These eight hubs (Putamen, Caudate, Hippocampus and Thalamus-Proper in the left and right cerebral hemisphere) are the highest-ranking according to their topological dimension, defined as the number of simplexes of all orders in which the node participates. Furthermore, we analyse the weight-dependent heterogeneity of simplexes. We demonstrate changes in the structure of identified core networks and topological entropy when the threshold weight is gradually increased. These results highlight the role of higher-order interactions in human brain networks and provide additional evidence for (dis)similarity between the female and male connectomes.
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Affiliation(s)
- Miroslav Andjelković
- Department of Theoretical Physics, Jožef Stefan Institute, 1000, Ljubljana, Slovenia
- Department of Thermal Engineering and Energy, Vinča Institute of Nuclear Sciences - National Institute of the Republic of Serbia, University of Belgrade, 1100, Belgrade, Serbia
| | - Bosiljka Tadić
- Department of Theoretical Physics, Jožef Stefan Institute, 1000, Ljubljana, Slovenia.
- Complexity Science Hub, Josefstaedter Strasse 39, Vienna, Austria.
| | - Roderick Melnik
- MS2Discovery Interdisciplinary Research Institute, M2NeT Laboratory and Department of Mathematics, Wilfrid Laurier University, 75 University Ave. W, Waterloo, ON, N2L 3C5, Canada
- BCAM - Basque Center for Applied Mathematics, Alameda de Mazarredo 14, 48009, Bilbao, Spain
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209
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Hill AT, Hadas I, Zomorrodi R, Voineskos D, Farzan F, Fitzgerald PB, Blumberger DM, Daskalakis ZJ. Modulation of functional network properties in major depressive disorder following electroconvulsive therapy (ECT): a resting-state EEG analysis. Sci Rep 2020; 10:17057. [PMID: 33051528 PMCID: PMC7555809 DOI: 10.1038/s41598-020-74103-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 08/12/2020] [Indexed: 12/18/2022] Open
Abstract
Electroconvulsive therapy (ECT) is a highly effective neuromodulatory intervention for treatment-resistant major depressive disorder (MDD). Presently, however, understanding of its neurophysiological effects remains incomplete. In the present study, we utilised resting-state electroencephalography (RS-EEG) to explore changes in functional connectivity, network topology, and spectral power elicited by an acute open-label course of ECT in a cohort of 23 patients with treatment-resistant MDD. RS-EEG was recorded prior to commencement of ECT and again within 48 h following each patient’s final treatment session. Our results show that ECT was able to enhance connectivity within lower (delta and theta) frequency bands across subnetworks largely confined to fronto-central channels, while, conversely, more widespread subnetworks of reduced connectivity emerged within faster (alpha and beta) bands following treatment. Graph-based topological analyses revealed changes in measures of functional segregation (clustering coefficient), integration (characteristic path length), and small-world architecture following ECT. Finally, post-treatment enhancement of delta and theta spectral power was observed, which showed a positive association with the number of ECT sessions received. Overall, our findings indicate that RS-EEG can provide a sensitive measure of dynamic neural activity following ECT and highlight network-based analyses as a promising avenue for furthering mechanistic understanding of the effects of convulsive therapies.
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Affiliation(s)
- Aron T Hill
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, University of Toronto, 1001 Queen Street West, Unit 4-1, Toronto, ON, M6J 1H4, Canada
| | - Itay Hadas
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, University of Toronto, 1001 Queen Street West, Unit 4-1, Toronto, ON, M6J 1H4, Canada
| | - Reza Zomorrodi
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, University of Toronto, 1001 Queen Street West, Unit 4-1, Toronto, ON, M6J 1H4, Canada
| | - Daphne Voineskos
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, University of Toronto, 1001 Queen Street West, Unit 4-1, Toronto, ON, M6J 1H4, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Faranak Farzan
- School of Mechatronic Systems Engineering, Centre for Engineering-Led Brain Research, Simon Fraser University, Surrey, BC, Canada
| | - Paul B Fitzgerald
- Epworth Centre for Innovation in Mental Health, Epworth Healthcare and Monash Alfred Psychiatry Research Centre, The Alfred and Monash University Central Clinical School, Commercial Rd, Melbourne, VIC, Australia
| | - Daniel M Blumberger
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, University of Toronto, 1001 Queen Street West, Unit 4-1, Toronto, ON, M6J 1H4, Canada.,Institute of Medical Science, University of Toronto, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Zafiris J Daskalakis
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, University of Toronto, 1001 Queen Street West, Unit 4-1, Toronto, ON, M6J 1H4, Canada. .,Institute of Medical Science, University of Toronto, Toronto, ON, Canada. .,Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
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210
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Sanchez-Rodriguez LM, Iturria-Medina Y, Mouches P, Sotero RC. Detecting brain network communities: Considering the role of information flow and its different temporal scales. Neuroimage 2020; 225:117431. [PMID: 33045336 DOI: 10.1016/j.neuroimage.2020.117431] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Revised: 09/22/2020] [Accepted: 09/29/2020] [Indexed: 12/16/2022] Open
Abstract
The identification of community structure in graphs continues to attract great interest in several fields. Network neuroscience is particularly concerned with this problem considering the key roles communities play in brain processes and functionality. Most methods used for community detection in brain graphs are based on the maximization of a parameter-dependent modularity function that often obscures the physical meaning and hierarchical organization of the partitions of network nodes. In this work, we present a new method able to detect communities at different scales in a natural, unrestricted way. First, to obtain an estimation of the information flow in the network we release random walkers to freely move over it. The activity of the walkers is separated into oscillatory modes by using empirical mode decomposition. After grouping nodes by their co-occurrence at each time scale, k-modes clustering returns the desired partitions. Our algorithm was first tested on benchmark graphs with favorable performance. Next, it was applied to real and simulated anatomical and/or functional connectomes in the macaque and human brains. We found a clear hierarchical repertoire of community structures in both the anatomical and the functional networks. The observed partitions range from the evident division in two hemispheres -in which all processes are managed globally- to specialized communities seemingly shaped by physical proximity and shared function. Additionally, the spatial scales of a network's community structure (characterized by a measure we term within-communities path length) appear inversely proportional to the oscillatory modes' average frequencies. The proportionality constant may constitute a network-specific propagation velocity for the information flow. Our results stimulate the research of hierarchical community organization in terms of temporal scales of information flow in the brain network.
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Affiliation(s)
- Lazaro M Sanchez-Rodriguez
- Department of Radiology and Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill Univ., Montreal, Canada; McConnel Brain Imaging Center, Montreal Neurological Institute, McGill Univ., Montreal, Canada; Ludmer Centre for Neuroinformatics and Mental Health, McGill Univ., Montreal, Canada.
| | - Yasser Iturria-Medina
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill Univ., Montreal, Canada; McConnel Brain Imaging Center, Montreal Neurological Institute, McGill Univ., Montreal, Canada; Ludmer Centre for Neuroinformatics and Mental Health, McGill Univ., Montreal, Canada
| | - Pauline Mouches
- Department of Radiology and Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Biomedical Engineering Graduate Program, University of Calgary, Calgary, Alberta, Canada
| | - Roberto C Sotero
- Department of Radiology and Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Biomedical Engineering Graduate Program, University of Calgary, Calgary, Alberta, Canada.
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211
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Stolicyn A, Harris MA, Shen X, Barbu MC, Adams MJ, Hawkins EL, de Nooij L, Yeung HW, Murray AD, Lawrie SM, Steele JD, McIntosh AM, Whalley HC. Automated classification of depression from structural brain measures across two independent community-based cohorts. Hum Brain Mapp 2020; 41:3922-3937. [PMID: 32558996 PMCID: PMC7469862 DOI: 10.1002/hbm.25095] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Revised: 05/16/2020] [Accepted: 05/25/2020] [Indexed: 12/30/2022] Open
Abstract
Major depressive disorder (MDD) has been the subject of many neuroimaging case-control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically-ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well-phenotyped community-based group of current MDD cases with clinical interview-based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, 'STRADL'). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types-SVM, penalised logistic regression or decision tree-either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population-based sample with self-reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses-remitted MDD in STRADL, and lifetime-experienced MDD in UK Biobank. The highest cross-validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self-reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime-experienced MDD (52.68-60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches.
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Affiliation(s)
- Aleks Stolicyn
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - Mathew A. Harris
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - Xueyi Shen
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - Miruna C. Barbu
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - Mark J. Adams
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - Emma L. Hawkins
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - Laura de Nooij
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - Hon Wah Yeung
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - Alison D. Murray
- Aberdeen Biomedical Imaging CentreUniversity of AberdeenLilian Sutton Building, ForesterhillAberdeenUK
| | - Stephen M. Lawrie
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - J. Douglas Steele
- School of Medicine (Division of Imaging Science and Technology)University of DundeeDundeeUK
| | - Andrew M. McIntosh
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - Heather C. Whalley
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
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212
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Shi J, Guo H, Liu S, Xue W, Fan F, Fan H, An H, Wang Z, Tan S, Yang F, Tan Y. Resting-state functional connectivity of neural circuits associated with primary and secondary rewards in patients with bipolar disorder. Soc Cogn Affect Neurosci 2020; 15:755-763. [PMID: 32734286 PMCID: PMC7511880 DOI: 10.1093/scan/nsaa100] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 06/08/2020] [Accepted: 07/11/2020] [Indexed: 01/27/2023] Open
Abstract
Objective We used resting-state functional connectivity (rsFC) to evaluate the integrity of the neural circuits associated with primary and secondary rewards in bipolar disorder (BD) with different mood phases. Methods Sixty patients with BD [21 patients with depressive episode of BD (BDD) and 41 patients with maniac episode of BD (BDM)] and 42 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging. rsFC was assessed using region of interest-wise analyses. Results Attenuation of rsFC at the orbitofrontal cortex (OFC) and the left ventral striatum (LVS) was observed in the secondary reward circuit of patients with BD compared to that of HCs. Among BDD, BDM and HCs, the rsFC between OFC and LVS in BDM was intermediate, while the rsFC between OFC and right ventral striatum/right amygdala in BDM was the highest; the corresponding rsFC values in BDD were the lowest. Furthermore, a positive correlation was found between rsFC and Young Mania Rating Scale scores in BDM. Conclusions This study suggests that there may be an abnormal rsFC between OFC and LVS in the second reward of patients with BD and the discrepant patterns of rsFC may exist between different mood states in patients with BD.
