101
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Fjaeldstad AW, Konieczny DT, Fernandes H, Gaini LM, Vejlø M, Sandberg K. The relationship between individual significance of olfaction and measured olfactory function. CURRENT RESEARCH IN BEHAVIORAL SCIENCES 2022. [DOI: 10.1016/j.crbeha.2022.100076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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102
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Dieleman J, Sescousse G, Kleinjan M, Otten R, Luijten M. Investigating the association between smoking, environmental tobacco smoke exposure and reward-related brain activity in adolescent experimental smokers. Addict Biol 2022; 27:e13070. [PMID: 34263512 PMCID: PMC9285048 DOI: 10.1111/adb.13070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 04/11/2021] [Accepted: 05/25/2021] [Indexed: 11/28/2022]
Abstract
Reduced anticipatory reward‐related activity, especially in the ventral striatum (VS), may underly adolescent vulnerability to develop nicotine dependence. It remains unclear whether nicotine uptake caused by environmental tobacco smoke (ETS) exposure, known to be associated with future smoking, might prompt similar changes in the brain's reward system, rendering adolescents vulnerable for development of nicotine dependence. To address this question, we tested whether current ETS exposure and monthly smoking are associated with VS hypoactivity for non‐drug rewards in experimental smoking adolescents. One‐hundred adolescents performed a monetary incentive delay task while brain activity was measured using fMRI. To test the hypothesized relationship, we used a variety of approaches: (1) a whole‐brain voxel‐wise approach, (2) an region‐of‐interest approach in the VS using frequentist and Bayesian statistics and (3) a small volume voxel‐wise approach across the complete striatum. The results converged in revealing no significant relationships between monthly smoking, ETS exposure and reward‐related brain activation across the brain or in the (ventral) striatum specifically. However, Bayesian statistics showed only anecdotal evidence for the null hypothesis in the VS, providing limited insight into the (non‐)existence of the hypothesized relationship. Based on these results, we speculate that blunted VS reward‐related activity might only occur after relatively high levels of exposure or might be associated with more long term effects of smoking. Future studies would benefit from even larger sample sizes to reliably distinguish between the null and alternative models, as well as more objective measures of (environmental) smoking via using devices such as silicone wristbands.
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Affiliation(s)
- Joyce Dieleman
- Department of Jeugd Trimbos Institute Utrecht Netherlands
- Behavioural Science Institute Radboud University Nijmegen Netherlands
| | | | - Marloes Kleinjan
- Department of Jeugd Trimbos Institute Utrecht Netherlands
- Interdisciplinary Social Sciences Utrecht University Netherlands
| | - Roy Otten
- Behavioural Science Institute Radboud University Nijmegen Netherlands
- Pluryn Research and Development Nijmegen Netherlands
- Arizona State University REACH Institute Tempe Arizona USA
| | - Maartje Luijten
- Behavioural Science Institute Radboud University Nijmegen Netherlands
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103
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Ngo GH, Khosla M, Jamison K, Kuceyeski A, Sabuncu MR. Predicting Individual Task Contrasts From Resting-state Functional Connectivity using a Surface-based Convolutional Network. Neuroimage 2021; 248:118849. [PMID: 34965456 PMCID: PMC10155599 DOI: 10.1016/j.neuroimage.2021.118849] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 11/20/2021] [Accepted: 12/21/2021] [Indexed: 12/23/2022] Open
Abstract
Task-based and resting-state represent the two most common experimental paradigms of functional neuroimaging. While resting-state offers a flexible and scalable approach for characterizing brain function, task-based techniques provide superior localization. In this paper, we build on recent deep learning methods to create a model that predicts task-based contrast maps from resting-state fMRI scans. Specifically, we propose BrainSurfCNN, a surface-based fully-convolutional neural network model that works with a representation of the brain's cortical sheet. BrainSurfCNN achieves exceptional predictive accuracy on independent test data from the Human Connectome Project, which is on par with the repeat reliability of the measured subject-level contrast maps. Conversely, our analyses reveal that a previously published benchmark is no better than group-average contrast maps. Finally, we demonstrate that BrainSurfCNN can generalize remarkably well to novel domains with limited training data.
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Affiliation(s)
- Gia H Ngo
- School of Electrical & Computer Engineering, Cornell University and Cornell Tech, United States
| | - Meenakshi Khosla
- School of Electrical & Computer Engineering, Cornell University and Cornell Tech, United States
| | | | | | - Mert R Sabuncu
- School of Electrical & Computer Engineering, Cornell University and Cornell Tech, United States; Radiology, Weill Cornell Medicine, United States.
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104
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NAS-optimized topology-preserving transfer learning for differentiating cortical folding patterns. Med Image Anal 2021; 77:102316. [PMID: 34979433 DOI: 10.1016/j.media.2021.102316] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 11/21/2021] [Accepted: 11/23/2021] [Indexed: 11/21/2022]
Abstract
Increasing evidence suggests that cortical folding patterns of human cerebral cortex manifest overt structural and functional differences. However, for interpretability, few studies leverage advanced techniques (e.g., deep learning) to investigate the difference among cortical folds, resulting in more differences yet to be extensively explored. To this end, we proposed an effective topology-preserving transfer learning framework to differentiate cortical fMRI time series extracted from cortical folds. Our framework consists of three main parts: (1) Neural architecture search (NAS), which is used to devise a well-performing network structure based on an initialized hand-designed super-graph in an image dataset; (2) Topology-preserving transfer, which takes the model searched by NAS as the source network, keeping the topological connectivity in the network unchanged, while transforming all 2D operations including convolution and pooling into 1D, therefore resulting in a topology-preserving network, named TPNAS-Net; (3) Classification and correlation analysis, which involves using the TPNAS-Net to classify 1D cortical fMRI time series for each individual brain, and performing a group difference analysis between autism spectrum disorder (ASD) and healthy control (HC) and correlation analysis with clinical information (i.e., age). Extensive experiments on two ASD datasets obtain consistent results, demonstrating that the TPNAS-Net not only discriminates cortical folding patterns at high classification accuracy, but also captures subtle differences between ASD and HC (p-value = 0.042). In addition, we discover that there is a positive correlation between the classification accuracy and age in ASD (r = 0.39, p-value = 0.04). These findings together suggest that structural and functional differences in cortical folding patterns between ASD and HC may provide a potentially useful biomarker for the diagnosis of ASD.
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105
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Mekki Y, Guillemot V, Lemaitre H, Carrion-Castillo A, Forkel S, Frouin V, Philippe C. The genetic architecture of language functional connectivity. Neuroimage 2021; 249:118795. [PMID: 34929384 DOI: 10.1016/j.neuroimage.2021.118795] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 11/11/2021] [Accepted: 12/08/2021] [Indexed: 02/08/2023] Open
Abstract
Language is a unique trait of the human species, of which the genetic architecture remains largely unknown. Through language disorders studies, many candidate genes were identified. However, such complex and multifactorial trait is unlikely to be driven by only few genes and case-control studies, suffering from a lack of power, struggle to uncover significant variants. In parallel, neuroimaging has significantly contributed to the understanding of structural and functional aspects of language in the human brain and the recent availability of large scale cohorts like UK Biobank have made possible to study language via image-derived endophenotypes in the general population. Because of its strong relationship with task-based fMRI (tbfMRI) activations and its easiness of acquisition, resting-state functional MRI (rsfMRI) have been more popularised, making it a good surrogate of functional neuronal processes. Taking advantage of such a synergistic system by aggregating effects across spatially distributed traits, we performed a multivariate genome-wide association study (mvGWAS) between genetic variations and resting-state functional connectivity (FC) of classical brain language areas in the inferior frontal (pars opercularis, triangularis and orbitalis), temporal and inferior parietal lobes (angular and supramarginal gyri), in 32,186 participants from UK Biobank. Twenty genomic loci were found associated with language FCs, out of which three were replicated in an independent replication sample. A locus in 3p11.1, regulating EPHA3 gene expression, is found associated with FCs of the semantic component of the language network, while a locus in 15q14, regulating THBS1 gene expression is found associated with FCs of the perceptual-motor language processing, bringing novel insights into the neurobiology of language.
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Affiliation(s)
- Yasmina Mekki
- NeuroSpin, Institut Joliot, CEA - Université Paris-Saclay, Gif-Sur-Yvette, 91191, France.
| | - Vincent Guillemot
- Hub de Bioinformatique et Biostatistique, Département Biologie Computationnelle, Institut Pasteur, USR 3756 CNRS, Paris, France
| | - Hervé Lemaitre
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, CNRS UMR 5293, Université de Bordeaux, Centre Broca Nouvelle-Aquitaine, Bordeaux, France
| | | | - Stephanie Forkel
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, CNRS UMR 5293, Université de Bordeaux, Centre Broca Nouvelle-Aquitaine, Bordeaux, France; Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France; Department of Neuroimaging, Institute of Psychiatry, Psychology and Neurosciences, King's College London, UK
| | - Vincent Frouin
- NeuroSpin, Institut Joliot, CEA - Université Paris-Saclay, Gif-Sur-Yvette, 91191, France
| | - Cathy Philippe
- NeuroSpin, Institut Joliot, CEA - Université Paris-Saclay, Gif-Sur-Yvette, 91191, France.
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106
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Morante M, Kopsinis Y, Chatzichristos C, Protopapas A, Theodoridis S. Enhanced design matrix for task-related fMRI data analysis. Neuroimage 2021; 245:118719. [PMID: 34775007 DOI: 10.1016/j.neuroimage.2021.118719] [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: 07/06/2021] [Revised: 09/20/2021] [Accepted: 11/09/2021] [Indexed: 10/19/2022] Open
Abstract
In this paper, we introduce a novel methodology for the analysis of task-related fMRI data. In particular, we propose an alternative way for constructing the design matrix, based on the newly suggested Information-Assisted Dictionary Learning (IADL) method. This technique offers an enhanced potential, within the conventional GLM framework, (a) to efficiently cope with uncertainties in the modeling of the hemodynamic response function, (b) to accommodate unmodeled brain-induced sources, beyond the task-related ones, as well as potential interfering scanner-induced artifacts, uncorrected head-motion residuals and other unmodeled physiological signals, and (c) to integrate external knowledge regarding the natural sparsity of the brain activity that is associated with both the experimental design and brain atlases. The capabilities of the proposed methodology are evaluated via a realistic synthetic fMRI-like dataset, and demonstrated using a test case of a challenging fMRI study, which verifies that the proposed approach produces substantially more consistent results compared to the standard design matrix method. A toolbox extension for SPM is also provided, to facilitate the use and reproducibility of the proposed methodology.
