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Kristanto D, Burkhardt M, Thiel C, Debener S, Gießing C, Hildebrandt A. The multiverse of data preprocessing and analysis in graph-based fMRI: A systematic literature review of analytical choices fed into a decision support tool for informed analysis. Neurosci Biobehav Rev 2024; 165:105846. [PMID: 39117132 DOI: 10.1016/j.neubiorev.2024.105846] [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: 01/22/2024] [Revised: 04/04/2024] [Accepted: 08/04/2024] [Indexed: 08/10/2024]
Abstract
The large number of different analytical choices used by researchers is partly responsible for the challenge of replication in neuroimaging studies. For an exhaustive robustness analysis, knowledge of the full space of analytical options is essential. We conducted a systematic literature review to identify the analytical decisions in functional neuroimaging data preprocessing and analysis in the emerging field of cognitive network neuroscience. We found 61 different steps, with 17 of them having debatable parameter choices. Scrubbing, global signal regression, and spatial smoothing are among the controversial steps. There is no standardized order in which different steps are applied, and the parameter settings within several steps vary widely across studies. By aggregating the pipelines across studies, we propose three taxonomic levels to categorize analytical choices: 1) inclusion or exclusion of specific steps, 2) parameter tuning within steps, and 3) distinct sequencing of steps. We have developed a decision support application with high educational value called METEOR to facilitate access to the data in order to design well-informed robustness (multiverse) analysis.
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Affiliation(s)
- Daniel Kristanto
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg 26129, Germany.
| | - Micha Burkhardt
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg 26129, Germany
| | - Christiane Thiel
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg 26129, Germany; Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Germany; Cluster of Excellence "Hearing4All", Carl von Ossietzky Universität Oldenburg, Germany
| | - Stefan Debener
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg 26129, Germany; Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Germany; Cluster of Excellence "Hearing4All", Carl von Ossietzky Universität Oldenburg, Germany
| | - Carsten Gießing
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg 26129, Germany; Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Germany.
| | - Andrea Hildebrandt
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg 26129, Germany; Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Germany; Cluster of Excellence "Hearing4All", Carl von Ossietzky Universität Oldenburg, Germany.
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2
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He X, Calhoun VD, Du Y. SMART (Splitting-Merging Assisted Reliable) Independent Component Analysis for Extracting Accurate Brain Functional Networks. Neurosci Bull 2024; 40:905-920. [PMID: 38491231 DOI: 10.1007/s12264-024-01184-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 12/08/2023] [Indexed: 03/18/2024] Open
Abstract
Functional networks (FNs) hold significant promise in understanding brain function. Independent component analysis (ICA) has been applied in estimating FNs from functional magnetic resonance imaging (fMRI). However, determining an optimal model order for ICA remains challenging, leading to criticism about the reliability of FN estimation. Here, we propose a SMART (splitting-merging assisted reliable) ICA method that automatically extracts reliable FNs by clustering independent components (ICs) obtained from multi-model-order ICA using a simplified graph while providing linkages among FNs deduced from different-model orders. We extend SMART ICA to multi-subject fMRI analysis, validating its effectiveness using simulated and real fMRI data. Based on simulated data, the method accurately estimates both group-common and group-unique components and demonstrates robustness to parameters. Using two age-matched cohorts of resting fMRI data comprising 1,950 healthy subjects, the resulting reliable group-level FNs are greatly similar between the two cohorts, and interestingly the subject-specific FNs show progressive changes while age increases. Furthermore, both small-scale and large-scale brain FN templates are provided as benchmarks for future studies. Taken together, SMART ICA can automatically obtain reliable FNs in analyzing multi-subject fMRI data, while also providing linkages between different FNs.
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Affiliation(s)
- Xingyu He
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, 30303, USA
| | - Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China.
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, 30303, USA.
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3
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Sharma R, Dillon K, Williams SEE, McIntosh R. Does emotion regulation network mediate the effect of social network on psychological distress among older adults? Soc Neurosci 2023; 18:142-154. [PMID: 37267049 DOI: 10.1080/17470919.2023.2218619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 05/12/2023] [Indexed: 06/04/2023]
Abstract
Socio-emotional interactions are integral for regulating emotions and buffering psychological distress. Social neuroscience perspectives on aging suggest that empathetic interpersonal interactions are supported by the activation of brain regions involved in regulating negative affect. The current study tested whether resting state functional connectivity of a network of brain regions activated during cognitive emotion regulation, i.e., emotion regulation network (ERN), statistically mediates the frequency of social contact with friends or family on psychological distress. Here, a 10-min resting-state functional MRI scan was collected along with self-reported anxiety/depressive, somatic, and thought problems and social networking from 90 community-dwelling older adults (aged 65-85 years). The frequency of social interactions with family, but not friends and neighbors, was associated with lower psychological distress. The magnitude of this effect was reduced by 33.34% to non-significant upon adding resting state ERN connectivity as a mediator. Follow-up whole-brain graph network analyses revealed that efficiency and centrality of the left inferior frontal gyrus and the right middle temporal gyrus relate to greater family interactions and lower distress. These hubs may help to buffer psychological problems in older adults through interactions involving empathetic and cognitive emotion regulation with close family.
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Affiliation(s)
| | - Kaitlyn Dillon
- Department of Psychology, University of Miami, Miami, Florida, USA
| | | | - Roger McIntosh
- Department of Psychology, University of Miami, Miami, Florida, USA
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4
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Kizilirmak JM, Soch J, Schütze H, Düzel E, Feldhoff H, Fischer L, Knopf L, Maass A, Raschick M, Schult A, Yakupov R, Richter A, Schott BH. The relationship between resting-state amplitude fluctuations and memory-related deactivations of the default mode network in young and older adults. Hum Brain Mapp 2023; 44:3586-3609. [PMID: 37051727 DOI: 10.1002/hbm.26299] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 03/09/2023] [Accepted: 03/20/2023] [Indexed: 04/14/2023] Open
Abstract
The default mode network (DMN) typically exhibits deactivations during demanding tasks compared to periods of relative rest. In functional magnetic resonance imaging (fMRI) studies of episodic memory encoding, increased activity in DMN regions even predicts later forgetting in young healthy adults. This association is attenuated in older adults and, in some instances, increased DMN activity even predicts remembering rather than forgetting. It is yet unclear whether this phenomenon is due to a compensatory mechanism, such as self-referential or schema-dependent encoding, or whether it reflects overall reduced DMN activity modulation in older age. We approached this question by systematically comparing DMN activity during successful encoding and tonic, task-independent, DMN activity at rest in a sample of 106 young (18-35 years) and 111 older (60-80 years) healthy participants. Using voxel-wise multimodal analyses, we assessed the age-dependent relationship between DMN resting-state amplitude (mean percent amplitude of fluctuation, mPerAF) and DMN fMRI signals related to successful memory encoding, as well as their modulation by age-related hippocampal volume loss, while controlling for regional grey matter volume. Older adults showed lower resting-state DMN amplitudes and lower task-related deactivations. However, a negative relationship between resting-state mPerAF and subsequent memory effect within the precuneus was observed only in young, but not older adults. Hippocampal volumes showed no relationship with the DMN subsequent memory effect or mPerAF. Lastly, older adults with higher mPerAF in the DMN at rest tend to show higher memory performance, pointing towards the importance of a maintained ability to modulate DMN activity in old age.
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Affiliation(s)
- Jasmin M Kizilirmak
- Cognitive Geriatric Psychiatry, German Center for Neurodegenerative Diseases, Göttingen, Germany
- Neurodidactics and NeuroLab, Institute for Psychology, University of Hildesheim, Hildesheim, Germany
- German Centre for Higher Education Research and Science Studies, Hannover, Germany
| | - Joram Soch
- Cognitive Geriatric Psychiatry, German Center for Neurodegenerative Diseases, Göttingen, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Hartmut Schütze
- Medical Faculty, Institute for Cognitive Neurology and Dementia Research, Otto-von-Guericke-University, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Magdeburg, Germany
| | - Emrah Düzel
- Medical Faculty, Institute for Cognitive Neurology and Dementia Research, Otto-von-Guericke-University, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Magdeburg, Germany
- German Center for Neurodegenerative Diseases, Magdeburg, Germany
| | | | | | - Lea Knopf
- Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Anne Maass
- German Center for Neurodegenerative Diseases, Magdeburg, Germany
| | | | - Annika Schult
- Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Renat Yakupov
- German Center for Neurodegenerative Diseases, Magdeburg, Germany
| | - Anni Richter
- Leibniz Institute for Neurobiology, Magdeburg, Germany
- Center for Intervention and Research on Adaptive and Maladaptive Brain Circuits Underlying Mental Health (C-I-R-C), Jena-Magdeburg-Halle, Germany
| | - Björn H Schott
- Cognitive Geriatric Psychiatry, German Center for Neurodegenerative Diseases, Göttingen, Germany
- Center for Behavioral Brain Sciences, Magdeburg, Germany
- Leibniz Institute for Neurobiology, Magdeburg, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
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5
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Han H, Ge S, Wang H. Prediction of brain age based on the community structure of functional networks. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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6
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Borkar K, Chaturvedi A, Vinod PK, Bapi RS. Ayu-Characterization of healthy aging from neuroimaging data with deep learning and rsfMRI. Front Comput Neurosci 2022; 16:940922. [PMID: 36172055 PMCID: PMC9511020 DOI: 10.3389/fncom.2022.940922] [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: 05/10/2022] [Accepted: 08/15/2022] [Indexed: 11/17/2022] Open
Abstract
Estimating brain age and establishing functional biomarkers that are prescient of cognitive declines resulting from aging and different neurological diseases are still open research problems. Functional measures such as functional connectivity are gaining interest as potentially more subtle markers of neurodegeneration. However, brain functions are also affected by “normal” brain aging. More information is needed on how functional connectivity relates to aging, particularly in the absence of neurodegenerative disorders. Resting-state fMRI enables us to investigate functional brain networks and can potentially help us understand the processes of development as well as aging in terms of how functional connectivity (FC) matures during the early years and declines during the late years. We propose models for estimation of the chronological age of a healthy person from the resting state brain activation (rsfMRI). In this work, we utilized a dataset (N = 638, age-range 20–88) comprising rsfMRI images from the Cambridge Centre for Aging and Neuroscience (Cam-CAN) repository of a healthy population. We propose an age prediction pipeline Ayu which consists of data preprocessing, feature selection, and an attention-based model for deep learning architecture for brain age assessment. We extracted features from the static functional connectivity (sFC) to predict the subject's age and classified them into different age groups (young, middle, middle, and old ages). To the best of our knowledge, a classification accuracy of 72.619 % and a mean absolute error of 6.797, and an r2 of 0.754 reported by our Ayu pipeline establish competitive benchmark results as compared to the state-of-the-art-approach. Furthermore, it is vital to identify how different functional regions of the brain are correlated. We also analyzed how functional regions contribute differently across ages by applying attention-based networks and integrated gradients. We obtained well-known resting-state networks using the attention model, which maps to within the default mode network, visual network, ventral attention network, limbic network, frontoparietal network, and somatosensory network connected to aging. Our analysis of fMRI data in healthy elderly Age groups revealed that dynamic FC tends to slow down and becomes less complex and more random with increasing age.
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7
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Amgalan A, Maher AS, Imms P, Ha MY, Fanelle TA, Irimia A. Functional Connectome Dynamics After Mild Traumatic Brain Injury According to Age and Sex. Front Aging Neurosci 2022; 14:852990. [PMID: 35663576 PMCID: PMC9158471 DOI: 10.3389/fnagi.2022.852990] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 04/05/2022] [Indexed: 11/17/2022] Open
Abstract
Neural and cognitive deficits after mild traumatic brain injury (mTBI) are paralleled by changes in resting state functional correlation (FC) networks that mirror post-traumatic pathophysiology effects on functional outcomes. Using functional magnetic resonance images acquired both acutely and chronically after injury (∼1 week and ∼6 months post-injury, respectively), we map post-traumatic FC changes across 136 participants aged 19-79 (52 females), both within and between the brain's seven canonical FC networks: default mode, dorsal attention, frontoparietal, limbic, somatomotor, ventral attention, and visual. Significant sex-dependent FC changes are identified between (A) visual and limbic, and between (B) default mode and somatomotor networks. These changes are significantly associated with specific functional recovery patterns across all cognitive domains (p < 0.05, corrected). Changes in FC between default mode, somatomotor, and ventral attention networks, on the one hand, and both temporal and occipital regions, on the other hand, differ significantly by age group (p < 0.05, corrected), and are paralleled by significant sex differences in cognitive recovery independently of age at injury (p < 0.05, corrected). Whereas females' networks typically feature both significant (p < 0.036, corrected) and insignificant FC changes, males more often exhibit significant FC decreases between networks (e.g., between dorsal attention and limbic, visual and limbic, default-mode and somatomotor networks, p < 0.0001, corrected), all such changes being accompanied by significantly weaker recovery of cognitive function in males, particularly older ones (p < 0.05, corrected). No significant FC changes were found across 35 healthy controls aged 66-92 (20 females). Thus, male sex and older age at injury are risk factors for significant FC alterations whose patterns underlie post-traumatic cognitive deficits. This is the first study to map, systematically, how mTBI impacts FC between major human functional networks.