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Affiliation(s)
- Jing Shi
- Psychiatry Research Center, Beijing HuiLongGuan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China
| | - Hua Guo
- Present Office, The Psychiatric Hospital of Zhumadian, Zhumadian, Henan 463000, China
| | - Sijia Liu
- Psychiatry Research Center, Beijing HuiLongGuan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China
| | - Wei Xue
- Department of Clinical Pharmacology, Beijing Hospital of the Ministry of Health, Beijing 100730, P.R. China
| | - Fengmei Fan
- Psychiatry Research Center, Beijing HuiLongGuan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China
| | - Hongzhen Fan
- Psychiatry Research Center, Beijing HuiLongGuan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China
| | - Huimei An
- Psychiatry Research Center, Beijing HuiLongGuan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China
| | - Zhiren Wang
- Correspondence: Zhiren Wang, Psychiatry Research Center, Beijing HuiLongGuan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China, 100096.
| | - Shuping Tan
- Psychiatry Research Center, Beijing HuiLongGuan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China
| | - Fude Yang
- Psychiatry Research Center, Beijing HuiLongGuan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China
| | - Yunlong Tan
- Psychiatry Research Center, Beijing HuiLongGuan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China
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213
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Lopes MA, Zhang J, Krzemiński D, Hamandi K, Chen Q, Livi L, Masuda N. Recurrence quantification analysis of dynamic brain networks. Eur J Neurosci 2020; 53:1040-1059. [PMID: 32888203 DOI: 10.1111/ejn.14960] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Revised: 08/03/2020] [Accepted: 08/27/2020] [Indexed: 01/02/2023]
Abstract
Evidence suggests that brain network dynamics are a key determinant of brain function and dysfunction. Here we propose a new framework to assess the dynamics of brain networks based on recurrence analysis. Our framework uses recurrence plots and recurrence quantification analysis to characterize dynamic networks. For resting-state magnetoencephalographic dynamic functional networks (dFNs), we have found that functional networks recur more quickly in people with epilepsy than in healthy controls. This suggests that recurrence of dFNs may be used as a biomarker of epilepsy. For stereo electroencephalography data, we have found that dFNs involved in epileptic seizures emerge before seizure onset, and recurrence analysis allows us to detect seizures. We further observe distinct dFNs before and after seizures, which may inform neurostimulation strategies to prevent seizures. Our framework can also be used for understanding dFNs in healthy brain function and in other neurological disorders besides epilepsy.
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Affiliation(s)
- Marinho A Lopes
- Department of Engineering Mathematics, University of Bristol, Bristol, UK.,Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, UK
| | - Jiaxiang Zhang
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, UK
| | - Dominik Krzemiński
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, UK
| | - Khalid Hamandi
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, UK
| | - Qi Chen
- Center for Studies of Psychological Application and School of Psychology, South China Normal University, Guangzhou, China
| | - Lorenzo Livi
- Departments of Computer Science and Mathematics, University of Manitoba, Winnipeg, MB, Canada.,Department of Computer Science, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
| | - Naoki Masuda
- Department of Engineering Mathematics, University of Bristol, Bristol, UK.,Department of Mathematics, University at Buffalo, State University of New York, Buffalo, NY, USA.,Computational and Data-Enabled Science and Engineering Program, University at Buffalo, State University of New York, Buffalo, NY, USA
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214
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Gender-Related and Hemispheric Effects in Cortical Thickness-Based Hemispheric Brain Morphological Network. BIOMED RESEARCH INTERNATIONAL 2020; 2020:3560259. [PMID: 32851064 PMCID: PMC7439209 DOI: 10.1155/2020/3560259] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 06/19/2020] [Accepted: 07/06/2020] [Indexed: 12/18/2022]
Abstract
Objective The current study examined gender-related differences in hemispheric asymmetries of graph metrics, calculated from a cortical thickness-based brain structural covariance network named hemispheric morphological network. Methods Using the T1-weighted magnetic resonance imaging scans of 285 participants (150 females, 135 males) retrieved from the Human Connectome Project (HCP), hemispheric morphological networks were constructed per participant. In these hemispheric morphologic networks, the degree of similarity between two different brain regions in terms of the distributed patterns of cortical thickness values (the Jensen–Shannon divergence) was defined as weight of network edge that connects two different brain regions. After the calculation and summation of global and local graph metrics (across the network sparsity levels K = 0.10‐0.36), asymmetry indexes of these graph metrics were derived. Results Hemispheric morphological networks satisfied small-worldness and global efficiency for the network sparsity ranges of K = 0.10–0.36. Between-group comparisons (female versus male) of asymmetry indexes revealed opposite directionality of asymmetries (leftward versus rightward) for global metrics of normalized clustering coefficient, normalized characteristic path length, and global efficiency (all p < 0.05). For the local graph metrics, larger rightward asymmetries of cingulate-superior parietal gyri for nodal efficiency in male compared to female, larger leftward asymmetry of temporal pole for degree centrality in female compared to male, and opposite directionality of interhemispheric asymmetry of rectal gyrus for degree centrality between female (rightward) and male (leftward) were shown (all p < 0.05). Conclusion Patterns of interhemispheric asymmetries for cingulate, superior parietal gyrus, temporal pole, and rectal gyrus are different between male and female for the similarities of the cortical thickness distribution with other brain regions. Accordingly, possible effect of gender-by-hemispheric interaction has to be considered in future studies of brain morphology and brain structural covariance networks.
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215
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Viher PV, Abdulkadir A, Savadijev P, Stegmayer K, Kubicki M, Makris N, Karmacharya S, Federspiel A, Bohlhalter S, Vanbellingen T, Müri R, Wiest R, Strik W, Walther S. Structural organization of the praxis network predicts gesture production: Evidence from healthy subjects and patients with schizophrenia. Cortex 2020; 132:322-333. [PMID: 33011518 DOI: 10.1016/j.cortex.2020.05.023] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 04/11/2020] [Accepted: 05/19/2020] [Indexed: 01/09/2023]
Abstract
Hand gestures are an integral part of social interactions and communication. Several imaging studies in healthy subjects and lesion studies in patients with apraxia suggest the praxis network for gesture production, involving mainly left inferior frontal, posterior parietal and temporal regions. However, little is known about the structural connectivity underlying gesture production. We recruited 41 healthy participants and 39 patients with schizophrenia. All participants performed a gesture production test, the Test of Upper Limb Apraxia, and underwent diffusion tensor imaging. We hypothesized that gesture production is associated with structural network connectivity as well as with tract integrity. We defined the praxis network as an undirected graph comprised of 13 bilateral regions of interest and derived measures of local and global structural connectivity and tract integrity from Finsler geometry. We found an association of gesture deficit with reduced global and local efficiency of the praxis network. Furthermore, reduced tract integrity, for example in the superior longitudinal fascicle, arcuate fascicle or corpus callosum were related to gesture deficits. Our findings contribute to the understanding of structural correlates of gesture production as they first present diffusion tensor imaging data in a combined sample of healthy subjects and a patient cohort with gestural deficits.
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Affiliation(s)
- Petra V Viher
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland; Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | - Ahmed Abdulkadir
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Peter Savadijev
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Diagnostic Radiology, McGill University, Montreal, Canada
| | - Katharina Stegmayer
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Marek Kubicki
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Nikos Makris
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Departments of Psychiatry, Neurology and Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Sarina Karmacharya
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Andrea Federspiel
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Stephan Bohlhalter
- Department of Clinical Research, University Hospital, Inselspital, Bern, Switzerland; Neurocenter, Luzerner Kantonsspital, Lucerne, Switzerland
| | - Tim Vanbellingen
- Department of Clinical Research, University Hospital, Inselspital, Bern, Switzerland; Neurocenter, Luzerner Kantonsspital, Lucerne, Switzerland; Gerontechnology and Rehabilitation Group, University of Bern, Bern, Switzerland
| | - René Müri
- Department of Clinical Research, University Hospital, Inselspital, Bern, Switzerland; Department of Neurology, University Hospital Inselspital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center of Advanced Neuroimaging, Institute of Neuroradiology, University of Bern, Bern, Switzerland
| | - Werner Strik
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Sebastian Walther
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
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216
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Moezzi B, Pratti LM, Hordacre B, Graetz L, Berryman C, Lavrencic LM, Ridding MC, Keage HAD, McDonnell MD, Goldsworthy MR. Characterization of Young and Old Adult Brains: An EEG Functional Connectivity Analysis. Neuroscience 2020; 422:230-239. [PMID: 31806080 DOI: 10.1016/j.neuroscience.2019.08.038] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 08/15/2019] [Accepted: 08/22/2019] [Indexed: 01/01/2023]
Abstract
Brain connectivity studies have reported that functional networks change with older age. We aim to (1) investigate whether electroencephalography (EEG) data can be used to distinguish between individual functional networks of young and old adults; and (2) identify the functional connections that contribute to this classification. Two eyes-open resting-state EEG recording sessions with 64 electrodes for each of 22 younger adults (19-37 years) and 22 older adults (63-85 years) were conducted. For each session, imaginary coherence matrices in delta, theta, alpha, beta and gamma bands were computed. A range of machine learning classification methods were utilized to distinguish younger and older adult brains. A support vector machine (SVM) classifier was 93% accurate in classifying the brains by age group. We report decreased functional connectivity with older age in delta, theta, alpha and gamma bands, and increased connectivity with older age in beta band. Most connections involving frontal, temporal, and parietal electrodes, and more than half of connections involving occipital electrodes, showed decreased connectivity with older age. Slightly less than half of the connections involving central electrodes showed increased connectivity with older age. Functional connections showing decreased strength with older age were not significantly different in electrode-to-electrode distance than those that increased with older age. Most of the connections used by the classifier to distinguish participants by age group belonged to the alpha band. Findings suggest a decrease in connectivity in key networks and frequency bands associated with attention and awareness, and an increase in connectivity of the sensorimotor functional networks with aging during a resting state.