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Affiliation(s)
- Manuel Morante
- Dept. of Electronic Systems, Aalborg University, Denmark; Computer Technology Institutes & Press "Diophantus" (CTI), Patras, Greece.
| | | | - Christos Chatzichristos
- Dept. Electrical Engineering (ESAT), Dynamical Systems, Signal Processing and Data Analytics (STADIUS), KU Leuven, Belgium
| | | | - Sergios Theodoridis
- Dept. of Electronic Systems, Aalborg University, Denmark; Dept. of Informatics and Telecommunications of the National and Kapodistrian University of Athens, Greece
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107
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Ni H, Song M, Qin J, Jiang T. Individual Discriminative Ability of Resting Functional Brain Connectivity is Susceptible to the Time Span of MRI Scans. Neuroscience 2021; 482:43-52. [PMID: 34914970 DOI: 10.1016/j.neuroscience.2021.12.008] [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: 07/15/2021] [Revised: 11/08/2021] [Accepted: 12/07/2021] [Indexed: 10/19/2022]
Abstract
Recent studies have suggested that resting-state brain functional connectivity (RSFC) has the potential to discriminate among individuals in a population. These studies mostly utilized a pattern of RSFC obtained from one scan to identify a given individual from the set of patterns obtained from the second scan. However, it remains unclear whether the discriminative ability would change with the extension of the time span between the two brain scans. This study explores the variations in the discriminative ability of RSFC on eight time spans, including 6 hours, 12 hours, 1 day, 1 month, 3-6 months, 7-12 months, 1-2 years and 2-3 years. We first searched for a set of the most discriminative RSFCs using the data of 200 healthy adult subjects from the Human Connectome Project dataset, and we then utilized this set of RSFCs to identify individuals from a population. The variations in the discriminative accuracies over different time spans were evaluated on datasets from a total of 682 unseen adult subjects acquired from four different sites. We found that although the accuracies were detectable at above-chance levels, the discriminative accuracies showed a significant decrease (F = 17.87, p < 0.01) along with the extension of brain imaging time span, from over 90% within one month to 66% at 2-3 years. Furthermore, the decreasing trend was robust and not dependent on the training set or analysis method. Therefore, we suggest that the discriminative ability of RSFC in identifying individuals should be susceptible to the length of time between brain scans.
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Affiliation(s)
- Huangjing Ni
- Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Ming Song
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
| | - Jiaolong Qin
- Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China; The Queensland Brain Institute, The University of Queensland, Brisbane, Qld, Australia.
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108
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Norbury A, Rutter SB, Collins AB, Costi S, Jha MK, Horn SR, Kautz M, Corniquel M, Collins KA, Glasgow AM, Brallier J, Shin LM, Charney DS, Murrough JW, Feder A. Neuroimaging correlates and predictors of response to repeated-dose intravenous ketamine in PTSD: preliminary evidence. Neuropsychopharmacology 2021; 46:2266-2277. [PMID: 34333555 PMCID: PMC8580962 DOI: 10.1038/s41386-021-01104-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 06/15/2021] [Accepted: 07/07/2021] [Indexed: 02/06/2023]
Abstract
Promising initial data indicate that the glutamate N-methyl-D-aspartate (NMDA) receptor antagonist ketamine may be beneficial in post-traumatic stress disorder (PTSD). Here, we explore the neural correlates of ketamine-related changes in PTSD symptoms, using a rich battery of functional imaging data (two emotion-processing tasks and one task-free scan), collected from a subset of participants of a randomized clinical trial of repeated-dose intravenous ketamine vs midazolam (total N = 21). In a pre-registered analysis, we tested whether changes in an a priori set of imaging measures from a target neural circuit were predictive of improvement in PTSD symptoms, using leave-one-out cross-validated elastic-net regression models (regions of interest in the target circuit consisted of the dorsal and rostral anterior cingulate cortex, ventromedial prefrontal cortex, anterior hippocampus, anterior insula, and amygdala). Improvements in PTSD severity were associated with increased functional connectivity between the ventromedial prefrontal cortex (vmPFC) and amygdala during emotional face-viewing (change score retained in model with minimum predictive error in left-out subjects, standardized regression coefficient [β] = 2.90). This effect was stronger in participants who received ketamine compared to midazolam (interaction β = 0.86), and persisted following inclusion of concomitant change in depressive symptoms in the analysis model (β = 0.69). Improvement following ketamine was also predicted by decreased dorsal anterior cingulate activity during emotional conflict regulation, and increased task-free connectivity between the vmPFC and anterior insula (βs = -2.82, 0.60). Exploratory follow-up analysis via dynamic causal modelling revealed that whilst improvement in PTSD symptoms following either drug was associated with decreased excitatory modulation of amygdala→vmPFC connectivity during emotional face-viewing, increased top-down inhibition of the amygdala by the vmPFC was only observed in participants who improved under ketamine. Individuals with low prefrontal inhibition of amygdala responses to faces at baseline also showed greater improvements following ketamine treatment. These preliminary findings suggest that, specifically under ketamine, improvements in PTSD symptoms are accompanied by normalization of hypofrontal control over amygdala responses to social signals of threat.
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Affiliation(s)
- Agnes Norbury
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sarah B Rutter
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Abigail B Collins
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sara Costi
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Manish K Jha
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sarah R Horn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Marin Kautz
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Morgan Corniquel
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Katherine A Collins
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Andrew M Glasgow
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jess Brallier
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lisa M Shin
- Department of Psychology, Tufts University, Medford, MA, USA
- Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
| | - Dennis S Charney
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - James W Murrough
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Adriana Feder
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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109
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Hakim N, Awh E, Vogel EK, Rosenberg MD. Inter-electrode correlations measured with EEG predict individual differences in cognitive ability. Curr Biol 2021; 31:4998-5008.e6. [PMID: 34637747 DOI: 10.1016/j.cub.2021.09.036] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 07/07/2021] [Accepted: 09/15/2021] [Indexed: 02/07/2023]
Abstract
Human brains share a broadly similar functional organization with consequential individual variation. This duality in brain function has primarily been observed when using techniques that consider the spatial organization of the brain, such as MRI. Here, we ask whether these common and unique signals of cognition are also present in temporally sensitive but spatially insensitive neural signals. To address this question, we compiled electroencephalogram (EEG) data from individuals of both sexes while they performed multiple working memory tasks at two different data-collection sites (n = 171 and 165). Results revealed that trial-averaged EEG activity exhibited inter-electrode correlations that were stable within individuals and unique across individuals. Furthermore, models based on these inter-electrode correlations generalized across datasets to predict participants' working memory capacity and general fluid intelligence. Thus, inter-electrode correlation patterns measured with EEG provide a signature of working memory and fluid intelligence in humans and a new framework for characterizing individual differences in cognitive abilities.
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Affiliation(s)
- Nicole Hakim
- Department of Psychology, University of Chicago, Chicago, IL 60637, USA; Institute for Mind and Biology, University of Chicago, Chicago, IL 60637, USA.
| | - Edward Awh
- Department of Psychology, University of Chicago, Chicago, IL 60637, USA; Institute for Mind and Biology, University of Chicago, Chicago, IL 60637, USA; Neuroscience Institute, University of Chicago, Chicago, IL 60637, USA
| | - Edward K Vogel
- Department of Psychology, University of Chicago, Chicago, IL 60637, USA; Institute for Mind and Biology, University of Chicago, Chicago, IL 60637, USA; Neuroscience Institute, University of Chicago, Chicago, IL 60637, USA
| | - Monica D Rosenberg
- Department of Psychology, University of Chicago, Chicago, IL 60637, USA; Neuroscience Institute, University of Chicago, Chicago, IL 60637, USA
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110
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Supporting Risk-Aware Use of Online Translation Tools in Delivering Mental Healthcare Services among Spanish-Speaking Populations. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:1011197. [PMID: 34745242 PMCID: PMC8568518 DOI: 10.1155/2021/1011197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/01/2021] [Accepted: 10/15/2021] [Indexed: 12/01/2022]
Abstract
Neural machine translation technologies are having increasing applications in clinical and healthcare settings. In multicultural countries, automatic translation tools provide critical support to medical and health professionals in their interaction and exchange of health messages with migrant patients with limited or non-English proficiency. While research has mainly explored the usability and limitations of state-of-the-art machine translation tools in the detection and diagnosis of physical diseases and conditions, there is a persistent lack of evidence-based studies on the applicability of machine translation tools in the delivery of mental healthcare services for vulnerable populations. Our study developed Bayesian machine learning algorithms using relevance vector machine to support frontline health workers and medical professionals to make better informed decisions between risks and convenience of using online translation tools when delivering mental healthcare services to Spanish-speaking minority populations living in English-speaking countries. Major strengths of the machine learning classifier that we developed include scalability, interpretability, and adaptability of the classifier for diverse mental healthcare settings. In this paper, we report on the process of the Bayesian machine learning classifier development through automatic feature optimisation and the interpretation of the classifier-enabled assessment of the suitability of original English mental health information for automatic online translation. We elaborate on the interpretation of the assessment results in clinical settings using statistical tools such as positive likelihood ratios and negative likelihood ratios.
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111
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Assari S. Cingulo-opercular and Cingulo-parietal Brain Networks Functional Connectivity in Pre-adolescents: Multiplicative Effects of Race, Ethnicity, and Parental Education. ACTA ACUST UNITED AC 2021; 6:76-99. [PMID: 34734154 DOI: 10.22158/rhs.v6n2p76] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Introduction A growing body of research has shown a diminished association between socioeconomic status (SES) indicators and a wide range of neuroimaging indicators for racial and ethnic minorities compared to majority groups. However, less is known about these effects for resting-state functional connectivity between various brain networks. Purpose This study investigated racial and ethnic variation in the correlation between parental education and resting-state functional connectivity between the cingulo-opercular (CO) and cingulo-parietal (CP) networks in children. Methods This cross-sectional study used data from the Adolescent Brain Cognitive Development (ABCD) study; we analyzed the resting-state functional Magnetic Resonance Imaging (rsfMRI) data of 8,464 American pre-adolescents between the ages of 9 and 10. The main outcome measured was resting-state functional connectivity between the CO and CP networks calculated using rsfMRI. The independent variable was parental education, which was treated as a nominal variable. Age, sex, and family marital status were the study covariates. Race and ethnicity were the moderators. Mixed-effects regression models were used for data analysis, with and without interaction terms between parental education and race and ethnicity. Results Higher parental education was associated with higher resting-state functional connectivity between the CO and CP networks. Race and ethnicity both showed statistically significant interactions with parental education on children's resting-state functional connectivity between CO and CP networks, suggesting that the correlation between parental education and the resting-state functional connectivity was significantly weaker for Black and Hispanic pre-adolescents compared to White and non-Hispanic pre-adolescents. Conclusions In line with the Minorities' Diminished Returns theory, the association between parental education and pre-adolescents resting-state functional connectivity between CO and CP networks may be weaker in Black and Hispanic children than in White and non-Hispanic children. The weaker link between parental education and brain functional connectivity for Blacks and Hispanics than for Whites and non-Hispanics may reflect racism, racialization, and social stratification that collectively minimize the returns of SES indicators, such as parental education for non-Whites, who become others in the US.
