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Falandays JB, Yoshimi J, Warren WH, Spivey MJ. A potential mechanism for Gibsonian resonance: behavioral entrainment emerges from local homeostasis in an unsupervised reservoir network. Cogn Neurodyn 2024; 18:1811-1834. [PMID: 39104666 PMCID: PMC11297877 DOI: 10.1007/s11571-023-09988-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 06/15/2023] [Accepted: 06/25/2023] [Indexed: 08/07/2024] Open
Abstract
While the cognitivist school of thought holds that the mind is analogous to a computer, performing logical operations over internal representations, the tradition of ecological psychology contends that organisms can directly "resonate" to information for action and perception without the need for a representational intermediary. The concept of resonance has played an important role in ecological psychology, but it remains a metaphor. Supplying a mechanistic account of resonance requires a non-representational account of central nervous system (CNS) dynamics. Towards this, we present a series of simple models in which a reservoir network with homeostatic nodes is used to control a simple agent embedded in an environment. This network spontaneously produces behaviors that are adaptive in each context, including (1) visually tracking a moving object, (2) substantially above-chance performance in the arcade game Pong, (2) and avoiding walls while controlling a mobile agent. Upon analyzing the dynamics of the networks, we find that behavioral stability can be maintained without the formation of stable or recurring patterns of network activity that could be identified as neural representations. These results may represent a useful step towards a mechanistic grounding of resonance and a view of the CNS that is compatible with ecological psychology.
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Affiliation(s)
| | - Jeffrey Yoshimi
- Department of Cognitive and Information Sciences, University of California, Merced, Merced, USA
| | - William H. Warren
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, USA
| | - Michael J. Spivey
- Department of Cognitive and Information Sciences, University of California, Merced, Merced, USA
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2
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Zhu H, Michalak AJ, Merricks EM, Agopyan-Miu AHCW, Jacobs J, Hamberger MJ, Sheth SA, McKhann GM, Feldstein N, Schevon CA, Hillman EMC. Spectral-switching analysis reveals real-time neuronal network representations of concurrent spontaneous naturalistic behaviors in human brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.08.600416. [PMID: 39026706 PMCID: PMC11257469 DOI: 10.1101/2024.07.08.600416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Despite abundant evidence of functional networks in the human brain, their neuronal underpinnings, and relationships to real-time behavior have been challenging to resolve. Analyzing brain-wide intracranial-EEG recordings with video monitoring, acquired in awake subjects during clinical epilepsy evaluation, we discovered the tendency of each brain region to switch back and forth between 2 distinct power spectral densities (PSDs 2-55Hz). We further recognized that this 'spectral switching' occurs synchronously between distant sites, even between regions with differing baseline PSDs, revealing long-range functional networks that would be obscured in analysis of individual frequency bands. Moreover, the real-time PSD-switching dynamics of specific networks exhibited striking alignment with activities such as conversation and hand movements, revealing a multi-threaded functional network representation of concurrent naturalistic behaviors. Network structures and their relationships to behaviors were stable across days, but were altered during N3 sleep. Our results provide a new framework for understanding real-time, brain-wide neural-network dynamics.
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Affiliation(s)
- Hongkun Zhu
- Department of Biomedical Engineering, Columbia University
- Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027
| | - Andrew J Michalak
- Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Edward M Merricks
- Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | | | - Joshua Jacobs
- Department of Biomedical Engineering, Columbia University
- Department of Neurological Surgery, Columbia University Medical Center, New York, 10032, New York, USA
| | - Marla J Hamberger
- Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Sameer A Sheth
- Department of Neurological Surgery, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Guy M McKhann
- Department of Neurological Surgery, Columbia University Medical Center, New York, 10032, New York, USA
| | - Neil Feldstein
- Department of Neurological Surgery, Columbia University Medical Center, New York, 10032, New York, USA
| | - Catherine A Schevon
- Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Elizabeth M C Hillman
- Department of Biomedical Engineering, Columbia University
- Department of Radiology, Columbia University Medical Center, New York, 10032, New York, USA
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027
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3
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De Ridder D, Siddiqi MA, Dauwels J, Serdijn WA, Strydis C. NeuroDots: From Single-Target to Brain-Network Modulation: Why and What Is Needed? Neuromodulation 2024; 27:711-729. [PMID: 38639704 DOI: 10.1016/j.neurom.2024.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 11/05/2023] [Accepted: 01/10/2024] [Indexed: 04/20/2024]
Abstract
OBJECTIVES Current techniques in brain stimulation are still largely based on a phrenologic approach that a single brain target can treat a brain disorder. Nevertheless, meta-analyses of brain implants indicate an overall success rate of 50% improvement in 50% of patients, irrespective of the brain-related disorder. Thus, there is still a large margin for improvement. The goal of this manuscript is to 1) develop a general theoretical framework of brain functioning that is amenable to surgical neuromodulation, and 2) describe the engineering requirements of the next generation of implantable brain stimulators that follow from this theoretic model. MATERIALS AND METHODS A neuroscience and engineering literature review was performed to develop a universal theoretical model of brain functioning and dysfunctioning amenable to surgical neuromodulation. RESULTS Even though a single target can modulate an entire network, research in network science reveals that many brain disorders are the consequence of maladaptive interactions among multiple networks rather than a single network. Consequently, targeting the main connector hubs of those multiple interacting networks involved in a brain disorder is theoretically more beneficial. We, thus, envision next-generation network implants that will rely on distributed, multisite neuromodulation targeting correlated and anticorrelated interacting brain networks, juxtaposing alternative implant configurations, and finally providing solid recommendations for the realization of such implants. In doing so, this study pinpoints the potential shortcomings of other similar efforts in the field, which somehow fall short of the requirements. CONCLUSION The concept of network stimulation holds great promise as a universal approach for treating neurologic and psychiatric disorders.
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Affiliation(s)
- Dirk De Ridder
- Section of Neurosurgery, Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand.
| | - Muhammad Ali Siddiqi
- Department of Electrical Engineering, Lahore University of Management Sciences, Lahore, Pakistan; Neuroscience Department, Erasmus Medical Center, Rotterdam, The Netherlands; Quantum and Computer Engineering Department, Delft University of Technology, Delft, The Netherlands
| | - Justin Dauwels
- Microelectronics Department, Delft University of Technology, Delft, The Netherlands
| | - Wouter A Serdijn
- Neuroscience Department, Erasmus Medical Center, Rotterdam, The Netherlands; Section Bioelectronics, Delft University of Technology, Delft, The Netherlands
| | - Christos Strydis
- Neuroscience Department, Erasmus Medical Center, Rotterdam, The Netherlands; Quantum and Computer Engineering Department, Delft University of Technology, Delft, The Netherlands
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4
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Tsai WX, Tsai SJ, Lin CP, Huang NE, Yang AC. Exploring timescale-specific functional brain networks and their associations with aging and cognitive performance in a healthy cohort without dementia. Neuroimage 2024; 289:120540. [PMID: 38355076 DOI: 10.1016/j.neuroimage.2024.120540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 02/02/2024] [Accepted: 02/12/2024] [Indexed: 02/16/2024] Open
Abstract
INTRODUCTION Functional brain networks (FBNs) coordinate brain functions and are studied in fMRI using blood-oxygen-level-dependent (BOLD) signal correlations. Previous research links FBN changes to aging and cognitive decline, but various physiological factors influnce BOLD signals. Few studies have investigated the intrinsic components of the BOLD signal in different timescales using signal decomposition. This study aimed to explore differences between intrinsic FBNs and traditional BOLD-FBN, examining their associations with age and cognitive performance in a healthy cohort without dementia. MATERIALS AND METHODS A total of 396 healthy participants without dementia (men = 157; women = 239; age range = 20-85 years) were enrolled in this study. The BOLD signal was decomposed into several intrinsic signals with different timescales using ensemble empirical mode decomposition, and FBNs were constructed based on both the BOLD and intrinsic signals. Subsequently, network features-global efficiency and local efficiency values-were estimated to determine their relationship with age and cognitive performance. RESULTS The findings revealed that the global efficiency of traditional BOLD-FBN correlated significantly with age, with specific intrinsic FBNs contributing to these correlations. Moreover, local efficiency analysis demonstrated that intrinsic FBNs were more meaningful than traditional BOLD-FBN in identifying brain regions related to age and cognitive performance. CONCLUSIONS These results underscore the importance of exploring timescales of BOLD signals when constructing FBN and highlight the relevance of specific intrinsic FBNs to aging and cognitive performance. Consequently, this decomposition-based FBN-building approach may offer valuable insights for future fMRI studies.
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Affiliation(s)
- Wen-Xiang Tsai
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
| | - Shih-Jen Tsai
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11217, Taiwan
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
| | - Norden E Huang
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
| | - Albert C Yang
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; Department of Medical Research, Taipei Veterans General Hospital, Taipei 11217, Taiwan; Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan.
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5
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Haiduk F, Zatorre RJ, Benjamin L, Morillon B, Albouy P. Spectrotemporal cues and attention jointly modulate fMRI network topology for sentence and melody perception. Sci Rep 2024; 14:5501. [PMID: 38448636 PMCID: PMC10917817 DOI: 10.1038/s41598-024-56139-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 03/01/2024] [Indexed: 03/08/2024] Open
Abstract
Speech and music are two fundamental modes of human communication. Lateralisation of key processes underlying their perception has been related both to the distinct sensitivity to low-level spectrotemporal acoustic features and to top-down attention. However, the interplay between bottom-up and top-down processes needs to be clarified. In the present study, we investigated the contribution of acoustics and attention to melodies or sentences to lateralisation in fMRI functional network topology. We used sung speech stimuli selectively filtered in temporal or spectral modulation domains with crossed and balanced verbal and melodic content. Perception of speech decreased with degradation of temporal information, whereas perception of melodies decreased with spectral degradation. Applying graph theoretical metrics on fMRI connectivity matrices, we found that local clustering, reflecting functional specialisation, linearly increased when spectral or temporal cues crucial for the task goal were incrementally degraded. These effects occurred in a bilateral fronto-temporo-parietal network for processing temporally degraded sentences and in right auditory regions for processing spectrally degraded melodies. In contrast, global topology remained stable across conditions. These findings suggest that lateralisation for speech and music partially depends on an interplay of acoustic cues and task goals under increased attentional demands.
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Affiliation(s)
- Felix Haiduk
- Department of Behavioral and Cognitive Biology, University of Vienna, Vienna, Austria.
- Department of General Psychology, University of Padua, Padua, Italy.
| | - Robert J Zatorre
- Cognitive Neuroscience Unit, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- International Laboratory for Brain, Music and Sound Research (BRAMS) - CRBLM, Montreal, QC, Canada
| | - Lucas Benjamin
- Cognitive Neuroscience Unit, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Cognitive Neuroimaging Unit, CNRS ERL 9003, INSERM U992, CEA, Université Paris-Saclay, NeuroSpin Center, 91191, Gif/Yvette, France
| | - Benjamin Morillon
- Aix Marseille University, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Philippe Albouy
- Cognitive Neuroscience Unit, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- International Laboratory for Brain, Music and Sound Research (BRAMS) - CRBLM, Montreal, QC, Canada
- CERVO Brain Research Centre, School of Psychology, Laval University, Quebec, QC, Canada
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6
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Parodi G, Zanini G, Chiappalone M, Martinoia S. Electrical and chemical modulation of homogeneous and heterogeneous human-iPSCs-derived neuronal networks on high density arrays. Front Mol Neurosci 2024; 17:1304507. [PMID: 38380114 PMCID: PMC10877635 DOI: 10.3389/fnmol.2024.1304507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 01/15/2024] [Indexed: 02/22/2024] Open
Abstract
The delicate "Excitatory/Inhibitory balance" between neurons holds significance in neurodegenerative and neurodevelopmental diseases. With the ultimate goal of creating a faithful in vitro model of the human brain, in this study, we investigated the critical factor of heterogeneity, focusing on the interplay between excitatory glutamatergic (E) and inhibitory GABAergic (I) neurons in neural networks. We used high-density Micro-Electrode Arrays (MEA) with 2304 recording electrodes to investigate two neuronal culture configurations: 100% glutamatergic (100E) and 75% glutamatergic / 25% GABAergic (75E25I) neurons. This allowed us to comprehensively characterize the spontaneous electrophysiological activity exhibited by mature cultures at 56 Days in vitro, a time point in which the GABA shift has already occurred. We explored the impact of heterogeneity also through electrical stimulation, revealing that the 100E configuration responded reliably, while the 75E25I required more parameter tuning for improved responses. Chemical stimulation with BIC showed an increase in terms of firing and bursting activity only in the 75E25I condition, while APV and CNQX induced significant alterations on both dynamics and functional connectivity. Our findings advance understanding of diverse neuron interactions and their role in network activity, offering insights for potential therapeutic interventions in neurological conditions. Overall, this work contributes to the development of a valuable human-based in vitro system for studying physiological and pathological conditions, emphasizing the pivotal role of neuron diversity in neural network dynamics.
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Affiliation(s)
| | | | | | - Sergio Martinoia
- Department of Informatics, Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genova, Genoa, Italy
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Di Ieva A. Fractal Analysis in Clinical Neurosciences: An Overview. ADVANCES IN NEUROBIOLOGY 2024; 36:261-271. [PMID: 38468037 DOI: 10.1007/978-3-031-47606-8_13] [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: 03/13/2024]
Abstract
Over the last years, fractals have entered into the realms of clinical neurosciences. The whole brain and its components (i.e., neurons and astrocytes) have been studied as fractal objects, and even more relevant, the fractal-based quantification of the geometrical complexity of histopathological and neuroradiological images as well as neurophysiopathological time series has suggested the existence of a gradient in the pattern representation of neurological diseases. Computational fractal-based parameters have been suggested as potential diagnostic and prognostic biomarkers in different brain diseases, including brain tumors, neurodegeneration, epilepsy, demyelinating diseases, cerebrovascular malformations, and psychiatric disorders as well. This chapter and the entire third section of this book are focused on practical applications of computational fractal-based analysis into the clinical neurosciences, namely, neurology and neuropsychiatry, neuroradiology and neurosurgery, neuropathology, neuro-oncology and neurorehabilitation, neuro-ophthalmology, and cognitive neurosciences, with special emphasis on the translation of the fractal dimension and other fractal parameters as clinical biomarkers useful from bench to bedside.