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Affiliation(s)
- Bahar Moezzi
- Cognitive Ageing and Impairment Neurosciences Laboratory, School of Psychology, Social Work and Social Policy, University of South Australia, Australia.
| | | | - Brenton Hordacre
- School of Health Sciences, University of South Australia, Australia
| | - Lynton Graetz
- Robinson Research Institute, Adelaide Medical School, University of Adelaide, Australia
| | - Carolyn Berryman
- Robinson Research Institute, Adelaide Medical School, University of Adelaide, Australia
| | - Louise M Lavrencic
- Cognitive Ageing and Impairment Neurosciences Laboratory, School of Psychology, Social Work and Social Policy, University of South Australia, Australia; Neuroscience Research of Australia, Australia
| | - Michael C Ridding
- Robinson Research Institute, Adelaide Medical School, University of Adelaide, Australia
| | - Hannah A D Keage
- Cognitive Ageing and Impairment Neurosciences Laboratory, School of Psychology, Social Work and Social Policy, University of South Australia, Australia
| | - Mark D McDonnell
- Computational Learning Systems Laboratory, School of Information Technology and Mathematical Sciences, University of South Australia, Australia
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217
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Tamburro G, di Fronso S, Robazza C, Bertollo M, Comani S. Modulation of Brain Functional Connectivity and Efficiency During an Endurance Cycling Task: A Source-Level EEG and Graph Theory Approach. Front Hum Neurosci 2020; 14:243. [PMID: 32733219 PMCID: PMC7363938 DOI: 10.3389/fnhum.2020.00243] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Accepted: 06/02/2020] [Indexed: 12/31/2022] Open
Abstract
Various methods have been employed to investigate different aspects of brain activity modulation related to the performance of a cycling task. In our study, we examined how functional connectivity and brain network efficiency varied during an endurance cycling task. For this purpose, we reconstructed EEG signals at source level: we computed current densities in 28 anatomical regions of interest (ROIs) through the eLORETA algorithm, and then we calculated the lagged coherence of the 28 current density signals to define the adjacency matrix. To quantify changes of functional network efficiency during an exhaustive cycling task, we computed three graph theoretical indices: local efficiency (LE), global efficiency (GE), and density (D) in two different frequency bands, Alpha and Beta bands, that indicate alertness processes and motor binding/fatigue, respectively. LE is a measure of functional segregation that quantifies the ability of a network to exchange information locally. GE is a measure of functional integration that quantifies the ability of a network to exchange information globally. D is a global measure of connectivity that describes the extent of connectivity in a network. This analysis was conducted for six different task intervals: pre-cycling; initial, intermediate, and final stages of cycling; and active recovery and passive recovery. Fourteen participants performed an incremental cycling task with simultaneous EEG recording and rated perceived exertion monitoring to detect the participants’ exhaustion. LE remained constant during the endurance cycling task in both bands. Therefore, we speculate that fatigue processes did not affect the segregated neural processing. We observed an increase of GE in the Alpha band only during cycling, which could be due to greater alertness processes and preparedness to stimuli during exercise. Conversely, although D did not change significantly over time in the Alpha band, its general reduction in the Beta bands during cycling could be interpreted within the framework of the neural efficiency hypothesis, which posits a reduced neural activity for expert/automated performances. We argue that the use of graph theoretical indices represents a clear methodological advancement in studying endurance performance.
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Affiliation(s)
- Gabriella Tamburro
- Behavioral Imaging and Neural Dynamics Center, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy.,Department of Neurosciences, Imaging and Clinical Sciences, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Selenia di Fronso
- Behavioral Imaging and Neural Dynamics Center, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy.,Department of Medicine and Aging Sciences, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Claudio Robazza
- Behavioral Imaging and Neural Dynamics Center, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy.,Department of Medicine and Aging Sciences, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Maurizio Bertollo
- Behavioral Imaging and Neural Dynamics Center, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy.,Department of Medicine and Aging Sciences, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Silvia Comani
- Behavioral Imaging and Neural Dynamics Center, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy.,Department of Neurosciences, Imaging and Clinical Sciences, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy
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218
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Khoo ATT, Kim PJ, Kim HM, Je HS. Neural circuit analysis using a novel intersectional split intein-mediated split-Cre recombinase system. Mol Brain 2020; 13:101. [PMID: 32616061 PMCID: PMC7331137 DOI: 10.1186/s13041-020-00640-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 06/23/2020] [Indexed: 11/10/2022] Open
Abstract
The defining features of a neuron are its functional and anatomical connections with thousands of other neurons in the brain. Together, these neurons form functional networks that direct animal behavior. Current approaches that allow the interrogation of specific populations of neurons and neural circuits rely heavily on targeting their gene expression profiles or connectivity. However, these approaches are often unable to delineate specific neuronal populations. Here, we developed a novel intersectional split intein-mediated split-Cre recombinase system that can selectively label specific types of neurons based on their gene expression profiles and structural connectivity. We developed this system by splitting Cre recombinase into two fragments with evolved split inteins and subsequently expressed one fragment under the influence of a cell type-specific promoter in a transgenic animal, and delivered the other fragment via retrograde viral gene transfer. This approach results in the reconstitution of Cre recombinase in only specific population of neurons projecting from a specific brain region or in those of a specific neuronal type. Taken together, our split intein-based split-Cre system will be useful for sophisticated characterization of mammalian brain circuits.
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Affiliation(s)
- Audrey Tze Ting Khoo
- Neuroscience and Behavioural Disorders Programme, Duke-National University of Singapore (NUS) Medical School, 8 College Road, Singapore, 169857, Singapore
| | - Paul Jong Kim
- Neuroscience and Behavioural Disorders Programme, Duke-National University of Singapore (NUS) Medical School, 8 College Road, Singapore, 169857, Singapore
| | - Ho Min Kim
- Graduate School of Medical Science & Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.,Center for Biomolecular & Cellular Structure, Institute for Basic Science (IBS), Daejeon, 34126, Republic of Korea
| | - H Shawn Je
- Neuroscience and Behavioural Disorders Programme, Duke-National University of Singapore (NUS) Medical School, 8 College Road, Singapore, 169857, Singapore.
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219
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Kim DJ, Min BK. Rich-club in the brain's macrostructure: Insights from graph theoretical analysis. Comput Struct Biotechnol J 2020; 18:1761-1773. [PMID: 32695269 PMCID: PMC7355726 DOI: 10.1016/j.csbj.2020.06.039] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 06/19/2020] [Accepted: 06/22/2020] [Indexed: 02/07/2023] Open
Abstract
The brain is a complex network. Growing evidence supports the critical roles of a set of brain regions within the brain network, known as the brain’s cores or hubs. These regions require high energy cost but possess highly efficient neural information transfer in the brain’s network and are termed the rich-club. The rich-club of the brain network is essential as it directly regulates functional integration across multiple segregated regions and helps to optimize cognitive processes. Here, we review the recent advances in rich-club organization to address the fundamental roles of the rich-club in the brain and discuss how these core brain regions affect brain development and disorders. We describe the concepts of the rich-club behind network construction in the brain using graph theoretical analysis. We also highlight novel insights based on animal studies related to the rich-club and illustrate how human studies using neuroimaging techniques for brain development and psychiatric/neurological disorders may be relevant to the rich-club phenomenon in the brain network.
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Key Words
- AD, Alzheimer’s disease
- ADHD, attention deficit hyperactivity disorder
- ASD, autism spectrum disorder
- BD, bipolar disorder
- Brain connectivity
- Brain network
- DTI, diffusion tensor imaging
- EEG, electroencephalography
- Graph theory
- MDD, major depressive disorder
- MEG, magnetoencephalography
- MRI, magnetic resonance imaging
- Neuroimaging
- Rich-club
- TBI, traumatic brain injury
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Affiliation(s)
- Dae-Jin Kim
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Byoung-Kyong Min
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
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220
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Gudmundsson L, Vohryzek J, Fornari E, Clarke S, Hagmann P, Crottaz-Herbette S. A brief exposure to rightward prismatic adaptation changes resting-state network characteristics of the ventral attentional system. PLoS One 2020; 15:e0234382. [PMID: 32584824 PMCID: PMC7316264 DOI: 10.1371/journal.pone.0234382] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 05/26/2020] [Indexed: 12/02/2022] Open
Abstract
A brief session of rightward prismatic adaptation (R-PA) has been shown to alleviate neglect symptoms in patients with right hemispheric damage, very likely by switching hemispheric dominance of the ventral attentional network (VAN) from the right to the left and by changing task-related activity within the dorsal attentional network (DAN). We have investigated this very rapid change in functional organisation with a network approach by comparing resting-state connectivity before and after a brief exposure i) to R-PA (14 normal subjects; experimental condition) or ii) to plain glasses (12 normal subjects; control condition). A whole brain analysis (comprising 129 regions of interest) highlighted R-PA-induced changes within a bilateral, fronto-temporal network, which consisted of 13 nodes and 11 edges; all edges involved one of 4 frontal nodes, which were part of VAN. The analysis of network characteristics within VAN and DAN revealed a R-PA-induced decrease in connectivity strength between nodes and a decrease in local efficiency within VAN but not within DAN. These results indicate that the resting-state connectivity configuration of VAN is modulated by R-PA, possibly by decreasing its modularity.
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Affiliation(s)
- Louis Gudmundsson
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), and University of Lausanne, Lausanne, Switzerland
- Neuropsychology and Neurorehabilitation Service, Centre Hospitalier Universitaire Vaudois (CHUV), and University of Lausanne, Lausanne, Switzerland
| | - Jakub Vohryzek
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), and University of Lausanne, Lausanne, Switzerland
- Department of Psychiatry, Hedonia Research Group, University of Oxford, Oxford, United Kingdom
| | - Eleonora Fornari
- CIBM (Centre d'Imagerie Biomédicale), Dept. of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), and University of Lausanne, Lausanne, Switzerland
| | - Stephanie Clarke
- Neuropsychology and Neurorehabilitation Service, Centre Hospitalier Universitaire Vaudois (CHUV), and University of Lausanne, Lausanne, Switzerland
| | - Patric Hagmann
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), and University of Lausanne, Lausanne, Switzerland
- Signal Processing Lab 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Sonia Crottaz-Herbette
- Neuropsychology and Neurorehabilitation Service, Centre Hospitalier Universitaire Vaudois (CHUV), and University of Lausanne, Lausanne, Switzerland
- * E-mail:
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221
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Saad JF, Griffiths KR, Korgaonkar MS. A Systematic Review of Imaging Studies in the Combined and Inattentive Subtypes of Attention Deficit Hyperactivity Disorder. Front Integr Neurosci 2020; 14:31. [PMID: 32670028 PMCID: PMC7327109 DOI: 10.3389/fnint.2020.00031] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 05/07/2020] [Indexed: 12/12/2022] Open
Abstract
Objective: Insights to underlying neural mechanisms in attention deficit hyperactivity disorder (ADHD) have emerged from neuroimaging research; however, the neural mechanisms that distinguish ADHD subtypes remain inconclusive. Method: We reviewed 19 studies integrating magnetic resonance imaging [MRI; structural (sMRI), diffusion, functional MRI (fMRI)] findings into a framework exploring pathophysiological mechanisms underlying the combined (ADHD-C) and predominantly inattentive (ADHD-I) ADHD subtypes. Results: Despite equivocal structural MRI results, findings from fMRI and DTI imaging modalities consistently implicate disrupted connectivity in regions and tracts involving frontal striatal thalamic in ADHD-C and frontoparietal neural networks in ADHD-I. Alterations of the default mode, cerebellum, and motor networks in ADHD-C and cingulo-frontoparietal attention and visual networks in ADHD-I highlight network organization differences between subtypes. Conclusion: Growing evidence from neuroimaging studies highlight neurobiological differences between ADHD clinical subtypes, particularly from a network perspective. Understanding brain network organization and connectivity may help us to better conceptualize the ADHD types and their symptom variability.