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Affiliation(s)
- Shervin Assari
- Department of Family Medicine, College of Medicine, Charles R. Drew University of Medicine and Science, Los Angeles, CA 90059, USA.,Department of Urban Public Health, Charles R. Drew University of Medicine and Science, Los Angeles, CA 90059, USA.,Marginalization-related Diminished Returns (MDRs) Research Center, Charles R. Drew University of Medicine and Science, Los Angeles, CA 90059, USA
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112
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White matter variability, cognition, and disorders: a systematic review. Brain Struct Funct 2021; 227:529-544. [PMID: 34731328 PMCID: PMC8844174 DOI: 10.1007/s00429-021-02382-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 09/03/2021] [Indexed: 11/23/2022]
Abstract
Inter-individual differences can inform treatment procedures and—if accounted for—have the potential to significantly improve patient outcomes. However, when studying brain anatomy, these inter-individual variations are commonly unaccounted for, despite reports of differences in gross anatomical features, cross-sectional, and connectional anatomy. Brain connections are essential to facilitate functional organization and, when severed, cause impairments or complete loss of function. Hence, the study of cerebral white matter may be an ideal compromise to capture inter-individual variability in structure and function. We reviewed the wealth of studies that associate cognitive functions and clinical symptoms with individual tracts using diffusion tractography. Our systematic review indicates that tractography has proven to be a sensitive method in neurology, psychiatry, and healthy populations to identify variability and its functional correlates. However, the literature may be biased, as the most commonly studied tracts are not necessarily those with the highest sensitivity to cognitive functions and pathologies. Additionally, the hemisphere of the studied tract is often unreported, thus neglecting functional laterality and asymmetries. Finally, we demonstrate that tracts, as we define them, are not correlated with one, but multiple cognitive domains or pathologies. While our systematic review identified some methodological caveats, it also suggests that tract–function correlations might still be a promising tool in identifying biomarkers for precision medicine. They can characterize variations in brain anatomy, differences in functional organization, and predicts resilience and recovery in patients.
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113
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Kiar G, Chatelain Y, de Oliveira Castro P, Petit E, Rokem A, Varoquaux G, Misic B, Evans AC, Glatard T. Numerical uncertainty in analytical pipelines lead to impactful variability in brain networks. PLoS One 2021; 16:e0250755. [PMID: 34724000 PMCID: PMC8559953 DOI: 10.1371/journal.pone.0250755] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 08/25/2021] [Indexed: 11/19/2022] Open
Abstract
The analysis of brain-imaging data requires complex processing pipelines to support findings on brain function or pathologies. Recent work has shown that variability in analytical decisions, small amounts of noise, or computational environments can lead to substantial differences in the results, endangering the trust in conclusions. We explored the instability of results by instrumenting a structural connectome estimation pipeline with Monte Carlo Arithmetic to introduce random noise throughout. We evaluated the reliability of the connectomes, the robustness of their features, and the eventual impact on analysis. The stability of results was found to range from perfectly stable (i.e. all digits of data significant) to highly unstable (i.e. 0 - 1 significant digits). This paper highlights the potential of leveraging induced variance in estimates of brain connectivity to reduce the bias in networks without compromising reliability, alongside increasing the robustness and potential upper-bound of their applications in the classification of individual differences. We demonstrate that stability evaluations are necessary for understanding error inherent to brain imaging experiments, and how numerical analysis can be applied to typical analytical workflows both in brain imaging and other domains of computational sciences, as the techniques used were data and context agnostic and globally relevant. Overall, while the extreme variability in results due to analytical instabilities could severely hamper our understanding of brain organization, it also affords us the opportunity to increase the robustness of findings.
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Affiliation(s)
- Gregory Kiar
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Yohan Chatelain
- Department of Computer Science and Software Engineering, Concordia University, Montréal, QC, Canada
| | | | - Eric Petit
- Exascale Computing Lab, Intel, Paris, France
| | - Ariel Rokem
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, United States of America
| | - Gaël Varoquaux
- Parietal Project-team, INRIA Saclay-ile de France, Paris, France
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Alan C Evans
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Tristan Glatard
- Department of Computer Science and Software Engineering, Concordia University, Montréal, QC, Canada
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114
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Testing the structure of human cognitive ability using evidence obtained from the impact of brain lesions over abilities. INTELLIGENCE 2021. [DOI: 10.1016/j.intell.2021.101581] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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115
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Kolodny T, Mevorach C, Stern P, Ankaoua M, Dankner Y, Tsafrir S, Shalev L. Are attention and cognitive control altered by fMRI scanner environment? Evidence from Go/No-go tasks in ADHD. Brain Imaging Behav 2021; 16:1003-1013. [PMID: 34705186 DOI: 10.1007/s11682-021-00557-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/05/2021] [Indexed: 10/20/2022]
Abstract
It is widely assumed that cognitive processes studied in fMRI are equivalent to cognitive processes engaged in the same experimental paradigms in typical behavioral lab settings. Yet very few studies examined this common assumption, and the results have been equivocal. In the current study we directly tested the effects of fMRI environment on sustained attention and response inhibition, using a Go/No-go task, among participants with (n = 42) and without (n = 21) attention deficit/hyperactivity disorder (ADHD). Participants with ADHD are characterized by deficits in these cognitive functions and may be particularly susceptible to environmental effects on attention. We found a substantial slowing of reaction time in the scanner for all participants, and a trend for enhanced sustained attention, particularly in ADHD participants with poor performance. We also report limited stability of individual differences in scores obtained in the lab and in the scanner. These findings call for cautious interpretation of neuroimaging task-related results, especially those obtained in clinical populations.
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Affiliation(s)
- Tamar Kolodny
- Department of Cognitive Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.
| | - Carmel Mevorach
- Department of Psychology and Centre of Human Brain Health, University of Birmingham, Birmingham, UK
| | - Pnina Stern
- Constantiner School of Education, Tel-Aviv University, Tel-Aviv, Israel
| | - Maya Ankaoua
- Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
| | - Yarden Dankner
- Constantiner School of Education, Tel-Aviv University, Tel-Aviv, Israel
| | | | - Lilach Shalev
- Constantiner School of Education, Tel-Aviv University, Tel-Aviv, Israel.,Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
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116
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de Bruijn AGM, van der Fels IMJ, Renken RJ, Königs M, Meijer A, Oosterlaan J, Kostons DDNM, Visscher C, Bosker RJ, Smith J, Hartman E. Differential effects of long-term aerobic versus cognitively-engaging physical activity on children's visuospatial working memory related brain activation: A cluster RCT. Brain Cogn 2021; 155:105812. [PMID: 34716033 DOI: 10.1016/j.bandc.2021.105812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 10/11/2021] [Accepted: 10/13/2021] [Indexed: 11/19/2022]
Abstract
Different types of physical activity are thought to differentially affect children's brain activation, via physiological mechanisms, or by activating similar brain areas during physical and cognitive tasks. Despite many behavioral studies relying on these mechanisms, they have been rarely studied. This study looks at both mechanisms simultaneously, by examining effects of two physical activity interventions (aerobic vs. cognitively-engaging) on children's brain activation. Functional Magnetic Resonance Imaging (fMRI) data of 62 children (48.4% boys, mean age 9.2 years) was analyzed. Children's visuospatial working memory related brain activity patterns were tested using a Spatial Span Task before and after the 14-week interventions consisting of four physical education lessons per week. The control group followed their regular program of two lessons per week. Analyses of activation patterns in SPM 12.0 revealed no activation changes between pretest and posttest (p > .05), and no differences between the three conditions in pretest-posttest changes in brain activation (p > .05). Large inter-individual differences were found, suggesting that not every child benefited from the interventions in the same way. To get more insight into the assumed mechanisms, further research is needed to understand whether, when, for whom, and how physical activity results in changed brain activation patterns.
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Affiliation(s)
- A G M de Bruijn
- Groningen Institute for Educational Research, University of Groningen, Grote Rozenstraat 3, 9712 TG Groningen, the Netherlands.
| | - I M J van der Fels
- Center for Human Movement Sciences, University Medical Center Groningen, University of Groningen, Antonius Deusinglaan 1, 9713 AV Groningen, the Netherlands.
| | - R J Renken
- Neuroimaging Center Groningen, University Medical Center Groningen, University of Groningen, Antonius Deusinglaan 2, 9713 AW Groningen, the Netherlands.
| | - M Königs
- Emma Neuroscience Group, Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Postbus 22660, 1100 DD Amsterdam, the Netherlands.
| | - A Meijer
- Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Van der Boechorststraat 7, 1081 BT Amsterdam, the Netherlands.
| | - J Oosterlaan
- Emma Neuroscience Group, Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Postbus 22660, 1100 DD Amsterdam, the Netherlands; Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Van der Boechorststraat 7, 1081 BT Amsterdam, the Netherlands.
| | - D D N M Kostons
- Groningen Institute for Educational Research, University of Groningen, Grote Rozenstraat 3, 9712 TG Groningen, the Netherlands.
| | - C Visscher
- Center for Human Movement Sciences, University Medical Center Groningen, University of Groningen, Antonius Deusinglaan 1, 9713 AV Groningen, the Netherlands.
| | - R J Bosker
- Groningen Institute for Educational Research, University of Groningen, Grote Rozenstraat 3, 9712 TG Groningen, the Netherlands.
| | - J Smith
- Center for Human Movement Sciences, University Medical Center Groningen, University of Groningen, Antonius Deusinglaan 1, 9713 AV Groningen, the Netherlands.
| | - E Hartman
- Center for Human Movement Sciences, University Medical Center Groningen, University of Groningen, Antonius Deusinglaan 1, 9713 AV Groningen, the Netherlands.
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117
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Wang P, Feng J, Wang Y, Zhu W, Wei S, Im H, Wang Q. Sex-specific static and dynamic functional networks of sub-divisions of striatum linking to the greed personality trait. Neuropsychologia 2021; 163:108066. [PMID: 34678357 DOI: 10.1016/j.neuropsychologia.2021.108066] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 10/15/2021] [Accepted: 10/16/2021] [Indexed: 11/30/2022]
Abstract
The study of greed has been broadly investigated and discussed in the field of social sciences, including economics, political science, and psychology. However, the neural mechanisms underlying greed personality trait (GPT) have received little attention from the cognitive neuroscience field and still remain unclear. In this study, we explored the associations between GPT and static/dynamic reward circuit-specifically its sub-regions' functional networks including caudate, nucleus accumbens (NAcc), and putamen. Behavioral analyses revealed significant associations of GPT with Past-Negative and Present-Fatalistic time attitude as well as attention impulsivity. Imaging analyses revealed a significant interaction effect between sex and GPT on the static reward functional networks. In particular, GPT was positively correlated with static caudate-NAcc, caudate-cerebellum, and NAcc-parahippocampus/medial orbitofrontal cortex (PHG/mOFC) for males but negatively correlated for females. GPT was also marginally and negatively correlated with static putamen-occipital pole functional connectivities among males. Interestingly, sex difference interaction patterns were further observed in the dynamic reward functional networks. Further, dynamic reward functional networks also exhibited some specific characteristics, manifesting in more brain regions involved for greedy behaviors. These findings suggest sex-specific static and dynamic functional networks underlying human dispositional greed, and also implicate the critical contributions of reward circuit, especially for sub-circuits of reward, on greed.
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Affiliation(s)
- Pinchun Wang
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China
| | - Jie Feng
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China
| | - Yajie Wang
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China
| | - Wenwei Zhu
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China
| | - Shiyu Wei
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China
| | - Hohjin Im
- Department of Psychological Science, University of California, Irvine, 92697-7085, CA, USA.
| | - Qiang Wang
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China; Key Research Base of Humanities and Social of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, 300387, China; Tianjin Social Science Laboratory of Students' Mental Development and Learning, Tianjin, 300387, China.