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Affiliation(s)
- Anar Amgalan
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, United States
| | - Alexander S. Maher
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, United States
| | - Phoebe Imms
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, United States
| | - Michelle Y. Ha
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, United States
| | - Timothy A. Fanelle
- Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, United States
- Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
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8
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Zhan K, Zheng Y, Yang Y, Zhen Y, Tang S, Zheng Z. Age-Related Changes in Micro Brain Characteristics Based on Relaxed Mean-Field Model. Front Aging Neurosci 2022; 14:830529. [PMID: 35517049 PMCID: PMC9062185 DOI: 10.3389/fnagi.2022.830529] [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: 01/15/2022] [Accepted: 03/17/2022] [Indexed: 11/30/2022] Open
Abstract
Brain health is an important research direction of neuroscience. In addition to the effects of diseases, we cannot ignore the negative effect of aging on brain health. There have been many studies on brain aging, but only a few have used dynamic models to analyze differences in micro brain characteristics in healthy people. In this article, we use the relaxed mean-field model (rMFM) to study the effects of normal aging. Two main parameters of this model are the recurrent connection strength and subcortical input strength. The sensitivity of the rMFM to the initial values of the parameters has not been fully discussed in previous research. We examine this issue through repeated numerical experiments and obtain a reasonable initial parameter range for this model. Differences in recurrent connection strength and subcortical input strength due to aging have also not been studied previously. We use statistical methods to find the regions of interest (ROIs) exhibiting significant differences between young and old groups. Further, we carry out a difference analysis on the process of change of these ROIs on a more detailed timescale. We find that even with the same final results, the trends of change in these ROIs are different. This shows that to develop possible methods to prevent or delay brain damage due to aging, more attention needs to be paid to the trends of change of different ROIs, not just the final results.
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Affiliation(s)
- Ke Zhan
- School of Mathematical Sciences, Beihang University, Beijing, China
- Institute of Artificial Intelligence, Beihang University, Beijing, China
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
| | - Yi Zheng
- School of Mathematical Sciences, Beihang University, Beijing, China
- Institute of Artificial Intelligence, Beihang University, Beijing, China
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
| | - Yaqian Yang
- School of Mathematical Sciences, Beihang University, Beijing, China
- Institute of Artificial Intelligence, Beihang University, Beijing, China
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
| | - Yi Zhen
- School of Mathematical Sciences, Beihang University, Beijing, China
- Institute of Artificial Intelligence, Beihang University, Beijing, China
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
| | - Shaoting Tang
- School of Mathematical Sciences, Beihang University, Beijing, China
- Institute of Artificial Intelligence, Beihang University, Beijing, China
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
- State Key Laboratory of Software Development Environment (NLSDE), Beihang University, Beijing, China
- Peng Cheng Laboratory, Shenzhen, China
- Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai, China
- *Correspondence: Shaoting Tang
| | - Zhiming Zheng
- School of Mathematical Sciences, Beihang University, Beijing, China
- Institute of Artificial Intelligence, Beihang University, Beijing, China
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
- State Key Laboratory of Software Development Environment (NLSDE), Beihang University, Beijing, China
- Peng Cheng Laboratory, Shenzhen, China
- Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai, China
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9
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Capogna E, Sneve MH, Raud L, Folvik L, Ness HT, Walhovd KB, Fjell AM, Vidal-Piñeiro D. Whole-brain connectivity during encoding: age-related differences and associations with cognitive and brain structural decline. Cereb Cortex 2022; 33:68-82. [PMID: 35193146 PMCID: PMC9758575 DOI: 10.1093/cercor/bhac053] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 01/25/2022] [Accepted: 01/26/2022] [Indexed: 11/14/2022] Open
Abstract
There is a limited understanding of age differences in functional connectivity during memory encoding. In the present study, a sample of cognitively healthy adult participants (n = 488, 18-81 years), a subsample of whom had longitudinal cognitive and brain structural data spanning on average 8 years back, underwent functional magnetic resonance imaging while performing an associative memory encoding task. We investigated (1) age-related differences in whole-brain connectivity during memory encoding; (2) whether encoding connectivity patterns overlapped with the activity signatures of specific cognitive processes, and (3) whether connectivity associated with memory encoding related to longitudinal brain structural and cognitive changes. Age was associated with lower intranetwork connectivity among cortical networks and higher internetwork connectivity between networks supporting higher level cognitive functions and unimodal and attentional areas during encoding. Task-connectivity between mediotemporal and posterior parietal regions-which overlapped with areas involved in mental imagery-was related to better memory performance only in older age. The connectivity patterns supporting memory performance in older age reflected preservation of thickness of the medial temporal cortex. The results are more in accordance with a maintenance rather than a compensation account.
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Affiliation(s)
- Elettra Capogna
- Corresponding author: Department of Psychology, University of Oslo, 0317 Oslo, Norway.
| | - Markus H Sneve
- Centre for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Liisa Raud
- Centre for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Line Folvik
- Centre for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Hedda T Ness
- Centre for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Kristine B Walhovd
- Centre for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway,Department of Radiology and Nuclear Medicine, Oslo University Hospital, 0424 Oslo, Norway
| | - Anders M Fjell
- Centre for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway,Department of Radiology and Nuclear Medicine, Oslo University Hospital, 0424 Oslo, Norway
| | - Didac Vidal-Piñeiro
- Centre for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
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10
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Bottino F, Lucignani M, Pasquini L, Mastrogiovanni M, Gazzellini S, Ritrovato M, Longo D, Figà-Talamanca L, Rossi Espagnet MC, Napolitano A. Spatial Stability of Functional Networks: A Measure to Assess the Robustness of Graph-Theoretical Metrics to Spatial Errors Related to Brain Parcellation. Front Neurosci 2022; 15:736524. [PMID: 35250432 PMCID: PMC8894326 DOI: 10.3389/fnins.2021.736524] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 12/28/2021] [Indexed: 12/12/2022] Open
Abstract
There is growing interest in studying human brain connectivity and in modelling the brain functional structure as a network. Brain network creation requires parcellation of the cerebral cortex to define nodes. Parcellation might be affected by possible errors due to inter- and intra-subject variability as a consequence of brain structural and physiological characteristics and shape variations related to ageing and diseases, acquisition noise, and misregistration. These errors could induce a knock-on effect on network measure variability. The aim of this study was to investigate spatial stability, a measure of functional connectivity variations induced by parcellation errors. We simulated parcellation variability with random small spatial changes and evaluated its effects on twenty-seven graph-theoretical measures. The study included subjects from three public online datasets. Two brain parcellations were performed using FreeSurfer with geometric atlases. Starting from these, 100 new parcellations were created by increasing the area of 30% of parcels, reducing the area of neighbour parcels, with a rearrangement of vertices. fMRI data were filtered with linear regression, CompCor, and motion correction. Adjacency matrices were constructed with 0.1, 0.2, 0.3, and 0.4 thresholds. Differences in spatial stability between datasets, atlases, and threshold were evaluated. The higher spatial stability resulted for Characteristic-path-length, Density, Transitivity, and Closeness-centrality, and the lower spatial stability resulted for Bonacich and Katz. Multivariate analysis showed a significant effect of atlas, datasets, and thresholds. Katz and Bonacich centrality, which was subject to larger variations, can be considered an unconventional graph measure, poorly implemented in the clinical field and not yet investigated for reliability assessment. Spatial stability (SS) is affected by threshold, and it decreases with increasing threshold for several measures. Moreover, SS seems to depend on atlas choice and scanning parameters. Our study highlights the importance of paying close attention to possible parcellation-related spatial errors, which may affect the reliability of functional connectivity measures.
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Affiliation(s)
- Francesca Bottino
- Medical Physics Department, Bambino Gesù Children’s Hospital IRCCS, Rome, Italy
| | - Martina Lucignani
- Medical Physics Department, Bambino Gesù Children’s Hospital IRCCS, Rome, Italy
| | - Luca Pasquini
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
- Neuroradiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | | | - Simone Gazzellini
- Neuroscience and Neurorehabilitation Department, Bambino Gesù Children’s Hospital – IRCCS, Rome, Italy
| | - Matteo Ritrovato
- Health Technology and Safety Research Unit, Bambino Gesù Children’s Hospital – IRCCS, Rome, Italy
| | - Daniela Longo
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Lorenzo Figà-Talamanca
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Maria Camilla Rossi Espagnet
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
- NESMOS, Neuroradiology Department, S. Andrea Hospital Sapienza Rome University, Rome, Italy
| | - Antonio Napolitano
- Medical Physics Department, Bambino Gesù Children’s Hospital IRCCS, Rome, Italy
- *Correspondence: Antonio Napolitano,
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11
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Foo H, Thalamuthu A, Jiang J, Koch F, Mather KA, Wen W, Sachdev PS. Age- and Sex-Related Topological Organization of Human Brain Functional Networks and Their Relationship to Cognition. Front Aging Neurosci 2022; 13:758817. [PMID: 34975453 PMCID: PMC8718995 DOI: 10.3389/fnagi.2021.758817] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 11/25/2021] [Indexed: 11/23/2022] Open
Abstract
Age and sex associated with changes in the functional brain network topology and cognition in large population of older adults have been poorly understood. We explored this question further by examining differences in 11 resting-state graph theory measures with respect to age, sex, and their relationships with cognitive performance in 17,127 United Kingdom Biobank participants (mean = 62.83 ± 7.41 years). Age was associated with an overall decrease in the effectiveness of network communication (i.e., integration) and loss of functional specialization (i.e., segregation) of specific brain regions. Sex differences were also observed, with women showing more efficient networks, which were less segregated than in men (FDR adjusted p < 0.05). The age-related changes were also more apparent in men than in women, which suggests that men may be more vulnerable to cognitive decline with age. Interestingly, while network segregation and strength of limbic network were only nominally associated with cognitive performance, the network measures collectively were significantly associated with cognition (FDR adjusted p ≤ 0.002). This may imply that individual measures may be inadequate to capture much of the variance in the neural activity or its output and need further refinement. The complexity of the organization of the functional brain may be shaped by the age and sex of an individual, which ultimately may influence the cognitive performance of older adults. Age and sex stratification may be used to inform clinical neuroscience research to identify older adults at risk of cognitive dysfunction.
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Affiliation(s)
- Heidi Foo
- Centre for Healthy Brain Aging, CHeBA, School of Psychiatry, University of New South Wales Medicine, Kensington, NSW, Australia
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Aging, CHeBA, School of Psychiatry, University of New South Wales Medicine, Kensington, NSW, Australia
| | - Jiyang Jiang
- Centre for Healthy Brain Aging, CHeBA, School of Psychiatry, University of New South Wales Medicine, Kensington, NSW, Australia
| | - Forrest Koch
- Centre for Healthy Brain Aging, CHeBA, School of Psychiatry, University of New South Wales Medicine, Kensington, NSW, Australia
| | - Karen A Mather
- Centre for Healthy Brain Aging, CHeBA, School of Psychiatry, University of New South Wales Medicine, Kensington, NSW, Australia.,Neuroscience Research Australia, Randwick, NSW, Australia
| | - Wei Wen
- Centre for Healthy Brain Aging, CHeBA, School of Psychiatry, University of New South Wales Medicine, Kensington, NSW, Australia
| | - Perminder S Sachdev
- Centre for Healthy Brain Aging, CHeBA, School of Psychiatry, University of New South Wales Medicine, Kensington, NSW, Australia.,Neuropsychiatric Institute, Euroa Centre, Prince of Wales Hospital, Randwick, NSW, Australia
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12
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Hermiller MS, Dave S, Wert SL, VanHaerents S, Riley M, Weintraub S, Mesulam MM, Voss JL. Evidence from theta-burst stimulation that age-related de-differentiation of the hippocampal network is functional for episodic memory. Neurobiol Aging 2022; 109:145-157. [PMID: 34740076 PMCID: PMC8671378 DOI: 10.1016/j.neurobiolaging.2021.09.018] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 05/11/2021] [Accepted: 09/17/2021] [Indexed: 01/03/2023]
Abstract
Episodic memory is supported by hippocampal interactions with a distributed network. Aging is associated with memory decline and network de-differentiation. However, the role of de-differentiation in memory decline has not been directly tested. We reasoned that hippocampal network-targeted stimulation could test these theories, as age-related changes in the network response to stimulation would indicate network reorganization, and corresponding changes in memory would suggest that this reorganization is functional. We compared effects of stimulation on fMRI connectivity and memory in younger versus older adults. Theta-burst network-targeted stimulation of left lateral parietal cortex selectively increased hippocampal network connectivity and modulated memory in younger adults. In contrast, stimulation in older adults increased connectivity throughout the brain, without network selectivity, and did not influence memory. These findings provide evidence that network responses to stimulation are de-differentiated in aging and suggest that age-related de-differentiation is relevant for memory. This manuscript is part of the Special Issue entitled "Cognitive Neuroscience of Healthy and Pathological Aging" edited by Drs. M. N. Rajah, S. Belleville, and R. Cabeza. This article is part of the Virtual Special Issue titled COGNITIVE NEUROSCIENCE OF HEALTHY AND PATHOLOGICAL AGING. The full issue can be found on ScienceDirect at https://www.sciencedirect.com/journal/neurobiology-of-aging/special-issue/105379XPWJP.