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Affiliation(s)
- Jacqueline Fifi Saad
- Brain Dynamics Centre, Westmead Institute for Medical Research, Westmead Hospital, Sydney, NSW, Australia.,The Discipline of Psychiatry, Sydney Medical School, The University of Sydney, Camperdown, NSW, Australia
| | - Kristi R Griffiths
- Brain Dynamics Centre, Westmead Institute for Medical Research, Westmead Hospital, Sydney, NSW, Australia.,The Discipline of Psychiatry, Sydney Medical School, The University of Sydney, Camperdown, NSW, Australia
| | - Mayuresh S Korgaonkar
- Brain Dynamics Centre, Westmead Institute for Medical Research, Westmead Hospital, Sydney, NSW, Australia.,The Discipline of Psychiatry, Sydney Medical School, The University of Sydney, Camperdown, NSW, Australia
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222
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Khater IM, Nabi IR, Hamarneh G. A Review of Super-Resolution Single-Molecule Localization Microscopy Cluster Analysis and Quantification Methods. PATTERNS (NEW YORK, N.Y.) 2020; 1:100038. [PMID: 33205106 PMCID: PMC7660399 DOI: 10.1016/j.patter.2020.100038] [Citation(s) in RCA: 131] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Single-molecule localization microscopy (SMLM) is a relatively new imaging modality, winning the 2014 Nobel Prize in Chemistry, and considered as one of the key super-resolution techniques. SMLM resolution goes beyond the diffraction limit of light microscopy and achieves resolution on the order of 10-20 nm. SMLM thus enables imaging single molecules and study of the low-level molecular interactions at the subcellular level. In contrast to standard microscopy imaging that produces 2D pixel or 3D voxel grid data, SMLM generates big data of 2D or 3D point clouds with millions of localizations and associated uncertainties. This unprecedented breakthrough in imaging helps researchers employ SMLM in many fields within biology and medicine, such as studying cancerous cells and cell-mediated immunity and accelerating drug discovery. However, SMLM data quantification and interpretation methods have yet to keep pace with the rapid advancement of SMLM imaging. Researchers have been actively exploring new computational methods for SMLM data analysis to extract biosignatures of various biological structures and functions. In this survey, we describe the state-of-the-art clustering methods adopted to analyze and quantify SMLM data and examine the capabilities and shortcomings of the surveyed methods. We classify the methods according to (1) the biological application (i.e., the imaged molecules/structures), (2) the data acquisition (such as imaging modality, dimension, resolution, and number of localizations), and (3) the analysis details (2D versus 3D, field of view versus region of interest, use of machine-learning and multi-scale analysis, biosignature extraction, etc.). We observe that the majority of methods that are based on second-order statistics are sensitive to noise and imaging artifacts, have not been applied to 3D data, do not leverage machine-learning formulations, and are not scalable for big-data analysis. Finally, we summarize state-of-the-art methodology, discuss some key open challenges, and identify future opportunities for better modeling and design of an integrated computational pipeline to address the key challenges.
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Affiliation(s)
- Ismail M. Khater
- Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Ivan Robert Nabi
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Ghassan Hamarneh
- Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
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223
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Lin YC, Baete SH, Wang X, Boada FE. Mapping brain-behavior networks using functional and structural connectome fingerprinting in the HCP dataset. Brain Behav 2020; 10:e01647. [PMID: 32351025 PMCID: PMC7303390 DOI: 10.1002/brb3.1647] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 03/12/2020] [Accepted: 03/20/2020] [Indexed: 11/22/2022] Open
Abstract
INTRODUCTION Connectome analysis of the human brain's structural and functional architecture provides a unique opportunity to understand the organization of the brain's functional architecture. In previous studies, connectome fingerprinting using brain functional connectivity profiles as an individualized trait was able to predict an individual's neurocognitive performance from the Human Connectome Project (HCP) neurocognitive datasets. MATERIALS AND METHODS In the present study, we extend connectome fingerprinting from functional connectivity (FC) to structural connectivity (SC), identifying multiple relationships between behavioral traits and brain connectivity. Higher-order neurocognitive tasks were found to have a weaker association with structural connectivity than its functional connectivity counterparts. RESULTS Neurocognitive tasks with a higher sensory footprint were, however, found to have a stronger association with structural connectivity than their functional connectivity counterparts. Language behavioral measurements had a particularly stronger correlation, especially between performance on the picture language test (Pic Vocab) and both FC (r = .28, p < .003) and SC (r = 0.27, p < .00077). CONCLUSIONS At the neural level, we found that the pattern of structural brain connectivity related to high-level language performance is consistent with the language white matter regions identified in presurgical mapping. We illustrate how this approach can be used to generalize the connectome fingerprinting framework to structural connectivity and how this can help understand the connections between cognitive behavior and the white matter connectome of the brain.
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Affiliation(s)
- Ying-Chia Lin
- Center for Advanced Imaging Innovation and Research (CAI2R), NYU School of Medicine, New York, NY, USA.,Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY, USA
| | - Steven H Baete
- Center for Advanced Imaging Innovation and Research (CAI2R), NYU School of Medicine, New York, NY, USA.,Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY, USA
| | - Xiuyuan Wang
- Center for Advanced Imaging Innovation and Research (CAI2R), NYU School of Medicine, New York, NY, USA.,Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY, USA
| | - Fernando E Boada
- Center for Advanced Imaging Innovation and Research (CAI2R), NYU School of Medicine, New York, NY, USA.,Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY, USA
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224
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Chemotherapy-induced brain changes in breast cancer survivors: evaluation with multimodality magnetic resonance imaging. Brain Imaging Behav 2020; 13:1799-1814. [PMID: 30937827 DOI: 10.1007/s11682-019-00074-y] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Chemotherapy related cognitive impairments are common in breast cancer patients undergoing chemotherapy. These cognitive dysfunctions are mainly attributable to chemotherapy related brain structural and functional alterations. Multimodality magnetic resonance imaging (MRI) can reveal brain gray matter volume loss, white matter microstructural disruption, reduced gray matter density, impaired cerebral blood flow and brain structural and functional connection networks at both local and global levels. This review outlines the potential applications of multimodality MR imaging techniques in chemotherapy induced cognitive deficit in breast cancer survivors and provides future research perspective in this field.
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225
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Panda EJ, Emami Z, Valiante TA, Pang EW. EEG phase synchronization during semantic unification relates to individual differences in children's vocabulary skill. Dev Sci 2020; 24:e12984. [PMID: 32384181 DOI: 10.1111/desc.12984] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 04/17/2020] [Accepted: 04/21/2020] [Indexed: 11/30/2022]
Abstract
As we listen to speech, our ability to understand what was said requires us to retrieve and bind together individual word meanings into a coherent discourse representation. This so-called semantic unification is a fundamental cognitive skill, and its development relies on the integration of neural activity throughout widely distributed functional brain networks. In this proof-of-concept study, we examine, for the first time, how these functional brain networks develop in children. Twenty-six children (ages 4-17) listened to well-formed sentences and sentences containing a semantic violation, while EEG was recorded. Children with stronger vocabulary showed N400 effects that were more concentrated to centroparietal electrodes and greater EEG phase synchrony (phase lag index; PLI) between right centroparietal and bilateral frontocentral electrodes in the delta frequency band (1-3 Hz) 1.27-1.53 s after listening to well-formed sentences compared to sentences containing a semantic violation. These effects related specifically to individual differences in receptive vocabulary, perhaps pointing to greater recruitment of functional brain networks important for top-down semantic unification with development. Less skilled children showed greater delta phase synchrony for violation sentences 3.41-3.64 s after critical word onset. This later effect was partly driven by individual differences in nonverbal reasoning, perhaps pointing to non-verbal compensatory processing to extract meaning from speech in children with less developed vocabulary. We suggest that functional brain network communication, as measured by momentary changes in the phase synchrony of EEG oscillations, develops throughout the school years to support language comprehension in different ways depending on children's verbal and nonverbal skill levels.
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Affiliation(s)
- Erin J Panda
- Neurosciences and Mental Health, SickKids Research Institute, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, ON, Canada.,Epilespy Research Program of the Ontario Brain Institute, Toronto, ON, Canada
| | - Zahra Emami
- Neurosciences and Mental Health, SickKids Research Institute, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, ON, Canada.,Division of Neurology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Taufik A Valiante
- Epilespy Research Program of the Ontario Brain Institute, Toronto, ON, Canada.,Krembil Research Institute, University Health Network and Toronto Western Hospital, Toronto, Ontario, Canada.,Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Elizabeth W Pang
- Neurosciences and Mental Health, SickKids Research Institute, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, ON, Canada.,Epilespy Research Program of the Ontario Brain Institute, Toronto, ON, Canada.,Division of Neurology, The Hospital for Sick Children, Toronto, ON, Canada
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226
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Antiepileptic drugs induce subcritical dynamics in human cortical networks. Proc Natl Acad Sci U S A 2020; 117:11118-11125. [PMID: 32358198 DOI: 10.1073/pnas.1911461117] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Cortical network functioning critically depends on finely tuned interactions to afford neuronal activity propagation over long distances while avoiding runaway excitation. This importance is highlighted by the pathological consequences and impaired performance resulting from aberrant network excitability in psychiatric and neurological diseases, such as epilepsy. Theory and experiment suggest that the control of activity propagation by network interactions can be adequately described by a branching process. This hypothesis is partially supported by strong evidence for balanced spatiotemporal dynamics observed in the cerebral cortex; however, evidence of a causal relationship between network interactions and cortex activity, as predicted by a branching process, is missing in humans. Here this cause-effect relationship is tested by monitoring cortex activity under systematic pharmacological reduction of cortical network interactions with antiepileptic drugs. This study reports that cortical activity cascades, presented by the propagating patterns of epileptic spikes, as well as temporal correlations decline precisely as predicted for a branching process. The results provide a missing link to the branching process theory of cortical network function with implications for understanding the foundations of cortical excitability and its monitoring in conditions like epilepsy.
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227
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Ji W, Ferdman D, Copel J, Scheinost D, Shabanova V, Brueckner M, Khokha MK, Ment LR. De novo damaging variants associated with congenital heart diseases contribute to the connectome. Sci Rep 2020; 10:7046. [PMID: 32341405 PMCID: PMC7184603 DOI: 10.1038/s41598-020-63928-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Accepted: 04/08/2020] [Indexed: 12/15/2022] Open
Abstract
Congenital heart disease (CHD) survivors are at risk for neurodevelopmental disability (NDD), and recent studies identify genes associated with both disorders, suggesting that NDD in CHD survivors may be of genetic origin. Genes contributing to neurogenesis, dendritic development and synaptogenesis organize neural elements into networks known as the connectome. We hypothesized that NDD in CHD may be attributable to genes altering both neural connectivity and cardiac patterning. To assess the contribution of de novo variants (DNVs) in connectome genes, we annotated 229 published NDD genes for connectome status and analyzed data from 3,684 CHD subjects and 1,789 controls for connectome gene mutations. CHD cases had more protein truncating and deleterious missense DNVs among connectome genes compared to controls (OR = 5.08, 95%CI:2.81-9.20, Fisher's exact test P = 6.30E-11). When removing three known syndromic CHD genes, the findings remained significant (OR = 3.69, 95%CI:2.02-6.73, Fisher's exact test P = 1.06E-06). In CHD subjects, the top 12 NDD genes with damaging DNVs that met statistical significance after Bonferroni correction (PTPN11, CHD7, CHD4, KMT2A, NOTCH1, ADNP, SMAD2, KDM5B, NSD2, FOXP1, MED13L, DYRK1A; one-tailed binomial test P ≤ 4.08E-05) contributed to the connectome. These data suggest that NDD in CHD patients may be attributable to genes that alter both cardiac patterning and the connectome.