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118
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The Study of Sailors’ Brain Activity Difference Before and After Sailing Using Activated Functional Connectivity Pattern. Neural Process Lett 2021. [DOI: 10.1007/s11063-021-10545-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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119
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Strege MV, Persons JB, Ressler KJ, Krawczak RA, Fang A, Goldin P, Siegle GJ. Integrating Neuroscience Into Clinical Practice: Current Opinions and Dialogue Between Drs. Jacqueline Persons and Kerry Ressler. THE BEHAVIOR THERAPIST 2021; 44:326-334. [PMID: 36643848 PMCID: PMC9836032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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120
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Lin Q, Yoo K, Shen X, Constable TR, Chun MM. Functional Connectivity during Encoding Predicts Individual Differences in Long-Term Memory. J Cogn Neurosci 2021; 33:2279-2296. [PMID: 34272957 PMCID: PMC8497062 DOI: 10.1162/jocn_a_01759] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
What is the neural basis of individual differences in the ability to hold information in long-term memory (LTM)? Here, we first characterize two whole-brain functional connectivity networks based on fMRI data acquired during an n-back task that robustly predict individual differences in two important forms of LTM, recognition and recollection. We then focus on the recognition memory model and contrast it with a working memory model. Although functional connectivity during the n-back task also predicts working memory performance and the two networks have some shared components, they are also largely distinct from each other: The recognition memory model performance remains robust when we control for working memory, and vice versa. Functional connectivity only within regions traditionally associated with LTM formation, such as the medial temporal lobe and those that show univariate subsequent memory effect, have little predictive power for both forms of LTM. Interestingly, the interactions between these regions and other brain regions play a more substantial role in predicting recollection memory than recognition memory. These results demonstrate that individual differences in LTM are dependent on the configuration of a whole-brain functional network including but not limited to regions associated with LTM during encoding and that such a network is separable from what supports the retention of information in working memory.
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121
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Brief segments of neurophysiological activity enable individual differentiation. Nat Commun 2021; 12:5713. [PMID: 34588439 PMCID: PMC8481307 DOI: 10.1038/s41467-021-25895-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 09/07/2021] [Indexed: 11/08/2022] Open
Abstract
Large, openly available datasets and current analytic tools promise the emergence of population neuroscience. The considerable diversity in personality traits and behaviour between individuals is reflected in the statistical variability of neural data collected in such repositories. Recent studies with functional magnetic resonance imaging (fMRI) have concluded that patterns of resting-state functional connectivity can both successfully distinguish individual participants within a cohort and predict some individual traits, yielding the notion of an individual's neural fingerprint. Here, we aim to clarify the neurophysiological foundations of individual differentiation from features of the rich and complex dynamics of resting-state brain activity using magnetoencephalography (MEG) in 158 participants. We show that akin to fMRI approaches, neurophysiological functional connectomes enable the differentiation of individuals, with rates similar to those seen with fMRI. We also show that individual differentiation is equally successful from simpler measures of the spatial distribution of neurophysiological spectral signal power. Our data further indicate that differentiation can be achieved from brain recordings as short as 30 seconds, and that it is robust over time: the neural fingerprint is present in recordings performed weeks after their baseline reference data was collected. This work, thus, extends the notion of a neural or brain fingerprint to fast and large-scale resting-state electrophysiological dynamics.
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122
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Singh A, Erwin-Grabner T, Goya-Maldonado R, Antal A. Transcranial Magnetic and Direct Current Stimulation in the Treatment of Depression: Basic Mechanisms and Challenges of Two Commonly Used Brain Stimulation Methods in Interventional Psychiatry. Neuropsychobiology 2021; 79:397-407. [PMID: 31487716 DOI: 10.1159/000502149] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 07/16/2019] [Indexed: 12/12/2022]
Abstract
Noninvasive neuromodulation, including repetitive trans-cranial magnetic stimulation (rTMS) and direct current stimulation (tDCS), provides researchers and health care professionals with the ability to gain unique insights into brain functions and treat several neurological and psychiatric conditions. Undeniably, the number of published research and clinical papers on this topic is increasing exponentially. In parallel, several methodological and scientific caveats have emerged in the transcranial stimulation field; these include less robust and reliable effects as well as contradictory clinical findings. These inconsistencies are maybe due to the fact that research exploring the relationship between the methodological aspects and clinical efficacy of rTMS and tDCS is far from conclusive. Hence, additional work is needed to understand the mechanisms underlying the effects of magnetic stimulation and low-intensity transcranial electrical stimulation (TES) in order to optimize dosing, methodological designs, and safety aspects.
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Affiliation(s)
- Aditya Singh
- Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
| | - Tracy Erwin-Grabner
- Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
| | - Roberto Goya-Maldonado
- Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
| | - Andrea Antal
- Department of Clinical Neurophysiology, University Medical Center Göttingen, Göttingen, Germany, .,Institute for Medical Psychology, Medical Faculty, Otto-v.-Guericke University Magdeburg, Magdeburg, Germany,
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123
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EEG Mental Stress Assessment Using Hybrid Multi-Domain Feature Sets of Functional Connectivity Network and Time-Frequency Features. SENSORS 2021; 21:s21186300. [PMID: 34577505 PMCID: PMC8473213 DOI: 10.3390/s21186300] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 09/07/2021] [Accepted: 09/16/2021] [Indexed: 12/28/2022]
Abstract
Exposure to mental stress for long period leads to serious accidents and health problems. To avoid negative consequences on health and safety, it is very important to detect mental stress at its early stages, i.e., when it is still limited to acute or episodic stress. In this study, we developed an experimental protocol to induce two different levels of stress by utilizing a mental arithmetic task with time pressure and negative feedback as the stressors. We assessed the levels of stress on 22 healthy subjects using frontal electroencephalogram (EEG) signals, salivary alpha-amylase level (AAL), and multiple machine learning (ML) classifiers. The EEG signals were analyzed using a fusion of functional connectivity networks estimated by the Phase Locking Value (PLV) and temporal and spectral domain features. A total of 210 different features were extracted from all domains. Only the optimum multi-domain features were used for classification. We then quantified stress levels using statistical analysis and seven ML classifiers. Our result showed that the AAL level was significantly increased (p < 0.01) under stress condition in all subjects. Likewise, the functional connectivity network demonstrated a significant decrease under stress, p < 0.05. Moreover, we achieved the highest stress classification accuracy of 93.2% using the Support Vector Machine (SVM) classifier. Other classifiers produced relatively similar results.
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124
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Guo T, Zhang Y, Xue Y, Qiao L, Shen D. Brain Function Network: Higher Order vs. More Discrimination. Front Neurosci 2021; 15:696639. [PMID: 34497485 PMCID: PMC8419271 DOI: 10.3389/fnins.2021.696639] [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: 04/17/2021] [Accepted: 07/26/2021] [Indexed: 12/20/2022] Open
Abstract
Brain functional network (BFN) has become an increasingly important tool to explore individual differences and identify neurological/mental diseases. For estimating a "good" BFN (with more discriminative information for example), researchers have developed various methods, in which the most popular and simplest is Pearson's correlation (PC). Despite its empirical effectiveness, PC only encodes the low-order (second-order) statistics between brain regions. To model high-order statistics, researchers recently proposed to estimate BFN by conducting two sequential PCs (denoted as PC 2 in this paper), and found that PC 2-based BFN can provide additional information for group difference analysis. This inspires us to think about (1) what will happen if continuing the correlation operation to construct much higher-order BFN by PC n (n>2), and (2) whether the higher-order correlation will result in stronger discriminative ability. To answer these questions, we use PC n -based BFNs to predict individual differences (Female vs. Male) as well as identify subjects with mild cognitive impairment (MCI) from healthy controls (HCs). Through experiments, we have the following findings: (1) with the increase of n, the discriminative ability of PC n -based BFNs tends to decrease; (2) fusing the PC n -based BFNs (n>1) with the PC 1-based BFN can generally improve the sensitivity for MCI identification, but fail to help the classification accuracy. In addition, we empirically find that the sequence of BFN adjacency matrices estimated by PC n (n = 1,2,3,⋯ ) will converge to a binary matrix with elements of ± 1.
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Affiliation(s)
- Tingting Guo
- School of Mathematics Science, Liaocheng University, Liaocheng, China
| | - Yining Zhang
- School of Mathematics Science, Liaocheng University, Liaocheng, China
| | - Yanfang Xue
- School of Mathematics Science, Liaocheng University, Liaocheng, China
| | - Lishan Qiao
- School of Mathematics Science, Liaocheng University, Liaocheng, China
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.,Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.,Department of Artificial Intelligence, Korea University, Seoul, South Korea
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125
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Lee KM, Ferreira-Santos F, Satpute AB. Predictive processing models and affective neuroscience. Neurosci Biobehav Rev 2021; 131:211-228. [PMID: 34517035 DOI: 10.1016/j.neubiorev.2021.09.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 02/10/2021] [Accepted: 09/07/2021] [Indexed: 01/17/2023]
Abstract
The neural bases of affective experience remain elusive. Early neuroscience models of affect searched for specific brain regions that uniquely carried out the computations that underlie dimensions of valence and arousal. However, a growing body of work has failed to identify these circuits. Research turned to multivariate analyses, but these strategies, too, have made limited progress. Predictive processing models offer exciting new directions to address this problem. Here, we use predictive processing models as a lens to critique prevailing functional neuroimaging research practices in affective neuroscience. Our review highlights how much work relies on rigid assumptions that are inconsistent with a predictive processing approach. We outline the central aspects of a predictive processing model and draw out their implications for research in affective and cognitive neuroscience. Predictive models motivate a reformulation of "reverse inference" in cognitive neuroscience, and placing a greater emphasis on external validity in experimental design.
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Affiliation(s)
- Kent M Lee
- Northeastern University, 360 Huntington Ave, 125 NI, Boston, MA 02118, USA.
| | - Fernando Ferreira-Santos
- Laboratory of Neuropsychophysiology, Faculty of Psychology and Education Sciences, University of Porto, Portugal
| | - Ajay B Satpute
- Northeastern University, 360 Huntington Ave, 125 NI, Boston, MA 02118, USA
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126
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Lumaca M, Vuust P, Baggio G. Network Analysis of Human Brain Connectivity Reveals Neural Fingerprints of a Compositionality Bias in Signaling Systems. Cereb Cortex 2021; 32:1704-1720. [PMID: 34476458 DOI: 10.1093/cercor/bhab307] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 08/04/2021] [Accepted: 08/05/2021] [Indexed: 12/16/2022] Open
Abstract
Compositionality is a hallmark of human language and other symbolic systems: a finite set of meaningful elements can be systematically combined to convey an open-ended array of ideas. Compositionality is not uniformly distributed over expressions in a language or over individuals' communicative behavior: at both levels, variation is observed. Here, we investigate the neural bases of interindividual variability by probing the relationship between intrinsic characteristics of brain networks and compositional behavior. We first collected functional resting-state and diffusion magnetic resonance imaging data from a large participant sample (N = 51). Subsequently, participants took part in two signaling games. They were instructed to learn and reproduce an auditory symbolic system of signals (tone sequences) associated with affective meanings (human faces expressing emotions). Signal-meaning mappings were artificial and had to be learned via repeated signaling interactions. We identified a temporoparietal network in which connection length was related to the degree of compositionality introduced in a signaling system by each player. Graph-theoretic analysis of resting-state functional connectivity revealed that, within that network, compositional behavior was associated with integration measures in 2 semantic hubs: the left posterior cingulate cortex and the left angular gyrus. Our findings link individual variability in compositional biases to variation in the anatomy of semantic networks and in the functional topology of their constituent units.