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Affiliation(s)
- Molly S. Hermiller
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL,Department of Biomedical Engineering, Columbia University, New York, NY,Department of Psychology, Columbia University, New York, NY,Corresponding author: Molly S. Hermiller, 615 West 131st Street, Studebaker, 4th Floor, New York, NY 10027,
| | - Shruti Dave
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Stephanie L. Wert
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Stephen VanHaerents
- Ken and Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Michaela Riley
- Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Sandra Weintraub
- Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL,Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - M.-Marsel Mesulam
- Ken and Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL,Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Joel L. Voss
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL,Ken and Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL,Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL
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13
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Bekele BM, Luijendijk M, Schagen SB, de Ruiter M, Douw L. Fatigue and resting-state functional brain networks in breast cancer patients treated with chemotherapy. Breast Cancer Res Treat 2021; 189:787-796. [PMID: 34259949 PMCID: PMC8505321 DOI: 10.1007/s10549-021-06326-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 07/05/2021] [Indexed: 11/29/2022]
Abstract
PURPOSE This longitudinal study aimed to disentangle the impact of chemotherapy on fatigue and hypothetically associated functional brain network alterations. METHODS In total, 34 breast cancer patients treated with chemotherapy (BCC +), 32 patients not treated with chemotherapy (BCC -), and 35 non-cancer controls (NC) were included. Fatigue was assessed using the EORTC QLQ-C30 fatigue subscale at two time points: baseline (T1) and six months after completion of chemotherapy or matched intervals (T2). Participants also underwent resting-state functional magnetic resonance imaging (rsfMRI). An atlas spanning 90 cortical and subcortical brain regions was used to extract time series, after which Pearson correlation coefficients were calculated to construct a brain network per participant per timepoint. Network measures of local segregation and global integration were compared between groups and timepoints and correlated with fatigue. RESULTS As expected, fatigue increased over time in the BCC + group (p = 0.025) leading to higher fatigue compared to NC at T2 (p = 0.023). Meanwhile, fatigue decreased from T1 to T2 in the BCC - group (p = 0.013). The BCC + group had significantly lower local efficiency than NC at T2 (p = 0.033), while a negative correlation was seen between fatigue and local efficiency across timepoints and all participants (T1 rho = - 0.274, p = 0.006; T2 rho = - 0.207, p = 0.039). CONCLUSION Although greater fatigue and lower local functional network segregation co-occur in breast cancer patients after chemotherapy, the relationship between the two generalized across participant subgroups, suggesting that local efficiency is a general neural correlate of fatigue.
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Affiliation(s)
- Biniam Melese Bekele
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Cancer Center Amsterdam, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Maryse Luijendijk
- Department of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- Brain and Cognition, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Sanne B Schagen
- Department of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- Brain and Cognition, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Michiel de Ruiter
- Department of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Linda Douw
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Cancer Center Amsterdam, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
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14
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Soch J, Richter A, Schütze H, Kizilirmak JM, Assmann A, Behnisch G, Feldhoff H, Fischer L, Heil J, Knopf L, Merkel C, Raschick M, Schietke C, Schult A, Seidenbecher CI, Yakupov R, Ziegler G, Wiltfang J, Düzel E, Schott BH. A comprehensive score reflecting memory-related fMRI activations and deactivations as potential biomarker for neurocognitive aging. Hum Brain Mapp 2021; 42:4478-4496. [PMID: 34132437 PMCID: PMC8410542 DOI: 10.1002/hbm.25559] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 05/29/2021] [Accepted: 06/03/2021] [Indexed: 12/21/2022] Open
Abstract
Older adults and particularly those at risk for developing dementia typically show a decline in episodic memory performance, which has been associated with altered memory network activity detectable via functional magnetic resonance imaging (fMRI). To quantify the degree of these alterations, a score has been developed as a putative imaging biomarker for successful aging in memory for older adults (Functional Activity Deviations during Encoding, FADE; Düzel et al., Hippocampus, 2011; 21: 803-814). Here, we introduce and validate a more comprehensive version of the FADE score, termed FADE-SAME (Similarity of Activations during Memory Encoding), which differs from the original FADE score by considering not only activations but also deactivations in fMRI contrasts of stimulus novelty and successful encoding, and by taking into account the variance of young adults' activations. We computed both scores for novelty and subsequent memory contrasts in a cohort of 217 healthy adults, including 106 young and 111 older participants, as well as a replication cohort of 117 young subjects. We further tested the stability and generalizability of both scores by controlling for different MR scanners and gender, as well as by using different data sets of young adults as reference samples. Both scores showed robust age-group-related differences for the subsequent memory contrast, and the FADE-SAME score additionally exhibited age-group-related differences for the novelty contrast. Furthermore, both scores correlate with behavioral measures of cognitive aging, namely memory performance. Taken together, our results suggest that single-value scores of memory-related fMRI responses may constitute promising biomarkers for quantifying neurocognitive aging.
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Affiliation(s)
- Joram Soch
- German Center for Neurodegenerative Diseases (DZNE)GöttingenGermany
- Bernstein Center for Computational Neuroscience (BCCN)BerlinGermany
| | - Anni Richter
- Leibniz Institute for Neurobiology (LIN)MagdeburgGermany
| | - Hartmut Schütze
- German Center for Neurodegenerative DiseasesMagdeburgGermany
- Otto von Guericke University, Medical FacultyMagdeburgGermany
| | | | - Anne Assmann
- German Center for Neurodegenerative DiseasesMagdeburgGermany
- Otto von Guericke University, Medical FacultyMagdeburgGermany
| | | | - Hannah Feldhoff
- Leibniz Institute for Neurobiology (LIN)MagdeburgGermany
- Otto von Guericke University, Medical FacultyMagdeburgGermany
| | - Larissa Fischer
- Leibniz Institute for Neurobiology (LIN)MagdeburgGermany
- Otto von Guericke University, Medical FacultyMagdeburgGermany
| | - Julius Heil
- Leibniz Institute for Neurobiology (LIN)MagdeburgGermany
- Otto von Guericke University, Medical FacultyMagdeburgGermany
| | - Lea Knopf
- Leibniz Institute for Neurobiology (LIN)MagdeburgGermany
- Otto von Guericke University, Medical FacultyMagdeburgGermany
| | - Christian Merkel
- Leibniz Institute for Neurobiology (LIN)MagdeburgGermany
- Otto von Guericke University, Medical FacultyMagdeburgGermany
| | - Matthias Raschick
- Leibniz Institute for Neurobiology (LIN)MagdeburgGermany
- Otto von Guericke University, Medical FacultyMagdeburgGermany
| | - Clara‐Johanna Schietke
- Leibniz Institute for Neurobiology (LIN)MagdeburgGermany
- Otto von Guericke University, Medical FacultyMagdeburgGermany
| | - Annika Schult
- Leibniz Institute for Neurobiology (LIN)MagdeburgGermany
- Otto von Guericke University, Medical FacultyMagdeburgGermany
| | - Constanze I. Seidenbecher
- Leibniz Institute for Neurobiology (LIN)MagdeburgGermany
- Center for Behavioral Brain Sciences (CBBS)MagdeburgGermany
| | - Renat Yakupov
- German Center for Neurodegenerative DiseasesMagdeburgGermany
| | - Gabriel Ziegler
- German Center for Neurodegenerative DiseasesMagdeburgGermany
- Otto von Guericke University, Medical FacultyMagdeburgGermany
| | - Jens Wiltfang
- German Center for Neurodegenerative Diseases (DZNE)GöttingenGermany
- Department of Psychiatry and PsychotherapyUniversity Medical Center GöttingenGöttingenGermany
| | - Emrah Düzel
- German Center for Neurodegenerative DiseasesMagdeburgGermany
- Otto von Guericke University, Medical FacultyMagdeburgGermany
- Center for Behavioral Brain Sciences (CBBS)MagdeburgGermany
| | - Björn Hendrik Schott
- German Center for Neurodegenerative Diseases (DZNE)GöttingenGermany
- Leibniz Institute for Neurobiology (LIN)MagdeburgGermany
- Center for Behavioral Brain Sciences (CBBS)MagdeburgGermany
- Department of Psychiatry and PsychotherapyUniversity Medical Center GöttingenGöttingenGermany
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15
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Huang Q, Ren S, Zhang T, Li J, Jiang D, Xiao J, Hua F, Xie F, Guan Y. Aging-Related Modular Architectural Reorganization of the Metabolic Brain Network. Brain Connect 2021; 12:432-442. [PMID: 34210172 DOI: 10.1089/brain.2021.0054] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
Background: Modules in brain network represent groups of brain regions that are collectively involved in one or more cognitive domains. Exploring aging-related reorganization of the brain modular architecture using metabolic brain network could further our understanding about aging-related neuromechanism and neurodegenerations. Materials and Methods: In this study, 432 subjects who performed 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) were enrolled and divided into young and old adult groups, as well as female and male groups. The modular architecture was detected, and the connector and hub nodes were identified to explore the topological role of the brain regions based on the metabolic brain network. Results: This study revealed that human metabolic brain network was modular and could be clustered into three modules. The modular architecture was reorganized from young to old ages with regions related to sensorimotor function clustered into the same module; and the number of connector nodes was reduced and most connector nodes were localized in temporo-occipital areas related to visual and auditory functions in old ages. The major gender difference is that the metabolic brain network was delineated into four modules in old female group with the nodes related to sensorimotor function split into two modules. Discussion: Those findings suggest aging is associated with reorganized brain modular architecture. Clinical Trial Registration number: ChiCTR2000036842.
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Affiliation(s)
- Qi Huang
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Shuhua Ren
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Tianhao Zhang
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
| | - Junpeng Li
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Donglang Jiang
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Jianfei Xiao
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Fengchun Hua
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Fang Xie
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Yihui Guan
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
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16
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Baciu M, Banjac S, Roger E, Haldin C, Perrone-Bertolotti M, Lœvenbruck H, Démonet JF. Strategies and cognitive reserve to preserve lexical production in aging. GeroScience 2021; 43:1725-1765. [PMID: 33970414 PMCID: PMC8492841 DOI: 10.1007/s11357-021-00367-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 04/09/2021] [Indexed: 10/28/2022] Open
Abstract
In the absence of any neuropsychiatric condition, older adults may show declining performance in several cognitive processes and among them, in retrieving and producing words, reflected in slower responses and even reduced accuracy compared to younger adults. To overcome this difficulty, healthy older adults implement compensatory strategies, which are the focus of this paper. We provide a review of mainstream findings on deficient mechanisms and possible neurocognitive strategies used by older adults to overcome the deleterious effects of age on lexical production. Moreover, we present findings on genetic and lifestyle factors that might either be protective or risk factors of cognitive impairment in advanced age. We propose that "aging-modulating factors" (AMF) can be modified, offering prevention opportunities against aging effects. Based on our review and this proposition, we introduce an integrative neurocognitive model of mechanisms and compensatory strategies for lexical production in older adults (entitled Lexical Access and Retrieval in Aging, LARA). The main hypothesis defended in LARA is that cognitive aging evolves heterogeneously and involves complementary domain-general and domain-specific mechanisms, with substantial inter-individual variability, reflected at behavioral, cognitive, and brain levels. Furthermore, we argue that the ability to compensate for the effect of cognitive aging depends on the amount of reserve specific to each individual which is, in turn, modulated by the AMF. Our conclusion is that a variety of mechanisms and compensatory strategies coexist in the same individual to oppose the effect of age. The role of reserve is pivotal for a successful coping with age-related changes and future research should continue to explore the modulating role of AMF.