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Affiliation(s)
- Weizhen Ji
- Departments of Pediatrics, New Haven, CT, USA
| | | | - Joshua Copel
- Departments of Pediatrics, New Haven, CT, USA
- Obstetrics, Gynecology and Reproductive Sciences, New Haven, CT, USA
| | | | | | - Martina Brueckner
- Departments of Pediatrics, New Haven, CT, USA
- Genetics, New Haven, CT, USA
- Yale Combined Program in Biological and Biomedical Sciences, New Haven, CT, USA
| | - Mustafa K Khokha
- Departments of Pediatrics, New Haven, CT, USA
- Genetics, New Haven, CT, USA
| | - Laura R Ment
- Departments of Pediatrics, New Haven, CT, USA.
- Neurology, Yale School of Medicine, 333 Cedar Street, New Haven, CT, USA.
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228
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Modolo J, Hassan M, Wendling F, Benquet P. Decoding the circuitry of consciousness: From local microcircuits to brain-scale networks. Netw Neurosci 2020; 4:315-337. [PMID: 32537530 PMCID: PMC7286300 DOI: 10.1162/netn_a_00119] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 12/09/2019] [Indexed: 01/25/2023] Open
Abstract
Identifying the physiological processes underlying the emergence and maintenance of consciousness is one of the most fundamental problems of neuroscience, with implications ranging from fundamental neuroscience to the treatment of patients with disorders of consciousness (DOCs). One major challenge is to understand how cortical circuits at drastically different spatial scales, from local networks to brain-scale networks, operate in concert to enable consciousness, and how those processes are impaired in DOC patients. In this review, we attempt to relate available neurophysiological and clinical data with existing theoretical models of consciousness, while linking the micro- and macrocircuit levels. First, we address the relationships between awareness and wakefulness on the one hand, and cortico-cortical and thalamo-cortical connectivity on the other hand. Second, we discuss the role of three main types of GABAergic interneurons in specific circuits responsible for the dynamical reorganization of functional networks. Third, we explore advances in the functional role of nested oscillations for neural synchronization and communication, emphasizing the importance of the balance between local (high-frequency) and distant (low-frequency) activity for efficient information processing. The clinical implications of these theoretical considerations are presented. We propose that such cellular-scale mechanisms could extend current theories of consciousness.
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Affiliation(s)
- Julien Modolo
- University of Rennes, INSERM, LTSI-U1099, Rennes, France
| | - Mahmoud Hassan
- University of Rennes, INSERM, LTSI-U1099, Rennes, France
| | | | - Pascal Benquet
- University of Rennes, INSERM, LTSI-U1099, Rennes, France
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229
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Chen A, Yang D, Yan C, Peng Z, Kim M, Laurienti PJ, Wu G. A NOVEL SPATIO-TEMPORAL HUB IDENTIFICATION METHOD FOR DYNAMIC FUNCTIONAL NETWORKS. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2020; 2020:1416-1419. [PMID: 32934768 PMCID: PMC7489755 DOI: 10.1109/isbi45749.2020.9098728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Functional connectivity (FC) has been widely investigated to understand the cognition and behavior that emerge from human brain. Recently, there is overwhelming evidence showing that quantifying temporal changes in FC may provide greater insight into fundamental properties of brain network. However, scant attentions has been given to characterize the functional dynamics of network organization. To address this challenge, we propose a novel spatio-temporal hub identification method for functional brain networks by simultaneously identifying hub nodes in each static sliding window and maintaining the reasonable dynamics across the sliding windows, which allows us to further characterize the full-spectrum evolution of hub nodes along with the subject-specific functional dynamics. We have evaluated our spatio-temporal hub identification method on resting-state functional resonance imaging (fMRI) data from an obsessive-compulsive disease (OCD) study, where our new functional hub detection method outperforms current methods (without considering functional dynamics) in terms of accuracy and consistency.
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Affiliation(s)
- Anqi Chen
- Intelligent Information Processing Laboratory and School of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang, China
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Defu Yang
- Intelligent Information Processing Laboratory and School of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang, China
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Chenggang Yan
- Intelligent Information Processing Laboratory and School of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang, China
| | - Ziwen Peng
- Department of Child Psychiatry, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, China
| | - Minjeong Kim
- Department of Computer Science, University of North Carolina at Greensboro, Greensboro, NC, USA
| | - Paul J Laurienti
- Department of Radiology, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Guorong Wu
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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230
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Tommasin S, De Giglio L, Ruggieri S, Petsas N, Giannì C, Pozzilli C, Pantano P. Multi-scale resting state functional reorganization in response to multiple sclerosis damage. Neuroradiology 2020; 62:693-704. [PMID: 32189024 DOI: 10.1007/s00234-020-02393-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 02/27/2020] [Indexed: 11/26/2022]
Abstract
PURPOSE In multiple sclerosis (MS), how brain functional changes relate to clinical conditions is still a matter of debate. The aim of this study was to investigate how functional connectivity (FC) reorganization at three different scales, ranging from local to whole brain, is related to tissue damage and disability. METHODS One-hundred-nineteen patients with MS were clinically evaluated with the Expanded Disability Status Scale and the Multiple Sclerosis Functional Composite. Patients and 42 healthy controls underwent a multimodal 3 T MRI, including resting-state functional MRI. RESULTS We identified 16 resting-state networks via independent component analysis and measured within-network, between-network, and whole-brain (global efficiency and degree centrality) FC. Within-network FC was higher in patients than in controls in default mode, frontoparietal, and executive-control networks, and corresponded to low clinical impairment (default mode network versus Expanded Disability Status Scale r = - 0.31, p < 0.01; right frontoparietal network versus Paced Auditory Serial Addition Test r = 0.33, p < 0.01). All measures of between-network and whole-brain FC, except default mode network global efficiency, were lower in patients than in controls, and corresponded to high disability (i.e., basal ganglia global efficiency versus Timed 25-Foot Walk r = - 0.25, p < 0.03; default mode global efficiency versus Expanded Disability Status Scale r = - 0.44, p < 0.001). Altered measures of within-network, between-network, and whole-brain FC were combined in functional indices that were linearly related to disease duration, Paced Auditory Serial Addition Test and lesion load and non-linearly related to Expanded Disability Status Scale. CONCLUSION We suggest that the combined evaluation of functional alterations occurring at different levels, from local to whole brain, could exhaustively describe neuroplastic changes in MS, while increased within-network FC likely represents adaptive compensatory processes, decreased between-network and whole-brain FC likely represent loss of functional network integration consequent to structural disruption.
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Affiliation(s)
- Silvia Tommasin
- Department of Human Neuroscience, Sapienza - University of Rome, Viale dell'Università 30, 00185, Rome, Italy
| | - Laura De Giglio
- Medicine Department, Neurology Unit, San Filippo Neri Hospital, Via Giovanni Martinotti, 20, 00135, Rome, RM, Italy
| | - Serena Ruggieri
- Department of Human Neuroscience, Sapienza - University of Rome, Viale dell'Università 30, 00185, Rome, Italy
| | - Nikolaos Petsas
- IRCCS Neuromed, (Pozzilli [IS], IT) Via Atinense, 18, 86077, Pozzilli, IS, Italy
| | - Costanza Giannì
- Department of Human Neuroscience, Sapienza - University of Rome, Viale dell'Università 30, 00185, Rome, Italy
| | - Carlo Pozzilli
- Department of Human Neuroscience, Sapienza - University of Rome, Viale dell'Università 30, 00185, Rome, Italy
- Sant'Andrea Hospital, MS Centre, Sapienza - University of Rome, Viale di Grottarossa 1035, 00189, Rome, Italy
| | - Patrizia Pantano
- Department of Human Neuroscience, Sapienza - University of Rome, Viale dell'Università 30, 00185, Rome, Italy.
- IRCCS Neuromed, (Pozzilli [IS], IT) Via Atinense, 18, 86077, Pozzilli, IS, Italy.
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231
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Abstract
Working memory is characterized by neural activity that persists during the retention interval of delay tasks. Despite the ubiquity of this delay activity across tasks, species and experimental techniques, our understanding of this phenomenon remains incomplete. Although initially there was a narrow focus on sustained activation in a small number of brain regions, methodological and analytical advances have allowed researchers to uncover previously unobserved forms of delay activity various parts of the brain. In light of these new findings, this Review reconsiders what delay activity is, where in the brain it is found, what roles it serves and how it may be generated.
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Affiliation(s)
- Kartik K Sreenivasan
- Division of Science and Mathematics, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
| | - Mark D'Esposito
- Helen Wills Neuroscience Institute and Department of Psychology, University of California, Berkeley, CA, USA.
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232
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Yao G, Duan D. High-resolution 3D tractography of fibrous tissue based on polarization-sensitive optical coherence tomography. Exp Biol Med (Maywood) 2020; 245:273-281. [PMID: 31813275 PMCID: PMC7370596 DOI: 10.1177/1535370219894332] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Fibrous tissues play important roles in many parts of the body. Their highly organized directional structure is essential in achieving their normal biomechanical and physiological functions. Disruption of the typical fiber organization in these tissues is often linked to pathological changes and disease progression. Tractography is a specialized imaging method that can reveal the detailed fiber architecture. Here, we review recent developments in high-resolution optical tractography using Jones matrix polarization-sensitive optical coherence tomography. We also illustrate the use of this new tractography technology for visualizing depth-resolved, three-dimensional fibrous structures and quantifying tissue damages in several major fibrous tissues.