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Affiliation(s)
- Massimo Lumaca
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, 8000 Aarhus C, Denmark
| | - Peter Vuust
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, 8000 Aarhus C, Denmark
| | - Giosuè Baggio
- Language Acquisition and Language Processing Lab, Department of Language and Literature, Norwegian University of Science and Technology, 7941 Trondheim, Norway
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127
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Huang K, Chen D, Wang F, Yang L. Prediction of dispositional dialectical thinking from resting-state electroencephalography. Brain Behav 2021; 11:e2327. [PMID: 34423595 PMCID: PMC8442598 DOI: 10.1002/brb3.2327] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 07/20/2021] [Accepted: 08/02/2021] [Indexed: 11/20/2022] Open
Abstract
This study aims to explore the possibility of predicting the dispositional level of dialectical thinking using resting-state electroencephalography signals. Thirty-four participants completed a self-reported measure of dialectical thinking, and their resting-state electroencephalography was recorded. After wave filtration and eye movement removal, time-frequency electroencephalography signals were converted into four frequency domains: delta (1-4 Hz), theta (4-7 Hz), alpha (7-13 Hz), and beta (13-30 Hz). Functional principal component analysis with B-spline approximation was then applied for feature reduction. Five machine learning methods (support vector regression, least absolute shrinkage and selection operator, K-nearest neighbors, random forest, and gradient boosting decision tree) were applied to the reduced features for prediction. The model ensemble technique was used to create the best performing final model. The results showed that the alpha wave of the electroencephalography signal in the early period (12-15 s) contributed most to the prediction of dialectical thinking. With data-driven electrode selection (FC1, FCz, Fz, FC3, Cz, AFz), the prediction model achieved an average coefficient of determination of 0.45 on 200 random test sets. Furthermore, a significant positive correlation was found between the alpha value of standardized low-resolution electromagnetic tomography activity in the right dorsal anterior cingulate cortex and dialectical self-scale score. The prefrontal and midline alpha oscillations of resting electroencephalography are good predictors of the dispositional level of dialectical thinking, possibly reflecting these brain structures' involvement in dialectical thinking.
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Affiliation(s)
- Kun Huang
- Center for Statistical Science and Department of Industrial Engineering, Tsinghua University, Beijing, China
| | - Dian Chen
- Department of Psychology, School of Social Sciences, Tsinghua University, Beijing, China
| | - Fei Wang
- Department of Psychology, School of Social Sciences, Tsinghua University, Beijing, China.,Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China
| | - Lijian Yang
- Center for Statistical Science and Department of Industrial Engineering, Tsinghua University, Beijing, China
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128
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Interindividual variability of functional connectome in schizophrenia. Schizophr Res 2021; 235:65-73. [PMID: 34329851 DOI: 10.1016/j.schres.2021.07.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 07/08/2021] [Accepted: 07/11/2021] [Indexed: 11/21/2022]
Abstract
Schizophrenia is a complex psychiatric disorder that displays an outstanding interindividual variability in clinical manifestation and neurobiological substrates. A better characterization and quantification of this heterogeneity could guide the search for both common abnormalities (linked to lower intersubject variability) and the presence of biological subtypes (leading to a greater heterogeneity across subjects). In the current study, we address interindividual variability in functional connectome by means of resting-state fMRI in a large sample of patients with schizophrenia and healthy controls. Among the different metrics of distance/dissimilarity used to assess variability, geodesic distance showed robust results to head motion. The main findings of the current study point to (i) a higher between subject heterogeneity in the functional connectome of patients, (ii) variable levels of heterogeneity throughout the cortex, with greater variability in frontoparietal and default mode networks, and lower variability in the salience network, and (iii) an association of whole-brain variability with levels of clinical symptom severity and with topological properties of brain networks, suggesting that the average functional connectome overrepresents those patients with lower functional integration and with more severe clinical symptoms. Moreover, after performing a graph theoretical analysis of brain networks, we found that patients with more severe clinical symptoms had decreased connectivity at both whole-brain level and within the salience network, and that patients with higher negative symptoms had large-scale functional integration deficits.
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129
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Elliott ML, Knodt AR, Hariri AR. Striving toward translation: strategies for reliable fMRI measurement. Trends Cogn Sci 2021; 25:776-787. [PMID: 34134933 PMCID: PMC8363569 DOI: 10.1016/j.tics.2021.05.008] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 05/17/2021] [Accepted: 05/18/2021] [Indexed: 12/27/2022]
Abstract
fMRI has considerable potential as a translational tool for understanding risk, prioritizing interventions, and improving the treatment of brain disorders. However, recent studies have found that many of the most widely used fMRI measures have low reliability, undermining this potential. Here, we argue that many fMRI measures are unreliable because they were designed to identify group effects, not to precisely quantify individual differences. We then highlight four emerging strategies [extended aggregation, reliability modeling, multi-echo fMRI (ME-fMRI), and stimulus design] that build on established psychometric properties to generate more precise and reliable fMRI measures. By adopting such strategies to improve reliability, we are optimistic that fMRI can fulfill its potential as a clinical tool.
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Affiliation(s)
- Maxwell L Elliott
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA.
| | - Annchen R Knodt
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Ahmad R Hariri
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
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130
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Gschwandtner U, Bogaarts G, Chaturvedi M, Hatz F, Meyer A, Fuhr P, Roth V. Dynamic Functional Connectivity of EEG: From Identifying Fingerprints to Gender Differences to a General Blueprint for the Brain's Functional Organization. Front Neurosci 2021; 15:683633. [PMID: 34456669 PMCID: PMC8385669 DOI: 10.3389/fnins.2021.683633] [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: 04/03/2021] [Accepted: 06/23/2021] [Indexed: 11/13/2022] Open
Abstract
An individual's brain functional organization is unique and can reliably be observed using modalities such as functional magnetic resonance imaging (fMRI). Here we demonstrate that a quantification of the dynamics of functional connectivity (FC) as measured using electroencephalography (EEG) offers an alternative means of observing an individual's brain functional organization. Using data from both healthy individuals as well as from patients with Parkinson's disease (PD) (n = 103 healthy individuals, n = 57 PD patients), we show that “dynamic FC” (DFC) profiles can be used to identify individuals in a large group. Furthermore, we show that DFC profiles predict gender and exhibit characteristics shared both among individuals as well as between both hemispheres. Furthermore, DFC profile characteristics are frequency band specific, indicating that they reflect distinct processes in the brain. Our empirically derived method of DFC demonstrates the potential of studying the dynamics of the functional organization of the brain using EEG.
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Affiliation(s)
- Ute Gschwandtner
- Department of Neurology and Neurophysiology, University Hospital of Basel, Basel, Switzerland
| | - Guy Bogaarts
- Department of Neurology and Neurophysiology, University Hospital of Basel, Basel, Switzerland.,Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland
| | - Menorca Chaturvedi
- Department of Neurology and Neurophysiology, University Hospital of Basel, Basel, Switzerland
| | - Florian Hatz
- Department of Neurology and Neurophysiology, University Hospital of Basel, Basel, Switzerland
| | - Antonia Meyer
- Department of Neurology and Neurophysiology, University Hospital of Basel, Basel, Switzerland
| | - Peter Fuhr
- Department of Neurology and Neurophysiology, University Hospital of Basel, Basel, Switzerland
| | - Volker Roth
- Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland
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131
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Sato JR, Biazoli CE, Zugman A, Pan PM, Bueno APA, Moura LM, Gadelha A, Picon FA, Amaro E, Salum GA, Miguel EC, Rohde LA, Bressan RA, Jackowski AP. Long-term stability of the cortical volumetric profile and the functional human connectome throughout childhood and adolescence. Eur J Neurosci 2021; 54:6187-6201. [PMID: 34460993 DOI: 10.1111/ejn.15435] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 08/18/2021] [Accepted: 08/28/2021] [Indexed: 11/26/2022]
Abstract
There is compelling evidence showing that between-subject variability in several functional and structural brain features is sufficient for unique identification in adults. However, individuation of brain functional connectomes depends on the stabilization of neurodevelopmental processes during childhood and adolescence. Here, we aimed to (1) evaluate the intra-subject functional connectome stability over time for the whole brain and for large scale functional networks and (2) determine the long-term identification accuracy or 'fingerprinting' for the cortical volumetric profile and the functional connectome. For these purposes, we analysed a longitudinal cohort of 239 children and adolescents scanned in two sessions with an interval of approximately 3 years (age range 6-15 years at baseline and 9-18 years at follow-up). Corroborating previous results using short between-scan intervals in children and adolescents, we observed a moderate identification accuracy (38%) for the whole functional profile. In contrast, identification accuracy using cortical volumetric profile was 95%. Among the large-scale networks, the default-mode (26.8%), the frontoparietal (23.4%) and the dorsal-attention (27.6%) networks were the most discriminative. Our results provide further evidence for a protracted development of specific individual structural and functional connectivity profiles.
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Affiliation(s)
- João Ricardo Sato
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, Santo André, Brazil.,Interdisciplinary Lab for Clinical Neurosciences (LiNC), Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil
| | - Claudinei Eduardo Biazoli
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, Santo André, Brazil.,Department of Biological and Experimental Psychology, Queen Mary University of London, London, UK
| | - André Zugman
- Interdisciplinary Lab for Clinical Neurosciences (LiNC), Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil
| | - Pedro Mario Pan
- Interdisciplinary Lab for Clinical Neurosciences (LiNC), Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil
| | - Ana Paula Arantes Bueno
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, Santo André, Brazil
| | - Luciana Monteiro Moura
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, Santo André, Brazil.,Interdisciplinary Lab for Clinical Neurosciences (LiNC), Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil
| | - Ary Gadelha
- Interdisciplinary Lab for Clinical Neurosciences (LiNC), Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil
| | - Felipe Almeida Picon
- National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil.,ADHD Outpatient Program & Developmental Psychiatry Program, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Edson Amaro
- National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil.,Department of Radiology, School of Medicine, University of São Paulo, São Paulo, Brazil
| | - Giovanni Abrahão Salum
- National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil.,ADHD Outpatient Program & Developmental Psychiatry Program, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Euripedes Constantino Miguel
- National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil.,Department of Psychiatry, School of Medicine, University of São Paulo, São Paulo, Brazil
| | - Luis Augusto Rohde
- National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil.,ADHD Outpatient Program & Developmental Psychiatry Program, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Rodrigo Affonseca Bressan
- Interdisciplinary Lab for Clinical Neurosciences (LiNC), Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil
| | - Andrea Parolin Jackowski
- Interdisciplinary Lab for Clinical Neurosciences (LiNC), Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents (CNPq), São Paulo, Brazil
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132
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Attar ET, Balasubramanian V, Subasi E, Kaya M. Stress Analysis Based on Simultaneous Heart Rate Variability and EEG Monitoring. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2021; 9:2700607. [PMID: 34513342 PMCID: PMC8407658 DOI: 10.1109/jtehm.2021.3106803] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 05/07/2021] [Accepted: 08/03/2021] [Indexed: 12/27/2022]
Abstract
OBJECTIVE Stress is a significant risk factor for various diseases such as hypertension, heart attack, stroke, and even sudden death. Stress can also lead to psychological and behavioral disorders. Heart rate variability (HRV) can reflect changes in stress levels while other physiological factors, like blood pressure, are within acceptable ranges. Electroencephalogram (EEG) is a vital technique for studying brain activities and provides useful data regarding changes in mental status. This study incorporates EEG and a detailed HRV analysis to have a better understanding and analysis of stress. Investigating the correlation between EEG and HRV under stress conditions is valuable since they provide complementary information regarding stress. METHODS Simultaneous electrocardiogram (ECG) and EEG recordings were obtained from fifteen subjects. HRV /EEG features were analyzed and compared in rest, stress, and meditation conditions. A one-way ANOVA and correlation coefficient were used for statistical analysis to explore the correlation between HRV features and features extracted from EEG. RESULTS The HRV features LF (low frequency), HF (high frequency), LF/HF, and rMSSD (root mean square of the successive differences) correlated with EEG features, including alpha power band in the left hemisphere and alpha band power asymmetry. CONCLUSION This study demonstrated five significant relationships between EEG and HRV features associated with stress. The ability to use stress-related EEG features in combination with correlated HRV features could help improve detecting stress and monitoring the progress of stress treatments/therapies. The outcomes of this study could enhance the efficiency of stress management technologies such as meditation studies and bio-feedback training.