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Affiliation(s)
- Monica Baciu
- Univ. Grenoble Alpes, CNRS LPNC UMR 5105, 38000, Grenoble, France.
| | - Sonja Banjac
- Univ. Grenoble Alpes, CNRS LPNC UMR 5105, 38000, Grenoble, France
| | - Elise Roger
- Univ. Grenoble Alpes, CNRS LPNC UMR 5105, 38000, Grenoble, France
| | - Célise Haldin
- Univ. Grenoble Alpes, CNRS LPNC UMR 5105, 38000, Grenoble, France
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17
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Kizilirmak JM, Fischer L, Krause J, Soch J, Richter A, Schott BH. Learning by Insight-Like Sudden Comprehension as a Potential Strategy to Improve Memory Encoding in Older Adults. Front Aging Neurosci 2021; 13:661346. [PMID: 34194316 PMCID: PMC8236646 DOI: 10.3389/fnagi.2021.661346] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 05/07/2021] [Indexed: 12/23/2022] Open
Abstract
Several cognitive functions show a decline with advanced age, most prominently episodic memory. Problem-solving by insight represents a special associative form of problem-solving that has previously been shown to facilitate long-term memory formation. Recent neuroimaging evidence suggests that the encoding network involved in insight-based memory formation is largely hippocampus-independent. This may represent a potential advantage in older adults, as the hippocampus is one of the earliest brain structures to show age-related volume loss and functional impairment. Here, we investigated the potential beneficial effects of learning by insight in healthy older (60-79 years) compared to young adults (19-28 years). To this end, we compared later memory performance for verbal riddles encoded incidentally via induced insight-like sudden comprehension in both age groups. We employed a variant of the Compound Remote Associate Task (CRAT) for incidental encoding, during which participants were instructed to judge the solvability of items. In a 24-h delayed surprise memory test, participants attempted to solve previously encountered items and additionally performed a recognition memory test. During this test, older adults correctly solved an equal proportion of new CRA items compared to young adults and both age groups reported a similar frequency of Aha! experiences. While overall memory performance was better in young participants (higher proportion of correctly solved and correctly recognized old CRA items), older participants exhibited a stronger beneficial effect of insight-like sudden comprehension on later recognition memory for CRA items. Our results suggest that learning via insight might constitute a promising approach to improve memory function in old age.
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Affiliation(s)
- Jasmin M. Kizilirmak
- German Center for Neurodegenerative Diseases, Göttingen, Germany
- Institute of Psychology, University of Hildesheim, Hildesheim, Germany
| | | | - Justus Krause
- Institute of Psychology, University of Hildesheim, Hildesheim, Germany
| | - Joram Soch
- German Center for Neurodegenerative Diseases, Göttingen, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Anni Richter
- Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Björn H. Schott
- German Center for Neurodegenerative Diseases, Göttingen, Germany
- Leibniz Institute for Neurobiology, Magdeburg, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
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18
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Zhou X, Ma N, Song B, Wu Z, Liu G, Liu L, Yu L, Feng J. Optimal Organization of Functional Connectivity Networks for Segregation and Integration With Large-Scale Critical Dynamics in Human Brains. Front Comput Neurosci 2021; 15:641335. [PMID: 33867963 PMCID: PMC8044315 DOI: 10.3389/fncom.2021.641335] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 02/23/2021] [Indexed: 12/04/2022] Open
Abstract
The optimal organization for functional segregation and integration in brain is made evident by the “small-world” feature of functional connectivity (FC) networks and is further supported by the loss of this feature that has been described in many types of brain disease. However, it remains unknown how such optimally organized FC networks arise from the brain's structural constrains. On the other hand, an emerging literature suggests that brain function may be supported by critical neural dynamics, which is believed to facilitate information processing in brain. Though previous investigations have shown that the critical dynamics plays an important role in understanding the relation between whole brain structural connectivity and functional connectivity, it is not clear if the critical dynamics could be responsible for the optimal FC network configuration in human brains. Here, we show that the long-range temporal correlations (LRTCs) in the resting state fMRI blood-oxygen-level-dependent (BOLD) signals are significantly correlated with the topological matrices of the FC brain network. Using structure-dynamics-function modeling approach that incorporates diffusion tensor imaging (DTI) data and simple cellular automata dynamics, we showed that the critical dynamics could optimize the whole brain FC network organization by, e.g., maximizing the clustering coefficient while minimizing the characteristic path length. We also demonstrated with a more detailed excitation-inhibition neuronal network model that loss of local excitation-inhibition (E/I) balance causes failure of critical dynamics, therefore disrupting the optimal FC network organization. The results highlighted the crucial role of the critical dynamics in forming an optimal organization of FC networks in the brain and have potential application to the understanding and modeling of abnormal FC configurations in neuropsychiatric disorders.
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Affiliation(s)
- Xinchun Zhou
- Key Laboratory of Theoretical Physics of Gansu Province, Lanzhou Center for Theoretical Physics, Lanzhou University, Lanzhou, China
| | - Ningning Ma
- School of Mathematical Sciences and Centre for Computational Systems Biology, Fudan University, Shanghai, China
| | - Benseng Song
- Key Laboratory of Theoretical Physics of Gansu Province, Lanzhou Center for Theoretical Physics, Lanzhou University, Lanzhou, China
| | - Zhixi Wu
- Key Laboratory of Theoretical Physics of Gansu Province, Lanzhou Center for Theoretical Physics, Lanzhou University, Lanzhou, China
| | - Guangyao Liu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
| | - Liwei Liu
- College of Electrical Engineering, Northwest University for Nationalities, Lanzhou, China
| | - Lianchun Yu
- Key Laboratory of Theoretical Physics of Gansu Province, Lanzhou Center for Theoretical Physics, Lanzhou University, Lanzhou, China
| | - Jianfeng Feng
- School of Mathematical Sciences and Centre for Computational Systems Biology, Fudan University, Shanghai, China.,School of Mathematical Sciences, School of Life Science and the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China
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19
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Hrybouski S, Cribben I, McGonigle J, Olsen F, Carter R, Seres P, Madan CR, Malykhin NV. Investigating the effects of healthy cognitive aging on brain functional connectivity using 4.7 T resting-state functional magnetic resonance imaging. Brain Struct Funct 2021; 226:1067-1098. [PMID: 33604746 DOI: 10.1007/s00429-021-02226-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 01/20/2021] [Indexed: 01/05/2023]
Abstract
Functional changes in the aging human brain have been previously reported using functional magnetic resonance imaging (fMRI). Earlier resting-state fMRI studies revealed an age-associated weakening of intra-system functional connectivity (FC) and age-associated strengthening of inter-system FC. However, the majority of such FC studies did not investigate the relationship between age and network amplitude, without which correlation-based measures of FC can be challenging to interpret. Consequently, the main aim of this study was to investigate how three primary measures of resting-state fMRI signal-network amplitude, network topography, and inter-network FC-are affected by healthy cognitive aging. We acquired resting-state fMRI data on a 4.7 T scanner for 105 healthy participants representing the entire adult lifespan (18-85 years of age). To study age differences in network structure, we combined ICA-based network decomposition with sparse graphical models. Older adults displayed lower blood-oxygen-level-dependent (BOLD) signal amplitude in all functional systems, with sensorimotor networks showing the largest age differences. Our age comparisons of network topography and inter-network FC demonstrated a substantial amount of age invariance in the brain's functional architecture. Despite architecture similarities, old adults displayed a loss of communication efficiency in our inter-network FC comparisons, driven primarily by the FC reduction in frontal and parietal association cortices. Together, our results provide a comprehensive overview of age effects on fMRI-based FC.
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Affiliation(s)
- Stanislau Hrybouski
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
| | - Ivor Cribben
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada.,Department of Accounting and Business Analytics, Alberta School of Business, University of Alberta, Edmonton, AB, Canada
| | - John McGonigle
- Department of Brain Sciences, Imperial College London, London, UK
| | - Fraser Olsen
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB, Canada
| | - Rawle Carter
- Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, T6G 2V2, Canada
| | - Peter Seres
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB, Canada
| | | | - Nikolai V Malykhin
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada. .,Department of Biomedical Engineering, University of Alberta, Edmonton, AB, Canada. .,Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, T6G 2V2, Canada.
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20
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Waschke L, Kloosterman NA, Obleser J, Garrett DD. Behavior needs neural variability. Neuron 2021; 109:751-766. [PMID: 33596406 DOI: 10.1016/j.neuron.2021.01.023] [Citation(s) in RCA: 109] [Impact Index Per Article: 36.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 11/16/2020] [Accepted: 01/22/2021] [Indexed: 01/26/2023]
Abstract
Human and non-human animal behavior is highly malleable and adapts successfully to internal and external demands. Such behavioral success stands in striking contrast to the apparent instability in neural activity (i.e., variability) from which it arises. Here, we summon the considerable evidence across scales, species, and imaging modalities that neural variability represents a key, undervalued dimension for understanding brain-behavior relationships at inter- and intra-individual levels. We believe that only by incorporating a specific focus on variability will the neural foundation of behavior be comprehensively understood.
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Affiliation(s)
- Leonhard Waschke
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, 14195 Berlin, Germany; Center for Lifespan Psychology, Max Planck Institute for Human Development, 14195 Berlin, Germany.
| | - Niels A Kloosterman
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, 14195 Berlin, Germany; Center for Lifespan Psychology, Max Planck Institute for Human Development, 14195 Berlin, Germany
| | - Jonas Obleser
- Department of Psychology, University of Lübeck, 23562 Lübeck, Germany; Center of Brain, Behavior, and Metabolism, University of Lübeck, 23562 Lübeck, Germany
| | - Douglas D Garrett
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, 14195 Berlin, Germany; Center for Lifespan Psychology, Max Planck Institute for Human Development, 14195 Berlin, Germany
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21
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Al-Fahad R, Yeasin M, Bidelman GM. Decoding of single-trial EEG reveals unique states of functional brain connectivity that drive rapid speech categorization decisions. J Neural Eng 2020; 17:016045. [PMID: 31822643 PMCID: PMC7004853 DOI: 10.1088/1741-2552/ab6040] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Categorical perception (CP) is an inherent property of speech perception. The response time (RT) of listeners' perceptual speech identification is highly sensitive to individual differences. While the neural correlates of CP have been well studied in terms of the regional contributions of the brain to behavior, functional connectivity patterns that signify individual differences in listeners' speed (RT) for speech categorization is less clear. In this study, we introduce a novel approach to address these questions. APPROACH We applied several computational approaches to the EEG, including graph mining, machine learning (i.e., support vector machine), and stability selection to investigate the unique brain states (functional neural connectivity) that predict the speed of listeners' behavioral decisions. MAIN RESULTS We infer that (i) the listeners' perceptual speed is directly related to dynamic variations in their brain connectomics, (ii) global network assortativity and efficiency distinguished fast, medium, and slow RTs, (iii) the functional network underlying speeded decisions increases in negative assortativity (i.e., became disassortative) for slower RTs, (iv) slower categorical speech decisions cause excessive use of neural resources and more aberrant information flow within the CP circuitry, (v) slower responders tended to utilize functional brain networks excessively (or inappropriately) whereas fast responders (with lower global efficiency) utilized the same neural pathways but with more restricted organization. SIGNIFICANCE Findings show that neural classifiers (SVM) coupled with stability selection correctly classify behavioral RTs from functional connectivity alone with over 92% accuracy (AUC = 0.9). Our results corroborate previous studies by supporting the engagement of similar temporal (STG), parietal, motor, and prefrontal regions in CP using an entirely data-driven approach.