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Affiliation(s)
- Antonio Di Ieva
- Computational NeuroSurgery (CNS) Lab & Macquarie Neurosurgery, Macquarie Medical School, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, NSW, Australia.
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Yamakou ME, Desroches M, Rodrigues S. Synchronization in STDP-driven memristive neural networks with time-varying topology. J Biol Phys 2023; 49:483-507. [PMID: 37656327 PMCID: PMC10651826 DOI: 10.1007/s10867-023-09642-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 08/07/2023] [Indexed: 09/02/2023] Open
Abstract
Synchronization is a widespread phenomenon in the brain. Despite numerous studies, the specific parameter configurations of the synaptic network structure and learning rules needed to achieve robust and enduring synchronization in neurons driven by spike-timing-dependent plasticity (STDP) and temporal networks subject to homeostatic structural plasticity (HSP) rules remain unclear. Here, we bridge this gap by determining the configurations required to achieve high and stable degrees of complete synchronization (CS) and phase synchronization (PS) in time-varying small-world and random neural networks driven by STDP and HSP. In particular, we found that decreasing P (which enhances the strengthening effect of STDP on the average synaptic weight) and increasing F (which speeds up the swapping rate of synapses between neurons) always lead to higher and more stable degrees of CS and PS in small-world and random networks, provided that the network parameters such as the synaptic time delay [Formula: see text], the average degree [Formula: see text], and the rewiring probability [Formula: see text] have some appropriate values. When [Formula: see text], [Formula: see text], and [Formula: see text] are not fixed at these appropriate values, the degree and stability of CS and PS may increase or decrease when F increases, depending on the network topology. It is also found that the time delay [Formula: see text] can induce intermittent CS and PS whose occurrence is independent F. Our results could have applications in designing neuromorphic circuits for optimal information processing and transmission via synchronization phenomena.
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Affiliation(s)
- Marius E Yamakou
- Department of Data Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstr. 11, 91058, Erlangen, Germany.
- Max-Planck-Institut für Mathematik in den Naturwissenschaften, Inselstr. 22, 04103, Leipzig, Germany.
| | - Mathieu Desroches
- MathNeuro Project-Team, Inria Center at Université Côte d'Azur, 2004 route des Lucioles - BP 93, 06902, Cedex, Sophia Antipolis, France
| | - Serafim Rodrigues
- Mathematical, Computational and Experimental Neuroscience, Basque Center for Applied Mathematics, Alameda de Mazzaredo 14, 48009, Bilbao, Spain
- Ikerbasque, Basque Foundation for Science, Plaza Euskadi 5, 48009, Bilbao, Spain
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Gao Y, Wu X, Yan Y, Li M, Qin F, Ma M, Yuan X, Yang W, Qiu J. The unity and diversity of verbal and visuospatial creativity: Dynamic changes in hemispheric lateralisation. Hum Brain Mapp 2023; 44:6031-6042. [PMID: 37772359 PMCID: PMC10619400 DOI: 10.1002/hbm.26494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 09/02/2023] [Accepted: 09/14/2023] [Indexed: 09/30/2023] Open
Abstract
The investigation of similarities and differences in the mechanisms of verbal and visuospatial creative thinking has long been a controversial topic. Prior studies found that visuospatial creativity was primarily supported by the right hemisphere, whereas verbal creativity relied on the interaction between both hemispheres. However, creative thinking also involves abundant dynamic features that may have been ignored in the previous static view. Recently, a new method has been developed that measures hemispheric laterality from a dynamic perspective, providing new insight into the exploration of creative thinking. In the present study, dynamic lateralisation index was calculated with resting-state fMRI data. We combined the dynamic lateralisation index with sparse canonical correlation analysis to examine similarities and differences in the mechanisms of verbal and visuospatial creativity. Our results showed that the laterality reversal of the default mode network, fronto-parietal network, cingulo-opercular network and visual network contributed significantly to both verbal and visuospatial creativity and consequently could be considered the common neural mechanisms shared by these creative modes. In addition, we found that verbal creativity relied more on the language network, while visuospatial creativity relied more on the somatomotor network, which can be considered a difference in their mechanism. Collectively, these findings indicated that verbal and visuospatial creativity may have similar mechanisms to support the basic creative thinking process and different mechanisms to adapt to the specific task conditions. These findings may have significant implications for our understanding of the neural mechanisms of different types of creative thinking.
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Affiliation(s)
- Yixin Gao
- Key Laboratory of Cognition and Personality (SWU)Ministry of EducationChongqingChina
- Faculty of PsychologySouthwest University (SWU)ChongqingChina
| | - Xinran Wu
- Institute of Science and Technology for Brain‐Inspired IntelligenceFudan UniversityShanghaiChina
| | - Yuchi Yan
- Key Laboratory of Cognition and Personality (SWU)Ministry of EducationChongqingChina
- Faculty of PsychologySouthwest University (SWU)ChongqingChina
| | - Min Li
- Key Laboratory of Cognition and Personality (SWU)Ministry of EducationChongqingChina
- Faculty of PsychologySouthwest University (SWU)ChongqingChina
| | - Facai Qin
- Key Laboratory of Cognition and Personality (SWU)Ministry of EducationChongqingChina
- Faculty of PsychologySouthwest University (SWU)ChongqingChina
| | - Mujie Ma
- Key Laboratory of Cognition and Personality (SWU)Ministry of EducationChongqingChina
- Faculty of PsychologySouthwest University (SWU)ChongqingChina
| | - Xiaoning Yuan
- Key Laboratory of Cognition and Personality (SWU)Ministry of EducationChongqingChina
- Faculty of PsychologySouthwest University (SWU)ChongqingChina
| | - Wenjing Yang
- Key Laboratory of Cognition and Personality (SWU)Ministry of EducationChongqingChina
- Faculty of PsychologySouthwest University (SWU)ChongqingChina
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU)Ministry of EducationChongqingChina
- Faculty of PsychologySouthwest University (SWU)ChongqingChina
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Wang Y, Yin J, Desai RH. Topological inference on brain networks across subtypes of post-stroke aphasia. ARXIV 2023:arXiv:2311.01625v1. [PMID: 37961747 PMCID: PMC10635302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Persistent homology (PH) characterizes the shape of brain networks through the persistence features. Group comparison of persistence features from brain networks can be challenging as they are inherently heterogeneous. A recent scale-space representation of persistence diagram (PD) through heat diffusion reparameterizes using the finite number of Fourier coefficients with respect to the Laplace-Beltrami (LB) eigenfunction expansion of the domain, which provides a powerful vectorized algebraic representation for group comparisons of PDs. In this study, we advance a transposition-based permutation test for comparing multiple groups of PDs through the heat-diffusion estimates of the PDs. We evaluate the empirical performance of the spectral transposition test in capturing within- and between-group similarity and dissimilarity with respect to statistical variation of topological noise and hole location. We also illustrate how the method extends naturally into a clustering scheme by subtyping individuals with post-stroke aphasia through the PDs of their resting-state functional brain networks.
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Affiliation(s)
- Yuan Wang
- Department of Epidemiology and Biostatistics University of South Carolina U.S.A
| | - Jian Yin
- Department of Biostatistics Nanjing Medical University China
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11
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Bar-On M, Baharav S, Katzir Z, Mirelman A, Sosnik R, Maidan I. Task-Related Reorganization of Cognitive Network in Parkinson's Disease Using Electrophysiology. Mov Disord 2023; 38:2031-2040. [PMID: 37553881 DOI: 10.1002/mds.29571] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 07/03/2023] [Accepted: 07/17/2023] [Indexed: 08/10/2023] Open
Abstract
BACKGROUND Cognitive deficits in Parkinson's disease (PD) patients are well described, however, their underlying neural mechanisms as assessed by electrophysiology are not clear. OBJECTIVES To reveal specific neural network alterations during the performance of cognitive tasks in PD patients using electroencephalography (EEG). METHODS Ninety participants, 60 PD patients and 30 controls underwent EEG recording while performing a GO/NOGO task. Source localization of 16 regions of interest known to play a pivotal role in GO/NOGO task was performed to assess power density and connectivity within this cognitive network. The connectivity matrices were evaluated using a graph-theory approach that included measures of cluster-coefficient, degree, and global-efficiency. A mixed-model analysis, corrected for age and levodopa equivalent daily dose was performed to examine neural changes between PD patients and controls. RESULTS PD patients performed worse in the GO/NOGO task (P < 0.001). The power density was higher in δ and θ bands, but lower in α and β bands in PD patients compared to controls (interaction group × band: P < 0.001), indicating a general slowness within the network. Patients had more connections within the network (P < 0.034) than controls and these were used for graph-theory analysis. Differences between groups in graph-theory measures were found only in cluster-coefficient, which was higher in PD compared to controls (interaction group × band: P < 0.001). CONCLUSIONS Cognitive deficits in PD are underlined by alterations at the brain network level, including higher δ and θ activity, lower α and β activity, increased connectivity, and segregated network organization. These findings may have important implications on future adaptive deep brain stimulation. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- May Bar-On
- Laboratory of Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Faculty of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Shaked Baharav
- Laboratory of Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Faculty of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Zoya Katzir
- Laboratory of Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Department of Neurology, School of Medicine, Tel Aviv University, Tel-Aviv, Israel
| | - Anat Mirelman
- Laboratory of Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Department of Neurology, School of Medicine, Tel Aviv University, Tel-Aviv, Israel
| | - Ronen Sosnik
- Faculty of Engineering, Holon Institute of Technology (HIT), Holon, Israel
| | - Inbal Maidan
- Laboratory of Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Department of Neurology, School of Medicine, Tel Aviv University, Tel-Aviv, Israel
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Simpson-Kent IL, Gataviņš MM, Tooley UA, Boroshok AL, McDermott CL, Park AT, Delgado Reyes L, Bathelt J, Tisdall MD, Mackey AP. Multilayer network associations between the exposome and childhood brain development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.23.563611. [PMID: 37961103 PMCID: PMC10634748 DOI: 10.1101/2023.10.23.563611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Growing up in a high poverty neighborhood is associated with elevated risk for academic challenges and health problems. Here, we take a data-driven approach to exploring how measures of children's environments relate to the development of their brain structure and function in a community sample of children between the ages of 4 and 10 years. We constructed exposomes including measures of family socioeconomic status, children's exposure to adversity, and geocoded measures of neighborhood socioeconomic status, crime, and environmental toxins. We connected the exposome to two structural measures (cortical thickness and surface area, n = 170) and two functional measures (participation coefficient and clustering coefficient, n = 130). We found dense connections within exposome and brain layers and sparse connections between exposome and brain layers. Lower family income was associated with thinner visual cortex, consistent with the theory that accelerated development is detectable in early-developing regions. Greater neighborhood incidence of high blood lead levels was associated with greater segregation of the default mode network, consistent with evidence that toxins are deposited into the brain along the midline. Our study demonstrates the utility of multilayer network analysis to bridge environmental and neural explanatory levels to better understand the complexity of child development.
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Affiliation(s)
- Ivan L. Simpson-Kent
- Institute of Psychology, Developmental and Educational Psychology Unit, Leiden University, Leiden, the Netherlands
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Mārtiņš M. Gataviņš
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ursula A. Tooley
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Washington University in St. Louis, USA
- Department of Neurology, Washington University in St. Louis, USA
| | - Austin L. Boroshok
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Anne T. Park
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Joe Bathelt
- Department of Psychology, Royal Holloway, University of London, Egham, Surrey, United Kingdom
| | - M. Dylan Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Allyson P. Mackey
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
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Yuasa K, Hirosawa T, Soma D, Furutani N, Kameya M, Sano M, Kitamura K, Ueda M, Kikuchi M. Eyes-state-dependent alterations of magnetoencephalographic connectivity associated with delayed recall in Alzheimer's disease via graph theory approach. Front Psychiatry 2023; 14:1272120. [PMID: 37941968 PMCID: PMC10628524 DOI: 10.3389/fpsyt.2023.1272120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 10/10/2023] [Indexed: 11/10/2023] Open
Abstract
IntroductionAlzheimer’s disease (AD) is a neurodegenerative disorder characterized by memory impairment and cognitive decline. Electroencephalography (EEG) and magnetoencephalography (MEG) studies using graph theory show altered “Small-Worldness (SW)” properties in AD. This study aimed to investigate whether eye-state-dependent alterations in SW differ between patients with AD and healthy controls, considering the symptoms of AD.MethodsNineteen patients with AD and 24 healthy controls underwent MEG under different conditions (eyes-open [EO] and eyes-closed [EC]) and the Wechsler Memory Scale-Revised (WMS-R) with delayed recall. After the signal sources were mapped onto the Desikan–Killiany brain atlas, the statistical connectivity of five frequency bands (delta, theta, alpha, beta, and gamma) was calculated using the phase lag index (PLI), and binary graphs for each frequency band were constructed based on the PLI. Next, we measured SW as a graph metric and evaluated three points: the impact of AD and experimental conditions on SW, the association between SW and delayed recall, and changes in SW across experimental conditions correlated with delayed recall.ResultsSW in the gamma band was significantly lower in patients with AD (z = −2.16, p = 0.031), but the experimental conditions did not exhibit a significant effect in any frequency band. Next, in the AD group, higher scores on delayed recall correlated with diminished SW across delta, alpha, and beta bands in the EO condition. Finally, delayed recall scores significantly predicted relative differences in the SW group in the alpha band (t = −2.98, p = 0.009).DiscussionGiven that network studies could corroborate the results of previous power spectrum studies, our findings contribute to a multifaceted understanding of functional brain networks in AD, emphasizing that the SW properties of these networks change according to disease status, cognitive function, and experimental conditions.