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Affiliation(s)
- Eyad Talal Attar
- Department of Biomedical and Chemical Engineering and SciencesFlorida Institute of TechnologyMelbourneFL32901USA
- Department of Electrical and Computer EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia
| | - Vignesh Balasubramanian
- Department of Biomedical and Chemical Engineering and SciencesFlorida Institute of TechnologyMelbourneFL32901USA
| | - Ersoy Subasi
- Department of Computer Engineering and SciencesFlorida Institute of TechnologyMelbourneFL32901USA
| | - Mehmet Kaya
- Department of Biomedical and Chemical Engineering and SciencesFlorida Institute of TechnologyMelbourneFL32901USA
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133
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Braver TS, Kizhner A, Tang R, Freund MC, Etzel JA. The Dual Mechanisms of Cognitive Control Project. J Cogn Neurosci 2021:1-26. [PMID: 34407191 PMCID: PMC10069323 DOI: 10.1162/jocn_a_01768] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
We describe an ambitious ongoing study that has been strongly influenced and inspired by Don Stuss's career-long efforts to identify key cognitive processes that characterize executive control, investigate potential unifying dimensions that define prefrontal function, and carefully attend to individual differences. The Dual Mechanisms of Cognitive Control project tests a theoretical framework positing two key control dimensions: proactive and reactive. The framework's central tenets are that proactive and reactive control modes reflect domain-general dimensions of individual variation, with distinctive neural signatures, involving the lateral pFC as a central node within associated brain networks (e.g., fronto-parietal, cingulo-opercular). In the Dual Mechanisms of Cognitive Control project, each participant is scanned while performing theoretically targeted variants of multiple well-established cognitive control tasks (Stroop, cued task-switching, AX-CPT, Sternberg working memory) in three separate imaging sessions, that each encourages utilization of different control modes plus also completes an extensive out-of-scanner individual differences battery. Additional key features of the project include a high spatio-temporal resolution (multiband) acquisition protocol and a sample that includes a substantial subset of monozygotic twin pairs and participants recruited from the Human Connectome Project. Although data collection is still continuing (target n = 200), we provide an overview of the study design and protocol, along with initial results (n = 80) revealing evidence of a domain-general neural signature of cognitive control and its modulation under reactive conditions. Aligned with Don Stuss's legacy of scientific community building, a partial data set has been publicly released, with the full data set released at project completion, so it can serve as a valuable resource.
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134
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Three types of individual variation in brain networks revealed by single-subject functional connectivity analyses. Curr Opin Behav Sci 2021. [DOI: 10.1016/j.cobeha.2021.02.014] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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135
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Song H, Rosenberg MD. Predicting attention across time and contexts with functional brain connectivity. Curr Opin Behav Sci 2021. [DOI: 10.1016/j.cobeha.2020.12.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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136
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Fedorenko E. The early origins and the growing popularity of the individual-subject analytic approach in human neuroscience. Curr Opin Behav Sci 2021. [DOI: 10.1016/j.cobeha.2021.02.023] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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137
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Intrinsic functional connectivity of the frontoparietal network predicts inter-individual differences in the propensity for costly third-party punishment. COGNITIVE AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2021; 21:1222-1232. [PMID: 34331267 DOI: 10.3758/s13415-021-00927-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/14/2021] [Indexed: 11/08/2022]
Abstract
Humans are motivated to give norm violators their just deserts through costly punishment even as unaffected third parties (i.e., third-party punishment, TPP). A great deal of individual variability exists in costly punishment; however, how this variability particularly in TPP is represented by the brain's intrinsic network architecture remains elusive. Here, we examined whether inter-individual differences in the propensity for TPP can be predicted based on resting-state functional connectivity (RSFC) combining an economic TPP game with task-free functional neuroimaging and a multivariate prediction framework. Our behavioral results revealed that TPP punishment increased with the severity of unfairness for offers. People with higher TPP propensity punished more harshly across norm-violating scenarios. Our neuroimaging findings showed RSFC within the frontoparietal network predicted individual differences in TPP propensity. Our findings contribute to understanding the neural fingerprint for an individual's propensity to costly punish strangers, and shed some light on how social norm enforcement behaviors are represented by the brain's intrinsic network architecture.
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138
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Takagi Y, Okada N, Ando S, Yahata N, Morita K, Koshiyama D, Kawakami S, Sawada K, Koike S, Endo K, Yamasaki S, Nishida A, Kasai K, Tanaka SC. Intergenerational transmission of the patterns of functional and structural brain networks. iScience 2021; 24:102708. [PMID: 34258550 PMCID: PMC8253972 DOI: 10.1016/j.isci.2021.102708] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 05/04/2021] [Accepted: 06/08/2021] [Indexed: 01/22/2023] Open
Abstract
There is clear evidence of intergenerational transmission of life values, cognitive traits, psychiatric disorders, and even aspects of daily decision making. To investigate biological substrates of this phenomenon, the brain has received increasing attention as a measurable biomarker and potential target for intervention. However, no previous study has quantitatively and comprehensively investigated the effects of intergenerational transmission on functional and structural brain networks. Here, by employing an unusually large cohort dataset (N = 84 parent-child dyads; 45 sons, 39 daughters, 81 mothers, and 3 fathers), we show that patterns of functional and structural brain networks are preserved over a generation. We also demonstrate that several demographic factors and behavioral/physiological phenotypes have a relationship with brain similarity. Collectively, our results provide a comprehensive picture of neurobiological substrates of intergenerational transmission and demonstrate the usability of our dataset for investigating the neurobiological substrates of intergenerational transmission.
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Affiliation(s)
- Yu Takagi
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- ATR Brain Information Communication Research Laboratory Group, Kyoto, Japan
| | - Naohiro Okada
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- International Research Center for Neurointelligence (WPI-IRCN), University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan
| | - Shuntaro Ando
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Research Center for Social Science & Medicine, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Noriaki Yahata
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Institute for Quantum Life Science, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
- Department of Molecular Imaging and Theranostics, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
| | - Kentaro Morita
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Rehabilitation, The University of Tokyo Hospital, Tokyo, Japan
| | - Daisuke Koshiyama
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Shintaro Kawakami
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kingo Sawada
- Office for Mental Health Support, Mental Health Unit, Division for Practice Research, Center for Research on Counseling and Support Services, The University of Tokyo, Tokyo, Japan
| | - Shinsuke Koike
- International Research Center for Neurointelligence (WPI-IRCN), University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan
- University of Tokyo Institute for Diversity and Adaptation of Human Mind (UTIDAHM), The University of Tokyo, Tokyo, Japan
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo, Tokyo, Japan
- University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB), Tokyo, Japan
| | - Kaori Endo
- Research Center for Social Science & Medicine, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Syudo Yamasaki
- Research Center for Social Science & Medicine, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Atsushi Nishida
- Research Center for Social Science & Medicine, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Kiyoto Kasai
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- International Research Center for Neurointelligence (WPI-IRCN), University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan
- University of Tokyo Institute for Diversity and Adaptation of Human Mind (UTIDAHM), The University of Tokyo, Tokyo, Japan
- University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB), Tokyo, Japan
| | - Saori C Tanaka
- ATR Brain Information Communication Research Laboratory Group, Kyoto, Japan
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139
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Pan G, Xiao L, Bai Y, Wilson TW, Stephen JM, Calhoun VD, Wang YP. Multiview Diffusion Map Improves Prediction of Fluid Intelligence With Two Paradigms of fMRI Analysis. IEEE Trans Biomed Eng 2021; 68:2529-2539. [PMID: 33382644 DOI: 10.1109/tbme.2020.3048594] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE To understand the association between brain networks and behaviors of an individual, most studies build predictive models based on functional connectivity (FC) from a single dataset with linear analysis techniques. Such approaches may fail to capture the nonlinear structure of brain networks and neglect the complementary information contained in FC networks (FCNs) from multiple datasets. To address this challenging issue, we use multiview dimensionality reduction to extract a coherent low-dimensional representation of the FCNs from resting-state and emotion identification task-based functional magnetic resonance imaging (fMRI) datasets. METHODS We propose a scheme based on multiview diffusion map to extract intrinsic features while preserving the underlying geometric structure of high dimensional datasets. This method is robust to noise and small variations in the data. RESULTS After validation on the Philadelphia Neurodevelopmental Cohort data, the predictive model built from both resting-state and emotion identification task-based fMRI datasets outperforms the one using each individual fMRI dataset. In addition, the proposed model achieves better prediction performance than principal component analysis (PCA) and three other competing data fusion methods. CONCLUSION Our framework for combing multiple FCNs in one predictive model exhibits improved prediction performance. SIGNIFICANCE To our knowledge, we demonstrate a first application of multiview diffusion map to successfully fuse different types of fMRI data for predicting fluid intelligence (gF).
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140
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Wu D, Li X, Feng J. Connectome-based individual prediction of cognitive behaviors via graph propagation network reveals directed brain network topology. J Neural Eng 2021; 18. [PMID: 34181582 DOI: 10.1088/1741-2552/ac0f4d] [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] [Received: 04/12/2021] [Accepted: 06/28/2021] [Indexed: 11/11/2022]
Abstract
Objective. Brain connectivity network supports the information flow underlying human cognitions and should reflect the individual variability in human cognitive behaviors. Various studies have utilized brain connectivity to predict individual differences in human behaviors. However, traditional studies viewed brain connectivity network as a one-dimensional vector, a method which neglects topological properties of brain connectivity network.Approach. To utilize these topological properties, we proposed that graph neural network (GNN) which combines graph theory and neural network can be adopted. Different from previous node-driven GNNs that parameterize on the node feature transformation, we designed an edge-driven GNN named graph propagation network (GPN) that parameterizes on the information propagation within brain connectivity network.Main results.Edge-driven GPN outperforms various baseline models such as node-driven GNN and traditional partial least square regression in predicting the individual total cognition based on the resting-state functional connectome. GPN also reveals a directed network topology encoding the information flow, indicating that higher-order association cortices such as dorsolateral prefrontal, inferior frontal and inferior parietal cortices are responsible for the information integration underlying total cognition.Significance. These results suggest that edge-driven GPN can better explore topological structures of brain connectivity network and can serve as a new method to associate brain connectome and human behaviors.