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Affiliation(s)
- Rakib Al-Fahad
- Department of Electrical and Computer Engineering, University of Memphis, Memphis, 38152 TN, USA
| | - Mohammed Yeasin
- Department of Electrical and Computer Engineering, University of Memphis, Memphis, 38152 TN, USA
- Institute for Intelligent Systems, University of Memphis, Memphis, TN, USA
| | - Gavin M. Bidelman
- Institute for Intelligent Systems, University of Memphis, Memphis, TN, USA
- School of Communication Sciences & Disorders, University of Memphis, Memphis, TN, USA
- University of Tennessee Health Sciences Center, Department of Anatomy and Neurobiology, Memphis, TN, USA
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22
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Zifman N, Levy-Lamdan O, Suzin G, Efrati S, Tanne D, Fogel H, Dolev I. Introducing a Novel Approach for Evaluation and Monitoring of Brain Health Across Life Span Using Direct Non-invasive Brain Network Electrophysiology. Front Aging Neurosci 2019; 11:248. [PMID: 31551761 PMCID: PMC6745309 DOI: 10.3389/fnagi.2019.00248] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 08/21/2019] [Indexed: 12/17/2022] Open
Abstract
Objective Evaluation and monitoring of brain health throughout aging by direct electrophysiological imaging (DELPHI) which analyzes TMS (transcranial magnetic stimulation) evoked potentials. Methods Transcranial magnetic stimulation evoked potentials formation, coherence and history dependency, measured using electroencephalogram (EEG), was extracted from 80 healthy subjects in different age groups, 25–85 years old, and 20 subjects diagnosed with mild dementia (MD), over 70 years old. Subjects brain health was evaluated using MRI scans, neurocognitive evaluation, and computerized testing and compared to DELPHI analysis of brain network functionality. Results A significant decrease in signal coherence is observed with age in connectivity maps, mostly in inter-hemispheric temporal, and parietal areas. MD patients display a pronounced decrease in global and inter-hemispheric frontal connectivity compared to healthy controls. Early and late signal slope ratio also display a significant, age dependent, change with pronounced early slope, phase shift, between normal healthy aging, and MD. History dependent analysis demonstrates a binary step function classification of healthy brain vs. abnormal aging subjects mostly for late slope. DELPHI measures demonstrate high reproducibility with reliability coefficients of around 0.9. Conclusion These results indicate that features of evoked response, as charge transfer, slopes of response, and plasticity are altered during abnormal aging and that these fundamental properties of network functionality can be directly evaluated and monitored using DELPHI.
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Affiliation(s)
- Noa Zifman
- QuantalX Neuroscience, Tel Aviv-Yafo, Israel
| | | | - Gil Suzin
- Sagol Center for Hyperbaric Medicine and Research, Assaf Harofeh Medical Center, Ramle, Israel
| | - Shai Efrati
- Sagol Center for Hyperbaric Medicine and Research, Assaf Harofeh Medical Center, Ramle, Israel.,Sackler School of Medicine and Sagol School of Neuroscience, Tel-Aviv University, Tel Aviv-Yafo, Israel
| | - David Tanne
- Sackler School of Medicine and Sagol School of Neuroscience, Tel-Aviv University, Tel Aviv-Yafo, Israel.,Stroke and Cognition Institute, Rambam Healthcare Campus, Haifa, Israel
| | - Hilla Fogel
- QuantalX Neuroscience, Tel Aviv-Yafo, Israel
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23
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Benito-León J, Sanz-Morales E, Melero H, Louis ED, Romero JP, Rocon E, Malpica N. Graph theory analysis of resting-state functional magnetic resonance imaging in essential tremor. Hum Brain Mapp 2019; 40:4686-4702. [PMID: 31332912 DOI: 10.1002/hbm.24730] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 07/05/2019] [Accepted: 07/08/2019] [Indexed: 11/10/2022] Open
Abstract
Essential tremor (ET) is a neurological disease with both motor and nonmotor manifestations; however, little is known about its underlying brain basis. Furthermore, the overall organization of the brain network in ET remains largely unexplored. We investigated the topological properties of brain functional network, derived from resting-state functional magnetic resonance imaging (MRI) data, in 23 ET patients versus 23 healthy controls. Graph theory analysis was used to assess the functional network organization. At the global level, the functional network of ET patients was characterized by lower small-worldness values than healthy controls-less clustered functionality of the brain. At the regional level, compared with the healthy controls, ET patients showed significantly higher values of global efficiency, cost and degree, and a shorter average path length in the left inferior frontal gyrus (pars opercularis), right inferior temporal gyrus (posterior division and temporo-occipital part), right inferior lateral occipital cortex, left paracingulate, bilateral precuneus bilaterally, left lingual gyrus, right hippocampus, left amygdala, nucleus accumbens bilaterally, and left middle temporal gyrus (posterior part). In addition, ET patients showed significant higher local efficiency and clustering coefficient values in frontal medial cortex bilaterally, subcallosal cortex, posterior cingulate cortex, parahippocampal gyri bilaterally (posterior division), right lingual gyrus, right cerebellar flocculus, right postcentral gyrus, right inferior semilunar lobule of cerebellum and culmen of vermis. Finally, the right intracalcarine cortex and the left orbitofrontal cortex showed a shorter average path length in ET patients, while the left frontal operculum and the right planum polare showed a higher betweenness centrality in ET patients. In conclusion, the efficiency of the overall brain functional network in ET is disrupted. Further, our results support the concept that ET is a disorder that disrupts widespread brain regions, including those outside of the brain regions responsible for tremor.
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Affiliation(s)
- Julián Benito-León
- Department of Neurology, University Hospital 12 de Octubre, Madrid, Spain.,Center of Biomedical Network Research on Neurodegenerative Diseases (CIBERNED), Madrid, Spain.,Department of Medicine, Faculty of Medicine, Complutense University, Madrid, Spain
| | - Emilio Sanz-Morales
- Medical Image Analysis Laboratory (LAIMBIO), Rey Juan Carlos University, Madrid, Spain
| | - Helena Melero
- Medical Image Analysis Laboratory (LAIMBIO), Rey Juan Carlos University, Madrid, Spain
| | - Elan D Louis
- Department of Neurology, Yale School of Medicine, Yale University, New Haven, Connecticut.,Department of Chronic Disease Epidemiology, Yale School of Public Health, Yale University, New Haven, Connecticut.,Center for Neuroepidemiology and Clinical Neurological Research, Yale School of Medicine, Yale University, New Haven, Connecticut
| | - Juan P Romero
- Faculty of Biosanitary Sciences, Francisco de Vitoria University, Madrid, Spain.,Brain Damage Unit, Hospital Beata Maria Ana, Madrid, Spain
| | - Eduardo Rocon
- Neural and Cognitive Engineering group, Center for Automation and Robotics (CAR) CSIC-UPM, Arganda del Rey, Spain
| | - Norberto Malpica
- Medical Image Analysis Laboratory (LAIMBIO), Rey Juan Carlos University, Madrid, Spain
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24
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Differences in structural and functional networks between young adult and aged rat brains before and after stroke lesion simulations. Neurobiol Dis 2019; 126:23-35. [DOI: 10.1016/j.nbd.2018.08.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 07/17/2018] [Accepted: 08/03/2018] [Indexed: 01/09/2023] Open
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25
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Xiang J, Xue J, Guo H, Li D, Cui X, Niu Y, Yan T, Cao R, Ma Y, Yang Y, Wang B. Graph-based network analysis of resting-state fMRI: test-retest reliability of binarized and weighted networks. Brain Imaging Behav 2019; 14:1361-1372. [DOI: 10.1007/s11682-019-00042-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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26
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Patterns of functional connectivity in an aging population: The Rotterdam Study. Neuroimage 2019; 189:432-444. [PMID: 30659958 DOI: 10.1016/j.neuroimage.2019.01.041] [Citation(s) in RCA: 100] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Revised: 01/09/2019] [Accepted: 01/15/2019] [Indexed: 12/22/2022] Open
Abstract
Structural brain markers are studied extensively in the field of neurodegeneration, but are thought to occur rather late in the process. Functional measures such as functional connectivity are gaining interest as potentially more subtle markers of neurodegeneration. However, brain structure and function are also affected by 'normal' brain ageing. More information is needed on how functional connectivity relates to aging, particularly in the absence of overt neurodegenerative disease. We investigated the association of age with resting-state functional connectivity in 2878 non-demented persons between 50 and 95 years of age (54.1% women) from the population-based Rotterdam Study. We obtained nine well-known resting state networks using data-driven methodology. Within the anterior default mode network, ventral attention network, and sensorimotor network, functional connectivity was significantly lower with older age. In contrast, functional connectivity was higher with older age within the visual network. Between resting state networks, we found patterns of both increases and decreases in connectivity in approximate equal proportions. Our results reinforce the notion that the aging brain undergoes a reorganization process, and serves as a solid basis for exploring functional connectivity as a preclinical marker of neurodegenerative disease.
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27
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Sato JR, Sato CM, Silva MKDC, Biazoli CE. Commute Time as a Method to Explore Brain Functional Connectomes. Brain Connect 2018; 9:155-161. [PMID: 30398376 DOI: 10.1089/brain.2018.0598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Graph theory has been extensively applied to investigate complex brain networks in current neuroscience research. Many metrics derived from graph theory, such as local and global efficiencies, are based on the path length between nodes. These approaches are commonly used in analyses of brain networks assessed by resting-state functional magnetic resonance imaging, although relying on the strong assumption that information flow throughout the network is restricted to the shortest paths. In this study, we propose the utilization of commute time as a tool to investigate regional centrality on the functional connectome. Our initial hypothesis was that an alternative approach that considers alternative routes (such as commute time) could provide further information into the organization of functional networks. However, our empirical findings on the ADHD-200 database suggest that at the group level, the commute time and shortest path are highly correlated. In contrast, at the subject level, we discovered that commute time is much less susceptible to head motion artifacts when compared with metrics based on shortest paths. Given the overall similarity between the measures, we argue that commute time might be advantageous particularly for connectomic studies in populations where motion artifacts are a major issue.
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Affiliation(s)
- João Ricardo Sato
- 1 Center of Mathematics, Computing and Cognition, Universidade Federal do ABC, Santo André, Brazil
| | - Cristiane Maria Sato
- 1 Center of Mathematics, Computing and Cognition, Universidade Federal do ABC, Santo André, Brazil
| | - Marcel K de Carli Silva
- 2 Department of Computer Science, Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil
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28
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Wang WW, Lu YC, Tang WJ, Zhang JH, Sun HP, Feng XY, Liu HQ. Small-worldness of brain networks after brachial plexus injury: A resting-state functional magnetic resonance imaging study. Neural Regen Res 2018; 13:1061-1065. [PMID: 29926834 PMCID: PMC6022461 DOI: 10.4103/1673-5374.233450] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/02/2018] [Indexed: 12/18/2022] Open
Abstract
Research on brain function after brachial plexus injury focuses on local cortical functional reorganization, and few studies have focused on brain networks after brachial plexus injury. Changes in brain networks may help understanding of brain plasticity at the global level. We hypothesized that topology of the global cerebral resting-state functional network changes after unilateral brachial plexus injury. Thus, in this cross-sectional study, we recruited eight male patients with unilateral brachial plexus injury (right handedness, mean age of 27.9 ± 5.4 years old) and eight male healthy controls (right handedness, mean age of 28.6 ± 3.2). After acquiring and preprocessing resting-state magnetic resonance imaging data, the cerebrum was divided into 90 regions and Pearson's correlation coefficient calculated between regions. These correlation matrices were then converted into a binary matrix with affixed sparsity values of 0.1-0.46. Under sparsity conditions, both groups satisfied this small-world property. The clustering coefficient was markedly lower, while average shortest path remarkably higher in patients compared with healthy controls. These findings confirm that cerebral functional networks in patients still show small-world characteristics, which are highly effective in information transmission in the brain, as well as normal controls. Alternatively, varied small-worldness suggests that capacity of information transmission and integration in different brain regions in brachial plexus injury patients is damaged.
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Affiliation(s)
- Wei-Wei Wang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Ye-Chen Lu
- Department of Hand Surgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Wei-Jun Tang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Jun-Hai Zhang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Hua-Ping Sun
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiao-Yuan Feng
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Han-Qiu Liu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
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29
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Yu Q, Du Y, Chen J, Sui J, Adali T, Pearlson G, Calhoun VD. Application of Graph Theory to Assess Static and Dynamic Brain Connectivity: Approaches for Building Brain Graphs. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2018; 106:886-906. [PMID: 30364630 PMCID: PMC6197492 DOI: 10.1109/jproc.2018.2825200] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Human brain connectivity is complex. Graph theory based analysis has become a powerful and popular approach for analyzing brain imaging data, largely because of its potential to quantitatively illuminate the networks, the static architecture in structure and function, the organization of dynamic behavior over time, and disease related brain changes. The first step in creating brain graphs is to define the nodes and edges connecting them. We review a number of approaches for defining brain nodes including fixed versus data-driven nodes. Expanding the narrow view of most studies which focus on static and/or single modality brain connectivity, we also survey advanced approaches and their performances in building dynamic and multi-modal brain graphs. We show results from both simulated and real data from healthy controls and patients with mental illnesse. We outline the advantages and challenges of these various techniques. By summarizing and inspecting recent studies which analyzed brain imaging data based on graph theory, this article provides a guide for developing new powerful tools to explore complex brain networks.