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Affiliation(s)
- Keigo Yuasa
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Tetsu Hirosawa
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Daiki Soma
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Naoki Furutani
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Masafumi Kameya
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Masuhiko Sano
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Koji Kitamura
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Minehisa Ueda
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Mitsuru Kikuchi
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
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Rodríguez-González V, Núñez P, Gómez C, Shigihara Y, Hoshi H, Tola-Arribas MÁ, Cano M, Guerrero Á, García-Azorín D, Hornero R, Poza J. Connectivity-based Meta-Bands: A new approach for automatic frequency band identification in connectivity analyses. Neuroimage 2023; 280:120332. [PMID: 37619796 DOI: 10.1016/j.neuroimage.2023.120332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 07/05/2023] [Accepted: 08/14/2023] [Indexed: 08/26/2023] Open
Abstract
The majority of electroencephalographic (EEG) and magnetoencephalographic (MEG) studies filter and analyse neural signals in specific frequency ranges, known as "canonical" frequency bands. However, this segmentation, is not exempt from limitations, mainly due to the lack of adaptation to the neural idiosyncrasies of each individual. In this study, we introduce a new data-driven method to automatically identify frequency ranges based on the topological similarity of the frequency-dependent functional neural network. The resting-state neural activity of 195 cognitively healthy subjects from three different databases (MEG: 123 subjects; EEG1: 27 subjects; EEG2: 45 subjects) was analysed. In a first step, MEG and EEG signals were filtered with a narrow-band filter bank (1 Hz bandwidth) from 1 to 70 Hz with a 0.5 Hz step. Next, the connectivity in each of these filtered signals was estimated using the orthogonalized version of the amplitude envelope correlation to obtain the frequency-dependent functional neural network. Finally, a community detection algorithm was used to identify communities in the frequency domain showing a similar network topology. We have called this approach the "Connectivity-based Meta-Bands" (CMB) algorithm. Additionally, two types of synthetic signals were used to configure the hyper-parameters of the CMB algorithm. We observed that the classical approaches to band segmentation are partially aligned with the underlying network topologies at group level for the MEG signals, but they are missing individual idiosyncrasies that may be biasing previous studies, as revealed by our methodology. On the other hand, the sensitivity of EEG signals to reflect this underlying frequency-dependent network structure is limited, revealing a simpler frequency parcellation, not aligned with that defined by the "canonical" frequency bands. To the best of our knowledge, this is the first study that proposes an unsupervised band segmentation method based on the topological similarity of functional neural network across frequencies. This methodology fully accounts for subject-specific patterns, providing more robust and personalized analyses, and paving the way for new studies focused on exploring the frequency-dependent structure of brain connectivity.
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Affiliation(s)
- Víctor Rodríguez-González
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain.
| | - Pablo Núñez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain; Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium
| | - Carlos Gómez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain
| | | | | | - Miguel Ángel Tola-Arribas
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain; Servicio de Neurología. Hospital Universitario Río Hortega, Valladolid, Spain
| | - Mónica Cano
- Servicio de Neurología. Hospital Universitario Río Hortega, Valladolid, Spain
| | - Ángel Guerrero
- Hospital Clínico Universitario, Valladolid, Spain; Department of Medicine, University of Valladolid, Spain
| | | | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
| | - Jesús Poza
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
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15
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Ewell A, Allard T, Botdorf M, Ji A, Riggins T. Emotion regulation and reactivity are associated with cortical thickness in early to mid-childhood. Dev Psychobiol 2023; 65:e22412. [PMID: 37607888 DOI: 10.1002/dev.22412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 04/06/2023] [Accepted: 07/05/2023] [Indexed: 08/24/2023]
Abstract
This study explored the neural correlates of emotion regulation and emotional reactivity in early to mid-childhood. A sample of 96 children (70% White, mid-to-high socioeconomic status) aged 3-8 years provided structural neuroimaging data and caregivers reported on emotion regulation and emotional reactivity. The amygdala, insula, inferior frontal gyrus, anterior cingulate cortex, and medial orbitofrontal cortex were explored as a priori regions of interest (ROIs). ROI analyses revealed that emotion regulation was positively associated with cortical thickness in the insula, whereas emotional reactivity was negatively associated with cortical thickness in the inferior frontal gyrus. Exploratory whole-brain analyses suggested positive associations between emotion regulation and both left superior temporal thickness and right inferior temporal thickness, as well as negative associations between emotional reactivity and left superior temporal thickness. There were no significant associations between emotional regulation or reactivity and amygdala volume or cortical surface area. These findings support the notion that surface area and cortical thickness are distinct measures of brain maturation. In sum, these findings suggest that children may rely on a wider set of neural regions for emotion regulation and reactivity than adults, which is consistent with theories of interactive specialization across the life span.
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Affiliation(s)
- Arcadia Ewell
- Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts, USA
| | - Tamara Allard
- Department of Psychology, University of Maryland, College Park, Maryland, USA
| | - Morgan Botdorf
- Department of Psychology, University of Maryland, College Park, Maryland, USA
- Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Angela Ji
- Department of Graduate Psychology, James Madison University, Harrisonburg, Virginia, USA
| | - Tracy Riggins
- Department of Psychology, University of Maryland, College Park, Maryland, USA
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16
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Tooley UA, Latham A, Kenley JK, Alexopoulos D, Smyser T, Warner BB, Shimony JS, Neil JJ, Luby JL, Barch DM, Rogers CE, Smyser CD. Prenatal environment is associated with the pace of cortical network development over the first three years of life. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.18.552639. [PMID: 37662189 PMCID: PMC10473645 DOI: 10.1101/2023.08.18.552639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Environmental influences on brain structure and function during early development have been well-characterized. In pre-registered analyses, we test the theory that socioeconomic status (SES) is associated with differences in trajectories of intrinsic brain network development from birth to three years (n = 261). Prenatal SES is associated with developmental increases in cortical network segregation, with neonates and toddlers from lower-SES backgrounds showing a steeper increase in cortical network segregation with age, consistent with accelerated network development. Associations between SES and cortical network segregation occur at the local scale and conform to a sensorimotor-association hierarchy of cortical organization. SES-associated differences in cortical network segregation are associated with language abilities at two years, such that lower segregation is associated with improved language abilities. These results yield key insight into the timing and directionality of associations between the early environment and trajectories of cortical development.
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Affiliation(s)
- Ursula A. Tooley
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110
| | - Aidan Latham
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110
| | - Jeanette K. Kenley
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110
| | | | - Tara Smyser
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110
| | - Barbara B. Warner
- Department of Pediatrics, Washington University in St. Louis, St. Louis, MO 63110
| | - Joshua S. Shimony
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110
| | - Jeffrey J. Neil
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110
| | - Joan L. Luby
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110
| | - Deanna M. Barch
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO 63110
| | - Cynthia E. Rogers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110
- Department of Pediatrics, Washington University in St. Louis, St. Louis, MO 63110
| | - Chris D. Smyser
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110
- Department of Pediatrics, Washington University in St. Louis, St. Louis, MO 63110
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110
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17
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Cai J, Xu M, Cai H, Jiang Y, Zheng X, Sun H, Sun Y, Sun Y. Task Cortical Connectivity Reveals Different Network Reorganizations between Mild Stroke Patients with Cortical and Subcortical Lesions. Brain Sci 2023; 13:1143. [PMID: 37626499 PMCID: PMC10452233 DOI: 10.3390/brainsci13081143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 07/24/2023] [Accepted: 07/27/2023] [Indexed: 08/27/2023] Open
Abstract
Accumulating efforts have been made to investigate cognitive impairment in stroke patients, but little has been focused on mild stroke. Research on the impact of mild stroke and different lesion locations on cognitive impairment is still limited. To investigate the underlying mechanisms of cognitive dysfunction in mild stroke at different lesion locations, electroencephalograms (EEGs) were recorded in three groups (40 patients with cortical stroke (CS), 40 patients with subcortical stroke (SS), and 40 healthy controls (HC)) during a visual oddball task. Power envelope connectivity (PEC) was constructed based on EEG source signals, followed by graph theory analysis to quantitatively assess functional brain network properties. A classification framework was further applied to explore the feasibility of PEC in the identification of mild stroke. The results showed worse behavioral performance in the patient groups, and PECs with significant differences among three groups showed complex distribution patterns in frequency bands and the cortex. In the delta band, the global efficiency was significantly higher in HC than in CS (p = 0.011), while local efficiency was significantly increased in SS than in CS (p = 0.038). In the beta band, the small-worldness was significantly increased in HC compared to CS (p = 0.004). Moreover, the satisfactory classification results (76.25% in HC vs. CS, and 80.00% in HC vs. SS) validate the potential of PECs as a biomarker in the detection of mild stroke. Our findings offer some new quantitative insights into the complex mechanisms of cognitive impairment in mild stroke at different lesion locations, which may facilitate post-stroke cognitive rehabilitation.
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Affiliation(s)
- Jiaye Cai
- Department of Neurology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310020, China; (J.C.); (H.C.); (Y.J.); (X.Z.); (Y.S.)
| | - Mengru Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China
| | - Huaying Cai
- Department of Neurology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310020, China; (J.C.); (H.C.); (Y.J.); (X.Z.); (Y.S.)
| | - Yun Jiang
- Department of Neurology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310020, China; (J.C.); (H.C.); (Y.J.); (X.Z.); (Y.S.)
| | - Xu Zheng
- Department of Neurology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310020, China; (J.C.); (H.C.); (Y.J.); (X.Z.); (Y.S.)
| | - Hongru Sun
- Department of Electrocardiogram, Dongyang Traditional Chinese Medicine Hospital, Dongyang 322100, China;
| | - Yu Sun
- Department of Neurology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310020, China; (J.C.); (H.C.); (Y.J.); (X.Z.); (Y.S.)
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China
- MOE Frontiers Science Center for Brain Science and Brain-Machine Integration, Zhejiang University, Hangzhou 310058, China
- State Key Laboratory for Brain-Computer Intelligence, Zhejiang University, Hangzhou 310016, China
| | - Yi Sun
- Department of Neurology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310020, China; (J.C.); (H.C.); (Y.J.); (X.Z.); (Y.S.)
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18
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Gonzalez-Castillo J, Fernandez IS, Lam KC, Handwerker DA, Pereira F, Bandettini PA. Manifold learning for fMRI time-varying functional connectivity. Front Hum Neurosci 2023; 17:1134012. [PMID: 37497043 PMCID: PMC10366614 DOI: 10.3389/fnhum.2023.1134012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 06/21/2023] [Indexed: 07/28/2023] Open
Abstract
Whole-brain functional connectivity (FC) measured with functional MRI (fMRI) evolves over time in meaningful ways at temporal scales going from years (e.g., development) to seconds [e.g., within-scan time-varying FC (tvFC)]. Yet, our ability to explore tvFC is severely constrained by its large dimensionality (several thousands). To overcome this difficulty, researchers often seek to generate low dimensional representations (e.g., 2D and 3D scatter plots) hoping those will retain important aspects of the data (e.g., relationships to behavior and disease progression). Limited prior empirical work suggests that manifold learning techniques (MLTs)-namely those seeking to infer a low dimensional non-linear surface (i.e., the manifold) where most of the data lies-are good candidates for accomplishing this task. Here we explore this possibility in detail. First, we discuss why one should expect tvFC data to lie on a low dimensional manifold. Second, we estimate what is the intrinsic dimension (ID; i.e., minimum number of latent dimensions) of tvFC data manifolds. Third, we describe the inner workings of three state-of-the-art MLTs: Laplacian Eigenmaps (LEs), T-distributed Stochastic Neighbor Embedding (T-SNE), and Uniform Manifold Approximation and Projection (UMAP). For each method, we empirically evaluate its ability to generate neuro-biologically meaningful representations of tvFC data, as well as their robustness against hyper-parameter selection. Our results show that tvFC data has an ID that ranges between 4 and 26, and that ID varies significantly between rest and task states. We also show how all three methods can effectively capture subject identity and task being performed: UMAP and T-SNE can capture these two levels of detail concurrently, but LE could only capture one at a time. We observed substantial variability in embedding quality across MLTs, and within-MLT as a function of hyper-parameter selection. To help alleviate this issue, we provide heuristics that can inform future studies. Finally, we also demonstrate the importance of feature normalization when combining data across subjects and the role that temporal autocorrelation plays in the application of MLTs to tvFC data. Overall, we conclude that while MLTs can be useful to generate summary views of labeled tvFC data, their application to unlabeled data such as resting-state remains challenging.
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Affiliation(s)
- Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD, United States
| | - Isabel S. Fernandez
- Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD, United States
| | - Ka Chun Lam
- Machine Learning Group, National Institute of Mental Health, Bethesda, MD, United States
| | - Daniel A. Handwerker
- Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD, United States
| | - Francisco Pereira
- Machine Learning Group, National Institute of Mental Health, Bethesda, MD, United States
| | - Peter A. Bandettini
- Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD, United States
- Functional Magnetic Resonance Imaging (FMRI) Core, National Institute of Mental Health, Bethesda, MD, United States
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19
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Cheng P, Li Y, Wang G, Dong H, Liu H, Shen W, Zhou W. Aberrant topology of white matter networks in patients with methamphetamine dependence and its application in support vector machine-based classification. Sci Rep 2023; 13:6958. [PMID: 37117256 PMCID: PMC10147725 DOI: 10.1038/s41598-023-33199-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 04/08/2023] [Indexed: 04/30/2023] Open
Abstract
Brain white matter (WM) networks have been widely studied in neuropsychiatric disorders. However, few studies have evaluated alterations in WM network topological organization in patients with methamphetamine (MA) dependence. Therefore, using machine learning classification methods to analyze WM network topological attributes may give new insights into patients with MA dependence. In the study, diffusion tensor imaging-based probabilistic tractography was used to map the weighted WM networks in 46 MA-dependent patients and 46 control subjects. Using graph-theoretical analyses, the global and regional topological attributes of WM networks for both groups were calculated and compared to determine inter-group differences using a permutation-based general linear model. In addition, the study used a support vector machine (SVM) learning approach to construct a classifier for discriminating subjects with MA dependence from control subjects. Relative to the control group, the MA-dependent group exhibited abnormal topological organization, as evidenced by decreased small-worldness and modularity, and increased nodal efficiency in the right medial superior temporal gyrus, right pallidum, and right ventromedial putamen; the MA-dependent group had the higher hubness scores in 25 regions, which were mainly located in the default mode network. An SVM trained with topological attributes achieved classification accuracy, sensitivity, specificity, and kappa values of 98.09% ± 2.59%, 98.24% ± 4.00%, 97.94% ± 4.26%, and 96.18% ± 5.19% for patients with MA dependence. Our results may suggest altered global WM structural networks in MA-dependent patients. Furthermore, the abnormal WM network topological attributes may provide promising features for the construction of high-efficacy classification models.