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Affiliation(s)
- Dongya Wu
- School of Information Science and Technology, Northwest University, Xi'an 710127, People's Republic of China
| | - Xin Li
- School of Mathematics, Northwest University, Xi'an 710127, People's Republic of China
| | - Jun Feng
- School of Information Science and Technology, Northwest University, Xi'an 710127, People's Republic of China.,State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, School of Information Science and Technology, Northwest University, Xi'an 710127, People's Republic of China
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141
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Widespread plasticity of cognition-related brain networks in single-sided deafness revealed by randomized window-based dynamic functional connectivity. Med Image Anal 2021; 73:102163. [PMID: 34303170 DOI: 10.1016/j.media.2021.102163] [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: 08/13/2020] [Revised: 06/07/2021] [Accepted: 07/02/2021] [Indexed: 11/22/2022]
Abstract
As an extreme type of partial auditory deprivation, single-sided deafness (SSD) has been demonstrated to lead to extensive neural plasticity according to multimodal neuroimaging studies. Among them, resting-state functional magnetic resonance imaging (rs-fMRI) offers valuable information on functional connectivities (FCs). However, most previous SSD rs-fMRI studies assumed that the extracted FC remains stationary during the entire fMRI scan and neglected dynamic functional activities. Existing fixed window-based dynamic FC analysis also ignores dynamic functional activities under different temporal terms. Additionally, due to the cost constraints of using MRI machines, using data-driven methods for unbiased hypothesis investigations may require more effective sample data augmentation techniques. To tackle these challenges and problems together, in this study, we proposed a dynamic window with a random length and position to extract participants' dynamic characteristics under different temporal terms and to extract more information from the dataset. Then, we proposed a nodal efficiency-based correlation matrix to describe the relationships of synergism between regions as features and applied a linear support vector machine (SVM) model to learn the importance of the features, which helped to identify SSD patients and healthy controls. A total of 68 participants (including 23 with left SSD, 20 with right SSD and 25 healthy controls) were enrolled. Our proposed approach with a random window showed clear improvement compared with traditional static and fixed window-based dynamic FC by using the linear SVM model. FCs related to the frontoparietal, somatomotor, dorsal attention, limbic and default mode networks played significant roles in differentiating SSD patients from healthy controls. Additionally, FCs between the somatomotor and frontoparietal networks made the greatest contribution to the classification model. Regarding brain regions, FCs related to the superior frontal gyrus, superior parietal lobule, superior temporal gyrus, amygdala, and orbital gyrus played significant roles. These findings suggest that networks and regions related to higher-order cognitive functions showed the most significant FC alterations in SSD, which may represent a compensatory collaboration of cognitive resources in SSD.
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142
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Ribeiro FL, Dos Santos FRC, Sato JR, Pinaya WHL, Biazoli CE. Inferring the heritability of large-scale functional networks with a multivariate ACE modeling approach. Netw Neurosci 2021; 5:527-548. [PMID: 34189376 PMCID: PMC8233119 DOI: 10.1162/netn_a_00189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 02/10/2021] [Indexed: 11/06/2022] Open
Abstract
Recent evidence suggests that the human functional connectome is stable at different timescales and is unique. These characteristics posit the functional connectome not only as an individual marker but also as a powerful discriminatory measure characterized by high intersubject variability. Among distinct sources of intersubject variability, the long-term sources include functional patterns that emerge from genetic factors. Here, we sought to investigate the contribution of additive genetic factors to the variability of functional networks by determining the heritability of the connectivity strength in a multivariate fashion. First, we reproduced and extended the connectome fingerprinting analysis to the identification of twin pairs. Then, we estimated the heritability of functional networks by a multivariate ACE modeling approach with bootstrapping. Twin pairs were identified above chance level using connectome fingerprinting, with monozygotic twin identification accuracy equal to 57.2% on average for whole-brain connectome. Additionally, we found that a visual (0.37), the medial frontal (0.31), and the motor (0.30) functional networks were the most influenced by additive genetic factors. Our findings suggest that genetic factors not only partially determine intersubject variability of the functional connectome, such that twins can be identified using connectome fingerprinting, but also differentially influence connectivity strength in large-scale functional networks. The functional connectome is a unique representation of the functional organization of the human brain. As such, it has been extensively used as an individual marker, a “fingerprint,” because of its high intersubject variability. Here, we sought to investigate the influence of genetic factors on intersubject variability of functional networks. Therefore, we extended the connectome fingerprinting analysis to the identification of twin pairs, and we estimated the heritability of functional networks by a multivariate ACE modeling approach with bootstrapping. We found that genetic factors not only partially determine intersubject variability of the functional connectome, such that monozygotic twin identification accuracy achieved 57.2% on average using whole-brain connectome in the fingerprinting analysis, but also differentially influence connectivity strength in large-scale functional networks.
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Affiliation(s)
- Fernanda L Ribeiro
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | | | - João R Sato
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | - Walter H L Pinaya
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | - Claudinei E Biazoli
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil
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143
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Oathes DJ, Balderston NL, Kording KP, DeLuisi JA, Perez GM, Medaglia JD, Fan Y, Duprat RJ, Satterthwaite TD, Sheline YI, Linn KA. Combining transcranial magnetic stimulation with functional magnetic resonance imaging for probing and modulating neural circuits relevant to affective disorders. WILEY INTERDISCIPLINARY REVIEWS. COGNITIVE SCIENCE 2021; 12:e1553. [PMID: 33470055 DOI: 10.1002/wcs.1553] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 12/02/2020] [Accepted: 12/23/2020] [Indexed: 12/14/2022]
Abstract
Combining transcranial magnetic stimulation (TMS) with functional magnetic resonance imaging offers an unprecedented tool for studying how brain networks interact in vivo and how repetitive trains of TMS modulate those networks among patients diagnosed with affective disorders. TMS compliments neuroimaging by allowing the interrogation of causal control among brain circuits. Together with TMS, neuroimaging can provide valuable insight into the mechanisms underlying treatment effects and downstream circuit communication. Here we provide a background of the method, review relevant study designs, consider methodological and equipment options, and provide statistical recommendations. We conclude by describing emerging approaches that will extend these tools into exciting new applications. This article is categorized under: Psychology > Emotion and Motivation Psychology > Theory and Methods Neuroscience > Clinical Neuroscience.
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Affiliation(s)
- Desmond J Oathes
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Nicholas L Balderston
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Konrad P Kording
- Department of Neuroscience, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Joseph A DeLuisi
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Gianna M Perez
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - John D Medaglia
- Department of Psychology, Drexel University, Philadelphia, Pennsylvania, USA.,Department of Neurology, Drexel University, Philadelphia, Pennsylvania, USA.,Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics (CBICA), Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Romain J Duprat
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Yvette I Sheline
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Kristin A Linn
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.,Center for Biomedical Image Computing and Analytics (CBICA), Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.,Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
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144
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Fan YS, Li H, Guo J, Pang Y, Li L, Hu M, Li M, Wang C, Sheng W, Liu H, Gao Q, Chen X, Zong X, Chen H. Tracking positive and negative symptom improvement in first-episode schizophrenia treated with risperidone using individual-level functional connectivity. Brain Connect 2021; 12:454-464. [PMID: 34210149 DOI: 10.1089/brain.2021.0061] [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: 12/25/2022] Open
Abstract
BACKGROUND To improve the treatment outcomes of patients with schizophrenia, research efforts have focused on identifying brain-based markers of treatment response. Personal characteristics regarding disease-related behaviors likely stem from inter-individual variability in the organization of brain functional systems. This study aimed to track dimension-specific changes in psychotic symptoms following risperidone treatment using individual-level functional connectivity (FC). METHODS A reliable cortical parcellation approach that accounts for individual heterogeneity in cortical functional anatomy was used to localize functional regions in a longitudinal cohort, consisting of 42 drug-naive first-episodes schizophrenia (FES) patients at baseline and after 8 weeks of risperidone treatment. FC was calculated in individually specified brain regions and used to predict the baseline severity and improvement of positive and negative symptoms in FES. RESULTS Distinct sets of individual-specific FC were separately associated with the positive and negative symptom burden at baseline, which could be used to track the corresponding symptom resolution in FES patients following risperidone treatment. Between-network connections of the fronto-parietal network (FPN) contributed the most to predicting the positive symptom domain. A combination of between-network connections of the default mode network, FPN, and within-network connections of the FPN contributed markedly to the prediction model of negative symptom. CONCLUSION This novel study, which accounts for individual brain variation, take a step toward establishing individual-specific theranostic biomarkers in schizophrenia.
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Affiliation(s)
- Yun-Shuang Fan
- University of Electronic Science and Technology of China, 12599, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Chengdu, Sichuan, China;
| | - Haoru Li
- University of Electronic Science and Technology of China, 12599, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Chengdu, Sichuan, China;
| | - Jing Guo
- University of Electronic Science and Technology of China, 12599, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Chengdu, Sichuan, China;
| | - Yajing Pang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China;
| | - Liang Li
- University of Electronic Science and Technology of China, 12599, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Chengdu, Sichuan, China;
| | - Maolin Hu
- Department of Psychiatry, the Second Xiangya Hospital, Central South University, Changsha, PR China, Changsha, China;
| | - Meiling Li
- University of Electronic Science and Technology of China, 610054, China, School of Life Science & Technology,, Chengdu, Sichuan, China.,Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA, Charlestown, United States;
| | - Chong Wang
- University of Electronic Science and Technology of China, 12599, The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, Chengdu, China.,University of Electronic Science and Technology of China, 12599, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Chengdu, China;
| | - Wei Sheng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China, chengdu, China;
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA, Charlestown, MA, United States;
| | - Qing Gao
- University of Electronic Science and Technology of China, 12599, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, China, 610054;
| | - Xiaogang Chen
- Department of Psychiatry, the Second Xiangya Hospital, Central South University, Changsha, PR China, Changsha, China;
| | - Xiaofen Zong
- Department of Psychiatry, the Second Xiangya Hospital, Central South University, Changsha, PR China, Changsha, China;
| | - Huafu Chen
- University of Electronic Science and Technology of China,, School of Life Science and Technology, University of Electronic Science and Technology of China, Sichuan,Chengdu 610054, China, chengdu, China, 610054;
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145
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Kwak S, Kim H, Kim H, Youm Y, Chey J. Distributed functional connectivity predicts neuropsychological test performance among older adults. Hum Brain Mapp 2021; 42:3305-3325. [PMID: 33960591 PMCID: PMC8193511 DOI: 10.1002/hbm.25436] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Revised: 03/23/2021] [Accepted: 03/25/2021] [Indexed: 01/30/2023] Open
Abstract
Neuropsychological test is an essential tool in assessing cognitive and functional changes associated with late-life neurocognitive disorders. Despite the utility of the neuropsychological test, the brain-wide neural basis of the test performance remains unclear. Using the predictive modeling approach, we aimed to identify the optimal combination of functional connectivities that predicts neuropsychological test scores of novel individuals. Resting-state functional connectivity and neuropsychological tests included in the OASIS-3 dataset (n = 428) were used to train the predictive models, and the identified models were iteratively applied to the holdout internal test set (n = 216) and external test set (KSHAP, n = 151). We found that the connectivity-based predicted score tracked the actual behavioral test scores (r = 0.08-0.44). The predictive models utilizing most of the connectivity features showed better accuracy than those composed of focal connectivity features, suggesting that its neural basis is largely distributed across multiple brain systems. The discriminant and clinical validity of the predictive models were further assessed. Our results suggest that late-life neuropsychological test performance can be formally characterized with distributed connectome-based predictive models, and further translational evidence is needed when developing theoretically valid and clinically incremental predictive models.