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Affiliation(s)
- Gang Yao
- Department of Biomedical, Biological & Chemical Engineering, University of Missouri, Columbia, MO 65211, USA
| | - Dongsheng Duan
- Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO 65211, USA
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233
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Schnellbächer GJ, Hoffstaedter F, Eickhoff SB, Caspers S, Nickl-Jockschat T, Fox PT, Laird AR, Schulz JB, Reetz K, Dogan I. Functional Characterization of Atrophy Patterns Related to Cognitive Impairment. Front Neurol 2020; 11:18. [PMID: 32038473 PMCID: PMC6993791 DOI: 10.3389/fneur.2020.00018] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Accepted: 01/08/2020] [Indexed: 12/12/2022] Open
Abstract
Introduction: Mild cognitive impairment (MCI) is a heterogenous syndrome considered as a risk factor for developing dementia. Previous work examining morphological brain changes in MCI has identified a temporo-parietal atrophy pattern that suggests a common neuroanatomical denominator of cognitive impairment. Using functional connectivity analyses of structurally affected regions in MCI, we aimed to investigate and characterize functional networks formed by these regions that appear to be particularly vulnerable to disease-related disruptions. Methods: Areas of convergent atrophy in MCI were derived from a quantitative meta-analysis and encompassed left and right medial temporal (i.e., hippocampus, amygdala), as well as parietal regions (precuneus), which were defined as seed regions for connectivity analyses. Both task-based meta-analytical connectivity modeling (MACM) based on the BrainMap database and task-free resting-state functional MRI in a large cohort of older adults from the 1000BRAINS study were applied. We additionally assessed behavioral characteristics associated with the seed regions using BrainMap meta-data and investigated correlations of resting-state connectivity with age. Results: The left temporal seed showed stronger associations with a fronto-temporal network, whereas the right temporal atrophy cluster was more linked to cortico-striatal regions. In accordance with this, behavioral analysis indicated an emphasis of the left temporal seed on language generation, and the right temporal seed was associated with the domains of emotion and attention. Task-independent co-activation was more pronounced in the parietal seed, which demonstrated stronger connectivity with a frontoparietal network and associations with introspection and social cognition. Correlation analysis revealed both decreasing and increasing functional connectivity with higher age that may add to pathological processes but also indicates compensatory mechanisms of functional reorganization with increasing age. Conclusion: Our findings provide an important pathophysiological link between morphological changes and the clinical relevance of major structural damage in MCI. Multimodal analysis of functional networks related to areas of MCI-typical atrophy may help to explain cognitive decline and behavioral alterations not tractable by a mere anatomical interpretation and therefore contribute to prognostic evaluations.
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Affiliation(s)
| | - Felix Hoffstaedter
- Research Centre Jülich, Institute of Neuroscience and Medicine (INM-1, INM-7, INM-11), Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Simon B Eickhoff
- Research Centre Jülich, Institute of Neuroscience and Medicine (INM-1, INM-7, INM-11), Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Svenja Caspers
- Research Centre Jülich, Institute of Neuroscience and Medicine (INM-1, INM-7, INM-11), Jülich, Germany.,Institute for Anatomy I, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany.,JARA-BRAIN, Jülich-Aachen Research Alliance, Jülich, Germany
| | - Thomas Nickl-Jockschat
- Iowa Neuroscience Institute, Carver College of Medicine, University of Iowa, Iowa City, IA, United States.,Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, IA, United States
| | - Peter T Fox
- Research Imaging Center, University of Texas Health Science Center, San Antonio, TX, United States.,Research Service, South Texas Veterans Administration Medical Center, San Antonio, TX, United States
| | - Angela R Laird
- Department of Physics, Florida International University, Miami, FL, United States
| | - Jörg B Schulz
- Department of Neurology, RWTH Aachen University, Aachen, Germany.,Research Centre Jülich, Institute of Neuroscience and Medicine (INM-1, INM-7, INM-11), Jülich, Germany.,JARA-BRAIN, Jülich-Aachen Research Alliance, Jülich, Germany
| | - Kathrin Reetz
- Department of Neurology, RWTH Aachen University, Aachen, Germany.,Research Centre Jülich, Institute of Neuroscience and Medicine (INM-1, INM-7, INM-11), Jülich, Germany.,JARA-BRAIN, Jülich-Aachen Research Alliance, Jülich, Germany
| | - Imis Dogan
- Department of Neurology, RWTH Aachen University, Aachen, Germany.,Research Centre Jülich, Institute of Neuroscience and Medicine (INM-1, INM-7, INM-11), Jülich, Germany.,JARA-BRAIN, Jülich-Aachen Research Alliance, Jülich, Germany
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EEG Feature Extraction Based on a Bilevel Network: Minimum Spanning Tree and Regional Network. ELECTRONICS 2020. [DOI: 10.3390/electronics9020203] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Feature extraction is essential for classifying different motor imagery (MI) tasks in a brain–computer interface (BCI). Although the methods of brain network analysis have been widely studied in the BCI field, these methods are limited by differences in network size, density, and standardization. To address this issue and improve classification accuracy, we propose a novel method, in which the hybrid features of the brain function based on the bilevel network are extracted. Minimum spanning tree (MST) based on electroencephalogram (EEG) signal nodes in different MIs is constructed as the first network layer to solve the global network connectivity problem. In addition, the regional network in different movement patterns is constructed as the second network layer to determine the network characteristics, which is consistent with the correspondence between limb movement patterns and cerebral cortex in neurophysiology. We attempt to apply MST to the classification of the MI EEG signals, and the bilevel network has better interpretability. Thereafter, a vector is formed by combining the MST fundamental features with the directional features of the regional network. Our method is validated using the BCI Competition IV Dataset I. Experimental results verify the feasibility of the bilevel network framework. Furthermore, the average classification performance of the proposed method reaches 89.50%, which is higher than that of other competing methods, thereby indicating that the bilevel network is effective for MI classification.
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235
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A brain connectivity characterization of children with different levels of mathematical achievement based on graph metrics. PLoS One 2020; 15:e0227613. [PMID: 31951604 PMCID: PMC6968862 DOI: 10.1371/journal.pone.0227613] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Accepted: 12/21/2019] [Indexed: 11/30/2022] Open
Abstract
Recent studies aiming to facilitate mathematical skill development in primary school children have explored the electrophysiological characteristics associated with different levels of arithmetic achievement. The present work introduces an alternative EEG signal characterization using graph metrics and, based on such features, a classification analysis using a decision tree model. This proposal aims to identify group differences in brain connectivity networks with respect to mathematical skills in elementary school children. The methods of analysis utilized were signal-processing (EEG artifact removal, Laplacian filtering, and magnitude square coherence measurement) and the characterization (Graph metrics) and classification (Decision Tree) of EEG signals recorded during performance of a numerical comparison task. Our results suggest that the analysis of quantitative EEG frequency-band parameters can be used successfully to discriminate several levels of arithmetic achievement. Specifically, the most significant results showed an accuracy of 80.00% (α band), 78.33% (δ band), and 76.67% (θ band) in differentiating high-skilled participants from low-skilled ones, averaged-skilled subjects from all others, and averaged-skilled participants from low-skilled ones, respectively. The use of a decision tree tool during the classification stage allows the identification of several brain areas that seem to be more specialized in numerical processing.
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236
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Muller AM, Meyerhoff DJ. Does an Over-Connected Visual Cortex Undermine Efforts to Stay Sober After Treatment for Alcohol Use Disorder? Front Psychiatry 2020; 11:536706. [PMID: 33362591 PMCID: PMC7758478 DOI: 10.3389/fpsyt.2020.536706] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 11/11/2020] [Indexed: 11/13/2022] Open
Abstract
A fine-tuned interplay of highly synchronized activity within and between the brain's communities is a crucial feature of the brain's functional organization. We wanted to investigate in individuals with alcohol use disorder (AUD) the degree to which the interplay of the brain's community-architecture and the extended brain reward system (eBRS) is affected by drinking status (relapse or abstinence). We used Graph Theory Analysis of resting-state fMRI data from treatment seekers at 1 month of abstinence to model the brain's intrinsic community configuration and their follow-up data as abstainers or relapsers 3 months later to quantify the degree of global across-community interaction between the eBRS and the intrinsic communities at both timepoints. After 1 month of abstinence, the ventromedial PFC in particular showed a significantly higher global across-community interaction in the 22 future relapsers when compared to 30 light/non-drinking controls. These differences were no longer present 3 months later when the relapsers had resumed drinking. We found no significant differences between abstainers and controls at either timepoint. Post hoc tests revealed that one eBRS region, the ventromedial PFC, showed a significant global across-community interaction with a community comprising the visual cortex in relapsers at baseline. In contrast, abstainers showed a significant negative association of the ventromedial PFC with the visual cortex. The increased across-community interaction of the ventromedial PFC and the visual cortex in relapsers at timepoint 1 may be an early indicator for treatment failure in a subgroup of AUD patients.
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Affiliation(s)
- Angela M Muller
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Dieter J Meyerhoff
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
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237
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Martínez DE, Rudas J, Demertzi A, Charland‐Verville V, Soddu A, Laureys S, Gómez F. Reconfiguration of large-scale functional connectivity in patients with disorders of consciousness. Brain Behav 2020; 10:e1476. [PMID: 31773918 PMCID: PMC6955826 DOI: 10.1002/brb3.1476] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Revised: 08/23/2019] [Accepted: 10/11/2019] [Indexed: 12/15/2022] Open
Abstract
INTRODUCTION Functional connectivity alterations within individual resting state networks (RSNs) are linked to disorders of consciousness (DOC). If these alterations influence the interaction quality with other RNSs, then, brain alterations in patients with DOC would be characterized by connectivity changes in the large-scale model composed of RSNs. How are functional interactions between RSNs influenced by internal alterations of individual RSNs? Do the functional alterations induced by DOC change some key properties of the large-scale network, which have been suggested to be critical for the consciousness emergence? Here, we use network analysis to measure functional connectivity in patients with DOC and address these questions. We hypothesized that network properties provide descriptions of brain functional reconfiguration associated with consciousness alterations. METHODS We apply nodal and global network measurements to study the reconfiguration linked with the disease severity. We study changes in integration, segregation, and centrality properties of the functional connectivity between the RSNs in subjects with different levels of consciousness. RESULTS Our analysis indicates that nodal measurements are more sensitive to disease severity than global measurements, particularly, for functional connectivity of sensory and cognitively related RSNs. CONCLUSION The network property alterations of functional connectivity in different consciousness levels suggest a whole-brain topological reorganization of the large-scale functional connectivity in patients with DOC.
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Affiliation(s)
- Darwin E. Martínez
- Department of Systems and Computing EngineeringFacultad de IngenieríaUniversidad Nacional de ColombiaBogotáColombia
- Department of Systems EngineeringUniversidad CentralBogotáColombia
| | - Jorge Rudas
- Department of BiotechnologyUniversidad Nacional de ColombiaBogotáColombia
| | - Athena Demertzi
- Coma Science GroupGIGA‐Research and Cyclotron Research CentreUniversity of LiègeLiègeBelgium
| | | | - Andrea Soddu
- Physics and Astronomy DepartmentBrain and Mind InstituteWestern UniversityLondonONCanada
| | - Steven Laureys
- Coma Science GroupGIGA‐Research and Cyclotron Research CentreUniversity of LiègeLiègeBelgium
| | - Francisco Gómez
- Departamento de MatemáticasFacultad de CienciasUniversidad Nacional de ColombiaBogotáColombia
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Kastanenka KV, Moreno-Bote R, De Pittà M, Perea G, Eraso-Pichot A, Masgrau R, Poskanzer KE, Galea E. A roadmap to integrate astrocytes into Systems Neuroscience. Glia 2020; 68:5-26. [PMID: 31058383 PMCID: PMC6832773 DOI: 10.1002/glia.23632] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 04/08/2019] [Accepted: 04/09/2019] [Indexed: 12/14/2022]
Abstract
Systems neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well-documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub-serve coding and higher-brain functions. First, we reviewed Systems-like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca2+ transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in a time scale of subseconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte-neuronal circuits, is, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration such as predictive coding and energy-efficient coding. Clarifying the relationship between astrocytic Ca2+ and brain coding may represent a leap forward toward novel approaches in the study of astrocytes in health and disease.