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Affiliation(s)
- Ping Cheng
- Department of Radiology, Ningbo Medical Treatment Center Lihuili Hospital, Ningbo University, 57# Xing Ning Road, Ningbo, Zhejiang, China
| | - Yadi Li
- Department of Radiology, Ningbo Medical Treatment Center Lihuili Hospital, Ningbo University, 57# Xing Ning Road, Ningbo, Zhejiang, China.
| | - Gaoyan Wang
- Department of Radiology, Ningbo Medical Treatment Center Lihuili Hospital, Ningbo University, 57# Xing Ning Road, Ningbo, Zhejiang, China
| | - Haibo Dong
- Department of Radiology, Ningbo Medical Treatment Center Lihuili Hospital, Ningbo University, 57# Xing Ning Road, Ningbo, Zhejiang, China
| | - Huifen Liu
- Department of Psychiatry, Ningbo Kangning Hospital, Ningbo University, 1# Zhuangyu South Road, Ningbo, Zhejiang, China
| | - Wenwen Shen
- Department of Psychiatry, Ningbo Kangning Hospital, Ningbo University, 1# Zhuangyu South Road, Ningbo, Zhejiang, China
| | - Wenhua Zhou
- Department of Psychiatry, Ningbo Kangning Hospital, Ningbo University, 1# Zhuangyu South Road, Ningbo, Zhejiang, China.
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20
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Grosu GF, Hopp AV, Moca VV, Bârzan H, Ciuparu A, Ercsey-Ravasz M, Winkel M, Linde H, Mureșan RC. The fractal brain: scale-invariance in structure and dynamics. Cereb Cortex 2023; 33:4574-4605. [PMID: 36156074 PMCID: PMC10110456 DOI: 10.1093/cercor/bhac363] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 08/09/2022] [Accepted: 08/10/2022] [Indexed: 11/12/2022] Open
Abstract
The past 40 years have witnessed extensive research on fractal structure and scale-free dynamics in the brain. Although considerable progress has been made, a comprehensive picture has yet to emerge, and needs further linking to a mechanistic account of brain function. Here, we review these concepts, connecting observations across different levels of organization, from both a structural and functional perspective. We argue that, paradoxically, the level of cortical circuits is the least understood from a structural point of view and perhaps the best studied from a dynamical one. We further link observations about scale-freeness and fractality with evidence that the environment provides constraints that may explain the usefulness of fractal structure and scale-free dynamics in the brain. Moreover, we discuss evidence that behavior exhibits scale-free properties, likely emerging from similarly organized brain dynamics, enabling an organism to thrive in an environment that shares the same organizational principles. Finally, we review the sparse evidence for and try to speculate on the functional consequences of fractality and scale-freeness for brain computation. These properties may endow the brain with computational capabilities that transcend current models of neural computation and could hold the key to unraveling how the brain constructs percepts and generates behavior.
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Affiliation(s)
- George F Grosu
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
- Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, Str. Memorandumului 28, 400114 Cluj-Napoca, Romania
| | | | - Vasile V Moca
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
| | - Harald Bârzan
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
- Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, Str. Memorandumului 28, 400114 Cluj-Napoca, Romania
| | - Andrei Ciuparu
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
- Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, Str. Memorandumului 28, 400114 Cluj-Napoca, Romania
| | - Maria Ercsey-Ravasz
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
- Faculty of Physics, Babes-Bolyai University, Str. Mihail Kogalniceanu 1, 400084 Cluj-Napoca, Romania
| | - Mathias Winkel
- Merck KGaA, Frankfurter Straße 250, 64293 Darmstadt, Germany
| | - Helmut Linde
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
- Merck KGaA, Frankfurter Straße 250, 64293 Darmstadt, Germany
| | - Raul C Mureșan
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
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21
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Yamakou ME, Kuehn C. Combined effects of spike-timing-dependent plasticity and homeostatic structural plasticity on coherence resonance. Phys Rev E 2023; 107:044302. [PMID: 37198865 DOI: 10.1103/physreve.107.044302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 03/23/2023] [Indexed: 05/19/2023]
Abstract
Efficient processing and transfer of information in neurons have been linked to noise-induced resonance phenomena such as coherence resonance (CR), and adaptive rules in neural networks have been mostly linked to two prevalent mechanisms: spike-timing-dependent plasticity (STDP) and homeostatic structural plasticity (HSP). Thus this paper investigates CR in small-world and random adaptive networks of Hodgkin-Huxley neurons driven by STDP and HSP. Our numerical study indicates that the degree of CR strongly depends, and in different ways, on the adjusting rate parameter P, which controls STDP, on the characteristic rewiring frequency parameter F, which controls HSP, and on the parameters of the network topology. In particular, we found two robust behaviors. (i) Decreasing P (which enhances the weakening effect of STDP on synaptic weights) and decreasing F (which slows down the swapping rate of synapses between neurons) always leads to higher degrees of CR in small-world and random networks, provided that the synaptic time delay parameter τ_{c} has some appropriate values. (ii) Increasing the synaptic time delay τ_{c} induces multiple CR (MCR)-the occurrence of multiple peaks in the degree of coherence as τ_{c} changes-in small-world and random networks, with MCR becoming more pronounced at smaller values of P and F. Our results imply that STDP and HSP can jointly play an essential role in enhancing the time precision of firing necessary for optimal information processing and transfer in neural systems and could thus have applications in designing networks of noisy artificial neural circuits engineered to use CR to optimize information processing and transfer.
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Affiliation(s)
- Marius E Yamakou
- Department of Data Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstr. 11, 91058 Erlangen, Germany
- Max-Planck-Institut für Mathematik in den Naturwissenschaften, Inselstr. 22, 04103 Leipzig, Germany
| | - Christian Kuehn
- Faculty of Mathematics, Technical University of Munich, Boltzmannstrasse 3, 85748 Garching bei München, Germany
- Complexity Science Hub Vienna, Josefstädter Strasse 39, 1080 Vienna, Austria
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22
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Kida T, Tanaka E, Kakigi R, Inui K. Brain-wide network analysis of resting-state neuromagnetic data. Hum Brain Mapp 2023; 44:3519-3540. [PMID: 36988453 DOI: 10.1002/hbm.26295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 03/16/2023] [Accepted: 03/20/2023] [Indexed: 03/30/2023] Open
Abstract
The present study performed a brain-wide network analysis of resting-state magnetoencephalograms recorded from 53 healthy participants to visualize elaborate brain maps of phase- and amplitude-derived graph-theory metrics at different frequencies. To achieve this, we conducted a vertex-wise computation of threshold-independent graph metrics by combining proportional thresholding and a conjunction analysis and applied them to a correlation analysis of age and brain networks. Source power showed a frequency-dependent cortical distribution. Threshold-independent graph metrics derived from phase- and amplitude-based connectivity showed similar or different distributions depending on frequency. Vertex-wise age-brain correlation maps revealed that source power at the beta band and the amplitude-based degree at the alpha band changed with age in local regions. The present results indicate that a brain-wide analysis of neuromagnetic data has the potential to reveal neurophysiological network features in the human brain in a resting state.
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Affiliation(s)
- Tetsuo Kida
- Higher Brain Function Unit, Department of Functioning and Disability, Institute for Developmental Research, Aichi Developmental Disability Center, Kasugai, Japan
- Department of Functioning and Disability, Institute for Developmental Research, Aichi Developmental Disability Center, Kasugai, Japan
- Department of Integrative Physiology, National Institute for Physiological Sciences, Okazaki, Japan
- Section of Brain Function Information, Supportive Center for Brain Research, National Institute for Physiological Sciences, Okazaki, Japan
| | - Emi Tanaka
- Brain and Mind Research Center, Nagoya University, Nagoya, Japan
| | - Ryusuke Kakigi
- Department of Integrative Physiology, National Institute for Physiological Sciences, Okazaki, Japan
| | - Koji Inui
- Department of Functioning and Disability, Institute for Developmental Research, Aichi Developmental Disability Center, Kasugai, Japan
- Department of Integrative Physiology, National Institute for Physiological Sciences, Okazaki, Japan
- Section of Brain Function Information, Supportive Center for Brain Research, National Institute for Physiological Sciences, Okazaki, Japan
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23
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Adebisi AT, Veluvolu KC. Brain network analysis for the discrimination of dementia disorders using electrophysiology signals: A systematic review. Front Aging Neurosci 2023; 15:1039496. [PMID: 36936496 PMCID: PMC10020520 DOI: 10.3389/fnagi.2023.1039496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 02/06/2023] [Indexed: 03/06/2023] Open
Abstract
Background Dementia-related disorders have been an age-long challenge to the research and healthcare communities as their various forms are expressed with similar clinical symptoms. These disorders are usually irreversible at their late onset, hence their lack of validated and approved cure. Since their prodromal stages usually lurk for a long period of time before the expression of noticeable clinical symptoms, a secondary prevention which has to do with treating the early onsets has been suggested as the possible solution. Connectivity analysis of electrophysiology signals has played significant roles in the diagnosis of various dementia disorders through early onset identification. Objective With the various applications of electrophysiology signals, the purpose of this study is to systematically review the step-by-step procedures of connectivity analysis frameworks for dementia disorders. This study aims at identifying the methodological issues involved in such frameworks and also suggests approaches to solve such issues. Methods In this study, ProQuest, PubMed, IEEE Xplore, Springer Link, and Science Direct databases are employed for exploring the evolution and advancement of connectivity analysis of electrophysiology signals of dementia-related disorders between January 2016 to December 2022. The quality of assessment of the studied articles was done using Cochrane guidelines for the systematic review of diagnostic test accuracy. Results Out of a total of 4,638 articles found to have been published on the review scope between January 2016 to December 2022, a total of 51 peer-review articles were identified to completely satisfy the review criteria. An increasing trend of research in this domain is identified within the considered time frame. The ratio of MEG and EEG utilization found within the reviewed articles is 1:8. Most of the reviewed articles employed graph theory metrics for their analysis with clustering coefficient (CC), global efficiency (GE), and characteristic path length (CPL) appearing more frequently compared to other metrics. Significance This study provides general insight into how to employ connectivity measures for the analysis of electrophysiology signals of dementia-related disorders in order to better understand their underlying mechanism and their differential diagnosis.
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Affiliation(s)
- Abdulyekeen T. Adebisi
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, Republic of Korea
| | - Kalyana C. Veluvolu
- School of Electronics Engineering, Kyungpook National University, Daegu, Republic of Korea
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24
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Liao PC, Zhou X, Chong HY, Hu Y, Zhang D. Exploring construction workers' brain connectivity during hazard recognition: a cognitive psychology perspective. INTERNATIONAL JOURNAL OF OCCUPATIONAL SAFETY AND ERGONOMICS 2023; 29:207-215. [PMID: 35098890 DOI: 10.1080/10803548.2022.2035966] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Monitoring brain activity is a novel development for hazard recognition in the construction industry. However, very few empirical studies have investigated the causal connections within the brain. This study aimed to explore the brain connectivity of construction workers during hazard recognition. Electroencephalogram data were collected from construction workers to perform image-based hazard recognition tasks. The Granger causality-based adaptive directed transfer function was used to simulate directed and time-variant information flow across the observed brain activity from the perspective of cognitive psychology. The results suggested a top-down modulation of behavioral goals originating from the dorsal attention network during hazard relocation. The sensory cortex predominantly serves as the information outlet center and interacts extensively with the frontal and visual cortices, reflecting a top-down attention reorientation mechanism for processing threatening stimuli. Our findings of brain effective connectivity supplement new evidence underpinning parallel distributed processing theory for workplace hazard recognition.
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Affiliation(s)
- Pin-Chao Liao
- Department of Construction Management, Tsinghua University, China
| | - Xiaoshan Zhou
- Department of Construction Management, Tsinghua University, China
| | - Heap-Yih Chong
- School of Design and the Built Environment, Curtin University, Australia
| | - Yinan Hu
- Department of Construction Management, Tsinghua University, China
| | - Dan Zhang
- Department of Psychology, Tsinghua University, China
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25
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Luboeinski J, Claro L, Pomi A, Mizraji E. Stabilization through self-coupling in networks of small-world and scale-free topology. Sci Rep 2023; 13:1089. [PMID: 36658183 PMCID: PMC9851597 DOI: 10.1038/s41598-023-27809-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 01/09/2023] [Indexed: 01/20/2023] Open
Abstract
Mechanisms that ensure the stability of dynamical systems are of vital importance, in particular in our globalized and increasingly interconnected world. The so-called connectivity-stability dilemma denotes the theoretical finding that increased connectivity between the components of a large dynamical system drastically reduces its stability. This result has promoted controversies within ecology and other fields of biology, especially, because organisms as well as ecosystems constitute systems that are both highly connected and stable. Hence, it has been a major challenge to find ways to stabilize complex systems while preserving high connectivity at the same time. Investigating the stability of networks that exhibit small-world or scale-free topology is of particular interest, since these topologies have been found in many different types of real-world networks. Here, we use an approach to stabilize recurrent networks of small-world and scale-free topology by increasing the average self-coupling strength of the units of a network. For both topologies, we find that there is a sharp transition from instability to asymptotic stability. Then, most importantly, we find that the average self-coupling strength needed to stabilize a system increases much slower than its size. It appears that the qualitative shape of this relationship is the same for small-world and scale-free networks, while scale-free networks can require higher magnitudes of self-coupling. We further explore the stabilization of networks with Kronecker-Leskovec topology. Finally, we argue that our findings, in particular the stabilization of large recurrent networks through small increases in the unit self-regulation, are of practical importance for the stabilization of diverse types of complex systems.