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Affiliation(s)
- Seyul Kwak
- Department of PsychologySeoul National UniversitySeoulRepublic of Korea
| | - Hairin Kim
- Department of PsychologySeoul National UniversitySeoulRepublic of Korea
| | - Hoyoung Kim
- Department of PsychologyChonbuk National UniversityJeonjuRepublic of Korea
| | - Yoosik Youm
- Department of SociologyYonsei UniversitySeoulRepublic of Korea
| | - Jeanyung Chey
- Department of PsychologySeoul National UniversitySeoulRepublic of Korea
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146
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Gao S, Xia X, Scheinost D, Mishne G. Smooth graph learning for functional connectivity estimation. Neuroimage 2021; 239:118289. [PMID: 34171497 DOI: 10.1016/j.neuroimage.2021.118289] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 06/15/2021] [Accepted: 06/17/2021] [Indexed: 01/04/2023] Open
Abstract
Functional connectivity (FC) estimated from functional magnetic resonance imaging (fMRI) signals is important in understanding neural representation and information processing in cortical networks. However, due to a lack of "ground truth" FC pattern, the reliability and robustness of FC estimates are usually examined in downstream FC analysis tasks, such as performing participant's identification (also known as "fingerprinting"). In this paper, we propose to learn FC via a smooth graph learning framework. In particular, we treat each time frame of the fMRI time series as a graph signal on an underlying functional brain graph, and estimate the smooth graph functional connectivity (SGFC) by learning the weighted graph adjacency matrix based on graph signal smoothness assumption. We demonstrate that our approach gives rise to a natural and sparse graph representation of FC from which reliable graph measures can be extracted. Reliability of SGFC is evaluated in the context of fingerprinting and compared to correlation FC (CFC). SGFC achieves higher fingerprinting accuracy across several different experiment settings; the improvement is even more significant when a shorter fMRI scanning length is used for FC estimation. In addition to being reliable, we also validate the cognitive relevance of SGFC by using it to predict fluid intelligence. Finally, in evaluating topological measures of the sparse graph, SGFC reveals a more small-world and modular structure compared to CFC. Together, our results suggest that the smooth graph learning framework produces a naturally sparse, reliable, and cognitive-relevant representation of functional connectivity.
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Affiliation(s)
- Siyuan Gao
- Department of Biomedical Engineering, Yale University, 06520, United States
| | - Xinyue Xia
- Neurosciences Graduate Program, University of California San Diego, 92093, United States
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, 06520, United States; Department of Radiology and Biomedical Imaging, Yale School of Medicine, 06510, United States; Department of Statistics and Data Science, Yale University, 06520, United States; Child Study Center, Yale School of Medicine, 06510, United States
| | - Gal Mishne
- Neurosciences Graduate Program, University of California San Diego, 92093, United States; Halicioğlu Data Science Institute, University of California San Diego, 92093, United States; Department of Electrical and Computer Engineering, University of California San Diego, 92093, United States.
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147
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Smith RJ, Alipourjeddi E, Garner C, Maser AL, Shrey DW, Lopour BA. Infant functional networks are modulated by state of consciousness and circadian rhythm. Netw Neurosci 2021; 5:614-630. [PMID: 34189380 PMCID: PMC8233111 DOI: 10.1162/netn_a_00194] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 03/22/2021] [Indexed: 01/05/2023] Open
Abstract
Functional connectivity networks are valuable tools for studying development, cognition, and disease in the infant brain. In adults, such networks are modulated by the state of consciousness and the circadian rhythm; however, it is unknown if infant brain networks exhibit similar variation, given the unique temporal properties of infant sleep and circadian patterning. To address this, we analyzed functional connectivity networks calculated from long-term EEG recordings (average duration 20.8 hr) from 19 healthy infants. Networks were subject specific, as intersubject correlations between weighted adjacency matrices were low. However, within individual subjects, both sleep and wake networks were stable over time, with stronger functional connectivity during sleep than wakefulness. Principal component analysis revealed the presence of two dominant networks; visual sleep scoring confirmed that these corresponded to sleep and wakefulness. Lastly, we found that network strength, degree, clustering coefficient, and path length significantly varied with time of day, when measured in either wakefulness or sleep at the group level. Together, these results suggest that modulation of healthy functional networks occurs over ∼24 hr and is robust and repeatable. Accounting for such temporal periodicities may improve the physiological interpretation and use of functional connectivity analysis to investigate brain function in health and disease.
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Affiliation(s)
- Rachel J. Smith
- Department of Biomedical Engineering, University of California, Irvine, CA, USA
| | - Ehsan Alipourjeddi
- Department of Biomedical Engineering, University of California, Irvine, CA, USA
| | - Cristal Garner
- Division of Neurology, Children’s Hospital of Orange County, Orange, CA, USA
| | - Amy L. Maser
- Department of Psychology, Children’s Hospital of Orange County, Orange, CA, USA
| | - Daniel W. Shrey
- Division of Neurology, Children’s Hospital of Orange County, Orange, CA, USA
- Department of Pediatrics, University of California, Irvine, Irvine, CA, USA
| | - Beth A. Lopour
- Department of Biomedical Engineering, University of California, Irvine, CA, USA
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148
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Neural correlates of the production effect: An fMRI study. Brain Cogn 2021; 152:105757. [PMID: 34130081 DOI: 10.1016/j.bandc.2021.105757] [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] [Received: 05/11/2020] [Revised: 05/16/2021] [Accepted: 05/18/2021] [Indexed: 11/20/2022]
Abstract
Recognition memory is improved for items produced at study (e.g., by reading them aloud) relative to a non-produced control condition (e.g., silent reading). This production effect is typically attributed to the extra elements in the production task (e.g., motor activation, auditory perception) enhancing item distinctiveness. To evaluate this claim, the present study examined the neural mechanisms underlying the production effect. Prior to a recognition memory test, different words within a study list were read either aloud, silently, or while saying "check" (as a sensorimotor control condition). Production improved recognition, and aloud words yielded higher rates of both recollection and familiarity judgments than either silent or control words. During encoding, fMRI revealed stronger activation in regions associated with motor, somatosensory, and auditory processing for aloud items than for either silent or control items. These activations were predictive of recollective success for aloud items at test. Together, our findings are compatible with a distinctiveness-based account of the production effect, while also pointing to the possible role of other processing differences during the aloud trials as compared to silent and control.
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149
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Wang X, Li Q, Zhao Y, He Y, Ma B, Fu Z, Li S. Decomposition of individual-specific and individual-shared components from resting-state functional connectivity using a multi-task machine learning method. Neuroimage 2021; 238:118252. [PMID: 34116155 DOI: 10.1016/j.neuroimage.2021.118252] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 05/31/2021] [Accepted: 06/07/2021] [Indexed: 10/21/2022] Open
Abstract
Resting-state functional connectivity (RSFC) can be used for mapping large-scale human brain networks during rest. There is considerable interest in distinguishing the individual-shared and individual-specific components in RSFC for the better identification of individuals and prediction of behavior. Therefore, we propose a multi-task learning based sparse convex alternating structure optimization (MTL-sCASO) method to decompose RSFC into individual-specific connectivity and individual-shared connectivity. We used synthetic data to validate the efficacy of the MTL-sCASO method. In addition, we verified that individual-specific connectivity achieves higher identification rates than the Pearson correlation (PC) method, and the individual-specific components observed in 886 individuals from the Human Connectome Project (HCP) examined in two sessions over two consecutive days might serve as individual fingerprints. Individual-specific connectivity has low inter-subject similarity (-0.005±0.023), while individual-shared connectivity has high inter-subject similarity (0.822±0.061). We also determined the anatomical locations (region or subsystem) related to individual attributes and common features. We find that individual-specific connectivity exhibits low degree centrality in the sensorimotor processing system but high degree centrality in the control system. Importantly, the individual-specific connectivity estimated by the MTL-sCASO method accurately predicts behavioral scores (improved by 9.4% compared to the PC method) in the cognitive dimension. The decomposition of individual-specific and individual-shared components from RSFC provides a new approach for tracing individual traits and group analysis using functional brain networks.
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Affiliation(s)
- Xuetong Wang
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China
| | - Qiongling Li
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China
| | - Yan Zhao
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China
| | - Yirong He
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China
| | - Baoqiang Ma
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China
| | - Zhenrong Fu
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China
| | - Shuyu Li
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China
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150
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Wang Z, Yuan Y, Jiang Y, You J, Zhang Z. Identification of specific neural circuit underlying the key cognitive deficit of remitted late-onset depression: A multi-modal MRI and machine learning study. Prog Neuropsychopharmacol Biol Psychiatry 2021; 108:110192. [PMID: 33285264 DOI: 10.1016/j.pnpbp.2020.110192] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 11/23/2020] [Accepted: 11/30/2020] [Indexed: 01/21/2023]
Abstract
Neuropsychological impairment is a key feature of late-onset depression (LOD), with deficits observed across multiple cognitive domains. And this neuropsychological impairment can persist even after the remission of depressive symptoms. However, none of previous studies have explored the pattern of cognitive deficit in remitted LOD (rLOD), and investigated the specific neural circuit underlying the key cognitive deficit of LOD. 40 rLOD patients and 36 controls underwent comprehensive neuropsychological assessments and magnetic resonance imaging (MRI) scans. The influence of executive function or information processing speed deficit on other cognitive domains was first investigated. We then applied a multivariate machine learning technique known as relevance vector regression to evaluate the potential of multiple-modal MRI (i.e., integrating whole-brain grey-matter [GM] volume and white-matter [WM] tract features) for making accurate predictions about the key cognitive deficit for individual rLOD patient. We revealed that the information processing speed appears to represent a key cognitive deficit in rLOD. Further the machine learning model identified a wide range of GM regions and WM tracts that significantly contributed to the prediction of individual performance on information processing speed (r = 0.50, P < 0.001). The GM regions mainly located in the frontal-subcortical and limbic systems; and the WM tracts mainly located in the frontal-limbic pathway, including the anterior corona radiata, fornix, posterior cingulate bundle, and uncinate fasciculus. This present study provide strongly evidence supporting the concept of rLOD that the core aspect of the cognitive deficits (i.e., information processing speed) is associated with disruption of the frontal-subcortical-limbic pathway.
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Affiliation(s)
- Zan Wang
- School of Medicine, Southeast University, Nanjing 210009, China; Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing 210009, China.
| | - Yonggui Yuan
- School of Medicine, Southeast University, Nanjing 210009, China; Department of Psychosomatics and Psychiatry, Affiliated ZhongDa Hospital of Southeast University, Nanjing 210009, China
| | - Ying Jiang
- Department of Neurology, the 962nd Hospital of the PLA Joint Logistic Support Force, Harbin 150080, China
| | - Jiayong You
- Department of Psychiatry, Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China
| | - Zhijun Zhang
- School of Medicine, Southeast University, Nanjing 210009, China; Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing 210009, China.
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