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Affiliation(s)
- Ksenia V. Kastanenka
- Department of Neurology, MassGeneral Institute for Neurodegenerative Diseases, Massachusetts General Hospital and Harvard Medical School, Massachusetts 02129, USA
| | - Rubén Moreno-Bote
- Department of Information and Communications Technologies, Center for Brain and Cognition and Universitat Pompeu Fabra, 08018 Barcelona, Spain
- ICREA, 08010 Barcelona, Spain
| | | | | | - Abel Eraso-Pichot
- Departament de Bioquímica, Institut de Neurociències i Universitat Autònoma de Barcelona, Bellaterra, 08193 Barcelona, Spain
| | - Roser Masgrau
- Departament de Bioquímica, Institut de Neurociències i Universitat Autònoma de Barcelona, Bellaterra, 08193 Barcelona, Spain
| | - Kira E. Poskanzer
- Department of Biochemistry & Biophysics, Neuroscience Graduate Program, and Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, California 94143, USA
- Equally contributing authors
| | - Elena Galea
- ICREA, 08010 Barcelona, Spain
- Departament de Bioquímica, Institut de Neurociències i Universitat Autònoma de Barcelona, Bellaterra, 08193 Barcelona, Spain
- Equally contributing authors
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239
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Onesto V, Accardo A, Vieu C, Gentile F. Small-world networks of neuroblastoma cells cultured in three-dimensional polymeric scaffolds featuring multi-scale roughness. Neural Regen Res 2020; 15:759-768. [PMID: 31638101 PMCID: PMC6975141 DOI: 10.4103/1673-5374.266923] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Understanding the mechanisms underlying cell-surface interaction is of fundamental importance for the rational design of scaffolds aiming at tissue engineering, tissue repair and neural regeneration applications. Here, we examined patterns of neuroblastoma cells cultured in three-dimensional polymeric scaffolds obtained by two-photon lithography. Because of the intrinsic resolution of the technique, the micrometric cylinders composing the scaffold have a lateral step size of ~200 nm, a surface roughness of around 20 nm, and large values of fractal dimension approaching 2.7. We found that cells in the scaffold assemble into separate groups with many elements per group. After cell wiring, we found that resulting networks exhibit high clustering, small path lengths, and small-world characteristics. These values of the topological characteristics of the network can potentially enhance the quality, quantity and density of information transported in the network compared to equivalent random graphs of the same size. This is one of the first direct observations of cells developing into 3D small-world networks in an artificial matrix.
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Affiliation(s)
- Valentina Onesto
- Center for Advanced Biomaterials for Healthcare, Italian Institute of Technology, Naples, Italy
| | - Angelo Accardo
- Laboratoire d'Analyse et d'Architecture des Systemes (LAAS), Centre National de la Recherche Scientifique, Universite de Toulouse, CNRS, Toulouse, France; Current address: Department of Precision and Microsystems Engineering, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands
| | - Christophe Vieu
- Laboratoire d'Analyse et d'Architecture des Systèmes (LAAS), Centre National de la Recherche Scientifique, Université de Toulouse, CNRS; Institut National des Sciences Appliquées - INSA, Toulouse, France
| | - Francesco Gentile
- Department of Electric Engineering and Information Technology, University Federico II, Naples, Italy
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240
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Rosell-Tarragó G, Díaz-Guilera A. Functionability in complex networks: Leading nodes for the transition from structural to functional networks through remote asynchronization. CHAOS (WOODBURY, N.Y.) 2020; 30:013105. [PMID: 32013516 DOI: 10.1063/1.5099621] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Accepted: 12/13/2019] [Indexed: 06/10/2023]
Abstract
Complex networks are essentially heterogeneous not only in the basic properties of the constituent nodes, such as their degree, but also in the effects that these have on the global dynamical properties of the network. Networks of coupled identical phase oscillators are good examples for analyzing these effects, since an overall synchronized state can be considered a reference state. A small variation of intrinsic node parameters may cause the system to move away from synchronization, and a new phase-locked stationary state can be achieved. We propose a measure of phase dispersion that quantifies the functional response of the system to a given local perturbation. As a particular implementation, we propose a variation of the standard Kuramoto model in which the nodes of a complex network interact with their neighboring nodes, by including a node-dependent frustration parameter. The final stationary phase-locked state now depends on the particular frustration parameter at each node and also on the network topology. We exploit this scenario by introducing individual frustration parameters and measuring what their effect on the whole network is, measured in terms of the phase dispersion, which depends only on the topology of the network and on the choice of the particular node that is perturbed. This enables us to define a characteristic of the node, its functionability, that can be computed analytically in terms of the network topology. Finally, we provide a thorough comparison with other centrality measures.
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Affiliation(s)
- Gemma Rosell-Tarragó
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, 08028 Barcelona, Spain
| | - Albert Díaz-Guilera
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, 08028 Barcelona, Spain
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241
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Tang L, Mostafa S, Liao B, Wu FX. A network clustering based feature selection strategy for classifying autism spectrum disorder. BMC Med Genomics 2019; 12:153. [PMID: 31888621 PMCID: PMC6936069 DOI: 10.1186/s12920-019-0598-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Accepted: 10/09/2019] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Advanced non-invasive neuroimaging techniques offer new approaches to study functions and structures of human brains. Whole-brain functional networks obtained from resting state functional magnetic resonance imaging has been widely used to study brain diseases like autism spectrum disorder (ASD). Auto-classification of ASD has become an important issue. Existing classification methods for ASD are based on features extracted from the whole-brain functional networks, which may be not discriminant enough for good performance. METHODS In this study, we propose a network clustering based feature selection strategy for classifying ASD. In our proposed method, we first apply symmetric non-negative matrix factorization to divide brain networks into four modules. Then we extract features from one of four modules called default mode network (DMN) and use them to train several classifiers for ASD classification. RESULTS The computational experiments show that our proposed method achieves better performances than those trained with features extracted from the whole brain network. CONCLUSION It is a good strategy to train the classifiers for ASD based on features from the default mode subnetwork.
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Affiliation(s)
- Lingkai Tang
- Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, S7N 5A9 Canada
| | - Sakib Mostafa
- Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, S7N 5A9 Canada
| | - Bo Liao
- School of Mathematics and Statistics, Hainan Normal University, Haikou, 571158 China
| | - Fang-Xiang Wu
- Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, S7N 5A9 Canada
- Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, S7N 5A9 Canada
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242
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Peraza-Goicolea JA, Martínez-Montes E, Aubert E, Valdés-Hernández PA, Mulet R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Netw 2019; 123:52-69. [PMID: 31830607 DOI: 10.1016/j.neunet.2019.11.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Revised: 11/13/2019] [Accepted: 11/18/2019] [Indexed: 10/25/2022]
Abstract
In this work, we propose a natural model for information flow in the brain through a neural message-passing dynamics on a structural network of macroscopic regions, such as the human connectome (HC). In our model, each brain region is assumed to have a binary behavior (active or not), the strengths of interactions among them are encoded in the anatomical connectivity matrix defined by the HC, and the dynamics of the system is defined by the Belief Propagation (BP) algorithm, working near the critical point of the network. We show that in the absence of direct external stimuli the BP algorithm converges to a spatial map of activations that is similar to the Default Mode Network (DMN) of the brain, which has been defined from the analysis of functional MRI data. Moreover, we use Susceptibility Propagation (SP) to compute the matrix of long-range correlations between the different regions and show that the modules defined by a clustering of this matrix resemble several Resting State Networks (RSN) determined experimentally. Both results suggest that the functional DMN and RSNs can be seen as simple consequences of the anatomical structure of the brain and a neural message-passing dynamics between macroscopic regions. With the new model, we explore predictions on how functional maps change when the anatomical brain network suffers structural alterations, like in Alzheimer's disease and in lesions of the Corpus Callosum. The implications and novel interpretations suggested by the model, as well as the role of criticality, are discussed.
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Affiliation(s)
- Julio A Peraza-Goicolea
- Group of Complex Systems and Statistical Physics, Department of Theoretical Physics, University of Havana, Havana, Cuba; Department of Physics, Florida International University, Miami, FL, USA.
| | - Eduardo Martínez-Montes
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba; Advanced Center for Electrical and Electronic Engineering (AC3E), Universidad Técnica Federico Santa María, Valparaíso, Chile.
| | - Eduardo Aubert
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba.
| | | | - Roberto Mulet
- Group of Complex Systems and Statistical Physics, Department of Theoretical Physics, University of Havana, Havana, Cuba.
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243
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Shin KJ, Lee HJ, Park KM. Alterations of individual thalamic nuclei volumes in patients with migraine. J Headache Pain 2019; 20:112. [PMID: 31818256 PMCID: PMC6902536 DOI: 10.1186/s10194-019-1063-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2019] [Accepted: 11/29/2019] [Indexed: 01/07/2023] Open
Abstract
Background The aim of this study is to investigate the alterations of thalamic nuclei volumes and the intrinsic thalamic network in patients with migraine. Methods We enrolled 35 patients with migraine without aura and 40 healthy controls. All subjects underwent three-dimensional T1-weighted imaging. The thalamic nuclei were segmented using the FreeSurfer program. We investigated volume changes of individual thalamic nuclei and analyzed the alterations of the intrinsic thalamic network based on volumes in the patients with migraine. Results Right and left thalamic volumes as a whole were not different between the patients with migraine and healthy controls. However, we found that right anteroventral and right and left medial geniculate nuclei volumes were significantly increased (0.00985% vs. 0.00864%, p = 0.0002; 0.00929% vs. 0.00823%, p = 0.0005; 0.00939% vs. 0.00769%, p < 0.0001; respectively) whereas right and left parafascicular nuclei volumes were decreased in the patients with migraine (0.00359% vs. 0.00435%, p < 0.0001; 0.00360% vs. 0.00438%, p < 0.0001; respectively) compared with healthy controls. The network measures of the intrinsic thalamic network were not different between the groups. Conclusions We found significant alterations of thalamic nuclei volumes in patients with migraine compared with healthy controls. These findings might contribute to the underlying pathogenesis of the migraine. Trial registration None.
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Affiliation(s)
- Kyong Jin Shin
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, South Korea
| | - Ho-Joon Lee
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, South Korea
| | - Kang Min Park
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, South Korea.