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Affiliation(s)
- Jannik Luboeinski
- grid.7450.60000 0001 2364 4210Department of Computational Neuroscience, III. Institute of Physics – Biophysics, University of Göttingen, Göttingen, Germany ,grid.11630.350000000121657640Biophysics and Systems Biology Section, Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay
| | - Luis Claro
- grid.11630.350000000121657640Biophysics and Systems Biology Section, Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay
| | - Andrés Pomi
- grid.11630.350000000121657640Biophysics and Systems Biology Section, Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay
| | - Eduardo Mizraji
- grid.11630.350000000121657640Biophysics and Systems Biology Section, Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay
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26
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Gonzalez-Castillo J, Fernandez I, Lam KC, Handwerker DA, Pereira F, Bandettini PA. Manifold Learning for fMRI time-varying FC. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.14.523992. [PMID: 36789436 PMCID: PMC9928030 DOI: 10.1101/2023.01.14.523992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Whole-brain functional connectivity ( FC ) measured with functional MRI (fMRI) evolve over time in meaningful ways at temporal scales going from years (e.g., development) to seconds (e.g., within-scan time-varying FC ( tvFC )). Yet, our ability to explore tvFC is severely constrained by its large dimensionality (several thousands). To overcome this difficulty, researchers seek to generate low dimensional representations (e.g., 2D and 3D scatter plots) expected to retain its most informative aspects (e.g., relationships to behavior, disease progression). Limited prior empirical work suggests that manifold learning techniques ( MLTs )-namely those seeking to infer a low dimensional non-linear surface (i.e., the manifold) where most of the data lies-are good candidates for accomplishing this task. Here we explore this possibility in detail. First, we discuss why one should expect tv FC data to lie on a low dimensional manifold. Second, we estimate what is the intrinsic dimension (i.e., minimum number of latent dimensions; ID ) of tvFC data manifolds. Third, we describe the inner workings of three state-of-the-art MLTs : Laplacian Eigenmaps ( LE ), T-distributed Stochastic Neighbor Embedding ( T-SNE ), and Uniform Manifold Approximation and Projection ( UMAP ). For each method, we empirically evaluate its ability to generate neuro-biologically meaningful representations of tvFC data, as well as their robustness against hyper-parameter selection. Our results show that tvFC data has an ID that ranges between 4 and 26, and that ID varies significantly between rest and task states. We also show how all three methods can effectively capture subject identity and task being performed: UMAP and T-SNE can capture these two levels of detail concurrently, but L E could only capture one at a time. We observed substantial variability in embedding quality across MLTs , and within- MLT as a function of hyper-parameter selection. To help alleviate this issue, we provide heuristics that can inform future studies. Finally, we also demonstrate the importance of feature normalization when combining data across subjects and the role that temporal autocorrelation plays in the application of MLTs to tvFC data. Overall, we conclude that while MLTs can be useful to generate summary views of labeled tvFC data, their application to unlabeled data such as resting-state remains challenging.
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Affiliation(s)
| | - Isabel Fernandez
- Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD
| | - Ka Chun Lam
- Machine Learning Group, National Institute of Mental Health, Bethesda, MD
| | - Daniel A Handwerker
- Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD
| | - Francisco Pereira
- Machine Learning Group, National Institute of Mental Health, Bethesda, MD
| | - Peter A Bandettini
- Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD,Machine Learning Group, National Institute of Mental Health, Bethesda, MD,FMRI Core, National Institute of Mental Health, Bethesda, MD
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27
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Ding L, Sun Q, Jiang N, He J, Jia J. The instant effect of embodiment via mirror visual feedback on electroencephalogram-based brain connectivity changes: A pilot study. Front Neurosci 2023; 17:1138406. [PMID: 37021135 PMCID: PMC10067600 DOI: 10.3389/fnins.2023.1138406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 02/28/2023] [Indexed: 04/07/2023] Open
Abstract
The therapeutic efficacy of mirror visual feedback (MVF) is attributed to the perception of embodiment. This study intends to investigate the instantaneous effect of embodiment on brain connectivity. Twelve healthy subjects were required to clench and open their non-dominant hands and keep the dominant hands still during two experimental sessions. In the first session, the dominant hand was covered and no MVF was applied, named the sham-MVF condition. Random vibrotactile stimulations were applied to the non-dominant hand with MVF in the subsequent session. Subjects were asked to pedal while having embodiment perception during motor tasks. As suggested by previous findings, trials of no vibration and continuous vibration were selected for this study, named the condition of MVF and vt-MVF. EEG signals were recorded and the alterations in brain connectivity were analyzed. The average node degrees of sham-MVF, MVF, and vt-MVF conditions were largely different in the alpha band (9.94, 11.19, and 17.37, respectively). Further analyses showed the MVF and vt-MVF had more nodes with a significantly large degree, which mainly occurred in the central and the visual stream involved regions. Results of network metrics showed a significant increment of local and global efficiency, and a reduction of characteristic path length for the vt-MVF condition in the alpha and beta bands compared to sham-MVF, and in the alpha band compared to MVF. Similar trends were found for MVF condition in the beta band compared to sham-MVF. Moreover, significant leftward asymmetry of global efficiency and rightward asymmetry of characteristic path length was reported in the vt-MVF condition in the beta band. These results indicated a positive impact of embodiment on network connectivity and neural communication efficiency, which reflected the potential mechanisms of MVF for new insight into neural modulation.
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Affiliation(s)
- Li Ding
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
- The National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Qiang Sun
- National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Sichuan, China
- Med-X Center for Manufacturing, Sichuan University, Sichuan, China
| | - Ning Jiang
- National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Sichuan, China
- Med-X Center for Manufacturing, Sichuan University, Sichuan, China
| | - Jiayuan He
- National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Sichuan, China
- Med-X Center for Manufacturing, Sichuan University, Sichuan, China
- Jiayuan He,
| | - Jie Jia
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
- The National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
- *Correspondence: Jie Jia,
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28
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Terstege DJ, Epp JR. Network Neuroscience Untethered: Brain-Wide Immediate Early Gene Expression for the Analysis of Functional Connectivity in Freely Behaving Animals. BIOLOGY 2022; 12:biology12010034. [PMID: 36671727 PMCID: PMC9855808 DOI: 10.3390/biology12010034] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/19/2022] [Accepted: 12/23/2022] [Indexed: 12/28/2022]
Abstract
Studying how spatially discrete neuroanatomical regions across the brain interact is critical to advancing our understanding of the brain. Traditional neuroimaging techniques have led to many important discoveries about the nature of these interactions, termed functional connectivity. However, in animal models these traditional neuroimaging techniques have generally been limited to anesthetized or head-fixed setups or examination of small subsets of neuroanatomical regions. Using the brain-wide expression density of immediate early genes (IEG), we can assess brain-wide functional connectivity underlying a wide variety of behavioural tasks in freely behaving animal models. Here, we provide an overview of the necessary steps required to perform IEG-based analyses of functional connectivity. We also outline important considerations when designing such experiments and demonstrate the implications of these considerations using an IEG-based network dataset generated for the purpose of this review.
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29
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Teng C, Wang M, Wang W, Ma J, Jia M, Wu M, Luo Y, Wang Y, Zhang Y, Xu J. Abnormal Properties of Cortical Functional Brain Network in Major Depressive Disorder: Graph Theory Analysis Based on Electroencephalography-Source Estimates. Neuroscience 2022; 506:80-90. [PMID: 36272697 DOI: 10.1016/j.neuroscience.2022.10.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 10/04/2022] [Accepted: 10/11/2022] [Indexed: 11/05/2022]
Abstract
Studies of scalp electroencephalography (EEG) had shown altered topological organization of functional brain networks in patients with major depressive disorder (MDD). However, most previous EEG-based network analyses were performed at sensor level, while the interpretation of obtained results was not straightforward due to volume conduction effect. To reduce the impact of this defect, the whole cortical functional brain networks of MDD patients were studied during resting state based on EEG-source estimates in this paper. First, scalp EEG signals were recorded from 19 patients with MDD and 20 normal controls under resting eyes-closed state, and cortical neural signals were estimated by using sLORETA method. Then, the correntropy coefficient of wavelet packet coefficients was performed to calculate functional connectivity (FC) matrices in four different frequency bands: δ, θ, α, β, respectively. Afterwards, topological properties of brain networks were analyzed by graph theory approaches. The results showed that the global FC strength of MDD patients was significantly higher than that of healthy subjects in α band. Also, it was found that MDD patients have abnormally increased clustering coefficient and local efficiency in both α and β bands compared to normal people. Furthermore, patients with MDD exhibited increased nodal clustering coefficients in the left lingual gryus and left precuneus in α band. In addition, β band global clustering coefficient was positively correlated with the scores of depression severity. Therefore, the findings indicated the cortical functional brain networks in MDD patients were disruptions, which suggested it would be one of potential causes of depression.
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Affiliation(s)
- Chaolin Teng
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China; Department of Aerospace Medicine, The Air Force Medical University, Xi'an, Shaanxi 710068, PR China
| | - Mengwei Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China
| | - Wei Wang
- Department of Psychiatry, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, PR China
| | - Jin Ma
- Department of Aerospace Medicine, The Air Force Medical University, Xi'an, Shaanxi 710068, PR China
| | - Min Jia
- Department of Psychiatry, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, PR China
| | - Min Wu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China
| | - Yuanyuan Luo
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China; Department of Psychology, Xi'an Mental Health Center, Xi'an, Shaanxi 710061, PR China
| | - Yu Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China
| | - Yiyang Zhang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China
| | - Jin Xu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China.
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30
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Stylianou O, Kaposzta Z, Czoch A, Stefanovski L, Yabluchanskiy A, Racz FS, Ritter P, Eke A, Mukli P. Scale-Free Functional Brain Networks Exhibit Increased Connectivity, Are More Integrated and Less Segregated in Patients with Parkinson's Disease following Dopaminergic Treatment. FRACTAL AND FRACTIONAL 2022; 6:737. [PMID: 38106971 PMCID: PMC10723163 DOI: 10.3390/fractalfract6120737] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Dopaminergic treatment (DT), the standard therapy for Parkinson's disease (PD), alters the dynamics of functional brain networks at specific time scales. Here, we explore the scale-free functional connectivity (FC) in the PD population and how it is affected by DT. We analyzed the electroencephalogram of: (i) 15 PD patients during DT (ON) and after DT washout (OFF) and (ii) 16 healthy control individuals (HC). We estimated FC using bivariate focus-based multifractal analysis, which evaluated the long-term memory ( H ( 2 ) ) and multifractal strength ( Δ H 15 ) of the connections. Subsequent analysis yielded network metrics (node degree, clustering coefficient and path length) based on FC estimated by H ( 2 ) or Δ H 15 . Cognitive performance was assessed by the Mini Mental State Examination (MMSE) and the North American Adult Reading Test (NAART). The node degrees of the Δ H 15 networks were significantly higher in ON, compared to OFF and HC, while clustering coefficient and path length significantly decreased. No alterations were observed in the H ( 2 ) networks. Significant positive correlations were also found between the metrics of H ( 2 ) networks and NAART scores in the HC group. These results demonstrate that DT alters the multifractal coupled dynamics in the brain, warranting the investigation of scale-free FC in clinical and pharmacological studies.
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Affiliation(s)
- Orestis Stylianou
- Department of Physiology, Semmelweis University, 1094 Budapest, Hungary
- Institute of Translational Medicine, Semmelweis University, 1094 Budapest, Hungary
- Berlin Institute of Health at Charité, University Hospital Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Department of Neurology with Experimental Neurology, Charité-University Hospital Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Zalan Kaposzta
- Department of Physiology, Semmelweis University, 1094 Budapest, Hungary
| | - Akos Czoch
- Department of Physiology, Semmelweis University, 1094 Budapest, Hungary
| | - Leon Stefanovski
- Berlin Institute of Health at Charité, University Hospital Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Department of Neurology with Experimental Neurology, Charité-University Hospital Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Andriy Yabluchanskiy
- Vascular Cognitive Impairment and Neurodegeneration Program, Oklahoma Center for Geroscience and Healthy Brain Aging, Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, 975 NE 10th Street, BRC, Oklahoma City, OK 73104, USA
| | - Frigyes Samuel Racz
- Department of Physiology, Semmelweis University, 1094 Budapest, Hungary
- Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, TX 78712, USA
| | - Petra Ritter
- Berlin Institute of Health at Charité, University Hospital Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Department of Neurology with Experimental Neurology, Charité-University Hospital Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience Berlin, 10115 Berlin, Germany
- Einstein Center for Neuroscience Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Einstein Center Digital Future, Wilhelmstraße 67, 10117 Berlin, Germany
| | - Andras Eke
- Department of Physiology, Semmelweis University, 1094 Budapest, Hungary
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 300 Cedar Street, New Haven, CT 06520, USA
| | - Peter Mukli
- Department of Physiology, Semmelweis University, 1094 Budapest, Hungary
- Vascular Cognitive Impairment and Neurodegeneration Program, Oklahoma Center for Geroscience and Healthy Brain Aging, Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, 975 NE 10th Street, BRC, Oklahoma City, OK 73104, USA
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31
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Zhu L, Yin H, Wang Y, Yang W, Dong T, Xu L, Hou Z, Shi Q, Shen Q, Lin Z, Zhao H, Xu Y, Chen Y, Wu J, Yu Z, Wen M, Huang J. Disrupted topological organization of the motor execution network in Wilson's disease. Front Neurol 2022; 13:1029669. [PMID: 36479050 PMCID: PMC9721349 DOI: 10.3389/fneur.2022.1029669] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 11/08/2022] [Indexed: 07/25/2023] Open
Abstract
OBJECTIVE There are a number of symptoms associated with Wilson's disease (WD), including motor function damage. The neuropathological mechanisms underlying motor impairments in WD are, however, little understood. In this study, we explored changes in the motor execution network topology in WD. METHODS We conducted resting-state functional magnetic resonance imaging (fMRI) on 38 right-handed individuals, including 23 WD patients and 15 healthy controls of the same age. Based on graph theory, a motor execution network was constructed and analyzed. In this study, global, nodal, and edge topological properties of motor execution networks were compared. RESULTS The global topological organization of the motor execution network in the two groups did not differ significantly across groups. In the cerebellum, WD patients had a higher nodal degree. At the edge level, a cerebello-thalamo-striato-cortical circuit with altered functional connectivity strength in WD patients was observed. Specifically, the strength of the functional connections between the cerebellum and thalamus increased, whereas the cortical-thalamic, cortical-striatum and cortical-cerebellar connections exhibited a decrease in the strength of the functional connection. CONCLUSION There is a disruption of the topology of the motor execution network in WD patients, which may be the potential basis for WD motor dysfunction and may provide important insights into neurobiological research related to WD motor dysfunction.