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Affiliation(s)
- Qingbao Yu
- Mind Research Network, Albuquerque NM 87106 USA
| | - Yuhui Du
- Mind Research Network, Albuquerque NM 87106 USA. And also with School of Computer & Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Jiayu Chen
- Mind Research Network, Albuquerque NM 87106 USA
| | - Jing Sui
- University of Chinese Academy of Sciences, Beijing 100049 China. And also with CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Science (CAS), University of CAS, Beijing 100190 China
| | - Tulay Adali
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
| | - Godfrey Pearlson
- Olin Neuropsychiatry Research Center, Hartford, CT 06106, USA. And also with Departments of Psychiatry and Neurobiology, Yale University, New Haven, CT 06520, USA
| | - Vince D Calhoun
- Mind Research Network, Albuquerque NM 87106 USA. And also with Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA
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30
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Masuda N, Sakaki M, Ezaki T, Watanabe T. Clustering Coefficients for Correlation Networks. Front Neuroinform 2018; 12:7. [PMID: 29599714 PMCID: PMC5863042 DOI: 10.3389/fninf.2018.00007] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2017] [Accepted: 02/19/2018] [Indexed: 11/30/2022] Open
Abstract
Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other) measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node) are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients were strongly correlated with and therefore may be confounded by the node's connectivity. The proposed methods are expected to help us to understand clustering and lack thereof in correlational brain networks, such as those derived from functional time series and across-participant correlation in neuroanatomical properties.
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Affiliation(s)
- Naoki Masuda
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
| | - Michiko Sakaki
- School of Psychology and Clinical Language Sciences, University of Reading, Reading, United Kingdom.,Research Institute, Kochi University of Technology, Kochi, Japan
| | - Takahiro Ezaki
- PRESTO, Japan Science and Technology Agency, Kawaguchi, Japan
| | - Takamitsu Watanabe
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
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31
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Abstract
Neuroimaging studies have shown that local brain lesions could result in abnormal information transfer far from the lesion site in acute ischemic stroke (AIS) patients; yet, little is known about alternations of the topological organization of whole-brain networks in AIS. By using resting state functional magnetic resonance imaging (MRI) and graph theory analysis, we systematically investigated the topological properties of the functional brain networks of 28 healthy controls (HC, age: 56.9 ± 0.45 years) and 29 AIS (age: 57.6 ± 0.21 years) with proximal anterior circulation occlusion within 12 h of symptom onset. In our results, both the AIS and HC groups exhibited small-world network organization, suggesting a functional balance between local specialization and global integration. However, compared with the HC, the AIS patients had a lower shortest path length and higher global efficiency, indicating a tendency of randomization in patients' functional brain networks. The AIS patients had an increased nodal degree in the precuneus (PCUN), middle frontal gyrus (MFG), medial part of the superior frontal gyrus (SFGmed), orbital part of the middle frontal gyrus, and the opercular part of the inferior frontal gyrus, and increased nodal efficiency in the PUCN, MFG, SFGmed, and the angular gyrus. The decreased nodal degree in AIS was found in the heschl gyrus (HES), and no significant decreased nodal efficiency was observed. The dysfunctional connections were mainly concentrated in the HES and prefrontal cortex. Furthermore, the altered nodal centrality of the MFG and abnormal functional connectivity in AIS were associated with patients' Mini-Mental State Examination scores. These results suggested that interrupted functional connectivity in language system organization after focal brain lesions could also result in disruptions in the topological organization of other brain circuits, and this may contribute to disturbances in cognition in AIS patients.
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32
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States and traits of neural irregularity in the age-varying human brain. Sci Rep 2017; 7:17381. [PMID: 29234128 PMCID: PMC5727296 DOI: 10.1038/s41598-017-17766-4] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Accepted: 11/30/2017] [Indexed: 12/11/2022] Open
Abstract
Sensory representations, and thus human percepts, of the physical world are susceptible to fluctuations in brain state or “neural irregularity”. Furthermore, aging brains display altered levels of neural irregularity. We here show that a single, within-trial, information-theoretic measure (weighted permutation entropy) captures neural irregularity in the human electroencephalogram as a proxy for both, trait-like differences between individuals of varying age, and state-like fluctuations that bias perceptual decisions. First, the overall level of neural irregularity increased with participants’ age, paralleled by a decrease in variability over time, likely indexing age-related changes at structural and functional levels of brain activity. Second, states of higher neural irregularity were associated with optimized sensory encoding and a subsequently increased probability of choosing the first of two physically identical stimuli to be higher in pitch. In sum, neural irregularity not only characterizes behaviourally relevant brain states, but also can identify trait-like changes that come with age.
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Siyah Mansoory M, Oghabian MA, Jafari AH, Shahbabaie A. Analysis of Resting-State fMRI Topological Graph Theory Properties in Methamphetamine Drug Users Applying Box-Counting Fractal Dimension. Basic Clin Neurosci 2017; 8:371-385. [PMID: 29167724 PMCID: PMC5691169 DOI: 10.18869/nirp.bcn.8.5.371] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
Introduction: Graph theoretical analysis of functional Magnetic Resonance Imaging (fMRI) data has provided new measures of mapping human brain in vivo. Of all methods to measure the functional connectivity between regions, Linear Correlation (LC) calculation of activity time series of the brain regions as a linear measure is considered the most ubiquitous one. The strength of the dependence obligatory for graph construction and analysis is consistently underestimated by LC, because not all the bivariate distributions, but only the marginals are Gaussian. In a number of studies, Mutual Information (MI) has been employed, as a similarity measure between each two time series of the brain regions, a pure nonlinear measure. Owing to the complex fractal organization of the brain indicating self-similarity, more information on the brain can be revealed by fMRI Fractal Dimension (FD) analysis. Methods: In the present paper, Box-Counting Fractal Dimension (BCFD) is introduced for graph theoretical analysis of fMRI data in 17 methamphetamine drug users and 18 normal controls. Then, BCFD performance was evaluated compared to those of LC and MI methods. Moreover, the global topological graph properties of the brain networks inclusive of global efficiency, clustering coefficient and characteristic path length in addict subjects were investigated too. Results: Compared to normal subjects by using statistical tests (P<0.05), topological graph properties were postulated to be disrupted significantly during the resting-state fMRI. Conclusion: Based on the results, analyzing the graph topological properties (representing the brain networks) based on BCFD is a more reliable method than LC and MI.
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Affiliation(s)
- Meysam Siyah Mansoory
- Department of Medical Physics & Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Department of Neuro-Imaging and Analysis, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Ali Oghabian
- Department of Medical Physics & Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Department of Neuro-Imaging and Analysis, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir Homayoun Jafari
- Department of Medical Physics & Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Research Center for Biomedical Technologies and Robotics, Tehran University of Medical Sciences, Tehran, Iran
| | - Alireza Shahbabaie
- Department of Neuro-Imaging and Analysis, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran.,Iranian National Center for Addiction Studies, Tehran University of Medical Sciences, Tehran, Iran.,Substance Abuse and Dependence Research Center, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
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Seidkhani H, Nikolaev AR, Meghanathan RN, Pezeshk H, Masoudi-Nejad A, van Leeuwen C. Task modulates functional connectivity networks in free viewing behavior. Neuroimage 2017; 159:289-301. [PMID: 28782679 DOI: 10.1016/j.neuroimage.2017.07.066] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2016] [Revised: 07/30/2017] [Accepted: 07/31/2017] [Indexed: 02/01/2023] Open
Abstract
In free visual exploration, eye-movement is immediately followed by dynamic reconfiguration of brain functional connectivity. We studied the task-dependency of this process in a combined visual search-change detection experiment. Participants viewed two (nearly) same displays in succession. First time they had to find and remember multiple targets among distractors, so the ongoing task involved memory encoding. Second time they had to determine if a target had changed in orientation, so the ongoing task involved memory retrieval. From multichannel EEG recorded during 200 ms intervals time-locked to fixation onsets, we estimated the functional connectivity using a weighted phase lag index at the frequencies of theta, alpha, and beta bands, and derived global and local measures of the functional connectivity graphs. We found differences between both memory task conditions for several network measures, such as mean path length, radius, diameter, closeness and eccentricity, mainly in the alpha band. Both the local and the global measures indicated that encoding involved a more segregated mode of operation than retrieval. These differences arose immediately after fixation onset and persisted for the entire duration of the lambda complex, an evoked potential commonly associated with early visual perception. We concluded that encoding and retrieval differentially shape network configurations involved in early visual perception, affecting the way the visual input is processed at each fixation. These findings demonstrate that task requirements dynamically control the functional connectivity networks involved in early visual perception.
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Affiliation(s)
- Hossein Seidkhani
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, P.O. Box 13145-1384, Tehran, Iran; Laboratory of Perceptual Dynamics, Brain & Cognition Research Unit, KU Leuven - University of Leuven, Tiensestraat 102, Leuven, 3000, Belgium
| | - Andrey R Nikolaev
- Laboratory of Perceptual Dynamics, Brain & Cognition Research Unit, KU Leuven - University of Leuven, Tiensestraat 102, Leuven, 3000, Belgium
| | - Radha Nila Meghanathan
- Laboratory of Perceptual Dynamics, Brain & Cognition Research Unit, KU Leuven - University of Leuven, Tiensestraat 102, Leuven, 3000, Belgium
| | - Hamid Pezeshk
- School of Mathematics, Statistics and Computer Science, University of Tehran and School of Biological Sciences, Tehran, Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, P.O. Box 13145-1384, Tehran, Iran. http://lbb.ut.ac.ir/
| | - Cees van Leeuwen
- Laboratory of Perceptual Dynamics, Brain & Cognition Research Unit, KU Leuven - University of Leuven, Tiensestraat 102, Leuven, 3000, Belgium; Department of Experimental Psychology II, TU Kaiserslautern, Postfach 3049, Kaiserslautern, 67653, Germany
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Mirzakhalili E, Gourgou E, Booth V, Epureanu B. Synaptic Impairment and Robustness of Excitatory Neuronal Networks with Different Topologies. Front Neural Circuits 2017; 11:38. [PMID: 28659765 PMCID: PMC5468411 DOI: 10.3389/fncir.2017.00038] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Accepted: 05/22/2017] [Indexed: 11/13/2022] Open
Abstract
Synaptic deficiencies are a known hallmark of neurodegenerative diseases, but the diagnosis of impaired synapses on the cellular level is not an easy task. Nonetheless, changes in the system-level dynamics of neuronal networks with damaged synapses can be detected using techniques that do not require high spatial resolution. This paper investigates how the structure/topology of neuronal networks influences their dynamics when they suffer from synaptic loss. We study different neuronal network structures/topologies by specifying their degree distributions. The modes of the degree distribution can be used to construct networks that consist of rich clubs and resemble small world networks, as well. We define two dynamical metrics to compare the activity of networks with different structures: persistent activity (namely, the self-sustained activity of the network upon removal of the initial stimulus) and quality of activity (namely, percentage of neurons that participate in the persistent activity of the network). Our results show that synaptic loss affects the persistent activity of networks with bimodal degree distributions less than it affects random networks. The robustness of neuronal networks enhances when the distance between the modes of the degree distribution increases, suggesting that the rich clubs of networks with distinct modes keep the whole network active. In addition, a tradeoff is observed between the quality of activity and the persistent activity. For a range of distributions, both of these dynamical metrics are considerably high for networks with bimodal degree distribution compared to random networks. We also propose three different scenarios of synaptic impairment, which may correspond to different pathological or biological conditions. Regardless of the network structure/topology, results demonstrate that synaptic loss has more severe effects on the activity of the network when impairments are correlated with the activity of the neurons.