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244
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Machine learning: assessing neurovascular signals in the prefrontal cortex with non-invasive bimodal electro-optical neuroimaging in opiate addiction. Sci Rep 2019; 9:18262. [PMID: 31797878 PMCID: PMC6892956 DOI: 10.1038/s41598-019-54316-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Accepted: 11/09/2019] [Indexed: 02/07/2023] Open
Abstract
Chronic and recurrent opiate use injuries brain tissue and cause serious pathophysiological changes in hemodynamic and subsequent inflammatory responses. Prefrontal cortex (PFC) has been implicated in drug addiction. However, the mechanism underlying systems-level neuroadaptations in PFC during abstinence has not been fully characterized. The objective of our study was to determine what neural oscillatory activity contributes to the chronic effect of opiate exposure and whether the activity could be coupled to neurovascular information in the PFC. We employed resting-state functional connectivity to explore alterations in 8 patients with heroin dependency who stayed abstinent (>3 months; HD) compared with 11 control subjects. A non-invasive neuroimaging strategy was applied to combine electrophysiological signals through electroencephalography (EEG) with hemodynamic signals through functional near-infrared spectroscopy (fNIRS). The electrophysiological signals indicate neural synchrony and the oscillatory activity, and the hemodynamic signals indicate blood oxygenation in small vessels in the PFC. A supervised machine learning method was used to obtain associations between EEG and fNIRS modalities to improve precision and localization. HD patients demonstrated desynchronized lower alpha rhythms and decreased connectivity in PFC networks. Asymmetric excitability and cerebrovascular injury were also observed. This pilot study suggests that cerebrovascular injury in PFC may result from chronic opiate intake.
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245
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McClintock CH, Worhunsky PD, Xu J, Balodis IM, Sinha R, Miller L, Potenza MN. Spiritual experiences are related to engagement of a ventral frontotemporal functional brain network: Implications for prevention and treatment of behavioral and substance addictions. J Behav Addict 2019; 8:678-691. [PMID: 31891313 PMCID: PMC7044576 DOI: 10.1556/2006.8.2019.71] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND AND AIMS Spirituality is an important component of 12-step programs for behavioral and substance addictions and has been linked to recovery processes. Understanding the neural correlates of spiritual experiences may help to promote efforts to enhance recovery processes in behavioral addictions. We recently used general linear model (GLM) analyses of functional magnetic resonance imaging data to examine neural correlates of spiritual experiences, with findings implicating cortical and subcortical brain regions. Although informative, the GLM-based approach does not provide insight into brain circuits that may underlie spiritual experiences. METHODS Spatial independent component analysis (sICA) was used to identify functional brain networks specifically linked to spiritual (vs. stressful or neutral-relaxing) conditions using a previously validated guided imagery task in 27 young adults. RESULTS Using sICA, engagement of a ventral frontotemporal network was identified that was engaged at the onset and conclusion of the spiritual condition in a manner distinct from engagement during the stress or neutral-relaxing conditions. Degree of engagement correlated with subjective reports of spirituality in the scanner (r = .71, p < .001) and an out-of-the-magnet measure of spirituality (r = .48, p < .018). DISCUSSION AND CONCLUSION The current findings suggest a distributed functional neural network associated with spiritual experiences and provide a foundation for investigating brain mechanisms underlying the role of spirituality in recovery from behavioral addictions.
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Affiliation(s)
- Clayton H. McClintock
- Spirituality Mind Body Institute, Teachers College, Columbia University, New York, NY, USA
| | - Patrick D. Worhunsky
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Jiansong Xu
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Iris M. Balodis
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA,Peter Boris Centre for Addictions Research, Department of Psychiatry and Behavioral Neurosciences, DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada
| | - Rajita Sinha
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA,Child Study Center, Yale University School of Medicine, New Haven, CT, USA,Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Lisa Miller
- Spirituality Mind Body Institute, Teachers College, Columbia University, New York, NY, USA
| | - Marc N. Potenza
- Child Study Center, Yale University School of Medicine, New Haven, CT, USA,Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA,Connecticut Mental Health Center, New Haven, CT, USA,Connecticut Council on Problem Gambling, Wethersfield, CT, USA,Corresponding author: Marc N. Potenza, MD, PhD; Department of Neuroscience, Yale University School of Medicine, 1 Church Street, 7th floor New Haven, CT 06510, USA; Phone: +1 203 737 3553; Fax: +1 203 737 3591; E-mail:
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Wu S, Zhao W, Rowson B, Rowson S, Ji S. A network-based response feature matrix as a brain injury metric. Biomech Model Mechanobiol 2019; 19:927-942. [PMID: 31760600 DOI: 10.1007/s10237-019-01261-y] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Accepted: 11/11/2019] [Indexed: 01/06/2023]
Abstract
Conventional brain injury metrics are scalars that treat the whole head/brain as a single unit but do not characterize the distribution of brain responses. Here, we establish a network-based "response feature matrix" to characterize the magnitude and distribution of impact-induced brain strains. The network nodes and edges encode injury risks to the gray matter regions and their white matter interconnections, respectively. The utility of the metric is illustrated in injury prediction using three independent, real-world datasets: two reconstructed impact datasets from the National Football League (NFL) and Virginia Tech, respectively, and measured concussive and non-injury impacts from Stanford University. Injury predictions with leave-one-out cross-validation are conducted using the two reconstructed datasets separately, and then by combining all datasets into one. Using support vector machine, the network-based injury predictor consistently outperforms four baseline scalar metrics including peak maximum principal strain of the whole brain (MPS), peak linear/rotational acceleration, and peak rotational velocity across all five selected performance measures (e.g., maximized accuracy of 0.887 vs. 0.774 and 0.849 for MPS and rotational acceleration with corresponding positive predictive values of 0.938, 0.772, and 0.800, respectively, using the reconstructed NFL dataset). With sufficient training data, real-world injury prediction is similar to leave-one-out in-sample evaluation, suggesting the potential advantage of the network-based injury metric over conventional scalar metrics. The network-based response feature matrix significantly extends scalar metrics by sampling the brain strains more completely, which may serve as a useful framework potentially allowing for other applications such as characterizing injury patterns or facilitating targeted multi-scale modeling in the future.
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Affiliation(s)
- Shaoju Wu
- Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA, 01605, USA
| | - Wei Zhao
- Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA, 01605, USA
| | - Bethany Rowson
- Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, USA
| | - Steven Rowson
- Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, USA
| | - Songbai Ji
- Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA, 01605, USA.
- Department of Mechanical Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA, 01609, USA.
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247
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López-Madrona VJ, Matias FS, Mirasso CR, Canals S, Pereda E. Inferring correlations associated to causal interactions in brain signals using autoregressive models. Sci Rep 2019; 9:17041. [PMID: 31745163 PMCID: PMC6863873 DOI: 10.1038/s41598-019-53453-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 10/26/2019] [Indexed: 12/22/2022] Open
Abstract
The specific connectivity of a neuronal network is reflected in the dynamics of the signals recorded on its nodes. The analysis of how the activity in one node predicts the behaviour of another gives the directionality in their relationship. However, each node is composed of many different elements which define the properties of the links. For instance, excitatory and inhibitory neuronal subtypes determine the functionality of the connection. Classic indexes such as the Granger causality (GC) quantifies these interactions, but they do not infer into the mechanism behind them. Here, we introduce an extension of the well-known GC that analyses the correlation associated to the specific influence that a transmitter node has over the receiver. This way, the G-causal link has a positive or negative effect if the predicted activity follows directly or inversely, respectively, the dynamics of the sender. The method is validated in a neuronal population model, testing the paradigm that excitatory and inhibitory neurons have a differential effect in the connectivity. Our approach correctly infers the positive or negative coupling produced by different types of neurons. Our results suggest that the proposed approach provides additional information on the characterization of G-causal connections, which is potentially relevant when it comes to understanding interactions in the brain circuits.
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Affiliation(s)
| | - Fernanda S Matias
- Cognitive Neuroimaging Unit, Commissariat à l'Energie Atomique (CEA), INSERM U992, NeuroSpin Center, 91191, Gif-sur-Yvete, France.,Instituto de Física, Universidade Federal de Alagoas, 57072-970, Maceió, Alagoas, Brazil
| | - Claudio R Mirasso
- Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (UIB-CSIC), Campus Universitat de les Illes Balears, E-07122, Palma de Mallorca, Spain
| | - Santiago Canals
- Instituto de Neurociencias, CSIC-UMH, Sant Joan d'Alacant, 03550, Spain
| | - Ernesto Pereda
- Departamento de Ingeniería Industrial, Escuela Superior de Ingeniería y Tecnología, IUNE, Universidad de La Laguna, Tenerife, 38205, Spain. .,Laboratory of Cognitive and Computational Neuroscience, CTB, UPM, Madrid, Spain.
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248
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Levin RJ. The Clitoris—An Appraisal of its Reproductive Function During the Fertile Years: Why Was It, and Still Is, Overlooked in Accounts of Female Sexual Arousal. Clin Anat 2019; 33:136-145. [DOI: 10.1002/ca.23498] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Accepted: 09/19/2019] [Indexed: 12/17/2022]
Affiliation(s)
- Roy J. Levin
- Independent Research Investigator Sheffield United Kingdom
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249
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Multi-view learning-based data proliferator for boosting classification using highly imbalanced classes. J Neurosci Methods 2019; 327:108344. [DOI: 10.1016/j.jneumeth.2019.108344] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2019] [Revised: 06/17/2019] [Accepted: 07/01/2019] [Indexed: 01/07/2023]
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250
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Farooq H, Chen Y, Georgiou TT, Tannenbaum A, Lenglet C. Network curvature as a hallmark of brain structural connectivity. Nat Commun 2019; 10:4937. [PMID: 31666510 PMCID: PMC6821808 DOI: 10.1038/s41467-019-12915-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Accepted: 10/03/2019] [Indexed: 12/14/2022] Open
Abstract
Although brain functionality is often remarkably robust to lesions and other insults, it may be fragile when these take place in specific locations. Previous attempts to quantify robustness and fragility sought to understand how the functional connectivity of brain networks is affected by structural changes, using either model-based predictions or empirical studies of the effects of lesions. We advance a geometric viewpoint relying on a notion of network curvature, the so-called Ollivier-Ricci curvature. This approach has been proposed to assess financial market robustness and to differentiate biological networks of cancer cells from healthy ones. Here, we apply curvature-based measures to brain structural networks to identify robust and fragile brain regions in healthy subjects. We show that curvature can also be used to track changes in brain connectivity related to age and autism spectrum disorder (ASD), and we obtain results that are in agreement with previous MRI studies. The brain can often continue to function despite lesions in many areas, but damage to particular locations may have serious effects. Here, the authors use the concept of Ollivier-Ricci curvature to investigate the robustness of brain networks.
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Affiliation(s)
- Hamza Farooq
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA.
| | - Yongxin Chen
- School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Tryphon T Georgiou
- Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA
| | - Allen Tannenbaum
- Departments of Computer Science and Applied Mathematics & Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Christophe Lenglet
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
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