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Tooley UA, Park AT, Leonard JA, Boroshok AL, McDermott CL, Tisdall MD, Bassett DS, Mackey AP. The Age of Reason: Functional Brain Network Development during Childhood. J Neurosci 2022; 42:8237-8251. [PMID: 36192151 PMCID: PMC9653278 DOI: 10.1523/jneurosci.0511-22.2022] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 07/25/2022] [Accepted: 09/03/2022] [Indexed: 01/27/2023] Open
Abstract
Human childhood is characterized by dramatic changes in the mind and brain. However, little is known about the large-scale intrinsic cortical network changes that occur during childhood because of methodological challenges in scanning young children. Here, we overcome this barrier by using sophisticated acquisition and analysis tools to investigate functional network development in children between the ages of 4 and 10 years ([Formula: see text]; 50 female, 42 male). At multiple spatial scales, age is positively associated with brain network segregation. At the system level, age was associated with segregation of systems involved in attention from those involved in abstract cognition, and with integration among attentional and perceptual systems. Associations between age and functional connectivity are most pronounced in visual and medial prefrontal cortex, the two ends of a gradient from perceptual, externally oriented cortex to abstract, internally oriented cortex. These findings suggest that both ends of the sensory-association gradient may develop early, in contrast to the classical theories that cortical maturation proceeds from back to front, with sensory areas developing first and association areas developing last. More mature patterns of brain network architecture, controlling for age, were associated with better visuospatial reasoning abilities. Our results suggest that as cortical architecture becomes more specialized, children become more able to reason about the world and their place in it.SIGNIFICANCE STATEMENT Anthropologists have called the transition from early to middle childhood the "age of reason", when children across cultures become more independent. We employ cutting-edge neuroimaging acquisition and analysis approaches to investigate associations between age and functional brain architecture in childhood. Age was positively associated with segregation between cortical systems that process the external world and those that process abstract phenomena like the past, future, and minds of others. Surprisingly, we observed pronounced development at both ends of the sensory-association gradient, challenging the theory that sensory areas develop first and association areas develop last. Our results open new directions for research into how brains reorganize to support rapid gains in cognitive and socioemotional skills as children reach the age of reason.
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Affiliation(s)
- Ursula A Tooley
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Anne T Park
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Julia A Leonard
- Department of Psychology, Yale University, New Haven, Connecticut 06520
| | - Austin L Boroshok
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Cassidy L McDermott
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Matthew D Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Dani S Bassett
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Department of Physics and Astronomy, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Santa Fe Institute, Santa Fe, New Mexico 87501
| | - Allyson P Mackey
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
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33
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Dresp-Langley B. Grip force as a functional window to somatosensory cognition. Front Psychol 2022; 13:1026439. [DOI: 10.3389/fpsyg.2022.1026439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 09/26/2022] [Indexed: 11/13/2022] Open
Abstract
Analysis of grip force signals tailored to hand and finger movement evolution and changes in grip force control during task execution provide unprecedented functional insight into somatosensory cognition. Somatosensory cognition is the basis of our ability to act upon and to transform the physical world around us, to recognize objects on the basis of touch alone, and to grasp them with the right amount of force for lifting and manipulating them. Recent technology has permitted the wireless monitoring of grip force signals recorded from biosensors in the palm of the human hand to track and trace human grip forces deployed in cognitive tasks executed under conditions of variable sensory (visual, auditory) input. Non-invasive multi-finger grip force sensor technology can be exploited to explore functional interactions between somatosensory brain mechanisms and motor control, in particular during learning a cognitive task where the planning and strategic execution of hand movements is essential. Sensorial and cognitive processes underlying manual skills and/or hand-specific (dominant versus non-dominant hand) behaviors can be studied in a variety of contexts by probing selected measurement loci in the fingers and palm of the human hand. Thousands of sensor data recorded from multiple spatial locations can be approached statistically to breathe functional sense into the forces measured under specific task constraints. Grip force patterns in individual performance profiling may reveal the evolution of grip force control as a direct result of cognitive changes during task learning. Grip forces can be functionally mapped to from-global-to-local coding principles in brain networks governing somatosensory processes for motor control in cognitive tasks leading to a specific task expertise or skill. Under the light of a comprehensive overview of recent discoveries into the functional significance of human grip force variations, perspectives for future studies in cognition, in particular the cognitive control of strategic and task relevant hand movements in complex real-world precision task, are pointed out.
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Gu F, Gong A, Qu Y, Lu L, Shi Q, Fu Y. Brain Network Research of Skilled Shooters in the Shooting Preparation Stage under the Condition of Limited Sensory Function. Brain Sci 2022; 12:brainsci12101373. [PMID: 36291306 PMCID: PMC9599685 DOI: 10.3390/brainsci12101373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/01/2022] [Accepted: 10/07/2022] [Indexed: 11/16/2022] Open
Abstract
Shooting is a sport dominated by psychological factors. Hence, disturbing the shooter's sensory function during aiming will seriously affect his psychological state and shooting performance. Electroencephalograph (EEG) measurements of 30 skilled marksmen in the shooting preparation stage under noisy disturbance, weak light, and normal conditions were recorded. Therefore, the differences in neural mechanisms in the shooter's brain during shooting aiming in different disturbance conditions were explored using an analytical approach that employs functional connectivity and brain network analysis based on graph theory. The relationship between these brain network characteristics and shooting performance was also compared. The results showed that (1) the average connection strength in the beta frequency band and connection intensity in the left and right temporal lobes of the shooters under noise disturbance were significantly higher than those under the other two conditions, and their brain networks also showed a higher global and local efficiency. In addition, (2) the functional connection intensity in the occipital region of the beta band was higher than that in the normal condition in the weak-light condition. The information interaction in the left parietal region also increased continually during the shooting process. (3) Furthermore, the shooters' eigenvector centrality in the temporal and occipital regions with limited sensory function in the two conditions was lower than those in the normal condition. These findings suggest that noise disturbance activates the arousal level of the shooter's brain and enhances the information processing efficiency of the brain network; however, it increases the mental workload. In weak-light conditions, shooters focus more on visual information processing during aiming and strengthen the inhibition of functions in the brain regions unrelated to shooting behavior. Audiovisual disturbance renders the cortical regions equivalent to the audiovisual perception function in the shooter's brain less important in the entire brain network than in the normal condition. Therefore, these findings reveal the effect of audiovisual disturbance on the functional network of the cortex in the shooting preparation stage and provide a theoretical basis for further understanding the neural mechanism of the shooting process under sensory disturbances.
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Affiliation(s)
- Feng Gu
- School of Information Engineering, Engineering University of People’s Armed Police, Xi’an 710086, China
| | - Anmin Gong
- School of Information Engineering, Engineering University of People’s Armed Police, Xi’an 710086, China
| | - Yi Qu
- School of Information Engineering, Engineering University of People’s Armed Police, Xi’an 710086, China
| | - Ling Lu
- School of Information Engineering, Engineering University of People’s Armed Police, Xi’an 710086, China
| | - Qidi Shi
- School of Automation and Information Engineering, Kunming University of Science and Technology, Kunming 650032, China
| | - Yunfa Fu
- School of Automation and Information Engineering, Kunming University of Science and Technology, Kunming 650032, China
- Correspondence:
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35
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Terstege DJ, Durante IM, Epp JR. Brain-wide neuronal activation and functional connectivity are modulated by prior exposure to repetitive learning episodes. Front Behav Neurosci 2022; 16:907707. [PMID: 36160680 PMCID: PMC9501867 DOI: 10.3389/fnbeh.2022.907707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 08/23/2022] [Indexed: 11/26/2022] Open
Abstract
Memory storage and retrieval are shaped by past experiences. Prior learning and memory episodes have numerous impacts on brain structure from micro to macroscale. Previous experience with specific forms of learning increases the efficiency of future learning. It is less clear whether such practice effects on one type of memory might also have transferable effects to other forms of memory. Different forms of learning and memory rely on different brain-wide networks but there are many points of overlap in these networks. Enhanced structural or functional connectivity caused by one type of learning may be transferable to another type of learning due to overlap in underlying memory networks. Here, we investigated the impact of prior chronic spatial training on the task-specific functional connectivity related to subsequent contextual fear memory recall in mice. Our results show that mice exposed to prior spatial training exhibited decreased brain-wide activation compared to control mice during the retrieval of a context fear memory. With respect to functional connectivity, we observed changes in several network measures, notably an increase in global efficiency. Interestingly, we also observed an increase in network resilience based on simulated targeted node deletion. Overall, this study suggests that chronic learning has transferable effects on the functional connectivity networks of other types of learning and memory. The generalized enhancements in network efficiency and resilience suggest that learning itself may protect brain networks against deterioration.
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36
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Srivastava P, Fotiadis P, Parkes L, Bassett DS. The expanding horizons of network neuroscience: From description to prediction and control. Neuroimage 2022; 258:119250. [PMID: 35659996 PMCID: PMC11164099 DOI: 10.1016/j.neuroimage.2022.119250] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 04/15/2022] [Accepted: 04/25/2022] [Indexed: 01/11/2023] Open
Abstract
The field of network neuroscience has emerged as a natural framework for the study of the brain and has been increasingly applied across divergent problems in neuroscience. From a disciplinary perspective, network neuroscience originally emerged as a formal integration of graph theory (from mathematics) and neuroscience (from biology). This early integration afforded marked utility in describing the interconnected nature of neural units, both structurally and functionally, and underscored the relevance of that interconnection for cognition and behavior. But since its inception, the field has not remained static in its methodological composition. Instead, it has grown to use increasingly advanced graph-theoretic tools and to bring in several other disciplinary perspectives-including machine learning and systems engineering-that have proven complementary. In doing so, the problem space amenable to the discipline has expanded markedly. In this review, we discuss three distinct flavors of investigation in state-of-the-art network neuroscience: (i) descriptive network neuroscience, (ii) predictive network neuroscience, and (iii) a perturbative network neuroscience that draws on recent advances in network control theory. In considering each area, we provide a brief summary of the approaches, discuss the nature of the insights obtained, and highlight future directions.
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Affiliation(s)
- Pragya Srivastava
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Panagiotis Fotiadis
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Neuroscience, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Linden Parkes
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Physics & Astronomy, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia PA 19104, USA; Santa Fe Institute, Santa Fe NM 87501, USA.
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37
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Liu X, He Y, Gao Y, Booth JR, Zhang L, Zhang S, Lu C, Liu L. Developmental differences of large-scale functional brain networks for spoken word processing. BRAIN AND LANGUAGE 2022; 231:105149. [PMID: 35777141 DOI: 10.1016/j.bandl.2022.105149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 06/03/2022] [Accepted: 06/13/2022] [Indexed: 06/15/2023]
Abstract
A dual-stream dissociation for separate phonological and semantic processing has been implicated in adults' language processing, but it is unclear how this dissociation emerges with development. By employing a graph-theory based brain network analysis, we compared functional interaction architecture during a rhyming and meaning judgment task of children (aged 8-12) with adults (aged 19-26). We found adults had stronger functional connectivity strength than children between bilateral inferior frontal gyri and left inferior parietal lobule in the rhyming task, between middle frontal gyrus and angular gyrus, and within occipital areas in the meaning task. Meanwhile, adults but not children manifested between-task differences in these properties. In contrast, children had stronger functional connectivity strength or nodal degree in Heschl's gyrus, superior temporal gyrus, and subcortical areas. Our findings indicated spoken word processing development is characterized by increased functional specialization, relying on the dorsal and ventral pathways for phonological and semantic processing respectively.
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Affiliation(s)
- Xin Liu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/ McGovern, Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
| | - Yin He
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/ McGovern, Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yue Gao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/ McGovern, Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - James R Booth
- Department of Psychology and Human Development, Vanderbilt University, Nashville, TN 37203, USA
| | - Lihuan Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/ McGovern, Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Shudong Zhang
- Faculty of Education, Beijing Normal University, Beijing 100875, China
| | - Chunming Lu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/ McGovern, Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Li Liu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/ McGovern, Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
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38
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Sharf T, van der Molen T, Glasauer SMK, Guzman E, Buccino AP, Luna G, Cheng Z, Audouard M, Ranasinghe KG, Kudo K, Nagarajan SS, Tovar KR, Petzold LR, Hierlemann A, Hansma PK, Kosik KS. Functional neuronal circuitry and oscillatory dynamics in human brain organoids. Nat Commun 2022; 13:4403. [PMID: 35906223 PMCID: PMC9338020 DOI: 10.1038/s41467-022-32115-4] [Citation(s) in RCA: 66] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 07/18/2022] [Indexed: 12/30/2022] Open
Abstract
Human brain organoids replicate much of the cellular diversity and developmental anatomy of the human brain. However, the physiology of neuronal circuits within organoids remains under-explored. With high-density CMOS microelectrode arrays and shank electrodes, we captured spontaneous extracellular activity from brain organoids derived from human induced pluripotent stem cells. We inferred functional connectivity from spike timing, revealing a large number of weak connections within a skeleton of significantly fewer strong connections. A benzodiazepine increased the uniformity of firing patterns and decreased the relative fraction of weakly connected edges. Our analysis of the local field potential demonstrate that brain organoids contain neuronal assemblies of sufficient size and functional connectivity to co-activate and generate field potentials from their collective transmembrane currents that phase-lock to spiking activity. These results point to the potential of brain organoids for the study of neuropsychiatric diseases, drug action, and the effects of external stimuli upon neuronal networks.