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Affiliation(s)
- Ehsan Mirzakhalili
- Department of Mechanical Engineering, University of MichiganAnn Arbor, MI, United States
| | - Eleni Gourgou
- Department of Mechanical Engineering, University of MichiganAnn Arbor, MI, United States.,Division of Geriatrics, Department of Internal Medicine, Medical School, University of MichiganAnn Arbor, MI, United States
| | - Victoria Booth
- Department of Mathematics, University of MichiganAnn Arbor, MI, United States.,Department of Anesthesiology, Medical School, University of MichiganAnn Arbor, MI, United States
| | - Bogdan Epureanu
- Department of Mechanical Engineering, University of MichiganAnn Arbor, MI, United States
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Geib BR, Stanley ML, Dennis NA, Woldorff MG, Cabeza R. From hippocampus to whole-brain: The role of integrative processing in episodic memory retrieval. Hum Brain Mapp 2017; 38:2242-2259. [PMID: 28112460 PMCID: PMC5460662 DOI: 10.1002/hbm.23518] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Revised: 12/19/2016] [Accepted: 01/04/2017] [Indexed: 01/21/2023] Open
Abstract
Multivariate functional connectivity analyses of neuroimaging data have revealed the importance of complex, distributed interactions between disparate yet interdependent brain regions. Recent work has shown that topological properties of functional brain networks are associated with individual and group differences in cognitive performance, including in episodic memory. After constructing functional whole-brain networks derived from an event-related fMRI study of memory retrieval, we examined differences in functional brain network architecture between forgotten and remembered words. This study yielded three main findings. First, graph theory analyses showed that successfully remembering compared to forgetting was associated with significant changes in the connectivity profile of the left hippocampus and a corresponding increase in efficient communication with the rest of the brain. Second, bivariate functional connectivity analyses indicated stronger interactions between the left hippocampus and a retrieval assembly for remembered versus forgotten items. This assembly included the left precuneus, left caudate, bilateral supramarginal gyrus, and the bilateral dorsolateral superior frontal gyrus. Integrative properties of the retrieval assembly were greater for remembered than forgotten items. Third, whole-brain modularity analyses revealed that successful memory retrieval was marginally significantly associated with a less segregated modular architecture in the network. The magnitude of the decreases in modularity between remembered and forgotten conditions was related to memory performance. These findings indicate that increases in integrative properties at the nodal, retrieval assembly, and whole-brain topological levels facilitate memory retrieval, while also underscoring the potential of multivariate brain connectivity approaches for providing valuable new insights into the neural bases of memory processes. Hum Brain Mapp 38:2242-2259, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Benjamin R. Geib
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth Carolina
| | - Matthew L. Stanley
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth Carolina
| | - Nancy A. Dennis
- Department of PsychologyPennsylvania State UniversityUniversity ParkPennsylvania
| | - Marty G. Woldorff
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth Carolina
| | - Roberto Cabeza
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth Carolina
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Gkigkitzis I, Haranas I, Kotsireas I. Biological Relevance of Network Architecture. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 988:1-29. [PMID: 28971385 DOI: 10.1007/978-3-319-56246-9_1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Mathematical representations of brain networks in neuroscience through the use of graph theory may be very useful for the understanding of neurological diseases and disorders and such an explanatory power is currently under intense investigation. Graph metrics are expected to vary across subjects and are likely to reflect behavioural and cognitive performances. The challenge is to set up a framework that can explain how behaviour, cognition, memory, and other brain properties can emerge through the combined interactions of neurons, ensembles of neurons, and larger-scale brain regions that make information transfer possible. "Hidden" graph theoretic properties in the construction of brain networks may limit or enhance brain functionality and may be representative of aspects of human psychology. As theorems emerge from simple mathematical properties of graphs, similarly, cognition and behaviour may emerge from the molecular, cellular and brain region substrate interactions. In this review report, we identify some studies in the current literature that have used graph theoretical metrics to extract neurobiological conclusions, we briefly discuss the link with the human connectome project as an effort to integrate human data that may aid the study of emergent patterns and we suggest a way to start categorizing diseases according to their brain network pathologies as these are measured by graph theory.
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Affiliation(s)
- Ioannis Gkigkitzis
- Department of Mathematics, East Carolina University, 124 Austin Building, East Fifth Street, Greenville, NC, 27858-4353, USA.
| | - Ioannis Haranas
- Department of Physics and Computer Science, Wilfrid Laurier University, Science Building, Room N2078, 75 University Ave. W., Waterloo, ON, Canada, N2L 3C5
| | - Ilias Kotsireas
- Department of Physics and Computer Science, Wilfrid Laurier University, Science Building, Room N2078, 75 University Ave. W., Waterloo, ON, Canada, N2L 3C5
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Abstract
The episodic long-term memory system supports remembering of events. It is considered to be the most age-sensitive system, with an average onset of decline around 60 years of age. However, there is marked interindividual variability, such that some individuals show faster than average change and others show no or very little change. This variability may be related to the risk of developing dementia, with elevated risk for individuals with accelerated episodic memory decline. Brain imaging with functional magnetic resonance imaging (MRI) of blood oxygen level-dependent (BOLD) signalling or positron emission tomography (PET) has been used to reveal the brain bases of declining episodic memory in ageing. Several studies have demonstrated a link between age-related episodic memory decline and the hippocampus during active mnemonic processing, which is further supported by studies of hippocampal functional connectivity in the resting state. The hippocampus interacts with anterior and posterior neocortical regions to support episodic memory, and alterations in hippocampus-neocortex connectivity have been shown to contribute to impaired episodic memory. Multimodal MRI studies and more recently hybrid MRI/PET studies allow consideration of various factors that can influence the association between the hippocampal BOLD signal and memory performance. These include neurovascular factors, grey and white matter structural alterations, dopaminergic neurotransmission, amyloid-Β and glucose metabolism. Knowledge about the brain bases of episodic memory decline can guide interventions to strengthen memory in older adults, particularly in those with an elevated risk of developing dementia, with promising results for combinations of cognitive and physical stimulation.
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Affiliation(s)
- L Nyberg
- Departments of Radiation Sciences and Integrative Medical Biology, Umeå University and Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden
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Chen X, Wang M, He J, Li W. Dynamic brain network evolution in normal aging based on computational experiments. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.09.056] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Wang Y, Ghumare E, Vandenberghe R, Dupont P. Comparison of Different Generalizations of Clustering Coefficient and Local Efficiency for Weighted Undirected Graphs. Neural Comput 2016; 29:313-331. [PMID: 27870616 DOI: 10.1162/neco_a_00914] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Binary undirected graphs are well established, but when these graphs are constructed, often a threshold is applied to a parameter describing the connection between two nodes. Therefore, the use of weighted graphs is more appropriate. In this work, we focus on weighted undirected graphs. This implies that we have to incorporate edge weights in the graph measures, which require generalizations of common graph metrics. After reviewing existing generalizations of the clustering coefficient and the local efficiency, we proposed new generalizations for these graph measures. To be able to compare different generalizations, a number of essential and useful properties were defined that ideally should be satisfied. We applied the generalizations to two real-world networks of different sizes. As a result, we found that not all existing generalizations satisfy all essential properties. Furthermore, we determined the best generalization for the clustering coefficient and local efficiency based on their properties and the performance when applied to two networks. We found that the best generalization of the clustering coefficient is [Formula: see text], defined in Miyajima and Sakuragawa ( 2014 ), while the best generalization of the local efficiency is [Formula: see text], proposed in this letter. Depending on the application and the relative importance of sensitivity and robustness to noise, other generalizations may be selected on the basis of the properties investigated in this letter.
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Affiliation(s)
- Yu Wang
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven 3000, Belgium
| | - Eshwar Ghumare
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven 3000, Belgium
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven 3000, Belgium, and Alzheimer Research Centre, KU Leuven, Leuven Institute for Neuroscience and Disease, KU Leuven, Leuven 3000, Belgium
| | - Patrick Dupont
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven 3000, Belgium; Alzheimer Research Centre, KU Leuven, Leuven Institute for Neuroscience and Disease, KU Leuven, Leuven 3000, Belgium; and Medical Imaging Research Center, KU Leuven and University Hospitals Leuven, Leuven 3000, Belgium
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Li W, Wang M, Zhu W, Qin Y, Huang Y, Chen X. Simulating the Evolution of Functional Brain Networks in Alzheimer's Disease: Exploring Disease Dynamics from the Perspective of Global Activity. Sci Rep 2016; 6:34156. [PMID: 27677360 PMCID: PMC5039719 DOI: 10.1038/srep34156] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2016] [Accepted: 09/08/2016] [Indexed: 11/25/2022] Open
Abstract
Functional brain connectivity is altered during the pathological processes of Alzheimer's disease (AD), but the specific evolutional rules are insufficiently understood. Resting-state functional magnetic resonance imaging indicates that the functional brain networks of individuals with AD tend to be disrupted in hub-like nodes, shifting from a small world architecture to a random profile. Here, we proposed a novel evolution model based on computational experiments to simulate the transition of functional brain networks from normal to AD. Specifically, we simulated the rearrangement of edges in a pathological process by a high probability of disconnecting edges between hub-like nodes, and by generating edges between random pair of nodes. Subsequently, four topological properties and a nodal distribution were used to evaluate our model. Compared with random evolution as a null model, our model captured well the topological alteration of functional brain networks during the pathological process. Moreover, we implemented two kinds of network attack to imitate the damage incurred by the brain in AD. Topological changes were better explained by 'hub attacks' than by 'random attacks', indicating the fragility of hubs in individuals with AD. This model clarifies the disruption of functional brain networks in AD, providing a new perspective on topological alterations.
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Affiliation(s)
- Wei Li
- School of Automation, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
- Image Processing and Intelligent Control Key Laboratory of Education Ministry of China, Wuhan, 430074, P. R. China
| | - Miao Wang
- China Ship Development and Design Center, Wuhan, 430064, P. R. China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Yuanyuan Qin
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Yue Huang
- School of Electrical and Electronic Engineering, East China Jiaotong University, Nanchang, 330013, P. R. China
| | - Xi Chen
- School of Automation, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
- Image Processing and Intelligent Control Key Laboratory of Education Ministry of China, Wuhan, 430074, P. R. China
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Lee A, Tan M, Qiu A. Distinct Aging Effects on Functional Networks in Good and Poor Cognitive Performers. Front Aging Neurosci 2016; 8:215. [PMID: 27667972 PMCID: PMC5016512 DOI: 10.3389/fnagi.2016.00215] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2016] [Accepted: 08/26/2016] [Indexed: 12/13/2022] Open
Abstract
Brain network hubs are susceptible to normal aging processes and disruptions of their functional connectivity are detrimental to decline in cognitive functions in older adults. However, it remains unclear how the functional connectivity of network hubs cope with cognitive heterogeneity in an aging population. This study utilized cognitive and resting-state functional magnetic resonance imaging data, cluster analysis, and graph network analysis to examine age-related alterations in the network hubs’ functional connectivity of good and poor cognitive performers. Our results revealed that poor cognitive performers showed age-dependent disruptions in the functional connectivity of the right insula and posterior cingulate cortex (PCC), while good cognitive performers showed age-related disruptions in the functional connectivity of the left insula and PCC. Additionally, the left PCC had age-related declines in the functional connectivity with the left medial prefrontal cortex (mPFC) and anterior cingulate cortex (ACC). Most interestingly, good cognitive performers showed age-related declines in the functional connectivity of the left insula and PCC with their right homotopic structures. These results may provide insights of neuronal correlates for understanding individual differences in aging. In particular, our study suggests prominent protection roles of the left insula and PCC and bilateral ACC in good performers.
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Affiliation(s)
- Annie Lee
- Department of Biomedical Engineering, National University of Singapore Singapore, Singapore
| | - Mingzhen Tan
- Department of Biomedical Engineering, National University of Singapore Singapore, Singapore
| | - Anqi Qiu
- Department of Biomedical Engineering, National University of SingaporeSingapore, Singapore; Clinical Imaging Research Center, National University of SingaporeSingapore, Singapore; Singapore Institute for Clinical Sciences, the Agency for Science, Technology and ResearchSingapore, Singapore
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43
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Wang B, Zhang M, Bu L, Xu L, Wang W, Li Z. Posture-related changes in brain functional connectivity as assessed by wavelet phase coherence of NIRS signals in elderly subjects. Behav Brain Res 2016; 312:238-45. [PMID: 27335218 DOI: 10.1016/j.bbr.2016.06.037] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2016] [Revised: 06/16/2016] [Accepted: 06/18/2016] [Indexed: 12/29/2022]
Abstract
Postural instability and falls are commonly seen because of aging and motor disabilities. This study aims to assess the posture-related changes in brain functional connectivity by wavelet phase coherence (WPCO) of oxyhemoglobin concentration change (Δ[HbO2]) signals measured through near-infrared spectroscopy (NIRS) in elderly subjects. The NIRS signals were continuously recorded from the prefrontal cortex and sensorimotor cortical areas in 39 healthy elderly subjects and 22 young healthy subjects during 20min resting and 10min standing states. Eight connection types were obtained from the recorded brain areas. The WPCO were calculated in five frequency intervals in each channel pair as follows: I, 0.6-2Hz; II, 0.145-0.6Hz; III, 0.052-0.145Hz; IV, 0.021-0.052Hz; and V, 0.0095-0.021Hz. Results show that posture change and age significantly interacts with the right prefrontal cortex (PFC) and left sensorimotor cortex (SMC) connectivity in interval V (F=5.010, p=0.028). The left and right PFC connectivity in interval I, the left and right SMC connectivity in interval IV, and the connectivity in interval V, including right PFC and right SMC connectivity, left PFC and left SMC connectivity, and right PFC and left SMC connectivity, showed a significant difference between the Group Elderly and Group Young in response to posture change (p<0.05). This study provides new insight into the mechanism of posture control, and results may be useful in assessing the risk of postural instability in aged persons.