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Affiliation(s)
- Tal Sharf
- Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA, 93106, USA. .,Department of Molecular, Cellular and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, 93106, USA. .,Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, 95064, USA.
| | - Tjitse van der Molen
- Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA, 93106, USA.,Department of Molecular, Cellular and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, 93106, USA
| | - Stella M K Glasauer
- Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA, 93106, USA.,Department of Molecular, Cellular and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, 93106, USA
| | - Elmer Guzman
- Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA, 93106, USA.,Department of Molecular, Cellular and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, 93106, USA
| | - Alessio P Buccino
- Department of Biosystems Science and Engineering, ETH Zürich, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Gabriel Luna
- Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA, 93106, USA.,Department of Molecular, Cellular and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, 93106, USA
| | - Zhuowei Cheng
- Department of Computer Science, University of California Santa Barbara, Santa Barbara, CA, 93106, USA
| | - Morgane Audouard
- Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA, 93106, USA.,Department of Molecular, Cellular and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, 93106, USA
| | - Kamalini G Ranasinghe
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Kiwamu Kudo
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Srikantan S Nagarajan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Kenneth R Tovar
- Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA, 93106, USA
| | - Linda R Petzold
- Department of Computer Science, University of California Santa Barbara, Santa Barbara, CA, 93106, USA
| | - Andreas Hierlemann
- Department of Biosystems Science and Engineering, ETH Zürich, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Paul K Hansma
- Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA, 93106, USA.,Department of Physics, University of California Santa Barbara, Santa Barbara, CA, 93106, USA
| | - Kenneth S Kosik
- Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA, 93106, USA. .,Department of Molecular, Cellular and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, 93106, USA.
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Pandey P, Tripathi R, Miyapuram KP. Classifying oscillatory brain activity associated with Indian Rasas using network metrics. Brain Inform 2022; 9:15. [PMID: 35840823 PMCID: PMC9287523 DOI: 10.1186/s40708-022-00163-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 06/28/2022] [Indexed: 11/10/2022] Open
Abstract
Neural signatures for the western classification of emotions have been widely discussed in the literature. The ancient Indian treatise on performing arts known as Natyashastra categorizes emotions into nine classes, known as Rasas. Rasa-as opposed to a pure emotion-is defined as a superposition of certain transitory, dominant, and temperamental emotional states. Although Rasas have been widely discussed in the text, dedicated brain imaging studies have not been conducted in their research. Our study examines the neural oscillations, recorded through electroencephalography (EEG) imaging, that are elicited while experiencing emotional states corresponding to Rasas. We identify differences among them using network-based functional connectivity metrics in five different frequency bands. Further, Random Forest models are trained on the extracted network features, and we present our findings based on classifier predictions. We observe slow (delta) and fast brain waves (beta and gamma) exhibited the maximum discriminating features between Rasas, whereas alpha and theta bands showed fewer distinguishable pairs. Out of nine Rasas, Sringaram (love), Bibhatsam (odious), and Bhayanakam (terror) were distinguishable from other Rasas the most across frequency bands. On the scale of most network metrics, Raudram (rage) and Sringaram are on the extremes, which also resulted in their good classification accuracy of 95%. This is reminiscent of the circumplex model where anger and contentment/happiness are on extremes on the pleasant scale. Interestingly, our results are consistent with the previous studies which highlight the significant role of higher frequency oscillations in the classification of emotions, in contrast to the alpha band that has shows non-significant differences across emotions. This research contributes to one of the first attempts to investigate the neural correlates of Rasas. Therefore, the results of this study can potentially guide the explorations into the entrainment of brain oscillations between performers and viewers, which can further lead to better performances and viewer experience.
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Affiliation(s)
- Pankaj Pandey
- Computer Science and Engineering, Indian Institute of Technology Gandhinagar, 382355, Gandhinagar, India.
| | - Richa Tripathi
- Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden-Rossendorf, Görlitz, Germany
| | - Krishna Prasad Miyapuram
- Computer Science and Engineering, Indian Institute of Technology Gandhinagar, 382355, Gandhinagar, India.,Centre for Cognitive and Brain Sciences, Indian Institute of Technology Gandhinagar, 382355, Gandhinagar, India
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40
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Network structure from a characterization of interactions in complex systems. Sci Rep 2022; 12:11742. [PMID: 35817803 PMCID: PMC9273794 DOI: 10.1038/s41598-022-14397-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/06/2022] [Indexed: 11/29/2022] Open
Abstract
Many natural and man-made complex dynamical systems can be represented by networks with vertices representing system units and edges the coupling between vertices. If edges of such a structural network are inaccessible, a widely used approach is to identify them with interactions between vertices, thereby setting up a functional network. However, it is an unsolved issue if and to what extent important properties of a functional network on the global and the local scale match those of the corresponding structural network. We address this issue by deriving functional networks from characterizing interactions in paradigmatic oscillator networks with widely-used time-series-analysis techniques for various factors that alter the collective network dynamics. Surprisingly, we find that particularly key constituents of functional networks—as identified with betweenness and eigenvector centrality—coincide with ground truth to a high degree, while global topological and spectral properties—clustering coefficient, average shortest path length, assortativity, and synchronizability—clearly deviate. We obtain similar concurrences for an empirical network. Our findings are of relevance for various scientific fields and call for conceptual and methodological refinements to further our understanding of the relationship between structure and function of complex dynamical systems.
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Li Q, Li X, Fu H, Tan K, Ge Y, Chu L, Zhang C, Yan Y. Role of Impoundments Created by Low-Head Dams in Affecting Fish Assemblages in Subtropical Headwater Streams in China. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.916873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Low-head dams are ubiquitous human disturbances that degrade aquatic ecosystem function worldwide. The localized effects of low-head dams have been relatively well documented; however, most previous studies have ignored the concealed process caused by native-invasive species. Based on fish assemblage data from the first-order streams of four basins in the Wannan Mountains, we used a quantitative approach to assess the effects of low-head dams on fish assemblages by distinguishing between native and native-invasive species using occurrence- and abundance-based data, respectively. Low-head dams significantly decreased native fish alpha diversity while favoring native-invasive fish. The opposite pattern between the two fish types partly masked changes in the whole fish assemblage. Meanwhile, the establishment of widespread native-invasive species and the loss of native species driven by low-head dams influenced the interaction network structure. The degree to which local fish assemblages were altered by low-head dams, i.e., beta diversity (β-diversity) was significantly higher for abundance-based approaches than for occurrence-based ones, suggesting that the latter underestimated the effects of low-head dams. Furthermore, the species contribution to β-diversity of native species was significantly higher than that of native-invasive species in both impoundments and free-flowing segments for abundance-based data. In communities or regions where native fish species are predominant, our results suggest that understanding which species contribute to β-diversity will offer new insights into the development of effective conservation strategies by taking the identities of native and native-invasive species into account.
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Signals of Potential Species Associations Offer Clues about Community Organisation of Stream Fish across Seasons. Animals (Basel) 2022; 12:ani12131721. [PMID: 35804620 PMCID: PMC9265093 DOI: 10.3390/ani12131721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 06/25/2022] [Accepted: 07/01/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Species interactions are one of the main factors affecting community assembly, yet the role of such interactions remains mostly unknown. Here, we investigated roles of potential species associations in fish community assembly in the Qiupu River, China. Our results suggested that potential species associations might have been underestimated in stream fish community assembly. The contribution of potential species associations to fish community assembly can be reflected by interaction network structures. Omnivorous species play an important role in maintaining network structure as they may have more associations with other species. This study highlights the importance of capturing species associations in river ecosystems across different geographical and environmental settings. Abstract Environmental filtering, spatial factors and species interactions are fundamental ecological mechanisms for community organisation, yet the role of such interactions across different environmental and spatial settings remains mostly unknown. In this study, we investigated fish community organisation scenarios and seasonal species-to-species associations potentially reflecting biotic associations along the Qiupu River (China). Based on a latent variable approach and a tree-based method, we compared the relative contribution of the abiotic environment, spatial covariates and potential species associations for variation in the community structure, and assessed whether different assembly scenarios were modulated by concomitant changes in the interaction network structure of fish communities across seasons. We found that potential species associations might have been underestimated in community-based assessments of stream fish. Omnivore species, since they have more associations with other species, were found to be key components sustaining fish interaction networks across different stream orders. Hence, we suggest that species interactions, such as predation and competition, likely played a key role in community structure. For instance, indices accounting for network structure, such as connectance and nestedness, were strongly correlated with the unexplained residuals from our latent variable approach, thereby re-emphasising that biotic signals, potentially reflecting species interactions, may be of primary importance in determining stream fish communities across seasons. Overall, our findings indicate that interaction network structures are a powerful tool to reflect the contribution of potential species associations to community assembly. From an applied perspective, this study should encourage freshwater ecologists to empirically capture and manage biotic constraints in stream ecosystems across different geographical and environmental settings, especially in the context of the ever-increasing impacts of human-induced local extinction debts and species invasions.
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Kaur A, Kahlon KS. Accurate Identification of ADHD among Adults Using Real-Time Activity Data. Brain Sci 2022; 12:brainsci12070831. [PMID: 35884638 PMCID: PMC9312518 DOI: 10.3390/brainsci12070831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 06/20/2022] [Accepted: 06/23/2022] [Indexed: 11/16/2022] Open
Abstract
Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopment disorder that affects millions of children and typically persists into adulthood. It must be diagnosed efficiently and consistently to receive adequate treatment, otherwise, it can have a detrimental impact on the patient’s professional performance, mental health, and relationships. In this work, motor activity data of adults suffering from ADHD and clinical controls has been preprocessed to obtain 788 activity-related statistical features. Afterwards, principal component analysis has been carried out to obtain significant features for accurate classification. These features are then fed into six different machine learning algorithms for classification, which include C4.5, kNN, Random Forest, LogitBoost, SVM, and Naive Bayes. The detailed evaluation of the results through 10-fold cross-validation reveals that SVM outperforms other classifiers with an accuracy of 98.43%, F-measure of 98.42%, sensitivity of 98.33%, specificity of 98.56% and AUC of 0.983. Thus, a PCA-based SVM approach appears to be an effective choice for accurate identification of ADHD patients among other clinical controls using real-time analysis of activity data.
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Affiliation(s)
- Amandeep Kaur
- Department of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar 143005, Punjab, India
- Correspondence: or ; Tel.: +91-9855-40-6833
| | - Karanjeet Singh Kahlon
- Department of Computer Science, Guru Nanak Dev University, Amritsar 143005, Punjab, India;
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Roy M, Senapati A, Poria S, Mishra A, Hens C. Role of assortativity in predicting burst synchronization using echo state network. Phys Rev E 2022; 105:064205. [PMID: 35854538 DOI: 10.1103/physreve.105.064205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 05/11/2022] [Indexed: 06/15/2023]
Abstract
In this study, we use a reservoir computing based echo state network (ESN) to predict the collective burst synchronization of neurons. Specifically, we investigate the ability of ESN in predicting the burst synchronization of an ensemble of Rulkov neurons placed on a scale-free network. We have shown that a limited number of nodal dynamics used as input in the machine can capture the real trend of burst synchronization in this network. Further, we investigate the proper selection of nodal inputs of degree-degree (positive and negative) correlated networks. We show that for a disassortative network, selection of different input nodes based on degree has no significant role in the machine's prediction. However, in the case of assortative network, training the machine with the information (i.e., time series) of low degree nodes gives better results in predicting the burst synchronization. The results are found to be consistent with the investigation carried out with a continuous time Hindmarsh-Rose neuron model. Furthermore, the role of hyperparameters like spectral radius and leaking parameter of ESN on the prediction process has been examined. Finally, we explain the underlying mechanism responsible for observing these differences in the prediction in a degree correlated network.
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Affiliation(s)
- Mousumi Roy
- Department of Applied Mathematics, University of Calcutta, 92, A.P.C. Road, Kolkata 700009, India
| | - Abhishek Senapati
- Center for Advanced Systems Understanding (CASUS), 02826 Görlitz, Germany
| | - Swarup Poria
- Department of Applied Mathematics, University of Calcutta, 92, A.P.C. Road, Kolkata 700009, India
| | - Arindam Mishra
- Division of Dynamics, Lodz University of Technology, Stefanowskiego 1/15, 90924 Lodz, Poland
| | - Chittaranjan Hens
- Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata 700108, India
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Phillips NS, Rao V, Kmetz L, Vela R, Medick S, Krull K, Kesler SR. Changes in Brain Functional and Effective Connectivity After Treatment for Breast Cancer and Implications for Intervention Targets. Brain Connect 2022; 12:385-397. [PMID: 34210168 PMCID: PMC9131353 DOI: 10.1089/brain.2021.0049] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background: Patients with breast cancer frequently report cognitive impairment both during and after completion of therapy. Evidence suggests that cancer-related cognitive impairments are related to widespread neural network dysfunction. The default mode network (DMN) is a large conserved network that plays a critical role in integrating the functions of various neural systems. Disruption of the network may play a key role in the development of cognitive impairment. Methods: We compared neuroimaging and neurocognitive data from 43 newly diagnosed primary breast cancer patients (mean age = 48, standard deviation [SD] = 8.9 years) and 50 frequency-matched healthy female controls (mean age = 50, SD = 10 years) before treatment and 1 year after treatment completion. Functional and effective connectivity measures of the DMN were obtained using graph theory and Bayesian network analysis methods, respectively. Results: Compared with healthy females, the breast cancer group displayed higher global efficiency and path length post-treatment (p < 0.03, corrected). Breast cancer survivors showed significantly lower performance on measures of verbal memory, attention, and verbal fluency (p < 0.05) at both time points. Within the DMN, local brain network organization, as measured by edge-betweenness centralities, was significantly altered in the breast cancer group compared with controls at both time points (p < 0.0001, corrected), with several connections showing a significant group-by-time effect (p < 0.003, corrected). Effective connectivity demonstrated significantly altered patterns of neuronal coupling in patients with breast cancer (p < 0.05). Significant correlations were seen between hormone blockade therapy, radiation therapy, chemotherapy cycles, memory, and verbal fluency test and edge-betweenness centralities. Discussion: This pattern of altered network organization in the default mode is believed to result in reduced network efficiency and disrupted communication. Subregions of the DMN, the orbital prefrontal cortex and posterior memory network, appear to be at the center of this disruption and this could inform future interventions. Impact statement This prospective study is the first to investigate how post-treatment changes in functional and effective connectivity in the regions of default mode network are related to cancer therapy and measures of memory and verbal learning in breast cancer patients. We demonstrate that the interactions between treatment, brain connectivity, and neurocognitive outcomes coalesce around a subgroup of brain structures in the orbital frontal and parietal lobe. This would suggest that interventions that target these regions may improve neurocognitive outcomes in breast cancer survivors.
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Affiliation(s)
- Nicholas S. Phillips
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Vikram Rao
- School of Nursing, University of Texas at Austin, Austin, Texas, USA
| | - Lorie Kmetz
- School of Nursing, University of Texas at Austin, Austin, Texas, USA
| | - Ruben Vela
- School of Nursing, University of Texas at Austin, Austin, Texas, USA
- Department of Diagnostic Medicine, Dell School of Medicine, University of Texas at Austin, Austin, Texas, USA
| | - Sarah Medick
- School of Nursing, University of Texas at Austin, Austin, Texas, USA
| | - Kevin Krull
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Shelli R. Kesler
- School of Nursing, University of Texas at Austin, Austin, Texas, USA
- Department of Diagnostic Medicine, Dell School of Medicine, University of Texas at Austin, Austin, Texas, USA
- Center for Computational Oncology, Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas, USA
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Trujillo JP, Özyürek A, Kan CC, Sheftel-Simanova I, Bekkering H. Differences in functional brain organization during gesture recognition between autistic and neurotypical individuals. Soc Cogn Affect Neurosci 2022; 17:1021-1034. [PMID: 35428885 PMCID: PMC9629468 DOI: 10.1093/scan/nsac026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 03/19/2022] [Accepted: 04/15/2022] [Indexed: 01/12/2023] Open
Abstract
Persons with and without autism process sensory information differently. Differences in sensory processing are directly relevant to social functioning and communicative abilities, which are known to be hampered in persons with autism. We collected functional magnetic resonance imaging data from 25 autistic individuals and 25 neurotypical individuals while they performed a silent gesture recognition task. We exploited brain network topology, a holistic quantification of how networks within the brain are organized to provide new insights into how visual communicative signals are processed in autistic and neurotypical individuals. Performing graph theoretical analysis, we calculated two network properties of the action observation network: 'local efficiency', as a measure of network segregation, and 'global efficiency', as a measure of network integration. We found that persons with autism and neurotypical persons differ in how the action observation network is organized. Persons with autism utilize a more clustered, local-processing-oriented network configuration (i.e. higher local efficiency) rather than the more integrative network organization seen in neurotypicals (i.e. higher global efficiency). These results shed new light on the complex interplay between social and sensory processing in autism.
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Affiliation(s)
- James P Trujillo
- Correspondence should be addressed to James P. Trujillo, Radboud University, Donders Centre for Cognition, Maria Montessori Building, Thomas van Aquinostraat 4, Nijmegen 6525 GD, The Netherlands. E-mail:
| | - Asli Özyürek
- Donders Institute for Brain, Cognition, and Behavior, Donders Centre for Cognition, Nijmegen, GD 6525, The Netherlands,Max Planck Institute for Psycholinguistics, Nijmegen, XD 6525, The Netherlands
| | - Cornelis C Kan
- Department of Psychiatry, Radboud University Medical Centre, Radboudumc, Nijmegen, GA 6525, The Netherlands
| | - Irina Sheftel-Simanova
- One Planet Research Centre, Radboud University Medical Centre, Radboudumc, Nijmegen, GA 6525, The Netherlands
| | - Harold Bekkering
- Donders Institute for Brain, Cognition, and Behavior, Donders Centre for Cognition, Nijmegen, GD 6525, The Netherlands
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Zhang S, Tang J, Huang J, Suo G, Zhou Z, You B, Dai Y, Liu Y. Whole-Brain Dynamic Resting-State Functional Network Analysis in Benign Epilepsy with Centrotemporal Spikes. IEEE J Biomed Health Inform 2022; 26:3813-3821. [PMID: 35380976 DOI: 10.1109/jbhi.2022.3164907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Benign epilepsy with centrotemporal spikes (BECTS), the most common type of epilepsy among children, is considered a network disorder. Both fMRI and EEG source imaging (ESI) studies have indicated that BECTS is associated with static resting-state functional network (SFN) alterations (e.g., decreased global efficiency) in source space. However, we find that the abovementioned alterations are not significant when the SFN calculations are performed in the scalp space using only clinical routine low-density (e.g., 19 channels) EEG recordings (shown in our results). In the context of EEG microstates, it is clear that networks in the scalp space with resting-state EEG recordings dynamically reconfigure in a well-organized way based on different functional states. We are therefore inspired to propose a whole-brain dynamic resting-state functional network (DFN) computation method based on resting-state low-density EEG recordings with four classical microstates in scalp space. Notably, on the one hand, this approach is suitable for clinical conditions, and, on the other hand, the dynamic alternations calculated with a DFN may promote our understanding of how the networks change in BECTS. We analysed the changes in a DFN in six frequency bands (, low, high, and) in patients with BECTS compared to those for healthy controls. Superior to traditional SFNs, the proposed DFN can reveal significant differences between individuals with BECTS and healthy controls (e.g., lower global efficiency), thus matching traditional fMRI and ESI methods in the source space. Our method directly performs DFN computations from low-density EEG recordings and avoids complex ESI computations, making it promising for clinical applications, especially in the outpatient diagnosis stage.
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Jones D, Lowe V, Graff-Radford J, Botha H, Barnard L, Wiepert D, Murphy MC, Murray M, Senjem M, Gunter J, Wiste H, Boeve B, Knopman D, Petersen R, Jack C. A computational model of neurodegeneration in Alzheimer's disease. Nat Commun 2022; 13:1643. [PMID: 35347127 PMCID: PMC8960876 DOI: 10.1038/s41467-022-29047-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 02/17/2022] [Indexed: 12/25/2022] Open
Abstract
Disruption of mental functions in Alzheimer's disease (AD) and related disorders is accompanied by selective degeneration of brain regions. These regions comprise large-scale ensembles of cells organized into systems for mental functioning, however the relationship between clinical symptoms of dementia, patterns of neurodegeneration, and functional systems is not clear. Here we present a model of the association between dementia symptoms and degenerative brain anatomy using F18-fluorodeoxyglucose PET and dimensionality reduction techniques in two cohorts of patients with AD. This reflected a simple information processing-based functional description of macroscale brain anatomy which we link to AD physiology, functional networks, and mental abilities. We further apply the model to normal aging and seven degenerative diseases of mental functions. We propose a global information processing model for mental functions that links neuroanatomy, cognitive neuroscience and clinical neurology.
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Affiliation(s)
- D Jones
- Department of Neurology, Mayo Clinic, Rochester, MN, 55905, USA.
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA.
| | - V Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
| | - J Graff-Radford
- Department of Neurology, Mayo Clinic, Rochester, MN, 55905, USA
| | - H Botha
- Department of Neurology, Mayo Clinic, Rochester, MN, 55905, USA
| | - L Barnard
- Department of Neurology, Mayo Clinic, Rochester, MN, 55905, USA
| | - D Wiepert
- Department of Neurology, Mayo Clinic, Rochester, MN, 55905, USA
| | - M C Murphy
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
| | - M Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, 32224, USA
| | - M Senjem
- Department of Information Technology, Mayo Clinic, Rochester, MN, 55905, USA
| | - J Gunter
- Department of Information Technology, Mayo Clinic, Rochester, MN, 55905, USA
| | - H Wiste
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA
| | - B Boeve
- Department of Neurology, Mayo Clinic, Rochester, MN, 55905, USA
| | - D Knopman
- Department of Neurology, Mayo Clinic, Rochester, MN, 55905, USA
| | - R Petersen
- Department of Neurology, Mayo Clinic, Rochester, MN, 55905, USA
| | - C Jack
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
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Zaccaro A, Piarulli A, Melosini L, Menicucci D, Gemignani A. Neural Correlates of Non-ordinary States of Consciousness in Pranayama Practitioners: The Role of Slow Nasal Breathing. Front Syst Neurosci 2022; 16:803904. [PMID: 35387390 PMCID: PMC8977447 DOI: 10.3389/fnsys.2022.803904] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 02/17/2022] [Indexed: 12/24/2022] Open
Abstract
The modulatory effect of nasal respiration on integrative brain functions and hence consciousness has recently been unambiguously demonstrated. This effect is sustained by the olfactory epithelium mechanical sensitivity complemented by the existence of massive projections between the olfactory bulb and the prefrontal cortex. However, studies on slow nasal breathing (SNB) in the context of contemplative practices have sustained the fundamental role of respiratory vagal stimulation, with little attention to the contribution of the olfactory epithelium mechanical stimulation. This study aims at disentangling the effects of olfactory epithelium stimulation (proper of nasal breathing) from those related to respiratory vagal stimulation (common to slow nasal and mouth breathing). We investigated the psychophysiological (cardio-respiratory and electroencephalographic parameters) and phenomenological (perceived state of consciousness) aftereffects of SNB (epithelium mechanical – 2.5 breaths/min) in 12 experienced meditators. We compared the nasal breathing aftereffects with those observed after a session of mouth breathing at the same respiratory rate and with those related to a resting state condition. SNB induced (1) slowing of electroencephalography (EEG) activities (delta-theta bands) in prefrontal regions, (2) a widespread increase of theta and high-beta connectivity complemented by an increase of phase-amplitude coupling between the two bands in prefrontal and posterior regions belonging to the Default Mode Network, (3) an increase of high-beta networks small-worldness. (4) a higher perception of being in a non-ordinary state of consciousness. The emerging scenario strongly suggests that the effects of SNB, beyond the relative contribution of vagal stimulation, are mainly ascribable to olfactory epithelium stimulation. In conclusion, slow Pranayama breathing modulates brain activity and hence subjective experience up to the point of inducing a non-ordinary state of consciousness.
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Affiliation(s)
- Andrea Zaccaro
- Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy
- Department of Neuroscience, Imaging and Clinical Sciences, “G. d’Annunzio” University of Chieti-Pescara, Chieti, Italy
| | - Andrea Piarulli
- Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy
- Giga Consciousness, Coma Science Group, University of Liège, Liège, Belgium
- *Correspondence: Andrea Piarulli,
| | - Lorenza Melosini
- Pneumology Branch, Azienda Ospedaliero Universitaria Pisana, Pisa, Italy
| | - Danilo Menicucci
- Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy
| | - Angelo Gemignani
- Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy
- Clinical Psychology Branch, Azienda Ospedaliero Universitaria Pisana, Pisa, Italy
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50
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Wu X, Kong X, Vatansever D, Liu Z, Zhang K, Sahakian BJ, Robbins TW, Feng J, Thompson P, Zhang J. Dynamic changes in brain lateralization correlate with human cognitive performance. PLoS Biol 2022; 20:e3001560. [PMID: 35298460 PMCID: PMC8929635 DOI: 10.1371/journal.pbio.3001560] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 01/31/2022] [Indexed: 12/12/2022] Open
Abstract
Hemispheric lateralization constitutes a core architectural principle of human brain organization underlying cognition, often argued to represent a stable, trait-like feature. However, emerging evidence underlines the inherently dynamic nature of brain networks, in which time-resolved alterations in functional lateralization remain uncharted. Integrating dynamic network approaches with the concept of hemispheric laterality, we map the spatiotemporal architecture of whole-brain lateralization in a large sample of high-quality resting-state fMRI data (N = 991, Human Connectome Project). We reveal distinct laterality dynamics across lower-order sensorimotor systems and higher-order associative networks. Specifically, we expose 2 aspects of the laterality dynamics: laterality fluctuations (LF), defined as the standard deviation of laterality time series, and laterality reversal (LR), referring to the number of zero crossings in laterality time series. These 2 measures are associated with moderate and extreme changes in laterality over time, respectively. While LF depict positive association with language function and cognitive flexibility, LR shows a negative association with the same cognitive abilities. These opposing interactions indicate a dynamic balance between intra and interhemispheric communication, i.e., segregation and integration of information across hemispheres. Furthermore, in their time-resolved laterality index, the default mode and language networks correlate negatively with visual/sensorimotor and attention networks, which are linked to better cognitive abilities. Finally, the laterality dynamics are associated with functional connectivity changes of higher-order brain networks and correlate with regional metabolism and structural connectivity. Our results provide insights into the adaptive nature of the lateralized brain and new perspectives for future studies of human cognition, genetics, and brain disorders. Hemispheric lateralization constitutes a core architectural principle of human brain organization, often argued to represent a stable, trait-like feature, but how does this fit with our increasing appreciation of the inherently dynamic nature of brain networks? This neuroimaging study reveals the dynamic nature of functional brain lateralization at resting-state and its relationship with language function and cognitive flexibility.
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Affiliation(s)
- Xinran Wu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Xiangzhen Kong
- Department of Psychology and Behavioral Sciences, Zhejiang University, Zhejiang, China
| | - Deniz Vatansever
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Zhaowen Liu
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Kai Zhang
- School of Computer Science and Technology, East China Normal University, Shanghai, China
| | - Barbara J. Sahakian
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Department of the Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, United Kingdom
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
| | - Trevor W. Robbins
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Department of the Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, United Kingdom
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
- Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China
- Shanghai Center for Mathematical Sciences, Shanghai, China
| | - Paul Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Jie Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- * E-mail:
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