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Affiliation(s)
- Bitan Wang
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University, Jinan, 250061, PR China
| | - Ming Zhang
- Interdisciplinary Division of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong Special Administrative Region
| | - Lingguo Bu
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University, Jinan, 250061, PR China
| | - Liwei Xu
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University, Jinan, 250061, PR China
| | - Wei Wang
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University, Jinan, 250061, PR China
| | - Zengyong Li
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University, Jinan, 250061, PR China; National Research Center for Rehabilitation Technical Aids, Beijing 100176, PR China.
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44
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Disrupted functional brain connectome in unilateral sudden sensorineural hearing loss. Hear Res 2016; 335:138-148. [DOI: 10.1016/j.heares.2016.02.016] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2015] [Revised: 02/18/2016] [Accepted: 02/22/2016] [Indexed: 12/20/2022]
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45
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A Novel Brain Network Construction Method for Exploring Age-Related Functional Reorganization. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2016; 2016:2429691. [PMID: 27057155 PMCID: PMC4789383 DOI: 10.1155/2016/2429691] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2015] [Revised: 01/12/2016] [Accepted: 01/31/2016] [Indexed: 11/17/2022]
Abstract
The human brain undergoes complex reorganization and changes during aging. Using graph theory, scientists can find differences in topological properties of functional brain networks between young and elderly adults. However, these differences are sometimes significant and sometimes not. Several studies have even identified disparate differences in topological properties during normal aging or in age-related diseases. One possible reason for this issue is that existing brain network construction methods cannot fully extract the "intrinsic edges" to prevent useful signals from being buried into noises. This paper proposes a new subnetwork voting (SNV) method with sliding window to construct functional brain networks for young and elderly adults. Differences in the topological properties of brain networks constructed from the classic and SNV methods were consistent. Statistical analysis showed that the SNV method can identify much more statistically significant differences between groups than the classic method. Moreover, support vector machine was utilized to classify young and elderly adults; its accuracy, based on the SNV method, reached 89.3%, significantly higher than that with classic method. Therefore, the SNV method can improve consistency within a group and highlight differences between groups, which can be valuable for the exploration and auxiliary diagnosis of aging and age-related diseases.
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46
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Deng Z, Chandrasekaran B, Wang S, Wong PCM. Resting-state low-frequency fluctuations reflect individual differences in spoken language learning. Cortex 2015; 76:63-78. [PMID: 26866283 DOI: 10.1016/j.cortex.2015.11.020] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2015] [Revised: 10/23/2015] [Accepted: 11/28/2015] [Indexed: 10/22/2022]
Abstract
A major challenge in language learning studies is to identify objective, pre-training predictors of success. Variation in the low-frequency fluctuations (LFFs) of spontaneous brain activity measured by resting-state functional magnetic resonance imaging (RS-fMRI) has been found to reflect individual differences in cognitive measures. In the present study, we aimed to investigate the extent to which initial spontaneous brain activity is related to individual differences in spoken language learning. We acquired RS-fMRI data and subsequently trained participants on a sound-to-word learning paradigm in which they learned to use foreign pitch patterns (from Mandarin Chinese) to signal word meaning. We performed amplitude of spontaneous low-frequency fluctuation (ALFF) analysis, graph theory-based analysis, and independent component analysis (ICA) to identify functional components of the LFFs in the resting-state. First, we examined the ALFF as a regional measure and showed that regional ALFFs in the left superior temporal gyrus were positively correlated with learning performance, whereas ALFFs in the default mode network (DMN) regions were negatively correlated with learning performance. Furthermore, the graph theory-based analysis indicated that the degree and local efficiency of the left superior temporal gyrus were positively correlated with learning performance. Finally, the default mode network and several task-positive resting-state networks (RSNs) were identified via the ICA. The "competition" (i.e., negative correlation) between the DMN and the dorsal attention network was negatively correlated with learning performance. Our results demonstrate that a) spontaneous brain activity can predict future language learning outcome without prior hypotheses (e.g., selection of regions of interest--ROIs) and b) both regional dynamics and network-level interactions in the resting brain can account for individual differences in future spoken language learning success.
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Affiliation(s)
- Zhizhou Deng
- Center for the Study of Applied Psychology and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China; Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Hong Kong
| | - Bharath Chandrasekaran
- Department of Communication Sciences and Disorders, Moody College of Communication, The University of Texas at Austin, Austin, TX, USA; Department of Psychology, College of Liberal Arts, The University of Texas at Austin, Austin, TX, USA; Department of Linguistics, College of Liberal Arts, The University of Texas at Austin, Austin, TX, USA; Institute of Mental Health Research, College of Liberal Arts, The University of Texas at Austin, Austin, TX, USA; Institute for Neuroscience, The University of Texas at Austin, Austin, TX, USA
| | - Suiping Wang
- Center for the Study of Applied Psychology and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China.
| | - Patrick C M Wong
- Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Hong Kong; Brain and Mind Institute, The Chinese University of Hong Kong, Hong Kong.
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Tromp D, Dufour A, Lithfous S, Pebayle T, Després O. Episodic memory in normal aging and Alzheimer disease: Insights from imaging and behavioral studies. Ageing Res Rev 2015; 24:232-62. [PMID: 26318058 DOI: 10.1016/j.arr.2015.08.006] [Citation(s) in RCA: 201] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2015] [Accepted: 08/20/2015] [Indexed: 12/30/2022]
Abstract
Age-related cognitive changes often include difficulties in retrieving memories, particularly those that rely on personal experiences within their temporal and spatial contexts (i.e., episodic memories). This decline may vary depending on the studied phase (i.e., encoding, storage or retrieval), according to inter-individual differences, and whether we are talking about normal or pathological (e.g., Alzheimer disease; AD) aging. Such cognitive changes are associated with different structural and functional alterations in the human neural network that underpins episodic memory. The prefrontal cortex is the first structure to be affected by age, followed by the medial temporal lobe (MTL), the parietal cortex and the cerebellum. In AD, however, the modifications occur mainly in the MTL (hippocampus and adjacent structures) before spreading to the neocortex. In this review, we will present results that attempt to characterize normal and pathological cognitive aging at multiple levels by integrating structural, behavioral, inter-individual and neuroimaging measures of episodic memory.
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Affiliation(s)
- D Tromp
- Laboratoire de Neurosciences Cognitives et Adaptatives (LNCA - UMR 7364 - CNRS/UDS) - 21 rue Becquerel, 67087 Strasbourg, France.
| | - A Dufour
- Laboratoire de Neurosciences Cognitives et Adaptatives (LNCA - UMR 7364 - CNRS/UDS) - 21 rue Becquerel, 67087 Strasbourg, France; Centre d'Investigations Neurocognitives et Neurophysiologiques (CI2N - UMS 3489 - CNRS/UDS) - 21 rue Becquerel, 67087 Strasbourg, France
| | - S Lithfous
- Laboratoire de Neurosciences Cognitives et Adaptatives (LNCA - UMR 7364 - CNRS/UDS) - 21 rue Becquerel, 67087 Strasbourg, France
| | - T Pebayle
- Centre d'Investigations Neurocognitives et Neurophysiologiques (CI2N - UMS 3489 - CNRS/UDS) - 21 rue Becquerel, 67087 Strasbourg, France
| | - O Després
- Laboratoire de Neurosciences Cognitives et Adaptatives (LNCA - UMR 7364 - CNRS/UDS) - 21 rue Becquerel, 67087 Strasbourg, France.
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Tan Q, Zhang M, Wang Y, Zhang M, Wang B, Xin Q, Li Z. Age-related alterations in phase synchronization of oxyhemoglobin concentration changes in prefrontal tissues as measured by near-infrared spectroscopy signals. Microvasc Res 2015; 103:19-25. [PMID: 26525098 DOI: 10.1016/j.mvr.2015.10.002] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2015] [Revised: 09/24/2015] [Accepted: 10/21/2015] [Indexed: 11/26/2022]
Abstract
The prefrontal cortex plays an important role in planning complex cognitive behavior, personality expression, and decision making. This study aims to assess the phase synchronization of signals of the oxyhemoglobin concentration changes (Δ[HbO2]) in the left and right prefrontal tissues through near-infrared spectroscopy (NIRS) with wavelet phase coherence (WPCO) method. The NIRS signals were continuously recorded from the left and right prefrontal lobes in 43 healthy elderly subjects (age: 69.6 ± 8.4 years) and 40 young healthy subjects (age: 24.5 ± 1.7 years) during the resting state. Phase synchronization between the left and right prefrontal oscillations in six frequency intervals (I, 0.6-2 Hz; II, 0.145-0.6 Hz; III, 0.052-0.145 Hz; IV, 0.021-0.052 Hz; V, 0.0095-0.021 Hz; and VI, 0.005-0.0095 Hz) was analyzed using the WPCO method. The WPCO values of elderly subjects were significantly lower in frequency intervals I (F=7.376, p=0.010) and III (F=6.418, p=0.016) than those of the young subjects. Low phase coherence in intervals I and III indicates reduced synchronization of cardiac activity in the prefrontal area and weakened prefrontal functional connectivity, respectively.
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Affiliation(s)
- Qitao Tan
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University, Jinan 250061, PR China
| | - Ming Zhang
- Interdisciplinary Division of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, SAR, PR China
| | - Yi Wang
- Department of Dermatology, Ji'nan Central Hospital, 250013, PR China
| | - Manyu Zhang
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University, Jinan 250061, PR China
| | - Bitan Wang
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University, Jinan 250061, PR China
| | - Qing Xin
- Hospital of Shandong University, Jinan 250061, PR China
| | - Zengyong Li
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University, Jinan 250061, PR China.
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Li X, Wang H. Identification of functional networks in resting state fMRI data using adaptive sparse representation and affinity propagation clustering. Front Neurosci 2015; 9:383. [PMID: 26528123 PMCID: PMC4607787 DOI: 10.3389/fnins.2015.00383] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Accepted: 10/02/2015] [Indexed: 02/04/2023] Open
Abstract
Human brain functional system has been viewed as a complex network. To accurately characterize this brain network, it is important to estimate the functional connectivity between separate brain regions (i.e., association matrix). One common approach to evaluating the connectivity is the pairwise Pearson correlation. However, this bivariate method completely ignores the influence of other regions when computing the pairwise association. Another intractable issue existed in many approaches to further analyzing the network structure is the requirement of applying a threshold to the association matrix. To address these issues, we develop a novel scheme to investigate the brain functional networks. Specifically, we first establish a global functional connection network by using the Adaptive Sparse Representation (ASR), adaptively integrating the sparsity of ℓ1-norm and the grouping effect of ℓ2-norm for linear representation and then identify connectivity patterns with Affinity Propagation (AP) clustering algorithm. Results on both simulated and real data indicate that the proposed scheme is superior to the Pearson correlation in connectivity quality and clustering quality. Our findings suggest that the proposed scheme is an accurate and useful technique to delineate functional network structure for functionally parsimonious and correlated fMRI data with a large number of brain regions.
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Affiliation(s)
- Xuan Li
- Key Lab of Child Development and Learning Science of Ministry of Education, Institute of Child Development and Education, Research Center for Learning Science, Southeast University Nanjing, China
| | - Haixian Wang
- Key Lab of Child Development and Learning Science of Ministry of Education, Institute of Child Development and Education, Research Center for Learning Science, Southeast University Nanjing, China
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PCC characteristics at rest in 10-year memory decliners. Neurobiol Aging 2015; 36:2812-20. [DOI: 10.1016/j.neurobiolaging.2015.07.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2014] [Revised: 06/29/2015] [Accepted: 07/02/2015] [Indexed: 01/31/2023]
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